esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Figure 1: The influence of the distance between two points on the wavefront phase aberration difference for beams with different wavelengths. Figure 2: Influence of the turbulence inner scale on the difference in wavefront aberrations of beams with different wavelengths: (**a**) \(l_{0}\) = 0.01 m \(-l_{0}\) = 0.15 m and (**b**) \(l_{0}\) = 0.20 m \(-l_{0}\) = 0.40 m.  
It is assumed that the turbulence inner scale \(l_{0}\) is 0.10 m, turbulence outer scale \(L_{0}\) is 50 m, and atmospheric refractive index structure constant corresponds to \(C_{n}^{2}=1\times 10^{-12}\) m\({}^{-2/3}\). Signal light with a wavelength of 1550 nm and beacon lights with wavelengths of 632.8 nm, 530 nm, 850 nm, and 950 nm traverse the atmospheric channel along a shared optical path spanning 10 km. Variations in wavefront phase aberration across different points on the received cross-section are influenced by these wavelength discrepancies, as depicted in Figure 3. In Figure 3a, within the receiving aperture cross-section, the distance between two points exhibits minimal impact on the wavefront aberration difference between beams of distinct wavelengths, consistently maintaining a stable level. Notably, when the beacon light wavelength is 1280 nm, the disparity between the signal light and beacon light wavefront remains below 1, rendering it practically negligible. Figure 3b highlights that as the wavelength disparity between the signal light and the beacon light diminishes, the wavefront aberration difference progressively decreases. When the wavelengths of the signal light and the beacon light align, the wavefront aberration difference reaches 0. Thus, only when the signal light and beacon light wavelengths closely match, the accuracy of adaptive optics for wavefront correction can be maximized. Consequently, in practical applications, the wavelength difference between beacon light and signal light should be considered a critical factor when selecting the beacon light wavelength. Signal Optical Wavefront Aberration Correction for Dual-Wavelength Free-Space Optical Communication Systems
# A New Calibration Method for Broadband Complex Resistivity Measurement System of Rocks and Ores
## 3 Analysis and Calibration of the Instrument's Acquisition Circuit  
The distributed capacitance of the instrument's internal acquisition channels can significantly affect data collection. This distributed capacitance creates a low-pass filtering effect that attenuates high-frequency signals, leading to waveform distortion and phase shifts. This manifests in the circuit as leakage currents, which reduce the input impedance of the instrument and consequently decrease measurement accuracy [37]. This impact is predominantly observed at high frequencies, as the internal distributed capacitance of the instrument is typically in the picoFarad (pF) range, exerting minimal influence at low frequencies.  
Figure 3 displays the instrument response results (uncalibrated) of the CSUZA-FW08 when testing a 100 \(\Omega\) pure resistor, employing an adaptive testing method. In this scenario, the instrument selects a sampling resistor (100 \(\Omega\)) that closely matches the sample's resistance for measurement. When the frequency is below 1 kHz, the instrument's resistance  
measurement accuracy can reach 0.3%, while the phase measurement accuracy can achieve 0.1 mrad. However, at high frequencies (>1 kHz), both the resistance and phase begin to deviate from their actual values, with phase shifts reaching up to 2.8 mrad at 100 kHz. This testing result primarily reflects the influence of the distributed capacitance within the instrument's internal acquisition channels. This effect further intensifies as the resistance of the measured sample increases, potentially obscuring the intrinsic polarization characteristics of the rock samples. To minimize the impact on test results, it is essential to analyze the magnitude of the channel's distributed capacitance, compensate for measurement errors, and address circuit design and component selection. We designed two circuit models for analysis, placing the sampling resistor at the end of the circuit or on the signal source side. This can be easily achieved by altering the connection direction of the signal source, thus referring to these configurations as the direct connection and reverse connection forms of the signal source. We will compare the advantages, disadvantages, and differences between these two circuit design forms.
## 2 Materials and Methods
### Classification of a Weight-Tuned Decision Tree  
To perform the final classification, a Weight-Tuned Decision Tree was used, which provides a definition and efficiently handles imbalanced learning.  
* Weight adjustment: Weights are assigned to classes to deal with unbalanced datasets.
* Tree construction: A decision tree based is constructed based on feature partitioning to maximize information gain.  
* Cross-validation: K-fold cross-validation is used to ensure the robustness of the model and reduce the effect of data-partitioning randomness.
* Performance averaging: The average of the results across the folds is calculated to obtain a reliable performance estimate. Mathematically, the Weight-Tuned Decision Tree can be described as follows [11]: Train a decision tree classifier on reduced features \(\mathbf{z}_{f}^{(i)}\). Let \(\mathbf{y}^{(i)}\in\{0,1\}\) be the label (e.g., benign/malignant). The classifier is trained as follows: \[\hat{g}^{(i)}=f_{\text{tree}}\ \left(\mathbf{z}_{f}^{(i)};\theta\right)\] (7) where \(f_{\text{tree}}\) is a decision tree; \(\theta\) includes split criteria and sample weights tuned via cross-validation: \[\theta=\underset{\theta}{\text{arg}min}\ \mathcal{L}_{\text{tree}}\ +\lambda\cdot\Omega(\theta)\] (8)  
\(\mathcal{L}_{\text{tree}}\) denotes classification loss (e.g., Gini impurity or entropy); \(\Omega(\theta)\) denotes regularization (e.g., tree depth, number of leaves, etc.); \(\lambda\) denotes regularization strength.  
The best \(\theta\) is selected via cross-validation, optimizing performance metrics like accuracy, F1-score, or AUC.  
The final prediction pipeline, including all the algorithms above, is as follows:  
\[\mathbf{x}^{(i)}\ \overset{\text{Deep MLP}}{\rightarrow}\ \mathbf{h}^{(i)}\ \overset{\text{FFA}}{\rightarrow}\ \mathbf{z}_{f}^{(i)}\ \overset{\text{WT-CVDT}}{\rightarrow}\ \hat{g}^{(i)} \tag{9}\]
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 3 System Modeling and Problem Description  
System models for dynamic edge-server placement include network, coverage, delay, and cost models. In detail, a network model considers the network connectivity between the mobile users and edge servers, a coverage model considers the edge-server service range and coverage capability, a latency model focuses on the communication latency between the edge servers and users, and a cost model considers the fixed and mobile edge-server costs. These four models are introduced below.
## 1 Introduction
In [22], the authors assessed the validity of the Shannon-Hartley equation when altering a single parameter--the spreading factor (\(\alpha\))--and its impact on G3-PLC system performance. In contrast, this paper aims to investigate how variations in the modulating signal profile and the modulating signal sampling frequency affect the G3-PLC system performance using the same equation. The paper is as follows: Section 2 illustrates the SS-modulation technique theory, the variable that can be controlled to create the randomized pattern. Section 3 explains the PLC system types, components, and operation. Sections 4 and 5 provide the experimental setup and results, respectively, while Section 6 summarizes the work performed, concludes, and discusses the future work.
## 3 Establishment of CPPS Real-Time Hardware-in-the-Loop Co-Simulation Platform
### Modeling and Simulation of Control Center  
The co-simulation platform consisted of several independent simulation entities, which demanded different requirements in terms of of continuity, step accuracy, and driving mode. To make the information from one simulator compatible with that of another, it was necessary to apply a time synchronization strategy to ensure a unified simulation clock. For the hardware-in-the-loop simulation, the powerand information devices were simulated by software to ensure the simulation speed and accessibility, while control devices were simulated by real-word devices to improve the simulation authenticity.  
The physical control center was responsible for inhibiting the accident diffusion after fault. It was composed of the master station and the stability control devices. The master station was based on the ARM processor (developed by Acorn Co., Ltd., Cambridge, UK) running on a Linux system and developed in C language, which possesses extendibility and real-time performance. The master station is responsible for receiving the status information from the power grid and the control instructions from the stability control device, and dealing with the data processing, situational awareness, and synchronous interactive control. The master station kept monitoring the grid operation status through measurement units. After the fault, the master station compared fault type and location with the preset strategy table, selected the optimal strategy and sent the commands to the stability control devices. The master station consisted of four modules: (1) the protocol analysis module used for parsing the data packets from the measurement units and control devices; (2) the database module used for storing the real-time state of power grid; (3) the fault detection module used for detecting whether the system was faulty; and (4) the control module used for generating optimal control strategies after the fault. The stability control device was developed based on an embedded Linux system, which was responsible for receiving and implementing control commands from the master station into practice, and maintained the operation stability in the power system simulation. Therefore, the coordinated and consolidated control of distributed simulators could be reached.
## 6 Conclusions and Future Work  
The EU has among its 2020 policies a Common Agricultural Policy to ensure food availability for the population of the EU. Precision Agriculture (PA) is encompassed in these policies, as it aids in optimizing the crops and facilitating crop management using technology. Sensor nodes and drones are some of the technologies that can be utilized in PA to monitor the crops in real time. In this paper, we have considered the use of remote sensing drones as mobile gateways to provide a guide to determine the best drone parameters for successful data transmission between sensor nodes and the gateway in PA systems to monitor and manage crops. The drone gathers the data from the sensor nodes by flying over them and connecting wirelessly to them using WiFi.  
We have defined some parameters such as the _fh_ or the _fv_ as fixed parameters, while others such as the antenna (or _dc_) might be adapted to reach the connectivity. Different scenarios were simulated to determine the optimal drone parameters for the drone to be able to gather the data from the sensor nodes. The results indicate that for the highest node density, there is only one case in which the drone can be used when the drone has a flying height of 24 m or lower and velocities equal or lower to 1 m/s, and we include an antenna with a coverage of 25 m. For the rest of the node densities, we can find several combinations that ensure that the drone can be efficiently used as a mobile gateway. Nonetheless, in maximum flying velocity, no connectivity is achieved for the medium node density.  
For future work, we will perform real tests with different nodes, different wireless technologies such as ZigBee, and different soil node settings. We will also evaluate the performance of remote sensing drones utilized as mobile gateways with the implementation of fault-tolerance and energy-saving algorithms for its use in smart irrigation systems, as it is detailed in [52]. In addition, we will measure how the extra weight due to the node and antenna affects the flying capabilities of different drones.  
Methodology, L.G., L.P., J.M.J.; formal analysis, L.G.; investigation, L.G., L.P., J.M.J.; resources, P.V.M.; writing--original draft preparation, L.G., L.P., J.M.J.; writing--review and editing, J.L., P.L.; supervision, J.L., P.L.; project administration, J.L., P.V.M., P.L.; funding acquisition, J.L., P.V.M., P.L. All authors have read and agreed to the published version of the manuscript.  
This work is partially founded by the European Union with the \"Fondo Europeo Agricola de Desarrollo Rural (FEADER)--European inviter en zonas rurales\", the MAPAMA, and Comunidad de Madrid with the IMIDRA, under the mark of the PDR-CM 2014-2020\" project number PDR18-XEROCESPED, by the European Union through the ERANETMED (European Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR, and by Conselleria de Educacion, Culture y Depotre with the Subvenciones para la contratacion de personal investigador en fase postdoctoral, grant number APOSTD/2019/04.  
The authors declare no conflict of interest.
# Rotation of Liquid Metal Droplets Solely Driven by the Action of Magnetic Fields
###### Abstract  
The self-rotation of liquid metal droplets (LMDs) has garnered potential for numerous applications, such as chip cooling, fluid mixture, and robotics. However, the controllable self-rotation of LMDs utilizing magnetic fields is still underexplored. Here, we report a novel method to induce self-rotation of LMDs solely utilizing a rotating magnetic field. This is achieved by rotating a pair of permanent magnets around a LMD located at the magnetic field center. The LMD experiences Lorenz force generated by the relative motion between the droplet and the permanent magnets and can be rotated. Remarkably, unlike the actuation induced by electrochemistry, the rotational motion of the droplet induced by magnetic fields avoids the generation of gas bubbles and behaves smoothly and steadily. We investigate the main parameters that affect the self-rotational behaviors of LMDs and validate the theory of this approach. We further demonstrate the ability of accelerating cooling and a mixer enabled by the self-rotation of a LMD. We believe that the presented technique can be conveniently adapted by other systems after necessary modifications and enables new progress in microfluidics, microelectromechanical (MEMS) applications, and micro robotics.  
liquid metal; self-rotation; mixer; Lorentz force; EGaIn +
Footnote †: journal: Applied Physics Letters
## 6 Simulation and Test Results
### Parameter Measurement Tests
Using this data and employing the fitting calculation with the formula \(K_{V}=\omega/U_{m}\), the speed of the rotor motor is determined to be 100 rpm/V. From Figure 12b, it can be seen that at different voltage values, \(I_{0}\) remains approximately constant at 1.98 A. The comprehensive design parameters and measurement parameters of the FPTLIR are finally obtained as listed in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Chord length \(c(r)\) & 0.065–0.127r & m \\ Twist angle \(\theta(r)\) & 0.451–1.098r & rad \\ Air density \(\rho\) & 1.169 & kg/m\({}^{3}\) \\ Propeller blade number \(N\) & 1 & \\ Length of blade base\(R_{1}\) & 0.073 & m \\ Length of blade tip \(R_{2}\) & 0.342 & m \\ Linear parameter \(k\) & 5.081 & rad\({}^{-1}\) \\ Resistance coefficient \(c_{d}\) & 0.005 & \\ Motor resistance \(R_{m}\) & 0.2 & \(\Omega\) \\ Zero load current \(I_{0}\) & 1.98 & A \\ Motor velocity constant \(K_{V}\) & 100 & rpm/V \\ \multicolumn{2}{c}{Rotor location \(l\)} & 0.821 & m \\ \multicolumn{2}{c}{Mass \(m\)} & 38 & kg \\ Horizontal equivalent plate area \(S_{HE}\) & 0.157 & m\({}^{2}\) \\ Vertical equivalent plate area \(S_{VE}\) & 0.444 & m\({}^{2}\) \\ Moment of inertia \(x\)\(I_{x}\) & 5.8 & kg\(\cdot\)m\({}^{2}\) \\ Moment of inertia \(y\)\(I_{y}\) & 5.8 & kg\(\cdot\)m\({}^{2}\) \\ Moment of inertia \(z\)\(I_{z}\) & 8.5 & kg\(\cdot\)m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Design parameters and measurement parameters of FPTLIR. Figure 12: Collection data of the test platform. (**a**) Rotor motor with load test bench data diagram; (**b**) test bench data diagram of rotor motor without load.
## 6 Experiments and Results
### Discussion  
In summary, reduction with clustering, oversampling, and classification stages were tested for the IoTID20 dataset with a selected values of oversampling ratio and \(k\) for \(k\)-means clustering technique using SLFN classifier and SVM-SMOTE oversampling technique.  
The reduction with clustering stage produced an undersampled but representative dataset having the same distribution of normal and intrusion activities. The SLFN classification technique and \(k\)-means clustering with \(k\) value of 3 generated better performance than other classification techniques and other values of \(k\) by having the highest value of GM. In addition, the performance of the SVM-SMOTE with a value of 0.9 for the over  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Technique** & **ACC** & **PREC\({}_{\text{I}}\)** & **PREC\({}_{\text{N}}\)** & **REC\({}_{\text{I}}\)** & **REC\({}_{\text{N}}\)** & **GM** \\ \hline SLFN-SVM-SMOTE (\({}_{\text{r = 0.9, k = 3}}\)) & 0.9351 & 0.5211 & **0.9969** & **0.9578** & 0.9335 & **0.9453** \\ SVM & 0.9713 & 0.9179 & 0.9737 & 0.6066 & 0.9963 & 0.7774 \\ SGD & 0.9668 & 0.8760 & 0.9707 & 0.5619 & 0.9946 & 0.7475 \\ LR & 0.9572 & 0.8094 & 0.9625 & 0.4350 & 0.9930 & 0.6573 \\ SLFN & **0.9842** & **0.9879** & 0.9841 & 0.7637 & **0.9994** & 0.8736 \\ NB & 0.3199 & 0.0862 & 1.0000 & 1.0000 & 0.2733 & 0.5228 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of SLFN-SVM-SMOTE (\({}_{\text{r = 0.9, k = 3}}\)) with basic classifiers.  
Figure 9: The box plot for the GM value for different oversampling techniques.  
sampling ratio gave better results than other oversampling techniques and other values of oversampling ratio. Finally, the proposed approach outperformed the other basic classifiers in terms of GM.  
The experiments were limited to the classification of the intrusion identification label, which could extended to include the category label and the subcategory label for the IoTID20 dataset. It also considered the specific distribution of the activities for the IoTID20 dataset and could be tested on different datasets having different distribution of activities. Other works in the future are expected to be observed for the IoTID20 dataset where a comparison and empirical evaluation can hold. In addition, an issue might arise if there is a significant difference between the sizes of the resulted clusters in the first stage (i.e., some clusters are very small compared to other clusters). In this case, the pattern of such instances will be underrepresented for the training algorithm and consequently lead to misclassification of similar instances.
## 3 Results and Discussion
### Textural and Chemical Characterisation of Activated Carbons
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Activated Carbon** & \(\mathbf{A_{BET}}\)/ & \(\mathbf{A_{d}}\)/ & \(\mathbf{V_{s}}\)/ & \(\mathbf{V_{DR}}\)/ & \(\mathbf{L_{0}}\)/ & & & & \\
**m\({}^{2}\) g\({}^{-1}\)** & \(\mathbf{m^{2}}\) g\({}^{-1}\) & \(\mathbf{cm^{3}}\) g\({}^{-1}\) & \(\mathbf{cm^{3}}\) g\({}^{-1}\) & \(\mathbf{nm}\) & & & & \\ \hline Norit 1240X & 975 & 60 & 0.43 & 0.23 & 1.04 & 84.4 & 0.19 & 0.49 & 9.73 \\ Norit 1240W & 971 & 38 & 0.43 & 0.22 & 1.11 & 86.7 & 0.07 & 0.54 & 10.49 \\ Panreac & 941 & 223 & 0.41 & 0.23 & 0.97 & 89.3 & 0.53 & 0.38 & 8.12 \\ Merck & 927 & 170 & 0.36 & 0.23 & 1.02 & 85.2 & 0.22 & 0.32 & 7.27 \\ Merck-NaOH-0.5 & 994 & 194 & 0.38 & 0.25 & 1.02 & 84.9 & 0.43 & 0.59 & 7.76 \\ Merck-NaOH-2.5 & 976 & 162 & 0.40 & 0.24 & 1.02 & 84.1 & 0.47 & 0.57 & 8.67 \\ Merck-NaOH-5 & 968 & 188 & 0.37 & 0.24 & 1.00 & – & – & – & 8.06 \\ Merck-HNO3-10 & 979 & 175 & 0.40 & 0.25 & 1.08 & 71.8 & 1.47 & 0.41 & 2.88 \\ Merck-HNO3-20 & 966 & 177 & 0.38 & 0.24 & 1.09 & 70.4 & – & – & 2.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical and textural characteristics, obtained by application of the \(\alpha_{\rm s}\), DR and BET methods, to N\({}_{2}\) adsorption isotherms, obtained on different ACs as received and after being submitted to acid or basic treatments. Figure 1: Nitrogen adsorption isotherms obtained, at 77 K, on the original ACs. (Adsorption branch— open symbols and desorption branch—close symbols).
# Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS-The Case of Bangkok
## 4 Methodology
### Step III: Scenario Analysis  
Scenario analysis consisted of two phases, LAP and VRPTW. The two phases of scenario analysis are further detailed as follows:  
* Phase I: LAP  
In the first phase, from the set of candidate locations, the potential locations were selected and assign retailers to selected DCs. These were formulated by minimizing the number of locations and maximize coverage. The location-allocation analysis in ArcGIS network analyst tools starts by generating an origin-destination matrix by calculating the shortest-path costs. Then, it builds an edited version of the cost matrix, which can be obtained by generating a set semi randomized initial solutions, and it applies a vertex substitution heuristic to refine these solutions for creating a group of good solutions [35]. A metaheuristic combines the group of good solutions to create better solutions [35]. Finally, the metaheuristic returns the best solution with the current one when there is no additional significant improvement [35]. Therefore, the combination of an edited matrix, semi randomized initial solutions, a vertex substitution heuristic, and a refining metaheuristic can quickly yield the near-optimal results [35].  
Moreover, both time and distance were specified as an impedance cutoff. The analysis assigned 141 delivery points (retailers) to candidate DCs. Moreover, the capacity of selected DCs was calculated to compare the balancing capacity of selected DCs for each scenario. Figure 7 shows an example of LAP in scenario 5 by maximizing coverage facility.  
* Phase II: VRPTW  
In the second phase, the vehicle routing problem was solved using the locations of selected DC from phase I: LAP. The optimal vehicle routing was also obtained using ArcGIS network analyst tools  
Figure 7: Examples of location–allocation problem (LAP) in ArcGIS software (scenario 5).  
based on a tabu search metaheuristic. It is designed for getting a solution very close to a global optimum in a general optimization problem [36]. The principle of a metaheuristic is to explore the search space composed of all feasible solutions in order to achieve the optimal solution that minimizes the objective function [37]. The tabu search is concerned with finding new and more effective ways of taking advantage of the mechanisms associated with both adaptive memory and responsive exploration [38].  
In addition, both distance and time were used as impedances for all scenarios. The analysis focused on the vehicle routing from DC to the retailer and from wholesaler to retailer, the number of required trucks, total travel time, and total travel distance. In order to minimize total cost, we aimed to minimize the number of required trucks, total travel time, and total distance. In this research, all possible scenarios were implemented using ArcGIS; for example, Figure 8 shows the results of VRP in scenario 5, which optimized the route by using VRPTW.