esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Occurrence Modes for Arsenic in Coal and Its Influence on the Environment  
#### 5.4.1 Occurrence Modes for Arsenic in Coal  
Arsenic is one of the common trace elements in coal and has attracted much attention due to its great harm to the environment. The arsenic content in coal varies depending on the type of coal, the coal-forming environment, and the geological background [3]. The average arsenic concentrations in the worldwide low-rank coals and hard coals are 7.6 ppm and 9 ppm, respectively [34], while the average arsenic concentrations in coals for China and Australia are 3.79 ppm and 3 ppm, respectively [3]. In addition, the arsenic concentration in coal and its combustion products can reach up to 35,000 ppm [3].  
The occurrence modes of arsenic in coal are diverse (Sections 5.1 and 5.2, [3; 10; 13; 43]). At present, the common understanding in academia is that the majority of arsenic in most coals occurs in pyrite as an isomorphic substitute for sulfur, or solid solutions (this work [3; 55]). Arsenic may be enriched in pyrite, and its genesis can be either a syngenetic or epigenetic origin [3; 56]. However, this does not mean that the arsenic content in coal rich in pyrite will be high. For example, studies show that framboidal pyrite in the Pennsylvania coal of the Appalachian Basin is usually deficient in arsenic [3; 57]. Meanwhile, arsenic in coal is also related to clay minerals, phosphates, and arsenates (this work, [3; 8]). Arsenic in coal occurs not only in minerals but may also be associated with organic matter in some cases (this work, [3]). However, it is mainly found in low-rank coal. Whether it is definitely present in high-rank coal requires further evidence [3].  
Based on previous studies and this work, we believe that (1) arsenic in coal is predominantly in the form of pyrite, and (2) arsenic in coal is associated with organic matter in low-rank coal and to a lesser extent in high-rank coal.
## 5 Performance Evaluation
### Experiment
#### 5.4.2 Ablation Study of G-TIAG  
To explore the contribution of each component in G-TIAG to the classification results, we conduct an ablation study for G-TIAG. Specifically, we sequentially remove one component from G-TIAG and analyze the change in classification performance.  
The GCN model denotes the model with the GRU component removed from G-TIAG, and the GCN-GRU model denotes the model with the self-attention removed from G-TIAG. The overall classification ability of the three classifiers is evaluated using two indicators, which are accuracy and processing rate. The results are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Normal & 0.91 & 0.90 & 0.90 & 0.94 \\ Dos & 0.91 & 0.89 & 0.90 & 0.93 \\ DDoS & 0.88 & 0.88 & 0.87 & 0.89 \\ PortScan & 0.91 & 0.87 & 0.89 & 0.90 \\ Bot & 0.69 & 0.71 & 0.70 & 0.77 \\ Web attack & 0.66 & 0.72 & 0.69 & 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification results of G-TIAG.  
Figure 8: Confusion matrix of G-TIAG.  
The results in Table 8 show a slight decrease in the classification performance when any components are removed from G-TIAG. When the GRU is removed, the accuracy decreases from 0.89 to 0.84, which indicates that the GRU can capture the correlation among longer sequence features and improve the classification results appropriately. When the self-attention is removed, the accuracy slightly decreases from 0.89 to 0.86, which indicates that the self-attention can focus on the more valuable features and fully exploit the correlation features among input instances.  
Compared with G-TIAG, the processing efficiency of GCN is substantially improved, and the processing efficiency of GCN-GRU is also improved. The introduction of the GRU and self-attention increases the complexity of the G-TIAG. Therefore, if classification performance is pursued in a practical scenario, devices with higher arithmetic power can be used in exchange for higher classification results.  
To detailly understand the performance of the three methods on benign data and five attacks, we choose two indicators for evaluation, which are the accuracy and the F1-Score. The results are shown in Figure 9.  
We find that the classification ability of G-TIAG for benign data and Dos, DDoS, and PortScan has been improved compared with the other two methods. For Bot and Web Attacks, the classification ability has been improved substantially compared with GCN, and the classification ability has been improved marginally compared with GCN-GRU. Therefore, we can conclude from the above work that the GRU component contributes more to the classification results than self-attention.
## 5 Offline Trail Positioning System
An example of the system performance and the databases stored in different FatBeacons can be seen in Figure 10(a). In Figure 10(a), different points corresponding to POIs can be seen. These POIs are highlighted with different letters to simplify the example. When the user starts hiking and detects the first FatBeacon, highlighted with the letter A in the image of Figure 10(a), the application asks the hiker to select one of the possible destinations for this trail. Then, the application stores the last FatBeacon found, in this case A, and the final destination of the user, in this case H (see Figure 10(b)). The hiker continues the trail route, discovering different FatBeacons, like B, C and D, and the application informs the hiker about whether he/she is on the right track after discovering each one of the FatBeacons (see Figure 12). If the user goes on the wrong track and finds for example the FatBeacon in the POI denoted by E, the application alerts the hiker that he/she is following the wrong way. In that case, the application discards this FatBeacon and does not store it in the database. Later, the user again finds the FatBeacon of the POI denoted by D, and the application informs that he/she is on the correct track. Now, the user finds the FatBeacons F and G, and the application informs that he/she is on the right track. Once  
Figure 10: Damaged trail signals. Figure 9: Guide signals. the hiker finds the FatBeacon of the POI denoted by H, the application informs that he/she has reached his/her destination. This simple example shows the performance of the developed offline trail positioning system, whose decision algorithm can be seen in Algorithm 1. Figure 11: Map of the trail with FatBeacon distribution and user route example. Figure 12: FatBeacon route database (B and C have been omitted). if (destination == chosenDestination) {  inform(\"Right Way\");  return true; } }  inform(\"Wrong Way\");  return false; } } %label{algorithm1} ```  
The above fragment of the position algorithm shows how the developed system works to inform users about the trails. Some functions, like extractBBDD (String previous), are used to process the information received from the FatBeacon and to extract the useful data. Besides, the function inform(String message) sends the user a notification and a voice message through the application. Some screenshots of the developed application can be seen in Figure 13.
# Solution Merging in Matheuristics for Resource Constrained Job Scheduling
## 2 Resource Constrained Job Scheduling
### Model 1  
A common technique in the context of exact methods for scheduling is to discretise time [33]. Let \(\mathcal{T}=\{1,\ldots,t_{\max}\}\) be a set of time intervals (with \(t_{\max}\) being sufficiently large) and let \(x_{jt}\) be a binary variable for all \(j\in J\) and \(t\in\mathcal{T}\), which takes value 1 if the processing of job \(j\) completes at time \(t\)By defining the weighted tardiness for a job \(j\) at time \(t\) as \(w_{ji}:=\max\{0,w_{j}\left(t-d_{j}\right)\}\), we can formulate an MIP for the RCJS problem, as follows.  
min \[\sum_{j\in\mathcal{J}}\sum_{t\in\mathcal{T}}\left(w_{ji}\cdot x_{ ji}\right)\] s.t. \[\sum_{t\in\mathcal{T}}x_{ji}=1 \forall\ j\in\mathcal{J}\] (12) \[x_{jit}=0 \forall\ t\in\{1,\ldots,r_{j}+p_{j}-1\},\quad\forall\ j\in \mathcal{J}\] (13) \[\sum_{t\in\mathcal{T}}t\cdot x_{bt}-\sum_{t\in\mathcal{T}}t\cdot x _{at}\geq p_{b} \forall\ (a,b)\in\mathcal{C}\] (14) \[\sum_{l=t}^{t+p_{j}}x_{jl}+\sum_{l=t}^{t+p_{k}}x_{kt}\leq 1 \forall\ j,k\in\mathcal{J},t\in\mathcal{T}\] (15) \[\sum_{j\in\mathcal{J}}\sum_{t=t}^{t+d_{j}-1}g_{j}\cdot x_{jl}\leq \mathcal{G} \forall\ t\in\mathcal{T}.\] (16)  
Constraints (12) ensure that all jobs are complete. Constraints (13) ensure that the release times are satisfied and are typically implemented by excluding all of these variables from the model. Constraints (14) take care that the precedences between jobs \(a\) and \(b\) are satisfied and Constraints (16) ensure that no more than one job is processed at the same time on one machine. Constraints (16) ensure that the resource constraints are satisfied.  
This model is certainly the most natural way to formulate the RCJS problem, though not necessarily the computationally most effective. The linear programming (LP) bounds could be strengthened by replacing each precedence constraint of the form (14), with a set of constraints specifying that \(\sum_{t<\tau+p_{b}}x_{bt}\leq\sum_{t\leq\tau}x_{at}\ \forall\tau\in\mathcal{T}\). Additionally, the branching behaviour for this formulation tends to be very unbalanced: forcing a fractional variable to take value 1 in the branch and the bound tree can be expected to have a large effect with the completion time of the job now fixed. On the other hand, setting some \(x_{jit}=0\) is likely to result in a very similar LP solution in the branch-and-bound child node with perhaps \(x_{j,t-1}\) or \(x_{j,t+1}\) having some positive (fractional) value with relatively little change to the completion time objective. Finally, the repeated uses of sums over \(\mathcal{T}\) in the constraint mean that the coefficient density is relatively high, potentially impacting negatively on the solving time for each linear program within the branch and bound method. All of these considerations typically lead to the following alternative formulation being preferred in the context of branch and bound.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 5 Proposed Solution
### System Scalability  
The proposed framework is scalable and can be used with new criteria for slice selection, as well as different access network technologies. The interaction between NSSF DAF instances, even in different administrative domains, provides a network of information about the quality of available slices in certain regions (e.g., neighborhoods, universities, cities).  
_NSSF DAF_ also allows for the addition of new modules, for example, a broker service for IoT devices that support Message Queuing Telemetry Transport (MQTT) or other publish/subscribe protocols. It can also facilitate the attachment of a Mobility Management (MM) module, selection of Access Points (APs) for RAN based on Wi-Fi 6.  
Framework deployment can be performed using DevOps tools, and it can still be deployed in containers or virtual machines on a public cloud or local datacenter. This feature can be used on private 5G networks.
## 4 Simulation and Experimental Data
### Simulation Data
The proposed algorithm can eliminate the imaging sidelobes of moving targets, and control the positioning deviation within 0.30 m, without increasing the complexity of the system. In addition, in terms of the calculation time, compared with no compensation, the proposed algorithm only takes 0.10 s, which is the same as the RCMCA.
## 3 Implementation of Raising Power Loss Equalizing Degree (PLED) by Convex Optimization Commutation  
### Algorithms of Convex Quadratic Optimization Commutation  
As Figure 1 shows, the commutation algorithm must unite with the controllers in real-time. Since the focus of this paper is to raise PLED by convex optimization commutation instead of the real-time control, thereinafter all commutations are implemented using quadprog routine with an active-set algorithm on Matlab R2012b, Lenovo-PC with Intel(R) Core(TM) i5-3230M CPU @ 2.60 GHz.  
The quadprog routine in Matlab Optimization Toolbox [22] is specifically programmed to solve the quadratic objective optimization problems with equality, inequality, and boundary constraints, in spite of these problems being convex or non-convex. This routine also facilitates users to select a few different algorithms--active-set, interior-point, and trust-region-reflective algorithms among others. These algorithms, searching for the optimal solution in the feasible region, can still converge fast and operate efficiently on a dedicated high-speed microprocessor [23; 24; 25; 26].
## 6 Performance Evaluation of ASC-RL
### Event Correlations Inside ASC-RL  
This parameter is used to predict the next state and depends on various factors. Probabilistic temporal logic is applied to check the contents of bit values in different sensing cycles, and event correlation was derived from cognitive sensors. As shown in Figure 7, correlations between different cognitive sensors were derived and mapped with high and low correlations. The range of values is from \"\(-1\)\" to \"+1\", in which \"+1\" is a complete positive correlation with increasing and decreasing correlating values. A 0 correlation means that these sensor values have no dependency, and they have no bits that correlate with each other. A \"\(-1\)\" correlation is an inverse correlation; as one increases, the other decreases. If there is a strong correlation, like 0.85, it means that these sensors sense similar nature data with the same bit repetitions. In weak correlations, such as 0.05, these sensors  
\begin{table}
\begin{tabular}{c c c c} \hline
**Figure** & **Parameters** & **Key Observations** & **Optimal Condition** \\ \hline \multirow{2}{*}{Figure 5} & \multirow{2}{*}{Energy–Noise} & Lesser noise with optimum energy & \multirow{2}{*}{Lower noise, moderate energy} \\  & & & \(\text{bad }R_{t}\) & \\  & & & Good signal and lower noise result in & \\  & & & better \(R_{t}\), a weaker signal or high & \\  & & & noise reduces \(R_{t}\) & \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 5: JDC for noise and energy.  
Figure 6: JDC for noise and signal strength.  
sense rare types of data, or they are deployed very far apart from each other. In a negative correlation, such as \(-0.05\), it works inversely; when one increases, the other decreases. In ASC-RL, this correlation is helpful in state change based on the correlation.  
In correlating the values in Figure 7 with the values in Figure 1, four states for any sensor are shown with a structured Markov transition matrix. The probability of a sensor remaining in its current state is defined by the diagonal element \(\mu\), but the off-diagonal elements are defined as \(\frac{1-\mu}{3}\). This equally divides the remaining probability among transitions to the other three states. Higher values of \(\mu\) suggest stronger persistence in the current state. This means that each node has a constant and balanced probability of changing states. On the other hand, Figure 7, illustrates the relationships between event patterns in ten sensors. Some sensors show stronger correlations, such as \(CS_{1},CS_{4},CS_{7}\), and \(CS_{1}0\), as they share a base signal. It can be correlated with Figure 1, as this phenomenon is consistent, which states that sensors with high \(\mu\) and similar functions are more likely to exhibit state changes over time. The heatmap in Figure 7 illustrates that organized transitions in a Markov model can result in visible correlations between sensors.
## 4 Experimental Validations
### Inspection of Small Flaws Compared to the Wavelength
#### 4.2.1 Inspections with Compressional (P) Waves  
The specimen has been first inspected in a TOFD configuration for which the probes radiate P45\({}^{\circ}\) waves. Figure 14a depicts the device, and the inspection configuration is described in Figure 14b.  
The experimental and simulated Bscans obtained using the 1 MHz probes and the 3D hybrid FEM model in simulation for the notch are provided in Figure 15. The Bscans chronologically display the lateral wave, the echo from the defect and the echo due to the P waves backwall reflection. At this low frequency (1 MHz), we observe a single echo arising from the entire flaw. The experimental and simulated results are very similar: the observed relative amplitudes of all echoes are very well reproduced in simulation. Details on the simulation of lateral wave or backwall reflection in TOFD can be found in [13]. The comparison of maximal amplitude for the flaw echo lead to a simulation/experience gap of \(-\)1.6 dB for the 3D hybrid FEM model against \(-\)2.2 dB for the 3D PTD model.  
Figure 14: (**a**) Photograph of the setup; (**b**) TOFD inspection configuration using immersion probes: study of P45 echoes scattered by the flaw surrounded in blue.  
Figure 13: Specim used for the following experimental validations.  
The 3D hybrid FEM model provides a slightly better prediction than the analytical model for small flaws and TOFD P waves at 1 MHz.
# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 4 Results and Discussion
### Optimal Location of Emergency Bells  
The optimal locations for new emergency bells were determined by considering the existing emergency bells. Based on a surveillance area of 50 m, 17,437 unmonitored crimes were targeted to determine optimal locations for new emergency bells. Precisely, the yellow dots indicate the ideal positions for emergency bells, the red dots represent the coordinates of crime incidents, and the blue dots show where emergency bells have already been installed (Figure 6b).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Administrative** & \multirow{2}{*}{**N \({}^{*}\)Bell**} & \multirow{2}{*}{**N \({}^{*}\)CCTV**} & **Area.CCTV** & **Request.** & **Area.Build** & **Float.Pop.D** & **Float.Pop.N** & **N \({}^{*}\)Crime** \\
**Dong** & & & **(\%) \(\ast\)** & **CCTV \(\ast\)** & **(km\({}^{2}\))** & ******** & **** **(2015\({}^{-}\)2021)** \\ \hline Suwan-dong & 92 & 282 & 0.51 & 27 & 19.417353 & 740,313.71 & 497,229.83 & 4223 \\ Hanam-dong & 62 & 232 & 0.41 & 18 & 74.018946 & 797,992.87 & 413,941.94 & 1621 \\ Chipyeong-dong & 53 & 192 & 0.29 & 6 & 14.49957 & 588,982.6122 & 347,001.45 & 7601 \\ Yongbong-dong & 48 & 176 & 0.31 & 27 & 6.403424 & 676,030.6806 & 497,929.84 & 3886 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The high-level cluster derived through cluster analysis.
# Spatial Performance Indicators for Traffic Flow Prediction
## 3 Materials and Methods  
### Spatial KPIs  
This study aims to enhance the evaluation of traffic prediction by incorporating spatial KPIs alongside standard accuracy KPIs. This study focuses on three specific measures from the domain of GIS and network graph analysis: spatial autocorrelation measured by Global Moran's I, the spatial clustering of high/low values measured by Getis-Ord General G, and node centrality measured by the Adapted PageRank Algorithm Modified (APAM).
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Methods Based on Deep Learning
#### 2.6.2 Methods Based on Generating Adversarial Network
The thermal target's prominence in the infrared image and the textural features in the visible image can both be preserved in the fused image in this fashion.
## 2 Data and Model Experiment
### WRF-Chem Model Setup  
#### 2.3.1 Description of WRF-Chem Model  
WRF-Chem is a chemical transport model developed by the community led by NOAA/ESRL (Earth System Research Laboratory, The United States National Oceanic and Atmospheric Administration) [55; 56], and it is widely used for analysing heavy pollution processes and the effectiveness of emission control measures [22; 27; 57].
## 3 Proposed Method
### Kd  
Given a dataset \(\mathcal{D}=\{(x_{n},y_{n})\}_{n=1}^{N}\) consisting of \(N\) samples with \(C\) classes, \(y_{n}\in\{1\dots C\}\). The KD method uses the student network to learn the information delivered by the teacher network, thereby improving the performance of the student network. For ease of presentation, \(t\) and \(s\) are used to distinguish between the teacher and student networks, respectively. The temperature-softened network output is introduced in the conventional KD [14] to obtain supervised information. At this point, the probability of the network output for each category \(j\in\{1\dots C\}\) is expressed as:  
\[p(x,\tau)=\sigma\big{(}z_{j}/\tau\big{)}=\frac{e^{\big{(}z_{j}/\tau\big{)}}}{ \sum_{i=1}^{C}e^{\big{(}z_{i}/\tau\big{)}}} \tag{1}\]  
where \(x\) denotes the input sample, \(z_{j}\) is the output of the network before the softmax function [37] is applied, and \(\sigma(\cdot)\) is the softmax function. \(\tau\) is the hyper-parameter temperature, which controls the smoothness of the probability distribution of the output. The conventional KD method attempts to minimize the gap between the output of the teacher and student networks at the same temperature to allow the student network to mimic the teacher network. The distance between the teacher and student, i.e., the distillation loss, is denoted as:  
\[\sum_{i=1}^{N}D_{KL}(p_{t}(x_{i},\tau),p_{s}(x_{i},\tau)) \tag{2}\]  
where \(D_{KL}\) is the distance function Kullback-Leibler divergence [38] that measures the distance between the teacher and student networks. \(p_{t}(x_{i},\tau)\) and \(p_{s}(x_{i},\tau)\) denote the output of the teacher network and that of the student network after temperature smoothing, respectively. The distance between the student network and true label, i.e., the classification loss, is expressed as:  
\[\sum_{i=1}^{N}L_{CE}(p_{s}(x_{i},1),y_{i}) \tag{3}\]where \(L_{CE}\) denotes the cross-entropy function and \(y_{i}\) refers to the true label of this sample. The overall loss of the student network consists of the distillation and classification losses.  
\[\begin{split} Loss_{KD}&=Loss_{\text{{cla}}}+Loss_{ \text{{dis}}}\\ &=\sum_{i=1}^{N}\Bigl{(}(1-\alpha)L_{CE}(p_{s}(x_{i},1),y_{i})+ \alpha\tau^{2}\text{D}_{KL}(p_{t}(x_{i},\tau),p_{s}(x_{i},\tau))\Bigr{)}\end{split} \tag{4}\]  
where \(\alpha\) is the hyper-parameter used to balance the two losses and since the order of magnitude of the classification loss gradient is approximately \(\tau^{2}\) times that of the distillation loss gradient. The distillation loss is multiplied by \(\tau^{2}\) to keep the contribution of the two loss gradients consistent.