esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 6 Discussion and Analysis  
Concrete pavements are widely used because of their long-term cost-effectiveness, structural strength, and durability. However, the abrasion of concrete pavement due to various factors has affected its durability and performance. The rate of abrasion is greatly influenced by the interplay of material qualities, workmanship, environmental factors, and vehicle loads; therefore, better comprehension of the causes and mitigation techniques is essential. Numerous research has linked compressive strength to abrasion resistance, emphasizing the need to use high-performance mix compositions. Although the standard test for assessing abrasion resistance provides valuable insight, there is a need for standardized testing procedures that accurately mimic real-world conditions. There have been various studies on the use of supplementary cementitious materials (SCMs), fibers, and admixtures to improve abrasion resistance due to developments in material science. However, the trade-offs regarding material availability, workability, strength, economic analysis, and environmental issues need further analysis.  
Compared to conventional linear and nonlinear regression models, the prediction of concrete performance related to abrasion resistance has been enhanced by the developments in computing techniques (machine learning) approaches, especially through the extensive use of artificial neural networks (ANNs) and random forests (RFs). According to previous studies, these models perform better in forecasting abrasive wear, particularly when several variables interact in intricate ways, providing increased efficiency, cost savings, and decreased material waste. Despite these developments, choosing the appropriate model and using high-quality training datasets that depict the real-life scenario are still essential for obtaining high predictive accuracy, indicating that future studies should focus on creating standardized processes.
# Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths
## 4 Multi-Frequency RTK Positioning Accuracy Analysis
### Positioning Accuracy

Table 6: TCCU–TGBU (30 km) average of medium baseline RTK RMS and ambiguity fixing rate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & **Fixing** & \multicolumn{3}{c}{**RMS**} \\ \cline{2-5}  & **Rate** & **N (m)** & **E (m)** & **U (m)** \\ \hline B1C/B2a & 92.29\% & 0.015 & 0.009 & 0.029 \\ B1I/B3I & 97.69\% & 0.017 & 0.010 & 0.030 \\ L1L2 & 99.13\% & 0.015 & 0.010 & 0.030 \\ B1I/B3I(BDS-3) & 89.09\% & 0.016 & 0.010 & 0.033 \\ B1C/B2a/B3I & 92.68\% & 0.014 & 0.009 & 0.028 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: TGDA-TGCU (96 km) average of medium baseline RTK RMS and ambiguity fixing rate. Figure 8: TGD2-TGCU (128 km) long baseline RTK positioning accuracy.
# Protection of Digital Elevation Model--One Approach
## 3 Results  
Leaking of data from this system means significant damage to this system, but also other systems (business organizations, government organizations, and the state). Security is a major problem for these systems, and it lies in its nature [44]: collection, processing, transmission, and storage of data. Security threats exploit the vulnerabilities of sensitive information such as authenticity, integrity, confidentiality, and non-repudiation.  
In this section, we propose a model for non-repudiation and protected DEM data in security-sensitive systems. The model meets the following security requirements: confidentiality, authenticity, integrity, non-repudiation of transactions, and trust of the parties in the transaction. The fulfillment of the security requirements of the model is achieved by the combined application of cryptography and PKI.  
The digital signature and digital certificate are used to fulfill the requirements of the authenticity, integrity, and non-repudiation of the transaction. A digital certificate and a PKI are applied to the trust of the parties in the transaction. Whereas symmetric cryptography is used to satisfy the confidentiality of transactions, asymmetric cryptography has applied the confidentiality of the secret key in the transaction.
# Feasibility of LED-Assisted CMOS Camera: Contrast Estimation for Laser Tattoo Treatment
###### Abstract  
Understanding the residual tattoo ink in skin after laser treatment is often critical for achieving good clinical outcomes. The current study aims to investigate the feasibility of a light-emitting diode (LED)-assisted CMOS camera to estimate the relative variations in tattoo contrast after the laser treatment. Asian mice were tattooed using two color inks (black and red). The LED illumination was a separate process from the laser tattoo treatment. Images of the ink tattoos in skin were acquired under the irradiation of three different LED colors (red, green, and blue) for pre- and post-treatment. The degree of contrast variation due to the treatment was calculated and compared with the residual tattoo distribution in the skin. The black tattoo demonstrated that the contrast consistently decreased after the laser treatment for all LED colors. However, the red tattoo showed that the red LED yielded an insignificant contrast whereas the green and blue LEDs induced a 30% (\(p<0.001\)) and 26% (\(p<0.01\)) contrast reduction between the treatment conditions, respectively. The proposed LED-assisted CMOS camera can estimate the relative variations in the image contrast before and after the laser tattoo treatment.  
contrast variation; CMOS; LED; tattoo treatment Article
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 3 The Proposed Approach  
The framework of the proposed approach is shown in Figure 2. The wavelet coefficients that required threshold de-noising are determined by wavelet energy, and then further de-noising through sub-band smoothing filter to obtain the final de-noised smoothed ECG signal. A detailed explanation on every step is provided below.
## 4 Results and Analysis
### Urban Burning Area Detection
The Russian army had taken full control of the urban area of Mariupol, and the subsequent fighting basically centered on the Azov steel works, so the burning areas were located in and around the plant. Ukrainian forces surrendered on 16 May, and Sentinel-2A satellite imagery on 28 May showed no burning area in or around the Azovstal steel and iron works, bringing the nearly three-month battle for Mariupol to an end. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**CEM**} & \multicolumn{3}{c}{**IFB-CEM**} \\ \cline{2-7}  & **Recall** & **Precision** & **F1 Score** & **Recall** & **Precision** & **F1 Score** \\ \hline
4 March 2021 & 0.959 & 0.839 & 0.895 & 0.980 & 0.959 & 0.969(10.074) \\
13 January 2022 & 0.982 & 0.900 & 0.939 & 0.964 & 0.947 & 0.955(10.016) \\
13 March 2022 & 0.981 & 0.912 & 0.945 & 0.943 & 0.963 & 0.953(10.008) \\
14 March 2022 & 1.000 & 0.922 & 0.959 & 0.979 & 0.979 & 0.979(10.020) \\
19 March 2022 & 0.972 & 0.886 & 0.927 & 0.956 & 0.959 & 0.957(10.030) \\
21 March 2022 & 1.000 & 0.877 & 0.934 & 0.972 & 0.986 & 0.979(10.045) \\
22 March 2022 & 0.965 & 0.933 & 0.949 & 0.948 & 0.976 & 0.962(10.013) \\
24 March 2022 & 0.973 & 0.899 & 0.934 & 0.978 & 0.973 & 0.975(10.041) \\
29 March 2022 & 0.986 & 0.907 & 0.944 & 0.986 & 0.986 & 0.986(10.042) \\
3 April 2022 & 0.984 & 0.861 & 0.919 & 0.984 & 0.964 & 0.974(0.055) \\
7 April 2022 & 1.000 & 0.807 & 0.893 & 1.000 & 0.979 & 0.989(0.096) \\
15 April 2022 & 0.933 & 0.667 & 0.778 & 0.933 & 0.933 & 0.933(10.115) \\
30 April 2022 & 1.000 & 0.333 & 0.500 & 1.000 & 0.667 & 0.800(10.300) \\
1 May 2022 & 0.833 & 0.625 & 0.714 & 1.000 & 0.831 & 0.908(10.194) \\
3 May 2022 & 1.000 & 0.667 & 0.800 & 0.883 & 0.883 & 0.883(10.083) \\
8 May 2022 & 0.833 & 0.625 & 0.714 & 0.700 & 0.840 & 0.764(10.050) \\
9 May 2022 & 0.667 & 0.571 & 0.615 & 0.533 & 0.815 & 0.645(10.030) \\
28 May 2022 & 0.500 & 1.000 & 0.667 & 0.500 & 1.000 & 0.667(-) \\ \hline \end{tabular}
\end{table} 
Table 1: The optimal extraction results before and after the method improvement. Figure 9: The temporal–spatial distribution and area statistics of burning areas during the conflict.
###### Abstract  
This study proposes a bilateral attention U-Net with a dissimilarity attention gate (DAG) for change detection on remote sensing imageries. The proposed network is designed with a bilateral dissimilarity encoding for the DAG calculation to handle reversible input images, resulting in high detection rates regardless of the order of the two input images for change detection. The DAG exploits all the combinations of joint features to avoid spectral information loss fed into an attention gate on the decoder side. The effectiveness of the proposed method was evaluated on the KOMPSAT-3 satellite images dataset and the aerial change detection dataset (CDD). Its performance was better than that of conventional methods (specifically, U-Net, ATTUNet, and Modified-UNet++) as it achieved average \(F_{1}\)-score and kappa coefficient (KC) values of 0.68 and 66.93, respectively, for the KOMPSAT-3 dataset. For CDD, it achieved \(F_{1}\)-score and KC values of 0.70 and 68.74, respectively, which are also better values than those achieved by conventional methods. In addition, we found that the proposed bilateral attention U-Net can provide the same changed map regardless of whether the image order is reversed.  
convolutional neural network; deep learning remote sensing; satellite images; change detection Article  
Bilateral Attention U-Net with Dissimilarity Attention Gate for Change Detection on Remote Sensing Imageries.
# Multiple-Diffraction Subtractive Double Monochromator with High Resolution and Low Stray Light
## 1 Introduction
We derived a unified formula for multiple diffractions in grating-based spectrometers and established the quantitative stray-light suppression principle of the subtractive double monochromator. Based on this theoretical foundation, we designed, constructed, and experimentally validated a prototype MSDM system operating in the 1250-1650 nm near-infrared band. Compared with the conventional single-diffraction dispersion method, the angular resolution of a double-diffraction method could be enhanced by a factor of 5-7, and the subtractive double monochromator weakened the stray light by a hundredfold. The experimental results show that the resolution is 18.8 pm within the 1250-1650 nm band, and the OSNR reaches approximately 70 dB. The MSDM serves as the core spectroscopic module for both emission and absorption spectrometers. While the current prototype has been experimentally validated for the near-infrared band (1250-1650 nm), the underlying design principle can be readily extended to other spectral ranges, including visible and UV bands, through optical component adaptation. In the telecommunications wavelength band, it can detect optical communication channels, while in the UV-visible range, it enables elemental analysis. This study holds great promise for high-precision and high-sensitivity spectral detection.
# Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents
## 4 Calculation Equation for Air Demand
### Analysis of the Interfacial Shear Stress Formulation  
Previous studies have indicated that the variation coefficient (\(\omega\)) in Equation (5) can be expressed as a function of the air-water interface amplitude, which is related to the root mean square of the normal fluctuating velocity (\([\overline{(\
u^{\prime})^{2}}]^{0.5}\)) near the air-water interface [13; 14; 25]. A study of uniform open-channel flow revealed that the mean normal pulsating velocity (\([\overline{(\
u^{\prime})^{2}}]^{0.5}\)) and friction velocity (\(u^{*}\)) satisfies Equation (9) [26; 27], and Equation (10) can be derived from the force balance equation [26]. \([\overline{(\
u^{\prime})^{2}}]^{0.5}\) can be calculated from the hydraulic radius (\(R\)) and hydraulic slope (\(J\)), as shown in Equation (11).  
\[(\overline{(\
u^{\prime})^{2}}]^{0.5})_{Z=h_{w}}=1.48e^{-0.7} \tag{9}\]  
\[u^{*}=(gRJ)^{0.5} \tag{10}\]  
\[[\overline{(\
u^{\prime})^{2}}]^{0.5}=1.48\times e^{-0.7}\times u^{*}=0.735u^{* }=0.735(gRJ)^{0.5} \tag{11}\]  
Combining [PERSON]'s formula, [PERSON]'s formula, and [PERSON]'s coefficient formula in Equation (12), \((RJ)^{0.5}\) can be calculated in Equation (13) [28].  
\[\begin{split}&(RJ)^{0.5}=\frac{U_{w}}{C}\\ & C=\frac{R^{1/6}}{n^{\prime}}\\ & n^{\prime}=0.038R^{1/6}(\frac{17\mu_{w}}{\rho_{w}U_{w}R}+\frac{ 0.25\Delta}{R})^{0.125}\\ &(RJ)^{0.5}=0.038(\frac{17\mu_{w}}{\rho_{w}U_{w}R}+\frac{0.25 \Delta}{R})^{0.125}U_{w}\end{split} \tag{12}\]  
Equations (11) and (13) show that the inverted roughness (\(\Delta\)) has a strong effect on the normal fluctuating velocity (\([\overline{(\
u^{\prime})^{2}}]^{0.5}\)), which can be explained by the fact that roughening of the invert can break the laminar sublayer and increase the surface turbulence  
Figure 12: Flow diagram of the solution procedure for air demand.  
intensity [28; 29; 30; 31; 32]. Thus, the relationship in Equation (14) can be rationally assumed for the free-surface flow pattern.  
\[\omega\sim[\overline{({v^{\prime}})}^{2}]^{0.5}\sim g^{0.5}(\frac{17\mu_{w}}{\rho _{w}U_{w}R}+\frac{0.25\Delta}{R})^{0.125}U_{w} \tag{14}\]  
On the basis of the model test data of case (5), the variation coefficient (\(\omega_{0}\)) was calculated via the method described in Section 4.1, combining the loss coefficient (\(\delta_{i}\)) of the \(i\)th vent computed via Equation (15) [33]. The optimal fit (Equation (16), Figure 13) was obtained through linear least-squares regression, yielding a mean absolute percentage error (MAPE) of 6.95% across the experimental dataset. The relationship demonstrates strong predictive accuracy (R\({}^{2}\) = 0.937) with 95% confidence bounds of the regression coefficient. This statistical validation aligns with established methodologies for hydraulic parameter estimation [34].  
\[\delta_{i}=\frac{\lambda_{i}s_{i}}{d_{i}}+\sum\xi_{i}\approx 0.015\times 16+1.8=2.04 \tag{15}\]  
\[\omega=1.3648\times 10^{-5}g^{0.5}(\frac{17\mu_{w}}{\rho_{w}U_{w}R}+\frac{0.25 \Delta}{R})^{0.125}U_{w} \tag{16}\]
## 3 Details of the Proposed Algorithm
### Update of Auxiliary Variable: \(\mathbf{z}\)  
The optimization of \(\mathbf{z}\) in each iteration only includes the evaluation of the proximal operator with constant step-size \(t_{z}=1\), which is equivalent to performing the element-wise soft-thresholding step as  
\[\mathbf{z}^{(k+1)}=S_{\tau/\rho}\Big{(}\mathbf{z}^{(k)}-\
abla g_{1}(\mathbf{z} ^{(k)})/\rho\Big{)} \tag{31}\]  
with  
\[\
abla g_{1}(\mathbf{z}^{(k)})=\rho(\mathbf{z}^{(k)}+\mathbf{\Phi}\mathbf{x}^ {(k+1)}-\mathbf{y}+\gamma^{(k)}/\rho). \tag{32}\]  
Similarly, we integrate the extrapolation step of variable \(\mathbf{z}\) to improve the convergence rate:  
\[\mathbf{v}^{(k+1)}=\mathbf{z}^{(k)}+\frac{\mu_{z}^{(k-1)}}{\mu_{z}^{(k)}}( \mathbf{w}_{z}^{(k)}-\mathbf{z}^{(k)})+\frac{\mu_{z}^{(k-1)}-1}{\mu_{z}^{(k)}} (\mathbf{z}^{(k)}-\mathbf{z}^{(k-1)}), \tag{33}\]  
where \(\mathbf{w}_{z}^{(k)}=S_{\tau/\rho}\Big{(}\mathbf{v}^{(k)}-\
abla g_{1}( \mathbf{v}^{(k)})/\rho\Big{)}\), and \(\mu_{z}^{(k)}\) follows the same update procedure as \(\mu_{x}^{(k)}\) (refer to (26)). The pseudocode of the update process of \(\mathbf{z}\) is summarized in Algorithm 3.  
```
0: Set \(\mathbf{z}^{(0)}=\mathbf{y}-\mathbf{\Phi}\mathbf{x}^{(0)}\), \(\mu_{z}^{(0)}=\mu_{z}^{(-1)}=1\).
0: Get \(\mathbf{x}^{(k)}\), \(\mathbf{z}^{(k-1)}\), \(\mathbf{z}^{(k-2)}\), \(\mathbf{w}_{z}^{(k-1)}\), \(\gamma^{(k-1)}\) and \(G(\mathbf{z}^{(k-1)})\) from previous iteration.
1: Compute the extrapolation point \(\mathbf{v}^{(k)}\) by (33) and the gradient \(\
abla g_{1}(\mathbf{v}^{(k)})\) by (32).
2: Evaluate the proximal operator: \(\mathbf{w}_{z}^{(k)}=S_{\tau/\rho}\Big{(}\mathbf{v}^{(k)}-\
abla g_{1}( \mathbf{v}^{(k)})/\rho\Big{)}\).
3: Compute \(G(\mathbf{w}_{z}^{(k)})\) by (17) and \(\mathbf{z}^{(k)}=\underset{\mathbf{z}}{\operatorname{argmin}}\ \{G(\mathbf{z}):\mathbf{z}=\mathbf{w}_{z}^{(k)}, \mathbf{z}^{(k-1)}\}\).
4: Update the extrapolation parameter \(\mu_{z}^{(k)}\) by (26).
```  
**Algorithm 3** Update of Auxiliary Variable \(\mathbf{z}\)
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Issues on Image-Based SHM
#### 4.4.4 Automation  
Local deformations in steel using DIC are only possible in the presence of stress or strain [63]. An image-based monitoring system, for any structure, may be centered on recognizing physical changes that persist when a material is subjected to several types of loads, applied vertically/horizontally/longitudinally, acting on a structure. Numerous cracks form in different sizes and shapes due to the aging of equipment or materials used in constructing a building. However, there are at least a hundred members, if not a thousand, in any civil infrastructure. It is tedious if these deformations, which are often the reason why structural members fail, are traditionally monitored. Automated crack inspection and characterization in bolts on a steel structure are feasible [83]. This creates the opportunity for the creation of a database that records the status of structural elements, not only for steel, but also for other materials such as concrete in buildings, bridges, and roads. Automating the process is highly achievable through: (i) picture capture from several viewpoints; (ii) the recognition of object patches that may contain damage; (iii) the grouping of identical items across images; and (iv) the detection of crack damage triggered by objects.
## 5 Implementation of the Methods and Algorithms Used
### Implementation of the Solution, the Posed Optimisation Problem  
The task of optimisation was to find and indicate the optimal location of a potential emitter in the area under consideration. The area covered by the research was defined as the Opole County together with the city of Opole, with a territorial assumption consistent with the maps allowed by the licence. The program (Figure 3) developed uses the MC optimisation methods, the calculations of which are based on the Pasquill plume dispersion model.  
In the first step, the prepared application downloads technical parameters of the emitter and meteorological data, then a text file containing coordinates of x and y points, as well as the assigned values of the \(Z_{0}\) land roughness length and the state of the existing pollution concentration. According to the MC optimisation method used, the coordinates from among those stored in the file are drawn using a pseudo-random number generator [42]. In the next step, the calculation is carried out using the chosen dispersion model. Their concentrations are calculated for each point in the area exposed to the emitter. Then, at the points of the grid which have been considered, the concentrations found in the obtained values are added. The next step is to compare them with the standard set at 50 \(\upmu\)g/m\({}^{3}\). Yet another step is to calculate a coefficient which is the ratio of the number of points in the area in question where the standard was not exceeded to the number of all points. Obtaining a value of \(1\) for the calculated coefficient indicates that placing the emitter in this particular location will not cause the concentration standards to be exceeded in any of the points of  
Figure 3: Block diagram describing how the application works.  
the verified area. Obtaining a different value means that the emission levels are exceeded, and the search for a location is being continued in another iteration. After completion of the above calculation stage, the next potential location of the emitter is processed. It is possible to adjust the number of draws (analysed locations) in order to increase the accuracy of the algorithm, unfortunately this causes a rapid increase in calculation time. If it turns out that it is not possible to meet the assumed emission criteria at any point in the area, the algorithm returns the coordinates of the location of the emitter that will have the least negative impact on the natural environment. As a result, the algorithm returns the coordinates of the optimal location within the area of emission concerned, where the coefficient (the ratio of \"points with the pollution standard not exceeded\" to \"all points in the area under consideration\"), which is a value in the range \(\langle\)0-1\(\rangle\), is the highest. If there is a need for a deeper understanding and possible application of the discussed algorithm, it is possible to contact the authors of the paper directly for consultation, as well as to obtain additional tips that may explain the details of the design and implementation.
# Mini-LED and Micro-LED: Promising Candidates for the Next Generation Display Technology
## 1 Introduction
Therefore, the relatively mature mini-LED is expected to be the first commercialized variety while the micro-LED display technology is still in its nascent state. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mini-LED** & **Micro-LED** \\ \hline
**Size (\(\upmu\)m)** & 100–200 & \textless{}100 \\
**Purpose** & Backlight for LCD & Self-emitting display \\
**Features** & High dynamic range, power saving, thin & High contrast, high efficiency, high resolution, high response time \\
**Yield** & \textgreater{}80\% & Hard to estimate \\
**Application** & LCD backlight—From small to large LCD panel & Micro-projection display, display from small to large size \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between mini-LED and micro-LED.
## 4 Numerical Examples
### Benchmark Problem: Simple Inversion Analysis Based on the Results of Forward Analysis
#### 4.1.3 Validation Checks of Conventional and Step-by-Step Inversion Analysis  
Conventional and step-by-step inversion analysis are carried out in this section with the combinations of material parameters in Table 2.  
Figure 4: Vertical displacement values of monitoring point.  
Figure 3: Outline and discrete grids of the benchmark model.
# Finding Bottlenecks in Message Passing Interface Programs by Scalable Critical Path Analysis
## 5 Profiling MPI Applications
### Load Imbalance Estimation
#### 5.6.3 Pattern-Based  
In [38; 39; 42; 43; 44; 45], the authors analyzed the performance of MPI applications using the following patterns.  
* Late Sender. This property refers to the amount of time lost when the MPI_Recv call is sent before the corresponding MPI_Send is executed.
* Late Receiver. This property applies to the opposite case. MPI_Send is blocked until the corresponding receive operation is called. This can happen for several reasons. Either the default implementation works in synchronous mode, or the size of the message being sent exceeds the available buffer space, and the operation is blocked until the data are transmitted to the recipient. The behavior is similar to MPI_SSend waiting for the message to be delivered. The downtime is measured, and the sum of all downtime periods is returned as a severity value.
* Messages in Wrong Order. This property concerns the problem of sending messages out of order. For example, the sender can send messages in a certain order, and the recipient can expect them to arrive in the reverse order.
* Wait at Barrier. This property corresponds to the downtime caused by the load imbalance when the barrier is called. The idle time is calculated by comparing the execution time of the process for each MPI_Barrier call. To work correctly, the implementation of this property requires the participation of all processes in each call of the collective barrier operation. The final value is simply the sum of all measured downtime periods.