esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Experimental Results  
Estimating the proposed protocol's performance, we take into account the network lifetime, network stability period, total residual energy, residual energy deviation, and the number of received data packets by the sink node. These metrics are described as follows:  
* Network lifetime: It is the time between the start of the network running and the death of the first node. Besides, it is known as the network stability period.
* Network instability period: It is the time between the death of the first node and the death of all nodes.
* Residual energy: This metric is calculated by considering the total residual energy of all sensor nodes. Usually, this value is presented as a percentage indicator of the total remaining energy of the network, which is represented in Equation (20). \[p(t)=\frac{\sum_{i=1}^{N}E_{r}(n_{i},t)}{\sum_{i=1}^{N}E_{o}(n_{i},t)}\] (20) where \(p(t)\) is the percentage of the total residual energy in the round \(t\). The total residual energy in round \(t\) is \(\sum_{i=1}^{N}E_{r}(n_{i},t)\) and the sum of the initial energy \(E_{o}\) of all the sensor nodes in the network is \(\sum_{i=1}^{N}E_{o}(n_{i},t)\).
* Residual energy deviation: The variation between the node with the most remaining energy and the node with the least remaining energy. The residual energy deviation in the round \(t\) is calculated as follows in Equation (21). \[D_{R}(t)=E_{r_{max}}(\mathbf{n},t)-E_{r_{min}}(\mathbf{n},t)\] (21)where \(E_{r_{max}}(\mathbf{n},t)\) and \(E_{r_{min}}(\mathbf{n},t)\) are the maximum and minimum residual energy among the set of nodes \(\mathbf{n}\) in the network considering round \(t\). However, to facilitate the analysis of the results, the residual energy deviation is reported in percentage units.
* Throughput: The number of data packets received by the BS.  
Our method has been compared against other popular techniques such as Low Energy Adaptive Clustering Hierarchy Protocol (LEACH) [14], Stable Election Protocol (SEP) [21], and Distributed Energy-Efficient Clustering Algorithm (DEEC) [23].  
For consistency in the experiments, parameter settings of Table 1 have been considered for all the routing protocols in comparison. Regarding the tuning parameters of the YSGA algorithm, Table 2 shows the adopted values in which the authors have reported the best performance of the YSGA. In Addition to the settings of Table 1, Table 3 shows the values of additional parameters considered in every method in comparison.  
The simulation was implemented in MATLAB R2019a(The MathWorks, Inc. Natick, Massachusetts, USA), in a computer with a processor Intel(R) Core (TM)i7-8550 [EMAIL_ADDRESS] GHz1.99 GHz.
## 4 Simulation and Analysis
### Comparison between SS-LCBBA and Baseline Algorithms
#### 4.2.3 The Maneuver Intention Prediction Network Parameter Settings  
The intention recognition network uses the network structure shown in Section 2.2, where each stacked layer has the same network structure, with 128 neurons in the fully connected layer (FC) and 512, 256, and 128 neurons in the three LSTM layers. The number of parallel networks \(P_{n}\) is selected according to the recognition accuracy test results and the complexity of the intention parameters, \(A_{1}\). The number of parallel networks for the intention recognition network of the \(QEG\) trajectory was set to \(P_{n}=2\). The number of parallel networks for the \(SG\) trajectory recognition network was set to \(P_{n}=3\). The joint prediction network used the network structure, and each stacked network had the same structure, with the numbers of neurons in two FC of 256 and 128, in order; and the number of neurons in three LSTM layers of 512, 256, and 128, in order. The number of parallel networks \(P_{n}\) was chosen according to the type of longitudinal maneuver. The length of the input sequence was set to 50.
## 4 Discussion
### Comparison of Space Closing Efficacy between Various Loop Springs  
The parameter \(C\)/\(N_{b}\) corresponds to the space closing speed. However, it did not verify whether \(C\)/\(N_{b}\) is proportional to the closing speed in clinical settings. For example, when \(C\)/\(N_{b}\) is doubled, it is unknown whether the closing speed is doubled. It could only be predicted that the larger \(C\)/\(N_{b}\), the faster the closing speed. We used \(C\) and \(C\)/\(N_{b}\) to compare the efficacies of various springs.  
In Figure 8, the spring plotted on the right side allowed a longer closing distance and on the upper side allowed a faster closing speed. A spring that can move speedily over a long distance must be plotted in the upper right area in Figure 8, but no such springs existed. In clinical settings, an appropriate spring should be selected for each case by giving priority to either the closing distance or speed.  
For all springs, when the gable bend was increased, although the closing distance \(C\) decreased, the closing speed \(C\)/\(N_{b}\) increased significantly. It was concluded that the gable bend was very effective for speedy space closure. It should be noted that an excessive gable bend causes plastic deformation of the spring during the activation.  
When the helical loop was replaced with the vertical loop, the closing distance \(C\) decreased. On the other hand, when the loop height was increased, \(C\) increased. In both cases, the change in \(C\)/\(N_{b}\) was not significant. To increase the closing distance without a significant decrease in closing speed, the loop should be made as high as possible. However, if the loop is too high, it will be uncomfortable for the patient.  
When the wire size was increased to 0.018 \(\times\) 0.025 inches, the closing speed \(C\)/\(N_{b}\) increased, but the closing distance \(C\) decreased. Since an increase in wire size is equivalent to an increase in Young's modulus, the closing speed increases when the spring is made of stainless steel with a larger Young's modulus (200 GPa).  
So far, the moment-to-force ratio has been used to evaluate the mechanical property of a retraction spring. However, in the present simulation, the force system and the resulting movement pattern changed as the teeth moved. Hence, we attempted to evaluate the space closing efficacy based on the long-term movement of the teeth. Although this method is slightly more advanced than before, its validity must be verified in the future.
## 4 The Proposed Scheme HFECS  
Information entropy is the statistical measure of the degree of uncertainty of an information source and it has always played a significant role in uncertainty decision analysis [13]. Likewise, hesitant fuzzy entropy is defined as the statistical measure of degree of hesitant fuzzy uncertainty, which depends on MADM to make a decision about certain events. In heterogeneous clustering schemes of WSNs, the role of CH within a cluster needs to be substituted using some decision criteria in order to avoid the hotspot problem. Using a single parameter such as the energy level of the sensor nodes is not sufficient enough to make an accurate decision about CH selection after every round of communication. In order to ensure that the best possible working sensor node is chosen as CH in every round of communication, we have to incorporate MADM in our clustering scheme. The hesitant fuzzy set including multi attributes and alternatives could help us out in implementing MADM for opportunistic clustering in WSNs [40; 41].  
Our main objective is to design a hesitant fuzzy entropy-based self-organizing opportunistic clustering scheme which can prolong the network lifetime and bring an improvement in overall energy consumption of sensor nodes. In our proposed scheme, we determined the entropy weight coefficients using hesitant fuzzy entropy to select the best possible CH through election procedure and perform reliable data fusion at CH level to reduce the information flow. Since we are dealing with MADM problem, we considered several parameters like time frequency parameter \(TF_{V_{i}V_{j}}\) (depends on \(W_{v}/S_{v}\) and status transition frequency \(F_{ST}\)), residual energy \(R_{E}\), link quality factor \(LQR\) in terms of signal-to-noise ratio, distance to BS \(D_{toBS}\), and number of supported sensor nodes \(N_{support}\) as our different attributes of hesitant fuzzy set to compute the hesitant fuzzy entropy.  
We have three different types of nodes in our network which can lead to energy heterogeneity and might result in three different types of CH, i.e., \(CH_{normal}\),\(CH_{int}\), and \(CH_{adv}\), with the initial energy \(E_{n}\), \(E_{int}\), and \(E_{adv}\), respectively, but in order to avoid frequent change in the CH role, we used only two different types of CH, i.e., \(CH_{int}\) and \(CH_{adv}\), for calculating \(N_{support}\). However, the percentage of energy consumption for \(CH_{int}\) (\(E_{CH}/E_{int}\)) is more than that of \(CH_{adv}\) (\(E_{CH}/E_{adv}\)) due to the reason that \(E_{adv}>E_{int}\). As the consumed energy in relation to available energy is uneven for CHs, it leads us to determine the optimum number of sensor nodes which can be supported by each type of CH. Therefore, we used the remaining energy and average of \(E_{int}\) and \(E_{adv}\) to find the optimum number of sensor nodes supported by both \(CH_{int}\) and \(CH_{adv}\) in Equation (7) as:  
\[N_{support}=\frac{(E_{total}-E_{consumed})*(N-N_{C})}{\left(N_{C}*\frac{(E_{int} +E_{adv})}{2}\right)} \tag{7}\]
## 3 Results and Discussion
Configuration 3 significantly outweighs the other solutions in terms of the number of lane violations and the ability to control UGV in a limited space evaluation. Trial 2 also consisted of maneuvering in limited space. However, it had a differentiated cornering radius and relatively open spaces, providing greater maneuverability when avoiding obstacles. It required the operator to possess not only good spatial orientation but also the ability to detect, recognize, and avoid obstacles. Trial 2's results compared to trial 1 show significantly greater differences in travel times depending on the camera configuration. The shortest average travel time was obtained for configuration 3 (422 s), and it was shorter than the time obtained for configuration 2 by 16% and 23% shorter than the time obtained for configuration 1 (Figure 10). It should be noted that all operators obtained the best results in configuration 3 and the worst results in configuration 1. However, the complexity of the task caused a significant increase in the average travel time in comparison to the case of direct observation; for configuration 3, the time extension was 150%, for configuration 2, it was 197%, and for configuration 1, it was 226%. When analyzing the ability to carry out the detection and recognition of obstacle tasks, the test indicates a significant variation in results depending on camera configuration in teleported systems (Figure 11). The average of undetected obstacles on one pass was 0.014 for configuration 3, 0.14 for configuration 2, and 1.2 for configuration 1. Configuration 3 also provided the best results in terms of obstacle recognition. In this case, configuration 1 is the worst. The average number of unrecognized obstacles per pass is 22 times higher than for the best configuration (configuration 3). Figure 9: Number of lane violations per ride for 3 camera configurations—trial 1. The number of obstacles hit can be used as an indicator of the ability to construct a mental map. From this point of view, configuration 3 proved to be the most effective (Figure 12); only 0.2 contacts with obstacles per ride were recorded. In configuration 1, there were 80% more, and in configuration 2, there was as much as 420% more. A comparison of the number of lane violations (Figure 12) shows that configuration 1 turned out to be the most effective--on average 0.15 violations during one ride. The increased number of lane violations in configuration 3 (0.32 violations per ride) probably resulted from greater operator confidence and execution of the task at much higher speeds. Figure 11: Average of number of undetected and unrecognized obstacles per ride for 3 camera configurations—trial 2. Figure 10: Average travel time for 3 camera configurations and driving with direct observation of the surrounding—trial 2.
## 5 Conclusions  
In this study, we present a construction framework for polar codes that is specifically designed for VLC channels. Simulation results showed that the proposed VLC system, utilizing these codes, achieved a coding gain of at least 5 dB. Adjusting the polar code parameters further reduced the system's BER. Notably, across various indoor heights, the BER of the VLC system using our approach was significantly lower than that of an uncoded system, with this advantage becoming more pronounced as communication distances increased. Our scheme effectively reduced transmission errors in the VLC system, greatly extending the range for reliable communication. We also introduced a mapping strategy that combined polar codes with PNC in the VLC system. The results revealed distinctive performance characteristics between the proposed methodology and conventional transmission strategies. At a BER of \(10^{-5}\), this performance differential was small, yet our approach conserved two time slots and effectively doubled the throughput. In contrast to the DNC-Polar relay method, our approach saved one time slot, boosting throughput by half. Furthermore, it necessitated only a single receiving equipment set at the relay node, reducing equipment costs by half. Overall, the proposed framework enhanced VLC system throughput, improved telecommunication quality, and was well-suited for low-latency VLC systems.  
Figure 20: The throughput comparision of relay schemes.  
**Author Contributions:** Conceptualization, M.Z. and C.L.; methodology, M.Z.; software, M.Z.; validation, M.Z., Y.Z., and D.S.; formal analysis, M.Z.; investigation, M.Z.; resources, M.Z.; data curation, M.Z. and D.S.; writing--original draft preparation, M.Z. and Y.Z.; writing--review and editing, Y.Z., J.Y., and N.Z.; visualization, M.Z.; supervision, C.L.; project administration, C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Science Foundation of China (NSFC) with grant no. 62271514, the Science, Technology and Innovation Commission of Shenzhen Municipality with grant no. JCYJ20210324120002007, and ZDSYS20210623091807023, the Foundation of State Key Laboratory of Public Big Data (No. PBD2023-01), and the Open Project of Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection (No. 2023B1212010011).  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Data are available from the authors, on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 A Beam-Hopping-Based Global Beam Scheduling and User Resource Allocation Algorithm  
In this section, we propose a beam-hopping-based joint beam scheduling and resource allocation framework for a resource-constrained heterogeneous GEO-LEO satellite network. The proposed framework consists of three core components: beam-footprint partitioning, time-slot-based beam scheduling, and joint frequency-power allocation. The first objective function P1 is addressed through a two-step process involving beam partitioning and beam scheduling, with the goal of maximizing the number of scheduled spatio-temporal-frequency resources. The second objective function P2 is solved via frequency and power allocation, aiming to maximize the total system throughput.  
Section 3.1 presents the optimization of beam-footprint partitioning using an ILP approach. Section 3.2, we address the scheduling of beam activations across time slots by proposing a global beam-slot allocation strategy that accounts for spatio-temporal dynamics. Section 3.3 introduces a joint optimization of beam-level power allocation and user-level frequency and power assignment.
# A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities
## 2 Proposed REC Model
### Variables--Unknown or Changing Parts of the Model  
* _P\({}_{j}\)_: Rated power of generator \(j\) [kW];
* _ESS\({}_{cap}\)_: Capacity of the centralized energy storage system [kWh];
* _E\({}_{i}^{load}(t,y)\)_: Energy consumed by member \(i\) for each year \(y\) [kWh]
* _E\({}_{i}^{prod}(t,y)\)_: Energy produced by the sum of generators of user \(i\) [kWh];
* _E\({}_{i}^{selfcons}(t,y)\)_: Energy self-consumed by user \(i\) [kWh];
* _E\({}_{i}^{surplus}(t,y)\)_: Energy surplus of user \(i\) [kWh];
* _E\({}_{i}^{deficit}(t,y)\)_: Energy deficit of user \(i\) [kWh];
* _E\({}_{cc}^{off}(t,y)\)_: Energy offered from the members of the community that have a surplus [kWh];
* _E\({}_{cc}^{req}(t,y)\)_: Energy required from the members of the community that have a deficit [kWh];* \(E_{e}^{sharedRT}(t,y)\): Energy shared in real time within the community [kWh];
* \(E_{ec}^{shared}(t,y)\): Energy shared within the REC considering also energy provided by the ESS [kWh];
* \(E_{ec}^{-market}(t,y)\): Energy bought from the market [kWh];
* \(E_{ec}^{-market}(t,y)\): Energy sold to the market [kWh];
* \(E_{ec}^{stored}(t,y)\): Energy stored in the ESS at time \(t\) (initial instant of the time window) [kWh];
* \(E_{ec}^{-ESS}(t,y)\): Energy withdrawn from the ESS [kWh];
* \(E_{ec}^{-\sim ESS}(t,y)\): Energy injected into the ESS [kWh];
* \(PE^{-market}(t,y)\): Price of the energy sold to the market for each year \(y\) [\(\in\)/kWh];
* \(PE^{-market}(t,y)\): Price of the energy bought from the market for each year \(y\) [\(\in\)/kWh];
* \(Inv(y)\): Investment cost [\(\in\)];
* \(Cost(y)\): Operational cost [\(\in\)];
* \(Cost_{ESStep}(y)\): Equivalent cost for the energy storage replacement [\(\in\)];
* \(Sav(y)\): Saving obtained self-consuming/sharing energy [\(\in\)];
* \(Rev(y)\): Revenues obtained selling the surplus to the market or by mean of the incentives [\(\in\)];
* \(Fin_{inc}(y)\): Financial incentives for the community [\(\in\)].
# Quasi-Uniform Density Non-Solid Infill Strategy for Axisymmetric Non-Planar Additive Manufacturing
## 2 Methodology
### Numerically Optimizing the Algorithm  
Implementing the repulsion energy method on a curved manifold is computationally demanding because each energy evaluation involves solving for geodesic distances and repulsion forces. Several measures are introduced to mitigate this cost while preserving the algorithm's reliability. The first approach is the explicit approximation of the repulsion forces. Rather than numerically calculating gradients by evaluating the potential in the vicinity of each point, a chord-length discretization (5) is employed to approximate geodesic curves and estimate the gradient. This approximation substantially reduces the frequency with which the geodesic equation is solved, improving runtimes.  
\[\left(\tfrac{\mathrm{d}t_{ij}}{\mathrm{d}u}\quad\tfrac{\mathrm{d}t_{ij}}{ \mathrm{d}\theta}\right)\approx\sum_{k=1}^{n-1}\frac{\mathcal{G}(u(\tau_{k+1} ),\theta(\tau_{k+1}))-\mathcal{G}(u(\tau_{k}),\theta(\tau_{k}))}{\|\mathcal{G }(u(\tau_{k+1}),\theta(\tau_{k+1}))-\mathcal{G}(u(\tau_{k}),\theta(\tau_{k})) \|}\
abla_{\{\mathbf{\varsigma}\}}\mathcal{S}(\tau_{k}) \tag{5}\]  
The second approach is the non-dimensionalization of both the total energy and the forces by their initial values, denoted as \(E_{0}\) and \(F_{0}\). This normalization alleviates the stiffness of the potential function and aids convergence. Without such scaling, small point displacements can trigger disproportionately large variations in the energy gradient, potentially halting the optimization.  
The third and last approach is the selection of a close-to-solution initial seed distribution. While a random seed may suffice in principle, three alternative methods may provide more favorable starting configurations that typically converge more rapidly. A geodesic blue noise sampling approach (GBNS) filters randomly generated points by imposing minimum geodesic distances, thereby yielding a distribution already aligned with the manifold curvature [51; 52]. A second possibility treats the manifold as approximately flat by using Euclidean repulsion energy (ERE) to position points. Although this imposes a degree of geometric simplification, it is often faster to compute, and allows for non-linear mesh restrictions such as non-rectangular slice polygons. A third option employs Euclidean blue noise sampling (EBNS), which similarly maintains minimum distances using a Euclidean metric rather than the full geodesic one. These refined initializations help the subsequent geodesic repulsion algorithm initialize closer to the solution, providing significant time savings.  
Collectively, these optimizations ensure that point placement converges more reliably and in practical runtimes, even for build platforms characterized by complex curvature.
###### Abstract  
In the evolving landscape of device-free localization techniques, Wi-Fi channel state information (CSI) emerges as a pivotal tool for environmental sensing. This study introduces a novel fingerprint localization algorithm. It employs an improved Hybrid Grey Wolf Particle Swarm Optimization (IPSOGWO) in combination with Multi-Output Support Vector Regression (MSVR) to enhance indoor positioning accuracy. To counteract the limitations of standard DBSCAN and PCA in noise reduction and feature extraction from complex nonlinear data, we propose an adaptive denoising algorithm based on spatial clustering (A-DBSCAN) and an autoencoder to efficiently denoise and extract features from CSI amplitude to improve the localization accuracy. Additionally, we introduce a new position update strategy, bolstering the optimization efficiency of the PSOGWO algorithm. This refined approach is instrumental in determining the globally optimal hyperparameters in MSVR, leading to enhanced model prediction accuracy. Two indoor scenario experiments were conducted to evaluate our method, yielding average localization errors of 0.59 m and 1.12 m, marking an improvement in localization performance compared to existing methods.  
indoor positioning; channel state information; hybrid optimization algorithm; MSVR