esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Electrochemical Discharge Behaviors  
To compare electrochemical discharge capacity and self-discharge behavior, we applied 1.0 wt% of S-CuO, L-CuO, and C-CuO as additives for the Zn anodes. Discharging was performed using a constant current mode up to 0.2 V. We made and compared two cells for each material to characterize the self-discharge behavior. One was characterized after being stored for 1 h and the other after being stored for 12 h, both at room temperature. Figure 5 shows the discharge profiles after (a) 1 h storage and (b) 12 h storage. Discharge capacity and capacity retention rate are summarized in Table 2. The S-CuO showed the highest discharge capacity compared to other nano CuO additives after 1 h storage. L-CuO showed the lowest performance, with 399.91 mAh/g. Furthermore, after 12 h storage,  
Figure 4: EDX mapping images of (**a**) S-CuO, (**b**) L-CuO, and (**c**) C-CuO; EDX spectra of (**d**) S-CuO, (**e**) L-CuO, and (f**) C-CuO.  
S-CuO showed excellent performance with 359.42 mAh/g of discharge capacity, and 83.8% retention rate. However, the other forms of CuO showed lower performance than pure Zn, with a retention rate below 80%. The electrochemical discharge performance of the Zn-air cell was improved by the S-CuO additive. Each type of nano CuO has different morphology, therefore, the electrochemical behaviors are different in the electrolyte during discharge [38]. There is a difference in reactivity within the electrolyte because the surface area also depends on the morphology. The structure also affects the diffusion rate of ions As the rate changes, the distance at which ions spread also varies, which directly affects the performance of the batteries [39]. One study reported that the benefits of the sphere-like form increased battery capacity and preservation [40]. Among the CuO forms in this study, S-CuO has a sphere-like form, and its surface area is also wide due to primary particles. This morphological advantage leads to an increase in the spread rate of OH\({}^{-}\) within the electrolyte, which is considered to be relatively capacity-preserving compared to other forms of CuO.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Specific Discharge Capacity (mAh/g)**} & \multirow{2}{*}{**Capacity**} \\ \cline{2-2} \cline{4-4}  & **1 h Storage** & & **12 h Storage** \\ \hline Pure Zn & 476.48 & 390.14 & 81.8\% \\ S-CuO 1.0 wt\% & 428.8 & 359.42 & 83.8\% \\ L-CuO 1.0 wt\% & 399.91 & 302.92 & 75.7\% \\ C-CuO 1.0 wt\% & 420.15 & 328.78 & 78\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific discharge capacity and capacity retention rate of nano CuO additives after 1 h and 12 h storage.  
Figure 5: Specific discharge profiles of nano CuO additives after (**a**) 1 h storage and (**b**) 12 h storage.
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 2 System Model  
The distribution of the wavelet-OFDM MIMO system is illustrated within this section. This depends on the transmitter, the channel, in addition to the receiver which is utilizing the multiplexed method. The number of transmitting and receiving antennas is N and M, respectively.
# Analyzing Impact and Systemwide Effects of the SlowROS Attack in an Industrial Automation Scenario
## 6 Tests, Results, and Analysis
### Connection Request Flooding Bypass
We observed that, when the client eventually closes its connections by sending FINs, the server responds with a FIN/ACK for the connections that are considered established on both sides, whereas it responds with RST to the connections established only on the client side. According to the TCP specification, the transmission of RST instead of FIN confirms that the corresponding connection was never established on the server side. The mitigation has indeed a significant impact because, at the maximum connection request rate reached in our experiments (\(T_{SYN}=158\,\mathrm{\SIUnitSymbolMicro s}\)), about 55% of the connections \"established\" by the client are silently discarded and never reach the ROS Master. What makes the SYN flooding protection mechanism even more relevant in this context is that it is part of the standard TCP/IP protocol stack and does not depend on the installation and configuration of any dedicated kernel modules and utilities like, for instance, Iptables [54]. Therefore, it can be assumed to be ubiquitously present on virtually all Linux-based systems. Unfortunately, from the victim's point of view, the SYN flooding protection can, however, be easily circumvented exactly as we did for our experiments, that is, by ensuring that the attacker's connection request rate stays sufficiently low. This approach is also in  
Figure 6: SYN retransmissions and number of non-established connections on the server side as a function of the connection request period. line with the typical stealthiness goal of slow DoS attacks that, among other requirements, stipulates a carefully controlled and limited peak bandwidth usage. As a final remark, the actual \(T_{\text{SYN}}\) thresholds for the two behaviors just described (about \(2.5\,\mathrm{ms}\) and \(1\,\mathrm{ms}\), respectively) may likely depend on the specific hardware capabilities and software configuration of the server operating system. This aspect has not been further investigated in the present work and will be the subject of future work concerning attack strategy optimization.
In summary, compared with conventional deep neural networks (DNNs), PINNs demonstrate significant advantages and broad application prospects in the design of novel worm drive, particularly under conditions where experimental and simulation data are limited but comprehensive theoretical support from meshing theory is available. Consequently, the employment of PINN methodology is proposed to advance curvature interference theory. Through the application of PINNs, the initial-value issues inherent in the system of nonlinear equations are reformulated as a physical boundary-value problem. The approach facilitates solutions for the system of nonlinear equations. By adjusting boundary conditions within the neural network loss function during training, solutions for multiple key points along the curvature interference limit line are obtained. Multi-solution domains and residual connections [25] are incorporated into the enhanced PINN architecture, thereby enabling issues of gradient vanishing and local optima entrapment to be mitigated, with reliable convergence to globally optimal solutions ensured. Compared with conventional curvature interference analysis methods [14], the proposed PINN-based analysis method embeds the governing nonlinear equations into the loss function architecture, enabling equation resolution through the minimization of the loss function. Secondly, the method can integrate the nonlinear curvature interference boundary equations as boundary constraints for parametric optimization, achieving the simultaneous realization of global design optimization and curvature interference avoidance [26]. This study presents an advanced approach to curvature interference analysis through the application of Physics-Informed Neural Networks (PINN). Traditional curvature interference analysis involves determining the curvature interference limit line by solving curvature interference equations post-design. However, a critical research gap remains in ensuring both interference-free operation and optimal meshing performance in the contact zone during the initial design phase, particularly concerning the inverse determination of design parameters. Given that experimental and simulation data are limited during the initial design phase but the meshing principles are well established, this study transforms the curvature interference conditions into physical constraints within the PINN framework, constructing a loss function that incorporates the curvature interference limit function. This method overcomes the limitations of traditional forward solution analysis for curvature interference boundaries and lays the groundwork for the subsequent inverse determination of design parameters using PINN technology. The derivation strategy for the formulas in this manuscript is as shown in Figure 1.
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 2 System Model
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation (s)** & **Description** \\ \hline \(\mathcal{N},\mathcal{M},\mathcal{K}\) & Set of SBSs, MSs, and subchannels with cardinality \(N,M\), and \(K\) \\ \(M_{n}\) & Number of MSs associated with SBS \(n\) \\ \(B\) & Spectrum bandwidth \\ \(h_{ii}(k)\) & Channel gain of transmission on link \(i\) on subchannel \(k\) \\ \(h_{ji}(k)\) & Channel gain of interference from the transmitting SBS of link \(j\) \\  & to the receiving MS of link \(i\) on subchannel \(k\) \\ \(P_{\max}\) & Maximum transmit power of each SBS \\ \(P_{i,k}\) & Transmit power of link \(i\) on subchannel \(k\) \\ \(\gamma_{i,k}\) & SINR for link \(i\) on subchannel \(k\) \\ \(I_{i,k}\) & Co-channel interference to link \(i\) on subchannel \(k\) \\ \(\sigma^{2}\) & AWGN power \\ \(C_{i}\) & Capacity of link \(i\) \\ \(C\) & Total capacity \\ \(\mathcal{G}\) & Strategic form game \\ \(\mathcal{S}_{i}\) & Set of strategies of player \(i\in\mathcal{M},\mathcal{S}_{i}=\{1,2,\ldots,K\}\) \\ \(\mathcal{S},\mathcal{S}_{-i}\) & Cartesian product \(\prod_{j\in\mathcal{M}}\mathcal{S}_{j}\), and \(\prod_{j\in\mathcal{M}\setminus\{i\}}\mathcal{S}_{j}\) \\ \(s_{i}\) & Strategy of player \(i\), \(s_{i}\in\mathcal{S}_{i}\) \\ \(s_{-i}\) & Strategy of all players except \(i\), \(\mathcal{S}_{-i}\in\mathcal{S}_{-i}\) \\ \(u_{i}\) & Utility of player \(i\) \\ \(\mathrm{BR}_{i}\) & BR strategy of player \(i\) \\ \(\mathbf{P}\) & Potential function \\ \(W_{k}(s)\) & Total welfare from all links that are using subchannel \(k\) \\ \(\mathbf{P}_{i}\) & Subchannel selection probability vector of link \(i\) \\ \(p_{i,k}(t)\) & Probability of choosing subchannel \(k\) for sensing at time slot \(t\) by link \(i\) \\ \(\tilde{r}_{i}\) & Normalized utility increment of link \(i\) \\ \(\beta\) & Learning step-size \\ \(r_{n}\) & Circular coverage radius of SBS \(n\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of notations. AWGN: additive white Gaussian noise; BR: best-response; SINR: signal-to-interference-plus-noise ratio.
# Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
## 2 Methodology  
The methodology proposed for the VTA approach is described in this section. The main goal of this study is to develop a system that allows the classification of the unbalance fault in IM using a low-dimensionality feature vector. This low-dimensionality feature vector is composed of correlation and variance texture features. An overview of the proposed methodology is shown in Figure 1. From this figure, it can be observed that the classification system is divided into two stages: training-validation, and testing. In the training-validation stage, two different subsets of vibration signals are used to extract the correlation and variance texture features. In the testing stage, the correlation and variance texture features are extracted from a different subset of vibration signals to evaluate the final classifier and measure the performance from the VTA model.
# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 5 Conclusions
### Practical Application  
In this study, a semi-supervised classification algorithm was proposed by fully considering the practical problems of insufficient label data amount and missing categories. We reduced the algorithm's dependence on labeled data and increased the processing capacity of abnormal track labeled datasets. The algorithm has backward compatibility. When the amount of data marking was sufficient, and the data type marking was not missing, it still achieved a considerable performance index, which could be used for data processing under normal circumstances.  
So, this algorithm could be used as a beneficial supplement to the current track data processing system, to enhance the applicability of the system, and to lay the foundation for subsequent applications, such as target identity attribute recognition and behavior monitoring.
# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 5 Experiment  
### Experiment Setup  
All programming work is based on the deep learning framework of PyTorch1.13 and its Python3.9 counterpart. We used an NVIDIA A100 GPU to train our model. In terms of data sets, we used two publicly available real-world traffic data, I-80 and US-101 of the NGSIM dataset [30]. This set of data is the trajectory of a vehicle captured at a frequency of 10 Hz in a real highway scene. Each dataset consists of three subsets corresponding to scenarios characterized by light, moderate, and heavy traffic conditions. The duration of each scene is 15 min. Track information is divided into segments every 8 s, with a 3-s segment used as historical track input and a subsequent 5-s segment used as track prediction. Prior to training, each track segment was downsampled at a rate of 5 Hz. The data set is divided into training set, verification set and test set in a ratio of 0.7:0.1:0.2.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Latency Aware and Energy-Efficient Routing  
These protocols extend the network lifetime despite the poor processing and storage capacity of nodes. These protocols can generally be divided into four broad subcategories, which are: cluster, multipath, location, heuristic and swarm-based protocols. In the following, we detail these classes.
# Green Strategies for Molecularly Imprinted Polymer Development
## Green Strategies in MIP Development
### Electropolymerization-Assisted MIPs  
Electropolymerization brings many beneficial aspects, since it allows the control of the thickness of the polymer layer. The polymer can be attached to the sensor surface with different types of geometries, and is compatible with combinatorial and high-throughput strategies [115; 116]. In general, this approach is performed when MIP sensitive devices are developed, such as capacitive, electrochemical, voltametric, or amperimetric sensors.  
The [PERSON] group [116] described for the first time, the electrosynthesis of a glucose-MIP on the electrode of a quartz crystal. The electropolymerization of \(o\)-phenylenediamine was performed in acetate buffer (pH 5.2) and glucose by cyclic voltammetry in the range 0.0-0.8 V. The results showed that this is a feasible way to prepare biomimetic sensors for glucose. Also [PERSON] et al. [117] reported a phenylalanine-MIP layer on gold electrodes produced by electropolymerization of phenol as monomer. Polyphenol MIP monolayer with affinity towards phenylalanine was produced at room temperature by cyclic voltammetry at 0.3 V. The results showed a decrease on the electrical capacitance when increasing the template concentration. [PERSON] et al. [118] reported a glucose-MIP layer on gold electrode synthesized via electropolymerization of \(o\)-phenylenediamine in the presence of the template glucose, dissolved in acetate buffer solution (pH 5.18) by cyclic voltammetry, and the resulting sensor was shown to be stable and reproducible, exhibiting higher selectivity to glucose than for ascorbic acid or fructose.  
Electropolymerization is being widely used in the synthesis of MIPs, especially advantageous in the preparation of sensing devices. Many other templates have been used, such as cholesterol [119; 120], fluorescein, rhodamine, and 2,4-dichlorophenoxyacyacetic acid (2,\(A\)-D) [121], fenvalerate [122], caffeine [123], morphine [124], paracetamol [125], 4,6-dinitro-\(o\)-cresol [126], ascorbic acid [127], sulfamethoxazole [128], triclosan [129], folic acid [130], BPA [131], naproxen, paracetamol, and THO [132], methyl-parathion pesticide [133], bovine hemoglobin (BHb) [134], and estradiol (E\({}_{2}\)) [135].  
[PERSON] et al. [136] reported human ferritin and human papillomavirus-derived E7 proteins MIP-layered carbon nanotube tips via an electropolymerization method for protein detection. The polyphenol (PPn) was deposited at carbon nanotube tip surface by cyclic voltammetry in PBS containing phenol between 0.0 and 0.9 V, and further, the protein was added to the solution, and more five cycles of voltage were applied. As a result, MIP sensor was obtained as nano-sized particles at the sensor surface, providing a label-free electrochemical detection with ability to selectively recognize the proteins over calmodulin.
## 1 Introduction
A genetic algorithm is used in [6] to find the optimal order of distances in IDW. An adaptive version of the IDW method is introduced in [4], where the authors suggest that the spatial pattern of the sampled stations in the neighborhood could influence the weighting parameter. The algorithm selects the weights in IDW based on the stations' density around the unsampled location. It gave better estimations than KG on the study cases. However, the choices of the membership functions and the number of relevant neighbors are heuristics, and there is no guarantee that they will perform well in other scenarios. Some machine learning models were also investigated in [3], with application to spatial interpolation of environmental variables. They are compared with several traditional spatial interpolation methods, among which is inverse distance squared. The study found that the combination of random forest and IDW, respectively random forest with OK were the best methods, with similar accuracies. At the same time, OK was found to perform similarly to IDW in the tested scenarios. A hybrid method that combines IDW with support vector machines is reported in [14], applied to multiyear annual precipitation. In [15], temporal predictions obtained with an ensemble approach are used as inputs to spatial interpolation by IDW, with improved overall results. The main difficulty faced when applying IDW is setting the value of the power parameter. This is usually done before the algorithm is applied. The usual approach for searching for the optimal value of the power parameter is by exhaustive search, by sampling all possible values in a given interval, at a user chosen step size [2]. The results of this method depend on the search window. The method is only guaranteed to find a local optimum [2]. In this paper, we target the optimization of the process of setting the power parameter in IDW, using a nature-inspired metaheuristic--Particle Swarm Optimization. We automate the process of identifying the suitable parameter, while maintaining, if not optimizing, the prediction accuracy of the standard IDW method. Experiments are performed on maximum annual precipitation data gathered in the Dobrogea region (Romania). An evaluation of the proposed hybrid method is performed, against standard IDW and Kriging. The paper structure is the following. Section 2 begins with a brief presentation of the meteorological stations locations in the study area. Sections 2.2 and 2.3 present the spatial interpolation methods IDW and KG. The Particle Swarm Optimization (PSO) metaheuristic is presented in Section 2.4. The section ends with the presentation of the hybrid algorithm Optimizing IDW (OIDW), obtained by optimizing the \(\beta\) parameter of IDW with PSO. The experimental settings are described in Section 3, while the results are discussed in Section 4. The paper ends with the conclusion section.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 4 Track Fault Detection Using Machine Learning
### Multibody Dynamics Simulation  
To analyse the effects of railway track deterioration on car-body vibrations, we performed a railway vehicle travel simulation using SIMPACK, a multibody dynamics software package. The model used for the simulation was a single conventional railway vehicle, the parameters for which are shown in Table 1. Figure 4 shows the model used in the simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Unit** & **Value** \\ \hline Car-body mass & kg & 25,000 \\ Bogie mass & kg & 3100 \\ Wheelset mass & kg & 1500 \\ Car-body inertia & kgm\({}^{2}\) & 84,100 \\ Bogie inertia & kgm\({}^{2}\) & 1734.75 \\ Wheelset inertia & kgm\({}^{2}\) & 735 \\ Car-body base & m & 14.1 \\ Wheel base & m & 2.1 \\ Primary suspension vertical stiffness & kN/m & 2120 \\ Secondary suspension vertical stiffness & kN/m & 200 \\ Primary suspension lateral stiffness & kN/m & 5590 \\ Secondary suspension lateral stiffness & kN/m & 131.3 \\ Primary suspension vertical damping & kNs/m & 78.4 \\ Secondary suspension vertical damping & kNs/m & 48 \\ Primary suspension lateral damping & kNs/m & 78.4 \\ Secondary suspension lateral stiffness & kNs/m & 58.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle parameters.  
Figure 3: Track irregularities.  
The model consists of seven rigid bodies, one car body, two bogies, and four wheelsets, each having six degrees of freedom. Thus, 42 degrees of freedom exist in the model. It is necessary to connect each rigid body with an appropriate joint such that the vehicle exhibits a realistic motion. One car-body, two bogies, and four wheelsets were connected by spring and damper elements. These spring and damper elements were set with spring constants and damping coefficients in three axial directions.  
Using this vehicle model, we calculated the vertical acceleration, lateral acceleration, and roll rate above the centre of the front bogie at the car body. This simulation study is carried out based on the straight track. Alignment irregularity in curved track is not considered in this paper but should be considered in the next work.
## 2 Graph Based Critical Sensor Assessment
### Improving the Critical Sensors  
In fact, as we will see now, the algorithms presented in this paper identify (through their locations) the most critical sensors whose information is instrumental in devising the best evacuation paths. Therefore  
Figure 2: Graphical representation of the output of Algorithm 2, where line thickness increases with visit count. The cyan star on the ground floor marks the exit. Results shown for a subset of all building locations.  
the lifetime of such sensors in presence of an emergency (such as a fire) should be \"hardened\", e.g., by upgrading them with heat-resistant components, or adding redundancy.  
We will illustrate this by showing via simulation how the proportion of successful evacuees increases when critical sensors are hardened. In particular we identify the sensors to be hardened using the top-ranked thirteen locations (out of 240) identified by Algorithm 1 and 2, and the results are given in Figure 3. Results are normalized against a best-case scenario where all sensors are indestructible (green bars). A worst-case scenario where all nodes fail under fire (red bars) is also shown for comparative purposes. Both algorithms appear to perform well with a slight advantage for Algorithm 2. This can be explained by the fact that only seven locations out of thirteen differ amongst the two sets. A detailed analysis of the simulations also reveals that each algorithm may be best suited for particular types of graphs:  
* **Algorithm 1** is most effective in intricate graphs featuring staircases, corridors and partitioned space, where an evacuation plan must be decided early based on the designation of strategic areas.
* **Algorithm 2** is best suited for open spaces, where bypassing the fire is generally trivial and the critical decisions relate to the availability of exits and how to approach them.  
The building graph used in this simulation happens to be a blend of both types of spaces, which may explain why neither of the algorithms has a clear advantage.