esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Spatiotemporal Adaptive Prediction Network
#### 2.2.3 CNN Framework  
To extract the local spatial features of assembly steel strut axial force data and capture geological characteristics pertinent to foundation pit construction, this study employs a CNN for feature extraction. As illustrated in Figure 4, the temporal and spatial features of the axial force data are fused with geological features to form a unified representation that is subsequently processed by the CNN. This method facilitates the extraction of high-level features that capture complex spatial and geological relationships, ultimately enhancing the accuracy of assembly steel strut axial force predictions.  
The feature map \(X\in\mathbb{R}^{H\times W\times C}\), generated from the self-attention-enhanced LSTM, where \(H\) and \(W\) denote the height and width of the feature map and \(C\) represents the number of channels, is used as input. A convolutional kernel \(K\in\mathbb{R}^{k\times k\times C}\) is applied to perform convolution operations and extract local spatial features. The convolution calculation is defined as follows:  
\[Y(i,j)=\sum_{p=1}^{C}\sum_{n=1}^{k}\sum_{m=1}^{k}X(i+m-1,j+n-1,p)\cdot K(m,n,p)+b \tag{10}\]  
where \(Y(i,j)\) is the value of the output feature map at position \((i,j)\), \(b\) is the bias term, \(p\) is the input channel index, and \(m\), \(n\) are indices along the spatial dimensions of the kernel. The convolution operation slides the kernel over the input feature map to extract spatial features, using the ReLU activation function to introduce nonlinearity and improve the model's capacity to capture complex patterns:  
\[Y_{\text{ReLU}}(i,j)=\text{max}(0,Y(i,j)) \tag{11}\]
## 3 Results and Discussion
### Preparation and Characterization of the Material
#### 3.1.2 Morphological Studies  
SEM micrographs (Figure 1) were used to study the surface morphology of the prepared composite material. SEM images of bare TiO\({}_{2}\) show tightly packed clusters of aggregates of the core 40 nm particles. The micrographs for TiO\({}_{2}\) show particles that appear pseudospherical in shape, which is one of the characteristic forms for unmodified anatase [17]. TiO\({}_{2}\)-Fe\({}_{3}\)O\({}_{4}\) particles reveal similar aggregation behavior as bare TiO\({}_{2}\). In the case of TiO\({}_{2}\)-Fe\({}_{3}\)O\({}_{4}\), it appears that TiO\({}_{2}\) particles were grafted onto much bigger, micrometric particles of Fe\({}_{3}\)O\({}_{4}\) forming a coating over Fe\({}_{3}\)O\({}_{4}\)'s surface similar to a core-shell structure. The synthesized Fe\({}_{3}\)O\({}_{4}\) was expected to have a much more spherical shape. However, based on the micrographs, it can be said that the magnetite took the shape of irregular, micrometric grain aggregates.
## 5 Results and Discussion
### Satellite-Based Versus In-Situ Data
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Event (Product)**} & \multicolumn{3}{c}{**Metric**} \\ \cline{2-4}  & **RMSE (mm)** & **MAE (mm)** & **BIAS (\%)** \\ \hline
2015 (TMPA 0.25\({}^{\circ}\)) & 11.51 & 7.45 & 0.63 \\
2015 (TMPA 0.1\({}^{\circ}\)) & 11.23 & 7.35 & 0.64 \\
2015 (IMERG 0.1\({}^{\circ}\)) & 10.67 & 6.72 & \(-\)0.00 \\
2016 (TMPA 0.25\({}^{\circ}\)) & 10.43 & 8.93 & 0.69 \\
2016 (TMPA 0.1\({}^{\circ}\)) & 10.72 & 9.03 & 0.68 \\
2016 (IMERG 0.1\({}^{\circ}\)) & 10.56 & 8.07 & 0.36 \\
2017 (TMPA 0.25\({}^{\circ}\)) & 0.82 & 0.72 & \(-\)1.62 \\
2017 (TMPA 0.1\({}^{\circ}\)) & 0.76 & 0.57 & \(-\)0.81 \\
2017 (IMERG 0.1\({}^{\circ}\)) & 1.20 & 0.89 & \(-\)1.71 \\
2018 (TMPA 0.25\({}^{\circ}\)) & 1.94 & 1.47 & 0.96 \\
2018 (TMPA 0.1\({}^{\circ}\)) & 1.91 & 1.37 & 1.01 \\
2018 (IMERG 0.1\({}^{\circ}\)) & 1.88 & 1.36 & 1.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias (BIAS) for each recorded event with spatial resolutions specified. Figure 4: Spearman correlation (\(R_{s}\)) and \(p\)-value (\(p_{\text{s}}\)) between remote sensing data, at spatial resolutions of (**a**) TMPA 0.25\({}^{\circ}\), (**b**) TMPA 0.1\({}^{\circ}\), and (**c**) IMERG 0.1\({}^{\circ}\) and rain gauge records. The solid line represents the fitted linear regression.
## 3 Problem Statement  
In this section, we formulate the optimization problem for computation offloading and resource allocation in mobile edge computing networks. The primary objective of this problem is to minimize the total energy consumption across the system while simultaneously achieving extremely low latency. This is achieved by optimizing the computation offloading strategy for each user \(s_{k}^{m}\), determining the computation resource allocation at the edge \(f_{l,k}\), and managing the radio resource allocation \(p_{k}\) while adhering to the maximum delay tolerance set for each task of the IoT device \(t_{k}^{max}\). The problem statement is outlined as follows:  
\[\min_{\{s_{k}^{m},f_{l,k}p_{k}\}}E_{k}^{service}\] s.t. \[\text{C1}: s_{k}^{m}\in\{0,1\},k\in\mathcal{K},m\in\{local,edge,cloud\},\] \[\text{C2}: s_{k}^{local}+s_{k}^{edge}+s_{k}^{cloud}\leq 1,k\in\mathcal{K},\] \[\text{C3}: \underline{\sum_{k=1}^{K}}s_{k}^{edge}f_{l,k}\leq f_{l}^{max}, \forall k\in\mathcal{K},\] \[\text{C4}: s_{k}^{edge}SINR_{k,l}\geq\gamma_{min},\forall k\in\mathcal{K},\] \[\text{C5}: s_{k}^{cloud}SINR_{k,j}\geq\gamma_{min},\forall k\in\mathcal{K},\] \[\text{C6}: \underline{\sum_{k}}s_{k}^{edge}p_{k,l}\leq P_{max},k\in\mathcal{K},\] \[\text{C7}: \underline{\sum_{k}}s_{k}^{cloud}p_{k,j}\leq P_{max},k\in\mathcal{ K},\] \[\text{C8}: D_{k}^{service}\leq t_{k}^{max},k\in\mathcal{K} \tag{15}\]  
where the constraint (15) implies that the user \(k\) is allowed to choose only one mode at a time. \(\text{C3}\) denotes that the frequency of each E-AP \(f_{l}\) is constrained by maximum frequency \(f_{l}^{max}\), which is the number of users that can be scheduled to each E-AP \(l\). \(\text{C4}\) ensures the QoS requirements for better system performance by satisfying \(\gamma_{min}\), which is considered as a lower bound. The computing power consumption at the edge cannot go beyond the allocated power \(P_{total}\), which is reflected by C6. Similarly, the C7 shows the upper bound of uplink transmission to be \(P_{max}\) allocated power. The objective variables of the optimization problem shown in (15) consist of \(p_{k}\), \(f_{l,k}\), and \(s_{k}^{m}\), which are power allocation, computing resource allocation, and mode selection, respectively. The traditional approaches are incapable of solving the non-convex mixed-integer non-linear fractional programming problem (MINLFP) as presented in (15). It is especially challenging regarding the dynamic and dense environment of wireless networks. Due to the non-trivial nature of the problem due to the high demand for energy minimization, a distributed learning-based A3C scheme is advocated to resolve (15). A3C is a cutting-edge scheme based on distributed learning with multiple independent agents with their weights. They interact in a complex environment with multiple copies of the environment in parallel toward achieving a common goal [31].
## 3 Experiment Setup  
In order to verify the model of the MBBP-REH and investigate its characteristics, corresponding experimental setups are designed.  
The experiment setup of the rotational energy harvesting system is shown in Figure 3a. Figure 3b shows the prototype of the developed MBBP-REH. Three MBBP-REHs with different excitation magnets arrangement are designed and examined their performances, which are referred to as MBBP-REH1, MBBP-REH2, and MBBP-REH3, respectively. MBBP-REH1 has two excitation magnets that produce attractive force with the center magnet of the buckled beam. MBBP-REH2 has two excitation magnets that generate repulsive force with the center magnet of the buckled beam. MBBP-REH3 has two magnets which produce an attractive force and a repulsive force with the center magnet of the buckled beam, respectively. Table 1 shows the geometric dimensions and the material properties of the proposed prototype. \(D\) = 20 mm, \(d_{0}\) = 11.2 mm, and \(R\) = 25 mm when there is no special description. The revolving host is driven by a steeper motor (SS1704A20A-01, Samsr motor) with a step angle of 1.8\({}^{\circ}\). The rotation speed can be set by the computer through the stepper motor controller, and a matched driver (MD-2322, Samsr motor) is utilized to drive the stepper motor. The voltage that is generated by the rotational energy harvester is collected by a signal acquisition card (6361, NI), then transferred to the computer.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 2 Peer-To-Peer (P2P) and Ad-Hoc Networks
### Balloon Networks  
As a special type of ad hoc networks, aerial and hierarchical balloon networks proved feasible from testing [19] and include optical and radio frequency links between balloons, which consist of  
Figure 1: End-to-end network topology for low-power wide-area network (LPWAN) and carrier networks. LID (Location ID) is carried within LPWAN to the gateway, which connects to public networks in the form of calling party address (PAN) of existing signalling systems such as public switched telephone network (PSTN) or cellular networks.  
super-nodes and sub-nodes. Each super-node balloon comprises a free-space optical communication system for data communications with one or more other super-node balloons, which comprises a radio-frequency communication system operable for data communications [20; 21]. A wireless telecommunications system with a base station positioned on an airborne platform is provided and can communicate with satellite-based routing processes [22]. The system can also utilise a ground-based wireless switching system to communicate with a plurality of airborne base stations positioned above a geographic area, as well as the balloons' geographic zones [23].
## 2 Experimental Development
### Mechanical Characterization of Small-Scale Prototypes  
The dimensions of the small-scale prototypes were determined following the methodology of [PERSON] et al. [7; 8] for both materials. The structure consisted of four columns, each subjected to a 19.25 kN applied weight. The scaled design period of the isolated structure was 1.15 s, with a nominal lifetime of 100 years. Seven seismic records representative of Italian regions were used. The resulting prototype dimensions are shown in Figure 4 [8].  
Two tests were performed. In the first shear compression test, a horizontal displacement protocol was applied following [PERSON]'s [15] recommendations (Figure 5a). Six deformation levels were tested, namely, 25%, 50%, 67%, 100%, 100%, and 75% of the maximum chosen deformation (29 mm) with a period of 1.15 s (Figure 6a). The test was conducted at Purdue University's Intelligent Infrastructure Systems Laboratory (IISL).  
The second cyclic compression test was conducted in three phases to measure displacements under a variable compression load (Figure 5b). First, a compressive load was applied at 0.01 mm/s until reaching the design load of 19 kN. Then, three cycles were performed at \(\pm\)30% of the design load at 0.05 mm/s. Finally, the compression load was fully removed at 0.01 mm/s (Figure 6b). This test was conducted at PUJB laboratories.  
Figure 4: Dimensions of the UN-FREI and UR-FREI small-scale prototypes [7].  
Figure 5: Prototype tests: (**a**) shear compression test, and (**b**) cyclic compression test.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 4 Discussion
A key limitation of this study is the use of AS fruits collected from a single geographic location (Northeast Forestry University, China) at one maturity stage, restricting the generalizability of the findings. Plant provenance, genetic variability, and environmental factors significantly affect bioactive compound accumulation and post-harvest responses to drying treatments. Previous studies confirmed that temperature fluctuations, photoperiod, soil composition, and environmental stressors (e.g., drought, UV radiation, salinity) substantially modulate secondary metabolite profiles in plants [83; 84]. Additionally, regional variability markedly influences phenolic content and antioxidant capacity [85; 86]. Future studies should incorporate multi-location, multi-season, and multi-maturity sampling, as well as different cultivars, to enhance the robustness, representativeness, and applicability of the conclusions across diverse environmental conditions. Limited scope of biological activity assessments. This study assessed primary bioactive indicators (TPC, TFC, and antioxidant activity via DPPH and ABTS assays) but did not explore other significant biological activities relevant to functional applications of AS fruits. Recent studies indicate that AS fruits may possess additional bioactivities, including anti-inflammatory, antimicrobial, hypoglycemic, immunomodulatory, cardiovascular-protective,and hepatoprotective effects [87; 88; 89]. Future research should, therefore, include comprehensive in vitro and in vivo evaluations using cellular and animal models to thoroughly characterize the bioactivities influenced by different drying methods.
## 5 Experimental Results
### Experimental Results for BCC Algorithm
#### 5.2.2 Power Consumption Analysis of BCC
Hence, at higher injection rate, CES and GRD consumed less power compared to BCC. However, this reduction of power came at the cost of lower throughput as CES and GRD algorithms did not consider the network traffic characteristics while consolidating. However, the increase in power consumption in BCC for S2S-WiDCN was not as drastic as in the case of wired data center. This is demonstrated by the trend arrows in Figure 8d. This was because, in the S2S-WiDCN architecture, a server had the potential to sustain a maximum of seven simultaneous links at a time with other servers in its vertical plane and horizontal line. On the contrary, in the wired architecture, there existed only one link per server albeit, of higher bandwidth. As a result, for the wired DCN with BCC, many of the VM migration attempts failed due to the violation of the inequality of (5) compared to the S2S-WiDCN. This suggested that the bandwidth constrained network-aware consolidation, BCC, was more effective on S2S-WiDCN.
# Multiple-Attribute Decision Making ELECTRE II Method under Bipolar Fuzzy Model
## 2 The BF-ELECTRE II Method
### Construction of Outranking Graphs
It is clearly shown that the alternative \(\mathcal{P}_{2}\) is chosen as the best possible location for all these MCDM methods under bipolar fuzzy environment. Thus, the proposed bipolar fuzzy ELECTRE II method can be successfully applied to solve the MCDM problems with bipolar fuzzy information. This method not only gives the solution of problem but also ranks the alternatives in ascending order and the alternative with minimum choice value is chosen as best action. ### Comparison of BF-ELECTRE II with Fuzzy ELECTRE II

* Bipolar fuzzy set plays an important role in human decision making, as it deals with two-sided information or bipolar reasoning of human thinking. Positive information represents what is granted to be possible or true, while negative information represents what is considered to be impossible or may be false. There are many real world problems which are based on bipolarity or double-sided information (instead of one-sided), as we described in the numerical examples. We use bipolar fuzzy set in which the performance rating of each alternative consists of two membership values: positive and negative. The positive membership value of an alternative shows the benefit or satisfaction behavior of that alternative towards the criteria and the negative membership value represents the cost or dissatisfaction degree of alternatives. We use bipolar fuzzy ELECTRE II method to rank the alternatives in ascending order or to chose the best possible action. * Fuzzy ELECTRE II is successfully applied to solve problems having only one sided information, that is, alternatives are ranked only on the basis of positive membership or satisfaction degree of alternatives. By using fuzzy structure or set to decision making, we are unable to provide information about the dissatisfaction behavior or degree of alternatives corresponding to different
# Design and Numerical Study of Induction-Heating Graphitization Furnace Based on Graphene Coils
## 2 Numerical Model
### Mathematical Model  
#### 2.2.1 Basic Assumptions  
The simulation of an induction graphitizing furnace involves several physical fields with strong coupling. In order to ensure scientific results and reduce the difficulty of model solving, the following simplified assumptions must be formulated for the model:  
1. All materials in the model are pure;
2. All materials in the model are isotropic;
3. No consideration is given to mechanical stresses or deformations due to temperature changes;
4. No consideration is given to the phase transition.
## 3 Modeling Optimization Problems with General Uncertainty  
Real-world optimization problems encounter various sources of uncertainty, leading to the importance of including the uncertainty terms in the problem formulation. Thus, the travel time or demand in transportation problems usually become random or fuzzy variables, instead of being fixed values. This leads to a change in the problem formulation to include these variables. A generic problem formulation including stochastic and fuzzy uncertainty is shown in Equations (1)-(6). The problem formulation represents a solution \(s\), belonging to a set of solutions \(S\), that optimizes the objective function \(Z\). This objective function has stochastic, \(X\), and fuzzy, \(Y\), variables, as depicted in Equation (1). Both stochastic and fuzzy variables are defined in the problem context. The stochastic and fuzzy variables have defined probability distributions and fuzzy sets, respectively. The set of constraints in the problem formulation could have \(|I|\) deterministic constraints, as indicated in Equation (2). An example of such a constraint would be the transported freight not exceeding vehicle capacity, which is a typical restriction found in many vehicle routing problems. In addition to these constraints, non-deterministic constraints are defined, such as in Equations (3)-(5). These constraints include stochastic or fuzzy variables, resulting in \(|J|\) stochastic constraints, \(|K|\) fuzzy constraints, and \(|M|\) constraints with fuzzy and stochastic variables. Thus, the constraints become probabilistic. For example, the travel time \(l_{m}(X,Y)\) is required to be within a pre-defined threshold \(t_{m}\), as depicted in Equation (5). The limit and the travel time are affected by the \(X\) and \(Y\) variables. In the context of the model, sets \(I\), \(J\), \(K\), and \(M\) are just sets of natural numbers.  
\[\text{optimize }f(s)=Z(s,X,Y) \tag{1}\]  
subject to:  
\[g_{i}(s)\leq t_{i}\qquad\forall i\in I \tag{2}\]  
\[Pr\Big{(}g_{j}(s,X)\leq l_{j}(X)\Big{)}\leq t_{j}\qquad\forall j\in J \tag{3}\]  
\[Pr\Big{(}g_{k}(s,Y)\leq l_{k}(Y)\Big{)}\leq t_{k}\qquad\forall k\in K \tag{4}\]  
\[Pr\Big{(}g_{m}(s,X,Y)>l_{m}(X,Y)\Big{)}\geq t_{m}\qquad\forall m\in M \tag{5}\]  
\[s\in S \tag{6}\]
# Training Autoencoders Using Relative Entropy Constraints
## 2 Background
### Solving for Decoder Parameters  
An autoencoder is an unsupervised learning algorithm. If the data itself is regarded as a kind of label, that is, \(\mathbf{O}=\mathbf{X}\), then the autoencoder can be trained by the mode of supervised learning. Assuming that the loss function of the autoencoder adopts the squared error loss function, and the activation function of the output layer adopts a linear activation function, the optimization objective of the autoencoder can be defined as:  
\[\min_{\mathbf{W}_{d}}\|\mathbf{W}_{d}\mathbf{H}-\mathbf{X}\|^{2}+\lambda\| \mathbf{W}_{d}\|^{2}, \tag{6}\]  
where \(\lambda(\lambda>0)\) is a weight parameter, which is used to control the importance of the first item and the second item in the optimization objective.  
The optimization problem (6) has an analytical solution. When the number of columns of the hidden layer output matrix \(\mathbf{H}\) is greater than the number of rows (\(M>J\)), the calculation formula of \(\mathbf{W}_{d}\) is:  
\[\mathbf{W}_{d}=\mathbf{X}\mathbf{where **I** is an identity matrix of size \(J\times J\). When the number of rows of the hidden layer output matrix **H** is greater than or equal to the number of columns (\(J\geq M\)), the calculation formula of \(\textbf{W}_{d}\) is:  
\[\textbf{W}_{d}=\textbf{X}(\textbf{H}^{T}\textbf{H}+\lambda\textbf{I})^{-1} \textbf{H}^{T}, \tag{8}\]  
where **I** is an identity matrix of size \(M\times M\).  
When the forward algorithm is used to train the autoencoder, the encoder model parameters are obtained by bundling the decoder model parameters. Therefore, the calculation formula of the encoder model parameters is:  
\[\textbf{W}_{e}=\textbf{W}_{d}^{T}. \tag{9}\]  
In summary, taking the number of training samples greater than the number of hidden nodes (\(M>J\)) as an example, the autoencoder forward training algorithm based on relative entropy constraints, REAE (Algorithm 2), can be described as:  
```
1: Solve for the feature mapping parameters \(\textbf{W}_{p}\) and \(\textbf{b}_{p}\) by optimizing the loss function (5) using gradient descent.
2: Forward propagate data, and use the model parameters obtained in step (1) to calculate the hidden layer output matrix **H**.
3: Calculate the decoder model parameters \(\textbf{W}_{d}\) according to the Formula (7).
4: Use the Formula (9) to calculate the encoder model parameters \(\textbf{W}_{e}\).
```  
**Algorithm 2** Forward training algorithm for autoencoders based on relative entropy constraints.  
When the number of hidden nodes is greater than or equal to the number of training samples (\(J\geq M\)), the third step of Algorithm 2 can use the Formula (8) to calculate the decoder model parameters \(\textbf{W}_{d}\).
## 4 Results from the Literature Review
### Connected Car
#### 4.2.7 Advances in Public Transport  
Public transport systems are continuously evolving to better meet societal transport needs while minimizing their environmental impact. Several recent innovations also demonstrated potential benefits in global crisis scenarios. A review of the literature highlights five notable examples of such advancements (Table 14).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Solution** & & **Role in Case of Potential Crises** \\ \hline \hline \multirow{7}{*}{Autonomous buses} & \(\bullet\) & Lack of expenses for operators to employ drivers and the possibility of reducing transportation costs (economic crisis) \\  & \(\bullet\) & Reducing greenhouse gas emissions and lower carbon footprint \\  & (environmental crisis and health crisis) \\  & \(\bullet\) & Possibility of using it for contactless transport of infected people, samples, and tests (health crisis) \\  & \(\bullet\) & Higher energy efficiency and profitability of transport during sudden \\  & \(\bullet\) & Possibility to generate savings by flexibly adjusting courses, shortening travel times, and increasing vehicle occupancy (economic crisis) \\ On-demand public transport & \(\bullet\) & Reducing harmful gas emissions per passenger (environmental crisis and health crisis) \\  & \(\bullet\) & Increasing the resilience of the public transport system to potential sudden \\  & \(\bullet\) & changes in demand for passenger transport (health crisis) \\ \hline \hline \end{tabular}
\end{table}
Table 14: The role of recent advances in public transport during potential global crises.