esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor-critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.  
reconfigurable intelligent surfaces; EV charging networks; SIoT; causal structure; A3C; reinforcement learning; RIS phase shift; energy efficiency +
Footnote †: journal: _Future Internet_  
In smart electric vehicle charging networks, the communication system adjusts multiple features to achieve desirable communication outcomes. For example, factors such as communication power allocation at charging stations, the presence of active or passive relays [9], the communication channel quality, and the presence of obstacles may affect the overall communication conditions [10]. As actions are taken to optimize the communication network, the system's communication state changes accordingly. In [11], this is represented using Markov Decision Processes (MDPs), with the communication quality serving as the reward factor. In this scenario, the numbers of interventions and states both exponentially increase.  
To address these challenges, this paper introduces a novel framework leveraging emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-based reinforcement learning techniques.
###### Abstract  
Employing a small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and hotspot analysis, this study identified 81 potential landslides in a 768.7 km\({}^{2}\) area of Xiaojin county, eastern Tibetan Plateau. Subsequent time-series deformation analysis revealed that these potential landslides are in the secondary creep stage. The newly identified landslides were compared to a landslide inventory (LI), established through field surveying, in terms of causative factors, including altitude, slope, relief amplitude, distance to river, distance to road, and slope curvature. From the comparison, the InSAR technique showed the following advantages: (1) it identified 25 potential landslides at high altitudes (>3415 m) in addition to the low-altitude landslides identified through the field survey. (2) It obtained approximately 37.5% and 70% increases in the number of potential landslides in the slope angle ranges of 20\({}^{\circ}\)-30\({}^{\circ}\) and 30\({}^{\circ}\)-40\({}^{\circ}\), respectively. (3) It revealed significant increases in potential landslides in every relief amplitude bin, especially in the range from 58 m to 92 m. (4) It can highlight key geological factors controlling landslides, i.e., the stratigraphic occurrence and key joints as the InSAR technique is a powerful tool for identifying landslides in all dip directions. (5) It reveals the dominant failure modes, such as sliding along the soil-rock interface and/or interfaces formed by complicated combinations of discontinuities. This work presents the significant potential of InSAR techniques in gaining deeper knowledge on landslide development in alpine forest regions.  
alpine forest region; InSAR; landslide inventory; hotspot analysis Article  
[PERSON], [PERSON] [PERSON]; [PERSON]; [PERSON] Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China  
1  
[PERSON]; [PERSON]; [PERSON]; [PERSON] Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China  
2
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 6 Conclusions and Future Work  
This paper addresses optimizing the MC's charging schedule in WRSNs, which considers target coverage and connectivity constraints. Unlike the existing approaches, ours took into account the charging location and the charging time in the newly proposed Fuzzy Q-charging. Fuzzy Q-charging has an optimal charging time determination algorithm that relies on Fuzzy logic to adjust the energy charging level dynamically. The algorithm has been utilized at every charging point to maximize the number of alive sensors. Moreover, Fuzzy Q-charging uses Q-learning in an optimal charging scheme to maximize the target number. We have extensively evaluated Fuzzy Q-charging in comparison to the previous charging schemes in WRSNs. The evaluation results show that Fuzzy Q-charging outperforms the others. Specifically, Fuzzy Q-charging prolongs the network lifetime infinitely in certain conditions of the target and sensor numbers, while the other algorithms cannot. In other cases, Fuzzy Q-charging extends the network lifetime by 6.8 times on average and 33.9 times in the best case, compared to the existing algorithms. In the future, we plan to extend this work to handle the WRSNs with multiple mobile chargers.  
Conceptualization: V.Q.L., P.L.N.; Methodology: P.L.N., K.N.; Software, validation: V.Q.L., A.D.N., T.H.N.; Formal analysis, investigation: P.L.N., V.Q.L., A.D.N.; Writing--original draft preparation, Visualization: A.D.N., V.Q.L.; Writing--review and editing: P.L.N., K.N.; Project administration: K.N., T.H.N.; Resources: T.H.N.; Funding acquisition: K.N., P.L.N. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Ministry of Education and Training of Vietnam under grant number B2020-BKA-13. This research was partly supported by ROIS NII Open Collaborative Research 2021-21S1201, JSPS KAKENHI Grant Number 19K20251, 20H04174. La Van Quan was funded by Vingroup Joint Stock Company and supported by the Domestic Master/Ph.D. Scholarship Programme of Vingroup Innovation Foundation (VINIF), Vingroup Big Data Institute (VINBIGDATA), code VINIF.2020.Th.S.BK.03.  
Not applicable.  
The authors declare no conflict of interest.
# Hybrid Beamforming for Reduction of Inter-Beam Interference in Millimeter-Wave Cellular Systems
## 4 Conclusions  
In this paper, we proposed digital precoding techniques for mm-wave systems with a hybrid beamformer, which can reduce the IBI received from neighboring cells as well as adjacent beams in the serving cell. We performed simulations, and the results show that while the Type-1 precoder is simple, it cannot reduce the IBI from neighboring cells, resulting in an error floor in BER performance. They also show that Type-2/Type-2/Type-3 precoders can achieve high achievable rates or low BERs because they can reduce the IBI from the serving cell and neighboring cells. The advantages and disadvantages of the precoders can be summarized as follows. In the Type-2 case, the number of \(RF\) chains required at the \(BS\) is the same as in a single-cell case. However, all neighboring \(BS\)s should share data streams, increasing the network overhead. In the Type-3 case, the digital precoder can be designed independently by each \(BS\). However, the number of \(RF\) chains required at the \(BS\) increases. In the Type-4 case, the number of \(RF\) chains required at the \(BS\) is reduced to that of a single-cell case. However, the number of \(RF\) chains required at the \(MS\) increases. In addition, we showed that the processing times required for the estimation of the effective combined network channel can be significantly reduced using the CBRS technique. Finally, we showed that the proposed precoders (Type-2/Type-2/Type-3) with the estimated effective network channel can significantly reduce the IBI in mm-wave cellular systems. As a future work, the performance of the hybrid precoder needs to be analyzed in the environment where \(MS\)s move.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01057628), and by Chung-Ang University Graduate Research Scholarship in 2017.  
**Author Contributions:** (ONLY for Research Articles): [PERSON] and [PERSON] developed a cell selection technique for mm-Wave cellular systems with hybrid beamforming. [PERSON] supervised the project.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 7: Comparison of BERs of proposed hybrid beamforming techniques when the estimated channel is used.
# A Filter Pruning Method of CNN Models Based on Feature Maps Clustering
## 4 Filter Pruning Based on Feature Maps Clustering
### K-Means Method
#### 4.1.2 Clustering  
After the dimensional reduction of the feature maps, the K-Means algorithm can be used to cluster the feature maps. The specific algorithm steps are as follows:  
1. Choose the initial centers of K classes appropriately. Generally, the class centers are initialized by guessing or in a random way.
2. For any sample, calculate its distance to K centers and classify the sample into the class, to whose center the distance is the shortest.
3. Calculate the average of all data points belonging to the same class, that is calculate the average of each dimension of the vector and use the average as the new class center.
4. Repeat steps 2 and 3 until convergence occurs, i.e., the centers of all classes do not change anymore.  
Figure 5 is an example of the K-Means algorithm. Where A, B, C, D, and E represent different samples, two orange circles represent different centers. According to the distances between the samples and the centers, A, B, and C are clustered in one group, and D and E are clustered in the other group eventually.  
The advantages of this algorithm are simple steps and fast operations. Most importantly, it is easy to be implemented and can be operated in parallel. However, this algorithm also has some drawbacks because the number of classes K should be set in advance and improper selection will lead to poor results. The key to the algorithm lies in the selection of initial centers and distance formula. Moreover, how to determine the clustering number K is also an important problem to be solved.
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 6 Bioremediation Applications Using Adapted Microbes  
### Microbially Assisted Phoremediation: From the Lab to the Field Applications  
Plant-microbe interactions are complex and greatly influence ecosystem functions. Numerous authors emphasize the importance of describing interaction mechanisms between plants and microbes to develop adapted, efficient, and sustainable bioremediation strategies [7,27]. Even though biological and bioengineering research generates more and more precious knowledge that ameliorates our understanding of the contribution of microbes to plants' stress-resistance capacity and adaptability, research is, most of the time, based on controlled or semi-controlled experiments on simplified conditions. A study from 2010 [50] asserted that, at this time, no in situ survey was conducted to concretely test the efficiency of the bioremediation of metals with halophilic microbes. In 2015, only 6.6% of the publications dealing with phytoremediation were based on field experiments [180]. Moreover, most of these publications are related to non-saline ecosystems. Indeed, extracting metals with plants or microbes seems easy to do in controlled conditions, and numerous articles make recommendations to foster bioremediation applications, but when confronted with the reality of the field, efficient bioremediation remains a difficult task. The influence of microbial communities on plant growth and their sensitivity to pathogens [27], as well as abiotic factors such as salinity, organic matter, pH, redox potential [1], and seasonality [181] determines the phytoremediation yield and success.
## Abbreviations  
A2C & Advantage actor-critic  
A3C & Asynchronous advantage actor-critic  
AI & Artificial intelligence  
ANN & Artificial neural network  
BCT & Blockchain technology  
BRL & Batch reinforcement learning  
BRP & Balance responsible party  
CEM & Competitive energy market  
CO & Community-operated  
DDPG & Deep deterministic policy gradients  
DER & Distributed energy resources  
DG & Distributed generators  
DPG & Deterministic policy gradients  
DQN & Deep Q network  
DSO & Distribution system operator  
ESS & Energy storage systems  
ETPA & Energy Trading Platform Amsterdam  
EV & Electric vehicle  
FSP & Flexible service providers  
GA & Genetic algorithms  
GOPACS & Grid Operators Platform for Congestion Solutions  
HER & Hindsight experience replay  
HS-DDPG & Hot start deep deterministic policy gradient  
IISM & Institute of Information Systems and Marketing  
IoT & Internet of Things  
ILQR & Iterative linear quadratic regulator  
LAMP & Landau Microgrid Project  
LEC & Local energy community  
LEM & Local energy market  
LES & Local energy system  
LSEM & Large-scale energy markets  
MBFM & Modified Bessel function model  
MBVE & Model-based value estimation  
MDP & Markov decision process  
MG & Microgrid  
MGM & Microgrid market  
ML & Machine learning  
NAF & Normalized advantage function  
P2P & Peer-to-peer  
PG & Policy gradient  
PILCO & Probabilistic inference for learn control  
POMDP & Partially observable Markov decision process  
PPO & Proximal policy optimization  
PSO & Particle swarm optimization  
Q & Action  
QR-DQN & Distributional reinforcement learning with quantile regression  
REM & Retail electricity market  
RES & Renewable energy sources  
RL & Reinforcement learning  
SAC & Soft critic actor  
SAM & Strategic assessment model  
SARSA & State-action-reward-state-action  
SPOE & Swiss Federal Office of Energy  
SoC & State of charge  
SM & Smart meter
## 4 ITS Threats Analysis and Classification
### ITS Involved Entities  
From a security point of view, different entities might be involved in ITS systems [13], including:  
* The drivers: Drivers are the most important element of ITS, since they have to make vital decisions and can interact with the driving assistance systems to ensure their safety;  
Figure 8: Examples of ITS threats, attacks and countermeasures.  
* The on-board unit (OBU): OBU refers to both the driver and the vehicle in the literature. OBUs can be classified into: (i) normal OBUs, which operate in a normal way; and (ii) malicious OBUs, which try to mislead the system;
* The road side unit (RSU): Similarly to OBU, RSUs can be classified into: (i) normal RSU terminals; and (ii) malicious RSU terminals, which try to mislead the system;
* Third party entities: Third party entities can be trusted or semi-trusted, and are responsible for managing the security certificates, as well as the diverse secrets/public key pairs. Examples of such entities include the transportation regulatory agencies and the vehicle manufacturers;
* The attackers: Attackers try to violate the security of ITS systems by using several techniques. These attackers can be classified into different categories, as discussed in the following subsection.
## 1 Introduction
#### Emergency Scenario
Section 3 presents the vital sign evaluation, specific data features, signal compression, security on medical information, the WBSN architecture for emergency scenarios and relevant facts of Emergency E-Health practices. In Section 4, we analyze communication protocols, energy efficient MAC schemes and scheduling conflicts. In the fifth section, we discuss briefly the assessment of an energy optimization model for a WBSN. Section 6 presents the importance of video technology in m-Health systems. Finally, Sections 7 and 8 present the future challenges and conclusions, respectively.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 3 Methodology
### Optical Signal Detection and Processing  
The modulated optical signal emitted by the laser diode was received by the silicon photocell, which generated a voltage output proportional to the incident light intensity. This signal was then amplified using the ICL7611 operational amplifier before being digitized by the microcontroller's analog-to-digital converter.  
Two primary performance metrics were extracted from the digitized data:  
* Optical Output Power (mW): Recorded as a function of both drive current and modulation frequency.
* Laser Diode Efficiency: Derived from the slope of the optical power versus current curve, normalized for electrical input.  
To support reproducibility and clarity in signal processing, Table 1 provides the key physical and electrical parameters of the VACTEC7-21G72S photocell used in the receiver subsystem.  
This methodology facilitated accurate and repeatable comparison of the three tested wavelengths, allowing optical power and conversion efficiency to be evaluated under standardized electrical and environmental conditions. The modular and reconfigurable  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Detection Area & \(2\times 4\) cm\({}^{2}\) \\ \hline Open-Circuit Voltage & U\({}_{\text{oc}}=500\) mv \\ \hline Short-Circuit Current & I\({}_{\text{sc}}=5\) mA \\ \hline Standard Illumination & E\({}_{\text{v}}=600\) Lx \\ \hline Parallel Cells (N\({}_{1}\)) & \(1\) \\ \hline Series Cells (N\({}_{2}\)) & \(1\) \\ \hline Load Resistance & R\({}_{\text{h}}=0\)\(\sim 1000\)\(\Omega\) \\ \hline Internal Capacitance & 0.2 uF \\ \hline Operating Temperature & T \(=295\) K \\ \hline Field of View & \(\Psi=90^{\circ}\) \\ \hline Measurement Distance & d \(=1.0\) m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the VACTEC7–21G72S silicon photocell.  
design of the testbed further supports future extensions for longer-range or fog-simulated FSO testing.
## 3 Analysis of the DQL Method
### Convergence Analysis
#### 3.1.3 Convergence of the DQL Method  
The DQL method's approach, as previously stated, is a two-stage procedure. When \(k\leq\textsc{max\_search}\), all the direct, quadratic and linear steps are enabled, and the method focuses on the efficiency of finding a better solution. When \(k>\textsc{max\_search}\), the method disables the quadratic and linear steps and switches the solution acceptance rule to the sufficient decrease rule. This switch allows us to prove the convergence of the method. In particular, if the objective function \(f\) is a function that satisfies Assumptions 1 and 4, then the method fits under Assumptions 2 and 3. Thus, Theorem 2 applies to the DQL method.  
The following Theorem shows that Assumption 2 holds for the DQL method.  
**Theorem 3**: _Let Assumption 1 hold. If there exists an \(\alpha>0\) such that \(\delta^{k}>\alpha\), for all \(k\), then the DQL method visits only a finite number of points._  
Since the number of points evaluated in an iteration is finite and the number of iterations in the exploration stage is finite, the evaluated points in the exploration stage of the DQL method is finite.  
In the convergence stage, the DQL method accepts an improvement \(x\) if \(f(x)<f(x^{k})-(\delta^{k})^{2}\). Therefore, Theorem 1 can be applied to the convergence stage of the DQL method. Therefore, the DQL method visits only a finite number of points.  
Let \(D^{k}\) be the rotation matrix produced by the DQL method at the iteration \(k\). We denote the set of the columns of \(\begin{bmatrix}D^{k}&-D^{k}\end{bmatrix}\) as \(D^{k}\). We have the following proposition.  
**Proposition 1**: _Let \(\tilde{D}^{k}\) be the set of search directions generated by the DQL method at the iteration \(k\) and \(n\) be the dimension of the search space. Then, (a) \(\lVert\tilde{d}\rVert=1\) for any \(\tilde{d}\in\tilde{D}^{k}\), (b) \(cm(\tilde{D}^{k})=\frac{1}{\sqrt{n}}\)._  
This is easy to confirm.  
Proposition 1 indicates that in the DQL method, the cosine measure of the set of search directions and the norm of the search directions are constant, so we can find a lower bound \(\epsilon_{1}\) for the cosine measure of the set of search directions and a upper bound \(\epsilon_{2}\) for the norm of the search directions. Therefore, Assumption 3 holds for the DQL method.  
We present the following convergence theorem for the DQL method.  
**Theorem 4**: _Let \(\{x_{k}\}\) be the sequence of iterations produced by the DQL method to a function \(f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{n}\) with a compact level set \(L(x_{0})\). In addition, let \(\
abla f\) be Lipschitz continuous in an open set containing \(L(x_{0})\). Then, the DQL method results in_  
\[\liminf_{k\rightarrow+\infty}\lVert\
abla f(x_{k})\rVert=0, \tag{26}\]and the sequence of iterates \(\{x_{k}\}\) has a limit point \(x_{\star}\) for which_  
\[\
abla f(x_{\star})=0. \tag{27}\]  
Proof.: Theorem 2 applies, since Assumptions 1-4 hold for the DQL method.