esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Model
### Model Architecture
Then, this information is passed through a single fully connected linear layer. Critic networks also take as an input the action calculated by the actor network for the same state information. This information is passed through a fully connected layer. Then, the output from state and action embeddings is combined using a transformation fully connected (TFC) layer as described in [40], which is followed by the activation function. Finally, a Q-value decoder obtains an estimated state-action pair value. The full critic network structure is depicted in Figure 5, and the parameter values are described in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Actor Network** & \\ \hline
**Layer** & **Input Size** & **Output Size** \\ \hline
**Edge Encoder**: & & \\ Linear & 7 & 40 \\ CELU & 40 & 40 \\ Linear & 40 & 80 \\ CELU & 80 & 80 \\ Max Aggregation & (n, 80) & (1, 80) \\
**Node Encoder**: & & \\ Linear & 25 & 400 \\ CELU & 400 & 400 \\ Concatenation & 400 + 80 & 480 \\
**Action Decoder**: & & \\ Linear & 480 & 300 \\ CELU & 300 & 300 \\ Linear & 300 & 2 \\ Tanh & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network parameters and structure of actor network. Figure 5: Critic network structure. Inputs of ego agent and other agent information are depicted in gray. Edge embedder described in Figure 5 is stylized in light red. Fully connected layers are represented in yellow and CELU in dark purple. Concatenation, which combines the ego agent features with embedded edge features, is represented with the green circle.
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### The Role of Cryptographic Methods in Blockchain-Based Data Storage Systems
#### 3.2.9 Security vs. Performance Trade-Offs in Cryptographic Techniques
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Cryptographic Technique** & **Security Level** & **Computational Overhead** & **Performance Impact** & **Use Cases** & **References** \\ \hline Asymmetric Encryption (RSA, ECDSA, Ed25519) & High & High due to key size and modular exponentiation & Slow for large-scale transactions, high processing cost & Digital signatures, secure authentication, blockchain consensus mechanisms & [114; 126; 127; 128] \\ \hline Symmetric Encryption (AES-256, ChaCha20) & High & Low, optimized for speed and efficiency & Fast and efficient, suitable for real-time processing & Encrypted storage, secure messaging, VPN encryption & [117; 122; 123; 124; 129] \\ \hline Zero-Knowledge Proofs (ZKP–SNARKs, STARKs) & Very High & Very High, requires complex proof generation & Slows down authentication due to proof verification time & Anonymous transactions, identity verification, privacy-focused blockchain applications & Anonymous transactions, identity verification, [56; 109; 115; 130; 131; 132] \\ \hline Homomorphic Encryption (Fully Encryption—FHE) & Very High & Extremely High, impractical for real-time operations & Extremely slow, impractical for large-scale blockchain storage & Cloud computing, privacy-preserving smart contracts & [120; 133; 134; 135; 136] \\ \hline Hashing Mechanisms (SHA-256, Keccak, Blake2) & Medium & Low, designed for quick computation & Minimal impact, designed for rapid verification & Blockchain transaction integrity, data verification, proof-of-work mechanisms & [16; 23; 24; 25] \\ \hline \end{tabular}
\end{table} 
Table 7: Security vs. Performance Trade-offs in Cryptographic Techniques. overhead, making them less practical for real-time blockchain applications. In contrast, symmetric encryption and hashing mechanisms balance security and performance efficiently, making them the preferred choices for scalable blockchain storage and authentication systems. Selecting an appropriate cryptographic technique depends on the specific application requirements, available computational resources, and performance constraints.
## 4 Experiments  
The goal of this section is to evaluate each approach of the Grassmann clustering algorithms and the (dis)similarities discussed in Section 3. These methods are then empirically analyzed and compared in terms of the clustering performance. To achieve this goal, a human motion capture-derived MTS dataset from CMU Graphics Lab Motion Capture (MOCAP) [55] is utilized in this experimental study. The following subsections provide details of the database, experimental protocols, and the results with discussions. All experiments were conducted using a PC with a 3.6 GHz CPU, 32 GB memory, and on the Matlab platform.
# Modeling of Fatigue-Strength Development
### The Maturity Method
#### 1.2.1 Maturity Functions  
The Nurse-Saul and the Arrhenius maturity functions are two models that are popularly used to compute maturity indices of concrete [41]. Nurse-Saul maturity function computes the maturity index of a concrete sample as the product of its cure temperature and the cure duration [42]. Despite its simplicity in application, Nurse-Saul maturity function has the limitation of assuming concrete maturity to be a linear function of temperature. Previous research has proved that the maturity temperature relationship is more exponential than it is linear [43]. The Nurse-Saul maturity function is given by Equation (1).  
\[\mathrm{M}=\sum_{\mathrm{o}}^{\mathrm{t}}\left(\mathrm{T}-\mathrm{T}_{\mathrm{ o}}\right)\times\Delta\mathrm{t} \tag{1}\]  
where,  
M = Maturity index at age t, (\({}^{\circ}\)C-hours or \({}^{\circ}\)C-days)  
T = Average concrete temperature during the time interval \(\Delta\mathrm{t}\) (\({}^{\circ}\)C)  
T\({}_{\mathrm{o}}\) = Datum temperature, the temperature below which no curing occurs (\({}^{\circ}\)C)  
t = Elapsed time (hours or days)  
\(\Delta\mathrm{t}\) = Time interval (hours or days)  
The Arrhenius maturity function, Equation (2), was proposed by [PERSON] and [PERSON] [44], to address the non-linear relationship between the rate of initial strength gain and cure temperature in concrete strength-age functions [45].  
\[\mathrm{t}_{\mathrm{e}}=\sum_{0}^{\mathrm{t}}\left\{\mathrm{e}^{\frac{\mathrm{ T}}{\mathrm{K}}\left(\frac{1}{2\beta_{\mathrm{s}}+\mathrm{T}}-\frac{1}{2\beta_{ \mathrm{s}}+\mathrm{T}}\right)}\right\}\times\Delta\mathrm{t} \tag{2}\]  
where,\(\mathrm{t_{e}}\) = The equivalent age at the reference curing temperature (hours or days)  
E = The apparent activation energy, characterizing temperature sensitivity of the mix (J/mol)  
R = The universal gas constant, 8.314 (J/mol-K)  
T = The average temperature of the concrete during interval \(\Delta\mathrm{t}\), (\({}^{\circ}\)C)  
T\({}_{\mathrm{r}}\) = The reference temperature (20 \({}^{\circ}\)C in Europe and 23 \({}^{\circ}\)C in North America)  
\(\Delta\mathrm{t}\) = Time interval (hours or days)  
The equivalent age represents the curing period, at a chosen reference temperature, that would yield concrete maturity equivalent to that which would be attained by curing at the actual concrete temperature.
## 2 Materials and Method Methodology
### Data Sources and Processing
#### 2.3.2 Residential Community Data  
This study used fine-grained residential population aggregation points as the research units. The Points of Interest (POI) for residential communities in Wuhan's central urban area as of the end of 2021 were collected from housing trading websites Anjuke ([[https://wuhan.anjuke.com/community/](https://wuhan.anjuke.com/community/)]([https://wuhan.anjuke.com/community/](https://wuhan.anjuke.com/community/)), accessed on 25 December 2021) and Lianjia ([[https://wh.lianjia.com/xiaoqu/](https://wh.lianjia.com/xiaoqu/)]([https://wh.lianjia.com/xiaoqu/](https://wh.lianjia.com/xiaoqu/)), accessed on 25 December 2021). For communities lacking information on the number of households or year of construction, we supplemented this data by consulting real estate agents and observing street view maps. After data cleaning, a total of 5555 community records were obtained. We selected data for communities built before or during 2014, resulting in 4728 communities (Figure 2c), indicating an addition of 827 new communities during this period (Figure 2d). Following the same steps, we identified a total of 5247 communities built before 2017.  
Previous research has found hexagonal grids to be advantageous compared to traditional square grids because they ensure equal distances from any point to the grid center in all six directions [44; 45]. This characteristic not only maintains data accuracy but also  
Figure 2: Location of the study area **(a,b)**, distribution of residential communities **(c,d)**, and street view sampling points **(e–g)**.  
reduces computational complexity and minimizes boundary effects that can introduce sample bias [46]. Consequently, this study implemented a hexagonal grid network with a side length of 250 m. Points representing residential areas within these hexagons were aggregated at the grid centroids, resulting in 1355 aggregated residential points for the year 2014 (Figure 3). By 2021, the number of aggregated residential clusters increased by 216. The clusters that persisted throughout this period were categorized as constant clusters to facilitate the subsequent quantitative analysis of changes. The total population at each aggregated point was calculated based on the total number of households after aggregation. The formulas are shown as follows:  
\[P_{K}=\overline{M}\times\sum_{i\in A_{k}}R_{i} \tag{1}\]  
where \(P_{K}\) represents the total population at point \(k\), \(A_{k}\) is the hexagon at point \(k\), \(R_{i}\) denotes the number of households at a specific residential point \(i\) within \(A_{k}\), and \(\overline{M}\) is the average number of people per household.  
According to the annual _Wuhan Statistical Yearbook_, the average number of people per household in Wuhan for the years 2014, 2017, and 2021 was 2.64, 2.66, and 2.93, respectively. The total population for 2021 calculated from this data was cross-checked with the population figures in the _Wuhan Statistical Yearbook 2022_, resulting in a margin of error of 3.84%. This confirmed the reliability of the population data used in this study.
## 3 Results and Discussions
### Effect of Lactic Acid Bacteria on Rice Straw Silage
plantarum_; LPA: maize silage treated with _L. plantarum_; LPA: maize silage treated with _L. acidophilus_. 2 SEM: standard error of the mean. 3  
The in vitro rumen fermentation characteristics of ensiled rice straw are shown in Table 6. The DMD of rice straw silage was different (\(p<0.05\)) at 6 and 24 h of incubation due to the treatments. At the end of the 24 h incubation, the PC and LA treatments had the highest DMD compared to other treatments. Our result was in contrast with the study conducted by [PERSON] et al. [50], which had shown no differences in DMD of rice straw treated by _L. acidophilus_. The pH of the ensiled rice straw was significantly affected (\(p<0.05\)) by treatment, with the pH of all LAB treatments being lower than that of NC. However, by 24 h of digestion, all treatments had similar pH. There was no significant difference in gas production across all treatments (\(p=0.157\)). Ammonia-N differed significantly across the treatments at 6 h of incubation (\(p<0.05\)). The LA and LPL treatments had higher concentration of ammonia-N compared to the rest of the treatments. The rice straw silage treated with the LA had the highest CP, and consequently, the amount of protein and amino acids available for deamination onto ammonia-N is likely to be increased. However, another factor that influences the degradation of protein is energy availability [51]. Rice straw has low amounts of energy due to the high level of cell wall content, with some indigestible components (i.e., lignin and silica). Such discrepancy in terms of energy and protein availability to ruminal microorganisms may lead to waste on ammonia-N.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Treatment (\(n=3\))**} & \multirow{2}{*}{**SEM 2**} & \multirow{2}{*}{\(p\)} \\ \cline{2-2} \cline{5-6}  & **NC 1** & & & & & & \\ \hline DMD, \% & & & & & & \\ \(6\) h & \(15.69\) 2 [FOOTNOTE:2]Footnote 2: NC: Negative control ( maize silage without any additives); PC: Positive control ( maize silage treated with commercial silage additives); LPL: maize silage treated with _L. plantarum_; LPA: maize silage treated with _L. This study investigated cultures of LAB as inoculums for both maize and rice straw silage. These two silage materials are quite different in chemical composition and other characteristics. Generally, maize silage has relatively lower DM content and higher OM compared to rice straw silage. CP content was higher in maize silage compared to rice straw silage. On the other hand, rice straw silage had higher NDF and ADF values. Both silages showed declining pH after 40 days of fermentation by LAB. With the organic acid compounds, maize silage had higher lactate-acetate ratio than rice straw silage.
###### Abstract
In one study, a memetic reinforcement learning-based MPPT algorithm was developed, and high tracking accuracy was achieved under partial shading [30]. In another study, the MPPT performance was enhanced by a dynamic leader-based collective intelligence approach and fast and stable tracking was achieved [31]. In addition, it was shown in another study that the global maximum could be effectively found under partial shading conditions using a biologically inspired memetic salp swarm algorithm [32]. These advanced methods significantly increase the efficiency of PV systems. MPPT techniques based on deep learning (DL)-RL and ANN-MLP quickly adapt to environmental changes under partial shading conditions, find the global maximum power point more effectively and increase system efficiency. This results in higher energy production and more stable system performance compared to traditional methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Adaptability** & **Complexity** &
\begin{tabular}{c} **Dynamic** \\ **Performance** \\ \end{tabular} \\ \hline P and O & Moderate & Low & Low & Poor \\ ANN-Based & High & High & High & Good \\ RL-Based & Very High & Very High & Very High & Excellent \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarizes the key characteristics of the different MPPT methods. In the study, ANN, RL, and conventional MPPT methods are designed and compared and their efficiency is analyzed in a system including PVs, energy storage, and load. The system is modeled using MATLAB, incorporating real-world climate data from Ankara, including monthly solar irradiance, temperature, and sunlight hours. The ANN-based MPPT uses a multi-layer perceptron (MLP) to increase PV efficiency, whereas the RL-based MPPT optimizes energy production by Q-learning. The baseline for comparison is a traditional MPPT approach. A 7.5 kW PV system with a 20 kWh battery system is the subject of hourly and monthly simulations. The rest of the paper is organized as follows: In Section 2, the details of the system designed with MATLAB are explained. Detailed analysis of the MPPT methods used in the study are expressed in Section 3. In Section 4, the simulation results are provided, and the conclusion is given in Section 5.
## 1 Introduction
The key contributions of this paper are as follows:  
1. A multi-objective client selection strategy: we propose RBPS, a game-theoretic method based on NE, that jointly considers accuracy, battery level, and privacy sensitivity. The method dynamically adjusts each client's reward using contextual device information. 2. Cooperative participation logic: clients with the highest rewards are selected to contribute and receive updated models. Non-selected clients remain inactive, reducing unnecessary battery usage and limiting the exposure of sensitive data. 3. Realistic simulation of heterogeneous devices: we simulate a broad range of clients, including smartphones, edge nodes, and IoT devices, with assigned profiles reflecting real-world battery, privacy, and accuracy characteristics. 4. Comprehensive empirical evaluation: we perform extensive experiments using four benchmark datasets (MNIST, EMNIST, Fashion-MNIST, and CIFAR-10) over 300 communication rounds. Our evaluation spans various client pool sizes (10, 25, 50, 100, 1000, and 5000) and includes both homogeneous client configurations (e.g., all IoT or all smartphones) and heterogeneous client pools that mix devices of different capabilities. We also compare RBPS against several state-of-the-art (SOA) methods, including Oort, FedCS, FRS, and FedProm, to assess its effectiveness in balancing accuracy, battery preservation, and privacy. 5. Analysis of trade-offs: our results highlight that RBPS provides a better balance across accuracy, battery sustainability, and privacy preservation, particularly in large, heterogeneous client pools where other methods either sacrifice performance or overburden specific device classes. By addressing all three core FL objectives in a unified and adaptive framework, RBPS offers a scalable, fair, and sustainable solution for FL in real-world, battery-constrained environments. The remainder of this paper is organized as follows. Section 2 reviews existing works related to client selection in FL, highlighting their limitations in handling multi-objective trade-offs. Section 3 introduces the necessary preliminaries and concepts used throughout this paper. In Section 4, we present our game theoretic client selection model based on NE. Section 5 discusses how we realistically model resource constraints such as battery level and privacy. Section 6 explains the experimental setup and evaluation procedure. Section 7 presents the results and discussion, including insights on the impact of client pool sizes and a comparison with existing methods. Finally, Section 8 concludes this paper and outlines directions for future work.
# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
## 6 Limitations and Future Research  
Our paper has provided insights into the impact information and communication technologies on agricultural production; however, there are some limitations that should be mentioned and future studies should focus on such areas. As a result of data limitations, our study focused on information and communication technologies, such as fixed telephone subscriptions, mobile cellular subscription, and individuals using the internet; it is important to note that future studies should focus on other aspects of ICTs and digital technologies.  
The findings of the study may not be generalizable to other regions outside of Africa or to other agricultural sectors that were not included in the study. Hence, there is need for future research to focus on other agricultural sectors and regions outside of Africa.  
The study relies on panel data from 32 African countries. These countries were selected because of data availability over the period studied. Future studies may increase the scope (number of countries and period) to gain deeper and better insights on the topic.  
While the study identifies bidirectional causality between various factors and agricultural production, it is important to note that the causality may not be straightforward or direct. There may be other factors that affect the relationship between ICTs and agricultural production. Future studies should investigate such factors.  
**Author Contributions:** Conceptualization, R.U.O., D.A.A., R.A.-A., F.F.A., C.J.O. and J.G.N.; Formal analysis, R.U.O. and C.J.O.; Investigation, R.U.O.; Literature Search and Review, D.A.A., R.A.-A., F.F.A., C.J.O. and J.G.N.; Methodology, R.U.O.; Resources, J.G.N.; Supervision, R.U.O., D.A.A., R.A.-A. and F.F.A.; Validation, D.A.A., R.A.-A. and F.F.A.; Visualization, D.A.A., R.A.-A. and F.F.A.; Writing--original draft, R.U.O., D.A.A., R.A.-A., F.F.A., C.J.O. and J.G.N.; Writing--review and editing, R.U.O., D.A.A., R.A.-A., F.F.A., C.J.O. and J.G.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data used in this paper are publicly available.  
**Acknowledgments:** This paper was supported by the KU Research Professor Program of Konkuk University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The upcoming fifth generation (5G) wireless networks making use of higher-frequency spectrum bands suffer from serious propagation issues due to high path loss and beam directivity requirements. This promotes the device-to-device communications to boost the transmission reliability at the network edges, providing remarkable benefits in terms of the energy and spectrum efficiency, essential for a wide class of sensors networks and Internet-of-Things. More in general, applications where devices are usually constrained in computational and transmission range capabilities. In such a context, the selection of the proper number of devices arranged as a relay plays a crucial role. Towards this goal, this paper proposes an efficient relay selection scheme minimizing both the delivery transmission delay and the overall energy consumption, i.e., the overall number of relays to be used. By focusing on a multicast content delivery application scenario the problem of interest is formulated as a one-sided preferences matching game. In addition, the strategy designed takes into account specific information, named reputation coefficient, associated to each device jointly with link propagation conditions for allowing the selection of suitable relays for disseminating the content among the devices. The effectiveness of the proposed solution is underpinned by computer simulations, and the performance is evaluated in terms of power consumption, end-to-end delay, and number of selected relays. As confirmed by results, the proposed approach improves network performance compared to the greedy approach, the random algorithm, a scheme previously proposed in literature, and with two game theory-based strategies.  
relay selection; device-to-device communications; matching game; multicast context Article
## 3 Overview Communication Technology for Smart Grid
### Power Line Communication  
Transmission lines enable data exchange between devices on the power line. A power line carrier communication system is implemented to realize the modulation of a carrier signal. The orthogonal frequency division multiplexing-based data rate of information may be several Mbps, while the carrier frequency on transmission lines is about 100-200 kHz. However, the circuit can be many kilometers in length [24].  
Different types of contact in power lines use different frequency bands. The purpose of the power distribution system is the initial transmission of the AC power source in a typical frequency of 50 or 60 Hz. Therefore, the line power is only able to perform at high frequencies. The problem is the propagation of a limiting factor in the communication of the wires. In addition, interference of communication signals may exist and high harmonics are generated by the non-linear loads present in the grids. An advanced active filter must be used to overcome these problems. In smart grid applications, the Powerline Carrier (PLC) system is used in the connection of the communication area network between the smart meter and the local data concentrator. The advantage of PLC is related to its building costs, as its extensive infrastructure reduces installation costs. Its shortcomings include the existence of high-order harmonics, interference of the communication signal and limited frequency of communication [25].