esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity
## 4 IoT-Based Smart Healthcare Systems
### Telemedicine  
Telemedicine allows for the provision of remote medical services and relies upon Internet infrastructure and communication technologies. Overall, these enhance the response capacities of medical services and staff, improve patients' health, and reduce treatment costs. In [72], [PERSON] et al. introduced an AI-based IoHT system to analyze collected data from medical devices mounted on patients' bodies. The information collected from medical sensors is transferred through the GSM system to the Azure data center for data handling and decision making. In [73], [PERSON] et al. designed a novel IoHT system for rural residents by monitoring their main survival indicators. The patients wore an RFID tag for identification.  
When patients' vital indicators, such as blood pressure and heart rate, have abnormal changes, the IoHT system will generate alarms, and then, send them to doctors, hospitals, or caregivers. [PERSON] et al. [74] designed an IoHT system for remote patient monitoring by combining web services and cloud computing-based solutions. Indeed, the authors developed an ECG Android App to monitor the electrocardiogram indicators of patients. The obtained signals are transferred to Microsoft's Azure platform for handling. Moreover, they also used a hybrid cloud, where patients' sensitive medical records are pushed into private clouds, and general medical information is pushed into public clouds.
# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
###### Abstract  
The presence of concrete cracks in structures indicates possible structural deterioration, but it is quite difficult to measure crack width accurately. While much research has been conducted on crack detection using image processing, there is a gap in the accurate quantification of crack width in millimeters. Current methods either measure in pixels or require the attachment of scales or markers onto a measured surface, which can pose safety hazards in hard-to-reach areas. This paper addresses these issues by proposing a novel image-based method for measuring concrete crack width in millimeters using a laser beam and image processing. The proposed method was validated in the laboratory by capturing images of concrete cracks with two cameras of different resolutions, each attached with lasers. The lasers projected a circular laser beam onto the surface of the concrete under inspection. The images were then pre-processed, segmented, and skeletonized for crack width measurement in pixels. The relationship between the laser diameter and camera distance from the surface was used to convert the measured crack width from pixels to millimeters. The method was performed with high accuracy, as indicated by the absolute error. The largest absolute error was 0.57 mm, while the smallest absolute error was 0.02 mm. The proposed method allows real-world interpretation of results in millimeters. As a result, measured crack widths can easily be compared to allowable limits in international standards, which are typically expressed in metric or SI units. The proposed method can also promote safer inspections in areas of low accessibility by attaching the laser to devices such as drones.  
crack detection; crack width measurement; laser beam; image processing Article
# Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network
## 1 Introduction
The model consists of four core components: graph learning, temporal convolution, graph convolution, and down-sampling convolution. The local information and deep spatiotemporal information of multivariate time series data can be learned by the model. Dependencies between sequences can be captured by a graph learning module. The temporal information is captured by the 1D convolution of the temporal convolution module. In the graph convolution module, the spatial dependencies between variables are extracted through relational graphs. The spatiotemporal information in sequences with different sparsity is captured by down-sampling convolution modules. The experiments in this paper show that the model has good prediction performance and generalization ability. The main contributions of this paper are as follows:  
1. A more general time series forecasting model based on graph convolutional networks and deep spatiotemporal features is proposed in this paper. 2. The attention mechanism is added to the graph convolution module to realize the filtering of spatiotemporal information. 3. A down-sampling convolution module is proposed to extract deep spatiotemporal information of time series to improve the performance of the model. The remainder of this paper is organized as follows: Section 2 presents the related work and research status. Section 3 introduces the definition of the problem and presents the overall framework and algorithmic flow of the model. Section 4 describes the experimental part and gives the experimental results of different methods in the dataset, analysis of experimental results, and ablation experiments. Finally, Section 5 is the conclusion of this paper.
## 3 Results and Discussion
### Effect of Wettability on T\({}_{2}\) Distribution and T\({}_{2}\)LM
However, for a weakly water-wet system, it is suggestive that some of the pore surfaces are wetted by both water and oil, creating a complication. This is further understood qualitatively through the gradient of the trend line from the subtle shift towards faster relaxation. The effect of surface relaxation affects water differently from oil; hence a relative shift to help identify the degree of water wetness is impossible. The proposed  
The reservoir used in this research has a gas cap, hence the reservoir was delineated into gas and oil zones. Although there was no core data for the gas zone, the developed mathematical model was applied. When used in the mathematical model, the average T\({}_{2}\)LM for the gas zone gave an Amott index of 0.55, which shows a strongly water-wet condition. Gas is a non-wetting fluid because oil and water propitiously wet pore spaces in reservoir formations [59]. In general, the oil zone interval from the plot showed a positive relationship with increasing water saturation, indicating that the zone is water-wet. To know the type of water-wet condition, the average of the T\({}_{2}\)LM was incorporated in the model, and results indicated a moderately water-wet condition with an Amott index of 0.26. This agrees with the average experimental Amott results for the entire oil interval of 0.25. However, the zonation of the oil zones based on decreasing water saturation or vice versa showed several water-wet conditions, shown in Table 6 above. Zones, where core wettability results were available agreed with the results from corresponding depths. Based on the observed trends and average T\({}_{2}\)LM, the last interval exhibited moderately to weakly water-wet conditions. This occurrence is due to its saturation being above 0.85. As earlier explained, at high water saturation of 0.85 and above, the residual oil exists in the micropores, where it wets the surface of the pores. As a result, the micropores are wet by oil whiles water wets the macropores, exhibiting some weakly water-wet conditions. This interval was added to verify this statement. Alternatively, the occurrence of low water saturation and low T\({}_{2}\)LM refers to intervals of low permeability as well as a low free fluid index.
## 3 Numerical Results
### Six-Pole Solitons
Although the \"7-pole\" soliton can maintain its profile and power over a relatively long distance, the perturbation growth rate that we calculate is \(\mathrm{max}[\mathrm{Re}(\lambda)]=0.0043\), which means this soliton has minor linear instability, as shown in Figure 9h. The physical mechanisms of these  
Figure 7: The power curves of six-pole solitons in PT-symmetric triangular lattices with different \(d\) and \(W_{0}\). The gray area in the \(\mu\)–\(W_{0}\) plane is the optical energy band of the triangular lattices, and the white area is the first bandgap. The solid lines correspond to the stable solitons, while the dashed lines correspond to the unstable ones. two different transverse instabilities are as follows. The strong nonlocality broadens the refractive index distribution of each soliton hump, and those broadened areas mainly overlap on the center of the six-pole soliton, which creates a hotspot of the refractive index. Then, the soliton energy \"flows\" into this hotspot and enhances the overlap effect. Eventually, the power of this hotspot is equal to the other humps of the soliton. Figure 8: The evolution dynamics of stable six-pole solitons with \(d=0.5,\mu=10\). (**a**,**b**) correspond to the transverse profile of the solitons at \(z=0\) and \(z=140\), respectively. (**c**) shows the phase structure of the six-pole soliton. (**d**,**e**) correspond to \(\Delta\) and \(P\) during propagation. (**f**) shows the spectrum of the perturbation growth rates. Figure 9: The dynamics of strongly nonlocal six-pole solitons. The first row corresponds to the unstable soliton with \(d=2,\mu=8.4\), and the second row corresponds to the stable soliton with \(d=2,\mu=10.4\). (**a**,**b**,**e**,**f**) show their transverse profiles. (**c**,**g**) show the power of the soliton, \(P\), during propagation. (**d**,**h**) show the spectrums of their perturbation growth rates. It is worth emphasizing that the existence range of six-pole solitons is similar to the existence range of dipole solitons in Figure 6, except that the existence range of six-pole solitons becomes narrower more quickly when nonlocality becomes stronger.
## 2 Materials and Methods
### Flexural Testing  
Upon production of fifteen ceramic discs per group (\(n\) = 120, 8 groups), flexural strength measurements were conducted according to ISO 6872:2015 [1]. A three-point bending test was performed using a universal testing machine (Universal Testing Machine, ZwickRoell, Ulm, Germany) at a crosshead speed of 1 mm/min on a 10 mm span. Each specimen was loaded until ceramic failure occurred, and fracture load data were obtained.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Brand**} & \multirow{2}{*}{**Manufacturer**} & \multirow{2}{*}{**Ceramic Chemical Composition**} & \multicolumn{2}{c}{**Ceramic Modeling Liquid**} \\  & & & **(min)** & **(\({}^{\circ}\)C/min)** & **Pressure** & **(\({}^{\circ}\)C)** & **HT (min)** \\ \hline CR & 580 & 6 & 55 & Yes & 920 & 1 \\ \hline IV & 403 & 4 & 60 & Yes & 910 & 1 \\ \hline NR & 400 & 8 & 65 & Yes & 980 & 1 \\ \hline VT & 400 \({}^{\circ}\)C & 6 & 55 & Yes & 880 \({}^{\circ}\)C & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Firing procedures of the dental ceramics tested. ST: starting temperature, DT: drying time, FT: final temperature, TRI: temperature rate of increase, HT: holding time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Brand**} & \multirow{2}{*}{**Manufacturer**} & \multirow{2}{*}{**Ceramic Chemical Composition**} & \multicolumn{2}{c}{**Ceramic Modeling Liquid**} \\  & & & **Chemical Composition** & **(Weight \%)** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Noritake super \\ porcelain EX \\ (\(n\)) \\ \end{tabular} } & Kuraray Noritake & \begin{tabular}{c} Leucite-containing feldspathic porcelain \\ SiO2 (64.5\%); Al2O3 (14.4\%) \\ \end{tabular} & Polyethyleneglycol \\  & Kuraray Noritake & \begin{tabular}{c} CaO (\textless{}1.0\%); MgO (\textless{}1.0\%) \\ K2O (8.7\%); Na2O (9.2\%) \\ Li2O (<1.0\%); B2O3 (0\%); \\ pigments (\textless{}1\%) \\ \end{tabular} & \begin{tabular}{c} Water \\ Additives \\ \% is not disclosed by the \\ manufacturer \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} VITA VM13 \\ (V) \\ \end{tabular} } & VITA Zahnfabrik H. & \
\begin{tabular}{c} Leucite-containing feldspathic porcelain \\ SiO2 (55–72\%); Al2O3 (12–16\%); \\ K2O (8–10\%); Na2O (4–6\%); TiO2 (<1\%); \\ CeO2 (<1\%); ZrO2 (<1\%); CaO (1–2\%); \\ B2O3 (1–2\%); BaO (1–3\%); SnO2 (<1\%); \\ Mg, Fe, and P oxides (<0.1\%) \\ \end{tabular} & Purified water (\textgreater{}99\%); &
\begin{tabular}{c} L’organic components \\ (<1\%) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The recorded values of the fracture load were used to calculate the biaxial flexural strength. The expected flexural strength (\(\sigma\) in MPa) of a rectangular sample under a load in a three-point bending setup is calculated using the following formula [13; 14]: \(\sigma=3\cdot\mathrm{F_{max}}\cdot\mathrm{L}/2\cdot\mathrm{b}\cdot\mathrm{d^{2}}\), where \(\mathrm{F_{max}}\) is the maximum force (Newton) applied to achieve fracture of the specimen.
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 3 Results
### Experiment Result  
#### 3.2.1 Energy Consumption and Processing Delay  
Figures 5 and 6 show the satellite energy consumed by each mission and the processing delay of each mission when the number of on-board missions is set to 15.  
In Figure 5, we can see that LOC achieves the lowest energy consumption because it does not utilize any GEO satellite resources. However, it is undesirable because LOC violates the QoS constraints. GOC achieves the highest energy consumption by completely offloading missions to GEO satellites for processing. SSPCO achieves a lower energy consumption for all on-board missions because it coordinates the computing power of LEO and GEO satellites to save satellite energy as much as possible while guaranteeing the QoS requirements of on-board missions.  
From Figure 6, it can be seen that the processing delay of each mission of LOC is much larger than other methods. This is due to the limited computing power of LEO satellites, and processing missions entirely on LEO satellites leads to a huge delay. GOC obtains the lowest processing delay by completely offloading the missions to GEO satellites with high computing power. GACO and PSOCO have lower processing delays compared with SSPCO. This is due to the fact that SSPCO takes into account the time cost of LEO satellite computation, GEO satellite computation, and mission transmission, and prioritizes the QoS  
Figure 10: Impact of different QoS constraints on the total energy consumption.  
Figure 9: Impact of different QoS constraints on the overall degree of QoS violation.  
constraints of on-board missions rather than simply pursuing the minimization of mission processing delay.
# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
## 3 Methods
### Yolo-Pgc  
Tomato ripeness detection poses numerous intricate challenges within the realm of computer vision. Tomatoes display extensive morphological variation throughout their growth cycle, taking on diverse shapes, color patterns, and size ranges. Additionally, agricultural settings add further complications due to factors such as leaf occlusion, fruit clustering, and inconsistent illumination conditions. As depicted in Figure 1, this module integrates three essential components: feature decomposition, SS2D dynamic, and variable illumination conditions. Contemporary deep learning methodologies often demonstrate suboptimal performance when confronting these challenges, particularly in scenarios involving small target detection, severe occlusion, and complex background interference. To address these limitations, comprehensive architectural improvements are proposed to the YOLOv11 framework, as illustrated in Figure 2. The key architectural enhancements include the following elements. First, our enhanced architecture replaces the conventional C2 PSA structure with the PSSD module, significantly improving model robustness and detection stability in complex scenarios. By leveraging efficient feature extraction and dynamic modeling mechanisms, the PSSD module shows superior adaptation to variable detection environments. Second, replacing the original C3k2 module with our novel CIF2M module boosts target detection capabilities in complex backgrounds while enabling more precise target-background segmentation. Third, integrating the GHVC module in place of the C3K2 structure improves occlusion handling through an efficient feature reconstruction mechanism. The structural optimization scheme proposed in this study is designed specifically for the tomato ripeness detection task, and effectively improves model performance in complex agricultural scenarios by introducing a multiscale feature extraction mechanism and contextual information modeling techniques. The core features of each module and its design basis are detailed in Table 1.  
Experimental results show that the optimized model exhibits excellent adaptability and stability under challenging conditions such as target scale change, occlusion interference, and complex backgrounds. The exceptional adaptability and stability of the enhanced  
Figure 1: Structure of PSSD.
## 5 Experimental Results and Discussion
### Verification Test of the Performance of the Working Circuit System
#### 5.1.1 Lift Cylinder Performance Test  
Test the performance of the lifting cylinder: manipulate the picking head from the ground to the highest position, and then from the highest down to the ground (Figure 12b); this action was repeated five times. At the same time, the time required for the lift cylinder to rise and fall and the displacement value of the cylinder piston rod at the highest position of the picking head were recorded. Finally, the average value was calculated as the expansion and contraction speed of the cylinder. When the picking head was lifted to the highest position, then the engine was turned off and left for 30 min, the displacement of the cylinder piston rod was measured again, and the displacement deviation was calculated twice as the displacement deviation of the cylinder and the deviation rate was calculated by measuring five times, and the test results of the lifting cylinder were shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Times** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Cylinder** \\ **Displacement** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Boost Speed** \\ **(m/s)** \\ \end{tabular} & \begin{tabular}{c} **Descent Time** \\ **(s)** \\ \end{tabular} & \
\begin{tabular}{c} **Reach Back** \\ **Speed (m/s)** \\ \end{tabular} &
\begin{tabular}{c} **Displacement** \\ **Deviation** \\ **Amount (mm)** \\ \end{tabular} \\ \hline
1 & 365.5 & 4.9 & 0.075 & 4.0 & 0.093 & 1.9 \\
2 & 368.2 & 4.7 & 0.079 & 3.8 & 0.096 & 2.1 \\
3 & 364.3 & 4.8 & 0.076 & 3.7 & 0.095 & 1.6 \\
4 & 368.3 & 5.0 & 0.074 & 3.9 & 0.094 & 1.3 \\
5 & 369.8 & 4.7 & 0.078 & 4.0 & 0.092 & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Lift cylinder test results.  
Figure 11: The speed curve of the cylinder before and after adding the balance valve.
# A Novel Multi-Task Performance Prediction Model for Spark
## 4 Experimentation  
### Dataset and Experimental Environment  
We used four virtual machines to build a Spark cluster with four nodes, using a master-slave architecture, where one node is the master node, and the remaining three nodes are the slave nodes, Worker. The operating system of the virtual machines is CentOS 7.9, the memory of each node is 12 GB, the number of processors is 4, and the number of cores of each processor is 3, i.e., the total number of virtual CPU cores of each node is 12, the hard disk capacity is 100 GB, and the network adapter uses NAT mode. The total number of virtual CPU cores is 12, the hard disk capacity is 100 GB, the network adapter is in NAT mode, and the four virtual machines can communicate with each other. The physical machine has 64.0 GB of memory (63.9 GB available), a system type of 64-bit operating system, and an x64-based processor, all of which are hardware configurations that can support the operation of the Spark cluster.  
During the experiment, the Spark cluster built by a virtual machine can efficiently execute multiple benchmarking tasks. We adopt distributed computing, which can fully utilize the computing resources of the cluster, greatly improve the computation efficiency, and shorten the computation time. At the same time, the use of virtual machines also makes the construction of the cluster more flexible and convenient, which can be quickly built and adjusted to improve the operability and stability of the experiment. In this experiment, we choose WordCount, PageRank, Pi, and Logistic Regression (LR) as Spark benchmark test tasks. Among them, the WordCount task needs to count the words in a text file, the PageRank task needs to calculate the PageRank value of a web page, the Pi task needs to calculate the value of \(\pi\), and the LR task needs to classify the data. The data for the four tasks we used were automatically generated on a virtual machine by HiBench 7.1.1, a big data benchmarking suite that tests the performance of different distributed computing frameworks (e.g., Hadoop, Spark, etc.) under a variety of datasets and workloads. The data sizes performed by the four applications are shown in Table 1.  
For each application, we set 50 different data sizes for experimentation. For the WordCount application, we used different sizes of text data for the experiments. The size of the experimental data starts from 100 MB and increases by 100 MB each time until it reaches 5000 MB. At each data size, we run the application and collect its performance metrics such as runtime, CPU utilization, etc. For PageRank, Pi, and LR applications, we adopt a similar strategy, except that the content and format of the data are adjusted accordingly to the specific application.
# Sentinel-1 GRD Preprocessing Workflow +
## 2 Workflow
### Calibration  
Calibration is the procedure that converts digital pixel values to radiometrically calibrated SAR backscatter. The information required to apply the calibration equation is included within the Sentinel-1 GRD product; specifically, a calibration vector included as an annotation in the product allows simple conversion of image intensity values into sigma nought values. The calibration reverses the scaling factor applied during level-1 product generation, and applies a constant offset and a range-dependent gain, including the absolute calibration constant.  
In the proposed preprocessing workflow, a LUT to produce sigma nought values is proposed, in order to generate radiometrically calibrated SAR backscatter with respect to the nominally horizontal plane. Sigma specifies the strength of reflection in terms of the geometric cross section of a conducting sphere, and represents the radar cross section of a distributed target over that expected from an area of one square meter. The sigma nought has a significant variation with the incidence angle, wavelength, and polarisation, as well as with properties of the scattering surface.