esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### The Response of \(S_{\mathrm{GPP}}\) and \(S_{\mathrm{ER}}\) to Changes in Water Availability  
In our study, a positive sensitivity of carbon dioxide flux to precipitation signifies a synchronous relationship, whereas a negative value indicates an inverse correlation. When \(S_{\mathrm{GPP}}\) and \(S_{\mathrm{ER}}\) are positive, \(S_{\mathrm{GPP}}\) shows a marked non-linear response to changes in AI and MAP (Figures 6A and 7A). Specifically, \(S_{\mathrm{GPP}}\) and \(S_{\mathrm{ER}}\) are highest in arid and semi-arid regions. \(S_{\mathrm{GPP}}\) and \(S_{\mathrm{ER}}\) decline rapidly with increasing AI when AI < 0.65 and 0.85, but they stabilize when AI > 0.65 and 0.85. It is widely recognized that \(S_{\mathrm{GPP}}\) is typically higher in arid regions and lower in more mesic ecosystems [11; 25; 66]. This may be attributed to the fact that water availability is a key limiting factor for vegetation growth in these areas [67; 68], coupled with the higher water-use efficiency of local vegetation [69]. In contrast, in humid regions, factors such as light and soil nutrients may play a more significant role in limiting vegetation growth [70].  
Regarding non-linearity, [PERSON] and [PERSON] (2001) were the first to find that vegetation productivity responds to precipitation in a non-linear manner [71]. With further research,an increasing body of evidence supports the idea that grassland ecosystems exhibit positive asymmetry in their response to precipitation, meaning that productivity increases in wet years exceed declines in dry years [39; 72], which is beneficial for the ecosystem's carbon sink [73]. Conversely, negative asymmetry is observed in dry years [74], which is detrimental to the carbon sink. On the other hand, experiments in grassland have also shown that ER and its components exhibit asymmetric responses to changes in precipitation [39; 44].  
The asymmetry between carbon dioxide flux and precipitation may be attributed to two main factors: firstly, the asymmetric response of ANPP to precipitation was a key reason for the non-linear nature of carbon dioxide flux in response to precipitation changes [40; 44]; secondly, the asymmetric relationship between soil moisture and precipitation exacerbates the non-linear relationship of carbon dioxide flux. Experimental studies have shown that reduced precipitation significantly decreases surface soil water content (SWC), while increased precipitation has limited effects, further intensifying the non-linear response of carbon dioxide flux across different precipitation gradients [44]. Moreover, overall, \(S_{\text{ER}}\) exhibits a significant decreasing trend over a wider range of AI and MAP changes (Figures 6B and 7B), indicating that ER is more sensitive to variations in moisture while possessing some regulatory capacity. This phenomenon may be attributed to the soil's ability to store and buffer water [75] and to the effective utilization of deep moisture by plant roots [76; 77], which together enhance the ecosystem's adaptability to changes in moisture.
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 MODIS GPP  
To assess the capacity of vegetation to sequester carbon, the 8-day MODIS GPP product with a resolution of 0.5 km (MOD17A2H) was used, and annual GPP values for the study period were calculated with the help of ArcGIS 10.8. The product incorporates recently updated Biome Property look-up tables and an improved version of the daily GMAP meteorological data as input parameters [31], which have been extensively employed at both global and regional scales [32].
## 3 Results  
### The Optimized Parameters of Typical Vegetation Types  
The optimized parameters of temperate grasses, alpine meadows, shrubs, evergreen needle leaf forests, deciduous broad leaf forests, one crop a year, and two crops a year are shown in the Table 1. Most parameters required calibration through the coupled differential evolution algorithm with the Biome-BGC model, while a few parameters used the model's default values. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Parameter** & **GRA** & **AM** & **Shrub** & **ENF** & **DBF** & **Crop1** & **Crop2-W** & **Crop2-M** & **Units** \\ \hline Year day to start new growth & 130 & 120 & * & * & * & 138 & 60 & 180 & day \\ \hline Year day to end litterfall & 280 & 275 & * & * & * & 283 & 180 & 300 & day \\ \hline Transfer growth period as fraction of growing season & 0.9912 & 0.6201 & 0.6217 & * & 0.0782 & 0.5021 & 0.6623 & 0.7976 & / \\ \hline Litterfall as fraction of growing season & 0.2810 & 0.2959 & 0.6207 & * & 0.8964 & 0.5514 & 0.1985 & 0.2541 & / \\ \hline Annual leaf and fine root turnover fraction & 1 & 1 & 0.8871 & 0.3023 & 1 & 0.4 & 1.0 & 0.1657 & 1 a\({}^{-1}\) \\ \hline Annual live wood turnover fraction & 0 & 0 & 0.7 & 0.2528 & 0.7 & 0 & 0 & 0 & 1 a\({}^{-1}\) \\ \hline Annual whole-plant mortality fraction & 0.01 & 0.06 & 0.005 & 0.0001 & 0.005 & 0.005 & 0.005 & 0.01 & 1 a\({}^{-1}\) \\ \hline Annual fire mortality fraction & 0.005 & 0.05 & 0.005 & 0.0001 & 0.0025 & 0.005 & 0.005 & 0.01 & 1 a\({}^{-1}\) \\ \hline New fine root C: new leaf C & 2.1644 & 1.6664 & 5.1482 & 5.6219 & 2.1620 & 0.1 & 1.9773 & 1.1619 & ratio \\ \hline New stem C: new leaf C & 0 & 0 & 0.7497 & 0.5200 & 3.0301 & 0 & 0 & 0 & ratio \\ \hline New live wood C: new total wood C & 0 & 0 & 0.2664 & 0.0867 & 0.1 & 0 & 0 & 0 & ratio \\ \hline New root C: new stem C & 0 & 0 & 0.4017 & 0.3031 & 0.23 & 0 & 0 & 0 & ratio \\ \hline Current growth proportion & 0.6744 & 0.7684 & 0.2370 & 0.5355 & 0.1865 & 0.05 & 0.1059 & 0.5937 & / \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameter table for temperate grasses (GRA), alpine meadows (AM), shrubs, evergreen needle leaf forests (ENF), deciduous broad leaf forests (DBF), one crop a year (Crop1), wheat (Crop2-W), and maize (Crop2-M). * represents that this type of vegetation does not involve this parameter. The corresponding parameters are not working.
# An Efficient Carrier Synchronization Scheme for Demodulation Systems
## 5 Conclusions  
Compared with the conventional scheme, the proposed scheme simplifies the carrier synchronization from three steps to two steps and dramatically reduces the multiplication of carrier synchronization, which is relatively simple and practical. In the pilot-aided VM system, the BER curve of the proposed carrier synchronization scheme is almost the same as the theoretical BER curve. The operating frequency of the proposed scheme can be up to 200 MHz. Therefore, it has a wide range of application prospects in high-speed equipment.  
Conceptualization, W.H. and Z.W.; methodology, W.H.; software, W.H.; validation, W.H. and R.M.; formal analysis, W.H.; investigation, W.H. and R.M.; resources, W.H.; data curation, W.H.; writing--original draft preparation, W.H.; writing--review and editing, M.L. and R.M.; visualization, R.M.; supervision, W.H. and Z.W.; project administration, Z.W. and W.H. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Literature 2** & **Literature 3** & **Literature 4** & **This Paper** \\ \hline \multirow{4}{*}{Coarse CFR} & \multirow{4}{*}{D\&M (M = 2)} & \multirow{2}{*}{Fitz (M = 2)} & Modified & \multirow{2}{*}{RLR} \\  & & & & & \\  & & & & & (M = 9) \\ \cline{2-5}  & Multiplier & 34 & 73 & 281 & 30 \\ \cline{2-5}  & Arc-tangent & \multirow{2}{*}{1} & \multirow{2}{*}{2} & \multirow{2}{*}{1} & \multirow{2}{*}{1} \\  & operations & & & & \\ \hline \multirow{4}{*}{Fine CFR} & \multirow{4}{*}{L\&R (M = 9)} & Simple Pilot & Simple Pilot & \multirow{4}{*}{None} \\  & & Block & Block & Block & \\  & & Correlation & Correlation & & \\ \cline{1-1} \cline{2-5}  & Multiplier & 288 & 36 & 36 & 0 \\ \cline{1-1} \cline{2-5}  & Arc-tangent & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{0} \\ \cline{1-1}  & operations & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware complexity comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resource** & **Frame** & **CFR Algorithm** & **CFR Algorithm** & **CR Scheme** \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware utilization.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Class A) (Subject Number: XDA153501, Sub-Subject Number: XDA15350103).  
The authors declare no conflict of interest.
## 4 Discussion
### The Correlations of 16-Day SIF-EVI  
Figure 5 shows significant relationships of SIF-EVI over the course of 16 days and at a 1 km spatial resolution. Significant correlations were found from Julian day 145 to 241, and R\({}^{2}\) values of 0.30 on Julian day 145 increased to 0.52 on Julian day 193 and subsequently decreased to 0.24 on Julian day 241.  
Many researchers have studied the relationships between SIF and EVI. [PERSON], et al. [21] found R\({}^{2}\) = 0.80 between GOSAT SIF and MODIS EVI. [PERSON], et al. [56] obtained a strong linear regression (R = 0.69) between MODIS EVI and SIF in a cropland at the OCO-2 footprint level. [PERSON], et al. [11] found a significant correlation between OCO-2 SIF and MODIS EVI, which was slightly stronger than the relationship between OCO-2 SIF and flux-tower observed GPP. Using eddy flux tower data, [PERSON], et al. [45] observed strong linear relationships (R\({}^{2}\) = 0.76) between SIF and absorbed photosynthetically active radiation (APAR) which was quite similar to that between SIF and EVI. In this research, significant correlations between EVI and SIF were found at the point scale. However, due to saturation in forest regions, nonlinearity was apparent between NDVI and SIF.
# Attention-Based Hypergraph Neural Network: A Personalized Recommendation
## 3 Proposed Method
### Heterogeneous Hypergraph and Attention Fusion
#### 3.4.1 Construction of Heterogeneous Hypergraphs
First, for the hypergraph with students as hyperedges and courses as vertices, the hypergraph convolution formula is defined as:  
\[x_{s\to c}^{l+1}=\sigma(N_{s\to c}^{-\frac{1}{2}}H_{s\to c}W_{s\to c}S_{s\to c}^{-1}S_{s \to c}^{-1}H_{s\to c}^{T}N_{s\to c}^{-\frac{1}{2}}X_{s\to c}^{l}P_{s\to c}) \tag{7}\]  
Second, for the hypergraph with courses as hyperedges and students as vertices, the hypergraph convolution formula is defined as:  
\[x_{c\to s}^{l+1}=\sigma(N_{c\to s}^{-\frac{1}{2}}H_{c\to s}W_{c\to s}S_{c \to s}^{-1}H_{c\to s}^{T}N_{c\to s}^{-\frac{1}{2}}X_{c\to s}^{l}P_{c\to s}) \tag{8}\]  
Third, for the hypergraph with teachers as hyperedges and students as vertices, the hypergraph convolution formula is defined as:  
\[x_{t\to s}^{l+1}=\sigma(N_{t\to s}^{-\frac{1}{2}}H_{t\to s}W_{t\to s}S_{t \to s}^{-1}H_{t\to s}^{T}N_{t\to s}^{-\frac{1}{2}}X_{t\to s}^{l}P_{t\to s}) \tag{9}\]  
Fourth, for the hypergraph with courses that have similar elective patterns as hyperedges and students as vertices, the hypergraph convolution formula is defined as:  
\[x_{c\to s^{\prime}}^{l+1}=\sigma(N_{c^{\prime}\to s^{\prime}}^{-\frac{1}{2}}H_ {c^{\prime}\to s^{\prime}}W_{c^{\prime}\to s^{\prime}}S_{c^{\prime}\to s^{ \prime}}^{-1}H_{c^{\prime}\to s^{\prime}}^{T}N_{c^{\prime}\to s^{ \prime}}^{-\frac{1}{2}}X_{c^{\prime}\to s^{\prime}}^{l}P_{c^{\prime}\to s^{ \prime}}) \tag{10}\]  
Fifth, for the hypergraph with teachers as hyperedges and courses as vertices, the hypergraph convolution formula is defined as:  
\[x_{t\to c}^{l+1}=\sigma(N_{t\to c}^{-\frac{1}{2}}H_{t\to c}W_{t\to c}S_{t \to c}^{-1}H_{t\to c}^{T}N_{t\to c}^{-\frac{1}{2}}X_{t\to c}^{l}P_{t\to c}) \tag{11}\]  
In the matrix multiplications described in Equations (7)-(11) above, the embedding fusion representation learning from hypergraph vertices to hyperedges is achieved, capturing high-order features of online students' courses and preferences. Multiple users and multiple courses are processed using hypergraph convolution calculations, and the results are concatenated for further operations. In the convolution operations of the five heterogeneous hypergraphs described above, each vertex aggregates information through its associated hyperedges. However, the relationships between different hyperedges and vertices possess varying degrees of importance. To further enhance the model's capability to capture the significance of different relationships and nodes, an attention mechanism is introduced into the model.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 3 Selected Use Cases
### Anticipated Cooperative Collision Avoidance (ACCA)
#### 3.3.4 What Are the Benefits of 5G Compared to 4G for ACCA?  
As defined in the previous subsections, there are certain requirements and functionalities that depend on the network infrastructure and need to be provided by the telco operator. A main requirement is the capability of the infrastructure to respond in \"real time\", being able to collect events signaled by vehicles, process them and signal them back to other vehicles in the geographic position. This capability requires bounded latency and reliability guarantees that cannot be provided by current 4G infrastructures, and 5G support for URLLC (Ultra-Reliable Low-Latency Communications) traffic is expected to be critical.  
Additionally, considering that critical geoservices need to be processed as close as possible to the vehicles and potential dangers, it is key to enable a 5G MEC capability in order to support:  
* the need of high and guaranteed reliability of the connectivity between the vehicle and the off-board geoservice, which is distributed in the cloud edge;
* the need of low and guaranteed latency of the connectivity between the vehicle and the off-board geoservice, which is distributed in the cloud edge;
* the need of backend interconnection between the central cloud and the distributed edge cloud to guarantee a seamless service connectivity under roaming or handover conditions.
# Distributed Combinatorial Maps for Parallel Mesh Processing
## 4 A Parallel Algorithm of Mesh Processing Based on \(3\)-dmaps
### Message Passing  
In order to transfer messages between the different blocks of the distributed combinatorial maps, we can either broadcast messages to everybody or send messages only to the concerned blocks.  
1. If we broadcast messages, each message is sent to all tasks. When receiving a message, a task must first test if it is concerned by the message or not. This test is achieved by searching the critical dart in the associative array of critical darts. Since this test must be done anyway (to know if the cell was already subdivided or not, _cf._ lines 3 and 11 in Algorithm 2), there is no overhead in complexity. The only additional cost is due to the communication: many messages could be exchanged.  
Figure 7: A 3-dmap \(DM^{\prime}\) composed of 3 blocks also representing the 3-map of Figure 1b. The central edge between the four cubes belongs to the 3 blocks \(M^{1},M^{2},M^{3}\), and is split in 2 edges in block \(M^{2}\).  
If we do not broadcast messages, we use the possibility introduced in Section 3.3 where each critical dart stores the number of the other block containing this critical dart. With this number, it is then possible to directly send a message to the concerned block. In this case, the number of messages sent is smaller, but there is a small overhead in memory space to store these block numbers.
## 2 Methodology
### Base Cation Balance Calculations  
A simple mass balance approach was adopted for each of the three base cations: Ca, Mg and K. Atmospheric deposition and soil mineral weathering represent the principal sources, with tree uptake and soil leaching the main sinks [29]. Budgets were calculated for oak, Scots pine and Sitka spruce on each of the main forest soil types:  
\[\text{Cation balance}/\Delta\text{st}=\text{inputs}\left[\text{AD}+\text{W}+ \text{EV}+\text{F}\right]-\text{outputs}\left[\text{Bh}+\text{L}\right]\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site Name** & **Soil Type (FAO)** & **Soil Horizon** & **Soil pH (H\({}_{2}\)O)** & **C\%** & **N\%** & **Soil CEC mmol c kg\({}^{-4}\)** & **Soil BS\%** \\ \hline Alice Holt & Eutric vertisol & Ah & 5.4 & 2.69 & 0.56 & 23.10 & 95 \\  & BCg & 6.2 & 1.08 & 0.027 & 25.83 & 96 \\ Savernake & Eutric vertisol & E & 4.7 & 3.47 & 0.138 & 15.73 & 22 \\  & 2 BCtg & 6.2 & 1.21 & 0.068 & 23.15 & 99 \\  & \multirow{2}{*}{Cmibic podzol} & Ah & 4.3 & 8.27 & 0.456 & 12.92 & 9 \\  & & Bs & 5.1 & 5.01 & 0.279 & 6.71 & 7 \\  & & Ah & 5.3 & 1.98 & 0.482 & 4.93 & 92 \\  & & Bw & 7.0 & 0.31 & 0.285 & 3.23 & 99 \\ Sherwood & Cabric podzol & Ah & 4.1 & 2.69 & 0.161 & 6.51 & 4 \\  & BC & 4.5 & 1.37 & 0.064 & 2.58 & 7 \\  & & E & 4.2 & 3.87 & 0.111 & 10.03 & 9 \\  & & Bs & 4.8 & 3.42 & 0.082 & 2.35 & 13 \\  & & E & 4.3 & 2.90 & 0.103 & 2.57 & 36 \\  & & Bs & 4.4 & 1.30 & 0.041 & 1.36 & 16 \\  & & Ah(g) & 4.0 & 6.52 & 0.357 & 17.46 & 6 \\  & & Bs & 3.8 & 1.66 & 0.060 & 12.41 & 33 \\  & & Ah & 5.1 & 2.5 & 0.185 & 6.3 & 52 \\  & & Bs & 5.9 & 0.4 & 0.027 & 0.9 & 54 \\  & & A & 4.0 & 4.44 & 0.29 & 16.36 & 3 \\  & & Bs & 4.3 & 3.51 & 0.05 & 13.18 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Soil type and soil horizon related pH, carbon (C), nitrogen (N), cation exchange capacity (CEC by BaCl\({}_{2}\) extraction method) and base saturation (BS) of 10 Intensive Forest Monitoring Level II sites in the UK. Soil characteristics are shown only for the two horizons for each plot where the soil solution samplers are situated.  
Figure 1: Location of ICP Forest Level II sites in the UK.  
\(\Delta\)St--The overall change in the soil stock of base cations  
AD--Atmospheric deposition (wet and dry) of base cations  
W--Release of base cations from weathering of soil minerals  
EV--Release of base cations from the decomposition of pre-forestation vegetation  
F--Base cation inputs in fertiliser applications  
Bh--Tree uptake and removal of base cations in harvested ligneous biomass  
L--Loss of base cations by leaching in soil run-off/ drainage waters  
All terms of the cation balance are expressed in kilograms per hectare per year (kg ha\({}^{-1}\) y\({}^{-1}\)). The method for determining each term is described below.
# Wind-Wave-Current Coupled Modeling of the Effect of Artificial Island on the Coastal Environment
## 2 Materials and Methods
### Mathematical Model Building
#### 2.3.1 Hydrodynamic Model  
The hydrodynamic model adopts the SCHISM system, the full name of which is the Semi-implicit Cross-scale Hydroscience Integrated System Model, developed by the Virginia Oceanographic Institute [13]. SCHISM was widely applied in the research of many ocean engineering problems [12]. To be specific, SCHISM is not a model, but a simulation system. The core of the SCHISM simulation system is a three-dimensional hydrodynamic model based on the assumption of hydrostatic pressure, which drives other coupled modules, including a sediment model, water ecological model, water quality model, wave model, oil spill model, sea ice model, hydraulic building model, etc. The three-dimensional hydrodynamic calculation module was developed in unstructured grid mode, which was suitable for computationally complex geometric boundaries and complex terrain. The governing equations of the hydrodynamic model included a continuity equation and a momentum equation:  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}+\frac{\partial w} {\partial z}=0 \tag{1}\]  
\[\frac{du}{dt}=fv-g\frac{\partial\eta}{\partial x}+K_{mh}(\frac{\partial^{2}u} {\partial x^{2}}+\frac{\partial^{2}u}{\partial y^{2}})+\frac{\partial}{ \partial z}(K_{mv}\frac{\partial u}{\partial z}) \tag{2}\]  
\[\frac{dv}{dt}=-fu-g\frac{\partial\eta}{\partial y}+K_{mh}(\frac{\partial^{2}v }{\partial x^{2}}+\frac{\partial^{2}v}{\partial y^{2}})+\frac{\partial}{ \partial z}(K_{mv}\frac{\partial v}{\partial z}) \tag{3}\]  
where \((x,y)\) is the Cartesian plane coordinate (m); Z is the vertical coordinate, where upward is positive; t is the time (s); \(Z_{ini}\) is used to calculate the initial water level (m); \(\eta\) is the fluctuation of the free water level (m); \(h\) is the water depth (m); \(u,\ v\) are normal and tangential velocity (m/s); \(g\) is the acceleration of gravity (m/s\({}^{2}\)) and is 9.81; \(f\) is the Coriolis force  
Figure 6: Water level and wind field recorded during Typhoon 19, 2017, in Gangbei Town. (**a**) Curve of typhoon water level and wind speed. (**b**) Typhoon transit wind speed rose chart.  
coefficient (1.0487974 \(\times\) 10\({}^{-4}\)); \(\rho\) is the density of water; and \(K_{mh}\), \(K_{mv}\) are the horizontal and vertical turbulent viscosity coefficients (m\({}^{2}\)/s) in the flow momentum equation.
## 3 Results
### Heatwave Detection and Measurement
#### 3.2.2 Satellite-Based Land Surface Temperature Heatwave  
The result reveals the annual total number of daytime HWN, which ranges from two to nine. Pathum Thani emerges as the most affected region, followed by eastern Bangkok in terms of HWN prevalence. In terms of the annual HWF, which ranges from 10 to 39 days, the northern part of Pathum Thani exhibits the highest frequency. Regarding HWD, figures range from 4 to 11 days, with the rural regions of northern Saraburi experiencing the longest durations. The highest HWM during these events was recorded in the urban areas of the Don Muang region of Bangkok, with temperatures ranging from 33 \({}^{\circ}\)C to 42 \({}^{\circ}\)C. Additionally, the highest temperatures for the hottest HWA ranged from 35 \({}^{\circ}\)C to 45 \({}^{\circ}\)C, also observed in urban areas. To summarize, the annual distribution and characteristics of daytime heatwaves across different regions indicate that Pathum Thani and Eastern Bangkok are most prone to experiencing a higher number of HWN. Northern Pathum Thani endures the highest HWF of these events. Meanwhile, the rural areas of Northern Saraburi are subjected to the longest HWD. The urban regions, particularly in Don Muang, Bangkok, are notable for recording the highest HWM and temperatures on the hottest HWA. The data suggest that heatwaves are more frequent in peri-urban areas, while rural regions tend to experience longer-lasting heatwaves.  
On the other hand, the analysis delineates the annual total number of nighttime HWN, which ranges from 3 to 12 events. Downtown Bangkok emerges as the area most affected by heatwave occurrences, closely followed by Northern Pathum Thani. The frequency of annual nighttime HWF varies from 13 to 62 days per year, with downtown Bangkok experiencing the highest frequency. Regarding HWD, the analysis shows a range from 4 to 12 days, with the longest observed in downtown Bangkok. The annual mean cumulative nighttime HWM spans from 21 \({}^{\circ}\)C to 27 \({}^{\circ}\)C, with the central districts of downtown Bangkok recording the highest values. The range of the warmest nighttime HWA extends from 21 \({}^{\circ}\)C to 28 \({}^{\circ}\)C, with the highest temperatures observed in central Bangkok. To conclude, the annual indices for nighttime heatwave events indicate that downtown Bangkok and northern Pathum Thani are the most significantly affected regions, both in terms of the number of nighttime HWN and their HWF. Downtown Bangkok also records the longest HWD as well as the highest nighttime HWM and warmest nighttime HWA. The analysis suggests that nighttime heatwaves are more frequent and prolonged compared to their daytime counterparts, albeit with lower maximum temperatures. Urban areas, especially downtown Bangkok, are particularly vulnerable to severe nighttime heatwaves.