esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing of Sewing Work Levels Using a Power Monitoring System
## 3 Methodology
### Romote Sensing for Sewing Work Using a Power Monitoring System  
A power monitoring system (PMS) was used in the proposed method to provide remote sensing of sewing operations. The PMS applied in this study, as shown in Figure 3, was used to connect a wireless communications network to the sewing machine's power plug in order to collect and analyze power consumption data at low cost without requiring any additional equipment modification [31]. Power monitoring devices included current sensors and Wi-Fi communication modules. The system was applied to 20 garment production lines employing approximately 50 operators, which confirmed that valid data were collected and analyzed. The power consumption was verified through the operation of approximately 1000 power monitoring devices, and the accuracy and stability of the current sensor were verified while the supply of stable voltage was confirmed. The collected power consumption data was transmitted to a cloud server to extract time information describing the unit process of the sewing machine operator using a pattern analysis algorithm.  
Several pattern analysis algorithms have been researched for such applications based on data mining or deep learning algorithm approaches. In this study, the symbo  
Figure 3: Power monitoring system for remote sensing of sewing work. MES: Manufacturing execution system.  
(SAX) and the dynamic time warping (DTW) approaches were used to analyze the work patterns in the power consumption data and extract the working time of the unit process [32; 33]. In the application of the pattern analysis algorithm performed in this study, the hardware parameters were applied equally to all tasks, and the parameters of the algorithm analyzing the pattern of each sewing task were supplemented by adjusting the DTW sensitivity of the algorithm. The power pattern analysis algorithm applied in this study measures the quantity and time of work based on the power profile generated in completing one task, including sewing and non-sewing. For information on extracting the working time of sewing work using a PMS, refer to [PERSON] et al. [31].  
The operator's working time information extracted through pattern analysis was linked to the operator name, process name, and theoretical or existing work time information contained in the manufacturing execution system (MES). Using this matched information matrix, a baseline for each operator or task was created based on the theoretical work time or previous work time. The generated level scale was then corrected to reflect the real-time measurements of work time and defect rate, thereby calculating the scales for \(L_{S}\) and \(L_{D}\).
## 4 Discussion on the Corresponding Processes in Ionosphere
### The Movement of Midlatitude Trough during the Magnetic Storm
The local time of CSES and Swarm-A was beyond midnight at about 02:00, and both of them showed abrupt movement in the northern hemisphere at the main phase on 26 August, but a smooth boundary was revealed at the same time in the southern hemisphere, in which the different season and regional effects should be taken into account. The strong correlations between electric fields and Ne were \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Satellite LT** & **Swarm-A 14 LT** & **Swarm-B 09 LT** & **Swarm-A 02 LT** & **Swarm-B 21 LT** \\
**Latitudes/\({}^{\circ}\) N** & **0–15 15–30 30–45 45–60** & **0–15 15–30 30–45 45–60** & **0–15 15–30 30–45 45–60** & **0–15 15–30 30–45 45–60** \\ \hline r of speed/TEC in & \(-0.10\) –0.22 & \(-0.26\) –0.27 & \(-0.37\) –0.37 & \(-0.43\) –0.40 \\
24–31 August & \(\mathbf{-0.35}\) –0.19 & \(\mathbf{-0.34}\) –0.33 & \(-0.430\) –0.45 & \(-0.40\) –0.41 \\ \hline r of P/TEC in & & & & & \\
24–31 August & 0.35 0.38 **0.54** 0.43 & 0.37 **0.50** 0.47 0.37 & 0.098 **0.19** –0.040 –0.13 & **0.64** 0.60 0.44 0.32 \\ \hline r of speed/TEC in & \(-0.013\) –0.104 & 0.026 –0.04 & \(-0.35\) –0.40 & \(-0.51\) –0.52 \\
27–31 August & \(\mathbf{-0.26}\) –0.054 & \(\mathbf{-0.20}\) –0.16 & \(\mathbf{-0.51}\) –0.42 & \(\mathbf{-0.53}\) –0.46 \\ \hline r of P/TEC in & & & & \\
27–31 August & 0.096 0.11 0.16 **0.20** & 0.088 0.22 0.28 **0.35** & 0.033 –0.019 & \\  & & & & \\
27–31 August & 0.088 0.22 0.28 **0.35** & \(-0.050\) –0.064 & 0.32 **0.37** 0.32 0.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The correlation between TEC from Swarm satellites and solar wind speed and pressure. The bold values present the maxima. illustrated by CSES in local nighttime with the similar spatial distribution characteristics of electric fields at 225 Hz and Ne to depict the mid-latitude trough in the northern and southern hemispheres, as in Figures 3 and 5, including with Ne from Swarm-A in Figure 10, which demonstrates the strong interaction of ULF/ELF electric field with electron density at medium latitudes at local nighttime, during the whole magnetic storm time period.
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 3 Background
### Load Disaggregation Algorithm  
The Load Disaggregation Algorithm (LDA) is defined as an algorithm that takes data on aggregated electricity loads from multiple appliances, as input, and outputs disaggregated loads for individual appliances [PERSON] et al. (2015). Combining LDA with a non-intrusive approach to obtain data, it forms a method for NILM. Assuming that labels with information about appliance loads are available for some of the load data, the problem of disaggregation is similar to a supervised learning problem known from Machine Learning (ML) or a problem of statistical regression [PERSON] et al. (2015).  
There are generality two approaches to pattern recognition: supervised and non-supervised. In supervised training it is assumed that the ground truth of your system is known. For a NILM application this means that the true consumption of the appliances needs to be available for the training process. This means that sub-meters must be placed to measure the appliances individually, which often is costly and time consuming. In non-supervised methods only the main meter readings are available, and no information about the appliances are known. This type of algorithms will usually try to cluster the readings in distinct groups cosponsoring to a guess of what appliances that correspond to the reading.
## 2 Research Methodology
### General System
#### 2.3.1 Server Distribution Mechanism  
An original server, also known as a content service provider, is a server that contains the original copy of a website or web application's content, such as HTML, images, videos, and other media. When a user requests a resource from a website, the request is sent to the original server, which in turn will respond according to the requested resource. The original server is a physical server or it can be a virtual machine hosted in a data center or cloud environment. It is responsible for generating dynamic content, processing user input, and handling back-end logic. The original server in this work can also be used as a central storage for multimedia content that is accessed and shared by multiple users. These multimedia files are stored in databases, file systems, or other types of storage.  
In a server provider, to distribute the multimedia content (video and audio), we need a distribution protocol (server push mechanism) to replicate these contents to the optimal edge servers. Therefore, according to the investigations that have been done in Section 2, FASP [28], File Catalyst protocol (FCP) [29], and Signiant [30] have been tested as distribution protocols to select the optimal and most efficient protocol. These protocols must be configured to transmit an adaptive video streaming from the original to the edge server. The CDN system adopts these protocols to duplicate the segmentation of adaptive video streaming.
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
## 1 Introduction
### Construction Stages  
To study the influence of the original bridge structure settlements that causes by the shield excavation and foundation underpinning design plan, it is necessary to take the full construction steps into consideration by using numerical simulation model. Bases on the site actual construction scheme, it takes 15 construction stages to simulate the whole process of shield tunnel crossed through the existing bridges foundation areas and the mainly construction steps are described as follows: (0) The initial phase, to simulate the initial phase of the existing bridge and the displacement was wiped out to eliminate the impact that may be caused by the following construction steps. (1) Foundation excavation, to excavate the bridge foundation area and foundation underpinning working space can be created. (2) Underpinning piles and cap construction, to construct the underpinning piles and cap under the bearing platform on both sides. (3) Prestress tension construction, to tension the prestressed steel strands placed at the bottom of the underpinning bearing platform. (4) Bridge foundation piles underpinning and replacement, to start the jacks placed under the underpinning bearing platform and to load it to the designed force, then to complete the bridge foundation replacement process. (5) Existing foundation piles truncation, to passivate the original bridge foundation piles in simulation model. (6) Seal piles construction, to connect the underpinning bearing platform with the new pile caps on the both sides, and to unload the jacks. (7) Shield tunneling initial construction, to launch the shield machine and dug into the existing bridge foundation piles area. (8) Shield tunnel crossing through construction, to continue the shield construction and let it cross through the existing bridge foundation piles area. (9) Completed the construction of shield tunnel under the existing bridge pile foundation part. (10)-(14) Shield machine moved away from the existing bridge piles foundation area.
## 3 Results and Discussion  
The total number of measurements was 108 in one block, with 864 measured or calculated data. Due to the random factor AR, nine measurements were performed at each monitored cutting speed. The WKR ratio was calculated and the degree of charring was subsequently determined from the measured data. The data were then tested by the Shapiro-Wilk test of good agreement of the measured data, with the normal distribution at a significance level of 0.05. Due to the fact that the measured data did not meet the assumption of a normal distribution, a normal distribution was achieved by a nonlinear Box-Cox transformation (Table 2).  
Figure 2: Photo used for measuring the width of the cutting kerf and heat-affected zone (**left**), and photo edited by segmentation (**right**) by Digital Microscopy (DM).  
The results of the measured data after the transformation enabled the use of parametric mathematical-statistical methods of experimental evaluation (in the next parts asterisk symbol * is used for transformed values). Descriptive statistics were subsequently produced for all monitored transformed responses (Table 3).  
It follows from the descriptive statistics tables for each monitored transformed response that the cutting kerf width at the board top is larger than the cutting kerf width at the board bottom. The term \"upper board's surface\" refers to the side where the primary radiation beam interacts with the material. The values of the variation coefficients are smaller on the upper board's surface compared to the values of the variation coefficients of the observed responses on the board bottom. This is related to the properties of the wood from the point of view of heat transfer, as well as to the power of the radiation beam required for thermal decomposition of the wood in the cutting process. Larger values of variation coefficients also point to defects in the form of burns on the surface of the board bottom, which are caused by the sudden blowing of burns from the space of the cutting kerf with auxiliary gas (air).
## 4 Results and Discussion
### Effect of Major Dust Events  
Major dust outbreaks load the atmosphere with a high concentration of dust particles. Figure 11 displays the effect of 11 March 2009, a day with a major dust storm, on solar irradiance over the AP. The figure clearly illustrates the correlation between high aerosol loading and lower solar irradiance. On 3 March 2009, about 7 days before the storm, high aerosol loading can be observed in the central and southern parts of the AP (Figure 11c). This high aerosol loading resulted in low solar irradiance around the same locations (Figure 11a) where dust loading was high. On 18 March 2009, about 7 days after the storm, lower dust loading can be observed over the same regions of the AP (Figure 11d), with higher solar irradiance (Figure 11b).  
Figure 8: Intra-annual direct solar irradiance over AP during (**a–d**) January–April 2016, respectively.  
Figure 10: Locations with the highest solar irradiance in the Arabian Peninsula are highlighted in yellow. Points a–f represent locations used to check the efficiency of the machine learning ResNet model.  
Figure 9: Intra-annual direct solar irradiance over AP during (**a–d**) January–April 2017, respectively.  
Figure 12 shows the effect of dust loading on solar irradiance during the major dust events on 27 March 2012. (Figure 12a,b show the solar irradiance, while Figure 12c,d indicate the corresponding aerosol loading.) On 20 March 2012, 1 week before the major dust event, high aerosol loading was observed over the AP, especially near the Red Sea coast, the AG, and western Iran, with corresponding lower solar radiation levels (Figure 12a,c). On the other hand, on 4 April 2012, 1 week after the storm, low aerosol concentration was observed near the Rub Al Khali desert, with corresponding high solar irradiance.  
Figure 11: Effect of 11 March 2009, a major dust event, on solar irradiance over the AP on (**a**) 3 March 2009, (**b**) 18 March 2009; and on AOD over the AP for (**c**) 3 March 2009, and (**d**) 18 March 2009.
# Coexistence in Wireless Networks: Challenges and Opportunities  
[PERSON]  
1 Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia; [EMAIL_ADDRESS] (K.A.  
delivering low-latency, high-speed connectivity, uniform service with high traffic volume, support for countless connected devices, and exceptional mobility across various scenarios. To address the escalating demand for high data rate services, fifth-generation (5G) technology is being developed to enhance network performance and introduce new capabilities such as high-speed data transmission, ultra-low latency, and massive connectivity [7].  
To enhance spectrum efficiency and effectively support high data rates, one viable approach is to identify additional available spectrum resources. Nevertheless, improving spectrum efficiency poses a formidable challenge, particularly in sub-3 GHz bands, where the spectrum is congested, fragmented, and inefficiently utilized across heterogeneous wireless networks, resulting in interference issues. The 3400-3600 MHz frequency band offers a wider contiguous bandwidth compared to lower frequency bands and has superior propagation characteristics relative to higher frequency bands. Consequently, it has emerged as a pivotal frequency band for the commercial deployment of 5G systems [8].
# Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
## 4 Sea Trial Verification and Result Analysis
### Sea Trials in the South China Sea  
To verify the actual performance and reliability of the tsunami real-time monitoring system proposed in this article, a complete deployment and testing of the system was conducted on 25 September 2020 in the 54-meter-deep sea area of the South China Sea to verify the overall reliability of the tsunami real-time monitoring system function.  
The overall deployment sequence of the system is as follows: first, place the underwater unit in the water, then place the parallel release device in the water, fix its lower-end under force on the mooring pile, release it with a hook, and slowly place the Kevlar cable in the water until the weight block is in place. Use an A-frame to lift the weight block into the water, release the hook, and complete the system deployment.  
The sea trial began on the morning of 25 September 2020. At 9:52, the entire system was powered on, and the data reception was normal. The deployment was completed at 10:30. At 16:30, the system was released to the water surface through the deck unit and was towed to the side of the boat by another small boat (Figure 10). It was successfully recovered using the onboard A-frame. After the system recycling, upon reviewing the original data, it was found that after the deployment mode ended, it successfully entered the standard mode at 13:30, and the pressure data were collected completely. The system status information was transmitted back to the shore station data center every hour. After entering standard mode, the first small water-surface-communication buoy was successfully released, and 10 min later, the second small buoy was successfully released (the simulation test showed that the first small buoy was damaged). The redundant design of water-surface-communication was running well. Table 3 shows the partial data of the sea trial, and the data results indicate that the system runs stably and the data are good.  
Figure 10: Small-boat recovery underwater unit.  
Figure 9: Release device shore test.
## 2 Materials and Methods  
### Polymer Samples  
The first material was polycarbonate (PC). The chemical structure of PC can be seen in Figure 1a. As a source for this sample material, commercial recordable compact disks (CDs) of the brand Platinum(r) (CD-R, 700 MB, BestMedia, Hohldorf, Germany) were used, which were cut into small pieces using a manual knife (to ensure the lowest possible stress and damage to the material). To make sure the CDs were really made from PC, Fourier transform infrared spectroscopy in the attenuated total reflection mode (FTIR-ATR) was performed on a small test sample, which is shown in Figure S1a in the Supplementary Materials. The spectrometer used was a Bruker-Equinox 55 (Bruker, Karlsruhe, Germany). In the spectral range between 400 and 4000 cm\({}^{-1}\), the same absorption peaks occurred as reported in the literature for PC, for instance in [21]. As the second polymer, cellulose acetate butyrate (CAB) was used. CAB is a biocompatible and biodegradable polymer with its main part being the biomaterial cellulose with attached acetyl rests of acetic acid and acyl rests of butyric acid. A representation of the molecule can be seen in Figure 1b. Two different CAB materials were used. One was produced via an extrusion process (further referred to as extruded CAB). For the extrusion process to work, some softener was added to the CAB (substance not mentioned by manufacturer of raw material). The fabrication of the extruded CAB was performed by heating up small CAB pearls (CELLIDOR B 501-10, MOCOM Compounds GmbH & Co. KG, Hamburg, Germany) and pressing them into the desired shapes. The other one was made by heating up CAB powder (product number 419044, average molecular weight M\({}_{\text{n}}\approx\) 70,000), which was produced by Sigma Aldrich (Merck, Darmstadt, Germany), and then pressing it from either one or both sides (further referred to as pure CAB). Sigma Aldrich (Merck, Darmstadt, Germany) claims in the specification sheet provided with this material that it has a purity of better than 97% and that the infrared spectrum conforms to the structure [22]. For the extruded CAB, we have no information about the purity, but a comparison of the FTIR-ATR spectra, shown in Figure S1b,c in the Supplementary Materials, show that the spectral lines of the pure CAB and extruded CAB are practically identical. Only the line intensities of the extruded CAB were comparably weaker, which was due to worse contact with the ATR prisms due to the higher stiffness of this thick material and its rather rough surface.  
The typical sample size of the polymers used for laser-structuring and the cell experiments was approximately 2 cm \(\times\) 2 cm, with a thickness of 1.2 mm for PC, 4 mm for the extruded CAB and about 0.4 mm for the pure CAB, respectively. In some cases, broken parts of these samples were used. The original form of the extruded CAB was bone-shaped, with a length of 17 cm and a width of 2 cm at the ends and 1 cm in the middle of the extruded parts.
# Simulation Study on Thermal Wake Characteristics of Underwater Vehicle under Rotary Motion
## 5 Conclusions  
In this paper, the superposition grid technique is used to simulate the rotating motion of an underwater vehicle in uniform fresh water, and the spatial evolution law of thermal wake and the distribution of surface temperature field are obtained. The conclusion is as follows:  
1. The trajectory of the discharged cooling water differs greatly when the underwater vehicle is in direct navigation and rotating motion. The former is distributed symmetrically, the latter asymmetrically. The cooling water in the state of rotation gradually develops into a thermal wake with strong asymmetry after separation from the body, and the thermal wake on the rotating side is above the thermal wake on the deviating side.
2. The thermal wake center on the rotating side experienced the process of floating, sinking and then floating, while the thermal wake center on the deviating side experienced the process of sinking and floating. The thermal wake on both sides is greatly affected by the whirlpool caused by the body. The whirling side whirlpool rotates clockwise and the deviating side whirlpool rotates counterclockwise, which better explains the phenomenon of the different trajectories of the thermal wake on both sides.
3. The thermal wake temperature of the rotating side is always lower than that of the deviating side, which is related to the difference in vortex strength on both sides. The fluid velocity and vortex strength of the rotating side are large, and the thermal wake of the rotating side has strong heat transfer with the surrounding seawater, leading to faster temperature decay.
4. When the thermal wake rises to the surface of the water under the rotating motion of the underwater vehicle, it forms a temperature anomaly area on the water surface, which is composed of two hot spots.  
Conceptualization, G.L.; Software, G.L.; Data curation, G.L.; Writing--original draft preparation, G.L.; Supervision, Y.D. and L.Y.; Funding acquisition, Y.D. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by National Natural Science Foundation of China (NO.52271354).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 20: The curve of maximum difference \(\Delta\Gamma\) on line AB with time.