esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The statistical analysis of the data using PCA and PLS-DA should be performed with two different scaling: UV scaling and Pareto scaling. These two scalings are commonly used in multivariate analysis to account for different variations and emphasize important features in the data. Once the PLS-DA stage is reached, it is important to conduct a permutation test for both UV scaling and Pareto scaling. The permutation test helps assess the statistical significance and predictive ability of the models generated with each scaling. By comparing the results of the permutation test, you can determine which scaling provides a better description and prediction of the data. After selecting the preferred scaling based on the results of the permutation test, the statistical analysis can continue with further techniques such as OPLS-DA, S-Plot, and VIP-Plot. OPLS-DA is an extension of PLS-DA that helps identify variables responsible for group separation while removing confounding variation. The S-Plot and VIP-Plot are graphical tools that aid in the interpretation of the models, highlighting important variables and their contributions to the separation between groups.
# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 4 Stability-Aware Geographic Routing in EH-WSNs (SAGEEH)
### SAGREH Protocol
(ii) To prevent backward packet transmission, the selected forwarding node must locate in a half circular are toward the sink node. To determine a node in the half circle, the angle \(\varphi_{SI}\) between the two lines, SI and SD, is calculated. Based on the law of cosines, angle \(\varphi_{SI}\) can be calculated as follows \[\varphi_{SI}=ar\cos\left(\frac{d_{SI}^{2}+d_{SD}^{2}-d_{ID}^{2}}{2d_{SI}d_{SD }}\right)\] (13) where \(d_{SI}\) and \(d_{ID}\) are the distance between nodes S and I, and the distance between nodes I and D, respectively. If \(\varphi_{SI}\) is less than or equal to 90\({}^{\circ}\), node I locates in the half circle toward node D. (iii) Its residual energy is greater than or equal to its packet energy \(E_{e}\). These conditions are summarized in Equation (14) as \[\left\{ \begin{array}{ll}d_{SI}\leq&R\\ \varphi_{SI}\leq&90^{\circ}\\ RE_{I}\leq&E_{e}\end{array} \right.\] (14)
* **Step 2:** The above forwarding node selection process in the route exploring phase is repeated until node D is reached. Aside from using the residual energy and location information for path selection, the estimated \(PRR\) of the wireless link is also employed to select the most stable path from all available paths. The detailed process of stable path selection is presented as follows. * **Step 1:** Initially, source node (S) broadcasts its RREQ packet to its neighbors. The RREQ consists of its node ID and location, the Path \(PRR\) field which is initially set to 1, sequence number to prevent from routing loop, and Time to Live (TTL) to avoid RREQ from flooding throughout the network. * **Step 2:** A sensor node which receives the RREQ packet from previous node will store previous node's ID in its Route Cache, uses the previous node's location and its current location to calculate the PRR of the link between it and previous node by Equation (11) in Section 3.2, then updates Path \(PRR\) field in RREQ by multiplying this value by the previous one. Moreover, this sensor node will decrease TTL by one before broadcasting to its neighbors. * **Step 3:** The above step is repeated until the RREQ reaches sink node (D). After knowing the PRRs of paths, the sink node will select the path whose PRR is the highest; _i.e._, \[\arg\max(\prod_{i,j\in P_{k}}PRR_{ij})\] (15) where \(i\), and \(j\) represent node \(v_{i}\) and node \(v_{j}\), respectively. After selecting the path whose \(PRR\) is the highest, the sink node adds this path information in the Route Reply (RREP) sending back to source node. Figure 4: An example scenario of the selection of a neighbor node based on the residual battery, energy harvesting, and location information in the proposed stability-aware geographic routing in energy-harvesting wireless sensor network (SAGREH) routing protocol. * **Step 4:** After source node receives RREP from sink node, it will forward data packets through this stable path.
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 4 Simulation Results
### Connection-Dependent Broadcast Flows  
Figure 9 shows the end-to-end packet delivery ratio for various source-destination distances. All the edge-based approaches achieve significantly better performance than the conventional approach without edge computing. This is because the packet forwarding through the edge node could efficiently reduce the MAC layer contention by using smaller numbers of sender nodes, resulting in a better wireless resource utilization. When the beacon size is small (\"Edge (BeaconSz = 56 B)\"), it is difficult to achieve a high packet delivery ratio as the small size beacon overestimates the link quality. Since the proposed scheme uses different sizes of beacons for different requirements, the link quality is evaluated more accurately, and this contributes to a significant improvement of packet delivery ratio as compared with other approaches that use constant packet size for beacons.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**gamma0\_** & **gamma1\_** & **gamma2\_** & **d0\_gamma\_** & **d1\_gamma\_** \\ \hline
2.0 & 2.0 & 2.0 & 200 & 500 \\ m0\_m & m1\_m & m2\_ & d0\_m\_ & d1\_m\_ \\
1 & 1 & 1 & 80 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of Nakagami Model.  
Figure 8: Virtual multi-hop data forwarding (dashed circle shows the transmission range).  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Street Scenario** \\ \hline Topology & 1700 m \(\times\) 1700 m \\ Number of vehicles & 619 \\ Mobility generator & SUMO [27] + TraNS [28] \\ Packet size & randomly selected from [56 B, 512 B, 1024 B] \\ Broadcast data rate & 15 packets per second \\ MAC & IEEE 802.11p (broadcast: 3 Mbps, unicast: 27 Mbps) \\ Propagation model & Nakagami Model \\ Simulation time & 500 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Environment.  
The end-to-end packet delivery ratio for different numbers of concurrent unicast traffic flows is shown in Figure 10. Although \"Edge (BeaconSz = 1024 B)\" is able to provide a high packet delivery ratio, the performance drops drastically with the number of traffic flows as a large size beacon underestimates the link quality, resulting in a large number of hops than the optimal one. The proposed scheme is able to attain a better performance in various number of traffic flows by selecting routes with consideration of packet payload sizes. Another contributor for the high packet delivery ratio is the edge-based packet forwarding approach which could reduce the number of forwarder nodes as compared with the conventional non-edge approaches.
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 3 Data-Skew-Handling Algorithms
### Multi-Dimensional Range Partition
#### 3.4.3 Partitioning the Non-Heavy Cells  
* We have a list of non-heavy cells denoted by \(C=\{c_{1},c_{2},\ldots,c_{|C|}\}\).
* Each \(c_{i}\in C\) consists of a sub-range \((R_{i},L_{j})\), and its workload is denoted by \(w(c_{i})\). For example, a non-heavy cell \(c_{1}\) is denoted by \((R_{i},L_{j},w(c_{1}))\).
* These non-heavy cells are partitioned into different partitions \(P=\{P_{1},P_{2},\ldots,P_{p}\}\).
* A list of non-heavy cells assigned to partition \(P_{i}\) is denoted as \(C_{i}\).
* The number of non-heavy cells of \(C_{i}\) is denoted as \(|C_{i}|\).
* The total workload of partition \(P_{i}\) is defined by \(W_{i}=\Sigma_{c_{i}\in C_{i}}w(c_{i})\).  
We use the assign algorithm to distribute the non-heavy cells to the partitions. First, the non-heavy cells in \(C\) are sorted in descending order of workload \(w(c_{i})\). For each non-heavy cell \(c_{i}\in C\), we choose partition \(P_{i}\) so that \(P_{i}\) has the minimum number of non-heavy cells and minimum total workload. The algorithm is shown in Algorithm 4.  
```
0:\(C=\{c_{1},c_{2},\ldots,c_{|C|}\}\), list of non-heavy cells sorted in descending order of \(w(c)\)
0:\(\{C_{1},C_{2},\ldots,C_{p}\}\), partitions of the non-heavy cells
1:for each cell \(c_{i}\in C\)do
2:\(P_{i}=\text{getNextPartition()}\)
3:\(C_{i}=C_{i}\cup\{c_{i}\}\)
4:endfor
5:return\(C_{1},C_{2},\ldots,C_{p}\) getNextPartition
0:\(P_{id}\), the partition selected for the non-heavy cells
1:\(\minCell=\infty\), \(\minLoad=\infty\), \(\text{id}=0\)
2:for each partition \(P_{i}\)do
3:if\(minCell>|C_{i}|\)then
4:\(\minCell=|C_{i}|\)
5:endif
6:endfor
7:for each partition \(P_{i}\)do
8:if\(minCell=|C_{i}|\) and \(minLoad<W_{i}\)then
9:id = i
10:\(\minLoad=W_{i}\)
11:endif
12:endfor
13:return\(P_{id}\)
```  
**Algorithm 4** Assignment algorithm  
For example, we have a list of non-heavy cells sorted in descending order of workload, \(C=\{(R_{1},L_{1},4),(R_{2},L_{2},4),(R_{2},L_{2},4),(R_{2},L_{2},4),(R_{4},L_ {4},4),(R_{4},L_{3},0)\}\), and four partitions \(P=\{P_{0},P_{1},P_{2},P_{3}\}\). The three cells \((R_{2},L_{2},4)\) are because the original cell is chopped into three cells. Initializing with \(C_{i}=\{\}\) and \(W_{i}=0\), we have:  
* \(P_{1}=\{(R_{1},L_{1},4)\},|C_{1}|=1,W_{1}=4\)
* \(P_{2}=\{(R_{2},L_{2},4)\},|C_{2}|=1,W_{2}=4\)
* \(P_{3}=\{(R_{2},L_{2},4)\},|C_{3}|=1,W_{3}=4\)
* \(P_{4}=\{(R_{2},L_{2},4)\},|C_{4}|=1,W_{4}=4\)
* \(P_{1}=\{(R_{1},L_{1},4),(R_{4},L_{4},4)\},|C_{1}|=2,W_{1}=8\)
* \(P_{2}=\{(R_{2},L_{2},4),(R_{4},L_{3},0)\},|C_{2}|=2,W_{2}=4\)  
The result of assigning the non-heavy cells into partitions is shown in Figure 11.
## 5 Proposed Dynamic Cluster Head Election Algorithm
### Proposed Optimum Cluster Head Election Method
The energy needed to transmit the data between two sensor points \(E_{nerg2 sens}\) can be expressed as:  
\[E_{nerg2 sens}=E_{trans}\cdot N_{trans}\_dbits+\epsilon\textit{amplif}\textit{ coe-dis}^{\alpha} \tag{1}\]  
where \(E_{trans}(J/b)\) is the wasted energy in transceiver operation, \(N_{trans}\_dbits\), is the number of transmitted data bits, \(\textit{amplif}\textit{ coe}(J/b/m^{2})\) is an amplification coefficient used to ensure the lowest bit error rate due to achieving the reliability of receiver, \(\alpha\) is a factor of 2 to 5, depending on the transmission environment of the networks, and _dis_ refers to the distance between two sensor nodes, which can be expressed as:  
\[E_{trans}=Vol_{\textit{wrk-curr}_{trans}}/T_{\textit{data-rate}} \tag{2}\]  
where \(V_{w}\) indicates to working voltage, _curr_ indicates to current for transmission, and \(T_{\textit{data-rate}}\) indicates to the transmission data rate. In contrast, we can express the energy consumed to receive the data from the other side \(E_{\textit{reciv-data}}\) as:  
\[E_{\textit{reciv-data}}=E_{trans}\cdot N_{trans}\_dbits \tag{3}\]  
Equation (1) reveals that the consumed energy in the fixed distance is directly proportional to the data bit numbers. Conversely, the greater distance between the two nodes means more energy has been consumed.
# NIRS-EMG for Clinical Applications: A Systematic Review
## 2 Electromyography
### EMG in Motor Control  
Several algorithms for EMG decomposition have been used in the literature, with the aim of extracting relevant features for clinical practice. For a detailed analysis, several works can be consulted [36]. In this paragraph, a specific valuable approach to EMG analysis for clinical purposes is briefly reviewed: The muscle synergy framework.  
Time-domain multi-channel EMG measurements can be used for coupling with algorithms that find coherent co-activation patterns shared by different muscles. Such approaches have found recent application in the study of motor control and medical practice, in the framework of muscle synergies [42]. Starting from the rectified and filtered EMG signal (time-domain), the decomposition approach attempts to identify the invariant muscle co-activations underlying neuromotor organization under the conditions of variability of movement. The invariant elements are the motor synergies, which are a model of the implementation of the modular neuromotor organization at a neural level. Muscle synergies have been employed in several fields and include, among others, comparison of algorithm performance and interpretation for synergy extraction [43], investigation of muscles synergies in upper-limb physiological motor control [44; 45] or after neurological lesions [46; 47; 48; 49], applications to locomotion [50], and postural perturbations [51].
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
## 4 Conclusions  
This article proposes a pentagonal multilayered wideband metamaterial absorber for the visible and near-infrared region. The overall diminution of the MA unit cell is 0.5\(\lambda\)\(\times\) 0.5\(\lambda\)\(\times\) 0.8\(\lambda\). The proposed MMA has strong absorptivity in the visible and near-infrared regions, with 92% average absorption throughout 400 nm to 2400 nm wavelength. The MA attained 2000 nm bandwidth with a peak absorption of 99.6% at 598 nm. The SiO\({}_{2}\) coating on Tu resonating layers plays a vital role in incident light reflection prevention. The proposed metamaterial absorber features consistent oblique incidence angle stability for both TM and TE polarization and polarization-independent characteristics. In order to interpret the absorption mechanism, the metamaterial property, magnetic field, electric field, and surface current distribution are investigated. Finally, the MA obtained 90.76% A\({}_{\text{AM1.5}}\) photon conversion efficiency, thus making the MA applicable in solar energy harvesting applications. Therefore, the proposed MA has excellent potential in various applications, including thermal emitters, infrared imaging, photodetectors and energy harvesters. **Author Contributions:** Conceptualization, A.M., M.L.H. and T.A. ; Formal analysis, A.M., H.R., M.L.H., M.T.I. and T.A. ; Funding acquisition, M.S.I, M.T.I. and M.S.S. ; Investigation, A.M., A.S.A., M.S.I. and T.A. ; Methodology, A.M., M.T.I., M.L.H., H.R. and T.A. ; Visualization, A.M.; Writing an original draft, A.M.; Writing review and editing, A.M., M.L.H., T.A., H.R., A.S.A., M.T.I, M.S.I. and M.S.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research work was funded by the Universiti Kebangsaan Malaysia, grant no: GUP-2022-011. The authors also acknowledge the support provided by King Abdullah City for Atomic and Renewable Energy (K.A.CARE) under K.A.CARE-King Abdulaziz University Collaboration Program. The authors are thankful to Deanship of Scientific Research, King Abdulaziz University for providing financial vide grant number (RG-14-135-43). **Data Availability Statement:** All data are fully available without restriction. **Acknowledgments:** This research work was funded by the Universiti Kebangsaan Malaysia, grant no: GUP-2022-011. The authors also acknowledge the support provided by King Abdullah City for Atomic and Renewable Energy (K.A.CARE) under K.A.CARE-King Abdulaziz University Collaboration Program. The authors are thankful to Deanship of Scientific Research, King Abdulaziz University for providing financial vide grant number (RG-14-135-43). **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Results
### The Influence of Cyclic Shocks on Thermal Diffusivity
#### 3.2.1 The Influence of Shock Application Temperature for the Same Number of Shock Cycles  
Previous studies on the microstructural changes induced in the Inconel 718 alloy through cyclic thermal stresses, performed at low heating/cooling rates [27; 28], have highlighted diffusion processes. These processes are much less evident for samples subjected to thermal shocks generated by solar energy.  
The variation in the thermal diffusivity of the standard sample with measurement temperature shows a significant maximum at 700 \({}^{\circ}\)C and an inflection point at approximately 820 \({}^{\circ}\)C (Figure 13). A similar maximum was also observed by [PERSON] and [PERSON] at 750 \({}^{\circ}\)C [33]. This variation can be explained by structural transformations that occur during the heat treatment.  
The variation in thermal diffusivity with shock temperature indicates that for the same number of shocks, the values are quite close and remain below those of the standard sample, with small maxima around 700 \({}^{\circ}\)C, within the range of 600 \({}^{\circ}\)C to 800 \({}^{\circ}\)C (Figure 13).  
Figure 12: Microstructures formed at 1000 \({}^{\circ}\)C for (**a**) 12 cycles and (**b**) 9 thermal shock cycles.  
Figure 13: _Cont._The increase in the thermal diffusivity values of nickel-based superalloys in their as-delivered state with the measuring temperature was determined by [PERSON], [PERSON], and [PERSON] [34] for single-crystal Ni-based superalloys, as well as by [PERSON] [35]  
Figure 13: The variation in the thermal diffusivity of the samples subjected to the same number of thermal shocks depending on the shock threshold temperature: (**a**) 3 thermal shock cycles; (**b**) 9 thermal shock cycles; (**c**) 12 thermal shock cycles; (**d**) 20 thermal shock cycles.  
and [PERSON]. [PERSON], [PERSON], and [PERSON] [36] for Inconel 718. In the evolution of the thermal diffusivity curve with the measuring temperature determined by [PERSON], an inflection point appears around 700 \({}^{\circ}\)C, while [PERSON] and collaborators note a peak around this temperature. The research presented in this paper shows the maximum evolution at 700 \({}^{\circ}\)C for the Inconel alloy in its as-delivered state, as well as peaks at 700 \({}^{\circ}\)C and 900 \({}^{\circ}\)C for samples subjected to cyclic thermal shocks.  
The present research reveals the influence of thermal shock temperature, as well as the number of shocks applied at the same temperature, on the evolution of thermal diffusivity curves with the measuring temperature.
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 4 Traffic-Aware Clustering-Based Routing Protocol (TACRP)
### Traffic Management Unit  
The Traffic Management Unit (TMU) takes control of the whole network, mainly providing route plans. It collects certain information, for instance, present position of vehicles and their target place, as well as the vehicle density at various routes. According to the vehicle information, a certain number of vehicles in a particular location are grouped into clusters. Vehicle selection for each cluster is based on priority, where the highest priority is given to emergency vehicles, then to passenger and goods vehicles. Each cluster is managed by its cluster head (CH). A group of clusters at a particular location is monitored by the RSU. The TMU is the controlling authority, which gathers the vehicles information from the RSU and then process the data [30]. The TMU collects the information from the RSU about the vehicles and the vehicular information and then processes it. By using the information, the vehicles are ranked. Vehicles are clustered according to area. The following data are collected from the vehicles by the CH for the process of clustering: vehicle type, as per priority, traveling speed of the vehicle, ranging from 0 to 90 Km/h, and residual energy of the vehicle and its address. The vehicles are grouped according to the destination point. Vehicles having the same destination location occupy the highest priority.  
The path selection process from source to destination is maintained by the TMU which conducts an analysis of the vehicle density of all the roads. Once the process of clustering path selection has started, paths are chosen based on priority, less congestion, optimal and less dense roads to the destination. In most cases, vehicles with the same priority select the same path to reach the destination during the process of communication.
## 3 Approximate DFT
### Fast Algorithm and Complexity Analysis  
Represented by the low-complexity matrix \(\hat{\mathbf{F}}_{32}\), the selected ADFT admits the following factorization:  
\[\hat{\mathbf{F}}_{32}=\mathbf{W}_{8}\cdot\mathbf{W}_{7}\cdot\mathbf{W}_{6} \cdot\mathbf{W}_{5}\cdot\mathbf{W}_{4}\cdot\mathbf{W}_{3}\cdot\mathbf{W}_{2} \cdot\mathbf{W}_{1}, \tag{5}\]  
where \(\mathbf{W}_{i}\), \(i=1,2,\ldots,8\), are sparse, low-complexity matrices. In the Appendix A, we provide the explicit definition of matrices \(\mathbf{W}_{i}\), \(i=1,2,\ldots,8\).  
For comparison, Table 1 lists the arithmetic operation count for the exact 32-point DFT computed according to its definition and to the radix-2 Cooley-Tukey FFT. For this operation counting, we assume that (i) the input signal is purely real-valued; (ii) trivial multiplications by \(0,\pm 1\), or \(\pm j\) are not counted; and (iii) a multiplication between a real number and a complex number is performed as two real multiplications--not a complex multiplication [99]. Due to (i) and (iii), the 704 non-trivial multiplicands of \(\hat{\mathbf{F}}_{32}\) are equivalent to 1408 real multiplications.  
Figure 1: Simulated magnitude response of (**a**) ideal 32-point DFT and (**b**) low-complexity 32-point ADFT [97; 98].  
The evaluation of the number of additions considered the sum of the number of real- and imaginary-part additions. It was also provided the arithmetic complexity for the discussed 32-point ADFT by its definition and by means of its fast algorithm (factorization). The complexity analysis of the radix-2 Cooley-Tukey FFT is detailed in [99].  
Table 2 shows the arithmetic complexity for each individual matrix term of the \(\hat{\mathbf{F}}_{32}\) factorization. The matrix \(\mathbf{W_{8}}\) does not present arithmetic costs because it consists of only sign-changing and data-swapping (real- and imaginary-part interchanging) operations. The data interchange is a consequence of the multiplication by \(j\) (a rotation by \(\pi/2\)).