esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 3 System Model and Problem Description
### Problem Description
#### 3.2.1 Task Delay  
The service time in an edge computing system comprises multiple components, including the task transmission time from the terminal device to the edge node, the task processing time at the edge node, and the waiting time for task execution. A critical factor influencing this service time is the data transmission rate within the communication channel, which significantly impacts overall system performance:  
\[R_{i}=Wlog_{2}(1+P_{i}H/\sigma^{2}),i\in T \tag{1}\]  
In the presented model, we utilize several parameters for the communication channel and task allocation. Specifically, \(W\) represents the communication bandwidth, \(\sigma^{2}\) denotes the channel noise power, \(P_{i}\) indicates the transmit power of the node, and H represents the channel gain. To indicate whether task i is executed on edge node j, we adopt the notation \(\alpha_{i,j}\):  
\[\alpha_{i,j}=\left\{\begin{array}{cc}1&,If\text{ {task }}i\text{ {can be executed in edge node j}}\\ 0&,others\end{array}\right. \tag{2}\]  
All the tasks are carried out by at least one node, and each node caters to at least a solitary device:  
\[\sum_{j\in E}\alpha_{i,j}\geq 1,\forall i\in T\quad\text{and}\quad\sum_{i\in T }\alpha_{i,j}\geq 1,\forall j\in E \tag{3}\]  
The transmission time of a service is denoted as \(T^{trans}_{i,j}\). The size of this transmission time is influenced by the number of inputs and outputs of a task and can be expressed as follows:  
\[T^{trans}_{i,j}=\alpha_{i,j}(d_{in}(j)+d_{out}(j)/R_{i}),i\in T,j\in E \tag{4}\]  
The task processing time of the edge node is contingent on the processing capability of the node itself and the load of the task, which can be expressed as follows:  
\[T^{exe}_{i,j}=\alpha_{i,j}(y(j)/f(i)),i\in T,j\in E \tag{5}\]  
Upon reaching the edge node, the task is queued, awaiting processing by the node. The queuing time can be calculated using the following formula:  
\[T^{queue}_{i,j}=\alpha_{i,j}(leav_{j}-arri_{j}),i\in T,j\in E \tag{6}\]  
where \(leav_{i}\) represents the departure time of the \(ith\) task, while \(arri_{i}\) signifies the arrival time of the \(ith\) task. Consequently, the service time of each task can be expressed as:  
\[T_{i,j}=T^{trans}_{i,j}+T^{exe}_{i,j}+T^{queue}_{i,j} \tag{7}\]The maximum task latency \(T_{max}\) is used to describe the computational performance of edge nodes in this study:  
\[T_{max}=argmax_{i\in T,j\in E}(T_{i,j}) \tag{8}\]
# Evaluation of Algal Control Measures in Eutrophic Reservoir Based on Aquatic Ecosystem Models
## 2 Materials and Methods  
### Overview of the Study Area  
The Shanzai Reservoir is located in the lower reaches of the Ao River. The geographic coordinates at the center of the reservoir are 26\({}^{\circ}\)17\({}^{\prime}\)51\({}^{\prime\prime}\) north latitude and 119\({}^{\circ}\)21\({}^{\prime}\)35\({}^{\prime\prime}\) east longitude (Figure 1) [38]. The entire basin area of the reservoir is 1646 km\({}^{2}\), with a total capacity of 1.06 \(\times\) 10\({}^{8}\) m\({}^{3}\). It is situated in the middle reaches of the Ao River, with the dam site located approximately 6 km upstream from Tangban Village in Lianjiang County. This is a third-level reservoir developed on the main stem of the Ao River, with primary functions including water supply, flood control, power generation, irrigation, and aquaculture [39]. The Shanzai Reservoir is the second water supply source for Fuzhou, serving the important task of supplying water to 1.6 million residents. It not only has to ensure the quantity, but also the quality of the water supply.  
The water flow direction of the Shanzai Reservoir starts from the north, where the Qili River meets and merges with the Rixi River flowing from the southwest to the east towards the center of the reservoir. The main stream gradually inclines towards the dam, forming an irregular elongated shape, with an average depth of about 25-30 m and the deepest part reaching approximately 50 m in front of the dam. The hydraulic retention time is approximately 20.8 days, indicating that it is a seasonal regulating reservoir. The vertical water temperature distribution in the Shanzai Reservoir is characterized by a single convection type, forming a stable thermal stratification from spring to summer to autumn.  
Figure 1: **(a)** Fujian Province, in the yellow section, is located on the southeastern coast of China; **(b)** Fuzhou City, in the pink section, is located at the eastern end of central Fujian Province; **(c)** the boundary of Shanzai Reservoir. These points are hydrological and water quality monitoring sites.  
The thermal stratification disappears in winter, and the vertical water body approaches a uniform mixing.  
With the economic and social development of the catchment area, the reservoir has faced new or intensified challenges, including the following: (1) the increased pressure of eutrophication in the reservoir area; (2) the increased risk of seasonal algal blooms; (3) the availability of total nitrogen (TN) and total phosphorus (TP) as a nutrient source. As a result, the optimization of the aquatic biological community structure, and the reduction in the algal bloom risk have become key issues in the Shanzai Reservoir.
# The Additive Manufacturing of Glass: A Critical Review
## 2 Glass AM Techniques
### Direct Methods
#### 2.1.1 Directed Energy Deposition (DED)
Additionally, DED objects produced at a larger scale present a need for further process design. Unlike metal DED (which may also involve a spooled or blown powder feedstock), the glass feedstock may have limitations in the associated feeding mechanism. Exceeding a critical diameter, glass filaments become inflexible (rigid, yet brittle) and cannot be easily wound around a spool, for example. Considering high deposition rates, a trade-off must be made in the feedstock used. Large inflexible filaments facilitate higher deposition rates, but there is issue when objects exceed the material provided in one length of filament. There must be consideration of how new feedstock material is added while not interrupting the process of material deposition. Alternatively, limiting the filament diameter allows spooling and the easier delivery of the feedstock material [26; 34; 36; 37; 38] but must facilitate higher feed rates to achieve a material deposition rate suitable for large-scale objects. Powder-fed DED may also mitigate this issue but has seen limited exploration in glass AM at the time of review. Such a process has been demonstrated only for single-layer glass structures, without the analysis of a multilayered 3D object [39; 40].
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 2 Methods
### Swin Transformer Block  
The traditional transformer block includes an MSA (Multi-head Self-Attention) module, MLP (MultiLayer Perceptron) and LN (Layer Normalization) layer. The output \(s^{l}\) of layer \(l\) is represented as [35]  
\[\hat{z}^{l} =\text{MSA}\left(\text{LN}(z^{l-1})\right)+z^{l-1}, \tag{1}\] \[z^{l} =\text{MLP}\left(\text{LN}(\hat{z}^{l})\right)+\hat{z}^{l} \tag{2}\]  
where \(z^{l}\) represents the output of the Transformer block.  
Figure 2: The architecture of U-Net-like network based on Swin Transformer block, which is composed of encoder, bottleneck, decoder and skip connections. Spatial fusion modules are embedded in the encoder.  
The computational costs of Transformer blocks grow quadratically as the number of tokens increase, which limits its application, especially on high-resolution remote sensing images. Therefore, Swin Transformer [41] proposes the shifted window mechanism. The Swin Transformer blocks consist of two sequential units, i.e., Window-based Multi-head Self-Attention (W-MSA) and Shifted Window-based Multi-head Self-Attention (SW-MSA), which is shown in Figure 3.  
In the W-MSA, the image is divided into non-overlapping windows of a certain size of \(N\), and each window contains \(N\times N\) patches. Self-attention is conducted within local windows, relieving the problem of high computing costs in MSA. To keep the correlation across windows, the SW-MSA introduces shifted windows partitioning, which brings information communication between adjacent non-overlapping windows of the previous layer.  
Self-attention is conducted within windows, each of which covers \(N\times N\) patches. \(N\) is set to 8 to fit in with the resolution in this experiment. These two Swin Transformer blocks are renamed as Window-Transformer (W-Trans) block and Shifted Window-Transformer (SW-Trans) block. Two successive W-Trans and SW-Trans can be formulated as [41]  
\[\hat{z}^{l} =\text{W-MSA}\Big{(}\text{LN}(z^{l-1})\Big{)}+z^{l-1}, \tag{3}\] \[z^{l} =\text{MLP}\Big{(}\text{LN}(\hat{z}^{l})\Big{)}+\hat{z}^{l},\] (4) \[\hat{z}^{l+1} =\text{SW-MSA}\Big{(}\text{LN}(z^{L})\Big{)}+z^{l},\] (5) \[z^{l+1} =\text{MLP}\Big{(}\text{LN}(\hat{z}^{l+1})\Big{)}+\hat{z}^{l+1} \tag{6}\]  
where \(z^{l}\) represents the output of the W-Trans block, and \(z^{l+1}\) represents the output of the SW-Trans block. Self-attention [49; 50] is calculated as  
\[\text{Attention}(Q,K,V)=\text{SoftMax}\Big{(}\frac{QK^{T}}{\sqrt{d}}+B\Big{)}V, \tag{7}\]  
where \(Q,K,V\in\mathbb{R}^{N^{2}\times d}\) represent the query, key and value matrices, respectively. \(N^{2}\) denotes the number of patches of a window. \(B\) and \(d\), respectively, represent the relative position bias and the dimension of the key or query. \(T\) denotes matrix transposition.  
Figure 3: (**a**) The structure of a standard Transformer block [35]. (**b**) Two consecutive Swin Transformer blocks [41], which are called Window-based Transformer block and Shifted Window-based Transformer block, respectively.
## 2 Experiments  
A commercial prismatic pouch lithium-ion cell was tested in this study. The cathode material of the cell was LiNi\({}_{x}\)Co\({}_{y}\)Mn\({}_{1-x-y}\)O\({}_{2}\) (NMC). The basic parameters of the cell are listed in Table 1. The experiments were conducted in a test bench made by DIGATRON, which had a current range of \(-100\) to \(+100\) A and a voltage range of 0 to 20 V. The tested cell was put into a temperature chamber to keep the ambient temperature. The temperature chamber was made by Dongguan Bell Company and the type was TEMI580.  
Firstly, the capacity test was designed to find the standard capacity of the test cell (Cell#1). The capacity test process was as follows: Cell#1 was placed in the temperature chamber at 25 \({}^{\circ}\)C for 3 h. Then, Cell#1 was discharged at a constant discharge current (1/3 C) to 2.5 V. After waiting for 1 h, Cell#1 was fully charged using the constant current-constant voltage method. This process was repeated three times, and the average value of the test capacity was taken as the standard capacity of the test cell. Secondly, the hybrid pulse power characterization (HPPC) test was carried out to find the open circuit voltage (OCV). The test steps can be found in References. [2; 6], specific steps are not listed here for brevity.  
Finally, Cell#1 was fully charged at 1/3 C. Then, the discharge process was performed under the NEDC cycles. PC software (BTS-600) was used to record the voltage and current of the cell in real time. Figure 1b,c show these test data, which were used for the model parameter identification and the SOC estimation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Nominal** & **Nominal** & **Lower Cut-Off** & **Upper Cut-Off** & **Maximum Charge** \\
**Capacity (Ah)** & **Voltage (V)** & **Voltage (V)** & **Voltage (V)** & **Current (A)** \\ \hline
32.5 & 3.75 & 2.5 & 4.15 & 65 \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of the test cell (Cell#1).
## 3 Numerical Simulation  
### Model Parameters  
Utilizing the on-site construction plan, a three-dimensional numerical computational physical model was established using FLAC3D. Suitable model boundaries are selected based on specifications and actual conditions. The model boundaries are set as follows: the upper surface is a free boundary, and all other boundaries are fixed against displacement. The model applies an elastoplastic theory and uses the Mohr-Coulomb criterion within its constitutive framework. The parameters for soil and support are provided in Table 1.  
Based on the measured terrain on site, a three-dimensional numerical model was established for the section from station ZG20+375 to ZG20+425. The model dimensions are 60 m wide, 40 m long, and extend from the ground surface to an elevation of 160 m, comprising a total of 203,364 elements and 200,431 nodes.  
The numerical analysis primarily simulates the process from the advanced grouting of small pipes to excavation up to station ZG20+400. The excavated area is supported by steel arches spaced every 0.5 m intervals and sprayed with a 20 cm thick concrete layer. During the calculation process, cable elements are used to simulate the advanced small pipes. The small pipes are inserted at an angle of 5\({}^{\circ}\), with a length of 5 m, longitudinal overlapping  
Figure 1: Location map of the study area.  
length of 1 m, and spacing of 0.4 m. The three-dimensional computational model is shown in Figure 2.
## 4 Results
### Spatial Autocorrelation Analysis of CES  
The results from the spatial autocorrelation analysis (Figure 5) indicated no statistically significant difference between the spatial distribution of CES in parks and a random distribution pattern (Moran's I = 0.018, Z = 0.59). Additional data supporting these results can be found in Appendix A Table 13. This result suggested that the CES distribution across Macau parks lacked significant spatial clustering or dispersion, displaying characteristics of a random distribution. This finding was closely related to the urban spatial structure of Macau. Park layouts were influenced by geographical constraints and land-use planning, resulting in the absence of high-value clusters or distinct regional groupings. Although differences were identified between parks in multiple CES dimensions, these variations did  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Coefficient**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{_p_} \\ \cline{2-2} \cline{4-5}  & **B** & & & \\ \hline Recreation and Tourism & 0.21 & 0.30 & 1.87 & 0.12 \\ Esthetic Appreciation & \(-\)0.13 & \(-\)0.17 & \(-\)0.71 & 0.51 \\ Public Facilities & \(-\)0.59 & \(-\)0.69 & \(-\)3.69 & 0.01 \\ Education and Knowledge & \(-\)0.05 & \(-\)0.05 & \(-\)0.51 & 0.63 \\ Dissemination & & & & \\ Landscape Experience & 0.95 & 0.83 & 4.62 & 0.006 \\ Social Interaction & 0.83 & 0.73 & 3.79 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Multiple regression coefficients for sentiment scores of six CES dimensions.  
not appear directly associated with their geographical locations or spatial configurations. The distribution of CES in Macau parks might be influenced by multiple factors, such as park size, park content, and surrounding socioeconomic contexts. Interactions among these factors might partially mask potential spatial clustering patterns of cultural services. Therefore, further research is recommended to explore how visitor experiences could be enhanced by optimizing the functional layouts and landscape designs of parks.  
Figure 4: Radar chart of CES evaluation for Macau parks. (**a**) Vasco da Gama Garden; (**b**) Dr. Carlos D’Assumpcao Park; (**c**) Montanha Russa Municipal Park; (**d**) Lou Lim Ioc Park; (**e**) Chunambeiro Park; (**f**) Flora Garden; (**g**) S. Francisco Garden; (**h**) Monte Fort Park; (**i**) Luis de Camoes Park; (**j**) Guia Hill Municipal Park; (**k**) Hac Sa Van Park; (**l**) Dr. [PERSON] Yat Sen Municipal Park. Additional data supporting these results can be found in Appendix A Table A2.
## 3 Results and Discussions
### Shape Memory Properties
#### 3.3.2 Shape Recovery Ratio and Shape Fixity  
The shape recovery ratio of the PEG-SMPUs, shown in Figure 9(a), varies from 98 to 82%, depending on the molar ratio \(\gamma\). PEG-H1 has the highest shape recovery ratio of 98%, and that of PEG-H4 is only 82%. The PEG-SMPUs show a better recovery ratio compared to those of the PCL-SMPUs, indicating that the high M\({}_{\rm W}\) polyol SMPUs are more flexible at elevated temperature, in contrast to the room temperature thermal mechanical properties shown above. This may be attributed to the longer molecular chains of the high M\({}_{\rm W}\) polyol soft segment, easy deformation of the SMPUs at above T\({}_{\rm g}\) as compared to low molecular weight polyols.  
Figure 8: Summary of recovery stress of different SMPUs at 60 \({}^{\circ}\)C and 50% strain.  
Figure 9: Shape recovery and shape fixity of the PEG **(a)** and PCL SMPUs **(b)** at 60 \({}^{\circ}\)C and 50% strain.  
Figure 9(b) summarizes the shape fixity for both series of the samples. The shape fixity is in the range of 82 \(\sim\) 95% for the PEG-SMPUs. It increases with the decrease of \(\gamma\). PEG-H1 with the highest \(\gamma\) shows the lowest shape fixity of about 82%, while that of PEG-H4 with \(\gamma\) = 0.5 is \(\sim\)95%. The shape fixity of the PEG-SMPUs is higher than PCL-SMPUs due to the high stiffness and high crystallinity as discussed above. Similarly, introduction of the IPDI restricts the segmental and short-range conformational movements of the soft segments, which leads to a quick fixing of the soft segment chains during cooling below the transition temperature. This trend is in agreement with the observation made for the PCL-SMPUs, which have a slightly lower fixity than the PEG-SMPUs with the same MDI to IPDI molar ratios. Generally, the high recovery ratio is always accompanied with poor fixity for shape memory polymers, and high recovery ratio and high fixity are the most desirable properties for SMPs. The PEG-SMPUs have a high recovery ratio and high fixity as well as high maximum stress, demonstrating their excellent shape memory effect as compared to those of the PCL-SMPUs, if the maximum strain of \(\sim\)400% is allowed which is the normal case for typical applications.
## 3 Proposed Methodology
### The T5 model for Sentence Generation  
Using a large-scale study for sentence generation, researchers came up with the T5 (Text-to-Text Transfer Transformer) model [29]. Since the architecture restricts each model to a single task, T5-like models can be adjusted to do several tasks. After constructing a feature vector, it is used as the input to the T5 sentence generator classifier equipped with the RoBERTa model during model training and evaluation. The features of the input data are represented with \(X_{i}=\{x_{1},x_{2},\ldots.x_{n}\}\) where it contains both discrete features and symbols. In the T5 model, the text token is from the deepfake news dataset using the segment embedding technique. In this technique, there is no sentence separation between deepfake news sentence labelling and sentence generation. The well-defined classification denoted as ([CLS]) is added as an initial token. Later, a separable token ([SEP]) is embedded as a final token. Thereafter, the text news token may be forwarded to the T5 model from sentence generation based on Equation (7):  
\[T=T_{5}(x_{1},x_{2},\ldots.x_{n}) \tag{7}\]  
where \(T=(t_{1},t_{2},\ldots.t_{n})\) and \(t_{i}\) is denoted as a token embedded word representation.  
The T5 model contains several layers, which contain sublayers too. Each layer in the T5 model has multi-head self-attention to retrieve connected tokens. The self-attention sublayer measures weighted scores represented with \(a_{ij}\) and \(ws_{ij}\), these are calculated with Equations (8) and (9) as follows:  
\[a_{ij}=\frac{\left(t_{i}m_{g}\right)\left(t_{j}m_{k}\right)}{\sqrt{O_{d}}} \tag{8}\]  
\[ws_{ij}=\frac{\exp\left(a_{ij}\right)}{\sum_{k=1}^{N}\exp\left(a_{ij}\right)} \tag{9}\]Furthermore, the multi-head self-attention mechanism used a mask matrix to identify a text news segment in deepfake news generation and detection. The attention was calculated under the following conditions:  
(i) \(j\)th element by ignoring the ith element. It means that \(M_{i,j}=0\) and \(\left(M_{i,j}-1\right)\times\infty=-\infty\) is attained, and the self-attention was designed with Equation (10):  
\[\begin{split} sim(i,j)=softmax\left(\frac{QK^{p}}{\sqrt{d_{i_{i,j} }}}\right)+\left(M_{i,j}-1\right)\times\infty,\\ =softmax(-\infty)\end{split}\] (10) (ii) \(i\)th element by ignoring the \(j\)th element. It means that \(M_{i,j}=1\) and \(\left(M_{i,j}-1\right)\times\infty=0\) is attained, and the self-attention was designed with Equation (11):  
\[\begin{split} sim(i,j)=softmax\left(\frac{QK^{p}}{\sqrt{d_{i_{i,j }}}}\right)+\left(M_{i,j}-1\right)\times\infty,\\ =softmax\left(\frac{QK^{p}}{\sqrt{d_{i_{i,j}}}}\right)\end{split} \tag{11}\]  
where \(p\) represents the matrix parameters mask \(M\in 0,1^{p\times p}\) in measuring the self-attention of the T5 model. Here, M denotes the mask, and Q is used as a parameter value to optimize the function.
## 4 Demand Side Management System under Evaluation
### Communication Module for NB-PLC  
The communication module used for field measurements is the Texas Instruments (TI) transceiver with power line modem developer kit (TMDSPLCKIT-V3 C2000), whose specifications are shown in Figure 1[18]. This transceiver is a state of art communication kit that has an AFE daughter card incorporated in the front end analog PLC (AFE03I). This tool kit can use various communication applications such as OFDM, IEEE-1901.2, PRIME, etc. All of these applications can be enabled through SFSK and controlled through PLC software suit. The OFDM communication system effectively utilizes the bandwidth. It also incorporates flexible coding schemes and efficient power management. In fact, the OFDM is a tailormade system for a harsh communication environment. This T1 transceiver uses a frequency band between 3 and 500 kHz with 46 kbps data rate. The transceiver uses the state of art Robust (ROBO) mode and modulation schemes. This makes it work easily for the repetition code with BPSK. The SNR and signal strength at the receiver are key parameters for analyzing the chosen sites. Figure 3 shows the graphical user interface (GUI) of the tool kit. Here we can easily see various metrics associated with network performance including sub-band SNR, bit error rate, and signal strength indicator.
## 3 Methodology  
In this study, we propose a unified framework that predicts each frame hand key point information along with object category information from first-person RGB video, while also being able to output hand action categories across consecutive video frames. The framework is illustrated in Figure 2, comprising two modules: the Hand Pose Estimation Module (HPEM) and the Action Recognition Module (ARM). In the Hand Pose Estimation Module, each video frame is first passed through a feature extractor and then fed into multiple transformer encoders. We construct a dual residual network structure and use a multi-layer perceptron to predict the hand key point coordinates and object categories for each frame. The hand pose results, object categories, and feature information of each frame are concatenated for the subsequent task. In the Action Recognition Module, the vectors of each frame are aggregated by a multi-layer transformer encoder. The temporal dynamics of the hand between video frames are captured to infer or estimate the motion trajectory, ultimately enabling the classification of hand action categories over the video sequence. In the remainder of this section, we examine each sub-module in more detail.