esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Data Processing Approach for the SHPB Test Based on PSO-TWER
## 2 Related Work  
### Time-Window Energy Ratio Method  
For waveform signals of the incident wave, the energy difference between the time window after the incident-wave start moment and the energy in the time window before arrival is relatively large, so the energy ratio can judge the arrival moment of the waveform signal. Assuming that the waveform electrical signal recording channel is \([x_{n}]\), the time (t) is the center of the time window. Then, the length (M) of the time window is taken before and after the time (t) in the recording channel. The energy ratio between the frontal and posterior time windows can be calculated by Equation (1).  
\[R(i)=\left(\begin{array}{c}\sum\limits_{\begin{subarray}{c}i+M-1\\ i-1\\ k=i-M\end{subarray}}^{k=i}x_{k}^{2}\\ \sum\limits_{k=i-M}^{i-1}x_{k}^{2}\\ \end{array}\right)^{\frac{1}{2}} \tag{1}\]  
where \(x_{k}\) is the amplitude of k, \(R(i)\) is the energy ratio of the frontal time window and posterior time window, and M is the length of the time window. In the study of [PERSON] [22], differences in M influence the selection of arrival times. The fluctuations generated by external disturbances lead to noise signals recorded during the acquisition of the voltage signal by the Hopkinson test equipment. As Figure 1a,b show, the noise signals can be observed in the whole signal [12]. In a small-scale range, noise signals can be regarded as miniature starting points with high frequency and low amplitude. A and B is the time-window of the incident wave and transmitted wave, respectively. Therefore, as Figure 1d shows, the selection of the arrival moment is more susceptible to the influence of noise signals when the time-window length (M) is too small. Figure 1e shows variation in the arrival moment for time-window length M, and the mutation evident in M is 500.  
Figure 1: The representative voltage curves and time-window energy ratios: (**a**) noise signal in the initial and final period; (**b**) noise signal in the initial period; (**c**) energy ratio of the two-window method; (**d**) first-break times; (**e**) first break for the time-window lengths.
## 6 Training and Simulation Results
### Computed a Gini Policing Index (GPI)
#### 6.2.1 Summary: Gini Policing Index Analysis  
To assess the fairness of the patrol strategies generated by the DDQN and PPO agents, a simplified Gini policing index was calculated. This index aims to measure the inequality in the distribution of patrol visits relative to crime rates across different socioeconomic zones. For this analysis, Washington D.C. Wards were used as a proxy for socioeconomic zones, and the real crime incident data from the Washington DC dataset was used to determine the 'crime count (risk) per Ward'.  
Figure 7: Crime incident locations and density overlaid on Washington DC map. _The heatmap shows the spatial intensity of crime incidents, with darker areas indicating higher-density clusters._The simulated patrol paths of the retrained DDQN and PPO agents were mapped to these Wards to obtain patrol visit frequencies per Ward. The Gini index was then calculated based on the ratio of patrol visits to crime counts for each Ward. A Gini index closer to 0 indicates a more equitable distribution of patrols relative to crime, while an index closer to 1 suggests greater inequality.
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
###### Abstract  
A typical pulsed thermography procedure results in a sequence of infrared images that reflects the evolution of temperature over time. Many features of defects, such as shape, position, and size, are derived from single image by image processing. Hence, determining the key frame from the sequence is an important problem to be solved first. A maximum standard deviation of the sensitive region method was proposed, which can identify a reasonable image frame automatically from an infrared image sequence; then, a stratagem of image composition was applied for enhancing the detection of deep defects in the key frame. Blob analysis had been adopted to acquire general information of defects such as their distributions and total number of defects. A region of interest of the defect was automatically located by its key frame combined with blob analysis. The defect information was obtained through image segmentation techniques. To obtain a robustness of results, a method of two steps of detection was proposed. The specimen of polyvinyl chloride with two artificial defects at different depths as an example was used to demonstrate how to operate the proposed method for an accurate result. At last, the proposed method was successfully adopted to examine the damage of carbon fiber-reinforced polymer. A comparative study between the proposed method and several state-of-the-art ones shows that the former is accurate and reliable and may provide a more useful and reliable tool for quality assurance in the industrial and manufacturing sectors.  
Keywords:long pulse thermography; infrared image sequence; blob analysis; region of interest; standard deviation +
Footnote †: journal: Applied Physics Letters
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dependent Variables** & **Independent Variables** & \multirow{2}{*}{\(\beta\)} & **Error** & \multirow{2}{*}{_p-Value_} & **Lower CI 95\%** & **Upper CI 95\%** \\ \hline \multicolumn{6}{c}{Primary Stability} \\ \hline \multirow{3}{*}{ISQ day of the surgery \({}^{\text{A}}\)} & Macrodesign & \(-\)0.200 & 1.410 & 0.028 & \(-\)5.948 & \(-\)0.350 \\ \cline{2-6}  & Length & 0.175 & 1.315 & 0.046 & 0.049 & 5.269 \\ \cline{2-6}  & Insertion torque & 0.387 & 0.064 & \(<\)0.001 & 0.147 & 0.400 \\ \hline \multicolumn{6}{c}{Secondary stability} \\ \hline \multirow{3}{*}{ISQ 3 months \({}^{\text{B}}\)} & Location & 0.274 & 1.338 & 0.003 & 1.349 & 6.660 \\ \cline{2-6}  & Macrodesign & \(-\)0.277 & 1.333 & 0.003 & \(-\)6.667 & \(-\)1.375 \\ \cline{2-6}  & Age & 0.190 & 1.284 & 0.041 & 0.112 & 5.208 \\ \cline{1-1} \cline{5-6} \multicolumn{6}{c}{**A: Coefficient of determination \({\rm R}^{2}=0.278\) \(\rm F=12.589\); \(p<0.001\). \({}^{\text{B}}\): Coefficient of determination \({\rm R}^{2}=0.182\) \(\rm F=7.258\); \(p<0.001\).** & \multicolumn{3}{c}{**} \\ \hline \end{tabular}
\end{table} 
Table 8: Linear regression model between the stability and bone-density variables. the insertion torque, between 0.147 and 0.4 ISQ units were gained. Using longer implants resulted in a gain of 0.05-5.239 ISQ units, and utilizing conical-shaped implants resulted in a loss of 0.35-5.269 ISQ units (Table 7). The secondary stability linear regression model, assessed based on the ISQ after three months (ISQ3), depended mainly on the location of the implant in the arch (incisor-canines, bicuspid- molars), the implant macro-design (cylindrical, conical), and the age of the patient (\(\leq\)55, \(>\)55). According to the significant regression model (F = 7.258; \(p<0.001\)), which only predicted 18.2% of the results of the measured ISQ3 values, the average ISQ after three months at the incisor-canine area, 76.67 \(\pm\) 7.13, will increase by 1.35-6.66 units when the implant is placed more posteriorly (bicuspids and molars), and by 0.11-5.21 units in older patients. If conical-shaped implants are used, the ISQ3 value will decrease by 1.375-6.660 (Table 7).
# Detection of Frozen Soil Using Sentinel-1 SAR Data
## 5 Discussion
Further investigations are necessary to analyze the effect of frozen water content in crop on the SAR backscattering coefficient.
## 2 Validation, Evidence Based on Response Processes
### Applications of Evidence of RPs in Assessment  
Small-scale process data have gained increasing attention in cognitive ability testing due to advances in digital measurement, which enhance data collection by allowing RPsto be traced and recorded [29]. In large-scale assessments, such data support validation practices and test development [34; 37], including the analysis of computer-generated log files, screen captures, facial expressions, eye movements, video-recorded behaviors, and physiological sensor data [38].  
RP evidence is especially important for tests measuring higher-order thinking, such as DC assessments, or for developing new assessment formats [34]. Test-takers may use unintended pathways or strategies to solve items, potentially bypassing the cognitive skills being measured. For instance, [PERSON] et al. used ET data to evaluate how the layout of options in multiple-choice questions influenced responses, offering an alternative interpretation of test scores [39]. [PERSON] et al. employed ET data to validate response processes in clinical vignette-type multiple-choice questions, improving question design and providing feedback for teaching [40]. [PERSON] and [PERSON] explored whether ET data revealed emotional engagement in financial competence assessments [41]. RPs have also been studied extensively to assess the alignment between test-takers' responses and the intended construct [42; 43; 44; 45], and to inform group comparisons by explaining score differences across examinee groups [34; 46].
# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 5 Evaluation and Results
The frame on the right (**b**), show the logo just slightly perceptible. A different perspective is provided in Table 6 with the results for all the sub-segments aggregated by difficulty level (see Table 4). These demonstrate very high performance in the lower difficult streams (1 and 2), but also in the highest level (3) ones. The results obtained for the easier streams indicate that the algorithm performs very well in cases where the channel logo is clearly distinguishable from the background content. Moreover, note that the performance for levels 2 and 3 is very close (\(\sim\)90%). Streams with high quantity of visual clutter and with monochromatic logos (very similar to the programs being broadcasted) pose some obstacles to the proposed approach. This happens due to the logo absorption problem which is more predominant in monochromatic logos. Even manual observation of the logo poses severe difficulties since no perceived visual difference between the logo or background is noticeable. Another identified difficulty occurs when streams do not follow the base hypothesis in which programming content is displayed without logo and self advertisements are presented with the channel logo. For a more detailed and objective comparison with the state-of-the-art, the testing dataset presented in [3] was also manually annotated and processed with the proposed method. The results obtained and the baseline reported in [3] are presented in Table 7. To the best of our knowledge, the method presented in [3] is the most similar to our approach, as it follows the same principles of not requiring manual data labeling for processing input streams and being able to adapt to streams with different logos without manual adjustments or parameter corrections. From this comparison, it is clear that our proposal outperforms the state-of-the-art, obtaining almost ideal detections for this dataset. Note that the three streams that composed the dataset were assigned the lowest difficulty level, i.e., they are less challenging than most of the segments in the full dataset. Moreover, unlike the remaining dataset, the three streams start with the logo present, which simplifies the problem.
## 6 Application of Plasma Treatment in Advanced and Emerging Packages
### Package on a Package (PoP)  
Package-on-package (PoP) represents a widely adopted three-dimensional integration strategy, particularly prevalent in mobile applications, where components like memory and logic processors are vertically stacked using ball grid array (BGA) interconnects. This approach significantly enhances component density, enabling greater functionality within constrained footprints, and can improve electrical performance by shortening signal paths [153]. However, the successful fabrication and reliability of PoP structures critically depend on the quality of the inter-package connections. A specific challenge arises after the moulding process used to encapsulate the lower package components; residual moulding compound, commonly referred to as mould flash, can contaminate the BGA pads or other critical interconnect surfaces intended for the subsequent stacking step.  
If not removed, this mould flash acts as a barrier, potentially preventing proper wetting during solder reflow and compromising the formation of reliable electrical and mechanical joints between the stacked packages. The reviewed literature highlights the essential role of plasma treatment in addressing this issue, specifically documenting its use for cleaning mould flash immediately before the PoP stacking process [154]. In the referenced study, two different gas chemistries were evaluated for this cleaning application: a mixture based on carbon tetrafluoride and oxygen (CF\({}_{4}\)/O\({}_{2}\)), and another using argon and oxygen (Ar/O\({}_{2}\)). The CF\({}_{4}\)/O\({}_{2}\) plasma utilizes both physical bombardment (from the ions generated) and potent chemical etching (fluorine radicals reacting with the organic mould compound and potentially fillers), alongside oxygen radicals for ashing. The Ar/O\({}_{2}\) plasma, conversely, relies primarily on physical bombardment via energetic argon ions combined with the chemical ashing effect of oxygen radicals on the organic resin.  
The investigation concluded that the Ar/O\({}_{2}\) combination was an important and highly viable option, ultimately being preferred in the context presented. This preference was largely driven by practical factors surrounding the use of fluorine-based gases like CF\({}_{4}\), which can be subject to higher operational costs and more stringent environmental or regulatory controls in certain manufacturing locations. Moreover, as noted, fluorine-based chemistries are typically leveraged for more aggressive bulk etching processes due to their high reactivity, whereas the Ar/O\({}_{2}\) mixture offers effective cleaning with potentially less risk of unwanted substrate interaction or modification, making it well-suited for preparing the delicate interconnect surfaces prior to reliable PoP assembly [154].
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 5 Discussion
### The Dependency on the Number of SLR Sites  
The dependency of median RMS on the number of SLR sites for 3-, 5-, 7-, and 9-day solutions are displayed in Figure 17. For GEO C01, the number of SLR sites is no more than four, even in its 9-day solutions. Meanwhile, the number can reach 8 for BDS IGSO, and 12-14 for BDS MEO satellites. The influence of the SLR sites number on the accuracy of SLR orbit determinations is not negligible. Moreover, the more the number of SLR sites, the higher the accuracy of the multi-day arc solutions. When the number of SLR sites exceeds 5-6, the accuracy of multi-day arc solutions becomes more excellent and stable, especially in the T and N directions. In particular, there is no obvious regularity  
Figure 16: The dependency of median RMS on the number of SLR observations. The red, green, and blue dots, as well as dotted lines, represent values in the R, T, and N directions respectively. In particular, the hollow circle dotted line refers to the median RMS with a step size of 10.  
for C01, because rare success solutions are included in the multi-day arcs. Similarly, when the number of SLR sites exceeds 5, the RMS values become worse for IGSO C10 on account of fewer RMS statistics, especially in its T and N directions. In addition, the longer multi-day arc solutions have more stable RMS values because more SLR observations are included, even with the same number of SLR sites. Taking the BDS-3 MEO C21, for example, its 3-day solutions RMS becomes unstable as the number of SLR sites increases; however, the RMS values become more and more precise as the arc lengthens. In the analysis of the 3-day solution for BDS-2 C11 by [PERSON], [PERSON] and [PERSON] (2015), the trend of the median RMS values is smoother than that in this contribution with the increase in the number of SLR sites. This situation may be caused by the different selection of strategies and time spans in the SLR-only orbit determination. Above all, it can be deduced that on the one hand, the longer the multi-day arcs, the greater number of SLR sites it may contain; on the other hand, the increase in the number of SLR sites strengthens the geographical distribution geometry of SLR sites, which improves the accuracy of the SLR-only orbit determination, especially in T and N directions.  
Figure 17: The dependency of median RMS on the number of SLR sites. The definition of different colour dots and circles are the same as in Figure 16.
# Application of OCT in the Gastrointestinal Tract
## 3 OCT Applications in the GI Tract
### Hepatobiliary  
Hepatobiliary malignancies account for around 13% of global cancer mortalities and 3% of those in the US, with cholangiocarcinoma (CCA) accounting for 15-20% of hepatobiliary malignancies [80]. The assessment of strictures in the pancreatico-bilary tract can be challenging since they can be benign or cancerous. The current protocol is an endoscopic retrograde cholangiocarcinomareatography (ERCP) and random biopsies or brush cytology [81; 82], but the practice often leads to a low diagnostic yield because of the small amount of available tissue and the tortuous anatomy of the bile duct [83; 84]. Thus, a low-profile OCT probe was designed to enable passage through the pancreatic duct and demonstrated the ability to differentiate malignant from benign strictures [85; 86; 87]. [PERSON] et al. described that strictures with cholangiocarcinoma--acrinoma showed a hyperreflective surface with loss of inner wall layering, while benign areas had clear delineated ductal wall layering [88]. [PERSON], in a 22-patient study, found similar results and was able to quantify the thickness of each duct layer to differentiate benign, inflammatory and malignant tissue types [89]. This ability of OCT to potentially differentiate benign from cancerous lesions may obviate the need to remove tissue in the future.
## 3 Results
### Photocatalytic Performance Results  
As reported in previous studies, these catalysts exhibit better H2 production activity in the presence of a sacrificial agent than a virgin photocatalyst. It is well known that the amount of protons, i.e., H\({}^{+}\) ions, for H2 production in the presence of a sacrificial agent increases. The sacrificial agent provides H\({}^{+}\) ions, which are responsible for increased photocatalyst activity and water splitting. In this study, methanol was used as a sacrificial agent and it was found that this sacrificial agent increases the rate of H2 production. The increased H2 production rates with sacrificial agents can be easily seen in Figure 8a.  
A systematic study of the photocatalytic activity of the ZrO2-CdWO4 NC was performed for H2 production via water splitting with the sacrificial agent (methanol). H2 production via water splitting was performed under various conditions such as different catalyst amounts, time, temperatures, and sacrificial agents.  
The time effect has also been monitored for this study, and is shown in Figure 8a. Figure 8a confirms that H2 production increases as time increases. It can be seen from Figure 8b that the H2 production rate increases as the photocatalyst is added. The point is that the number of surface sites increases with the increase in photocatalyst concentration (facilitates visible light absorption), due to which the catalytic activity of the catalyst also increases. This is because the rate of H2 production showed a marked increase when increasing the concentration of the photocatalyst prepared in this study. As can be seen, the concentration of the photocatalyst ranged from 25 mg to 250 mg, and H2 production ranged from 165 umol/g to 1574 umol/g.  
Figure 8c shows the temperature effect on water splitting. The increased temperature facilitates the easy excitation of electrons from VB to CB, charge separation, and prevention of e\({}^{-}\)-h\({}^{+}\) recombination, which is responsible for enhanced water splitting. In addition, the temperature increase reduces the viscosity of water, which is also responsible for water  
Figure 7: PL spectra of ZrO2, CdWO4, and the ZrO2-CdWO4 NC.  
splitting. Different pH effects were also measured for H\({}_{2}\) production efficiency, as shown in Figure 8d, and pH 7.0 was observed as the optimum pH for good H\({}_{2}\) production.  
The effect of MeOH (sacrificial agent) concentration on H\({}_{2}\) production via water splitting was also observed. As shown in Figure 8e, H\({}_{2}\) production initially increases with an increase in the concentration of MeOH, and the H\({}_{2}\) production rate decreases. The reason behind the decrease in the H\({}_{2}\) production rate might be the blocking of the surface site on the high loading of MeOH. The blocking of surface sites may eventually decrease the visible light absorption for photocatalytic activity.  
Figure 8: Optimization results of H\({}_{2}\) production via water splitting using ZrO\({}_{2}\)-CdWO\({}_{4}\): (**a**) time effect, (**b**) catalyst amount effect, (**c**) temperature effect, (**d**) pH effect, and (**e**) MeOH vol% effect.
## 2 Preliminaries
### Principal Component Analysis
In this phase the s eigenvalues are sorted in descending order; in the same way the corresponding eigenvectors in the matrix \(\mathrm{V}\) are ordered, obtaining the matrix \(\mathrm{V}^{\prime}\), whose columns correspond to the ordered eigenvectors. 6. The normalized data matrix \(Z\) is transformed in the matrix of the principal components \(\mathrm{Z}^{\prime}\) multiplying \(Z\) by the ordered matrix of the eigenvectors \(\mathrm{V}^{\prime}\): \(Z^{\prime}=Z\mathrm{V}^{\prime}\). 7. The significant principal components are selected by analyzing the eigenvalues, sorted in descending order. Three heuristic criteria are generally used for the choice of the number of components: - Select only the main components corresponding to the eigenvalues whose sum, compared to the sum of all the eigenvalues, is greater than or equal to a specific threshold, for example, 80% or 90%. * Adopt the Kaiser criterion, in which only those components are selected which correspond to an eigenvalue greater than or equal to 1, or, equivalently, the components that have variance greater than the average. * Build the eigenvalue graph, called _Scree Plot_, and select the number of components corresponding to the _elbow point_ beyond which the graph the eigenvalues stabilizes. * The reduced dataset is constructed considering only the significant principal components.
## 4 Results and Discussion
### Steady-State Analysis
#### 4.1.4 Overall Engine Efficiency  
The overall efficiency of the baseline engine, which is calculated using the brake power achieved over the total fuel power, can reach levels higher than 35% at medium to high load conditions. On the other hand, the overall efficiency of the electrically-assisted turbocharged engine is given by the brake power achieved plus the energy harvested or provided by the e-turbo divided by the total fuel power. Figure 20 shows the comparison charts between the overall efficiency of the baseline engine and four cases studied in Phase 2 and Phase 3 of the steady-state simulations.  
It is evident in Figure 20 that the implementation of a motor-generator linked to the turbocharger leads to an overall engine efficiency increase at medium and high loads and engine speeds for all of the cases. The cases with a reduced turbine size (multiplier of 0.9) benefit from an increased overall efficiency across the whole map. Finally, the high pre-turbine pressures show a significant effect on the overall efficiency gain of the model.  
By assessing the work presented in the three phases of the steady-state simulations, the ideal engine layout can be summarized as shown in Figure 21.  
Figure 19: Effect of pre-turbine pressure % change on: (**a**) engine’s torque at full load; (**b**) M/G performance at full load; negative values indicate energy harvesting.