esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Research  
### Support Vector Machine  
A Support Vector Machine (SVM) [20] is applied to classify data by finding the best hyperplane that separates all data points of one class from those of another class. Here, the best hyperplane of the SVM means the one with the largest margin between the two classes. Margin means the maximal width of the hyperplane that has no interior data points.  
The support vectors are the data points on the boundary of the slab that are closest to the separating hyperplane.  
The training data is classified correctly by a given hyperplane. The hyperplane, represented by (w,b), is equally expressed by all pairs \(\left\{\lambda w,\lambda b\right\}\) for \(\lambda\in R^{+}\). Thus, we define the canonical hyperplane to be that which separates the data from the hyperplane by a \"distance\" of at least 1. That is to say, the relationship satisfies the following formula:  
\[\begin{cases}x_{i}^{\mathrm{T}}w+b\geq+1,&\left\{y_{i}=+1,\right.\\ x_{i}^{\mathrm{T}}w+b\leq-1,&\left\{y_{i}=-1,\right.\end{cases} \tag{1}\]  
or, more compactly, as follows:  
\[y_{i}\left(x_{i}^{\mathrm{T}}w+b\right)-1\geq 0,\forall i \tag{2}\]  
Based on the considerations presented above, the optimum separation hyperplane conditions from Equation (2) can be formulated into the following expression that represents a linear SVM:  
\[\begin{cases}\min\frac{1}{2}\left\|w\right\|^{2}\\ s.t.y_{i}\left(x_{i}^{\mathrm{T}}w+b\right)-1\geq 0,\forall i\end{cases} \tag{3}\]  
The optimization problem from Equation (3) represents the minimization of a quadratic function under linear constraints. The training data are converted into a feature space with kernel function, and different kernel function cause different effect of classification, thus, the kernel functions in SVM are important for the choice.  
Here, the Gaussian RBF kernel is used as follows:\[K(x_{i},x_{j})=\exp\left(-\left\|x_{j}-x_{i}\right\|^{2}\Big{/}\left(2\sigma^{2} \right)\right) \tag{4}\]  
Width coefficient penalty factor \(C\) and radial basis kernel factor \(\sigma\) need to be determined before support vector machine model corresponding to the classification. The parameters of SVM are optimized by the particle swarm optimization algorithm in improved _Particle Swarm Optimization-Support Vector machine_ (IPSO-SVM) method.
# COALA: A Protocol for the Avoidance and Alleviation of Congestion in Wireless Sensor Networks
## 5 Conclusions  
In this research article, a novel lightweight scheme named COALA, which aims at preventing the diffusion of imminent congestion in WSNs through alternative path routing was introduced.  
Figure 10: Number of Packets Lost vs. Traffic Load Rate for variable \(w_{\widetilde{BO}}\).  
Figure 9: Average Packet Transmission Time vs. Traffic Load Rate for variable \(w_{\widetilde{BO}}\).  
The innovation of this proposed scheme lies in the fact that it takes into consideration a certain number of both invariant and variable factors that affect the probability of congestion occurrence along with other crucial factors like energy efficiency.  
The efficacy of COALA protocol was evaluated through simulation tests in comparison with an advanced scheme of this kind, named DAIPaS Hard. The first comparative advantage of COALA is that it incorporates the use of the so-called vicinity index during the initial determination of routing paths, which makes nodes having a lot of possible data receivers and few data providers be favored. Therefore, traffic load may be distributed in a more balanced manner. Additionally, COALA introduces a multivariable cost function, which takes into consideration not only the metrics that DAIPaS Hard suggests, but also the popularity index, the vicinity index and the geographical distance of this node. Thus, congestion caused due to upper-level neighboring nodes that are repetitively busy, or have many probable data suppliers and few data recipients or are located at more distant locations, can be avoided. As a result, COALA achieves the reduction of transmission delays, lost packets rates, and energy dissipation.  
Additionally, it was shown that the simple yet effective algorithm of COALA is able to accommodate to the performance criteria of each individual application through the appropriate adjustment of the weighting of its main cost function.  
The authors of this article intend in future research work to enhance the herein-proposed protocol by either incorporating criteria based on well-known algorithms for energy efficiency [20; 21; 22], QoS [23] and security [24]. The convergence with game theoretic approaches [25], and the adaption of the proposed algorithm to standards for IPv6 routing in LLNs [26] are also under consideration.  
A part of the cost for the publishing of this article has been granted by the University of Surrey, and the London South Bank University.  
[PERSON] conceived the proposed protocol, designed the protocol algorithm and wrote the manuscript; [PERSON] contributed to the refinement of the protocol algorithm, developed the simulation environment and performed the simulation tests; [PERSON], [PERSON] and [PERSON] contributed to the refinement of the protocol algorithm and the interpretation of the simulation results.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Track Reconstruction  
Geolocation protocols are detailed in [PERSON] et al. (2018), and are briefly described here. Argos location estimates provided by SPOT tags were all opportunistic in nature, as a shark's fin needed to break the sea surface long enough to communicate with an orbiting satellite. As a result, positional information was often clustered in space and time, yet varied over the course of a deployment. SPOT tag location class Z positions were considered invalid and were removed from further analysis ([PERSON] et al., 2018). Transmitted data from PSATs were decoded with the manufacturer's cloud-based portal software. Positioned via light-based geolocation estimation, these tags offered the least accurate positions ([PERSON] et al., 2018), yet given a shark's occupancy in the epipelagic layer, good light-level readings were usually available throughout the deployment period. To estimate positions provided by PSATs, we performed a geolocation analysis using the manufacturer's proprietary Hidden Markov Model (HMM, WC-GPE3, Global Position Estimator Program suite, Wildlife Computers, Redmond, WA, USA). Alongside the positions estimated from the archived PSAT datasets, available known locations from SPOT tags, recapture events and acoustic detections were also included in the model. Acoustic positions were usually obtained in clusters, as a shark tended to be detected by the same receiver at a particular location. To reduce such clustering, we only included a single daily acoustic position when multiple detections were made. Although most published white shark speed estimates range between 0.3 to 1.3 ms\({}^{-1}\), we ran the HMM with 1, 2, 3, 4 and 5 ms\({}^{-1}\) as the prior to avoid spatial constraint of model likelihoods should sharks in this study move a larger distance for any period of the track. Solution scores among models using different swimming speeds were similar, indicating model results were convergent. We hence set the speed filter at 5 ms\({}^{-1}\). The final track for each shark was generated by selecting positions for each day under the following order of priority: (1) Argos/acoustic, (2) either dusk or dawn, (3) positions interpolated by the model. For each tagged shark, we estimated the distance travelled (in kilometres) by calculating Euclidean distance between waypoints.
For example, EDFAs are usually co-doped with Al ions, but the exact natures of the Al-related point defects induced under radiation are not clear yet, especially in the infrared spectral region. However, it is known that the RIA in the infrared is influenced by the presence of the tail of the AlOHCs, whose absorption peaks around 540 nm, and by another defect, whose structure remains unknown. For the EDFAs, while the pump transmission at 980 nm is affected by the AlOHC presence, the signal at 1550 nm is mainly impacted by this unknown center [25]. Under specific conditions such as EDFAs (where a pump signal is injected into the guide), the PB begins to compete with the radiation-induced defect generation by enhancing the recombination of defects. By injecting a pump signal at the appropriate wavelength and power into the fiber, metastable defects created by radiation can recombine not only thermally but also thanks to the energy supplied by the injected light. Then, PB allows us to reduce the RIA by accelerating its recovery processes [25; 26]. In the case of EDFA, the injected signal able to induce a PB phenomenon is the pump signal at 980 nm [27]. In a previous study, [PERSON] et al. [28] noted that by injecting the high-power pump signal into an EDF with a core co-doped with cerium during the 80% of the irradiation run time duration, the RIA at 980 nm is reduced by 20%, while the RIA at 1550 nm is reduced by 50%, compared to the case in which the pump signal is not injected into the fiber. Moreover, radiations can also influence the spectroscopic parameters of the fiber, as reported in [29; 30; 31; 32]. This work aims to analyze, through an experimental-simulation approach, the gain degradation induced by the presence of ionizing radiations in the space environment. Generally, for space applications in which EDFAs are used, the TID of kGy can be reached for the most challenging mission, so in this study, we will consider a maximum dose of 3 kGy (SiO\({}_{2}\)) that covers rather the current and future space needs. Since an EDFA using a Ce co-doped fiber presents a better radiation resistance than one based on telecom-grade active fiber, without Ce-doping or H\({}_{2}\) loading [20; 23], we will focus our study on such an EDFA. The gain degradation will be measured experimentally and evaluated using a simulation code based on the PSO algorithm [33; 34] which solves the rate equations of the involved energy levels and the power propagation equations. The radiation effects giving RIA and changing the spectroscopic parameters as radiative lifitime, emission and absorption cross sections were considered in the numerical code, too. This work highlights a good agreement between the measured and simulated gain degradation kinetics and allows us to understand the basic mechanisms that rule the gain degradation.
## 5 Conclusions  
This paper reports the performance evaluation of radio frequency interference measurements conducted at 6 GHz, 7 GHz, and 8 GHz. Extensive field measurements were conducted to experimentally detect and evaluate radio frequency interference in microwave links using a spectrum analyzer. The actual frequency scan measurements for the horizontal and vertical polarization and the analyzed frequency scans for the tested sites were presented. The probability density functions of the frequency scans covering the normalized histogram estimated normal distribution, normalized center lines, and the smoothed center lines were shown. Additionally, the cumulative distribution functions of the frequency scans for the tested sites for both polarizations were reported. The statistical error characteristics of the frequency scan comprising the mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), relative absolute error (RAE), mean squared error (MSE), each for the estimated normal distribution and the estimated fitness curve was derived. Further to this, the performance of the links was accessed using several error metrics, and the results revealed the presence of interference on the tested links. Additionally, the analysis of the frequency scan signals before and after the noise removal was demonstrated. The denoised signals compare favorably to related results in the existing literature. Last, the frequency scans would be beneficial in evaluating RFI measurements and spectrum planning in a related wireless environment. Future work would examine similar measurements in ISM bands and wideband scan RFI from 1 GHz to over 10 GHz. Additionally, the measured data could be used in developing robust RFI detection, mitigation, and optimization techniques.  
The manuscript was written through the contributions of all authors. [PERSON], [PERSON] and M.A.A. were responsible for the conceptualization of the topic; article gathering and sorting were conducted by [PERSON], A.L.I. and M.A.A.: manuscript writing and original drafting and formal analysis were carried out by A.L.I.; writing of the review and editing was completed by A.L.I.; [PERSON] and A.L.I. led the overall research activity. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The work of [PERSON] is supported by the Nigerian Petroleum Technology Development Fund (PTDF) and the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program under Grant 57473408.  
The authors declare that they have no conflicts of interest.
## 2 Materials and Methods  
### Study Area Description and Urbanization Level Classification  
Our study was conducted in Changchun, a typical medium-sized city, which is the capital of Jilin province, northeast China (43\({}^{\circ}\)42\({}^{\prime}\) to 44\({}^{\circ}\)03\({}^{\prime}\) N, 125\({}^{\circ}\)09\({}^{\prime}\) to 125\({}^{\circ}\)27\({}^{\prime}\) E). Covering approximately 524 km\({}^{2}\) (within the beltway) (Figure 1), Changchun had 3.659 million inhabitants at the end of 2014 (Changchun Statistical Bureau data). The climate is temperate humid or sub-humid continental monsoon, the mean annual precipitation is 567 mm, and the mean air temperature is 4.8 \({}^{\circ}\)C [26]. Dominant soil types are black soil, dark-brown soil, and chernozem [27].  
We used human disturbance time and ring roads to represent the urbanization level. Changchun historical maps and literature data [28] were used to distinguish human disturbance times. Five categories, based on the years at which soil was built-up (the years 1900, 1954, 1990, 2004, and 2014), indicated human disturbance time, i.e., 114, 60, 24, 10, and 0 years, respectively. Moreover, ring road numbers are another symbol to indicate the urbanization degree, which studies have shown can be used as a proxy for the degree of urbanization [29] or urban-rural gradients [21; 27]. Accordingly, the urban  
Figure 1: The location and samples of the study area. Notes: **(A)** Northeast China; **(B)** Changchun administrative region; and **(C)** map of study area, in which the shading represents the regions of different soil build-up time, the red lines indicate the different ring road boundaries and the dots are the sample points of different forest types: RF, roadside forest; AF, affiliated forest; LF, landscape and relaxation forest; EF, ecological and public welfare forest; and PF, production and management forest (the map was created by ArcGIS 10.3, Esri, RedLands, CA, USA).  
area was divided into five categories, i.e., 1 st, 2 nd, 3 rd, 4 th, and 5 th ring road regions (Figure 1). For understanding urbanization effects, the urban-rural gradient transect method is generally used to evaluate time and space substitutions. However, the choice of this transect is subjective and usually biases the urbanization level determined. The large urban dataset used in the present paper, as well as the use of an urban-rural gradient related to ring road development and urban disturbance time, help contribute to a more objective determination of the urbanization level, also reflecting the real urbanization process.
# Performance Investigation of Coated Carbide Tools in
## 4 Conclusions  
The optimization of coated carbide tools in milling procedures composes an essential task for improving machining efficiency, reducing costs, and extending tool life. The present work focused on investigating the wear behavior of coated tools under varying machining conditions, including feed rate, velocity, and radial depth, through an experimentally generated dataset. To enhance predictive capabilities, machine learning models such as artificial neural network, k-nearest neighbors, and support vector regression were developed to estimate tool life based on these machining parameters. In parallel, FEM simulations were also conducted to numerically model the wear mechanisms and analyze the milling process under different operating conditions. A comparative analysis of AI-based predictions and FEM simulations provided valuable insights into the strengths and limitations of both methodologies. In particular, AI techniques demonstrated the ability to deliver rapid, data-driven predictions with minimal computational cost, making them well-suited for real-time tool wear monitoring and manufacturing parameters optimization. On the other hand, FEM models provide a deeper physical understanding of the wear mechanisms at play, allowing for precise analysis of stress distribution, heat generation, and material deformation during milling procedures.  
The findings of this study contribute to the ongoing advancements of predictive manufacturing and process optimization in machining operations. By leveraging AI-driven models for tool wear prediction and FEM simulations for in-depth wear analysis, manufacturers can make informed decisions to enhance tool life, reduce downtime, and improve overall productivity. Future research may further refine the developed models by integrating more experimental data for the training phase of the AI algorithms, as well as investigate additional parameters such as coating composition and tool geometry. Moreover, future work could study hybrid AI models that combine multiple learning techniques for improved accuracy. Furthermore, deep learning, reinforcement learning, and convolutional neural networks could also be explored to refine tool wear predictions. Ultimately, the combination of AI and FEM methodologies could offer a powerful framework for the intelligent optimization of coated tools in milling applications, paving the way for more efficient and cost-effective manufacturing processes. By exploring these future directions, the machining industry can further enhance the accuracy, reliability, and applicability of predictive models for tool wear estimation and process optimization, contributing to smarter and more efficient manufacturing systems.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The author declares no conflicts of interest.
## 4 Experimental Setup and Results  
To demonstrate the efficiency of our approach we have defined a new business model for ISPs/Network Operators and we check its viability using a legacy GPON. In fact, we have set up a network scenario which offers different priority SLAs based on flexible Internet plans so that users contract a fixed guaranteed bandwidth (basic bandwidth) plus an extra bandwidth, and when network subscribers demand this extra bandwidth and there are available resources, it can be given at the expense of a special pricing. Contrary to most of the proposed online service reconfiguration policies or DBA algorithms managed by SDN which are implemented with simulations, we have experimentally demonstrated our proposal using a GPON testbed with legacy equipment.
## 4 SnZn-Based Solders
### Reliability  
During aging and reflow, the thickness of brittle Cu\({}_{5}\)Zn\({}_{8}\) interfacial layer increases with time, which brings a great threat to the reliability of SnZn solders. Some research reported that RE addition could suppress the coarsening of Cu\({}_{5}\)Zn\({}_{8}\) IMC, but it would also result in spontaneous growth of Sn whiskers and finally lead to the potential failure of electronics devices [90]. Research [91] shows that Ni nanoparticles can effectively increase the shear load of the solder joints due its grain refinement.  
The practical use of SnZn solders is limited because the existence of Zn-rich phase greatly reduces the oxidation resistance of the SnZn solder [92]. Under high temperature and humidity conditions, water vapor and oxygen oxidized both the surface microstructure and Zn-rich phase in the solder matrix, resulting in volume expansion and the increase of internal stress, which easily induces cracks along Sn grain boundaries. [PERSON] et al. [91] found the addition of Ni nanoparticles could aid the formation of IMCs in the grain boundaries, so the penetration of H\({}_{2}\)O and O\({}_{2}\) was inhibited as well as the formation of ZnO. It is also reported that Ag nanoparticles increased the thermal and humidity stability of the SnZn-based solder alloy [76]. As shown in Figure 16, the oxidation of Zn-rich phase was significantly inhibited with the addition of Ni and Ag nanoparticles.  
Figure 14: Spreading area of SnZn–xZrC (x = 0, 0.03, 0.06, 0.09, 0.12) composite solders [88].  
Figure 15: Macro morphology of SnZn–Al\({}_{2}\)O\({}_{3}\) composite solders after wetting on the 6061-aluminum alloy: (a) 0% Al\({}_{2}\)O\({}_{3}\); (b) 0.5% Al\({}_{2}\)O\({}_{3}\); (c) 1.0% Al\({}_{2}\)O\({}_{3}\)[89].
## 3 False Data Injection Attacks on RL-Based Charging Coordination
### Evaluations of FDI Attacks  
In this section, we first discuss the training process of a RL-based charging coordination system and then we investigate the impact of the FDI attacks discussed earlier on the performance of the system and the battery levels of honest and malicious consumers. We evaluate system performance using three metrics: fairness in power distribution, charging power usage, and total reward. Higher fairness and total reward indicate better performance, while lower charging power usage is preferred. To assess the impact of the FDI attacks on honest and malicious consumers, we track their SoC values--the greater the reduction in the honest consumers' batteries and the increase in the malicious consumers' batteries, the more negative the impact of the attacks. We examine two scenarios with different attacker proportions: 20% (two attackers) and 40% (four attackers) out of ten total consumers. Additionally, we test two charging power availability levels, \(C_{\max}=6\) and \(C_{\max}=10\), to assess the impact of the attack under different charging power availability. Attack severity is also varied, with two values for \(\alpha\) (0.1 and 0.7). The attack is more severe at \(\alpha=0.1\), where attackers significantly under-report their SoC values, causing greater disruption to the system. Due to space limitation, in this subsection, we present the detailed results of the first attack and summarize the results of the rest of the attacks in a Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attack No.** & **Attack Name** & **Mathematical Model** \\ \hline
1 & Random Reduction Factor & \(f_{1}(SoC_{i}^{(t)})=\alpha\times SoC_{i}^{(t)}\) \\ \hline
2 & Time-Varying Reduction Factor & \(f_{2}(SoC_{i}^{(t)})=\alpha_{t}\times SoC_{i}^{(t)}\) \\ \hline
3 & Zero SoC Reporting & \(f_{3}(SoC_{i}^{(t)})=0\) for \(t\in[t_{s},t_{e}]\), \(SoC_{i}^{(t)}\) otherwise \\ \hline
4 & Gradual Manipulation & \(f_{4}(SoC_{i}^{(t)})=SoC_{i}^{(t)}\times(1-\alpha_{t})\) \\ \hline
5 & Strategic Non-linear SoC Underreporting & \(f_{5}(SoC_{i}^{(t)})=\begin{cases}SoC_{i}^{(t)}\times(1-\beta)&\text{if high-demand period,}\\ SoC_{i}^{(t)}&\text{otherwise}\end{cases}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Attack Methods.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Reward** & **Fairness** & **Power Usage** & **Avg. Honest SoC** & **Avg. Malicious SoC** \\ \hline Baseline (No Attack) & 0\% & 0\% & 0\% & 28\% & 28\% \\ \hline Attack \#1 & \(-\)27.27\% & \(-\)19\% & +7.55\% & 26\% & 32\% \\ \hline Attack \#2 & \(-\)28.5\% & \(-\)21.0\% & +9.0\% & 23\% & 35\% \\ \hline Attack \#3 & \(-\)32.0\% & \(-\)2