esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structure of the Grid-Connected PV System  
Figure 1 shows a designed topology for a PV array. The topology comprises five interconnected branches: PV panel, MPPT controllers with PI controllers, a DC/DC boost converter, a 1-\(\varphi\) DC/AC bridge inverter and a low-pass filter (LCL), and a utility grid.  
A continuous mode boost converter and the inverter are designed to increase the DC output voltage and PV system integration with the grid network respectively. The LCL filter is connected between the output of the inverter and grid system by introducing a bulk filter capacitor to reduce ripple components of the grid parameters.  
The proposed MPPT controller is used to track GMPP and generate constant DC-bias voltage at the output of the boost converter. A phase-locked loop (PLL) is used in the VSI control system to synchronize the phase of the grid voltage and current with the measured inverter waveforms. The PLL controls the measured grid voltage by converting its fixed reference frame (\(\alpha\beta\)-components) into a synchronous reference frame (dq-components).  
A PR controller is selected over the PI controller to completely remove the SSE from the grid current and generate the smooth sine pulse with modulation (SPWM) pulses. The use of infinite gain in the PR controller assures high efficiency because it overcomes disturbances and realizes sinusoidal reference waveforms.
# Application of MMC-RPC in High-Speed Railway Traction Power Supply System Based on Energy Storage
## 5 Simulation Analysis
### Verification of Management Effect  
On the simulation platform, traction loads are respectively 15 MW on the left arm and 1 MW on the right arm of the traction network, MMC-RPC is put into operation in 0.6 s to verify the power quality management effect of the MMC-RPC system.  
Figures 16 and 17 show the power quality management ability of the MMC-RPC system in the traction power supply system under improved VSG control; it can be seen from Figure 17 that the three-phase current on the power grid side is unbalanced. After the MMC-RPC system transfers active power and compensates for reactive power, the current amplitude at the primary side of the traction substation gradually stabilizes at 80 A; At 0.75 s, the current changes with the load power and returns to the original value. The unbalance degree of the three-phase current is 0, and the current is gradually balanced. And the parameters of traction network are shown in Table 1.  
Figure 17 shows the current of the traction power supply arm. Due to the unbalance of locomotive load, the current unbalance on both sides is large. The peak current of the left power supply arm is 900 A, and the peak current of the right power supply arm is 50 A. After MMC-RPC is put into operation, the load current of the left and right power supply arms is balanced, and the amplitude is stable at 500 A. The current balance on both sides is realized, and the negative sequence current is eliminated.  
Figure 18 shows the active and reactive power compensated by MMC-RPC under improved VSG control. After being put into use in 0.6 s, MMC-RPC realizes power transmission stably and quickly, and reaches the expected power in 0.65 s; it can be seen from the figure that the power fluctuation of the system is very small.  
Figure 19 shows the harmonic THD value of the MMC-RPC output current; it can be seen from the figure that the current has a very small harmonic content. Compared with traditional RPC, MMC-RPC reduces the cost without additional filters.  
According to Figures 16-19, ES type MMC-RPC achieves a good power quality management effect, and has small harmonic components.  
\begin{table}
\begin{tabular}{c c} \hline
**Traction Network Parameters** & **Parameter Values** \\ \hline Rated voltage of power supply arm & 27.5 kV \\ Rated frequency of power supply arm & 50 Hz \\ Line impedance per unit length [30] & 0.215 + \(\mathrm{j}\)0.516 \(\mathrm{\Omega/km}\) \\ Locomotive load power (heavy load) & 15 MW \\ Locomotive load power (light load) & 1 MW \\ \hline \end{tabular}
\end{table}
Table 1: Traction network parameters.  
Figure 18: Output power under improved VSG control.
## 5 Evaluation and Results
### Comparative Assessment  
**RBAC vs. ABAC:** Traditional role-based access control models lack flexibility in handling dynamic, attribute-driven conditions. In contrast, the attribute-based access control model implemented here enabled nuanced decisions based on user attributes like clearance level and organizational affiliation. Experiments showed ABAC achieved a lower false grant rate compared to a simulated RBAC baseline.  
**Prior Work Comparison:** Previous blockchain-based ABAC models--for example, [PERSON] et al. [43]--report latencies ranging from 50 to 100 milliseconds, primarily within IoT contexts. In comparison, our system achieved an average latency of approximately 39 milliseconds while supporting cryptographically verifiable, multi-attribute policies specifically tailored to mining governance.
## 1 Introduction
Other studies have derived closed-form solutions for estimation errors [20], developed closed-form estimators for hybrid measurements (i.e., combining AOA with other techniques), and explored the simultaneous localization of multiple emitters [21]. Recent mathematical analyses, driven by the availability of different GNSS systems, have focused on determining how many satellites minimize the DOP for GNSS systems [22; 23], reducing computational costs [24], and developing approximate solutions using artificial intelligence in non-line-of-sight conditions [25]. The primary aim of this paper is to present a mathematical optimization model for the 3D spatial distribution of clusters of satellites in LEO, where the line of sight (LOS) constitutes a strong constraint. Previous investigations by the authors on this topic were presented in [26], where properties of symmetry and anti-symmetry related to the right ascension of the ascending node, the true anomaly, and the spacing angle of optimal deployments were predicted in simplified Earth-scanning mission scenarios. In this study, a theoretical model of constrained optimization was derived to account for an arbitrary number of satellites in LEO, capable of performing AOA measurements in case of target pointing and considering an emitter of signal on the surface of the Earth. An alternative formulation of the problem in terms of differences is introduced, simulated, and analyzed for a two-satellite configuration, incorporating variations in orbit radius and emitter latitude. The motivation behind this study is related to a set of operative problems on the update of an existing constellation. In this context, the developed framework considers the operative example, which consists of the optimal placement of a second companion satellite after the first one is launched. These activities are of interest for a set of new technological demonstrators based on CubeSats platforms and the miniaturization of arrays of antennas. One example of these missions is the SAILS (Spaceborne Autonomous Identification and Localization System) project supervised and funded by the Italian Space Agency, in which the Polytechnic University of Bari is collaborating as a scientific partner [27]. Section 2 describes the mathematical model of the spatial representation of the satellites and the signal emitter, along with the measurement and orbital models, and outlines the general optimization problem. Section 3 presents the optimization problem for two satellites, its alternative formulation in terms of differences, the main results of the simulations, and their analysis. Section 4 includes the conclusions of this study. Appendix A presents a glossary for the mathematical notation used in this paper.
# Design and Implementation of a System-on-Chip for Self-Calibration of an Angular Position Sensor
## 5 Conclusions  
Self-calibration of angle position sensors is a succinct method in practice. This paper designed a SoC with a calibration coprocessor in it. The SoC is capable of signal processing and transmission at the circuit level and has the ability to implement information fusion algorithms or other secondary development work. The coprocessor integrates a signal demodulation circuit that performs signal component detection using only addition and multiplication operations. On this basis, signal calibration and angle calculation modules are also integrated into the circuit. The entire system was implemented on FPGA and has processed the output signal of the CAPS. In summary, the proposed scheme is based on the design flow of digital integrated circuits, and studies simple but effective algorithms to implement hardware acceleration, and provides a SoC to improve the flexibility of the entire solution.  
The calibration algorithm was verified and analyzed using MATLAB. The RTL simulation results of the coprocessor showed that the maximum value of the absolute angle error can be reduced to 0.04%. FPGA-based experiments also confirmed that the SoC can reduce the maximum value of the absolute angle error to 4.36%. Furthermore, results showed that for sensor data collected in the experiments, the peak-to-peak error value was reduced to 3.96% and 4.31% under the SoC demodulation and calibration mode, respectively. Compared with the results of the previous work [9], the proposed scheme can better suppress the angular error.  
Accuracy improvement of the calculation modules in the circuit is one example of performance optimization. Optimizing the resource overhead and speed of the modules is also a research direction to improve the performance of the entire system. In addition, the implementation of digital chips for MEMS sensors will also be a focus of future research.  
Figure 19: Amplitude spectrum of two signals. (**a**) Amplitude spectrum of the sine channel; (**b**) amplitude spectrum of the cosine channel.  
**Author Contributions:** [PERSON] and [PERSON] designed the sensor. [PERSON] designed the proposed method and performed the experiments. [PERSON] and [PERSON] collected the experiment data. [PERSON] analyzed the experiment results. [PERSON] collected literature and evaluated design options. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 41871245 and the National High Technology Research and Development Program of China, grant number 2013 AA014103.  
**Acknowledgments:** The authors would like to thank [PERSON] for proofreading and [PERSON] for discussion.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# LLM-Based Cyberattack Detection Using Network Flow Statistics
## 2 Materials and Methods
### Training the Cyberattack Detection System
#### 2.3.3 Undersampling
Table 6 contains a similar analysis but classifies the rows as benign and malignant. After cleaning, the dataset had 13,352,392 benign rows and 2,352,951 malignant rows, which corresponds to 85.02% and 14.98%, respectively. At this point, the dataset had a pronounced imbalance. Hence, after applying the aforementioned undersampling of 20% benign rows of data, the proportions approached 53.16% benign network flows, with 46.84% being malignant. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of Attacks** & **Initial** & **After** & **Removed** & **Removed** \\
**Attacks** & **Rows** & **Cleaning** & **Rows** & **Proportion** \\ \hline Benign & **13,484,708** & **13,352,392** & **132,316** & **0.98\%** \\ Malignant & **2,748,235** & **2,352,951** & **395,284** & **14.38\%** \\ \hline  & 16,232,943 & 15,705,343 & 527,600 & 3.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of the data cleaning process grouped by benign/malignant. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Types of Attacks**} & \multicolumn{2}{c}{**After Cleaning**} & \multicolumn{2}{c}{**Undersampling 20\% Benign**} \\ \cline{2-5}  & **Rows** & **Prop.** & **Rows** & **Prop.** \\ \hline Benign & 13,352,392 & 85.02\% & 2,670,478 & 53.16\% \\ DDoS attacks-LOIC-HTTP & 576,175 & 3.67\% & 576,175 & 11.47\% \\ DDOS attack-HOIC & 668,461 & 4.26\% & 668,461 & 13.31\% \\ DoS attacks-Hulk & 434,873 & 2.77\% & 434,873 & 8.66\% \\ Bot & 282,310 & 1.80\% & 282,310 & 5.62\% \\ Infiltration & 160,604 & 1.02\% & 160,604 & 3.20\% \\ SSH-Bruteforce & 117,322 & 0.75\% & 117,322 & 2.34\% \\ DoS attacks-GoldenEye & 41,455 & 0.26\% & 41,455 & 0.83\% \\ DoS attacks-Slowloris & 10,285 & 0.07\% & 10,285 & 0.20\% \\ DDOS attack-LOIC-UDP & 1730 & 0.01\% & 1730 & 0.03\% \\ Brute Force -Web & 611 & 0.004\% & 611 & 0.01\% \\ Brute Force -XSS & 230 & 0.001\% & 230 & 0.005\% \\ SQL Injection & 87 & 0.001\% & 87 & 0.002\% \\ DoS attacks-SlowHTTPTest & 19,462 & 0.12\% & 19,462 & 0.39\% \\ FTP-BruteForce & 39,346 & 0.25\% & 39,346 & 0.78\% \\ \hline  & 15,705,343 & \multicolumn{2}{c}{5,023,429} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Dataset statistics by type of attack after applying undersampling. The proportions resulting from the imbalance of the selected dataset are specified in Tables 5 and 6. In addition to being useful for analyzing the distribution of data for each type of attack, these values are also useful to study the results, as well as to calculate the weighted metrics. It is worth highlighting that, in terms of rows per label, the dataset was still imbalanced, but slightly less than before, while in terms of proportions between benign and malignant network flows, the dataset was now balanced.
## Abbreviations  
### Nomenclature  
\(\varphi\) energy fraction of hydrogen  
Q\({}_{\text{H2}}\) the energy of hydrogen (J)  
Q\({}_{\text{gasoline}}\) the energy of gasoline (J)  
**Abbreviation**  
SI Spark Ignition  
HMD Hydrogen Mixture Distribution  
SHMD Stratified Hydrogen Mixture Distribution  
PHMD Premixed Hydrogen Mixture Distribution  
PPHMD Partially Premixed Hydrogen Mixture Distribution  
NHMD No Hydrogen Mixture Distribution  
CO Carbon Monoxide  
HC Hydrocarbon  
NOx Nitrogen Oxides  
RCCI Reactivity Controlled Compression Ignition  
COV Coefficient of Variation  
HDI Hydrogen Direct Injection  
SHDI Split Hydrogen Direct Injection  
EGR Exhaust Gas Recirculation  
CA Crank Angle  
BTDC before Top Dead Center
## 3 Results and Discussion
### Influence of Climate on NPP

Table 2: The statistical results of the second-order partial correlation between NPP, precipitation, and LST. while areas with a negative correlation accounted for 4.47%. In general, the NPP in southeastern Tibet was positively correlated with temperature, while NPP was negatively correlated with precipitation. This is because of the abundance of precipitation in the area and the influence of elevation factors, the LST of the Tibetan Plateau is lower all the time; the lower surface temperature is the main factor that limits the vegetation growth. However, the occurrence of precipitation events increases the total cloud cover and soil moisture, leading to a decrease in sunshine hours and solar radiation and decrease in surface temperature, further aggravating the inhibiting effect of low temperature on NPP in the abovementioned regions. Similarly, under the background of climate change, the continuous rise of LST will directly affect the extension of crop growing season. This has a positive co-advancement effect on NPP. This is consistent with previous studies that found a greater influence of surface temperature on NPP than that of precipitation in this region [51; 52]. Remote sensing can objectively reflect the influence of natural conditions and human factors on vegetation growth. In future research, the influence of climate factors and human activities on regional NPP should be studied individually to analyze the internal driving mechanism of NPP evolution from a more detailed perspective.
## 2 Wettability Measurement Methods
### Methods Based on Contact Angle Measurement
#### 2.2.2 Contact Angle Measurement Using micro-CT  
With the help of microcomputed tomography (micro-CT), it is possible to obtain a three-dimensional visualization of rocks, which makes it possible to extract information about oil and water distribution in pore structures, as well as pore distribution, interfacial tension, and absolute permeability [18]. In order to correct the mapping of pore space, filtration and segmentation operations are carried out in which water and oil saturation are used. Additionally, wettability is determined through contact angle, as shown in Figure 5.  
When scanning a sample with micro-CT, the integrity of the rock is not destroyed, which is an advantage. Limitations of this method are its accuracy, which depends on the resolution of micro-CT; errors during data segmentation; heterogeneity of wettability measured at different points of the sample; and its high cost [18; 19].  
Figure 4: Schematic of surface wetting angles \(\Theta\). \(\gamma_{SO}\), \(\gamma_{OW}\) and \(\gamma_{SW}\) are surface energies between surface and oil, oil and water, and surface and water, respectively. Here (**a**) the surface is water-wet, (**b**) intermediate-wet, (**c**) strongly oil-wet [15].  
Figure 5: Measured wetting angles on the processed image from micro-CT. Angles were measured through the non-wetting phase (s\(\mathrm{cCO_{2}}\)) and are shown by the pink arc. Angles indicated are additions measured through the wetting gray phase: (**A**) 53\({}^{\circ}\); (**B**) 42\({}^{\circ}\)[20].  
\begin{table}
\begin{tabular}{c c} \hline
**Type** & **Contact Angle (deg.)** \\ \hline water-wet & 0–80 \\ \hline intermediate-wet & 80–100 \\ \hline oil-wet & 100–160 \\ \hline strongly oil-wet & 160–180 \\ \hline \end{tabular}
\end{table}
Table 3: Dependence of wettability estimation on contact angle value [17].
## 2 System Description  
An overview of our developed AIRGAIT system is shown in Figure 1. This system consists of a BWS, a variable speed treadmill and an assistive lower limb gait training which comprises a robotic orthosis, spring, and parallel linkage. The spring and linkage are fixed in a sagittal plane so that the gait motion training at hip and knee joints can be realized. This research only focuses on improving the performance of the lower-limb robotic orthosis system.  
The structure of the developed lower-limb robotic orthosis is shown in Figure 2a. The robotic gait training system covers the thigh at the lower end of the hip joint and shank at the lower end of the knee joint. It is actuated by PAMs in antagonistic configuration as illustrated in Figure 2b. The PAM used in this research is a self-made McKibben artificial muscle with 1.0 inch diameter. Similar to human muscle, this PAM is able to reach a maximum contraction of 30% from the complete deflation length. The detailed parameters of PAMs are provided in Table 1. Proportional electric control valves ITV2000/3000 of SMC company are used to regulate the pressure of PAMs. The angle of knee and hip joints are measured by contactless Hall-IC named CP-20H of Midori Precisions. To implement the control algorithm, a CompactRIO platform developed by National Instrument is employed. It consists of a real-time processor for communication and signal processing, a field programmable gate array (FPGA) to run high-speed control. Besides, the sensors and control valves can be connected directly to the CompactRIO via industrial plug-in analog and digital inputs/outputs. The control algorithm is implemented and compiled by Labview software first and downloaded to CompactRIO for real-time control after that.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Actuators**} & \multicolumn{2}{c}{**Nominal Length [cm]**} & \multicolumn{2}{c}{**Nominal Pressure [\(\times\)100 kPa]**} \\ \cline{2-5}  & **Anterior** & **Posterior** & **Anterior** & **Posterior** \\ \hline Knee mono-articular & 40 & 40 & 3.5 & 1.0 \\ Hip mono-articular & 55 & 55 & 2.0 & 2.0 \\ Bi-articular & 65 & 65 & 3.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The nominal length and nominal pressure (\(P_{0}\)) of each pneumatic artificial muscles (PAMs).  
Figure 1: The AIRGAIT system.  
Figure 2: (a) The developed lower-limb robotic orthosis; (b) A typical antagonistic configuration.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 3 Materials and Methods
### Data Preprocessing  
The performance of the proposed models depends significantly on effective data preprocessing. Given the linguistic complexity of Arabic and differing model architectures, two preprocessing strategies were employed: character-level encoding for deep learning (DL) models and tokenization for transformer-based models. In both approaches, label encoding was applied to the target categories.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 3 Measurement Monitoring Techniques for Multipath Mitigation
### Multipath Detection for Excluding or De-Weighing Affected Measurements
This means that the C/N\({}_{0}\)-based detection metrics will be more sensitive to short-delay multipath, which imposes smaller errors on pseudoranges compared, for example, to a medium-delay multipath. In this regard, to cover a wider range of multipath scenarios, the C/N\({}_{0}\)-based detection metrics should be used along with other monitoring techniques that are more sensitive to medium and long-delay multipath (e.g., signal quality monitoring metrics defined at the receiver tracking stage [6; 24]). Moreover, for a given signal modulation and reflection path delay, the C/N\({}_{0}\) value will be more affected for lowerchipping-rate signals such as GPS L1 C/A and Galileo E1 than for high chipping-rate ones, such as GPS L5 and Galileo E5 [29]. * Geometry-free (GF) monitoring metric for detection  
Multipath can also be detected through the difference between pseudoranges on two frequencies. This combination, denoted \(m_{GF,i}^{f1/2}(k)\), cancels the geometric part of the measurements, leaving the frequency-dependent effects (mainly ionospheric delay) besides multipath and measurement noise:  
\[m_{GF,i}^{f1/2}(k)=P_{i}^{f1}(k)-P_{i}^{f2}(k)-\Delta d_{ion}^{f1/2}(k), \tag{13}\]  
where \(\Delta d_{ion}^{f1/2}(k)\) is the difference in the ionospheric effects and can be estimated and removed either through modeling the use of a nearby reference receiver or time-averaging; the remainder can be used to monitor the code multipath. Similar to the DC/N\({}_{0}\)-based monitoring metric, the GF metric is a combination of errors on two frequencies and thus does not provide separate information about multipath on each frequency. It can thus be used only for satellite-by-satellite detection and exclusion (or de-weighting). The GF monitoring metric is directly proportional to code multipath errors and from this point of view outperforms C/N\({}_{0}\)-based detection metrics (including both single-frequency C/N\({}_{0}\) and DC/N\({}_{0}\)-based detection metrics) which are more sensitive to short-delay multipath with a small impact on code tracking errors. The main advantage of the GF metric is its capability to be combined after a CMC-based error correction. Multipath errors can be first reduced by applying CMC-based error corrections and then the GF detection metric can be formed by differencing corrected pseudoranges on two frequencies. This will be discussed in Section 5 to perform a complementary combination of the monitoring techniques.