esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Application Parameters of Hydraulic Slotting Technology in Jointed Coal Reservoirs
###### Abstract  
Hydraulic slotting technology is typically used in coal mines to enhance permeability and prevent gas outbursts. Because a coal seam contains many cleats and joints, this study investigated the influence of conventional application parameters on the hydraulic slotting effect by numerical simulation and experimental testing. The cleats in the coal generated stress concentration and initiated with the water jet impact, which promoted the formation of a complex fracture network. The optimized arrangement included angles with an inclination of 20-45\({}^{\circ}\) between the borehole and the coal seam strike. The water jet pressure and rotation speed determined the shape of the slot. A high water jet pressure and low rotation speed promoted the formation of cracks at the end of the slot and strengthened the permeability-enhancing effect. Coal fragments could more easily peel off from the sides of the seam and block the borehole. The high water pressure and low rotation speed application parameters were optimized without blocking the borehole. Results obtained by field application revealed that the gas extraction flow after optimization was 1.3 times that of conventional hydraulic slotting. An appropriate angle between the cleats and borehole can more effectively increase the permeability of the coal seam and results in higher gas drainage flow. The results of this study can be useful as guidelines for field applications of hydraulic slotting technology.  
hydraulic slotting; water jet; permeability enhancement; gas exploitation; crack initiation
## 3 IoT Applications
### Internet of Medical Things (IoMT)  
The integration of health features into IoT devices makes the environment an IoMT. With the advancement of technology, the usage of IoMT devices is increasing. Moreover, the COVID-19 situation limits face-to-face meeting between patients and doctors. The pandemic has created a new era of IoMT for providing treatment to patients [47]. IoMT is making a network of people and medical devices (wireless medical devices and implanted medical devices). It utilizes wireless communication (Bluetooth, WiFi, 3G, 4G, 5G, ZigBee, etc.) to exchange health data with medical facilities like doctors, hospitals, medical experts, etc., [48]. With the advancement of microelectronics, medical devices have become intelligent and can monitor and report physical conditions such as blood pressure, heartbeat, oxygen level, etc. The devices can be placed in the body in the form of watches, belts, shoes, clothes, necklaces, etc., [49]. Moreover, IoMT has become the most significant change among the development in the medical sector as it brings not only the aged people but also every aged sick people in continuous monitoring and treatment. Especially people suffer even after recovering from COVID-19, and IoMT provides immediate treatment if  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Survey** & **Citation** & **Year** & **Objective** \\ \hline A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system & [PERSON] et al. [44] & 2022 & Machine learning based malware detection in the IoT network. \\ \hline  & & & Provided overview and comparison of IoT and IoE network; Provided overview of different IoT applications; Details discussion of IoT taxonomy, effects, and countermeasures; Centralized and blockchain based existing security solutions discussion in depth of IoT applications. \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._necessary. Many healthcare systems from all over the world adopt the IoMT system to provide treatment. However, nearly 50% of IoMT equipment is susceptible to attack, as per the 2020 CyberMDX research. IoMT network is distinct from other systems in that they have the potential to impact patients' lives and raise privacy problems if their identities are divulged [50]. Maintaining security and privacy is the primary concern of the IoMT system. According to a research from cybersecurity firm Critical Insights, cybersecurity incidents reached an all-time peak in 2021, compromising a record quantity of patients' personal health data. Healthcare attacks harmed 45 million people in 2021, rising from 34 million in 2020. As per the research, the number of breached data has tripled within only three years, up from 14 million in 2018 [51].
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 5 Experiment and Results
### Data Analysis and Insights  
Data stored on the blockchain is immutable and distributed across thousands of nodes, making it nearly impossible to alter--akin to writing in stone. The system demonstrated impressive performance, handling 980 transactions per second, which is comparable to processing approximately 1000 credit card payments every second. Over 12 h, the system achieved a 100% match between raw sensor data and blockchain hashes, showcasing impeccable data integrity.  
Data analysis revealed valuable insights into system performance. In factory A, temperature trends were monitored within a normal range of 23-27 \({}^{\circ}\)C, which was considered safe for machinery operation. However, twelve flagged events were detected over 72 h, including a spike to 28 \({}^{\circ}\)C at 3:00 a.m., which was traced back to a misconfigured sensor. Statistical methods, such as the Z-Score, were used to measure how \"unusual\" a data point was relative to the mean. For example, if the average human height is 5\({}^{\prime}\)6\({}^{\prime\prime}\), a person measuring 7\({}^{\prime}\)0\({}^{\prime\prime}\) would have a high Z-score. Any Z-score greater than three was flagged as abnormal, allowing for the early detection of potential issues. The temperature trends and anomalies are summarized in Table 2.
# Advanced Methods for Point Cloud Processing and Simplification
## 1 Introduction  
The main remote sensing task is to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects in a scanned space. The inseparable part constitutes their processing to obtain desired input data for the purpose of the application which the scanning was used for. There are many different applications where the processing of remote sensing is important; see the following subsections for how to deal and process point clouds.  
The main issue of our research is point cloud processing and simplification with the focus on the detection, statistical description, vectorization, and visualization of basic space features. This will allow the decreased memory consumption of physical data storage. For example, individual points of a planar surface composed of a thousand points are not so important for the subsequent processing. We rather look for vertices of the analyzed planar surface. Our contribution in point cloud processing is to show a different alternative way of processing. The main advantages of our approach lie in the combination of the physical space points quantization with image processing methods. This connection allows: obtaining important space features such as the area, perimeter and volume; the space and statistical descriptions of planar surfaces, including the descriptive pointamount decreasing by the vectorization of vertices; the correction and recovery of missing space data; and a planar surface presented as the image allows the alternative way of the physical points storage, including its visualization. It also serves to other researchers as the extension to the used methods presented in the following subsection. Moreover, the presented approach is usable not only for point clouds but also as the general processing and analysis of different levels for any 3D data.  
This paper is focused on the presentation of several new methods for point cloud processing such as the outlier points removal, estimation of the initial point cloud rotation, point cloud data correction and recovery, and the vertices detection of a planar surface. In the Introduction section is a survey about different point cloud processing methods for basic object detection and segmentation, the planar surface and space features estimation, and their typical application. The paper further includes the description of our developed point cloud processing pipeline and the presentation of the new methods in detail to give a comprehensive overview of our developed approach, including verification on real space data. In the results part, the processing of the complex point cloud with several rooms is presented. The results and algorithms are evaluated also in terms of precision against the physical space dimensions and comparison with the convex hull results. The recommendations for this processing approach are stated.
## 1 Introduction
Additionally, the control of the 3-SRR PM is enhanced by incorporating an artificial neural network (ANN) to accurately predict output values while accommodating input changes, overcoming the drawbacks of tuning PID control.
# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
## 3 Robust Spectral Dictionary Learning and Sparse Representation
### Robust Sparse Coding  
After training the spectral dictionary \(\mathbf{D}\) by the spectral dictionary learning method proposed in Section 3.1, the quantity \(\mathbf{\Psi}_{i}\) in the optimization problem (Equation (10)) was known. Thus, solving the corresponding robust sparse coding \(\hat{\mathbf{\alpha}}_{i}\) was the same as calculating the sparse coding in the dictionary learning and was regarded as an \(l_{1}\) norm measurement and \(l_{1}\) norm regularization convex optimization problem, expressed as follows:  
\[\hat{\mathbf{\alpha}}_{i}=\arg\min_{\mathbf{\alpha}_{i}}\lVert\mathbf{\Psi}_{i}\mathbf{\alpha} _{i}-\mathbf{y}_{o,i}\rVert_{1}^{1}+\lambda\lVert\mathbf{\alpha}_{i}\rVert_{1}, \tag{25}\]  
where \(\lVert\mathbf{\alpha}_{i}\rVert_{1}=\sum_{ij}\lvert\mathbf{\alpha}_{ij}\rvert\).  
Equation (25) can be converted to an equivalent-approximate robust sparse coding problem:  
\[\hat{\mathbf{\alpha}}_{i}=\arg\min_{\mathbf{\alpha}_{i}}\lVert\left(\begin{array}{c }\mathbf{y}_{o,i}\\ 0\end{array}\right)-\left(\begin{array}{c}\mathbf{\Psi}_{i}\\ \lambda\mathbf{I}\end{array}\right)\mathbf{\alpha}_{i}\rVert_{1}. \tag{26}\]  
Because the set of linear equations,  
\[\left[\begin{array}{c}\mathbf{y}_{o,i}\\ 0\end{array}\right]=\left[\begin{array}{c}\mathbf{\Psi}_{i}\\ \lambda\mathbf{I}\end{array}\right]\mathbf{\alpha}_{i}, \tag{27}\]is hyper-determined, Equation (26) satisfies the conditions outlined previously [29], and thus, the existence of a global optimal solution is guaranteed. Finally, the corresponding robust sparse coding \(\hat{\mathbf{a}}_{i}\) was obtained by solving Equation (26).
# MobiDabet: Mobile Telemonitoring System for Patients with Type 2 Diabetes Mellitus (T2 DM)
###### Abstract  
According to the International Diabetes Federation, in 2019, approximately 416.7 million people worldwide suffered from type 2 diabetes mellitus (T2 DM). T2 DM is a chronic degenerative disease of long term and slow progression. This condition requires a strict follow-up by physicians and nutritionists, combined with rigorous adherence to treatment by the patient to avoid possible complications. In this context, this paper describes the analysis, design, development, and preliminary usability assessment of a telemonitoring system focused on the monitoring, control, and remote nutritional therapy of people with T2 DM. The proposed system comprises two mobile web applications, one focused on the patient and another oriented to physicians and nutritionists. The central services that our system provides to the patient and health personnel are: generate risk alerts; consult food menu options; receive recommendations; consult results of the food intake frequency questionnaire; patient history management; record anthropometry of patients, and review health education material. We carry out a preliminary usability assessment of our system based on a field study with four physicians, two nutritionists, and seven patients with T2 DM. Based on the obtained results, our telemonitoring system shows a satisfactory/favorable opinion in terms of usability from the users' perspective.  
m-health; type 2 diabetes mellitus; telemonitoring system; mobile web applications +
Footnote †: journal: Applied Sciences
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 4 Analysis  
### Model Description  
Comprehensive analytical models necessitate a large amount of computational power and a lengthy analysis time. These parameters can be simplified by omitting superfluous information and components that have no effect on physical phenomena. Neglectable components such as connectors, chamfers, and fillets were omitted from the structural analytical model. The equipment mounting plate and separation mechanism use solid elements in finite element modeling. The solar panels use a shell unit, and node coupling connects the device mounting plate to it. The solar panels are rigidly coupled to the equipment mounting plate using MPC; each device on the mounting plate uses mass point simulation and is rigidly connected to the mounting point on the plate via MPC. According to the current scenario, contact boundary conditions between the satellite and the separation mechanism's guide rail are set. The analytical model for the separation mechanism and the MF satellite considered in this study was shown in Figure 7. The elements and nodes had a total of 661,028 and 895,933, respectively.
## 3 Results
### Player Load Results  
The analysis of the player load variable (Table 5), using a five-cluster k-means model, indicates that U14 basketball players distribute their physical effort across distinct intensity zones. In Zone 1 (<1.07 UA/min), players accumulate 3.41% of the total player load, with a cluster center of 0.91 UA/min. Zone 2 (1.07-1.36 UA/min) represents 19.66% of the total load, centered at 1.24 UA/min.  
Zone 3 (1.37-1.63 UA/min) accounts for 27.13% of the player load, with a cluster center of 1.50 UA/min, while Zone 4 (1.64-1.95 UA/min) comprises the largest share, 30.80%, centered at 1.77 UA/min. Finally, Zone 5 (>1.95 UA/min) represents 19.00% of the total load, with a cluster center of 2.13 UA/min (Table 5). These results suggest that the majority of physical demands are concentrated in moderate-to-high-intensity zones, with relatively few actions occurring at very low or maximum intensities.  
The distribution of player load intensities (Figure 5) among U14 male basketball players demonstrates a clear predominance of moderate to high physical effort during match play. Specifically, Zones 3 and 4--representing intensities between 1.37 and 1.95 AU/min--accounted for the largest proportion of total effort, with 27.13% and 30.80%, respectively. Combined, these zones represent 57.93% of all player load activity, indicating that the majority of gameplay is performed at sustained moderate-to-high intensities.  
Figure 4: Graphical interpretation of results from five-k means cluster of the variable speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Player Load** & \multirow{2}{*}{**Zone 1**} & \multirow{2}{*}{**Zone 2**} & \multirow{2}{*}{**Zone 3**} & \multirow{2}{*}{**Zone 4**} & \multirow{2}{*}{**Zone 5**} \\
**(u.a./min)** & & & & & \\ \hline Cluster centers & 0.91 & 1.24 & 1.50 & 1.77 & 2.13 \\ Ranges & \textless{}1.07 & 1.07 to 1.36 & 1.37 to 1.63 & 1.64 to 1.95 & \textgreater{}1.95 \\ n & 26 & 150 & 207 & 235 & 145 \\ \% & 3.41 & 19.66 & 27.13 & 30.80 & 19.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the five-k means cluster of the variable player load.  
Zone 5, corresponding to very high-intensity efforts (>1.95 AU/min), comprised 19.00% of total player load, with a cluster center of 2.13 AU/min. This substantial proportion of high-load activity suggests that U14 players are capable of sustaining significant physical demands during competition. In contrast, only 3.41% of total effort occurred in Zone 1 (<1.07 AU/min), indicating that very low-intensity activity is relatively rare (Figure 5).
## 2 Materials and Methods
### Workflow and Model Implementation  
The workflow of our deep learning approach of semantic segmentation on aquatic vegetation consisted of the following steps: (1) data preprocessing, (2) making label masks, (3) CNN model (U-Net architecture) training, (4) making predictions on new satellite imagery (i.e., inference) and producing example transmission risk maps for each image. Each step is described in the following sub-sections.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 1 Introduction
(2) SCM-IoT is a table-oriented coordination language instead of other tuple-oriented models such as Linda coordination model [20,21]. (3) Unlike the Linda model, the primitive operations of SCM-IoT are not based on the availability of tuples but based on the satisfaction of logical conditions. (4) In SCM-IoT, the small number of changes in the number of sensors and actuators in many IoT applications, such as home automation and healthcare, lead to a fixed table structure. However, the static nature of the table is not a conceptual limitation of the model but a non-functional constraint that ensures greater efficiency during coordination. A design based on a fixed-structure table may additionally give applications greater clarity by allowing all logically related entries (i.e., those that follow the same editing rules) to be grouped under the same table. This paper is structured as follows: Section 2 discusses works on SC, services coordination, services orchestration, and services choreography in the IoT context. In addition, the abstract models of distributed systems, colored Petri nets, and event-condition-action (ECA) rules are introduced. Next, in Section 3, we describe the methodology used. In Section 4, a case study is presented to describe the results obtained. In addition, in Section 5, we discuss the lack of dynamism and limitations. Finally, in Section 6, some conclusions and suggestions for future research are given.
## 2 State-of-the-Art in Light Dimming and Lights-Off Visible Light Communications
### Related Work
#### 2.2.3 Multi-Carrier Modulation  
Orthogonal Frequency Division Multiplexing (OFDM) is a special class of MCM system that has attracted attention in the optical communication field as a bandwidth-efficient multiplexing transmission.  
The two features related to multicarrier modulation are giving OFDM some advantages over other types of modulations: the ability to divide the OFDM spectrum into multiple sub-bands, which eases the design, and the adaptation of pilot subcarriers simultaneously with data carriers, offering a convenient possibility for phase and channel estimation [39]. An adaptive PWM dimming control scheme was used in [40] to achieve a BER of less than \(10^{-3}\) in a variable Multi-level Quadrature Amplitude Modulation (M-QAM) OFDM VLC system, where a number of points in each constellation M are adapted in direct relation with the duty cycle at first, followed by the change of the symbol rate. The strong point of this approach is that the required power to drive the LED lamp is much less than in the case of an OOK modulation.
## 3 Results and Discussion
### Composition of Milk and Fermented Milk  
Composition of raw bulk tank milk is shown in Table 1. Regarding fat content, a 10% increase was observed at the EL stage sampling as compared to PE sampling for diets BD (5.30% vs. 4.80%) and APD (5.07% vs. 4.56%), the increase was only slight for group on CD diet (4.91% vs. 4.73%). Fat content increased till the end of lactation reaching values of 5.39% in CD and 5.61% in BD. However, fat content in APD remained constant throughout the experiment. BD diet yielded the milk with the highest fat content throughout the study. Protein content was slightly lower in BD and APD with respect to CD at EL and ML stages (0.1 units \(p<\) 0.05). An increase in protein was observed at the end of lactation (LL) for all diets, reaching contents of 3.62%, 3.56% and 3.42% for CD, BD and APD, respectively. Protein content was always higher in milk from the CD diet. A parallel study conducted by [PERSON] et al. [8] with an inclusion of 40% broccoli and artichoke plant by-products throughout lactation, observed similar results with an increase of 0.5% in absolute terms of fat in milk from of diets with broccoli silage and a higher protein content for milk from a conventional diet. They also observed an increase in fat and protein throughout lactation. Other parallel studies [6; 7; 8; 29] but with a short-term inclusion showed that 40% broccoli silage by-products and 40% and 12.5% artichoke plant silage also increased the percentage of fat with respect to the conventional diet. However, the percentage of protein was hardly modified by the differentiated diets. The average macrocomposition of the milk fermented by YOMIX(tm) 300 starter culture is presented in Table 2. As to fat percentage, an increase throughout lactation was detected, being more pronounced in fermented milks from BD diet with an interval of 4.46-5.50% between PE and LL production, with an increase (\(p<\) 0.05) of 1.04 percentage units. Fermented milk derived from CD also showed the same tendency but slightly less pronounced with an increase of 0.52 percentage units between PE and LL production, followed by fermented milks made from APD (an increase of 0.43 percentage units). As to protein percentage, a similar trend was observed, increasing throughout lactation being LL production significantly higher than PE in all batches (0.38, 0.32 and 0.19 percentage units for CD, APD and BD, respectively). This increase was also observed ML stage in the CD batch. The tendency above described for protein content in milk with the same proportion
## Appendix A Throughput under Changing Data Traffic
### Transmission Overhead and Average Energy Consumption under Changing Data Traffic  
Figure 22 shows that transmission overhead reduces as data traffic increases. Nodes can retain messages in their buffers for longer periods of time under less data traffic due to less buffer occupancy. This results in more chances of replicating messages and increased transmission overhead. The replication techniques also retain their relative performance under different data traffic, with LARep incurring the lowest transmission overhead, followed by ADS, fixed-quota and PRoPHET without a replication technique, while LAMeD incurs the highest transmission overhead. Their performance in terms of average energy consumption corresponds to their transmission overhead. LARep records up to 96.4% reduction in energy consumption, as compared with 64.5% and 65.2% for fixed-quota and ADS, respectively.