esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Device-to-Device (D2D) communication is a promising technological innovation that is significantly considered to have a substantial impact on the next generation of wireless communication systems. Modern wireless networks of the fifth generation (5G) and beyond (B5G) handle an increasing number of connected devices that require greater data rates while utilizing relatively low power consumption. In this study, we present joint mode selection, channel assignment, and power allocation issues in a semi-distributed D2D scheme (SD-scheme) that underlays cellular networks. The objective of this study is to enhance the data rate, Spectrum Efficiency (SE), and Energy Efficiency (EE) of the network while maintaining the performance of cellular users (CUs) by creating a threshold of data rate for each CU in the network. Practically, we propose a centralized approach to address the mode selection and channel assignment problems, employing greedy and matching algorithms, respectively. Moreover, we employed a State-Action-Reward-State-Action (SARSA)-based reinforcement learning (RL) algorithm for a distributed power allocation scheme. Furthermore, we suggest that the sub-channel of the CU is shared among several D2D pairs, and the optimum power is determined for each D2D pair sharing the same sub-channel, taking into consideration all types of interferences in the network. The simulation findings illustrate the enhancement in the performance of the proposed scheme in comparison to the benchmark schemes in terms of data rate, SE, and EE.  
device-to-device (D2D); resource allocation; greedy algorithm; matching theory; power allocation; reinforcement learning (RL); SARSA +
Footnote †: journal:
## 2 Materials and Methods
### Proposed Assessment Method
#### 2.1.1 Colorfulness  
Colorfulness is an aspect of the visual perception, according to which the color of an object is perceived to be more or less chromatic [30]. The colorfulness features can be obtained mainly from color channels \(a\) and \(b\) of the CIELab space and they are orthogonal to the lightness channel. Two different colorfulness features are used in our proposed method. The first colorfulness feature is calculated by using Equation (1) [30].  
\[Col_{1}=0.02\times\log\left(\frac{\sigma_{a}^{2}}{\left|\mu_{a}\right|^{0.2}} \right)\times\log\left(\frac{\sigma_{b}^{2}}{\left|\mu_{b}\right|^{0.2}}\right) \tag{1}\]  
where \(\sigma_{a}^{2}\) and \(\sigma_{b}^{2}\) are the variance of \(a\) and \(b\) channels in CIELab and \(\mu_{a}\) and \(\mu_{b}\) are the mean values of the \(a\) and \(b\) domains, respectively. This feature value increases as the color adjustment level increases, which is shown in Figure 3a.  
Figure 2: Comparison of the subjective scores for LDR and HDR images. HDR images received significantly higher scores than LDR images.  
Unlike CQE, our proposed method applies a low-pass filter to the RGB space before converting it into the CIELab space. This is done so that the colorfulness features are not influenced by the components of the structural features. The low-pass filter is effective in minimizing the influence of the sharpness features of the images.  
The second colorfulness feature in our assessment method is obtained by using Equation (2).  
\[Col_{2}=\frac{1}{NM}\sum_{i=1}^{N}\sum_{j=1}^{M}\sqrt{\sum_{k=-K}^{K}\sum_{l=-L }^{L}w_{k,l}(R_{k,l}(i,j)-\mu(i,j))^{2}} \tag{2}\]  
where \(R=\sqrt{a^{2}+b^{2}}\) is the magnitude of the color components in the chromatic domain, \(w_{k,l}\) is the \(5\times 5\) sized 2D circularly symmetric Gaussian weighting function, and \(N\) and \(M\) are the width and height of an image, respectively. This feature also increases as the color adjustment level increases, which is shown in Figure 3b.  
The first feature in Equation (1) represents the global colorfulness obtained from the entire image while the second feature in Equation (2) represents the local colorfulness obtained from small image patches. Our proposed method uses these two colorfulness features to consider the global and local aspects of the HVS color perception [40]. These feature values are compared with the HDR colorfulness features that are established from HDR images by using the same Equations (1) and (2) in order to determine the final quality scores that correlate well with the subjective evaluations.
## 2 NB-IoT System Detailed Design and Implementation
### NPDSCH Receiver Chain Implementation
#### 2.2.10. Rate De-Matcher  
The function of this block is to reverse the rate matching process. The output of the rate de-matcher block is three original data streams that are generated from the tail biting convolutional encoder in the transmitter [33].  
As illustrated in Figure 13, the rate de-matcher mainly consists of a bit collection sub-block and de-interleaver sub-blocks. The bit collection sub-block includes a memory with size of \((3\times(TBS_{max}+24)\times\log_{2}(\max\) repetition size))\), which will be \((7680\times 12)\). The size of this memory is large because in this release, the maximum TBS is 2536 bits [32]. The input to this sub-block is the data stream of the circular buffer in the transmitter with any length according to the upper layer set parameter. The de-interleaver sub-block consists of four memories; each has a size of the number of columns multiplied by the number of rows for the maximum TBS, which equals \(32\times 80\)[34]. This sub-block should comprise three memories to store the whole data stream, but here, there are four memories, as the fourth memory is used for the consecutive input stream.  
Figure 13. Rate de-matcher block diagram.
## 6 Implementation
### Technical Details
#### 6.3.2 Middleware and Service Platform  
OSGi constitutes a standardized service platform and module system in Java, developing a holistic and dynamic component model. The components' services are materialized as bundles for deployment and are remotely installed/uninstalled and started/stopped without the need for reboot. The leading open source distribution, called Knopflerfish [77], is adopted by the CompoSecReasoner framework.  
JADE, apart from its pure distribution as a multi-agent platform, is also offered in the form of a JADE-OSGi bundle. Thus, the above mentioned CompoSecReasoner agent is deployed as a JADE-OSGi bundle, facilitating the real-time system administration functionality.  
The components port their services as Knopflerfish-OSGi bundles and interact with the associated JADE-OSGi bundle that runs the CompoSecReasoner agent. The software modules of the proposed framework are portrayed in Figure 4.  
The overall setting adopts a Service oriented Architecture (SoA). Each entity specifies semantic information such as the supported services and their type, in the standardized Device Profile for Web Services (DPWS), which is specified by the Organization for the Advancement of Structured Information Standards (OASIS). This enables device/service description, discovery, eventing as well as messaging functionality.
# Theoretical Zero-Thickness Broadband Holograms Based on Acoustic Sieve Metasurfaces
## 4 Discussion  
### Selection of Geometric Parameters  
Regarding the setting of the distance between the grid nodes, according to the Nyquist sampling theorem, the sampling condition in the calculation of the Rayleigh-Sommerfeld formula should satisfy [43]  
\[\Delta x\leq\frac{\lambda\sqrt{z^{2}+\Delta L^{2}/2}}{\Delta L}\]  
where, \(\Delta x\) is the sampling interval, which is the distance between the grid nodes (\(D\)), and \(\Delta L\) is the width of the diffraction field, which can be approximated as the diagonal length (\(50\sqrt{2}\lambda_{0}\)) of the proposed hologram. Substitute the relevant data into the above equation to obtain \(D=\Delta x\leq 23.9\) mm \(=2.39\lambda_{0}\).  
Figure 5: Acoustic broadband holographic imaging. (**a–f**) The normalized sound energy distributions in the image planes of the ASM-based hologram are calculated using the numerical simulation when the incident frequencies (\(f\)) are \(f_{0}/2\), \(f_{0}/1.5\), \(f_{0}/1.2\), \(1.2/f_{0}\), \(1.5/f_{0}\), and \(2f_{0}\), respectively. The imaging distances (\(z\)) are \(60\lambda_{0}\), \(80\lambda_{0}\), \(100\lambda_{0}\), \(144\lambda_{0}\), \(180\lambda_{0}\), and \(240\lambda_{0}\), respectively.  
Figure 6: Graph of RMSE against frequency. The RMSE values between the holographic images and the desired image shown in Figure 2a are calculated using numerical simulation based on the combination of the FEM and the BEM. The studied frequency range is from \(f_{0}/2\) to \(2f_{0}\).  
Regarding the setting of the image size, if the desired image size is set too small, the image detail size may be smaller than the minimum resolution distance (according to the Rayleigh criterion, the minimum resolution distance should be \(1.22\lambda z/a\), where \(a\) can be approximately regarded as the diagonal length of the proposed hologram), resulting in a loss of image details. If the desired image size is set too large, the paraxial approximation will not be satisfied or the information content [21] of the hologram will be less than that of the image. Therefore, in general, for acoustic holographic imaging, the desired image size is set to be equal to or similar to the hologram size in most research [3; 15; 16; 17; 18; 19; 21; 22; 23; 24; 25; 26; 27; 28].
# Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System
## 1 Introduction
proposed a novel fault diagnosis and detection method based on sparse representation classification (SRC), with the main contribution being the model training and multiclassification strategy [31]. To determine meaningful correlations among variables, the DBN was introduced to solve the redundant coupling problem and eliminate interference between process variables. This paper proposes a novel FDSR framework as the classification model to solve the classification of dimension-reduced features. To solve the classification problem with strong coupling and danger rejection limitation in modern industrial processes, the discriminative subdictionaries were learned corresponding to class labels, and the overcomplete dictionary was constructed by cascading subdictionaries orderly. Then, fault diagnosis was completed by determining the smallest reconstruction error, since the dictionary and coefficient are both sparse. The results are then compared to the backpropagation (BP) network, conventional sparse representation, and support vector machine. The advantages and efficacy of the proposed methods are illustrated by a case study on the TE process. The rest of the paper is organized as follows. Section 2 reviews the DBN techniques for feature extraction, which includes both the theory and their application in feature extraction. Section 3 provides a detailed illustration of the FDSR and the idea behind fault diagnosis. Section 4 introduces an overview of the proposed fault diagnosis methodology, then describes the experiment procedure and the datasets. The performance of the proposed method is also evaluated in this section. Finally, Section 5 summarizes the present research and potential future work.
# Multilevel Regularization Method for Building Outlines
## 2 Materials and Methods
### Local Regularization of Building Outline-Based Convex Hull Grouping  
#### 2.3.1 Grouping Unregularized Nodes Using the Convex Hull Method  
During the overall regularization stage, only the parts of the building outline with Hausdorff distances less than a set threshold to the MBR are subjected to regularization. To split and classify unregularized parts further, the convex hull of the building outline and node distribution characteristics are considered. The splitting and classification method is described below.  
Step 1: For each pair of adjacent nodes \(T_{i}\) and \(T_{i+1}\) in convex hull point set \(T\) of the building outline, identify two points \(P_{m}=T_{i}\) and \(P_{n}=T_{i+1}\) at the same positions in the initial point set \(P\) of the building outline. Then, form a node set \(V=\{P_{m+i},\,P_{m+i+1},\,\ldots,\,P_{n\cdot j}\}\) based on the continuous and unregularized nodes within the interval from \(P_{m}\) to \(P_{n}\) of point set \(P\).  
Step 2: Based on the number of points in set \(V\), as well as the shorter side length \(l\), area \(s\), and length-width ratio \(r\) of the MBR formed by \(V\), make an overall judgment of the overall outline characteristics, size, and spatial distribution of the point set, and categorize set \(V\) into three types: a point-like node set (Figure 9a), a line-like node set (Figure 9b), and an area-like node set (Figure 9c).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Total Number of** & **Number of Correctly** & \multirow{2}{*}{**Correct Ratio**} \\  & **Buildings** & **Counted** & \\ \hline MABR & 300 & 263 & 87.67\% \\ \hline Maximum number of & & & \\ co-linear nodes method & 300 & 286 & 95.33\% \\ \hline Proposed method & 300 & 300 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the accuracy of different MBR calculation methods.
## 3 System Model  
As shown in Figure 1, several users (satellite mobile terminals, Internet of Things gateways, vehicle-mounted mobile stations, shipborne mobile stations, etc.) communicate through LEO and GEO satellites. Terrestrial users are covered by a GEO satellite and \(n\) LEO satellites, and user's speed is negligible compared to the speed of LEO satellite. GEO and LEO satellites use different frequency bands, and all LEO satellites share the same frequency band. Each user can only collect the information of the covered satellites when deciding, and cannot know the status and decision results of other users, which means the users will make decisions independently.  
Due to the rapid movement of LEO satellites, the user may have to handover several times during the communication. When it comes to a new call or a handover call, the usermust decide which satellite to access, which is the purpose of the solution in this paper. The channel model of satellite-ground link and the service model is described in this section.
## 2 Methodology  
### Transportation Network Travel Speed Model  
The circuity refers to the ratio of the actual travel path distance to the Euclidean distance along the path between the origin (O) and destination (D) points of a trip. The circuity can be determined as follows:  
\[c_{ij}=\frac{a_{ij}}{d_{ij}} \tag{1}\]  
where \(c_{ij}\) is the circuity of the trip from the city \(i\) to the city \(j\); \(a_{ij}\) denotes the actual travel path distance of this trip; and \(d_{ij}\) is the Euclidean distance between the city pair.  
The original concept of circuitry solely considers the arrangement of the transportation network from the perspective of travel distance, while disregarding the influence of travel mode and transportation conditions. However, for intercity travel, passengers pay more attention to travel time than travel distance [14; 20; 31]. The passengers prefer trips with shorter travel duration rather than shorter distance. To overcome this constraint, this study redefines the notion of circuity, by introducing the ratio of the Euclidean distance between the origin and destination points to the actual travel time. This novel metric is referred to as the transportation network travel speed.  
\[v_{ij}=\frac{d_{ij}}{t_{ij}} \tag{2}\]  
where \(v_{ij}\) is the transportation network travel speed of the travel process between the city pair starting from city \(i\) to city \(j\), and \(t_{ij}\) is the average travel time of the travel path. A larger value of \(v_{ij}\) corresponds to a faster travel speed within the transportation network.
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 5 Results and Discussion
### Kay Juul 2-Performance Charts  
Obtaining the reference volumes of the vehicle, in Figures 12 and 13, the glider polar chart and the dive velocity chart are shown for the academic glider Kay Juul 2 with the updated polynomial coefficients, and in the Figure 13, the dive velocity chart, generating curves each 100 cc of volume capability, beginning with 100 cc and finishing with the max displaced volume from the neutral buoyancy state, \(V_{d0}\).  
It is observed that particularly in this case, with the adjustment in the polynomial regression for the hydrodynamic coefficients, the model has been improved. The minimal glide angle changed from 18.73\({}^{\circ}\) to 14.33\({}^{\circ}\), representing an improvement of 23.5% with respect to the previous model. The required volumes to perform an output velocity of 35 cm/s and 50 cm/s at 35\({}^{\circ}\) of glide angle, have a reduction of 22.74% with respect to the model without adjustment.  
As is commented in the analysis of Section 5.2, the improvement of the model updated for Kay Juul 2 has two main directions described as follows:  
* The value of \(K_{D0}\) in the updated model was decreased, obtaining an increment of the velocity at the same displaced volume with respect to the previous model. This means that to improve the velocity performance with respect to the displaced volume, it is necessary to reduce the resultant drag force at the zero angle of the glide angle. That could be done, for example: using a lower fineness ratio (rate between the external diameter and the length of the vehicle); reduction in the superficial roughness of the external shape; reduction of the dimensions of the vehicle; other techniques to reduce the basic drag.  
Figure 12: Glider polar chart—Kay Juul 2.  
Figure 13: Dive velocity chart—Kay Juul 2.  
* The limit glide angle was reduced. That means that the optimal lift and drag ratio was increased and the specific energy of consumption was reduced. If the reduction in the specific energy consumption is not a determinant to improve the velocity performance of the vehicle, it is observed in the comparative charts of the Section 5.2 that the curves are more stable. In combination with the reduction of the angle of attack, the velocity function in Equation (21) and the displaced volume in Equation (20) are improved because the factor \(K_{D2}\alpha^{2}\) is reduced too, obtaining more velocity with respect to the displaced volume of the VBS.  
Comparing the estimated performance of the glider Kay Juul 2 with respect to the models of the Slocum [27], the performance of the functions of flight angles is acceptable and the velocity curves are stable. The limit of the angles of attack is the second lower value of the analyzed models and the limit of the glide angle is on the average zone. The dimensions of the vehicle Kay Juul 2 are bigger than the Slocum dimension; therefore, the polynomial coefficients of the vehicle are also bigger, resulting in a lower resultant velocity at the same displaced volume as the Slocum models.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 2 System Model and Conventional Method
### Two-Step Linearization of Target State Prediction  
The motion model of target and the measurement model of target are represented as Equations (2) and (3), and they constitute a nonlinear detection system. They are nonlinear functions, thus the standard Kalman filter (KF) is no longer applicable, and the nonlinear filtering problem needs to be approximated as a linear filtering problem, so that a sub-optimal solution is obtained [20; 21]. Therefore, the Extended Kalman Filter (EKF) can be considered to predict or estimate the target state [22].  
In the above nonlinear system, as the nonlinear system needs to be linearized, that is, the Taylor series expansion is used to approximate the nonlinearity, which is transformed into the process of calculating the Jacobian matrix of the nonlinear functions \(f(\cdot)\) and \(h(\cdot)\), that is, the state transition matrix \(F(k)\) and the observation matrix \(H(k)\) are obtained. Therefore, the linearized system model can be obtained after linearization:  
\[\left\{\begin{array}{l}X(k{+}1)=F(k)X(k)+W(k)\\ Z(k)=H(k)X(k)+U(k)\end{array}\right. \tag{4}\]  
where  
\[F(k)=\left.\left[\begin{array}{ccc}1&0&-\theta(k)\\ 0&1&\frac{3}{2}-\theta(k)\\ 0&0&1\end{array}\right]\right|_{\theta(k)=\theta(k|k-1)} \tag{5}\]  
\[H(k)=\left.\left[\begin{array}{ccc}x(k)-x_{1}&y(k)-y_{1}&0\\ x(k)-x_{2}&y(k)-y_{2}&0\\ \vdots&\vdots&\vdots\\ x(k)-x_{n}&y(k)-y_{3}&0\end{array}\right]\right|_{X(k)=\hat{X}(k|k-1)} \tag{6}\]  
Note that Equation (6) is obtained using two-step linearization. \(x_{i}\) and \(y_{i}\) are defined as the X-axis and Y-axis of node \(s_{i}\), respectively.  
Based on the nonlinear system models (2) and (4), and the linearized system model (5), the state of moving target can be estimated or predicted by the Kalman filter. The main steps are as follows:  
\[\hat{X}(k{+}1|k)=f(\hat{X}(k|k)) \tag{7}\]  
\[P(k{+}1|k)=F(k)P(k|k)F^{T}(k)+\mathcal{Q}(k) \tag{8}\]  
\[K(k{+}1)=P(k{+}1|k)H(k)\cdot\left[H(k)P(k{+}1|k)H^{T}(k)+R(k)\right]^{-1} \tag{9}\]  
\[\hat{X}(k{+}1|k{+}1)=\hat{X}(k{+}1|k)+K(k{+}1)\left[Z(k{+}1)-h\left(\hat{X}(k {+}1|k)\right)\right] \tag{10}\]  
\[P(k{+}1|k{+}1)=(I-K(k{+}1)H(k))P(k{+}1|k) \tag{11}\]
# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 5 Simulation Evaluations  
In this section, we evaluate the performance of the proposed charging schedule through experimental simulation. The simulation is built in the widely-used OMNET++ simulator [30]. We also study the impact of important parameters on the performance, including the number of nodes deployed, the charger's traveling distance constraint, the charger's traveling speed and the node's received charging rate.
## 5 Challenges and Future Research Directions
### Overhead Information Exchange  
As connected devices and data traffic grow, the exchange of control and signaling information between small cells increases energy consumption and network congestion. Current signaling protocols struggle with inefficiencies, affecting both latency and power usage. Future research should aim to develop optimized communication algorithms, energy-efficient protocols, and lightweight signaling techniques to enhance network scalability and efficiency [61].