esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries: Network Model and Problem Definition
### Task Queue Description  
The queue model of this paper is shown in Figure 2. Each user device maintains a cache, a computing queue, and a transmission queue. After a task is generated, it first enters the cache to wait for an offloading decision. After the offloading decision is determined, the task can enter the calculation queue to perform local computing or enter the transmission queue to offload to edge layer devices. The \(ud_{i}\) lengths of the cache area, computing queue,and transmission queue recorded are \(q_{i}^{\text{comp}}\), \(q_{i}^{\text{cache}}\), and \(q_{i}^{\text{tran}}\), respectively. The available cache capacity of the user's device is \(R^{\text{ud}}\).  
Each edge server maintains a wireless transmission queue, a wired transmission queue, and \(num^{\text{core}}\) (the number of CPU cores in the edge server) computing queue. If a task is offloaded to the edge server, it can enter the server's computing queue or enter the server's wired transmission queue to be sent to other nodes or the cloud. If the task is completed within the edge server or cloud server, the computing result can be sent back to the user in the wireless transmission queue of the edge server. The \(en_{j}\) length of the computing queue and transmission queue, as well as the cache capacity of a single edge server, are \(R^{\text{en}}\), \(q_{j}^{\text{en-tran}}\), and \(q_{j}^{\text{en-comp}}\), respectively, and all queues comply with FIFO (First In First Out).  
Due to the different priorities of various tasks, a simple FIFO queue is difficult to meet practical needs. Therefore, this paper allows computing devices to perform queue insertion arrangements when receiving tasks. When the decision node makes an offload decision on task \(x_{t}^{i}\), the priority weight \(qf_{i,t}\) of the task can be determined. Task \(x_{t}^{i}\) is queued according to weight \(qf_{i,t}\) when it enters any queue, and, the higher the weight, the higher the priority.  
Figure 1: Cloud-edge collaboration model for MEC network.  
Figure 2: Task computing and transmission queues.  
To further solve queue congestion and improve throughput and delay performance, this paper also introduces an active queue management mechanism, which is described as follows:  
1. If the average queue length is less than the minimum threshold \(th_{min}\), newly arrived tasks are pushed into the queue;
2. If the average queue length is greater than the minimum threshold \(th_{min}\) and less than the maximum threshold \(th_{max}\), randomly drop the newly arrived task according to probability;
3. If the queue length has reached the maximum threshold \(th_{max}\), the newly arrived task is dropped \(p_{\text{drop}}\).
# Clustered Distributed Learning Exploiting Node Centrality and Residual Energy (CINE) in WSNs
## 1 Introduction
On the one hand, we consider performing ML operations in a WSN, and toward this aim we present a distributed learning approach that combines federated learning and gossiping. Additionally, from a networking point of view, we assume that WSN nodes are battery powered and, in general, have heterogeneous computing capabilities. Accordingly, we design a clustering algorithm where a few nodes, i.e., one per cluster, perform model training, while all of the others execute inference only. Then, to guarantee fairness and avoid the overload of a few nodes only along with the consequent exhaustion of their batteries, we propose a mechanism for cluster-head role rotation. Finally, in order to improve the model parameter dissemination process, the proposed mechanism is aware of the centrality of network nodes. The objective is to foster model parameter dissemination performed by nodes that have privileged positions in the topology of the network. The main contributions and the novelty of our proposed approach when compared to the previous solutions are as follows:  
* A clustering scheme for collaborative learning in WSNs that exploit the gossiping mechanism to exchange model parameters between clusters. Figure 1: Example of application of FL in a toy network topology. * The consideration of the machine learning capabilities, the residual energy, the centrality information, and the transmission power of the nodes to improve the fairness and speed up the convergence. * An intelligent scheme to transmit data chunks from cluster heads to cluster members only when it is necessary to reduce the communication load, at the same time enhancing the collaborative learning. Our proposed approach can be exploited in WSNs consisting of low-power devices that can only run inference, such as TensorFlow Lite [21]. The rest of this paper is organized as follows. In Section 2, we discuss the recent works on exploiting clustering in FL. In Section 3, we present the CINE algorithm by detailing the network protocol and the approaches used to include centrality. In Section 4, we assess the proposed protocol. Finally, in Section 5 some conclusions on the work are drawn.
## 5 Conclusions, Challenges, and Future Research Directions  
### Main Conclusions  
This study provides a comprehensive analysis of air quality forecasting, focusing on predicting and forecasting hourly PM\({}_{2.5}\) and PM\({}_{10}\) concentrations using a variety of machine learning (ML), deep learning (DL), feature engineering, and hybrid modelling approaches. The analysis leveraged a rich dataset spanning multiple years and encompassing various pollutants and meteorological parameters.  
The results demonstrate that model performance varies significantly between the training and testing phases, highlighting the importance of assessing generalization ability. While models such as GBoost, XGBoost, and GAM performed exceptionally well during training, LRR and DNN emerged as top performers in testing for PM\({}_{2.5}\) and PM\({}_{10}\) predictions, respectively, showcasing their robustness against overfitting.  
Through rigorous feature selection (FS) analysis, the study identified key predictors--notably benzene, CO, NO\({}_{\times}\), and meteorological variables--that strongly influence PM concentrations. The GEO-based feature selection method emerged as the most effective, providing highly accurate input sets for subsequent modelling. This method (GEO) identified that the following variables are the most relevant predictors for PM\({}_{2.5}\) and PM\({}_{10}\) (excluding relative humidity): Benzene, CO, m-Xylene, NO\({}_{2}\), NO\({}_{\times}\), o-Xylene, Ozone, Precipitation, Relative Humidity, SO\({}_{2}\), Temperature, Wind Direction, and Wind Velocity.  
Finally, implementing a Deep-NARMAX model for multi-step-ahead forecasting proved highly effective, capturing complex non-linear and temporal interactions. With R\({}^{2}\) values approaching 0.85 for PM\({}_{2.5}\) and 0.89 for PM\({}_{10}\), the model demonstrated very good predictive performance, underscoring its potential as a robust forecasting tool in environmental modelling.
## 3 Rotor Blade Design Approximation with RBFNN
From Table 5 and Figure 17, it was found that the number of training data had larger influence on the RBFNN meta-model accuracy than the SSP in the approximation of rotor blade design problem. From the results of the rotor design approximation, it was able to conclude that the decrement of less than 100 times of number of design variables for the number of training data showed the inferior accuracy due to insufficient learning data for the RBFNN meta-model. Among the learning parameters, the number of hidden layers were finally considered to evaluate the performance effects on the rotor blade design approximation. The error analyses were carried out applying one layer, two layers, and three layers to the RBFNN, respectively. In such those cases, 0.0 and 500 were applied in common for the SSP value and the number of training data. The results of RMSE and MAX were represented in Table 6 and Figures 18 and 19. The results of Table 6 and Figure 18 represented that the RMSEs of Mass, GJ, EI\({}_{22}\), and EI\({}_{33}\) were distributed in the range of 0.00590 to 0.00591, 0.00645 to 0.00649, 0.02023 to 0.02025, and 0.02375 to 0.02377, respectively, according to the variation of hidden layer. The MAXs of Mass, GJ, EI\({}_{22}\), and EI\({}_{33}\) were distributed in the range of 0.02473 to 0.02491, 0.01567 to 0.01597, 0.09622 to 0.09627, and 0.09031 to 0.09041, respectively, as represented in Table 6 and Figure 19. It was found that the influence of the number of hidden layers on the accuracy of the meta-model in the rotor blade design was scarcely detected comparing with the SSP and the number of training data. Although the effect of the number of hidden layers on the accuracy of the meta-model was insignificant, the RMSEs for all the design performance functions tended to slightly improve as the number of hidden layers was increased. The MAX results were also similar to the RSME results in case of the variation of the number of hidden layers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Error Type** & **\# of Hidden Layers** & **Mass** & **GJ** & **EI\({}_{22}\)** & **EI\({}_{33}\)** \\ \hline \multirow{4}{*}{RMSE} & 1 & 0.00590 & 0.00649 & 0.02025 & 0.02377 \\  & 2 & 0.00591 & 0.00645 & 0.02022 & 0.02375 \\  & 3 & 0.00590 & 0.00646 & 0.02023 & 0.02375 \\ \hline \multirow{4}{*}{MAX} & 1 & 0.02491 & 0.01597 & 0.09627 & 0.09041 \\  & 2 & 0.02477 & 0.01569 & 0.09621 & 0.09033 \\ \cline{1-1}  & 3 & 0.02473 & 0.01567 & 0.09622 & 0.09031 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of accuracy according to variation of hidden layer. Figure 19: Maximum error according to variation of hidden layer. Figure 18: RMSE according to variation of hidden layer.
## 3 Analysis of Factors Affecting Shear Behavior of Reinforced Concrete Beams  
Considering that finite element simulation can overcome the limitations of cumbersome experimental research processes, as well as large investments in labor, time, and material costs, in this section, the ABAQUS software will be employed to conduct an in-depth investigation into the influence of various parameters on the shear behavior performance of strengthened beams strengthened with the FRP grid-PCM composite system. As described in Section 1, existing experimental studies have shown that the factors affecting the shear behavior of strengthened beams include the thickness, spacing, and arrangement angle of the FRP grid, the elastic modulus of the FRP grid, the shear span ratio of the beam, the strength of the concrete, and the shear reinforcement ratio, etc. Among them, the reinforcement amount and elastic modulus of the FRP grid directly affect the shear behavior capacity it can provide; the shear span ratio will change the failure mode of the beam, thus affecting the shear contribution of the strengthening layer; and the shear resistance of the beam is mainly provided by four parts: concrete, stirrups, FRP grid, and PCM, which influence each other. However, the PCM mainly plays a bonding role, and its shear contribution is less compared with other parts. Based on the feasibility of numerical simulation, this section will focus on studying the influence of the reinforcement amount of the FRP grid, the elastic modulus of the FRP grid, the shear span ratio of the beam, the strength of the concrete, and the shear reinforcement ratio. In order to conveniently refer to each simulated beam and present the variables of each beam, they are uniformly named as \"B--variable and serial number\". B represents the beam, R represents the shear span ratio, and R1, R2, and R3 represent the shear span ratios of 1.6, 2.4, and 2.8, respectively; T represents the grid thickness, and the thicknesses of T1-T4 are 4, 3, 2, and 1 mm, respectively; I represents the grid spacing, and the spacings of I1-I4 are 75, 100, 125, and 150 mm, respectively; E represents the elastic modulus of the FRP grid, and the elastic modulus values of E1-E5 are 10, 15, 30, 40, and 50 GPa, respectively; H represents the concrete strength and H1-H3 represent 41, 33, and 29 MPa, respectively; and S represents the shear reinforcement ratio, and S1 and S2 represent the shear reinforcement ratios of 0.15% and 0.13%, respectively. For example, the reference beam S20-P-G-0 is now named B-1. The beam with a grid thickness change of 4 mm compared with the reference beam is named B-T1. The parameters of each simulated beam are summarized in Table 3, and the unstrengthened beam used for comparison is still represented by C20. Table 3 presents the FRP grid reinforcement amounts of each simulated beam, and its calculation formula is as follows:  
\[\rho_{g}=\frac{A_{g}}{bs_{g}} \tag{16}\]  
where \(A_{g}\) is the total cross-sectional area of all FRP grid limbs; \(b\) is the width of the beam; and \(s_{g}\) is the spacing of the FRP grid.
## 3 Results and Discussion  
### Adapter Characteristics  
Using the described method, actions were taken in response to the lack of filters for full-face masks in the COVID-19 ward of the local medical facility. The goal was to deliver a prototype of an adapter within 24 h to connect the Honeywell North Pano CL3 mask to the widely available Covidien DAR filter (Figure 3), rather than the dedicated Honeywell 1788155 filter, whose availability was very limited at the time. The next stage was to deliver the adapters to all of the medical staff working in the ward within 72 h after prototype approval.  
A secondary motivating factor for the use of the Covidien DAR filter was the medical staff complaints against the dedicated filter, including its considerable weight (395 g) and size limiting the view, all of which made the whole day's duty more strenuous. Of note, the weight of the Covidien DAR filter is 28 g and it does not obstruct the user's view due to its considerably smaller dimensions.  
The characteristics of the product were determined on the basis of an interview conducted with a team of doctors. These are described in Table 2 (operational criteria). By the same token, the demand was determined at the level of 300 units.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Area** & **Criteria** & **Requirements** \\ \hline \multirow{6}{*}{Usability} & ergonomics & low weight, easy to screw on, unlimited field of view \\ \cline{2-3}  & functionality & connection of the internal thread RD 40 of the full-face \\ \cline{2-3}  & sterilisation & yes \\ \cline{2-3}  & cytotoxicity & non-cytotoxic \\ \cline{2-3}  & life cycle & reusable, disposal in accordance with local regulations \\ \cline{2-3}  & & for biohazardous waste \\ \cline{2-3}  & antibacterial & yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Usability requirements of the adapter.  
Figure 3: (**a**) Full-face mask; (**b**) dedicated filter for face mask; (**c**) DAR filter.
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 8 Conclusions  
In this paper, while maintaining users' privacy, we explore the issues of utility in location-sharing systems. To address the problems of system efficiency and the flexibility of users' privacy-preserving requirements in location-sharing systems, we propose a search efficient privacy-preserving location-sharing solution (i.e., SELS). Specifically, based on the grid structure and users' query distances, we design a novel approach called associated grids to improve the efficiency of location-sharing  
Figure 11: The simulation results for scenario-4.  
Figure 10: The simulation results for scenario-3.  
systems while maintaining users' privacy. In addition, our scheme also provides a user-defined access control policy to meet users' flexible privacy-preserving requirements, which can effectively prevent the disclosure of users' privacy in social networks. The detailed efficiency analysis proves that our SELS can improve the efficiency of location-sharing systems. The security analysis also shows the privacy-preserving ability of our scheme. Under the condition of setting different parameters, we explore the effect of these parameters on location-sharing systems and compare our SELS with other schemes. Extensive experiments validate the practicability and performance of our scheme comprehensively.  
However, there are also some limitations in our SELS, such as the regional rationality of grid structure and the computing power cost of base stations. In the future, we will think of ways to lessen the limitations.  
Methodology, G.Y. and S.L.; software and validation, Y.X. and H.Z.; formal analysis, resources, and data curation, M.L. and Y.W.; writing--original draft preparation and writing--review and editing. G.Y. and Y.X.; supervision, S.L. and J.W.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the National Key R&D Program of China under Grant 2017 YFB0802300, BUPT Excellent Ph.D. Students Foundation (CX2019229), National statistical scientific research project of China (2018 LY61), BUPT Ph.D. Students Short term overseas study exchange program, National Natural Science Foundation of China (No.61902361).  
All the authors declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Quality of Service Based NOMA Group D2D Communications
## 2 System Model
### Power Allocation Coefficients Policies
#### 2.3.1 Policy I  
The Policy I utilises targeted rates of DRs to compute the power allocation coefficients. Similar to [7], the power allocation coefficient for DR \(i\) under Policy I is computed as:  
\[\beta_{i}=\frac{1/R_{i}}{\sum_{j=1}^{M}\frac{1}{R_{j}}}. \tag{2}\]  
The intuition behind Equation (2) is that power allocation coefficients could be utilised to maintain fairness among DRs [3; 26]. In case they are computed in proportion with users' targeted rates, then the highest ordered DR \(M\) would result in the highest SIC decoding order with maximum signal-to-interference-plus-noise ratio (SINR) threshold among all ordered DRs. Consequently, all of the lower ordered DRs always require the maximum SINR threshold dictated by the user \(M\), which would result in a biased treatment of lower ordered users (with lower targeted rates). Hence, in order to avoid this biasness, we propose to compute the power allocation coefficients as given by Equation (2).
## 2 Materials and Methods
### Speckle Whitening of Complex SAR Data
#### 2.1.2 Problem Formulation
\tag{6}\]  
where \(\hat{H}(\mathbf{f})\) denotes an estimate of the otherwise unknown true frequency response, \(H(\mathbf{f})\). Thus, the problem of blind deconvolution [37] becomes the spectral estimation of the SAR system's frequency response, \(H(\mathbf{f})\), which is necessary to build the inverse filter and recover the original whiteness of the data. Unless \(H(\mathbf{f})\) is otherwise known, its estimation can be carried out using least squares minimization in the Fourier domain using the SLC data [6]. In many cases, the tapering window is known, and its estimation is unnecessary. With reference to an SLC image acquired by the X-band COSMO-SkyMed satellite constellation and focused with a Hamming window, Figure 2 shows the in-range power spectrum, the inverse filter used by the last block in Figure 1, and the whitened power spectrum. The in-range power spectrum shown in Figure 2b resembles a Hamming window. The power spectrum after whitening is roughly flat. The amplitudes of original and whitened SLC images are shown in Figure 3a,b, respectively. The correlation coefficients (CCs), measured at unit lag from the complex data, are approximately equal to 0.30 in both range and azimuth before whitening and 0.05 after whitening. The estimation of image statistics from local windows of correlated samples is likely to be inaccurate because of the missing ergodicity of the set of samples. This drawback can be mitigated in part by using multiresolution analysis, where the autocorrelation function is downsampled toward the top level and survives only at the bottom layer [38].
## 3 Results
### Characterization Methods
#### 3.3.5 SEM and EDX Analyses  
The surface morphology of RPNS and the thermally activated samples were studied using SEM. The SEM micrographs shown in Figure 7A indicate the presence of some pores in RPNS. As the temperature used for thermal treatment increases, the number and size of the pores also increase, Figure 7B-D. The advanced pore structure in the thermally treated samples could be attributed to the decomposition of the three major components (hemicellulose, cellulose, and lignin) of the sample at different temperatures, with significantly different patterns in terms of produced gases. This finding was further confirmed by the TGA/_d_TGA analysis, Figure 5, and was previously reported [42]. Similarly, these findings are in alignment with the BET surface area and TGA analyses of other thermally treated adsorbents [19; 20; 21].  
Figure 6: Fourier- transform infrared (FT-IR) spectra of raw pistachio nutshells and thermally treated sample at 500 \({}^{\circ}\)C (PNS500).  
EDX analysis was used to study the elemental composition of tested adsorbents; RPNS and PNS500. The obtained results confirm that PNS500 was essentially constituted of carbon, 95.21%, compared to 80.57% for RPNS. Other elements like Na, Mg, and Ca exist, however, their concentration was less than 2%. The obtained data confirm the formation of carbonaceous material following the thermal treatment.
# Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
## 4 Experiments
### Effects of Contrastive Prototype Learning
#### 4.5.3 Comparison to Vanilla Supervised Learning  
An intuitive way to reach the same goal as **CPL** is to use the vanilla supervised pretraining paradigm. For instance, one can use a cross-entropy loss to train a regular action recognition model, \(F=\mathcal{C}\circ\mathcal{M}\), using the labeled data from the source domain (Equation (1)), and then use the trained model \(\mathcal{M}\) for transfer learning on the target domain (Equation (9)). This subsection presents two experiments to evaluate whether utilizing vanilla supervised learning can reach the same effectiveness as the proposed **CPL** strategy. First, we use conventional supervised method [5] and the proposed **CPL** method to train a comparative model \(F\) with full supervision from NTU RGB+D. Second, we fine-tuned \(\mathcal{C}\) (with \(\mathcal{M}\) fixed) using full supervision on 10% data samples from PKU-MMD Part I. Afterwards, we test the refined model on the test set of PKU-MMD Part I. The experimental results in Table 7 demonstrate that the fully supervised learning method offers limited transferability compared to the proposed **CPL** method. The fully supervised transfer learning only achieves 45.97% on the target domain, representing a 26.68% gap in accuracy compared to using **CPL**.