esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Client Selection in Satellite Federated Learning
## 4 Algorithm
### Client Selection
#### 4.2.2 Orbit Characteristic  
Unlike FL on the ground, one of the challenges of SFL is the orbital characteristics of satellites. In CS methods that do not consider orbital characteristics, it is highly likely that client satellites will disconnect from the server after completing local training, resulting in client resource waste. In extreme cases, the PS may not have immediate access to any available client satellites for some time, thereby being unable to distribute the global model immediately. In such cases, it is necessary to consider the orbital characteristics in CS.  
According to Section 3.2, we can calculate the duration of satellite access. Actually, we can assign the task of orbit simulation to the Systems Tool Kit (STK) ([[https://www.ansys.com/products/missions/ansys-stk](https://www.ansys.com/products/missions/ansys-stk)]([https://www.ansys.com/products/missions/ansys-stk](https://www.ansys.com/products/missions/ansys-stk)), accessed on 1 December 2023), a professional satellite-to-simulation tool. We can easily obtain the status of any satellite by this simulator. As shown in Figure 7, a satellite could be in two statuses; one is that it has \(T_{pre}\) time left to connect to the PS, and the other is that it has been connected, but has \(T_{in}\) time left to disconnect. In addition, once the client connects to the PS, \(T_{pre}\) will refresh as the remaining time until the next server access.  
We represent the maximum duration for a satellite to access the server at once as \(T_{cov}(T_{cov}\geq T_{in})\), and denote \(T_{wait}\) as the time that the PS will wait for the client. Then, we have \(T_{wait}\) according to Algorithm 2, where \(T_{wait}=1\) expresses that the client is unusable.  
Figure 7: Two statuses between the client satellite and the server.
## 4 Results and Discussion
### AR Prediction Results  
Figure 10 shows the predicted value and the measured value of the advance rate of the 238 rings' data. It can be seen that they were relatively close. Because the selection of the test set was random, the geographic space of these data was not continuous. However, through the display of the test set data, including the regression graph shown in Figure 11, the predicted value had a strong linear relationship with the true value. That was, within the allowable range of error, it can be considered that they satisfied a linear relationship. It can be concluded that the accuracy of the neural network model was quite reliable for predicting the advance rate, compared with traditional empirical formulas or numerical models.  
Figure 10: Comparison of AR obtained with the BPNN model and actual measured value on test set.  
In addition, Figure 12 shows the predicted value and measured value of the advance rate of the total 1188 rings in the whole shield tunneling propulsion process. Due to the complex and changeable geological conditions, it can be seen that the advance rate fluctuates greatly. Whether it was from the data error of a single point or the overall trend, the results obtained by the neural network were still very accurate, which also confirmed the reliability of Strategy B for the prediction of the advance rate in mixed ground.
## 3 System Performance
### Comparison between Other Sports Monitoring Systems  
In this section, we compare our system with other sports monitoring systems implemented on the literature (see Table 3). We have made a comparison taking into account general aspects of the systems. In this case, we have studied: if the systems are or not oriented to be implemented in teams, which are the wireless technology used and if they can make a multi-hop topology, what parameters could measure, their scalability, if the systems use some kind of security, it they are based on cloud technologies and finally the cost of the systems.  
Figure 8: Data dropped by buffer overflow and retry threshold exceeded.  
According to Table 3, our system is able to measure 7 parameters followed by the work [24], which can measure 4 parameters. Our system has to take into account some important parameters like GSR and EMG in order to know in a better way the evolution process of an athlete. Our system is oriented to work with one athlete, but there would not be any problem to monitor several users at the same time because as we have seen in the previous section, the amount of data sent to the cloud is small. For this reason, the network technology and server's capacity is enough to work without problems. This is an important feature because when we are working with personal information, privacy is factor. Our system is cloud based, so the updating process of the data is automatic and therefore the specialists can always consult all the data collected. Finally, we can see that our system, along with [26], is cost-effective. This is an important characteristic in order to implement these types of systems without having a high budget.  
Table 3 shows the comparison of our system with other solutions. Our system presented in this paper stands out because it can measure 7 parameters, it uses security to send the information, it could be implemented in teams due to its easy scalability and cost-effective. Finally, its management through the cloud facilitates its integration across platforms.
# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 7 Conclusions  
In a VECN, there are massive dynamic cooperative and competitive interactions between ICVs, and the privacy exposure will threaten the security of ICVs, especially for the high-priority security-related tasks. Therefore, we propose a privacy-preserving and priority-aware task offloading and computational-resource-allocation approach based on DRL to satisfy the stringent requirements of QoS and privacy for vehicles in dynamic VECN while guaranteeing the constraints of the limited resources for VEC servers. First, we employ a privacy-preserving framework that integrates the CA into the VECN. Furthermore, the cooperative optimization problem is formulated as an MDP to minimize the total weighted cost of the system. To solve the problem, we propose an approach named COPP-DDPG to learn the optimal actions of task offloading, resource allocation, and pseudonym changing decision in the dynamic VEC networks. The simulation results demonstrated the converge and superior performance of the proposed approach in comparison with the benchmark methods. In the future, we will continue to research using ring signature to further improve the VECN system's privacy protection under greater ICV density in the environment.  
Conceptualization, methodology, Y.W. and J.W.; software, validation, formal analysis, Y.W. and H.K.; investigation, writing--original draft preparation, Z.S.; writing--review and editing, J.W. and H.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 61572229 and 6171101066, Jilin Provincial Science and Technology Department of China (20210101415)C.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Control and Estimation under Information Constraints
### Coding/Decoding Schemes
#### 2.3.2 Zooming Strategies  
In _time-varying quantizers_[69; 70; 33; 71; 72] range \(M\) is updated with time. Using such a _zooming strategy_ improves the steady-state accuracy of the transmission procedure and at the same time prevents the encoder saturation at the process beginning. The values of \(M[k]\) can be precomputed (the _time-based_ zooming) [39; 73; 74], or current quantized measurements can be used at each step for updating \(M[k]\)(the _event-based_ zooming). For an audio channel, [PERSON] et al. [75] developed the coding schemes with the capacity to simultaneously encrypt and compress audio signals, which makes possible increasing necessity for transmitting sensitive audio information over insecure communication channels.  
The event-based zooming can be realized in the form of the _adaptive zooming_[76; 77; 78; 79], where the quantizer's range is adjusted automatically depending on the current variations of the transmitted signal.  
For the binary quantizer the following adaptive zooming algorithm was proposed and experimentally studied in [78]:  
\[\lambda[k]=(s[k]+s[k-1]+s[k-3])/3,\quad s[-1]=s[-2]=0, \tag{5}\] \[M[k+1]=m_{c}+\begin{cases}\rho M[k],&\text{if }|\lambda[k]|\leq 0.5, \\ M[k]/\rho,&\text{else},\end{cases}\] \[\bar{\sigma}[k]=M[k]s[k],\]  
where \(M[0]=M_{0}\) is an initial value of \(M[k]\) (the design parameter) and \(\bar{\sigma}[k]\) denotes the value of \(\sigma[k]\) recovered at the decoder side from binary values \(s[k]\). Unlike the case of the time-based zooming, the initial value \(M_{0}\) can be chosen arbitrarily, since \(M[k]\) is automatically adjusted and can be increased or decreased during the zoom-in or zoom-out stages.
# An Implementation of Actor-Critic Algorithm on Spiking Neural Network Using Temporal Coding Method
## 3 Actor-Critic Network Structure with TD
### Update the Policy  
Assuming \(\pi(A\,|\,S,\theta)\) is the probability of choosing action \(A\) and \(\theta_{il}\) is the weight value of synapse that links state neuron \(i\) and action neuron \(l\), the updating process of the value function is:  
\[\theta\leftarrow\theta+\alpha\delta\
abla_{\theta}\log\pi(A|S,\theta). \tag{13}\]  
According to (6),  
\[\pi(A|S,\theta)=p_{l}=q_{l}/\sum_{m=1}^{M}q_{m}. \tag{14}\]  
Assuming that state neuron \(i\) is the currently chosen state neuron, we only need to calculate \(\partial\log\pi(A\,|\,S,\theta)/\partial\theta_{il}\) when calculating \(\
abla_{\theta}\log\pi(A\,|\,S,\theta)\) since the other state neurons have no generations. The result is:  
\[\frac{\partial\log\pi(A\,|\,S,\theta)}{\partial\theta_{il}}=\begin{cases}-(1-p _{l})\cdot\frac{\partial t_{l}}{\partial\theta_{il}},&\text{\emph{chosen}} \\ p_{l}\cdot\frac{\partial t_{l}}{\partial\theta_{il}},&\text{\emph{otherwise}} \end{cases}. \tag{15}\]  
The calculating process of \(\partial t_{l}/\partial\theta_{il}\) is the same as that of \(\partial t_{j}/\partial\omega_{ij}\), which is:  
\[\frac{\partial t_{l}}{\partial\theta_{il}}=\frac{x\vartheta}{\theta_{il}^{2} }\frac{1}{\sum\limits_{t_{k}<t_{l}}\left[\frac{1}{\tau_{0}}\exp\left(-\frac{t _{l}-t_{k}}{\tau_{0}}\right)-\frac{1}{\tau_{1}}\exp\left(-\frac{t_{l}-t_{k}}{ \tau_{1}}\right)\right]}. \tag{16}\]  
The actor-critic network structure and the update formulas using temporally encoded information is provided. The actor-critic network structure with TD is shown as Algorithm 1.  
```
1:Input: the initial parameters of actor-critic network \(\theta^{(0)}\) and \(\omega^{(0)}\); the parameters of the network after the \(i\)th update \(\theta^{(i)}\) and \(\omega^{(i)}\); input signal; the maximum running time time _time_end_; the learning rate of actor-critic network \(\alpha_{\theta}>0,\ \alpha_{\omega}>0\);
2:Initialize the running time \(time=0\); the state \(S_{t}\).
3:Repeat
4: time\(\leftarrow\)time + 1
5: Input signal[time] to the actor-critic network
6: if actor network generates action signals then
7: Calculate \(\
abla_{\theta}log\pi(A|S,\theta)\) based on the selected action \(A\)
8: Update the state \(S^{\prime}\) and the reward signal \(R_{t+1}\)
9:endif
10: if critic network generates value function then
11: Calculate \(\
abla_{\omega}v(S,\omega)\)
12:endif
13: if value function gradient and policy gradient have been solved then
14:\(\delta\gets R(S^{\prime})+\gamma v(S^{\prime},\omega)-v(S,\omega)\)
15:\(\omega\leftarrow\omega+\beta\delta\
abla_{\omega}v(S,\omega)\)
16:\(\theta\leftarrow\theta+\alpha\delta\
abla_{\theta}log\pi(A|S,\theta)\)
17:\(S\gets S^{\prime}\)
18:endif
19:Until time > time_end
```  
**Algorithm 1** Actor-critic network structure with TD
## 4 Discussion
### Classification Performance and Uncertainties
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Reference/** & **Cropland** & **Foresland** & **Water** & **Humid** & **Pastureland** & **Dry** & **Total** & **UA** \\
**Classified** & **Cropland** & **Foresland** & **Body** & **Savanna** & **Pastureland** & **Dry** & **Savanna** & **Total** & **UA** \\ \hline Cropland & 83 & 0 & 0 & 1 & 0 & 0 & 84 & 99 \\ Forestland & 0 & 56 & 0 & 0 & 0 & 0 & 56 & 100 \\ Water Body & 0 & 0 & 14 & 0 & 0 & 0 & 14 & 100 \\ Humid & 0 & 0 & 0 & 19 & 2 & 15 & 36 & 53 \\ Savanna & 0 & 0 & 0 & 1 & 36 & 0 & 37 & 97 \\ Pastureland & 0 & 0 & 0 & 12 & 1 & 23 & 36 & 64 \\ Dry Savanna & 83 & 56 & 14 & 33 & 39 & 38 & 263 \\ PAI & 100 & 100 & 100 & 58 & 92 & 61 & & 88 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix among the major land use and land cover classes with the MapBiomas project. and long crop cycles in the estimates, or using a metric that clearly identifies two cycles in the same time series (19; 52; 57). The forestland showed a 3% difference between the two studies. In the MapBiomas project, the tree-dominated savannas, tree-dominated wetlands, and the Amazon rainforest were grouped in the forestland class. The area estimation of pastureland differed by only 1% in relation to the MapBiomas project, even though pasturelands are difficult to map because of the similar spectral response with the savanna grasslands and other land covers (5). Farmers often use natural fields for cattle ranching, making it difficult to differentiate them from native savanna grassland (61). In that manner, pastures were confused with dry savanna (Table 1). The commission error was better than the omission error, leading to a most likely overestimation of the area of pastures. Pasture conversion represented an average of 79% of new cropland area in Brazil and 20% of natural vegetation conversion during the 2000-2014 period, which represents approximately 8% of pasture conversion per year (59). The greatest difference in area estimation was found in the water body class, which was 4.1 times lower than the MapBiomas project estimation. The main reason for this difference was probably related to the lower spatial resolution of the PROBA-V (100 m) in comparison to that of the Landsat collection (30 m) classification of the MapBiomas. Another possible reason is the underestimation in this study of the Pantanal biome because of the use of the maximum shade fraction in the annual composite, different from that of the other initiatives (e.g., 16), which perform classification using wet season datasets when there is a lack of data. Another Brazilian initiative, called TerraClass, computed 4747 km\({}^{2}\) of water bodies in Mato Grosso state in 2014 using Landsat mosaic composites and visual interpretation (62). In this sense, the proposed approach minimizes the influence of the seasonality of the native vegetation of the savanna and wetlands by not determining single dry or wet periods, which, in turn, vary throughout Mato Grosso state (63) and may affect the area estimation in LULC mappings.
## 2 Materials and Methods
### BioEP/FF Composites' Characterization
#### 2.3.2 Thermal Properties  
Thermal stability at elevated temperatures of BioEP and BioEP/FF composites was studied by thermogravimetric analysis (TGA) using a TGA/SDT 851 thermobalance from Mettler-Toledo Inc. (Schwerzenbach, Switzerland). Samples with an average weight ranging from 7 to 9 mg were heated from 30 to 700 \({}^{\circ}\)C at a constant heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\). All samples were tested in triplicate in a nitrogen atmosphere with a constant nitrogen flow rate of 66 mL min\({}^{-1}\). The onset degradation temperature (\(T_{0}\)) was assumed at a weight loss of 5 wt%, and the maximum degradation rate temperature (\(T_{\rm max}\)) was obtained as the corresponding peak in the first derivative from TGA curves (DTG).
## 2 Study Area and Data  
### Study Area  
The Qinling-Daba Mountains in southern Shaanxi are located between 105\({}^{\circ}\)29\({}^{\prime}\)-111\({}^{\circ}\)2\({}^{\prime}\) E and 31\({}^{\circ}\)42\({}^{\prime}\)-34\({}^{\circ}\)26\({}^{\prime}\) N (Figure 1). The altitude is mostly in the range of 1000-3000 m [29]. In addition to the high mountains, basins and valleys formed due to the subsidence of the block [30]. These features include the Hanzhong Basin, Ankang Basin, Hanying Basin, and Xixiang Basin [31]. The Qinling-Daba Mountains span the Qinling, Northern China, and Yangzi plates and include widely distributed metamorphic rocks such as slate, phyllite, and schist, with a weak lithology. The lithological joints and fissures are well developed and strongly weathered, which is highly conducive to the occurrence of geohazards [32]. These mountains constitute an important boundary between northern and southern China, and there are significant differences in climate and rainfall between the northern and southern parts of the Qinling-Daba Mountains. The southern part belongs to the subtropical monsoon climate zone, while the northern part belongs to the temperate monsoon climate zone. The airflow enters a strong sinking area after crossing the Qinling-Daba Mountains, and the precipitation decreases sharply at that point. Due to the significant difference in terrain, there are also obvious vertical zonal climate characteristics [33]. The northern part has abundant rainfall at an altitude of 1000-1400 m. In the southern area, there is also high rainfall, and rainstorms and continuous rainfall are common due to the influence of the uplift and blocking of the airflow below 1000 m above sea level. These factors result in the frequent occurrence of geological disasters in the rainy season. Restricted by geological and geomorphological conditions, landslides are mainly shallow and small or medium-sized, and they can be mainly divided into soil and rock types according to material composition [33]. These landslides mainly occur from July to October and are strongly related to rainfall factors [34]. Landslides in the region are often interrelated in terms of causes and interdependent in time and space [35], showing regional aggregation. Such trends have been observed in the Ziyang-Hongshan landslide group, the Heihe-Ganyuyuan landslide group, the Ziyang-Gaoping landslide group, etc. Ankang, as a typical disaster-vulnerable area in the Qinling-Daba Mountains, is a representative example [35]. Therefore, the authors of this paper chose Ankang as a site in which to explore the adaptability of Sentinel\(-\)1A and ALOS/PALSAR\(-\)2 data.  
Figure 1: The Qinling-Daba Mountains and study area.
# Optimizing Internet of Things Honeypots with Machine Learning: A Review
## 6 Discussion
### Future Research Directions  
Building on the results of this paper, future research could explore several promising avenues for further developing the field of IoT honeypots and their integration with machine learning.  
One key future research direction is the real-world implementation and long-term evaluation of machine learning-enhanced honeypots. Current studies predominantly focus on short-term experiments and controlled environments, but long-term deployments in diverse IoT settings could provide deeper insights into the sustained effectiveness and adaptability of these systems against evolving attack strategies. Investigating how these honeypots perform under real-world constraints, including varying network conditions and emerging threat landscapes, remains a crucial challenge.  
Another important research direction involves optimizing machine learning models for IoT honeypots, particularly in terms of efficiency and resource consumption. Given the inherent limitations of IoT devices--such as restricted processing power and energy constraints--future studies should focus on developing lightweight and energy-efficient machine learning algorithms. This could include exploring novel model architectures, compression techniques, or hybrid approaches that balance computational demands with practical applicability.  
The integration of emerging technologies into IoT honeypots also presents a significant opportunity for future research. For instance, 5G networks could enhance the scalability and responsiveness of honeypots, while edge computing could help distribute computational loads efficiently. Additionally, incorporating blockchain technology could improve data integrity and secure information sharing between honeypot networks, reducing the risk of tampering and increasing trust in collected threat intelligence.  
Moreover, future research could investigate the potential of adversarial machine learning in IoT honeypots. Attackers are increasingly employing sophisticated evasion techniques to bypass security mechanisms. Studying how adversarial machine learning can be used both defensively (to improve honeypot deception) and offensively (to detect and counter adversarial attacks) could provide valuable insights into strengthening IoT security.  
Finally, interdisciplinary research combining cybersecurity, behavioral analysis, and artificial intelligence could further refine the effectiveness of honeypots. Understanding attacker behavior through AI-driven behavioral analytics could lead to more adaptive and intelligent honeypot designs that dynamically adjust their strategies based on real-time threat intelligence.  
By addressing these research directions, future studies can contribute to the advancement of IoT honeypots, ensuring they remain effective against increasingly sophisticated cyber threats while maintaining efficiency and scalability in real-world applications.
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## Appendix A Classification Algorithms Optimization  
Parameters optimization to achieve the best accurate results using k-NN and SVM has been conducted by experimenting with the possible combinations of parameters. The results of parameters fine-tuning for SVM and k-NN classifiers are shown in Figures 13 and 14 and explained below.  
The SVM classifier includes two major hyperparameters: the kernel and the penalty (C) value, as shown in Figure 13. The most common kernels for SVM are the examined linear, polynomial, and Radial Basis Function (RBF) kernels. Of these three, RBF and polynomial provided the most accurate results, with the polynomial performing slightly better for a range of penalty values C [0.01-10]. The penalty C metric can take a range of values that may exponentially affect classification accuracy. Therefore, log scales are usually preferred on this parameter, and the chosen values of C are 0.01, 0.1, 1, and 10. Therefore, penalty values below 0.01 or above 10 have not been examined. As shown in Figure 13, for C = 1 the SVM classifier accuracy peaks for either polynomial or RBF kernels. Nevertheless, the polynomial kernel has been selected due to its better accuracy response for C values below 1.  
The k-NN classifier includes two parameters: the number of neighbors (\(n\)) and the distance metric used. The number of neighbors is the most important hyperparameter of k-NN, and it usually takes odd values in the range of 1-21. Experiments showed that the accuracy follows a decreasing curve for \(n>9\) for our training dataset and for k-value \(n=9\), which is a local maximum point and, therefore, the optimal parameter choice. For distance metrics, [PERSON] and [PERSON]'s metrics presented similar accuracy results over the number n of k-values. Parameters calibration experiments showed that for both uniform and distance weightings, the contribution of neighborhood members had minimal impact, which is why they are not included in Figure 14.