esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Result Analysis and Discussion
### LVRT Enhancement by the Proposed Control Scheme under an Asymmetrical Fault
#### 5.3.2 Two-Line to Ground Fault (2 LG)  
The simulation results of the grid-connected SPV system with MPPT control are shown in Figure 13. Figure 13a shows the response of the MPPT control scheme when an asymmetrical two-line to ground (2 LG) fault (Phases 'a', 'b' to ground) occurs, with the voltage dropping to 0.21 pu from its nominal value (1.01 pu) for 150 ms, and therefore with voltage sag of around 20%. Figure 13b shows that the DCL voltage rises sharply to 1100 V, which trips the over-voltage protection circuitry. During a 2 LG fault, the active power can be delivered to the grid by the healthy phase, unlike during a 3 LG fault where no active power is injected into the grid. However, Figure 13c shows that the SPV system injects an average amount of 60 kW (i.e., 60% of the SPV rated power capacity) active power into the grid, which is not adequate under an unbalanced grid fault, since the grid point (PCC) voltage level is low. In this case, the inverter is overloaded (i.e., over-current). Figure 13d depicts that the SPV system injects less reactive power into the grid, which is not enough to support voltage recovery. Figure 14 shows the response of the proposed control scheme. DCL voltage is maintained at the maximum of 600 V, which has little oscillation, as shown in Figure 14a. Figure 14b illustrates that the SPV system output of active power has been effectively reduced to a null, and because of this the SPV inverter will not experience any severe over-current issues. At the same time, the SPV system injects an average amount of 200 kVAR of reactive power into the grid according to the GC in order to support voltage recovery, as shown in Figure 14c. In this case, the standard requires to provide 100% reactive current into the grid, it is shown in Figure 14d.  
Figure 12: Simulation results of proposed control scheme under an asymmetrical fault (1 LG). (a) DC-link voltage (DCL), (b) active power (P), (c) reactive power (Q), (d) reactive current (Iq).  
Figure 13: _Cont._Figure 13: Simulation results of the MPPT control scheme under an asymmetrical fault (2-LG). (**a**) Grid voltage at PCC, (**b**) DC-link voltage (DCL), (**c**) active power (P), (**d**) reactive power (Q).  
Figure 14: Simulation results of the proposed control scheme under an asymmetrical fault (2-LG). (**a**) DC-link voltage (DCL), (**b**) active power (P), (**c**) reactive power (Q), (**d**) reactive current (Iq).
## 3 Study on Rockburst Potential Assessment Based on Rock Mechanics Tests
### Assessment of Rockburst Potential Based on Rock Mechanics Test Results  
[PERSON] et al. [28] systematically generalized and summarized the evaluation indexes of rockburst potential and provided an understanding of the concept of rockburst potential. The evaluation of rockburst potential is mainly performed through the mechanical properties of the rock itself to assess the properties of the rock in a certain condition, which tends to occur to different degrees of the rockburst; i.e., this represents the internal cause of rockburst. In this study, the brittleness index and the maximum stored elastic strain energy index were used to assess the rockburst potential of the D\({}_{3}\)zg formation (dolomite) and C\({}_{1}\)d formation (limestone). The results of the assessment of rockburst potential of different stratigraphic rock properties are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Lithology** & **Sample Number** & **Diameter (mm)** & **Height (mm)** & **Peak Load (kN)** & **UTS (MPa)** \\ \hline \multirow{5}{*}{Limestone} & HY-6 & 50.02 & 24.81 & 9.83 & 5.04 \\  & HY-7 & 47.14 & 25.03 & 10.83 & 5.84 \\  & HY-8 & 49.61 & 25.02 & 10.88 & 5.58 \\  & HY-9 & 49.62 & 24.97 & 11.65 & 5.99 \\  & HY-10 & 48.62 & 24.98 & 7.84 & 4.11 \\  & Average & & & & 5.31 \\ \hline \end{tabular} Note: UTS indicates uniaxial tensile strength.  
\end{table}
Table 2: _Cont._  
Figure 6: Failure photos after partial uniaxial compression test.  
Figure 7: Failure photos after partial Brazilian split test.  
The results of the brittleness index criterion indicate that the D\({}_{3}\)zg formation (dolomite) tends to suffer from a moderate-level rockburst, and the C1d formation (limestone) tends to suffer from a slight-level rockburst. Predictions based on the maximum stored elastic strain energy index indicate that the D\({}_{3}\)zg formation (dolomite) tends to suffer from a slight rockburst, and the C1d formation (limestone) tends to suffer from a slight rockburst. Combined with the brittleness index criterion and the maximum stored elastic strain energy index results, the D\({}_{3}\)zg formation and the C1d formation have rockburst potential. However, the occurrence of rockburst must be evaluated in conjunction with the on-site geological conditions, stress states, construction factors, and other relevant parameters to achieve a comprehensive assessment.
## 1 Introduction
A first step to improve actual technology can be to monitor the performance of such systems and identify all the drawbacks of current technology. Recent studies explore photovoltaic technology integration into irrigation systems from distinct angles. Ref. [6] emphasizes economic viability and cost-effectiveness in arid areas. Ref. [10] focuses on intelligent algorithms to optimize energy efficiency and reliability. Ref. [11] addresses sizing optimization under irradiance uncertainty to enhance robustness. In comparison, our work uniquely uses high-resolution monitoring of transient conditions, enabling detailed diagnostics and improved photovoltaic pumping system resilience. Given the above, the current paper proposes a methodology for monitoring the performance of a PVPS through sensor measurements. The obtained data can be used in improving the actual technology by understanding how to properly operate and tune the key parameters of such a system to ensure a robust, durable, reliable, and highly efficient system. The current work begins with a short description of the PVPS, its main components and key parameters. The second part proposes a non-intrusive data logging and storing system for monitoring the performance of the PVPS. Finally, an analysis and discussion of the performance of the system based on sensor measurements are presented.
# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 3 A Brief Survey
\begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Paper** & **Search Method** & **Fitness Function** & **Means of Hybridization** \\ \hline P15 [78] & SCA-DE & \(f(x_{i})=\xi\times Err_{x_{i}}+(1-\xi)\times\)\(\left(1-\frac{|S|}{\mathcal{D}}\right)\), where \(Err_{x_{i}}\): LR’s error rate, \(|S|\): count of picked features, \(D\): total feature count and \(\xi\in[0,1]\). & The DE operators are applied as a local search strategy to help the SCA to avoid the local spot. \\ \hline P16 [79] & SA, WOA & \(Fitness=\alpha\gamma_{TR}(D)+\beta\times\left(\frac{|R|}{|N|}\right)\), where \(\gamma_{R}(D)\): CE, \(|\)R\(|\): length of the chosen substring, \(|\)N\(|\): length of whole feature set, \(\alpha\in[0,1]\) and \(\beta=(1-\alpha)\). \\ \hline P17 [6] & ABC, PSO & \(Fitness=\alpha\cdot\psi_{s}+\beta\frac{|\mathbb{N}|-|S|}{|N|}\), where \(\psi_{s}\): classifier performance with the subset \(S\), N: total attribute count, \(\beta\): size of the attribute subset and \(\alpha\): standard of the classification. & Each employed bee produces a new food source and exploits a good source. Each onlooker bee selects a supply based on the amount of its solution, creates a new food source and exploits the better one. PSO is used instead of scouting bees for the hunt for new sources after deciding the source to be left and assigning the employed bee as scout. \\ \hline P18 [80] & ACO, PSO & \(Fitness_{p}^{t}=\frac{1}{BER_{p}^{t}}\), where \(BER_{p}^{t}\): CE with the attribute vector chosen by particle \(p\) at the \(t^{th}\) iteration. & Here, both ACO and PSO are executed simultaneously by each individual. estimate the value of the chosen subset of each ant and particles by the classifier and pick the best one for the next generation. \\ \hline P19 [81] & MA with KHA & \(f_{\theta}=\omega\times E+(1-\omega)\frac{\Sigma\theta}{N}\), where \(f_{\theta}-\): fitness function with \(\theta\) number of features, N: total feature count, E: CE, \(\omega\): constant. & The suggested MAKHA hybrid technique employs foraging operation and physical random diffusion with crossover and mutation and uses someresult process and watch-jump process from the MA. \\ \hline P20 [9] & GA, SA & Percentage of recognition of the Bayesian classifier. & SA is used to select the chromosomes for the next generation. \\ \hline P21 [82] & GA-ACO & CA & In the proposed hybrid method, ACO utilizes the GA’s crossover and mutation strategy. This results in the exploration of ants near the optimum solution. The mechanism is again iterated after pheromone upgrading. \\ \hline P22 [83] & TS-BPSO & The KNN with LOOCV and SVM with OVR serves as estimators of the TS and BPSO objective functions. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# Reliability of a New Portable Dynamometer for Assessing Hip and Lower Limb Strength
## 4 Discussion
Specifically, knee extension strength has been repeatedly shown to be essential in monitoring the rehabilitation process following anterior cruciate ligament reconstruction [28; 29], while plantarlexor strength is associated with mid-portion Achilles tendinopathy [30]. The current study showed moderate to high intra- and inter-session reliability of testing strength of these muscle groups, suggesting that MuscleBoard(r) has the potential to be used in clinical settings related to knee and Achilles tendon injuries. Inter-session reliability was generally lower compared with intra-session reliability, with a higher number of measured parameters showing a statistically significant systematic bias. This was particularly evident for the NHE and HFLX tests. The reasons could be in participants' motor learning and task/measurement procedure familiarization, as they had already performed six repetitions before third measurement session started. It is, therefore, suggested that each participant perform one familiarization session before the first official measurement of these muscle groups with the MuscleBoard(r) device. Some limitations of the present study need to be acknowledged. The measurement for each muscle group was performed in only one body position. Different body positions could significantly influence produced peak torques or the reliability of the measurements. For example, HADD maximal strength can be evaluated with different hip and knee flexion angles, which could have a significant impact on peak HADD torques and peak muscle activity [31]. Further studies are needed to test the reliability of the MuscleBoard(r) dynamometer for different body positions within the same muscle group. Moreover, a certain degree of learning effect, which could have notably influenced the reliability between the sessions, cannot be ruled out. Hence, a more extensive familiarization (particularly for unilateral test) and, possibly, better stabilization with the arms is warranted when individuals or groups are being tested with a MuscleBoard(r) device for the first time.
# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 2 Related Works  
This paper presents an optimization of the original FullSubNet model and proposes the FullSubNet-pMix method. This method utilizes both the magnitude spectrum and the phase spectrum of the speech, allowing the information from these two spectra to interact with each other. The extracted information after interaction better reflects the characteristics of the noisy speech. Furthermore, hyperparameter optimization was performed on the model. The width of the sub-band frequency range was adjusted to achieve a more reasonable division of frequency bands. Additionally, the value of the Lookahead hyperparameter was changed to enable the model to consider future information more appropriately. Experimental results demonstrate that the proposed model outperforms the original model in terms of speech enhancement on the same training and test sets. Through ablation experiments, it was found that the respective improvements independently contribute to the enhancement of the model's speech enhancement performance.  
To achieve the capabilities described in the speech enhancement model, two key points need to be addressed: firstly, to obtain more valuable information during the pre-processing of speech before training; secondly, to effectively and fully utilize the acquired information during the model training process.  
In speech enhancement, the valuable information primarily includes the magnitude spectrum, phase spectrum, real spectrum, and imaginary spectrum of the speech. The real spectrum and imaginary spectrum contain both magnitude and phase information. In recent years, various approaches have been proposed to better utilize the extracted information. The PACDNN model [12] enhances both the magnitude spectrum and phase spectrum simultaneously. The CycleGan model [13] applies 2D convolutional processing to the magnitude spectrum and phase spectrum of the speech. One model [14] leverages the real spectrum and imaginary spectrum of the audio and utilizes encoder layers to reduce information loss during the training process. Another model [15] for speech enhancement directly utilizes deep complex-valued networks on the STFT representation, incorporating the phase information of the input speech. Some models [16; 17] takes the real and imaginary spectrum of the speech signal as input, which contain the phase information of the signal. The FullSubNet+ model [18] takes the magnitude spectrum, real spectrogram, and imaginary spectrogram of the speech signal as input, where the latter two spectrograms contain the phase information of the signal.
# A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine
## 2 Materials and Methods
### The Kernel Based Extreme Learning Machine  
#### 2.3.1 Extreme Learning Machine Overview  
A typical single implicit layer feedforward neural network structure is shown in Figure 3 [52], consisting of an input layer, an implicit layer and an output layer, with the input layer fully connected to the implicit layer and the implicit layer to the output layer neurons. The input layer has \(n\) neurons corresponding to \(n\) input variables, the hidden layer has \(l\) neurons, and the output layer has \(m\) neurons corresponding to \(m\) output variables. For the sake of generality, let the connection weights \(\omega\) between the input and hidden layers be:  
\[\omega=\begin{bmatrix}\omega_{11}&\omega_{12}&...&\omega_{1n}\\ \omega_{21}&\omega_{22}&...&\omega_{2n}\\...&...&...&...\\ \omega_{l1}&\omega_{l2}&...&\omega_{ln}\end{bmatrix} \tag{16}\]  
where \(\omega\) denotes the connection weight between the \(i\)th neuron in the input layer and the \(j\)th neuron in the hidden layer.  
Let the connection weight between the implicit layer and the output layer be \(\beta\) :  
\[\beta=\begin{bmatrix}\beta_{11}&\beta_{12}&...&\beta_{1n}\\ \beta_{21}&\beta_{22}&...&\beta_{2n}\\...&...&...&...\\ \beta_{l1}&\beta_{l2}&...&\beta_{ln}\end{bmatrix} \tag{17}\]  
where self \(\beta_{jk}\) denotes the connection weights between the \(j\)th neuron in the hidden layer and the \(k\)th neuron in the output layer.  
Let the threshold value \(b\) of the neuron in the hidden layer be  
\[b=\begin{bmatrix}b_{1}\\ b_{2}\\...\\ b_{l}\end{bmatrix} \tag{18}\]  
Let the input matrix \(X\) and output matrix \(Y\) of the training set with \(Q\) samples be  
\[X=\begin{bmatrix}x_{11}&x_{12}&...&x_{1n}\\ x_{21}&x_{22}&...&x_{2n}\\...&...&...&...\\ x_{n1}&x_{n2}&...&x_{nQ}\end{bmatrix} \tag{19}\]\[Y=\begin{bmatrix}y_{11}&y_{12}&...&y_{1n}\\ y_{21}&y_{22}&...&y_{2n}\\...&...&...&...\\ y_{m1}&y_{m2}&...&y_{mQ}\end{bmatrix} \tag{20}\]  
Let the activation function of the neurons in the hidden layer be \(g(x)\), then from Figure 1, the output \(T\) of the network is:  
\[T=\begin{bmatrix}t_{1}&t_{2}&...&t_{Q}\end{bmatrix}_{n\times Q} \tag{21}\]  
\[t_{j}=\begin{bmatrix}t_{1j}\\ t_{2j}\\...\\ t_{mj}\end{bmatrix}=\begin{bmatrix}\sum_{i=1}^{t}\beta_{i1}g(\omega_{i}x_{j}+b _{i})\\ \sum_{i=1}^{t}\beta_{i2}g(\omega_{i}x_{j}+b_{i})\\...\\ \sum_{i=1}^{t}\beta_{iim}g(\omega_{i}x_{j}+b_{i})\end{bmatrix}_{m\times 1} \tag{22}\]
# TinyGS vs. SatNOGS: A Comparative Analysis of Open-Source Satellite Ground Station Networks
## 5 Discussion
### Comparison of Costs
However, the use of the g5500-ardushield (compatible with Arduino Uno) developed by SatNOGS [25] provides a cost-saving option to control the Yaesu G-550, avoiding the purchase of relatively expensive controllers like the GS-232B. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Type** & **Approx. Price (EUR)** \\ \hline Raspberry & Raspberry Pi 3B & 40 \\ \hline SD card & Sandisk 32 GB, C10 & 12 \\ \hline SDR & RTL-SDR & 35 \\ \hline Filter & Broadcast FM block filter & 16 \\ \hline LNA & SPF51892 LNA (by & 21 \\  & RTL-SDR.com) & \\ \hline Rotator and Controller & SatNOGS rotator v3 and & 500 \\  & SatNOGS controller & 25 \\ \hline Diplexer & SatNOGS diplexer & 25 \\ \hline Antenna & Wimo X-Quad (432 MHz) & 235 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Costs of the different components in the construction of a reference rotator SatNOGS GS. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Type** & **Approx. Price (EUR)** \\ \hline LoRa module & TIGO LoRa32 V2 (433 MHz) & 27 \\ \hline LNA and filter & LNA and SAW Bandpass filter & \\  & Uputronics (435 MHz) & 52 \\ \hline Antenna & HYS Antenna Omni & \\  & (433 MHz) & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Costs of the different components in the construction of a reference TinyGS GS. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Type** & **Approx. Price (EUR)** \\ \hline Raspberry & Raspberry Pi 3B & 40 \\ \hline SD card & Sandisk 32 GB, C10 & 12 \\ \hline SDR & RTL-SDR v3 & 35 \\ \hline Filter & Broadcast FM Block Filter & 16 \\ \hline LNA & SPF51892 LNA (by & 21 \\  & RTL-SDR.com) & \\ \hline Antenna & HYS Antenna Omni & 40 \\  & (433 MHz) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Costs of the different components in the construction of a reference fixed SatNOGS GS. Finally, the costs associated with constructing a TinyGS ground station and a fixed SatNOGS ground station are quite affordable for the average enthusiast and/or other entities. However, these costs can be further mitigated through methods such as building the antenna oneself and opting for more economical components, among other considerations. Nevertheless, the establishment of a rotator SatNOGS ground station, particularly for the average enthusiast, entails a relatively higher expense.
## 2 Materials and Methods
### Semi-Empirical Capacity Fading Model  
After the acquisition of the charge-discharge data of a cell, we analyzed the discharge curve using the ECBE model to calculate the maximum capacity of the cell (Q\({}_{\text{m}}\)). Experimental SoH was computed for the batteries tested for various cycles using Equation (2) [8; 16; 24] using the maximum capacity of the batteries after the first cycle (Q\({}_{\text{max}}\) (fresh)) and after aging (Q\({}_{\text{max}}\) (aged)).  
\[\text{SoH}=\frac{Q_{\text{max}}(agd)}{Q_{\text{max}}(fresh)}. \tag{2}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Battery Characteristics** & **Description** \\ \hline Type Cylindrical & \\ Chemical system & NMC \\ Chemical component of the cathode & Li[Ni, Mn, Co]O\({}_{2}\) \\ Chemical component of the anode & Graphite \\ Nominal voltage & 3.62 V \\ Typical capacity & 2150 mAh \\ Charge cut-off voltage & 4.2 V \\ Discharge cut-off voltage & 2.75 V \\ Dimensions(mm) & \(18.4\times 65\) \\ Approx. weight & 44.5 g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Samsung 18,650 battery specification provided by the manufacturer [22].  
Figure 1: Variation of terminal voltage recorded during the charge–discharge period.  
The semi-empirical model used in this work is described by Equation (3) [16]. Parameters (k1, k2, k3) in the model can be extracted by substituting SoH obtained using Equation (2) from 3 different cycles into Equation (3). The extracted parameters were used to calculate the SoH over the entire battery life and denoted as estimated SoH in this work.  
\[\text{SoH}=1-\left(\frac{1}{2}\text{k}_{1}N^{2}+\text{k}_{2}N\right)-\frac{ \text{k}_{3}}{Q_{\text{max}(fresh)}}i, \tag{3}\]  
where k1, k2, and k3 are the coefficients that depend on battery operation conditions. k1 represents the capacity loss that increases rapidly at high ambient temperature, and k2 is a factor to account for capacity loss at normal temperature. k3 is a parameter that represents the capacity loss caused by the discharge rate. N represents the number of charge-discharge cycles the battery experienced at the time of this SoH calculation, and \(i\) is the discharging current.
# A Procedure to Prepare Sand-Clay Mixture Samples for Soil-Structure Interface Direct Shear Tests
## 4 Assessment of Sample Uniformity  
### Water Content  
To check whether the water was homogeneously distributed inside the specimen, a sand-clay mixture sample with 75% clay content was prepared by the S1 protocol. The uniformity of the sample can be evaluated by the water content (\(w\)). After the S1 preconsolidation was finished, the sand-clay mixture sample was cut into three slices, i.e., bottom, middle, and upper slices. Each slice was cut into four parts, the water content of each part was measured, finally, there were four points of water content on each slice, see Figure 6.  
Figure 5: The procedure of preparing sand–clay mixture samples for interface direct shear tests: (**a**) slurry prepared by S1 mixing protocol, (**b**) installation of the sample into of the designed preconsolidation cell, (**c**) step preconsolidation on the oedometer frame, (**d**) uninstall the cell, (**e**) trimming the specimen with a cutter, (**f**) move to the shear box, (**g**) put the sample into the shear box with a rod, (**h**) after flatting put a filter paper and (**i**) move the shear box and container to the interface apparatus.  
It can be seen from Figure 6 that the water content throughout the sand-clay specimen was identical and the specimen was homogeneous. From the top slice to the bottom slice, the maximum difference (\(\Delta w\)) in average water content throughout the three slices was only 0.25%, which is much lower than the \(\Delta w\) in [47] (1.02%) and in [48] (1.12%, 1.15% and 1.20%). In terms of the water content, we can conclude that the void ratio (\(e\)) of the sand-clay mixture prepared by the S1 protocol was extremely uniform throughout the specimen. This is due to the void ratio being revealed by the water content if the samples are fully saturated [47; 48].