esa-sceva/satcom-chunk-collection huggingface dataset:
# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 2 Materials and Methods
### Neural Network Architecture
The first component, the class identification loss, is formulated as follows:  
\[L_{class}=-\frac{1}{N\cdot\mathcal{C}}{\sum_{i=1}^{N}{\sum_{c=1}^{c}{y_{i,c} \text{log}\,\hat{y}_{i,c}}}}, \tag{1}\]  
where \(N\) represents the total number of samples, \(\mathcal{C}\) denotes the number of classes. \(y_{i,c}\) is the ground-truth label for sample \(i\) and class \(c\), and \(\hat{y}_{i,c}\) is the predicted probability of the sample belonging to class \(c\). This term ensures that the model effectively learns to classify each sample into the correct class by minimizing the negative log-likelihood of the correct class probability. To reinforce the distinction between target and non-target patterns, an additional target loss term is introduced. This term is defined as:  
\[L_{target}=-\frac{1}{2N}{\sum_{i=1}^{N}{\left({t_{i,1}\text{log}\,\hat{t}_{i, 1}+t_{i,2}\text{log}\,\hat{t}_{i,2}}\right)}}, \tag{2}\]  
where \(t_{i,1}\) and \(t_{i,2}\) represent the target labels, indicating whether the sample belongs to one of the five target classes (FRU, FRD, FLU, FLD, and TRA) or the non-target class. All the non-target classes are considered as a single class. The predicted probability values \(\hat{t}_{i,1}\) and \(\hat{t}_{i,2}\) are obtained by aggregating the softmax outputs for the target classes into a single probability while maintaining the probability of the non-target class. These predicted target values, \(\hat{t}_{i,1}\) and \(\hat{t}_{i,2}\), are defined as  
\[\hat{t}_{i,1}={\sum_{k=1}^{5}{p_{i,k}}}, \tag{3}\]  
\[\hat{t}_{i,1}={p_{i,6}}, \tag{4}\]  
where \(p_{i,c}\) denotes the predicted output of the model for each class. This formulation is designed to ensure that the model explicitly learns to distinguish between target and non-target patterns. The final loss function is expressed as the weighted sum of these two terms:  
\[L_{total}=L_{class}+\lambda\cdot L_{target}, \tag{5}\]  
where \(\lambda\) is a weighting factor that controls the contribution of the target loss term to the overall loss. By incorporating both loss terms, the proposed loss function aims to enhance classification accuracy while ensuring a robust differentiation between target and non-target classes.
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 4 Simulation and Discussion
### Fuzzy Controller Drive
#### 4.3.2 Applying or Removal of Step or Linear Load  
The results indicate that while applying the step load, the observed settling time is 0.0881 s, with a \(-\)0.1424% overshoot between 1.01 s to 1.02 s. While removing the load, the overshoot is \(-\)0.1213% (between 3.3 s-3.31 s) with a settling time of 4.99 s. When the load is linearly increasing from 1 s to 2.5 s to 2 nm, the settling time is 2.499 s with a 3 rpm steady state error and a \(-\)0.23% (1.5 s to 1.51 s) overshoot. After 2.5 s and up to 3.5 s, when the load reaches a steady state value of 2 nm, it has a settling time of 3.499 s. Between 3.5 to 4.5 s, the load linearly decreases and gradually settles with an overshoot of \(-\)0.06% with a 1 rpm error and a settling time of 4.5 s.
## Nomenclature  
\begin{tabular}{l l} \(C\) & cost or price, [USD/m\({}^{2}\) or USD/m\({}^{3}\)] \\ \(CDDs\) & cooling degree days \\ \(COP\) & coefficient of cooling system performance \\ E & east \\ \(ECSs\) & energy cost savings \\ \(g\) & discount rate [\%] \\ \(G_{\mathrm{s},\varepsilon}\) & solar constant \\ \(HDDs\) & heating degree days \\ \(h\) & heat transfer coefficient, [W/m\({}^{2\circ}\)C] \\ \(i\) & inflation rate [\%] \\ \(k\) & thermal conductivity, [W/m\({}^{\circ}\)C] \\ \(K_{T}\) & clearness index \\ LCCA & life cycle cost analysis \\ \(Hu\) & lower heating value, [J/m\({}^{3}\)] \\ \(LT\) & lifetime, [year] \\ N & north \\ NE & northeast \\ NW & northwest \\ \(PWF\) & present worth factor \\ \(\dot{q}_{s}\) & solar radiation incident, [W/m\({}^{2}\)] \\ \(R_{t,w}\) & total wall thermal resistances excluding the insulation layer, [m\({}^{2\circ}\)C/W] \\ S & south \\ SE & southeast \\ SW & southwest \\ \(T\) & temperature, [\({}^{\circ}\)C] \\ \(U\) & overall heat transfer coefficient, [W/m\({}^{2\circ}\)C] \\ W & west \\ \(x\) & thermal insulation thickness, [m] \\ Z & altitude \\ \(\alpha\) & solar absorption rate \\ \(\varepsilon\) & radiative emission coefficient of surface \\ \(\rho\) & reflectance of the ground \\ \(\beta\) & inclination angle of the surface \\ \(\gamma\) & azimuth angle \\ \(\sigma\) & Stefan-Boltzmann constant, [W/m\({}^{2\circ}\)C\({}^{4}\)] \\ \(\theta\) & incidence's angle of direct solar irradiance \\ \(\delta\) & declination angle \\ \(\varnothing\) & latitude angle \\ \(\omega\) & sunset hour angle \\ \(\eta\) & heating system efficiency \\ Subscript & \\ \(b\) & base \\ \(C\) & cooling \\ \(e\) & electricity \\ \(f\) & fuel \\ \(H\) & heating \\ \(i\) & inside \\ \(ins\) & insulation material \\ \(o\) & outside \\ \(opt\) & optimum \\ \(sol\)-\(air\) & solar air \\ \(surr\) & sky and surrounding surface \\ \end{tabular}
## 2 Data Preparation
### Internal Defects Classification from Collected B-Scan Images  
The ultrasonic B-scan image is a kind of continuous 2D image data that can display the cross-sectional image of the detected object. In B-scan images, the horizontal coordinates represent the probe movement distance, and the vertical coordinates represent the acoustic wave propagation time or distance. This intuitively reflects the detailed distribution in the probe detection range and the depth of the detected object from the detection surface.  
Based on the ultrasonic special transmission, reflection, and refraction characteristics of ultrasonic, when encountering a discontinuous medium, the wave will reflect and represent different colors and morphological characteristics. These discontinuous structures that can reflect the ultrasonic wave and can be received by the echo sensor are called ultrasonic reflector. The ultrasonic reflector includes both normal rail structures, such as screw holes, conductor holes, and rail joints, and rail defects, such as fishbolt hole cracks, crescent-shaped fatigue cracks, and rail base transverse cracks. According to the wave distribution regular, rail defects were first picked out from the ultrasonic reflectors. Then, the selected rail defects are further subdivided. The ultrasonic defects were manually labeled to fully learn these ultrasonic B-scan rail defect image features in the following training stage. Due to the lack of features in the B-scan images dataset and because the morphology of different categories of defects B-scan images are similar, this paper proposes a three-step classification method: the first step is a coarse classification method based on the colors and morphological characteristics of the channels, the second step is a fine classification method based on geometrics moments, and the third step is to frame in auxiliary line information during the labeling process to add location features. The specific classification method based on B-scan image features is divided into three parts, which are as follows:
## 2 Materials and Methods
### Fabrication of PES and sPES Membranes
#### 2.2.2 Fabrication of sPES Membranes  
The general fabrication diagram of sPES cation exchange membranes is shown in Figure 2a. The first step was sulfonation by CSA [18; 19; 20; 21]. As shown in Figure 2b, the sulfonation process would increase the content of the HO\({}_{3}\)S\({}^{-}\) group. Specifically, blank PES (bPES) weighed 30 g and was dissolved into 150 mL DCM. Then, 8 mL CSA was added, and the mixture was stirred for 60 min. The mixture was quenched with 300 mL methanol. The phase inversion method was applied to solidify sulfonated PES (sPES) by adding 170 g NMP to form sPES solution and slowly pouring sPES/NMP solution into DI to form sPES noodles. The noodles soaked for over 24 h to neutralize pH. The process was repeated three times to thoroughly neutralize pH. The solid sPES material was removed and placed under the fume hood to fully dry for subsequent use. In addition to the sulfonation dose of 8 mL CSA, greater degrees of sulfonation were attempted with doses of 16 mL and 24 mL CSA. However, the sPES cannot form a membrane with 24 mL CSA after purification. Also, with 16 mL CSA, the formed sPES membranes were fragile in mechanical strength, which meant they were fragile and easily broken. Thus, the sulfonation method with 8 mL CSA was chosen for the following experiments.  
After the dry sPES material was produced, the second step was identical to the fabrication process of PES membranes. The sPES dope solution was made for the membrane's fabrication process. Firstly, 30 g sPES was weighed and put into 170 g NMP to make a 15% sPES dope solution. Secondly, 2.0 g sPES dope solution was cast into each petri dish and put in the air to evaporate for 0.5 h, 1 h, 4 h, 8 h, 12 h, 16 h, 20 h, and 24 h, respectively, then they were submerged in DI water to form the sPES membranes.
# Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone
## 2 Materials and Methods
### Speed Estimation Method  
In order to better illustrate the proposed methodology, an example will be described step-by-step. A sample vibration recording corresponding to a two-pole induction motor fed from the supply mains, at no load (no mechanical load attached to the motor shaft), will be used as a case example. The average value of rotational speed measured by tachometers is \(\Omega_{m}=2820\) r/min, or in other words, a shaft mechanical rotation frequency of \(\omega_{m}=47\) Hz. Figure 4 shows the axial components of the time motor vibration, \(f_{X}(t)\), \(f_{Y}(t)\), and \(f_{Z}(t)\), obtained from the csv file recorded by means of the free application. Figure 5 shows the spectra of the components of the motor vibration signal, \(\mid\)\(F_{X}(j\cdot\omega)\), \(\mid\)\(F_{Y}(j\cdot\omega)\), and \(\mid\)\(F_{Z}(j\cdot\omega)\), obtained by the MATLAB-based routine. Finally, Figure 6 shows the RMS spectrum of the motor vibration, \(\mid\)\(F(j\cdot\omega)\), computed by means of (2).  
Next, a peak detection routine was used to determine the highest peak of the RMS spectrum of the motor vibration, \(\mid\)\(F(\omega_{F})\) \(\mid\)\(=\) MAX\(\mid\)\(F(\omega)\) \(\mid\). Accordingly, the proposed algorithm determined the rotor shaft mechanical frequency, \(\omega_{F}\), as the frequency corresponding to the highest magnitude of the RMS spectrum, that is, \(\mid\)\(F(\omega_{F})\) \(\mid\)\(=\) MAX\(\mid\)\(F(\omega)\) \(\mid\). In the example of Figure 6, the RMS spectrum of the motor vibration reached its maximum at a rotor shaft mechanical frequency of \(\omega_{F}=47.0215\) Hz (\(\mid\)\(F(\omega_{F}=47.0215\) Hz) \(\mid\)\(=\) MAX\(\mid\)\(F(\omega)\) \(\mid\) = 23.8414 p.u.). Finally, using (4), the estimation rotor speed was \(\Omega=2821.29\) r/min. The relative error of the estimation, compared to the measured value obtained using the tachometers (\(\Omega_{m}\)\(=2820\) r/min), was \(\varepsilon=0.0457\%\), demonstrating very good agreement.  
Figure 4: Time vibration signal of the motor fed from the supply mains, at no load (no mechanical load), at 2820 r/min for axis x (**top**), y (**middle**), and z (**bottom**).
## 3 Results and Discussion
### Topographical Analysis of Laser-Structured Surfaces  
Prior to laser structuring, the sample was mechanically polished to obtain a smooth and clean surface. The polished surface of CP Ti had an average surface roughness, Sa, of \(\sim\)15 nm. During laser processing using various laser processing parameters, different surface structures with various topographies are generated on the CP titanium. Figure 4 shows the topographical images of the laser-structured surfaces at A1, B1, C1, D1, E1, D4, and E1 regions obtained after laser ablation. It can be noted from Figure 4 that all surfaces of the laser-structured parts are rough. The A1 region (Figure 4a) shows that the surface structures do not have a lot of protrusions in comparison to the other four regions. In the B1, C1, D1, and E1 regions, the surface structures have numerous small protrusions. In D4, there are unprocessed and processed parts where the laser beams pass during scanning. The processed part is made up of almost V-shaped micro-grooves with a top width of \(\sim\)27 \(\upmu\)m and depth of \(\sim\)1.09 \(\upmu\)m. The unprocessed part remained because the hatching distance used could not facilitate the line overlapping (LO = \(-\)100%) of the laser tracts.  
Figure 4: Topography of the laser-structured CP Ti on the: (**a**) A1, (**b**) B1, (**c**) C1, (**d**) D1, (**e**) D4, and (**f**) E1 regions.  
The area parameters, such as surface roughness Sa, skewness Ssk, kurtosis Sku and surface slope Sdq, were determined using Gwydion 2.64 software and tabulated in Table 2. The Sa values obtained were on the nanoscale and this is due to the low laser fluence used which did not ablate the material deeply in the ablation process. Most of the Ssk values are less than zero (Ssk < 0), meaning that there were more valleys than peaks in processed regions [17,28]. For the Sku values, all regions have features with peaks and valleys which are not sharp (Sku < 3) [17,28]. The Sdq values are very small, showing that the degree of slopeness of the surfaces in all directions is low. Observation from Table 2 shows that Sdq increases with increasing laser fluence.
## 3 Numerical Experiments
### Field Example
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Well** & **FCRN** & **MFCRN** & **MFCRGRU** & **M2S-MFCRGRU** \\ \hline \multirow{4}{*}{Vp} & w1 & **0.066408/0.9419** & 0.070059/0.9359 & 0.072230/0.9316 & 0.077819/0.9259 \\  & w2 & 0.022538/0.9945 & 0.029373/0.9897 & **0.020107/0.9951** & 0.039119/0.9822 \\  & w3 & 0.041671/0.9739 & 0.043983/0.9709 & **0.037750/0.9784** & 0.058496/0.9526 \\  & w4 & 0.007013/0.9957 & 0.006798/0.9995 & **0.005852/0.9996** & 0.044270/0.9811 \\  & w5 & 0.007271/0.9994 & **0.006110/0.9995** & 0.006249/0.9996 & 0.044753/0.9736 \\  & w6 & 0.022889/0.9910 & **0.021399/0.9923** & 0.021433/0.9921 & 0.032104/0.9839 \\ \hline \multirow{4}{*}{Density} & w1 & **0.044346/0.9824** & 0.045243/0.9824 & 0.045739/0.9823 & 0.053312/0.9737 \\  & w2 & **0.023002/0.9950** & 0.035206/0.9875 & 0.023644/0.9940 & 0.034670/0.9866 \\ \cline{1-1}  & w3 & 0.037807/0.9872 & 0.038484/0.9865 & **0.031067/0.9918** & 0.043722/0.9826 \\ \cline{1-1}  & w4 & 0.012595/0.9994 & 0.010185/0.9994 & **0.008250/0.9998** & 0.035916/0.9891 \\ \cline{1-1}  & w5 & 0.010091/0.9995 & **0.008326**/0.9995 & 0.009420/**0.9997** & 0.040890/0.9871 \\ \cline{1-1}  & w6 & **0.024093/0.9947** & 0.026015/0.9943 & 0.024861/0.9946 & 0.036868/0.9875 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: NRMSE \(\downarrow\)/PCC \(\uparrow\) of the inversion results for different methods at the six wells. \(\downarrow\) means the lower the better and \(\uparrow\) means the higher the better. The best values are in bold. Figure 11: Inversion results of density on field data. (**a**) FCRN. (**b**) MFCRN. (**c**) MFCRGRU. (**d**) M2S-MFCRGRU. profile (Figure 14d) and density inversion profile (Figure 15d) of MS2-MFRGRU have the most complete stratigraphic structures with the best lateral continuity. Table 5 calculates the NRMSEs and PCCs of the Vp inversion and density inversion results predicted by the four methods at blind well w5. For both Vp and density, MS2-MFRGRU has the lowest NRMSE and the highest PCC. Thus, the generalization superiority and inversion effectiveness of M2S-MFRGRU are further demonstrated. Figure 14: Inline profile inversion results of Vp on the blind well test. (**a**) FCRN. (**b**) MFCRN. (**c**) MFCRGRU. (**d**) M2S-MFCRGRU. Figure 15: Inline profile inversion results of density on the blind well test. (**a**) FCRN. (**b**) MFCRN. (**c**) MFCRGRU. (**d**) M2S-MFCRGRU.
## 5 Conclusions  
This study examines the effectiveness of shots and goalkeeper performance across various positions and game situations. The main findings revealed that the context of the game significantly impacts the outcome of the action. In this regard, penalties proved to be the most effective situation for scoring goals, while, although regular plays and free kicks create more opportunities, they present lower success rates and higher save percentages. On the other hand, corner kicks and counterattacks showed a higher tendency to result in goals, highlighting their strategic relevance.  
The direction of the shot towards different areas of the goal is closely related to the success of the action. Shots directed near the posts, especially in the lower corners, were the most effective. Goalkeepers, in turn, demonstrated greater effectiveness in the central areas and at medium height.  
The shooting zones on the field are directly linked to the success of the shot. The six-yard area and its vertical extensions were identified as high-effective zones.  
No significant relationships were found between the body surface used and shot success. However, excitatory trends were observed for goals from header shots, with a higher probability of scoring when executed from the six-yard area and to the posts.  
Finally, the effectiveness of shots depended on the relationship between the attacking side and the striking foot, with far-post shots being more successful when the foot matched the wing and near-post shots being more easily saved when it did not. These results highlight the key role of shooting angles and goalkeeper positioning in shot success.
## 2 Materials and Methods  
### Experimental Materials and Instruments  
Instruments used in the present work included a handheld LCR (Inductance, capacitance, and resistance) meter (VICTOR 4082, Shenzhen, China, frequency range: 0-100 kHz, target indicators: impedance and phase angle), a Color Tec-PCM Plus 30 mm Benchtop Colorimeter (Color Tec Associates, Clinton, NJ, USA, target indicators: L, a, b, C, and H), a refractometer (PAL-1, ATAGO, Japan, measurement range: brix 0.0%-53.0%, measurement accuracy: brix \(\pm\)0.2%), a penetrometer (FM200, PCE, Germany) fitted with a 7.9-mm-diameter plunger, a titrator (DCB5000, BOECO, Germany), an electronic scale (OHAUS Adventurer AX2202, NJ, USA), and a digital caliper.  
Commercial fresh peaches (_Prunus persica_ ([PERSON] 'Spring Belle') obtained from a supermarket in Zagreb were used as the experimental sample. Two hundred peaches were placed in cold storage at 0 \({}^{\circ}\)C and numbered for use in the experiment. The measurement of nine quality indicators was completed within 48 h and was performed in sequential steps: color, shape index, volume, mass, dielectric properties, firmness, soluble solid concentration (SSC), and titratable acid (TA) [23; 24]. Two more quality indicators (density and sugar-acid ratio) were acquired by calculation.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 1 Introduction
### Cross-Site Scripting (XSS) Attacks: Characteristics and Analysis
#### 1.1.3 Attack Vectors and Exploitation Techniques  
XSS attacks can be delivered through the following:  
* URL parameters and query strings;
* Form inputs and submissions;
* HTTP headers;
* JSON data structures;
* Cookie values;
* DOM manipulation.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 4 Experimental Results
### Results Analysis
#### 4.2.2 The Fairness of Single Algorithm for Multiple Flows  
To investigate the fairness of sharing link bandwidth when multiple flows coexist in the same scheme, this study sent one data flow at 0 s, 10 s, and 20 s in the test to determine whether three data flows can finally share the link bandwidth evenly, as well as the time required to evenly share bandwidth and reach convergence. Using a 1 Gbps link bandwidth, 100 ms network delay, and zero random packet loss rate as an example, the tested fairness of each scheme is summarized as follows.  
The fairness results of the algorithms are presented in Figure 10, where it can be seen that the BBR had a higher throughput rate than the other algorithms when only one data flow was used. However, when two data flows of 10 s and 20 s are added, the throughput rates of the two data flows significantly differed. After 30 s, the throughput rates of the three data flows fluctuated, indicating that the BBR algorithm was unable to achieve an effective link bandwidth share. The results of the Cubic congestion algorithm for a network delay of 100 ms and a packet loss rate of zero are presented in Figure 11. After adding data flows at 10 s and 20 s intervals, the Cubic algorithm could average the throughput of three flows and ensure efficient link bandwidth sharing. The results of the BoostTCP congestion algorithm at a network delay of 100 ms and a packet loss rate of zero are presented in Figure 12. As demonstrated in Figure 12, the BoostTCP algorithm could maintain fairness between three data flows in a steady-state. However, the BoostTCP algorithm had a larger bandwidth-sharing fluctuation range than the Cubic algorithm, achieving an average value of 6.6 Mbps. BoostTCP could achieve a transmission rate of 320 Mbps after stabilization, which was faster than those of BBR and Cubic, indicating a more efficient use of network resources.  
Figure 11: The fairness test curves of the Cubic congestion control algorithm at a network delay of 100 ms and a packet loss rate of zero.  
Figure 10: The fairness curves of the BBR congestion control algorithm at a network delay of 100 ms and a packet loss rate of zero.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 5 Experimental Results and Discussion
### Proposed System Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**\#Params (M)**} & \multirow{2}{*}{**MACs (\(\times\)10\({}^{9}\))**} & \multirow{2}{*}{**Clean**} & \multicolumn{3}{c}{**Noise Type (WER)**} \\ \cline{5-6}  & & & & **Indoor** & **Outdoor** & **Transport** \\ \hline HuBERT Large [3] & 300 & 4324 & 3.62 & 8.64 & 7.58 & 5.23 \\ HuBERT Base [3] & 95 & 1669 & 6.43 & 11.80 & 10.82 & 8.89 \\ Robust HuBERT [15] & 95 & 1669 & 6.75 & 9.38 & 8.85 & 8.04 \\ DistilHuBERT [10] & 24 & 785 & 13.29 & 26.21 & 23.94 & 19.64 \\ DistilRobustHuBERT & 24 & 785 & 12.70 & 24.41 & 21.92 & 18.36 \\ RobustDistiler [17] & 24 & 785 & 14.03 & 19.93 & 18.53 & 17.22 \\ \hline Robust DistilHuBERT & 24 & 785 & 12.74 & 17.55 & 16.48 & 15.24 \\ Noise-Aware DistilHuBERT & 48 & 785 & 12.44 & 17.33 & 16.39 & 15.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison across different clean and noisy conditions with SNR between 0 and 30 dB. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Noise Type (WER)**} \\ \cline{2-4}  & **Indoor** & **Outdoor** & **Transport** \\ \hline HuBERT Large [3] & 17.76 & 14.96 & 8.26 \\ HuBERT Base [3] & 20.77 & 18.36 & 12.95 \\ Robust HuBERT [15] & 14.64 & 12.37 & 9.40 \\ DistilHuBERT [10] & 45.56 & 39.64 & 29.59 \\ DistilRobustHuBERT & 41.80 & 36.35 & 27.04 \\ RobustDistiler [17] & 28.86 & 24.99 & 20.99 \\ \hline Robust DistilHuBERT & 25.05 & 22.18 & 19.28 \\ Noise-Aware DistilHuBERT & 23.50 & 21.24 & 18.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison across different noisy conditions with SNR between 0 and 10 dB.