A1 Coefficient From Series of Heavy Rainy Days in the Period 1961-2007 [7]

Research to: Chen et al. (2008)[24] studied the synoptic scale development in the heavy rain event of October 30-31, 2008: mesoscale processes. The cause of this event was that in the tropics, a cold wave vortex formed on October 26 south of the Philippines, through interaction with the eastern disturbance, a small surface vortex existing over the sea and the East Asian cold wave, forming a strong moisture flow from the East Sea to Hanoi.

+ Rainfall characteristics related to the monsoon have also been studied by many works: Matsumoto (1997) [34] used a series of 5-day average rainfall data from 1975-1987 to determine the average start and end times of summer rainfall in the Indochina peninsula. The average seasonal variation during the start and phase repetition in Indonesia, India and the East Sea was determined based on 5-day average data of OLR (1975-1987) and 850 hPa level wind data (1980-1988). The study showed that the start of the summer season in the Indonesian island region is in late April to early May, earlier than in the coastal area along the Bay of Bengal. Meanwhile, Wang and Linho (2002) [26] have conducted studies on the spatial-temporal structure of rainfall characteristics due to the Pacific-Asian monsoon. The study showed that the large-scale outbreak of the Asian monsoon season consists of two phases. The first phase with increased rainfall over the South China Sea in mid-May establishes a planetary-scale monsoon band extending from the South Asian coast. Moron et al. (2008) [25] studied the spatial and temporal variability of the summer monsoon outbreak over the Philippines. The authors used a local criterion to determine the onset of the summer monsoon, which was defined as the first 5 consecutive wet days with a total rainfall of not less than 40 mm.

The trend of rainfall characteristics change has also been focused on by many studies such as Panmao Zhai, 2005[29] studied the change trend based on the Sen trend and Mann-Kendall test showed that the total annual rainfall has decreased significantly in south-east China, north China and the Sichuan basin but increased significantly in west China, the Yangtze River valley and the southeast coast. Spring rainfall has increased in south-east China and north China but decreased significantly in the middle reaches of the Yangtze River. The trend of summer rainfall is very similar to the annual total. Autumn rainfall

has generally decreased across eastern China. In eastern China, the decrease in the number of rainy days appears to be more dominant in the north while the effect of increased intensity is dominant in the south.

Wang Yi, 2009 [30] also investigated the rainfall trends in six rainfall indices in China for the seasons of 1961−2007 analyzed based on daily observations at 587 stations. The trends were estimated using the Sen method, with the Mann-Kendall test. The results showed that the geographic distribution patterns of the trends in seasonal extreme rainfall characteristics were similar to those of total rainfall. For winter, both the rainfall and total heavy rainfall increased over almost all of China. Increasing trends in extreme rainfall also occurred at many stations in southwest China in spring and the middle reaches of the Yangtze River and southern China in summer.

Figure 1.2.Trend in rainfall (PRCPTOT) and number of heavy rain days (R50). Trend is expressed as percentage change relative to the mean over available time data; Blue (red) symbols indicate increasing (decreasing) trends. Circular symbols indicate statistical significance at the 5% level [20].

Nobuhiko Endo,2009 [20] investigated trends in rainfall extremes using daily rainfall data from Southeast Asian countries from 1950 to 2000. The number of wet days (days with at least 1 mm of rainfall) showed a decreasing trend in these countries, while the average rainfall intensity of wet days showed an increasing trend. The heavy rainfall indices demonstrated that the number of stations with a significant increasing trend was larger than the decreasing trend. The number of heavy rain days increased in southern Vietnam, northern Myanmar, and the Visayas and Luzon Islands in

Philippines, while heavy rains decrease in northern Vietnam. The number of consecutive dry days decreases in the region dominated by winter monsoon rainfall. A reduction in dry season rain events is proposed in Myanmar.

Jehangir Ashraf Awan, 2014 [30] used K-means and hierarchical clustering methods to establish homogeneous rainfall zones in the East Asian monsoon region (20 ∘ N 50 ∘ N, 103 ∘ E -149 o E) using 30 years (1978 - 2007) of monthly rainfall data at 0.5 ∘ resolution . Different cluster validation indices were used to evaluate the number of homogeneous rainfall zones. Mann-Kendall tests and linear regression were used to analyze the seasonal and annual rainfall trends in the homogeneous rainfall zones. The study showed that the region has different rainfall regimes across different regions. Furthermore, significant increasing and decreasing trends were observed across different regions with strong seasonal variations indicating a worsening of climate risks, i.e. droughts and floods in the East Asian monsoon region.

Atsamon Limsakul, 2015 [32] studied the extreme rainfall of Thailand. Thailand tends to have heavier rainfall and more extreme events during La Nina years and negative PDO phases, and vice versa during El Nino years and positive PDO phases.

1.3.2 Domestic research

Nguyen Duc Ngu (1975, 2007) [3], [4] studied the impact of ENSO on weather, climate, environment and socio-economy in Vietnam. The study calculated and pointed out El Nino, La Nina episodes and their impact on some hydrometeorological factors such as temperature, rainfall, storm activity... for some specific areas in Vietnam.

Phan Van Tan (2010) [7] studied the global impact on extreme climate phenomena, based on data from 1961 to 2007, rainfall characteristics such as the highest monthly rainfall (Rx1day), the highest monthly rainfall of 5 consecutive days (Rx5day), total rainfall of days in the year greater than the 95th percentile (R95), the number of days in the month with daily rainfall greater than 50mm (R50) showed the monthly developments, and the trend of change of heavy rain (R50). The Southern region (N3) has a tendency to increase the number of days of heavy rain (coefficient a1 of linear regression over time).

15

Vu Thanh Hang and colleagues (2009) [12] used daily rainfall data at monitoring stations in seven climatic regions of Vietnam from 1961 to 2007 to determine the change trend of maximum daily rainfall. The analysis results showed that, during the period from 1961 to 2007, most of the region showed an increasing trend of maximum daily rainfall, except for the Northern Delta (B3), which increased sharply in recent years. The change also had differences between periods, in short periods the increasing/decreasing trend was not uniform between climatic regions.

Figure 1.3. Coefficient a1 from the series of heavy rain days in the period 1961-2007 [7]

Nguyen Dang Mau, Nguyen Minh Truong, Hidetaka Sasaki, Izuru Takayabu (2017) [15] have projected changes in the rainy season in the Vietnam region by the end of the 21st century using the NHRCM model. The results show that the JJA season rainfall may decrease by 0 - 40% in the North; increase by about 0 - 30% in the Central Highlands and the South compared to the baseline period. The SON season rainfall may increase by about 0 - 30% in the Central region. The projected increase/decrease in future rainfall is closely linked to the projected changes in large-scale circulation in the Vietnam region.

Nguyen Thi Hien Thuan and colleagues have studied the calculation of rainfall fluctuations through standard error analysis and percentage analysis. The study concluded that in ENSO years, rainfall in the months between the summer monsoon season fluctuates less than in the transition months between the dry season and the rainy season, especially April and May. The average rainfall after the El Nino event decreased more than in the El Nino year at most stations in the South. In contrast, La Nina years have increased average rainfall and average number of rainy days. Most areas

16

In the South, the rainy season starts late in El-Nino years, whereas the rainy season starts early in La-Nina years. The rainy season starts later in El-Nino than in La-Nina years [10].

Ngo Duc Thanh and Phan Van Tan (2012)[8] used the Mann-Kendall non-parametric test method and Sen's trend method to evaluate the change trend of 7 meteorological factors, period 1961-2007. The results showed that rainfall decreased in the North of the 17th parallel and increased in the South.

a) Percentage of Sen/year trend of daily rainfall and rainfall average year [8]	b) Linear trend of maximum daily rainfall (Rx) in the Southern region [12]
Figure 1.4. Percentage Sen/year trend of daily rainfall and annual mean rainfall and maximum daily rainfall.

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• Identify Rating Levels and Rating Scales zt2i3t4l5ee zt2a3gstourism,quan lan,quang ninh,ecology,ecotourism,minh chau,van don,geography,geographical basis,tourism development,science zt2a3ge zc2o3n4t5e6n7ts of the islanders. Therefore, this indicator will be divided into two sub-indicators: a1. Natural tourism attractiveness a2. Cultural tourism attractiveness b. Tourist capacity The two island communes in Quan Lan have different capacities to receive tourists. Minh Chau Commune is home to many standard hotels and resorts, attracting high-income domestic and international tourists. Meanwhile, Quan Lan Commune has many motels mainly built and operated by local people, so the scale and quality are not high, and will be suitable for ordinary tourists such as students. c. Time of exploitation of Quan Lan Island Commune: Quan Lan tourism is seasonal due to weather and climate conditions and festivals only take place on certain days of the year, specifically in spring. In Quan Lan commune, the period from April to June and from September to November is considered the best time to visit Quan Lan because the cultural tourism activities are mainly associated with festivals taking place during this time. Minh Chau island commune: Tourism exploitation time is all year round, because this is a place with a number of tourist attractions with diverse ecosystems such as Bai Tu Long National Park Research Center, Tram forest, Turtle Laying Beach, so besides coming to the beach for tourism and vacation in the summer, Minh Chau will attract research groups to come for tourism combined with research at other times of the year. d. Sustainability The sustainability of ecotourism sites in Quan Lan and Minh Chau communes depends on the sensitivity of the ecosystems to climate changes. landscape. In general, these tourist destinations have a fairly high level of sustainability, because they are natural ecosystems, planned and protected. However, if a large number of tourists gather at certain times, it can exceed the carrying capacity and affect the sustainability of the environment (polluted beaches, damaged trees, animals moving away from their habitats, etc.), then the sustainability of the above ecosystems (natural ecosystems, human ecosystems) will also be affected and become less sustainable. e. Location and accessibility Both island communes have ports to take tourists to visit from Van Don wharf: - Quan Lan – Van Don traffic route: Phuc Thinh – Viet Anh high-speed boat and Quang Minh high-speed boat, depart at 8am and 2pm from Van Don to Quan Lan, and at 7am and 1pm from Quan Lan to Van Don. There are also wooden boats departing at 7am and 1pm. - Van Don - Minh Chau traffic route: Chung Huong high-speed train, Minh Chau train, morning 7:30 and afternoon 13:30 from Van Don to Minh Chau, morning 6:30 and afternoon 13:00 from Minh Chau to Van Don. f. Infrastructure Despite receiving investment attention, the issue of infrastructure and technical facilities for tourism on Quan Lan Island is still an issue that needs to be resolved because it has a direct impact on the implementation of ecotourism activities. The minimum conditions for serving tourists such as accommodation, electricity, water, communication, especially medical services, and security work need to be given top priority. Ecotourism spots in Minh Chau commune are assessed to have better infrastructure and technical facilities for tourism because there are quite complete and synchronous conditions for serving tourists, meeting many needs of domestic and foreign tourists. 3.2.1.4. Determine assessment levels and assessment scales Corresponding to the levels of each criterion, the index is the score of those levels in the order of 4, 3, 2, 1 decreasing according to the standard of each level: very attractive (4), attractive (3), average (2), less attractive (1). 3.2.1.5. Determining the coefficients of the criteria For the assessment of DLST in the two communes of Quan Lan and Minh Chau islands, the students added evaluation coefficients to show the importance of the criteria and indicators as follows: Coefficient 3 with criteria: Attractiveness, Exploitation time. These are the 2 most important criteria for attracting tourists to tourism in general and eco-tourism in particular, so they have the highest coefficient. Coefficient 2 with criteria: Capacity, Infrastructure, Location and accessibility . Because the assessment area is an island commune of Van Don district, the above criteria are selected by the author with appropriate coefficients at the average level. Coefficient 1 with criteria: Sustainability. Quan Lan has natural and human-made ecotourism sites, with high biodiversity and little impact from local human factors. Most of the ecotourism sites are still wild, so they are highly sustainable. 3.2.1.6. Results of DLST assessment on Quan Lan island a. Assessment of the potential for natural tourism development  For Minh Chau commune: + Natural tourism attractiveness is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12. + Capacity is determined as average (2 points) and the coefficient is quite important (coefficient 2), then the score of Capacity criterion is 2 x 2 = 4. + Exploitation time is long (4 points), the most important coefficient (coefficient 3) so the score of the Exploitation time criterion is 4 x 3 = 12. + Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points + Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points. + Infrastructure is assessed as good (3 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 3 x 2 = 6 points. The total score for evaluating DLST in Minh Chau commune according to 6 evaluation criteria is determined as: 12 + 4 + 12 + 4 + 4 + 6 = 42 points Similar assessment for Quan Lan commune, we have the following table: Table 3.3: Assessment of the potential for natural ecotourism development in Quan Lan and Minh Chau communes Attractiveness of self-tourismof course Capacity Mining time Sustainability Location and accessibility Infrastructure Result Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti CommuneMinh Chau 12 12 4 8 12 12 4 4 4 8 6 8 42/52 Quan CommuneLan 6 12 6 8 9 12 4 4 4 8 4 8 33/52 b. Assessment of the potential for humanistic tourism development  For Quan Lan commune: + The attractiveness of human tourism is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12. + Capacity is determined to be large (3 points) and the coefficient is quite important (coefficient 2), then the score of the Capacity criterion is 3 x 2 = 6. + Mining time is average (3 points), the most important coefficient (coefficient 3) so the score of the Mining time criterion is 3 x 3 = 9. + Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points. + Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points. + Infrastructure is rated as average (2 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 2 x 2 = 4 points. The total score for evaluating DLST in Quan Lan commune according to 6 evaluation criteria is determined as: 12 + 6 + 6 + 4 + 4 + 4 = 36 points. Similar assessment with Minh Chau commune we have the following table: Table 3.4: Assessment of the potential for developing humanistic eco-tourism in Quan Lan and Minh Chau communes Attractiveness of human tourismliterature Capacity Mining time Sustainability Location and accessibility Infrastructure Result Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti Quan CommuneLan 12 12 6 8 9 12 4 4 4 8 4 8 39/52 Minh CommuneChau 6 12 4 8 12 12 4 4 4 8 6 8 36/52 Basically, both Minh Chau and Quan Lan localities have quite favorable conditions for developing ecotourism. However, Quan Lan commune has more advantages to develop ecotourism in a humanistic direction, because this is an area with many famous historical relics such as Quan Lan Communal House, Quan Lan Pagoda, Temple worshiping the hero Tran Khanh Du, ... along with local festivals held annually such as the wind praying ceremony (March 15), Quan Lan festival (June 10-19); due to its location near the port and long exploitation time, the beaches in Quan Lan commune (especially Quan Lan beach) are no longer hygienic and clean to ensure the needs of tourists coming to relax and swim; this is also an area with many beautiful landscapes such as Got Beo wind pass, Ong Phong head, Voi Voi cave, but the ability to access these places is still very limited (dirt hill road, lots of gravel and rocks), especially during rainy and windy times; In addition, other natural resources such as mangrove forests and sea worms have not been really exploited for tourism purposes and ecotourism development. On the contrary, Minh Chau commune has more advantages in developing ecotourism in the direction of natural tourism, this is an area with diverse ecosystems such as at Rua De Beach, Bai Tu Long National Park Conservation Center...; Minh Chau beach is highly appreciated for its natural beauty and cleanliness, ranked in the top ten most beautiful beaches in Vietnam; Minh Chau commune is also home to Tram forest with a large area and a purity of up to 90%, suitable for building bridges through the forest (a very effective type of natural ecotourism currently applied by many countries) for tourists to sightsee, as well as for the purpose of studying and researching. Figure 3.1: Thenmala Forest Bridge (India) Source: https://www.thenmalaecotourism.com/(August 21, 2019) 3.2.2. Using SWOT matrix to evaluate Quan Lan island tourism General assessment of current tourism activities of Quan Lan island is shown through the following SWOT matrix: Table 3.5: SWOT matrix evaluating tourism activities on Quan Lan island Internal agent Strengths- There is a lot of potential for tourism development, especially natural ecotourism and humanistic ecotourism.- The unskilled labor force is relatively abundant.- resource environmentunpolluted, still Weaknesses- Poorly developed infrastructure, especially traffic routes to tourist destinations on the island.- The team of professional staff is still weak.- Tourism products in general quite wild, originalintact general and DLST in particularalone is monotonous. External agents Opportunity- Tourism is a key industry in the socio-economic development strategy of the province and Van Don economic zone.- Quan Lan was selected as a pilot area for eco-tourism development within the framework of the green growth project between Quang Ninh province and the Japanese organization JICA.- The flow of tourists and especially ecotourism in the world tends toincreasing Challenge- Weather and climate change abnormally.- Competition in tourism products is increasingly fierce, especially with other localities in the province such as Ha Long, Mong Cai...- Awareness of tourists, especially domestic tourists, about ecotourism and nature conservation is not high. Through summary analysis using SWOT matrix we see that:  To exploit strengths and take advantage of opportunities, it is necessary to: - Diversify products and service types (build more tourism routes aimed at specific needs of tourists: experiential tourism immersed in nature, spiritual cultural tourism...) - Effective exploitation of resources and differentiated products (natural resources and human resources) div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s1 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s3 { color: #0D0D0D; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; vertical-align: -3pt; } div.maincontent .s7 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; vertical-align: -2pt; } div.maincontent .s8 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; vertical-align: -1pt; } div.maincontent .s9 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s10 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s11 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s12 { color: black; font-family:Symbol, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s13 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s14 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 5pt; } div.maincontent .s15 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 5pt; } div.maincontent .s16 { color: black; font-family:Cambria, serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s17 { color: #080808; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s18 { color: #080808; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s19 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 11pt; } div.maincontent .s20 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; } div.maincontent .s21 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 11pt; } div.maincontent .s22 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 11pt; } div.maincontent .s23 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s24 { color: #212121; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; tex
• A. Results of Testing the Reliability Coefficient of the Factor Scale from the Enterprise Side
• Qos Assurance Methods for Multimedia Communications zt2i3t4l5ee zt2a3gs zt2a3ge zc2o3n4t5e6n7ts low. The EF PHB requires a sufficiently large number of output ports to provide low delay, low loss, and low jitter. EF PHBs can be implemented if the output port's bandwidth is sufficiently large, combined with small buffer sizes and other network resources dedicated to EF packets, to allow the router's service rate for EF packets on an output port to exceed the arrival rate λ of packets at that port. This means that packets with PHB EF are considered with a pre-allocated amount of output bandwidth and a priority that ensures minimum loss, minimum delay and minimum jitter before being put into operation. PHB EF is suitable for channel simulation, leased line simulation, and real-time services such as voice, video without compromising on high loss, delay and jitter values. Figure 2.10 Example of EF installation Figure 2.10 shows an example of an EF PHB implementation. This is a simple priority queue scheduling technique. At the edges of the DS domain, EF packet traffic is prioritized according to the values ​​agreed upon by the SLA. The EF queue in the figure needs to output packets at a rate higher than the packet arrival rate λ. To provide an EF PHB over an end-to-end DS domain, bandwidth at the output ports of the core routers needs to be allocated in advance to ensure the requirement μ > λ. This can be done by a pre-configured provisioning process. In the figure, EF packets are placed in the priority queue (the upper queue). With such a length, the queue can operate with μ > λ. Since EF was primarily used for real-time services such as voice and video, and since real-time services use UDP instead of TCP, RED is generally not suitable for EF queues because applications using UDP will not respond to random packet drop and RED will strip unnecessary packets. 2.2.4.2 Assured Forwarding (AF) PHB PHB AF is defined by RFC 2597. The purpose of PHB AF is to deliver packets reliably and therefore delay and jitter are considered less important than packet loss. PHB AF is suitable for non-real-time services such as applications using TCP. PHB AF first defines four classes: AF1, AF2, AF3, AF4. For each of these AF classes, packets are then classified into three subclasses with three distinct priority levels. Table 2.8 shows the four AF classes and 12 AF subclasses and the DSCP values ​​for the 12 AF subclasses defined by RFC 2597. RFC 2597 also allows for more than three separate priority levels to be added for internal use. However, these separate priority levels will only have internal significance. PHB Class PHB Subclass Package type DSCP AF4 AF41 Short 100010 AF42 Medium 100100 AF43 High 100110 AF3 AF31 Short 011010 AF32 Medium 011100 AF33 High 011110 AF2 AF21 Short 010010 AF22 Medium 010100 AF23 High 010110 AF1 AF11 Short 001010 AF12 Medium 001100 AF13 High 001110 Table 2.8 AF DSCPs The AF PHB ensures that packets are forwarded with a high probability of delivery to the destination within the bounds of the rate agreed upon in an SLA. If AF traffic at an ingress port exceeds the pre-priority rate, which is considered non-compliant or “out of profile”, the excess packets will not be delivered to the destination with the same probability as the packets belonging to the defined traffic or “in profile” packets. When there is network congestion, the out of profile packets are dropped before the in profile packets are dropped. When service levels are defined using AF classes, different quantity and quality between AF classes can be realized by allocating different amounts of bandwidth and buffer space to the four AF classes. Unlike EF, most AF traffic is non-real-time traffic using TCP, and the RED queue management strategy is an AQM (Adaptive Queue Management) strategy suitable for use in AF PHBs. The four AF PHB layers can be implemented as four separate queues. The output port bandwidth is divided into four AF queues. For each AF queue, packets are marked with three “colors” corresponding to three separate priority levels. In addition to the 32 DSCP 1 groups defined in Table 2.8, 21 DSCPs have been standardized as follows: one for PHB EF, 12 for PHB AF, and 8 for CSCP. There are 11 DSCP 1 groups still available for other standards. 2.2.5.Example of Differentiated Services We will look at an example of the Differentiated Service model and mechanism of operation. The architecture of Differentiated Service consists of two basic sets of functions: Edge functions: include packet classification and traffic conditioning. At the inbound edge of the network, incoming packets are marked. In particular, the DS field in the packet header is set to a certain value. For example, in Figure 2.12, packets sent from H1 to H3 are marked at R1, while packets from H2 to H4 are marked at R2. The labels on the received packets identify the service class to which they belong. Different traffic classes receive different services in the core network. The RFC definition uses the term behavior aggregate rather than the term traffic class. After being marked, a packet can be forwarded immediately into the network, delayed for a period of time before being forwarded, or dropped. We will see that there are many factors that affect how a packet is marked, and whether it is forwarded immediately, delayed, or dropped. Figure 2.12 DiffServ Example Core functionality: When a DS-marked packet arrives at a Diffservcapable router, the packet is forwarded to the next router based on Per-hop behavior is associated with packet classes. Per-hop behavior affects router buffers and the bandwidth shared between competing classes. An important principle of the Differentiated Service architecture is that a router's per-hop behavior is based only on the packet's marking or the class to which it belongs. Therefore, if packets sent from H1 to H3 as shown in the figure receive the same marking as packets from H2 to H4, then the network routers treat the packets exactly the same, regardless of whether the packet originated from H1 or H2. For example, R3 does not distinguish between packets from h1 and H2 when forwarding packets to R4. Therefore, the Differentiated Service architecture avoids the need to maintain router state about separate source-destination pairs, which is important for network scalability. Chapter Conclusion Chapter 2 has presented and clarified two main models of deploying and installing quality of service in IP networks. While the traditional best-effort model has many disadvantages, later models such as IntServ and DiffServ have partly solved the problems that best-effort could not solve. IntServ follows the direction of ensuring quality of service for each separate flow, it is built similar to the circuit switching model with the use of the RSVP resource reservation protocol. IntSer is suitable for services that require fixed bandwidth that is not shared such as VoIP services, multicast TV services. However, IntSer has disadvantages such as using a lot of network resources, low scalability and lack of flexibility. DiffServ was born with the idea of ​​solving the disadvantages of the IntServ model. DiffServ follows the direction of ensuring quality based on the principle of hop-by-hop behavior based on the priority of marked packets. The policy for different types of traffic is decided by the administrator and can be changed according to reality, so it is very flexible. DiffServ makes better use of network resources, avoiding idle bandwidth and processing capacity on routers. In addition, the DifServ model can be deployed on many independent domains, so the ability to expand the network becomes easy. Chapter 3: METHODS TO ENSURE QoS FOR MULTIMEDIA COMMUNICATIONS In packet-switched networks, different packet flows often have to share the transmission medium all the way to the destination station. To ensure the fair and efficient allocation of bandwidth to flows, appropriate serving mechanisms are required at network nodes, especially at gateways or routers, where many different data flows often pass through. The scheduler is responsible for serving packets of the selected flow and deciding which packet will be served next. Here, a flow is understood as a set of packets belonging to the same priority class, or originating from the same source, or having the same source and destination addresses, etc. In normal state when there is no congestion, packets will be sent as soon as they are delivered. In case of congestion, if QoS assurance methods are not applied, prolonged congestion can cause packet drops, affecting service quality. In some cases, congestion is prolonged and widespread in the network, which can easily lead to the network being "frozen", or many packets being dropped, seriously affecting service quality. Therefore, in this chapter, in sections 3.2 and 3.3, we introduce some typical network traffic load monitoring techniques to predict and prevent congestion before it occurs through the measure of dropping (removing) packets early when there are signs of impending congestion. 3.1. DropTail method DropTail is a simple, traditional queue management method based on FIFO mechanism. All incoming packets are placed in the queue, when the queue is full, the later packets are dropped. Due to its simplicity and ease of implementation, DropTail has been used for many years on Internet router systems. However, this algorithm has the following disadvantages: − Cannot avoid the phenomenon of “Lock out”: Occurs when 1 or several traffic streams monopolize the queue, making packets of other connections unable to pass through the router. This phenomenon greatly affects reliable transmission protocols such as TCP. According to the anti-congestion algorithm, when locked out, the TCP connection stream will reduce the window size and reduce the packet transmission speed exponentially. − Can cause Global Synchronization: This is the result of a severe “Lock out” phenomenon. Some neighboring routers have their queues monopolized by a number of connections, causing a series of other TCP connections to be unable to pass through and simultaneously reducing the transmission speed. After those monopolized connections are temporarily suspended, Once the queue is cleared, it takes a considerable amount of time for TCP connections to return to their original speed. − Full Queue phenomenon: Data transmitted on the Internet often has an explosion, packets arriving at the router are often in clusters rather than in turn. Therefore, the operating mechanism of DropTail makes the queue easily full for a long period of time, leading to the average delay time of large packets. To avoid this phenomenon, with DropTail, the only way is to increase the router's buffer, this method is very expensive and ineffective. − No QoS guarantee: With the DropTail mechanism, there is no way to prioritize important packets to be transmitted through the router earlier when all are in the queue. Meanwhile, with multimedia communication, ensuring connection and stable speed is extremely important and the DropTail algorithm cannot satisfy. The problem of choosing the buffer size of the routers in the network is to “absorb” short bursts of traffic without causing too much queuing delay. This is necessary in bursty data transmission. The queue size determines the size of the packet bursts (traffic spikes) that we want to be able to transmit without being dropped at the routers. In IP-based application networks, packet dropping is an important mechanism for indirectly reporting congestion to end stations. A solution that prevents router queues from filling up while reducing the packet drop rate is called dynamic queue management. 3.2. Random elimination method – RED 3.2.1 Overview RED (Random Early Detection of congestion; Random Early Drop) is one of the first AQM algorithms proposed in 1993 by Sally Floyd and Van Jacobson, two scientists at the Lawrence Berkeley Laboratory of the University of California, USA. Due to its outstanding advantages compared to previous queue management algorithms, RED has been widely installed and deployed on the Internet. The most fundamental point of their work is that the most effective place to detect congestion and react to it is at the gateway or router. Source entities (senders) can also do this by estimating end-to-end delay, throughput variability, or the rate of packet retransmissions due to drop. However, the sender and receiver view of a particular connection cannot tell which gateways on the network are congested, and cannot distinguish between propagation delay and queuing delay. Only the gateway has a true view of the state of the queue, the link share of the connections passing through it at any given time, and the quality of service requirements of the traffic flows. The RED gateway monitors the average queue length, which detects early signs of impending congestion (average queue length exceeding a predetermined threshold) and reacts appropriately in one of two ways: − Drop incoming packets with a certain probability, to indirectly inform the source of congestion, the source needs to reduce the transmission rate to keep the queue from filling up, maintaining the ability to absorb incoming traffic spikes. − Mark “congestion” with a certain probability in the ECN field in the header of TCP packets to notify the source (the receiving entity will copy this bit into the acknowledgement packet). Figure 3. 1 RED algorithm The main goal of RED is to avoid congestion by keeping the average queue size within a sufficiently small and stable region, which also means keeping the queuing delay sufficiently small and stable. Achieving this goal also helps: avoid global synchronization, not resist bursty traffic flows (i.e. flows with low average throughput but high volatility), and maintain an upper bound on the average queue size even in the absence of cooperation from transport layer protocols. To achieve the above goals, RED gateways must do the following: − The first is to detect congestion early and react appropriately to keep the average queue size small enough to keep the network operating in the low latency, high throughput region, while still allowing the queue size to fluctuate within a certain range to absorb short-term fluctuations. As discussed above, the gateway is the most appropriate place to detect congestion and is also the most appropriate place to decide which specific connection to report congestion to. − The second thing is to notify the source of congestion. This is done by marking and notifying the source to reduce traffic. Normally the RED gateway will randomly drop packets. However, if congestion If congestion is detected before the queue is full, it should be combined with packet marking to signal congestion. The RED gateway has two options: drop or mark; where marking is done by marking the ECN field of the packet with a certain probability, to signal the source to reduce the traffic entering the network. − An important goal that RED gateways need to achieve is to avoid global synchronization and not to resist traffic flows that have a sudden characteristic. Global synchronization occurs when all connections simultaneously reduce their transmission window size, leading to a severe drop in throughput at the same time. On the other hand, Drop Tail or Random Drop strategies are very sensitive to sudden flows; that is, the gateway queue will often overflow when packets from these flows arrive. To avoid these two phenomena, gateways can use special algorithms to detect congestion and decide which connections will be notified of congestion at the gateway. The RED gateway randomly selects incoming packets to mark; with this method, the probability of marking a packet from a particular connection is proportional to the connection's shared bandwidth at the gateway. − Another goal is to control the average queue size even without cooperation from the source entities. This can be done by dropping packets when the average size exceeds an upper threshold (instead of marking it). This approach is necessary in cases where most connections have transmission times that are less than the round-trip time, or where the source entities are not able to reduce traffic in response to marking or dropping packets (such as UDP flows). 3.2.2 Algorithm This section describes the algorithm for RED gateways. RED gateways calculate the average queue size using a low-pass filter. This average queue size is compared with two thresholds: minth and maxth. When the average queue size is less than the lower threshold, no incoming packets are marked or dropped; when the average queue size is greater than the upper threshold, all incoming packets are dropped. When the average queue size is between minth and maxth, each incoming packet is marked or dropped with a probability pa, where pa is a function of the average queue size avg; the probability of marking or dropping a packet for a particular connection is proportional to the bandwidth share of that connection at the gateway. The general algorithm for a RED gateway is described as follows: [5] For each packet arrival Caculate the average queue size avg If minth ≤ avg < maxth div.maincontent .s1 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 15pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s3 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s7 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s8 { color: black; font-family:Arial, sans-serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .s9 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s10 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 6pt; } div.maincontent .s11 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s12 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; } div.maincontent .s13 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-d
• The Preposition “Auf” Viewed From a Cognitive Perspective Compared with Vietnamese
• Example Illustrating a Summary of an English Text

The 2016 Climate Change Scenario [1] shows that during the period 1958-2014, the average annual rainfall nationwide tends to increase slightly. In particular, the increase is highest in the winter and spring months; it decreases in the autumn months. In general, annual rainfall in the northern regions tends to decrease; in the southern regions it tends to increase (from 6.9% ÷ 19.8%/57 years). For the northern regions, rainfall mainly decreases most in the autumn months and increases slightly in the spring months. For the southern regions, rainfall in all seasons in all climate zones tends to increase; it increases most in the winter months (from 35.3% ÷ 80.5%/57 years) and spring months (from 9.2% ÷ 37.6%/57 years). Extreme rainfall has different trends among climatic zones: decreasing in most stations in the Northwest, Northeast, and Northern Delta.

Set and increase in most stations of other climatic zones.

+ In addition to rainfall characteristics, extreme rainfall has also been mentioned and studied: Ho Thi Minh Ha et al (2011) [13] identified extreme climate events from observation data series 1961-2007 and predicted future climate with RegCM3 model for Vietnam. The author classified extreme climate events including the number of hot days, cold nights and heavy rain days analyzed for 7 climate zones in Vietnam. RegCM3 results showed that heavy rain events during the rainy season tended to decrease in all regions except for 2 regions: Northwest and South Central.

Nguyen Thi Hoang Anh and CS (2012) [21] studied the rainfall characteristics related to tropical cyclones (TC). The study showed that the heaviest storm rainfall occurred from June to September for the northern region, while the total rainfall at the southern stations was mainly not due to TC rainfall. TC rainfall was concentrated in the Central region with the peak in October-November. During El Nino (La Nina) years, the ratio of TC and heavy rainfall in the Central region decreased (increased) in October-November. The La Nina phase had a stronger influence on rainfall than the El Nino phase.

Comment: Nowadays, the socio-economic development of our country, especially prioritizing development in remote areas in some key areas such as cultivation, animal husbandry and industrial crop development, hunger eradication and poverty reduction, crop restructuring, prevention and mitigation of damage caused by natural disasters and climate change. This is the responsibility of the hydrometeorological sector in general and meteorologists - climatologists in particular, requiring improvement of qualifications, ensuring that research works are more objective and reliable.

It can be said that research on rain is quite rich, concentrated in many research projects both abroad and in the country, including rain characteristics, rain trends, change trends and rain forecasts. Most of the research projects on rain change trends are on a national scale and long-term, few research projects are detailed for sub-climate regions and short-term periods (recent period).

CHAPTER 2

DATA AND RESEARCH METHODS

2.1 Data

2.1.1 Data from monitoring stations

The data set from 21 meteorological stations was inherited from research works and compiled from the Southern Hydrometeorological Station, so the rough errors were checked. Daily rainfall factors were collected to calculate and determine the rainfall characteristics from 21 meteorological stations. The diagram and list of meteorological stations used in the thesis are shown in Figure 2.1 and Table 2.1.

Rainfall observation data in the South are mostly available after 1979 when the South was completely liberated. The most complete data set is from 1984-2016 which has been collected by the thesis, however the objective of the thesis is to evaluate the changes of some rainfall characteristics during the rainy season in the last two decades, so the main assessment thesis focuses on rainfall data for the period 1996-2016 (21 years).

Table 2.1. List of meteorological stations in the Southern region

STT

Station	Province/City	Longitude	Latitude
1	Tan Son Hoa (HCM)	Ho Chi Minh City	10. 49	106. 40
2	Can Tho	Can Tho City	10. 02	105. 46
3	Phuoc Long	Binh Phuoc	11. 50	106. 59
4	Dong Xoai (Dong Phu)		11. 05	106. 8
5	Vung Tau	Vung Tau	10. 22	107. 05
6	Con Dao		8. 41	106. 35
7	Rach Gia	Kien Giang	10.00	105. 04
8	Phu Quoc		10. 13	103. 58
9	Ca Mau	Ca Mau	9. 11	105. 09
10	Three tri	Ben Tre	10. 15	106. 23
11	Chau Doc	An Giang	10. 47	105. 07
12	Bac Lieu	Bac Lieu	9. 18	105. 43
13	Cao Lanh	Dong Thap	10. 28	105. 38

Bien Hoa	Dong Nai	10. 57	106. 51
15	Vi Thanh	Hau Giang	9. 49	105. 27
16	Moc Hoa	Long An	10. 47	105. 56
17	Soc Trang	Soc Trang	9. 36	105. 58
18	Tay Ninh	Tay Ninh	11. 20	106. 07
19	My Tho	Tien Giang	10. 21	106. 24
20	Long	Tra Vinh	9. 59	106. 12
21	Vinh Long	Vinh Long	10. 16	105. 55

14

Figure 2.1 Southern region station diagram

2.1.2 Reanalysis rainfall data (TPT)

With the desire to have many data sources to verify and compare calculation results for rainfall distribution assessments in the South. Grid data is very useful for studying rainfall distribution over large areas, supplementing the lack of direct measurement stations. Japanese APHORODITE data (Asian precipitation Resolved Observational Data Integration Towards Evaluation of the Water