esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**A5**} \\ \hline
**Binding Energy (eV)** & **Counts** & **S/N Ratio** & **Spectral Line** & **ID** & **CAS Registry No.** \\ \hline
779.8 & 3122 & - & Ba 3d5/2 & BaO, BaCO\({}_{3}\) & 1304-28-5, 513-77-9 \\
530.4 & 209,903 & 83 & O 1s & -O, -CO\({}_{3}\) & multiple \\
438.0 & 12,348 & 4 & Ca 2s & Ca- & 1305-78-8, 471-34-1 \\
413.5 & 62,981 & 24 & Mo 3p1/2 & MoS\({}_{2}\) & 1317335 \\
401.3 & 29,293 & 10.7 & Sc 2p3/2 & Sc\({}_{2}\)O\({}_{3}\) & 12060081 \\
399.5 & 119,630 & 46.8 & Mo 3p3/2 & MoO\({}_{x}\) & 18868434 \\
344.9 & 1667 & - & Ca 2p3/2 & Ca- & 7440-70-2, 471-34-1 \\
288.4 & 39,144 & 14.6 & C 1s & -CO\({}_{3}\) & multiple \\
284.6 & 276,679 & 109.6 & C 1s & C (Adv.) & multiple \\
251.4 & 34,956 & 11 & Ba 4s & Ba- & 1304-28-5, 513-77-9 \\
232.0 & 267,459 & 106 & Mo 3d5/2 & Mo- & 1313-27-5 \\
177.7 & 458 & - & Ba 4p3/2 & Ba- & 1304-28-5, 513-77-9 \\
116.3 & 2450 & - & Al 2s & Al- & 1344-28-1 \\
89.8 & 5059 & - & Ba 4d5/2 & Ba- & 1304-28-5, 513-77-9 \\
63.2 & 16,425 & 6 & Mo 4s & Mo- & 7439-98-7 \\
37.9 & 41,223 & 16 & W 4f5/2 & W- & 1314-35-8 \\
24.8 & 9569 & 3 & Ca- & 1305-78-8, 471-34-1 \\
13.0 & 309 & - & Ba 5p3/2 & BaO & 1304-28-5 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: XPS data for point A4. Binding energy and counts taken from the overview spectra. S/N ratios were computed manually. A “-” indicates an S/N ratio below 1.5, implying signal indistinguishable from background. Spectral line and ID were taken from the NIST XPS Database [22]. See text for details. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**A4**} \\ \hline
**Binding Energy (eV)** & **Counts** & **S/N Ratio** & **Spectral Line** & **ID** & **CAS Registry No.** \\ \hline
779.8 & 4067 & - & Ba 3d5/2 & BaO, BaCO\({}_{3}\) & 1304-28-5, 513-77-9 \\
530.4 & 235,620 & 98 & O 1s & -O, -CO\({}_{3}\) & multiple \\
438.0 & 3638 & - & Ca 2s & Ca- & 1305-78-8, 471-34-1 \\
413.5 & 85,674 & 35 & Mo 3p1/2 & MoS\({}_{2}\) & 1317335 \\
401.3 & 31,362 & 12 & Sc 2p3/2 & Sc\({}_{2}\)O\({}_{3}\) & 12060081 \\
399.5 & 152,950 & 63 & Mo 3p3/2 & MoO\({}_{x}\) & 18868434 \\
344.9 & 595 & - & Ca 2p3/2 & Ca- & 7440-70-2, 471-34-1 \\
288.4 & 32,506 & 13 & C 1s & -CO\({}_{3}\) & multiple \\
284.6 & 239,737 & 100 & C 1s & C (Adv.) & multiple \\
251.4 & 40,140 & 16 & Ba 4s & Ba- & 1304-28-5, 513-77-9 \\
232.0 & 346,772 & 145 & Mo 3d5/2 & Mo- & 1313-27-5 \\
177.7 & 3029 & - & Ba 4p3/2 & Ba- & 1304-28-5, 513-77-9 \\
116.3 & 111 & - & Al 2s & Al- & 1344-28-1 \\
89.8 & 6316 & 2 & Ba 4d5/2 & Ba- & 1304-28-5, 513-77-9 \\
63.2 & 19,057 & 7 & Mo 4s & Mo- & 7439-98-7 \\
37.9 & 50,703 & 20 & W 4f5/2 & W- & 1314-35-8 \\
24.8 & 5345 & - & Ca 3p & Ca- & 1305-78-8, 471-34-1 \\
13.0 & 92 & -0 & Ba 5p3/2 & BaO & 1304-28-5 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: XPS data for point A5. Binding energy and counts taken from the overview spectra. S/N ratios were computed manually. A “-” indicates an S/N ratio below 1.5, implying signal indistinguishable from background. Spectral line and ID were taken from the NIST XPS Database [22]. See text for details.
###### Abstract  
Currently, remote sensing crop identification is mostly based on all available images acquired throughout crop growth. However, the available image and data resources in the early growth stage are limited, which makes early crop identification challenging. Different crop types have different phenological characteristics and seasonal rhythm characteristics, and their growth rates are different at different times. Therefore, making full use of crop growth characteristics to augment crop growth difference information at different times is key to early crop identification. In this study, we first calculated the differential features between different periods as new features based on images acquired during the early growth stage. Secondly, multi-temporal difference features of each period were constructed by combination, then a feature optimization method was used to obtain the optimal feature set of all possible combinations in different periods and the early key identification characteristics of different crops, as well as their stage change characteristics, were explored. Finally, the performance of classification and regression tree (Cart), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) classifiers in recognizing crops in different periods were analyzed. The results show that: (1) There were key differences between different crops, with rice changing significantly in period F, corn changing significantly in periods E, M, L, and H, and soybean changing significantly in periods E, M, N, and H. (2) For the early identification of rice, the land surface water index (LSWI), simple ratio index (SR), B11, and normalized difference tillage index (NDTI) contributed most, while B11, normalized difference red-edge3 (NDRE3), LSWI, the green vegetation index (Vlgreen), red-edge spectral index (RESI), and normalized difference red-edge2 (NDRE2) contributed greatly to corn and soybean identification. (3) Rice could be identified as early as 13 May, with PA and UA as high as 95%. Corn and soybeans were identified as early as 7 July, with PA and UA as high as 97% and 94%, respectively. (4) With the addition of more temporal features, recognition accuracy increased. The GBDT and RF performed best in identifying the three crops in the early stage. This study demonstrates the feasibility of using crop growth difference information for early crop recognition, which can provide a new idea for early crop recognition.  
eurmy 2023  
30 January 2023  
Accepted: 31 January 2023  
Submitted: 3 February 2023
## 2 Materials and Methods
### Characterization of EPS  
The purified EPS fraction was subjected to FTIR analysis using potassium bromide (KBr) disc method in order to study the functional groups. The EPS sample (2-5 mg) was mixed with KBr of IR spectroscopy grade in a smooth agate mortar. The mixture was then pressed under pressure of about 5-8 ton cm\({}^{-2}\) for about 20-30 s [4; 23]. The electrical charge on EPS was determined by electrophoresis method using dynamic light scattering in combination with an applied electric field [24]. The \({}^{1}\)H NMR and \({}^{13}\)C NMR analyses were conducted in D\({}_{2}\)O solvent. To obtain \({}^{1}\)H NMR spectra, EPS sample (0.7 mL) was dissolved in D\({}_{2}\)O, and a solution of tetramethylsilane (TMS) was used as the reference internal standard. The \({}^{1}\)H NMR and \({}^{13}\)C NMR spectra were recorded at 500 and 125.7 MHz, respectively. Mass spectra were collected with a JEOLJMS-AX 500 spectrophotometer.
## 2 Materials and Methods and Results
### Deadweight Calibrations
#### 2.2.4 Phase 3: Day-to-Day Reliability  
The third phase tested inter-session reliability, with the goal of determining how often we would need to recalibrate the load cells during human data collection. We calibrated every load cell 1 day, 1 week, and 6 months apart. For all these calibration sessions, we used the power station and calibrated the load cells after 30 min of warm-up time.  
For the statistical analysis of phase 3 (assessing between-session reliability), we used Bland-Altman analysis [17; 18] for limits of agreement (LOA) as well as calculated an intraclass correlation coefficient (ICC). For the Bland-Altman analysis, we matched observed volts by load and individual load cells and paired days (days 0, 1, 7, and 180). A random-effects model was used to estimate the ICC. Using this model, we also estimated the percentage of variation in voltage explained by the load, individual load cells, and day (session). For this analysis, the performance package in statistical software R was applied [19; 20].  
According to the random-effects model, the resulting ICC is 0.998, and the model estimated that about 98.86% of the variation in voltage was due to the load, 0.98% due to the individual load cells, and 0% due to the day (session), and about 0.15% was unexplained.  
For any paired days, there is no statistically significant difference in the average volt (i.e., no systematic bias, on average). The 95% LOA indicates that about 95% of the differences would be between \(-\)0.022 and 0.017 volts (days 1 vs. 0), and 95% LOAs for other paired days were shorter (Figure 7 and Table 1). There was a tendency that the absolute difference in volts was larger when the load was heavier, and among all day-to-day comparisons, the biggest absolute difference, observed between days 1 and 0, was 0.0546 volts, which was observed at load \(\sim\)200 kg. Therefore, we conclude that the load cells have excellent inter-session reliability (practically negligible differences between days). In practical terms, these results indicate that our calibrations will hold steady for at least 6 months before we need to recalibrate all our equipment.  
Figure 6: Stability of load–voltage relationship within each load cell over 7 h of run time.  
Phase 3 was the final phase of deadweight calibrations and reliability testing; in our last phase, we report on inter-trial reliability when human subjects pushed on the scrum machine.
## 6 Simulation Results
### Analysis of the Crack Detection Results of the OMP Algorithm
#### 6.2.1 Analysis of the Results for a Single Crack  
The localization of individual cracks was simulated and analyzed. The simulated estimation of the leak location is shown in Figure 5, where the number of spatial sample points determines the localization accuracy. The average error in crack location analysis for a single crack was 4.3547 m. The method was not applied in tunnel crack detection, and a comparison of the average errors in [12] shows that the simulation error is decreased, and the cumulative distribution functions (CDFs) of the estimated crack position errors are shown in Figure 6.  
As shown in Figure 5, the simulation results obtained using the CS-based Orthogonal Matching Pursuit (OMP) algorithm are presented. The widely spaced points in the figure represent the measurement nodes, which are the locations where the sensor detection data are obtained. The closely spaced nodes represent the spatial sample points, which correspond to the potential crack locations.  
As shown in Figure 6, the CDF of the error obtained from experimental simulation for a single crack indicates the distribution of the error. The error, denoted as \(|\hat{\kappa}-\kappa|\), represents the distance between the estimated position and the actual location of the crack, reflecting the accuracy of the estimation.
## 5 Results
### OCR Performance Analysis  
We analyzed the performance of the combined EasyOCR and Tesseract approach, focusing on its impact on the subsequent redaction task. While traditional OCR metrics like character error rate (CER) and word error rate (WER) are informative, they do not directly translate to the success or failure of PII redaction. A single character error in a name can be as detrimental as multiple errors in a non-PII word. Therefore, we qualitatively assessed the types of OCR errors and their potential consequences for PDPA compliance.  
Our observations revealed several common error patterns: misrecognition of Thai characters (due to the script's complexity), incorrect word segmentation (due to the lack of spaces between words in Thai), errors in recognizing numbers and symbols, and partial text extraction (missing parts of words or phrases). Many of these errors were associated with low OCR confidence scores, suggesting that incorporating confidence thresholds in future work could improve performance. These OCR imperfections directly influence the LLM's ability to identify PII. A misspelled or fragmented name, phone number, or hospital number is less likely to be recognized as such by the LLM. This underscores the critical dependency between OCR quality and overall redaction performance. Our mitigation strategies included combining the outputs of both EasyOCR and Tesseract (prioritizing recall), using a targeted LLM prompt designed to handle minor OCR errors, and focusing on maximizing the useful information extracted for redaction, even if not perfectly accurate. We acknowledge that perfect OCR on real-world, noisy label images is a significant challenge.
# Block Recovery Rate-Based Unequal Error Protection for Three-Screen TV
## 4 Experimental Results and Discussion
### H.265 SHVC Unequal Error Protection Using Raptor Codes without Layer Switching  
This subsection explains the experimental results of H.265 SHVC-based UEP without layer switching. For verifying the proposed methods, ultra-high definition (UHD) test sequence 'PeopleOnStreet' which is defined in the common test condition (CTC) of JCT-VC standard work was used. The resolution of base layer and enhancement layer were 1920 by 1080 and 3840 by 2160, respectively. QP values were also applied for those base layer and enhancement layers as 34 and 36, and three picture coding structures (random access (RA), all intra (AI), and low-delay B (LDB)) were tested. For the SHVC, SHM version 4.1 encoder was used, and the ratio of spatial scalability was 2:1 (2160 p for the enhancement layer and 1080 p for the base layer) [33; 34; 35]. The experiment was conducted without the simulation codes but the fully implemented UEP system. The experimental setup and results are as shown in Tables 1 and 2. In the result, the base layer was protected more and the enhancement layer was sacrificed for the base layer in given Raptor overheads (e.g., 5% and 10%) with base PLRs (e.g., 12% and 17%). The Table 2 shows the proposed UEP method provided better objective video qualities with Y-PSNR values.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **SHM 4.1. (JCT-VC SHVC Reference SW)**, \\
**Encoder and Decoder** & **OpenHEVCDecoder (Opensource SW), and** \\  & **Implemented Raptor En/Decoder** \\ \hline Test sequence name & PeopleOnStreet \\ Resolutions for base layer and enhancement layer & 1920 by 1080, 3840 by 2160 \\ QPs for base layer and enhancement layer & 34, 36 (base layer QP + 2) \\ Picture coding structures & RA, AI, LDB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environments. QPs, quantization parameters; RA, random access; AI, all intra; LDB, low-delay B.  
Figure 11: Network topology.
# Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
## 3 Neural Networks
### Gcnn-Lstm-At Network
#### 3.3.1 Graph Convolutional Network (GCN)  
The application of GCN allows the model to capture the graph structure in the input data [31]. By leveraging the connections between nodes in the input data, GCN can capture the interactions and dependencies among nodes. This is particularly beneficial in dealing  
Figure 6: The framework of the Inception module.  
Figure 7: The framework of CNN-AT algorithm.  
with data that exhibits complex correlations. The 12 locations on each tangerine have their own contextual information to some extent, while also being interdependent. To extract the relations between the 12 spectra, GCN processes the input data by generating graph-based features before putting it into the two-dimensional CNNs. The GCN is a layer-to-layer propagation, which is computed as:  
\[H^{l+1}=\sigma\Big{(}\hat{D}^{-\frac{1}{2}}\hat{A}\hat{D}^{-\frac{1}{2}}H^{l}W^{ l}\Big{)} \tag{4}\]  
where \(\sigma\) denotes the activation function (e.g., ReLU), \(W^{l}\) is the trainable weights, \(H^{l}\) is the matrix activation in layer \(l\) and \(H^{0}=X\). \(\hat{A}=A+I\), \(A\) refers to the adjacency matrix, \(I\) is the identity matrix and \(\hat{D}_{ii}=\sum_{j}\hat{A}_{ij}\).  
The given input \(X\in\mathbb{R}^{N\times D}\) represents \(N\) nodes with \(D\) features. The adjacency matrix \(A\) is calculated in a weighted way using the \(k\)-nearest neighbors algorithm (\(N=12,D=701,k=3\)). In this study, a two-layer GCN was employed, which is computed as:  
\[Z=f(X,A)=\Big{(}\hat{D}^{-\frac{1}{2}}\hat{A}\hat{D}^{-\frac{1}{2}}\text{ ReLU}\Big{(}\hat{D}^{-\frac{1}{2}}\hat{A}\hat{D}^{-\frac{1}{2}}XW^{0}\Big{)}W^{1} \Big{)} \tag{5}\]  
The output feature vector \(Z\) is duplicated two times and concatenated with the original spectra \(X\) (which is duplicated three times), obtaining an augmented feature of shape (\(n\), 60, 701). This information propagation mechanism enables the model to utilize global information for inference and prediction, rather than relying solely on local features of each node.
###### Abstract
[29] conceived and designed a GPS-based sun-tracker system that allows the vertical solar tracker to follow the sun's azimuth angle. They analyzed the performance of two solar panels that were positioned differently. The panel with SAT yielded around 22% more than the fixed PV panel in terms of average output power. In Central Vietnam, in [30], 250 W PV modules connected to the grid were adopted to test the effectiveness of an SAT solar tracking system. The experiments revealed that the tracking system consumed around 2-8% of the power produced. In addition to the solar tracker's configuration, the climate condition and geographical position affect the performance tracker. Even though the tracking system has the potential to increase the amount of energy gained by PV modules in many conditions, other studies suggest that it is not suitable for hot climates [31]. As a result, a thorough examination should be conducted before using the solar tracking system in any locality. An extended study across a broad region and many countries is required to fully comprehend the energy gained by the solar tracking system on a global scale. Thus, the experimental study presented in this paper explores the actual development and implementation of an STS, where the electrical performance of different sensor- and azimuth-based control schemes was investigated. First, a numerical model of the azimuth-based STS is derived. Then, a closed-loop LDR-based control scheme is highlighted. Therefore, the presented research effort adds to the body of knowledge on quantitative analysis and detailed comparison of generated, consumed, and net power of different tested SAT systems in a high solar irradiance region of Dammam, Saudi Arabia.
## 2 Numerical Simulation Method
### Grid Scheme and Boundary Conditions
#### 2.2.2 Boundary Conditions, Governing Equations and Turbulence Model
\]  
where \(\mu_{eff}=\mu+\mu_{t}\), \(\mu_{t}=\rho C_{k}\frac{\epsilon^{2}}{\epsilon}\), \(C_{t}=0.0845\), \(\alpha_{k}=\alpha_{\epsilon}=1.39\), \(C_{1\epsilon}=1.39\), \(C_{2\epsilon}=1.68\), \(R=\frac{C_{2}\rho\eta^{3}(1-\eta/\eta_{0})}{1+\beta\eta^{3}}\), \(\eta=\big{(}2E_{ij}\cdot E_{ij}\big{)}^{\frac{1}{2}}\frac{k}{\epsilon}\), \(E_{ij}=\frac{1}{2}\Big{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{j}}{\partial x_{i}}\Big{)}\), \(\eta_{0}=4.377\) and \(\beta=\)0.012. Because the fluid movement is obviously affected by the wall flow conditions, the flow Reynolds number in the near wall area is low and the turbulence development is not sufficient, it is necessary to treat the near wall flow of the solid wall of the pump device as a function; the tangential velocity of the water near the wall and the wall shear force can be obtained through the pairwise expression [28]  
\[ \begin{array}{c}u^{+}=\frac{1}{k}ln(y^{+})+C\\ u^{+}=\frac{u}{\mu_{\tau}}=\frac{\rho uvy}{\mu}\\ y^{+}=\frac{\rho\Delta y\mu_{\tau}}{\mu}\\ u_{\tau}=\Big{(}\frac{\tau_{w}}{\rho}\Big{)}^{\frac{1}{2}}\end{array} \]  
where \(y^{+}\) is the dimensionless distance, \(\tau_{w}\) is the wall shear stress, \(u\) is the velocity tangent to the wall, \(\Delta y\) is the actual distance from the wall, \(k\) is the [PERSON] constant and \(C\) is the constant related to the wall roughness (\(C=5.2\)).
## 1 Introduction
### Novel Contribution to VUG Analysis  
This study makes several significant contributions to the vertical urban growth monitoring studies:  
Innovative Stack Processing Approach: We advance the PSInSAR methodology by implementing a comprehensive stack processing approach specifically designed for monitoring temporal changes in urban vertical growth. While previous studies have applied PSInSAR for various urban applications [27; 28], our work is among the first to utilize a multi-temporal stack processing framework specifically optimized for tracking building height changes over time.  
Temporal Resolution and Coverage: By processing 375 COSMO-SkyMed images spanning from 2012 to 2020, we provide an unprecedented temporal coverage and resolution for monitoring vertical urban development, enabling detailed analysis of growth patterns and trends that would be undetectable in studies with limited temporal coverage and resolution.  
Multi-scale Analysis Framework: We developed and tested a methodological framework for analyzing vertical growth at multiple spatial scales, from 50 m grid level to district-level patterns, providing a comprehensive understanding of urban development dynamics.  
Comparative Validation Approach: We implement a validation strategy that compares our PSInSAR-derived height estimates with both global datasets (e.g., Global Human Settlement Layer, GHSL) and high-precision drone measurements, quantifying the accuracy and limitations of different data sources for VUG monitoring.  
While previous studies like VUG monitoring in Patna Urban Agglomeration [29], for detecting new buildings in Athens [30], and building height estimation in Chengdu [31] have demonstrated the utility of PSInSAR, they have not capitalized on the stack processing method, which can significantly refine VUG measurements. Our research comprehensively evaluates the effectiveness of the PSInSAR technique for quantifying and mapping complex patterns of vertical expansion in rapidly developing cities, demonstrating how the availability of a consistent, long-term, high-resolution satellite X-band SAR time series can benefit this process.  
By implementing a systematic validation process that includes comparing PSInSAR-derived height data with GHSL and independent validation data, we deliver a robust assessment of VUG in Wuhan. This approach ensures that our findings provide a detailed and precise understanding of the complex patterns of vertical expansion, addressing critical gaps in current urban remote sensing methodologies.