esa-sceva/satcom-chunk-collection huggingface dataset:
# GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning
###### Abstract
optimizes computational costs, it is vulnerable to malicious participants submitting erroneous information to disrupt the aggregation process, potentially enabling the inference of other participants' private information. To address the aforementioned problems, we propose a verifiable grouping federated learning scheme, referred to as GHEFL. This scheme is designed to prevent malicious servers from attempting to exploit participants' local data or performing incorrect aggregation operations that compromise model training. While ensuring the correctness of the aggregation results, the introduction of grouping guarantees the security of participant data and minimizes the workload on the server as much as possible. The main contributions are as follows:  
* We propose GHEFL, a verifiable federated learning scheme. This scheme allows each participant to verify the correctness of the aggregation process, preventing malicious servers from tampering with parameters uploaded by participants. Additionally, it prevents servers from returning incorrect aggregated results. * We adopt a dual-server architecture to replace the single-server architecture commonly used in traditional federated learning. This approach reduces the risk of performance degradation caused by the computational and storage overload of a single server, while also preventing malicious attempts by a single server to extract participants' private information. * In our scheme, we introduce a grouping mechanism by dividing participants into groups according to specific rules. Local parameter aggregation is first performed within each group and then the aggregated parameters are uploaded to the server on a group-by-group basis for global aggregation. This approach ensures the security of participant data and minimizes the information exchange between participants and the server compared to traditional federated learning. This article is organized as follows: Section 2 describes the system architecture and design goals. Section 3 describes the preliminary work. Section 4 describes the proposed scheme in detail. Section 5 performs the experimental and comparative analysis. Section 6 concludes this paper.
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 7 Automatic Classification Algorithms for EEG Signals
### Neural Networks  
The neural network (NN) is a type of machine learning algorithm that can \"learn\" from input data. The mathematical conception of learning using NN considers the application of a matrix transformation of the input information by a matrix called \"weight\". These weight changes are according to a bias, which make it possible for the algorithm to predict a new input [63]. This allows one not only to classify but also to cluster the information and predict future responses of a system [17].  
* Recurrent neural networks (RNNs). According to [47], they are neural networks that have circulation behavior thought calculations, and their output depends on the current input and the previous output.
* Long short-term memory (LSTM). LSTM contains memory cells that can preserve previous output states. [PERSON] and [PERSON] [64] described this architecture as capable of \"using their feedback connections to store representations of recent input events in the form of activations\", which means that this type of NN can store information.
* Convolutional neural networks (CNNs). The CNN is deep neural network architecture that relies on a specialized linear operation known as convolution [65]. This type of network can be a suitable model for processing various signals: images, audio, videos, and EEG.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Author** & **Year** & **Dataset** & **Algorithm** & **Accuracy\%** \\ \hline \hline \multirow{2}{*}{[PERSON] et al. [22]} & \multirow{2}{*}{2017} & BCI Competition IV. Nine & SVM & 76.24\% \\  & & healthy participants. & Improved & \\  & & Imagination & Binary & \\  & & & Gravitation & \\  & & & Search & \\  & & & Algorithm & \\  & & (IBGSA) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: EEG classification with ML algorithms.  
Figure 9: BCI implementation process.
# Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings
## Materials and Methods
### Deep Learning Model  
The multiplayer perceptron (MLP) is a widely-used neural network architecture for classification tasks; its low computational cost, structure simplicity, and comparably small dependence on the training data size make it a readily applicable neural network algorithm for classification [34]. In general, MLP is composed of three main components: The input layer, the hidden layers, and the output layer. Each layer is composed of nodes which receives the outputs from the previous layer as their input, referred to as activation values, \(s_{i}\). The activation values are multiplied by the respective weights \(w_{ij}\) and summed at consecutive layer nodes, followed by a chosen activation function \(f\). The output of each nodes \(y_{j}\) at layer \(l\) is described by the following equation:  
\[y_{j}=f\Big{(}\sum_{i=1}^{K_{i-1}}w_{ij}s_{i}\Big{)} \tag{1}\]  
where \(K_{i}\) is the number of nodes at the \(l\)th layer.  
While training, \(w_{ij}\) are adjusted to minimize the cross-entropy loss function described as the following:  
\[E=-\log\Big{(}\frac{e^{s_{ans}}}{\sum_{j=1}^{K_{i}}e^{s_{j}}}\Big{)} \tag{2}\]  
where \(s_{ans}\) is the output probability of correct classification.  
For our model, the input layer is a 32-dimensional layer where each dimension corresponds to the 32 sampling points of the data. As for the hidden layers, four cascades of layers each with 256 nodes were used. The number of layers and nodes for each layer were chosen heuristically since there are no known analytical methods for obtaining such parameters [35]. As for the activation function, we have used hyperbolic tangent function for the hidden layers, while for the output layer, which reduces dimension to equal classification categories and gives classification probability of each classification category, Softmax (1) activation function is applied so that the probability sum is equals to 1:  
\[\sigma_{i}(s)=\frac{e^{s_{i}}}{\sum_{j=1}^{K_{i}}e^{s_{j}}} \tag{3}\]  
where \(K_{i}\) is the number of nodes in the output layer. For our experiment, \(K_{i}\) is equal to the number of neural signal types. For the training data, \(\alpha\%\) of the PCA + K-means clustered data nearest to the cluster centers were used under the assumption that data near centers of cluster have lower probability of being misclassified; \(\alpha\) was set to 10 for our model. The methodology of PCA and k-means is further explained in Section 2.3.3. Figure 1 outlines the overall structures of the proposed model, and Figure 2 shows the structure of the MLP used in the model.  
The proposed spike sorting algorithm was written in Python and implement with Keras using a Tensorflow backend. When performed on a dataset with 3526 spikes using an Intel Xeon E5-1620 CPUequipped with a Nvidia Titan X GPU and 96 GB RAM, the model took approximately 9 s for training and 3 s for testing.
## 9 Quickest Spectrum Sensing
### Performance Metrics
#### CUSUM Test  
A popular approach for detecting the change is cumulative sum (CUSUM) test, originally proposed by [PERSON] in 1954 [78, 149]. The asymptotic optimality of CUSUM test was proved by [PERSON] in 1971 [150]. As a more difficult problem, the non-asymptotic optimality of CUSUM test was proved by [PERSON] in 1986 [151]. In CUSUM test, the test statistic, denoted by \(m_{t}\) at time slot \(t\), is given by  
\[m_{t}=\max(0,m_{t-1}+L(t)) \tag{55}\]  
where \(L(t)\) is the log likelihood of observation received at time slot \(t\), which is given by  
\[L(t)=\log\frac{P_{1}(X_{t})}{P_{0}(X_{t})} \tag{56}\]  
Intuitively, the test statistic \(m_{t}\) is the sum of log likelihood bounced by the boundary \(m_{t}=0\). Obviously, the larger \(m_{t}\) is, the more probably the change has happened (since the distribution is more biased to that after change). Then, the random stopping time of claiming the change is given by  
\[s=\min\left\{t|m_{t}\geq\gamma\right\} \tag{57}\]  
where \(\gamma\) is a predetermined threshold.  
Another equivalent form of CUSUM test is to set a random walk for every time slot: for time \(t\), we define random walk  
\[q_{t}(\tau)=\sum_{n=t}^{\tau}L(n) \tag{58}\]  
and stopping time  
\[s_{t}=\min\left\{\tau|q_{t}(\tau)\geq\gamma\right\} \tag{59}\]  
where the threshold \(\gamma\) is the same as that in (57). From the family of stopping times \(\left\{s_{t}\right\}_{t=1,2,...}\), we choose the earliest one as the time claiming the change of distribution, i.e.,  
\[s^{\prime}=\min\left\{s_{t},t=1,2,...\right\} \tag{60}\]  
It is easy to verify that \(s=s^{\prime}\). Therefore, both approaches are equivalent. In contrast to the former approach (we call it _single metric_ approach), the latter (we call it _multiple random walk_ approach) requires infinite memory (for each time slot, we need some memory to store the updated random walk value). Therefore, the former is more suitable for practical systems. However, the latter approach can provide some hints to approximate algorithms, as we will see.
# Carbon Nanotubes-Based Hydrogels for Bacterial Eradiation and Wound-Healing Applications
## 3 Development of CNT-Based Conductive Hydrogels
As discussed before, CNT functionalization with hydrogel is considered a potential hybrid candidate material for multiple applications in biomedical fields. CNT can be incorporated with hydrogels through their covalent and non-covalent functionalization using different treatment (chemical and mechanical) techniques [70]. Recently, [PERSON] et al., have nicely summarized the studies on developing CNT-based hybrid hydrogel for wound-healing applications [13]. In addition, they addressed most of the design and synthesis strategies for CNT hydrogel mixed materials and their diverse applications in the biomedical field. CNT hydrogels are associated with most of the properties of hydrogel polymeric networks. Generally, five polymerization techniques such as (i) covalent cross-linking (insitupolymerization), (ii) exsitu polymerization, (iii) physical cross-linking, (iv) polymer grafting, and (v) smart devices enable techniques that are exploited for the synthesis of CNT hydrogel hybrid. In both insitu and exsitu polymerizations (chemical treatment), CNT hydrogel outfits with excellent mechanical strength, a variety of shapes and surfaces,excellent yields, and it is easy to control the initial and final composition of hybrid gels into the hybrid [71; 72]. These techniques involve the introduction of nanofiller during reactions. In physical cross-linking techniques, hydrogels are physically cross-linked with CNT, which showed a high level of biocompatibility but low mechanical strength. These methods have been employed for the synthesis of CNT-based gelatin hydrogels [73]. The schematic representation for the synthesis of CNTs-based hydrogels is shown in Figure 3.  
Polymer grafting is employed for the synthesis of CNTs grafted hydrogels. This technique provides the functionalization of the CNTs surface with a polymeric chain, which results in the shielding of the CNT surface [13; 76]. This method offers a good dispersion of nanofillers along with their stronger interaction with polymers. Generally, protein and polypeptide-based CNT hydrogels have been synthesized through grafting techniques. Grafting-to and grafting-from polymerization for CNT polymers are two different grafting strategies involved in fabricating CNT hydrogels. In both approaches, the  
Figure 3: Scheme showing the synthesis of CNT-based hydrogels: (**a**) MnO\({}_{x}\)/CNT aerogels [74], (**b**) general process of the double network hydrogel [75]. functional groups of the polymer chains are reacted with CNTs, leading to the formation of bioactive materials for biomedical applications. In addition, the intelligent device enable technique is an exciting technique that was applied to develop responsive hydrogel nanocomposites-based smart devices [77]. This method has been widely applied for the synthesis of hydrogel materials that are pH and ionic responsive. Significant research on the development of CNTs-based hydrogels has been done to obtain the desired functionality, shape, and size for specific applications.
# Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS
## 4 Results and Discussion
### Mining Area  
Underground mining is also one of the causes of surface subsidence. The surface subsidence caused by mining is becoming more and more serious. As a result, land, cultivated land and farmland were destroyed and a large number of cave-in pits were produced, which had a great impact on people's life, property and social and economic development. Kunyang Phosphate Mine is a large open pit, consisting of a dump, reclamation area and  
Figure 8: Deformation rate in the east of Dianchi Lake and time series cumulative settlement diagram of P4.  
Figure 7: (**a**) Deformation rate in the northeast of Dianchi Lake; (**b**) Time series cumulative settlement diagram of P1, P2 and P3 points.  
6 slopes. Due to dense vegetation and other reasons, the high coherence points obtained in this area are few, resulting in less deformation information available. It can be seen from the results of this monitoring that there is an obvious settlement with a zonal distribution, and the maximum deformation rate exceeds 35 mm/year. The maximum deformation is located in the dump around the second mining area. With the passing of mining time, the deformation range gradually becomes larger, and the cumulative settlement of P5 points in this area exceeds 90 mm, as shown in Figure 9. At present, mining operations have been stopped in the first mining area and the northern part of the second mining area, and the reclamation work has been completed. The restricted mining area is gradually reduced, and the restricted mining area plays a good role in controlling the regional surface subsidence.
## 4 Discussion
anaerobic digestion facilities examined in this work ranged between 4 and 8 for installed CHP electric power between 50 and 400 kW, respectively. Clearly, energy sustainability is crucial, as a technology with low ESI (even below 1) can still be financially attractive due to incentives. Considering the economic feasibility, the findings of this study reveal that a feed-in tariff (FIT) of 0.21 \(\epsilon\) kWh\({}^{-1}\) is inadequate to render investment in anaerobic digestion facilities financially attractive for capacities less than 250 kW. Hence, it is imperative to either increase the FIT to 0.26 \(\epsilon\) kWh\({}^{-1}\) or provide investment subsidies for capital expenditures (CAPEX), or ideally, both. Investment in anaerobic digestion should yield value for the farmer, accompanied by a short payback period, preferably under 5 years. As evidenced by the results, significant economic returns are feasible for dairy cattle and sheep/goat enterprises. Poultry farms, despite their substantial consumption of fossil fuels (primarily for barn heating), incur overall fuel costs amounting to less than 5% of the annual turnover, thus mitigating the significance of this issue for farmers. Previous studies indicated that small-scale anaerobic digestion facilities are rarely implemented due to their limited financial attractiveness [30]. [PERSON] and [PERSON] [31] highlighted the barriers hindering the widespread adoption of anaerobic digestion technology in small and medium-sized livestock enterprises, including lack of interest by the farmer, high investment costs (attributed to the scarcity of qualified construction companies), limited access to bank loans, absence of suitable subsidies and support programs, and complex bureaucratic procedures for financing and obtaining permits/licenses. According to a survey conducted by [PERSON] et al. [32] in Switzerland, farmers with a small number of animals exhibit limited interest in biogas production. The primary motivation for adopting anaerobic digestion facilities is the potential income derived from selling electricity to the grid. Similarly, factors contributing to the limited adoption of anaerobic digestion technology include high investment costs, absence of subsidies, and difficulties in collaborating with neighboring companies to establish larger capacity plants. Furthermore, investing in anaerobic digestion technology often requires significant involvement from the farmer, spanning the design, construction, and especially operation phases, which may divert attention from their primary activities of animal breeding and/or milk production. Therefore, for the successful implementation of anaerobic digestion projects in small and medium-sized livestock enterprises, it is crucial to ensure a positive economic balance, coupled with low capital and operational expenses, while ensuring that farmers maintain their commitment to their core production activities.
# Experimental Investigations of a Tunnel Lining Segment Strengthened by In Situ Spraying Mortar
## 2 Experimental Overview
### In Situ Spraying Mortar Material Performance Experiment
#### 2.1.4 Interface Splitting Tensile Strength  
1. Test block preparation  
The mold size of the test block was 100 \(\times\) 100\(\times\) 100 mm. First, C55 and C65 concrete were poured into the test mold as the base, with a pouring height of 50 mm. C65 and C55 concrete base blocks were separated into four groups, with three blocks in each group. The surface was smoothed. After the concrete was finally solidified, a tool was used to hack the concrete surface in one-way and orthogonal directions to simulate different surface roughnesses. The concrete was cured for 7 days, and then H-70 mortar was poured in the first half of the test mold. The two groups of test blocks were cured for 28 days under standard curing conditions of 20 \(\pm\) 2 \({}^{\circ}\)C and humidity above 90%. The other two groups of test blocks were cured for 28 days in the same environment as the strengthened structure. The production scheme of the test blocks is shown in Table 1.  
(2) Testing method  
Interface-splitting tensile strength was tested by the method recommended in GB/T 50081, 2002 [18]. Parallel straight lines were drawn in the middle of the top and bottom surfaces of the test block so as to determine the position of the splitting surface. The test block was placed in the center of the pressure plate. Circular pads and bars were placed between the upper plate and the test block and between the test block and the lower plate. The pads and bars should be aligned with the center lines above and below the test block and perpendicular to the top surface of the test block. The testing machine was started, continuously and uniformly loaded, with a loading speed of 0.05 MPa/s\(\sim\)0.08 MPa/s. Block failure was tested, and the failure load was recorded. The experiment is shown in Figure 1. The calculation formula of compressive strength is as follows:  
\[f_{ts}=0.637\frac{F}{A} \tag{2}\]  
where \(f_{ts}\) is interface splitting tensile strength (MPa), \(F\) is failure load (N), \(A\) is area of the split surface of the test block (mm\({}^{2}\))
## 2 Performance Metrics, Geometry, and Material Properties  
### Performance Metrics  
Several metrics exist for evaluating the performance of cellular structures. In this paper, we will focus on peak force, mean crushing force, energy absorption, and specific energy absorption. _Peak force_-- initiating the plastic deformation of the cellular structure requires a large starting load that often shows up as a peak on the force-displace diagram. The peak force is denoted by \(F_{P}\). A low initial peak force is desirable so that the damage to the battery module would be minimized. _Mean force_--after the peak force, the cellular structure continues to crumple until it reaches the densification zone. Densification usually occurs around a 60% strain. This means force is denoted by \(F_{\mu}\), and it is calculated by taking the average of the forces from the peak force until the densification point. _Energy absorption_--\(E_{A}\), is calculated as the area under the force-displacement curve from the start of the crushing until it reaches the start of the densification zone marked by the steep rise of the force-displacement curve. Cellular structures with high-energy absorption capacity are desirable. _Specific energy absorption_--\(E_{SEA}\), is obtained by dividing the \(E_{A}\) by the crushed mass, \(M_{C}\). It allows us to compare different cellular structures with different masses that can have varying \(E_{A}\) capacities.
## Appendix B Neural-Network Travel-Time Prediction Time and Accuracy Results  
Table 14 provides neural-network prediction accuracy results for different transport networks and transfer-zone sample sizes based on 1000 randomly selected origin and destination queries. The root mean squared error (RMSE) of travel-time predictions are lower when there are many small transfer zones (as opposed to a few large ones), which makes sense considering that travel times between smaller zones will have a lower variance in possible shortest-path travel times. However, the storage space required for the neural networks increases with the square of the number of transfer zones. For the case of 100 transfer zones, we need to store 30,000 individual neural networks (100 zones times 100 zones times 3 open transport networks). For more than 100 transfer zones, this becomes a serious computational bottleneck for a current standard desktop computer. In terms of ML-TZSA, this means that different degrees of graph partitioning need to be used for neural-network travel-time predictions than for other journey-planning tasks when the transfer-zone sample size exceeds 100. The RMSE and mean absolute error (MAE) are always least for predictions in the car transport network. However, the relative MAE is always highest in the car transport network. This is because travel times are always lowest in the car network, so variance of travel times is naturally lower. Furthermore, predictions are more difficult to make in the car network because the roads exhibit a high variance in top speeds in comparison to paths in the other networks. The units of MAE are seconds, so for 100 transfer zones the expected prediction error lies between 30 s and 2.5 min. We except this error in exchange for the speed with which neural-network predictions can be made: small fractions of a millisecond.