esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fair Scheduling Algorithms in IEEE 802.16e Mobile WiMAX Networks
### Generalized Processor Sharing (GPS)  
GPS [8] is a simple throughput fair scheduling algorithm. The GPS concept is derived from the idea of CPU time sharing where each process receives equal amount of shared CPU time. Therefore, the resource allocation is unaware of packet boundary. In general, GPS can achieve ideal perfect fairness. In particular, full frame utilization can be accomplished in the context of a Mobile WiMAX frame-based allocation. Nevertheless, packet fragmentation may occur, resulting in reduced goodput--throughput after overhead--due to MAC (Media Access Control) header and fragmentation subheader overheads.  
For example, suppose there is a 300-byte Mobile WiMAX frame capacity, and there are four active mobile stations (MSs). To simplify the derivation, assume that all packets are 125 bytes in size. Then, only one 300/4 = 75-byte fragmented packet will be transmitted in every frame for each MS. With a large number of MSs, the ideal GPS is infeasible due to the effect of fragmentation overheads.
Foundation) invested USD 3.85 million in critical infrastructure cybersecurity projects [3]. The European Investment Bank Group has pledged EUR 8 billion for security investments until 2027, focusing on military mobility, space, green security, and critical infrastructure [4]. Siemens announced a USD 150 million investment in a high-tech manufacturing plant to support American data centers and critical infrastructure [5]. This review seeks to delve into the realm of critical infrastructure, specifically examining the integration of wireless sensor networks within these crucial systems. The review follows a systematic approach, meticulously scrutinizing the protocols employed in wireless sensor networks for critical infrastructure. It places a keen focus on unraveling the intricacies of security standards embedded within these networks, shedding light on the protective measures implemented. Furthermore, this review navigates through the landscape of manufacturing standards, emphasizing the pivotal considerations necessary in delivering components destined for integration into such networks. This comprehensive exploration aims to unravel the nuanced layers of wireless sensor network implementation in critical infrastructure, from protocol intricacies to robust security and manufacturing precision.  
This document's structure can be refined as follows: Section 2 delves into an indepth study of critical infrastructures at a global level, categorizing them comprehensively. Section 3 analyzes the technological applications of WSNs, focusing on the essential protocols and standards required for their deployment in critical infrastructures. Section 4 presents the methodology used for the bibliographic review and illustrates the results through diagrams. Finally, Section 5 engages in a discussion, studying the results obtained from the bibliographic research.
# Sustainable Operation of Active Distribution Networks
## 2 Description of the Suggested Multi-Agent System Structure
### Local Agents  
The building thermal load agent (BTL/A), building electrical load agent (BEL/A), plug-in electric vehicle agent (PEV/A), renewable energy sources agent (RES/A), and load agent (L/A) belong to the type of local agents. The inherent quantities of the distribution network components are calculated by local agents. Specifically, BTL/A and BEL/A were assigned to each building of the network and to model its thermal and electrical loads, respectively. Additionally, BTL/A calculates the indoor temperature of each building over the optimization horizon. The estimated indoor temperature of the building was used with its upper and lower limits to suitably calculate the flexibility of each building to increase or decrease its thermal power demand. Afterward, BTL/A and BEL/A send the necessary data to the supervising cluster agents CBTL/A and CBEL/A, respectively. PEV/A calculates the flexibility of each individual PEV to adjust its power and the bounds of its stored energy and the active and reactive power it exchanges with the distribution network. These quantities are forwarded to the CPEV/A, which supervises the cluster of PEVs. RES/A carries out extremely short-run predictions of the power produced by the renewable energy units it is assigned to. L/A is applied to each node of the distribution network and also estimates the node's active and reactive power requirement by performing very short-run predictions. RES/A and L/A provide their responses to ADNM/A.  
Figure 1: MAS based power management system structure for the active distribution network.
## 2 The Schmidt Decomposition and Its Recursion Tree Structure
### Schmidt Decomposition [30; 31]  
Let \(\ket{\psi}\) represent a \(n\)-qubit system (\(N=2^{n}\)), where qubits are ordered as \(q_{0}q_{1}\ldots q_{n-1}\). Using the Schmidt coefficients between the first qubit \(q_{0}\) and the rest of the system, i.e., \(q_{0}\)--\((q_{1}\ldots q_{n-1})\), we can represent the state in the following form:  
\[\ket{\psi}=\sigma_{1}\ket{u_{1}}\otimes\ket{v_{1}}+\sigma_{2}\ket{u_{2}} \otimes\ket{v_{2}}. \tag{4}\]  
In the above, while \(\sigma_{1}^{2}+\sigma_{2}^{2}=1\), \(\bra{u_{1}}u_{2}=1\) and \(\bra{v_{1}}v_{2}=1\). The Schmidt decomposition of a vector can be found by converting the vector into a \(2\times 2^{n-1}\) matrix and using the SVD described above. In that case, \(\sigma_{1}\) and \(\sigma_{2}\) are the singular values and \(\ket{u_{i}}\) and \(\ket{v_{i}}\)s are the singular vectors.  
Here, we can keep recursively taking the Schmidt decomposition of the larger vectors, i.e., \(\ket{v_{i}}\)s, and obtain the following:  
\[\ket{v_{1}} =\sigma_{3}\ket{u_{3}}\otimes\ket{v_{3}}+\sigma_{4}\ket{u_{4}} \otimes\ket{v_{4}},\text{ and} \tag{5}\] \[\ket{v_{2}} =\sigma_{5}\ket{u_{5}}\otimes\ket{v_{5}}+\sigma_{6}\ket{u_{6}} \otimes\ket{v_{6}}.\]  
Note that the size of any \(\ket{u_{i}}\) is still two by two. Once the size of \(\ket{v_{i}}\) also goes down to two, we stop the recursion. In the final analysis, we can write \(\ket{\psi}\) in the following decomposed form:  
\[\ket{\psi}= \sigma_{1}\ket{u_{1}}\otimes\sigma_{3}\ket{u_{3}}\otimes\sigma_{ 7}\ket{u_{7}}\otimes\ldots \tag{6}\] \[+\sigma_{1}\ket{u_{1}}\otimes\sigma_{3}\ket{u_{3}}\otimes\sigma_{ 8}\ket{u_{8}}\otimes\ldots\] \[+\] \[\vdots\] \[+\sigma_{2}\ket{u_{2}}\otimes\sigma_{6}\ket{u_{6}}\otimes\sigma_{ 14}\ket{u_{14}}\ldots\] \[+\cdots=\sum_{i}\ket{\psi_{i}},\]where \(\left|\psi_{i}\right\rangle\) represents a term in the summation, and the number of terms is exponential in the number of qubits. Figure 1 shows the recursion tree for the above equation. In the figure, each path from the root node to a leaf node represents a summation term in the equation. If any of the coefficients, \(\sigma_{i}\), is zero in this path, then the equivalent term in the summation is zero as well. Therefore, the number of non-zero terms is equal to the number of paths with non-zero coefficients.
## 4 Activity Recognition Based on Fusion of Point Cloud and Range-Doppler
### Using Only 3D Point Cloud Data as Input
#### 4.2.1 Three-Dimensional Convolutional Neural Network (3D CNN)  
For 3D point cloud data, we first consider that it reflects the spatial features of actions, and in the data pre-processing part above, we also voxelize each frame of data into a fixed size 3D array, so it is not difficult to think that we can use 3D CNN to extract the spatial features. However, considering the sparsity of point clouds (the number of point clouds in a frame is generally less than 10), we merge the point cloud from eight frames into one frame, that is, add eight consecutive three-dimensional arrays bit by bit to achieve a new three-dimensional array with a size of 10 \(\times\) 32 \(\times\) 32, which is denser. Then it is used as the input of the convolution network to train the network.  
The structure of the network is shown in Figure 4. In the convolution part, we use the 'Conv3D-Conv3D-Maxpooling' structure three times. The 3D convolution layers all use the convolution kernel of (3, 3, 3), the number of convolution kernels is 32, the padding is set to'same', the activation function is Relu, and the stride is set to (1, 1, 1). The pool size of the maxpooling layer is set to (2, 2, 2), the stride is set to (1, 1, 1), and the padding is set to 'valid'. Then the extracted features are flattened and input into the fully connected layer.  
2.2 Three-Dimensional Convolutional Neural Network and Long Short-Term Memory Network (3D CNN + LSTM)  
Considering that the activities are continuous, there is a timing characteristic between the received frames. Hence, we use a 3D CNN + LSTM network for training and classifi  
Figure 4: The structure of 3D CNN network.  
Figure 3: The visualization of range–Doppler data.  
cation. Because the acquisition rate is eight frames per second, and the duration of each activity is about one second, we pre-process the 3D point cloud data into a four-dimensional array of size 8 \(\times\) 10 \(\times\) 32 \(\times\) 32, which includes the merging of 3D point cloud data received in one second. Because the input of LSTM must contain the parameter timesteps, we use the 'TimeDistributed' layer wrapper to encapsulate the convolutional layer, pooling layer, and flatten layer, adding a time dimension to the input data. As mentioned above, we set the value on this dimension to 8.  
The structure of the network is shown in Figure 5. In the convolution part, we use the 'Conv3D-Conv3D-Maxpooling' structure three times. The parameter settings are the same as in the first network. Then the extracted features are flattened and input into the bidirectional LSTM layer, whose number of hidden layer units is 16. Finally, a fully connected layer is used for classification after regularization, whose activation function is Softmax.
# Machine Learning Techniques for Non-Terrestrial Networks
## 5 Machine Learning General Aspects  
As opposed to the conventional engineering approach, which relies on finding an algorithmic solution to the problem, ML leverages on building the domain knowledge from examples. The training process leads to the production of a _model_, which represents the rules governing the specific object of study. The assumption is that, after some epochs of training, the model will accurately represent the reality and, therefore, it can guarantee to behave at least as the engineered model. Some of the advantages of machine learning models are as  
Figure 11: Typical dual connectivity architecture, involving one TN and one NTN or two NTNs.  
follows: (i) they can adapt better to new scenarios, (ii) they perform well, even in exceptional cases, and (iii) they can spot trends and patterns, which are otherwise difficult to capture. However, many drawbacks have to be considered as well. ML requires a lot of training data, and the model accuracy heavily depends on the quality of the training set. Moreover, it is necessary that the training set is also balanced between each class it represents. Having a large amount of high quality data typically leads to better performance. Nevertheless, processing high data volumes can be time- and energy-consuming. These kinds of problems are slowing down the adoption of ML, especially on edge- and battery-powered devices.
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 2 Communication Signal Description and Dataset Generation
### Modulation Signal Description  
For any digital modulation signal, the transmission signal can be presented as  
\[s(t)=\sum_{n}a_{n}e^{i(w_{n}t+\phi)}g(t-nT_{b}) \tag{2}\]  
where \(w_{n}\) is the signal angular frequency, \(\phi\) is the carrier initial phase, \(T_{b}\) is the symbol period, \(a_{n}\) is the symbol sequence, \(g(t)\) is the shaping filter.  
For MFSK signal, it can be presented as  
\[a_{n}=1,w_{n}=w_{0}+\frac{2\pi}{M}i,i=0,1,\ldots,M-1 \tag{3}\]  
For MPSK signal, it can be presented as  
\[a_{n}=e^{i2\pi i/M},i=0,1,\ldots,M-1,w_{n}=w_{0} \tag{4}\]  
For MQAM signal, it can be presented as  
\[\left.\begin{array}{c}a_{n}=I_{n}+iQ_{n}\\ I_{n},Q_{n}=2i-\frac{M}{4}+1,i=0,1,\ldots,\frac{M}{4}-1,w_{n}=w_{0}\end{array}\right\} \tag{5}\]  
MAPSK constellations are robust against nonlinear channels due to their lower peak-to-average power ratio (PAPR), compared with QAM constellations. Therefore, APSK was mainly employed and optimized over nonlinear satellite channels during the last two decades. As recommended in DVB-S2 [37], it can be presented as:  
\[a_{n}=r_{k}\exp\left[i\left(\frac{2\pi}{n_{k}}i_{k}+\theta_{k}\right)\right] \tag{6}\]  
where \(r_{k}\) is the radius of the \(k\)th circle, \(n_{k}\) is the number of constellations in \(k\)th circle, \(i_{k}\) is the ordinal number of constellation points in the \(k\)th circle, \(\theta_{k}\) is the initial phase of the \(k\)th circle.
## 2 The Maximum Weighted Disjoint Dominating Sets Problem
### Illustrative Example  
Figure 1 provides an illustrative example of the MWDDS problem. Figure 1a,b provide a problem instance and the lifetime values of the nodes in terms of node weights, respectively. Note that, for the sake of simplicity, the lifetime values are normalized between 0 and 1. Figure 1c shows a feasible solution \(\mathcal{D}:=\{D_{1}=\{3,4\},D_{2}=\{1,5,6\}\}\), that is, solution \(D\) contains the two dominating sets \(D_{1}=\{3,4\}\) and \(D_{2}=\{1,5,6\}\). The lifetimes of nodes 3 and 4 are 0.775 and 0.424, respectively. Therefore, the lifetime of \(D_{1}\) is 0.424. Moreover, the lifetimes of nodes 1, 5, and 6 are 0.804, 0.782 and 0.021, respectively. This means that the lifetime of \(D_{2}\) is 0.021. As a consequence, the objective function value \(f(D)\) of \(D\) is the sum of 0.424 and 0.021, which results in 0.445. In contrast, Figure 1d shows the optimal solution to this problem instance, that is, \(\mathcal{D}^{*}:=\{D_{1}=\{1,3,5\},D_{2}=\{2,4,6\}\}\). According to the lifetime values of the single nodes, the lifetime of \(D_{1}\) is 0.775, while the lifetime value of \(D_{2}\) is 0.021. Therefore, the objective function value \(f(\mathcal{D}^{*})\) of \(\mathcal{D}^{*}\) is 0.796. Finally, note that, since this graph contains a node of degree 1, any valid solution contains at most two disjoint dominating sets.  
Figure 1: An illustrative example of the MWDDS problem.
# Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization
## 4 Results and Discussion
### Pathfinding Problems
Figure 9: The true solution of the \(\mathcal{P}4\) problem instance found by an exhaustive search: (**a**) the Pareto front formed by three discrete points (marked by symbols “\(\times\)”); and (**b**) the found paths for corresponding members of the Pareto front. Figure 11 replicates the maps of five pathfinding problem instances \(\mathcal{P}1\)-\(\mathcal{P}5\). This time, a final path from the Pareto-optimal set found by the VNDMOPSO algorithm is highlighted with a red curve. The path corresponding to the DSV with the maximal value of the hypervolume metric, \(\mathbf{u}_{\text{HV}}\), is selected from the Pareto-optimal set. Such a solution dominates the largest portion of the objective space and therefore should not prioritize any of the objectives: the minimal length of the path, and the minimal number of turning points. Figure 11: Selected Pareto-optimal solutions found by VNDMOPSO algorithm with maximal value of the HV metric for pathfinding problem instances \(\mathcal{P}1\)-\(\mathcal{P}5\) (**a–e**). Figure 10: The standard boxplots (min, 1 st quartile, median, 3 rd quartile, max value) of the dHV metric for algorithms VNDMOPSO, VNDGDE3, VLMOPSO, and SCMOPSO on five pathfinding problem instances \(\mathcal{P}1\)-\(\mathcal{P}5\).
## 4 Methodology
### Future Projections Based on Gems and Hydropower Capacity  
Different African research communities such as Climate Systems Analysis Group (CSAG), the University of Cape Town (South Africa), [8; 12; 18] have developed comprehensive future climate projection scenarios for the southern African region with the objective to assess climate variation impact in the energy sector, mainly focused on hydropower. The projections of future climate scenarios used are uncertain. The total of the 121 global climate models simulations (GCM) were analyzed, including those used in the fourth report in the International Panel on Climate Change (IPCC-AR4), and recently those used in the IPCC--AR5 GCMs. The reason to use GCMs based on future emission scenarios is because those are presently the main tools used to efficiently develop and analyze future climate scenarios. Statistical downscaling is involved in the statistical regression between GCMs outputs and local observations, resulting in the projected future local climate for specific stations and [12; 13; 14; 18].  
In total at least 20 GCMs have been developed for the region of southern Africa [5; 9; 10; 18]. These are relevant to use to assess climate variability for hydropower potential or water resources. Of these, five GCMs were selected: Coupled Global Climate Model (CGCM3.1), Commonwealth Scientific and Industrial Research Office (CSIRO3.0), Max Planck Institute for Meteorology Global Model, (ECHAM5), Community Climate System Model (CCSM3.0), and the Hadley Centre Coupled Model (HACDM3). Three scenarios were chosen for climate variability (A2, A1B and B2). These scenarios cover a wide range of emissions, from low (B2) to middle (A1B) and high (A2) [6; 13; 18].
## 2 Results and Discussion
### Effect of Attenuated UV Radiation on Three Major Classes of Volatiles
Therefore, the results in the 2010 vintage were further selected to investigate whether the effect of attenuated UV radiation altered metabolic pathways during grape development and to elucidate whether the attenuated UV-radiation applications were also dependent on development stages. Figure 2: Effect of the attenuated UV radiation on the concentration of three major classes of volatiles in developing “Cabernet Sauvignon” grapes. The abbreviation of UV treatments are described in the “Experimental section”. Data are means \(\pm\) SE of three replicates (\(n=3\)). At the same development stage, different letters represent significant difference among treatments (\(p\leq 0.05\)).
## 6 Results  
The proposed traffic model and EWA for dynamic network operation are implemented on a custom-based Java simulation framework. Simulations are performed on a computer with a 64-bit Intel 2.4 GHz CPU and 8 GB RAM. Moreover, the MILP formulation for the SBN design is solved with Cplex [46] using 16 64-bit Intel 2.7 GHz CPUs and 64 GB RAM. Near-optimal solutions were found within a 12-h time limit with optimization gaps below 1.7%.  
We consider two different settings of EWA input parameters: (i) \(W_{L}=0.1\), \(W_{H}=0.5\), and \(\psi=0.5\); and (ii) \(W_{L}=0.1\), \(W_{H}=0.9\), and \(\psi=0.9\). As a consequence, lightpaths are attempted to be released when their utilization is relatively low (i.e., less than 10%). On the other hand, when the lightpath utilization is greater than 50% (90%), additional lightpaths are established in order to avoid network overload. Moreover, the utilization of the last active lightpath on each logical link should not exceed 50% (90%) when a lightpath is released for energy saving. The simulation framework begins with all LCs in AM. For each set of traffic data, we perform a simulation of two days assuming the same traffic during both days. Results of the first day (transient period) of simulation are discarded in order to avoid its influence on final results. Differently from [6], we neglect the initial switching of LCs from the AM into SM in the final results.  
In the following, we analyze the influence of (i) temporal traffic variation; and (ii) spatial traffic variation on energy consumption and AF in a network using EWA with the given settings. Furthermore, we look also at (iii) the influence of HW parameters on the AF; and (iv) at network profitability. In all cases, we consider the three traffic scaling factors (229.52, 688.57, and 1147.62) and traffic variation over time as detailed in Section 5.1.
# Analysis of Electromagnetic Interference Effects of 5G Signals on Radio Altimeters
## 3 Simulation of 5G Signal Injection
### Interference Effect Simulation and Results Analysis
Although the power of the 5G signal entering the RA RF front-end is the same, the closer the center frequency of the 5G signal is to the operating band of the echo signal, the less the bandpass filter attenuates the 5G signal, and the higher the power of the 5G interference signal entering the LNA. As a result, the gain compression effect of the RF front-end becomes more pronounced.
## 4 Experiment
### Real Signal Experiment
#### 4.3.2 Frequency Domain Quality Analysis of Reconstructed Signals
Figure 14b shows the fitting diagram of the OR signal and reconstructed signal of the XJTU-SY dataset; the \(T_{\text{test}}\) is 0.0675. It can be observed that, compared with the OR signal, the IR signal has a lower \(T_{\text{test}}\) value, which means the latter has a higher fit accuracy than the original signal. However, the difference between the reconstructed signal and the original signal under the two accuracies is very small, almost completely coincident. This indicates that when the reconstructed \(T_{\text{test}}\) is lower than a certain value, the reconstructed signal has the most important feature information of the original signal, and the corresponding feature frequency can be displayed through empirical mode decomposition and other methods. Therefore, from the perspective of practical engineering applications, we believe that after a certain number of iterations, the iteration can be stopped when the signal has the corresponding characteristic frequency. While continuing iteration can improve reconstruction accuracy, it also slows down the convergence of the algorithm and increases the cost of unnecessary time. Figure 14: ADMM-1 DNet for reconstructing signal fitting graph. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**CWRU**} & \multicolumn{4}{c}{**XJTU-SY**} \\ \cline{2-9}  & **IR-\(T_{\text{test}}\)** & **BPFI** & **OR-\(T_{\text{test}}\)** & **BPFO** & **IR-\(T_{\text{test}}\)** & **BPFI** & **OR-\(T_{\text{test}}\)** & **BPFO** \\ \hline
**ISTA-Net** & 0.3977 & 170.2 & 0.3461 & 81.6 & 0.2824 & 223.7 & 0.3059 & 120.5 \\ \hline
**ADMM + K-SVD** & 0.2532 & 102.1 & 0.3046 & 148.2 & 0.4057 & 354.9 & 0.3961 & 79.5 \\ \hline
**ADMM-1 DNet** & 0.0019 & 159.7 & 0.0081 & 105.3 & 0.0497 & 197.3 & 0.0675 & 123.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reconstructed signal indicator results.