esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Project Description
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Name**} & \(\gamma\) & \(K_{\mathbf{h}}\) & \(\delta\) & \(\
u\) & \(K_{\mathbf{0}}\) & \(E\) & \(C\) & \(\varphi\) \\ \cline{3-10}  & & **KN/m\({}^{\mathbf{3}}\)** & **MPa/m** & **MPa** & **–** & **–** & **MPa** & **MPa** & \(\circ\) \\ \hline \textbackslash{}-1 ab1-2 & Silt and silty clay & 19.9 & 34 & – & 0.32 & 0.46 & – & 40.5 & 18.4 \\ \hline \(\gamma\)-0 & Residual soil & 19.6 & 24 & – & 0.31 & 0.45 & – & 27 & 16 \\ \hline \(\gamma\)-1 & Completely weathered granite & 20.6 & 40 & 16 & 0.3 & 0.42 & – & 28 & 18 \\ \hline \(\gamma\)-2 & strong-weathered granite & 22.5 & 100 & 39.06 & 0.29 & 0.41 & – & 35 & 21 \\ \hline \(\gamma\)-3 & Moderately weathered granite & 25.8 & 350 & 40.43 & 0.2 & 0.25 & 6000 & – & – \\ \hline \(\gamma\)-3-1 & Moderately weathered granite & 25.1 & 280 & 38.49 & 0.21 & 0.26 & 3500 & – & – \\ \hline \multirow{2}{*}{T2h-2} & Strong-weathered argillaceous silstone, silty mudstone, and mudstone & 21.5 & 90 & 0.49 & 0.29 & 0.41 & – & 34 & 20 \\ \hline \multirow{2}{*}{T2z-2} & Strong-weathered argillaceous limestone & 21.5 & 90 & 2.73 & 0.29 & 0.41 & – & 34 & 20 \\ \hline \multirow{2}{*}{T2z-3-2} & Moderately weathered limestone & 23.4 & 200 & 3.1 & 0.25 & 0.33 & 1500 & – & – \\ \hline \hline \end{tabular} Note: \(\gamma\) is volume weight, \(K_{\mathbf{h}}\) is horizontal bed coefficient, \(\delta\) is saturated uniaxial compressive strength, \(\
u\) is Poisson ratio, \(K_{\mathbf{0}}\) is coefficient of lateral earth pressure, \(E\) is elastic modulus, \(C\) is cohesion, \(\varphi\) is friction angle. “–” represents the missing data.  
\end{table} 
Table 2: Physicochemical properties of encountered ground types. Figure 5: Proportion of various strata along the longitudinal direction of the tunnel. Facing complex and variable terrain, the DZ423 earth pressure balance shield machine was selected for this construction. The opening rate of the cutter head is about 41%, and the diameter is 6440 cm, which will help reduce the possibility of sludge cake in the center of the cutter head. At the same time, cutters--such as hob, scraper and disc cutters--were installed, which had good adaptability to this complex formation. Additionally, many sensors in the shield machine can provide us with multiple mechanical parameter data such as thrust (T), cutterhead torque (CT), cutterhead rotation speed (CS), advance rate (AR), screw conveyor speed (SS), and other mechanical parameters during the excavation process, and it provided a large amount of data support for accurately predicting the advance rate of the EPB shield tunneling.
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 2 Materials and Methods
### Multi-Label Classification Model for Apple Disease and Pest Areas
#### 2.3.1 Data Input Layer  
To solve the problem of the low accuracy of single-source images using the diseases and pest classification algorithm, RGB and multispectral images were used as the data input of the model. This enhances the expression of the data information by using complementary information from multi-source images. Because multispectral images can reflect the physiological information of apples, to fully utilize the information in multispectral images, AMMFNet adopts the vegetation index selection algorithm introduced in Section 2.1 to process multispectral images into weighted vegetation indices. Furthermore, RGB images focus on intuitive effects and contain richer texture information. Therefore, AMMFNet uses RGB and multispectral images as multi-source inputs and transmits the processed RGB images and vegetation indices to the data fusion layer to obtain a more comprehensive representation of the characteristics of the apple orchard.
# Changes in the Building Stock of Da Nang between 2015 and 2017
###### Abstract  
This descriptor introduces a novel dataset, which contains the number and types of buildings in the city of Da Nang in Central Vietnam. The buildings were classified into nine distinct types and initially extracted from a satellite image of the year 2015. Secondly, changes were identified based on a visual interpretation of an image of the year 2017, so that new buildings, demolished buildings and building upgrades can be quantitatively analyzed. The data was aggregated by administrative wards and a hexagonal grid with a diameter of 250 m to protect personal rights and to avoid the misuse of a single building's information. The dataset shows an increase of 19,391 buildings between October 2015 and August 2017, with a variety of interesting spatial patterns. The center of the city is mostly dominated by building changes and upgrades, while most of the new buildings were constructed within a distance of five to six kilometers from the city center.  
buildings; change detection; remote sensing; urbanization; urban growth; Southeast Asia 2020 5, 42; doi:10.3390/data5020042
## 5 Case Study
### Capacity Assessment of the Beijing-Shanghai High-Speed Railway  
Apply the combined occupancy method to calculate the passing capacity of the entire Beijing-Shanghai railway and obtain the passing capacity, capacity utilization rate, operation chart occupancy time and traffic density of each section. The results are shown in Figure 12:  
Figure 11: Capacity assessment under different train numbers using the two methods.  
Figure 12: Capacity calculation results of the Beijing-Shanghai high-speed railway.  
According to Figure 12, under the existing conditions, among the sections of the Beijing-Shanghai high-speed railway, the Bengbu-Xuzhou section (red bar) has the largest capacity of 179 trains. The capacity utilization rate of the whole line of the Beijing Shanghai high-speed railway is more than 80%, except for the Beijing-Tianjin section.
## 7 Deploying EEG within IoT 5G Environment
### IoT Cloud  
The data gathered from the EEG sensing network are transmitted to the IoT cloud for long-term storage and further analysis. This cloud can be hosted on a remote server or a public cloud platform, such as Amazon Web Services. It offers remote access to users, enabling data analysis, monitoring, and remote control capabilities. The cloud platform facilitates resource sharing, making it a compelling choice for EEG authentication [116]. IIoT enhances device-to-device communication by enabling secure data sharing over the internet. Cloud technology in IoT also supports scalability in data storage and management from thousands of devices. It adds value to the data collected from IoT by enabling users and organizations to identify trends and insights, while also minimizing costs. Additionally, the cloud provides a more rapid response to large data volumes and improves the management of the IoT environment. To effectively handle EEG signal data, an IoT cloud must incorporate six essential levels [80; 117; 118].
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 3 Experimental Verification
### Comparative Analysis for the Feeding Performance
#### 3.5.2 Comparison of Feeding Effectiveness and Efficiency  
Table 6 explores the effects of two feeding methods--manual feeding and unmanned boat feeding--on the growth of sea cucumbers and feed conversion rates. This paper compares the growth of sea cucumbers over two months under manual feeding and unmanned boat feeding conditions within the same aquaculture area. The results indicate that the initial weight of sea cucumbers in both manual and unmanned boat farming areas was approximately 50.5 g, showing that the initial sizes of the sea cucumbers were very similar. However, there was a significant difference in the total weight gain between the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Scallop** & **Sea Cucumbers** & **Sea Crabs** \\ \hline AP & 89.6\% & 87.8\% & 94.3\% \\ mAP & & 90.5\% & \\ IoU & 0.78 & 0.72 & 0.81 \\ mIoU & & 0.77 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics for aquaculture species detection.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Treatment** & **Ranges** & **Manual Fed** & **S.D.** & **Ranges** & **UBP Fed** & \\  & **Ranges** & **Mean** & **S.D.** & **Ranges** & **Mean** & **S.D.** \\ \hline Time (min) & 45–51 & 47 & 4.3 & 19–20 & 20 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of feeding time.  
two feeding methods. Sea cucumbers fed manually showed a total weight gain ranging from 62.2 to 68.7 g, with an average of 65.3 g and a standard deviation of 5.4 g. By contrast, sea cucumbers fed by unmanned boats had a wider range of total weight gain, between 105.3 and 112.5 g, with an average of 109.5 g and a standard deviation of 6.8 g. The unmanned boat feeding method appears to promote greater weight gain in sea cucumbers, with an increase of 67.7% compared to manual feeding, suggesting a potential growth advantage of this feeding method. The unmanned boat farming area consumed a total of 67.5 kg of feed, while the manual feeding area consumed 89.2 kg of feed. The unmanned boat farming area saved approximately 24.33% of feed compared to the manual feeding area. The unmanned boat farming method exhibits higher feed utilization efficiency.  
These data indicate the potential advantages of unmanned boats over manual feeding methods in terms of sea cucumber growth and feed efficiency. Unmanned boat feeding can lead to better growth outcomes and more efficient feed utilization. These findings may hold practical significance for aquaculture, as improvements in growth conditions and feed efficiency can help increase yield and reduce production costs.
# Research on a Reductive Deep Chlorine Removal Process for Breaking Through the Solid Film Barrier
## 3 Results and Discussion
### Reaction Temperature  
To explore the influence of different temperatures on the dechlorination effect of ascorbic acid, we conducted experiments under the following conditions: n(Cu\({}^{2+}\))/n(Cl\({}^{-}\))/n(H\({}_{2}\)A) = 1.25:1:0.75, with an initial reaction pH value of 3.6 and a reaction time of 20 min. In  
Figure 9: Distribution of ascorbic acid species at different pH levels.  
addition, the experiment was conducted at different reaction temperatures, including 20 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 70 \({}^{\circ}\)C.  
As shown in Figure 10, with an increase in reaction temperature, the removal rate of chloride ions in the solution decreases. When the temperature rises from 20 \({}^{\circ}\)C to 70 \({}^{\circ}\)C, the chloride ion removal rate decreases from 90.8% to 51.7%. This indicates that the best chloride ion removal performance is achieved at 20 \({}^{\circ}\)C. The reasons for the decline in chloride ion removal rate with increasing temperature are as follows: (1) Decreased stability of ascorbic acid at higher temperatures: Ascorbic acid is relatively heat-resistant in its crystalline form, but it is highly sensitive to high temperatures in aqueous solutions. In the presence of O\({}_{2}\) and divalent copper ions, it is easily oxidized and converted into dehydroascorbic acid, which further degrades into biologically inactive diketogulonic acid. As the temperature increases, the degradation rate of ascorbic acid accelerates, leading to a decrease in the ascorbic acid available for reduction, thus reducing the chloride ion removal rate. (2) The increase in temperature affects the precipitation process of cuprous chloride. According to Figure 11, it can be seen that the solubility product constant of CuCl increases gradually with an increase in temperature, which leads to an increase in its solubility, which is unfavorable for the precipitation of cuprous chloride and makes the re-solubilization of cuprous chloride occur.  
Figure 11: Solubility product constants of CuCl at different temperatures.  
Figure 10: Effect of different reaction temperatures on effect of chlorine removal.
## 5 The Proposed Scheme
### Construction of ABE-RS
#### 5.1.7. Verification  
Verify\((AK_{uid},IPK_{uid})\rightarrow(0,1)\). The \(AA\) computes \((AK_{2})^{\alpha}\) and validates the following equation:  
\[AK_{1}/(AK_{2})^{\alpha}\overset{?}{=}IPK_{uid}. \tag{12}\]  
If the equation holds true, the user's authenticity is verified and the AA sends a secret value \(\tau\in\mathbb{Z}_{p}^{*}\) to the authenticated user via a secure channel to proceed to the subsequent search step. Otherwise, the request is discarded.
## 3 Results and Discussion
### In Vitro _Dissolution Data_  
The development of the present RP-HPLC method has made it a possibility to prove that the selected polymers for solid dispersion preparations have an influence on the dissolution rate of poorly soluble drugs, like ASA and CB. The percentage of dissolved drugs from solid dispersions and pure APIs is presented in Table 6. All three prepared solid dispersions showed an improvement in the percentage of the released drug after 60 min for both active substances, compared to the pure APIs. The reason for such improvements could be because APIs in SD are surrounded by the molecules of the hydrophilic polymers, which improves wetting and dissolution [19; 31; 32].  
After 60 min, in case of ASA, the SD containing povidone and poloxamer 407 had the highest improvement of dissolution rate (98.63% and 97.46%, respectively), in comparison with pure ASA (82.02%). For CB, the highest improvement of dissolution rate was for SD with poloxamer 407, 59.56% (32.20% for pure CB). SD with poloxamer 407 had the most significant results for both APIs (59.56% for CB and 97.46% for ASA). An increase in the ratio of polymers could have the effect of increasing the dissolution rate of CB, and promising results could be obtained with a SD using poloxamer 407.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Peak Area (mAU*s)**} & **Acetylsalicylic Acid** \\ \cline{2-7}
**Injections** & **50\% 1. day** & **50\% 2. day** & **100\% 1. day** & **100\% 2. day** & **125\% 1. day** & **125\% 2. day** \\ \hline
1 & 1,118,005 & 1,119,005 & 2,196,838 & 2,148,464 & 2,727,058 & 2,730,364 \\
2 & 1,116,341 & 1,121,366 & 2,208,228 & 2,146,395 & 2,730,215 & 2,725,634 \\
3 & 1,121,242 & 1,123,216 & 2,212,075 & 2,149,707 & 2,729,241 & 2,719,863 \\
4 & 1,115,786 & 1,118,345 & 2,216,265 & 2,148,082 & 2,715,653 & 2,740,793 \\
5 & 1,110,211 & 1,115,256 & 2,214,434 & 2,139,806 & 2,715,934 & 2,720,256 \\ Mean area peak (mAU*s) & 1,116,317 & 1,119,438 & 2,209,568 & 2,146,491 & 2,723,620 & 2,727,382 \\ RSD (\%) & 0.36 & 0.29 & 0.35 & 0.18 & 0.27 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Precision results for acetylsalicylic acid.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Peak Area (mAU*s)**} & **Clepidogrel Bisulfate** \\ \cline{2-7}
**Injections** & **50\% 1. day** & **50\% 2. day** & **100\% 1. day** & **100\% 2. day** & **125\% 1. day** & **125\% 2. day** \\ \hline
1 & 620,662 & 621,251 & 1,220,200 & 1,227,386 & 1,526,992 & 1,525,632 \\
2 & 619,432 & 619,534 & 1,225,194 & 1,215,347 & 1,519,965 & 1,518,649 \\
3 & 619,578 & 620,097 & 1,219,874 & 1,238,342 & 1,528,761 & 1,526,489 \\
4 & 621,392 & 619,987 & 1,218,465 & 1,237,379 & 1,523,235 & 1,517,398 \\
5 & 620,978 & 621,872 & 1,224,411 & 1,210,359 & 1,527,691 & 1,527,821 \\ Mean area peak (mAU*s) & 620,408 & 620,548 & 1,221,629 & 1,227,563 & 1,525,329 & 1,523,198 \\ RSD (\%) & 0.14 & 0.16 & 0.24 & 0.84 & 0.24 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Precision results for clopidogrel bisulfite.
## 3 Results and Discussion
### Empirical Line Quality Assessment  
An examination of calibrated MCA spectra and the corresponding ground-based measurement reveals good agreement. Generally, the spectra of both ground-based and calibrated MCA reflectance show the characteristic features of a green vegetation canopy with different amounts of biomass and soil covers (Figure 14). The depicted examples represent sites with high (28), medium (1) and low (5) amounts of fresh phytomass of lucerne.  
Reflectance in the VIS is low in the blue and red domain and shows the characteristic peak in the green domain. After the transition from VIS to NIR wavelengths around 712 nm, NIR reflectance varies between 0.33 and 0.65 at 761 nm and between 0.38 and 0.71 at 899 nm. The water absorption band around 953 nm reveals differences between the spectra regarding the relative decline of the reflectance compared to b\({}_{899}\). The example of a bare soil reflectance curve in Figure 14 indicates the general ability of calibrated MCA imagery to produce realistic spectra not exclusively for vegetation. The spectrum represents a small area free of vegetation within plot 7 and shows the typical monotonous increase of reflectance from VIS to NIR wavelengths within a realistic range of values.  
Figure 14: Comparison of the spectral response of lucerne extracted from calibrated Mini-MCA 12 bands with ground measured ASD Fieldspec reflectance and with bare soil reflectance (extracted from calibrated Mini-MCA 12 bands; ASD Fieldspec reflectance not available). The selected sites represent high (28), medium (1) and low (5) amounts of fresh phytomass of lucerne. The bare soil spectrum represents an area free of vegetation within plot 7.  
Figure 13: RGB composite image of the CarboZALF experimental area from calibrated Mini-MCA 12 bands b\({}_{658}\), b\({}_{551}\) and b\({}_{471}\).  
However, the VIS spectral response of lucerne in calibrated MCA imagery is generally higher than the ground based reflectance. In the NIR domain, MCA acquired reflectance closely matches the ground-based measurements. Coefficients of determination reveal high correlations between ground-based and image reflectance for the six NIR-bands b\({}_{761}\)-b\({}_{953}\), poor correlations for the four VIS bands b\({}_{471}\)-b\({}_{613}\) and the NIR band b\({}_{713}\) and a moderate correlation for the red band b\({}_{658}\) (Table 3). The corresponding root mean square errors (RMSE) are low in terms of absolute reflectance but differ extremely in relation to the range of values in the respective bands. The mean relative error (MRE %) illustrate the discrepancy between the VIS (33%-104%) and the NIR bands (4%-23%). We assume the poor matching in the VIS bands are partially caused by the saturated pixels of the white panels used for the creation of the empirical lines. Additional greyscale panels in the medium reflectance range (0.3-0.7) would have been helpful to make the empirical line relationship more robust [38]. Nonetheless, additional sources of error might be present since other authors reported similar discrepancies for the VIS response of the MCA [39].
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 5 Discussion
### Temporal and Spatial Evaluation
Our results highlight the need for time series in higher spatial resolution to more accurately capture the surface water dynamics in China.
# Enhanced Path Planning by Repositioning the Starting Point +
## 5 Conclusions
### Operational and Economic Implications  
The research findings reveal substantial practical benefits for drone operations across multiple industrial sectors. The optimization approach enables considerable energy savings through reduced flight distances, directly addressing the primary limitation of restricted battery capacity in current drone systems. Clear performance patterns emerged from the analysis, showing that improvements increase with drone count while decreasing with station density, providing mission planners with predictive capabilities for various operational scenarios.  
These distance reductions translate into tangible economic advantages, including extended operational range, reduced battery consumption, and improved mission success rates for commercial drone applications. From an environmental perspective, minimizing energy consumption through optimized flight paths contributes meaningfully to sustainable drone operations and supports broader conservation initiatives across industries.
# Tensor Robust Principal Component Analysis via Non-Convex Low Rank Approximation
## 5 Experiments
### Video Background Modeling  
#### 5.2.1 Qualitative Experiments  
In this part, we consider the background modeling problem, an essential task in video surveillance, which aims to separate the foreground objects from the background. The frames of background in video surveillance are highly correlated and thus it is reasonable to consider it as a low rank tensor. While the objects in the foreground usually only occupy a small part of the image in some frames and hence can regard it as the sparse part.  
To solve this problem, we test our algorithm on four example color videos, _Hall_, _MovedObject_, _Escalator_ and _Lobby_[[http://perception.i2r.a-star.edu.sg/bk_model/bk_index.html](http://perception.i2r.a-star.edu.sg/bk_model/bk_index.html)]([http://perception.i2r.a-star.edu.sg/bk_model/bk_index.html](http://perception.i2r.a-star.edu.sg/bk_model/bk_index.html)), and compare it with RPCA [4], Gamma [26], SNN [49] and TRPCA [20]. We extract \(N_{2}=200\) frames from these four videos. Suppose the size of frames in the given video is \(h\times w\), we stack each frame in each color channel as a column vector of size \(N_{1}\times 1\), where \(N_{1}=h\times w\), and then all column vectors into a matrix of size \(3N_{1}\times N_{2}\) for RPCA and Gamma, and into a tensor of size \(N_{1}\times N_{2}\times 3\) for SNN, TRPCA and t-Gamma. The parameter \(lambda\) is set to \(\lambda=\frac{1}{\max\{3N_{1},N_{2}\}}\) for RPCA and Gamma, and \(\lambda=\frac{1}{\max\{N_{1},N_{2}\}}\) for SNN, TRPCA and t-Gamma. The parameter \(\gamma\) in Gamma and t-Gamma is set to \(\gamma=\frac{1}{\sqrt{\min\{3N_{1},N_{2}\}}}\) and \(\gamma=\frac{3}{\sqrt{\min\{N_{1},N_{2}\}}}\) respectively. As suggested in [20], \(\lambda\) in SNN is set to \([200,2,20]\).  
The background separated results are depicted in Figures 3-6. In Figures 3 and 4, we can see that our method could reconstruct the background with fewer ghosting effects and more completely. From Figures 5 and 6, it could be observed that though all the method separate the background from the foreground effectively, the proposed algorithm are more robust to the dynamic foreground and illumination variation, e.g., only Gamma and t-Gamma can remove the steps of moving escalator in Figure 5, while the method Gamma cannot adapt to change of illumination in Figure 6.  
Figure 3: Background modeling: two example frames from Hall sequences are shown. (**a**) Original; (**b**) RPCA [4]; (**c**) Gamma [26]; (**d**) SNN [49]; (**e**) TRPCA [20]; (**f**) t-Gamma.  
Figure 4: Background modeling: two example frames from MovedObject sequences are shown. (**a**) Original; (**b**) RPCA [4]; (**c**) Gamma [26]; (**d**) SNN [49]; (**e**) TRPCA [20]; (**f**) t-Gamma.  
Figure 5: Background modeling: two example frames from Escalator sequences are shown. (**a**) Original; (**b**) RPCA [4]; (**c**) Gamma [26]; (**d**) SNN [49]; (**e**) TRPCA [20]; (**f**) t-Gamma.