esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Flexural Properties
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Nanocomposite**} & **Sorption, _Wsp_** & **Solubility, _Wsl_** & **Flexural** & **Flexural** \\  & (\(\upmu\)g/mm\({}^{3}\)) & (\(\upmu\)g/mm\({}^{3}\)) & **Modulus (GPa)** & **Strength (MPa)** \\ \hline Neat silica & 2.58 \(\pm\) 0.51 & 0.08 \(\pm\) 0.09 & 1.59 \(\pm\) 0.81 & 15.31 \(\pm\) 3.66 \\ MPS & 2.48 \(\pm\) 0.43 & 0.32 \(\pm\) 0.17 & 3.66 \(\pm\) 0.67 & 44.30 \(\pm\) 21.23 \\ OTMS & 2.29 \(\pm\) 0.15 & 0.56 \(\pm\) 0.13 & 1.75 \(\pm\) 0.71 & 15.38 \(\pm\) 23.45 \\ ACPS & 2.68 \(\pm\) 0.84 & 0.16 \(\pm\) 0.10 & 3.08 \(\pm\) 0.40 & 40.18 \(\pm\) 7.48 \\ MPS-OTMS & 2.34 \(\pm\) 0.26 & 0.49 \(\pm\) 0.15 & 2.04 \(\pm\) 0.62 & 22.21 \(\pm\) 12.63 \\ ACPS-OTMS & 2.52 \(\pm\) 0.25 & 0.39 \(\pm\) 0.16 & 2.60 \(\pm\) 0.92 & 30.65 \(\pm\) 10.53 \\ MPS-ACPS & 2.72 \(\pm\) 0.29 & 0.25 \(\pm\) 0.11 & 3.30 \(\pm\) 0.93 & 42.83 \(\pm\) 9.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Total median values \(\pm\) interquartile range (IQR) for flexural modulus, flexural strength, and sorption/solubility parameters for dental nanocomposite resins after storage in water at 37 \(\pm\) 1 \({}^{\circ}\)C for 7 days. et al. [31], when the functional silane SETMS led to the strength sequence MPS-SETMS composite > SETMS-OTMS composite > MPS-OTMS composite. It should be noticed here that it would be interesting to investigate the rheological properties of the resin prior to curing in order to assess the effect of surface functionality on the dispersion quality [70]. However, this could be the subject of another publication. Conceptualization, S.K., A.K.N., E.A.K. and D.S.A. ; methodology, S.K. and A.K.N. ; validation, E.A.K. and D.S.A. ; formal analysis, S.K. and A.K.N. ; investigation, S.K. and A.K.N. ; resources, E.A.K. ; data curation, A.K.N. ; writing--original draft preparation, A.K.N. ; writing--review and editing, E.A.K. and D.S.A. ; supervision, E.A.K. and D.S.A. ; project administration, A.K.N. ; funding acquisition, E.A.K. and D.S.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Figure 8: (**a**) Flexural modulus bar plots; (**b**) Flexural resistance box plots for the experimental dental nanocomposite resins filled with different silica nanofillers.
## 5 Modeling Details  
### Data Descriptions  
Due to the variety and volatility of energy consumption behaviors for residential and commercial consumers, short-term load forecasting is a complex prediction problem. Therefore, the performed strategy in the data collection of smart meters is randomly selecting customers to increase the robustness and reliability of the prediction model.  
Therefore, a real data set that consists of one hundred different consumers' information is used for the proof of concept. The data set contains in total 28,876 daily consumption profiles in Turkey of 56 households and 44 commercial buildings for 2018. After the data cleansing and outlier detection process, there are 22,505 load profiles that can be used for the proposed short-term load forecasting approach.  
The collected variables from smart meters are customer ID, multiplier coefficient, time information, consumption values, and customers class, with dynamic data acquisition resolution according to customer preferences such as every 15 min, half hourly, and hourly.
## 4 Experimental Results
### Effectiveness of the Proposed Semi-Supervised Learning
** & **1** & **2** & **4** & **8** & **16** & **32** & **64** & **128** \\ \hline \multirow{4}{*}{[20]} & \multirow{4}{*}{Multi-view} & & & 13.42 & 17.85 & 22.88 & 35.44 & 52.93 & 67.15 & 87.65 & 94.47 \\  & & ✓ & & 28.88 & 48.52 & 60.51 & 73.03 & 84.88 & 92.38 & 96.12 & 98.03 \\  & & ✓ & & 16.77 & 26.72 & 47.72 & 65.60 & 82.33 & 92.67 & 96.54 & 98.65 \\  & & ✓ & ✓ & 50.34 & 73.17 & 86.65 & 97.94 & 95.41 & 97.23 & **98.54** & **99.01** \\ \hline \multirow{4}{*}{**MssvNetLite (ours)**} & \multirow{4}{*}{Multi-view} & & & 6.03 & 7.58 & 9.67 & 15.12 & 26.33 & 48.74 & 68.56 & 85.52 \\  & & ✓ & & 25.81 & 35.10 & 47.52 & 67.58 & 82.32 & 94.96 & 95.88 & 98.31 \\  & & ✓ & & 13.99 & 18.22 & 34.16 & 56.97 & 71.88 & 86.36 & 94.06 & 95.70 \\  & & ✓ & & 45.23 & 57.0 & 81.03 & 89.24 & 94.70 & 97.07 & 98.41 &with a large amount of training data (\(\geq\)64), but rather slightly degrades it. This may be because the visual representation learned in the self-supervised learning stage is not so robust. As we can see from Table 3, FeatureNetLite with semi-supervised learning is significantly improved with the different numbers of training data and also outperforms other voxel-based approaches. Similar to the experimental results from [20], the classification performance of the voxel-based approach is not comparable with the MSV-based approach. Additionally, the results show that data augmentation and ImageNet pre-trained weight also can improve accuracy on small labeled sample learning. This is because data augmentation introduces variations to the dataset to help the model generalize better. In addition, pre-trained weights transfer the knowledge gained from other datasets (e.g., ImageNet) to the task of recognizing machining features.
# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 2 Materials and Methods
### Data Extraction  
Two performance metrics were evaluated: the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM). The PSNR and SSIM are commonly used metrics for assessing image quality, which can quantitatively show the effectiveness of the proposed networks. Both are full-reference metrics, which evaluate the quality of an image by comparing it to a high-quality reference image. Data were extracted if the article reported either the PSNR or SSIM with standard deviation for both the low-quality input images, as well as for the images generated by the proposed algorithm.  
The PSNR is defined as the ratio of the maximum power of a signal and the power of the distorting noise [30]. It reflects the pixel-based similarity between the reconstructed image and the corresponding high-quality reference [14]. This ratio between two images is expressed in decibels (dB), thus following a log\({}_{10}\) scale. A higher value indicates that the reconstructed image contains more details and provides a higher image quality [6; 31] and vice versa: a small value of the PSNR implies high numerical differences between images [31]. Given a reference image \(f\) and a test image \(g\), both of size \(M\times N\) with maximum intensity MAX\({}_{I}\) and the Mean Squared Error (MSE) between \(f\) and \(g\), the PSNR is calculated as follows:  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{\text{MAX}_{I}^{2}}{\text{MSE}(f,g)}\right) \tag{1}\]  
where MSE represents  
\[\text{MSE}(f,g)=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}(f_{ij}-g_{ij})^{2} \tag{2}\]  
The SSIM evaluates the perceived quality and assesses the perceptual-based similarity between paired images [14; 30]. It is considered to be correlated with the quality perception of the human visual system. Instead of using traditional error summation methods, the SSIM is designed by modeling any image distortion as a combination of three factors that are loss of correlation, luminance distortion, and contrast distortion. The SSIM index ranges between \(-1\) and \(1\), with a value of \(1\) indicating perfect correlation, \(0\) indicating no correlation, and \(-1\) indicating anti-correlation between the images [31].  
For a reference image \(f\) and a test image \(g\), where \(\mu\) denotes the mean, \(\sigma_{f}^{2}\) denotes the variance of \(f\), \(\sigma_{fg}\) denotes the covariance of \(f\) and \(g\), and \(C1\) and \(C2\) are two positive constants used to avoid a null denominator, the SSIM is defined as follows:  
\[\text{SSIM}(f,g)=\frac{(2\mu_{f}\mu_{g}+C_{1})(2\sigma_{fg}+C_{2})}{(\mu_{f}^ {2}+\mu_{g}^{2}+C_{1})(\sigma_{f}^{2}+\sigma_{g}^{2}+C_{2})}. \tag{3}\]  
Additionally, the extracted data were grouped by set type (in vivo, phantom, or simulation).
## 2 Oil Shale Properties
### Organic Matter Type in Oil Shale
The TOC values in the studied samples were found to be greater than 10 wt%, indicating a high organic richness. The total sulfur content was also high (T.S. > 2 wt%), indicating a marine environmental setting with low oxygen conditions. Based on the Biomarker, the ratios of different compounds such as C31-22R-hopane/C30-hopane, Gammacerane/C30-hopane, pristane/phytane, and steranes were measured. The biomarker distributions suggest high contributions of marine-derived organic matter that were preserved under highly hypersaline-reducing conditions. Based on the thermal index, the thermal maturity of the organic matter was evaluated using pyrolysis data (Tmax) and specific biomarker maturity parameters. Tmax, in pyrolysis, refers to the highest temperature reached during the thermal decomposition process. It represents the peak temperature at which the pyrolysis reactions occur most rapidly. The majority of oil shale samples had Tmax values lower than 430 \({}^{\circ}\)C, indicating they are in the immature stage of the oil generation window. Based on a petrological index, microscopic observations of organic matter were performed. The presence of alginite, amorphous organic matter, and planktonic foraminifera assemblages suggested a marine origin for the organic matter input. Based on the different evaluations, [PERSON] et al. determined the Jordanian oil shales were classified as being organically rich, deposited in a marine environment under highly hypersaline reducing conditions, and in the immature stage for oil generation, requiring artificial heating for commercial oil hydrocarbon generation. This categorization approaches offer valuable insights into evaluating the energy capacity, yield of hydrocarbons, and economic feasibility of distinct segments within oil shale. They aid in identifying the most effective extraction and refining methodologies to optimize the retrieval of valuable assets from this significant geological deposit.
## 2 Materials and Methods
### Statement of Interest and Research Questions
Some of them are quite broadened, lacking a structural measurement, or they do not examine at all users' security and privacy concerns, which are vital concepts for BS acceptance [30; 41]. In Greece, where, to the best of our knowledge, a large-scale research regarding users' BS acceptance is lacking, it is even more worthy to explore this issue. Consequently, questions concerning Greek users' perceptions and familiarity with BS, as well as the socio-demographic factors that affect them to accept this type of technology, are set emphatically. Furthermore, users' concerns regarding BS implementation are examined. Our study focuses on the Greek University of the Aegean. Especially for Greek Universities, this type of study could be very useful since the Ministry of Education has established a new law regarding HEIs security (Law 4777/2021, F.E.K. 25/A/17-2-2021-Article 16). Toward this, the consideration of the BS adoption in order to ensure the authorized access of staff and students within HEIs internal areas would be an interesting case. The University of the Aegean is a network university, with a large student population (17.312), established in six campuses spread across six islands of the Aegean Sea, consisting of six faculties and eighteen departments. Therefore, besides the convenient access, due to its multi-faceted diversity (e.g., geographically and field of study), this research population can provide useful insight for BS acceptance. The survey is distributed to the undergraduate, postgraduate and doctoral students. In this light, the following fundamental descriptive research questions are formulated, which guide our study:* RQ1: What are University of the Aegean students' perceptions and familiarity regarding BS adoption and use? * RQ2: What are the socio-demographical factors that affect University of the Aegean students' acceptance of BS? Our research questions aim to provide a description of many of the demographic and social variables under consideration and to assist in understanding which of them are important, while revealing current patterns regarding students' BS acceptance and adoption. In this regard, we aim to examine Greek users' BS acceptance in public educational services, focusing on the perspectives of Greek students and on the role of their socio-demographic profile, going beyond previous literature, as a research need that has to be addressed.
# Assessment of Electromagnetic Exposure to a Child and a
## 7 Conclusions  
A comprehensive investigation into electromagnetic (EM) exposure and thermal safety has been conducted, focusing on three distinct subjects: a girl of five years named [PERSON], a gestating female at seven months gestation known as Pregnant II, and her fetus. This study employed anatomically precise computational virtual humans along with a cellular device functioning at two different frequency bands, 1000 MHz and 1800 MHz. A total of 48 separate arrangements involving these numerical phantoms in various configurations and with different phone orientations were meticulously analyzed. The study presented detailed results of SAR10g, SAR-wb, and Tmax for every anatomical virtual human, including the fetus within the confines of an elevator cabin. The results here add to the findings in our prior work [55]. Here, contrary to [55], we examined how exactly, and in which scale, the positioning of the humans affects the quantities measured for all the utilized models, including the seven-month fetus.  
Notably, it becomes evident that an integrated approach is essential to fully understand the complexities of EM exposure. Each configuration offers unique insights into how specific absorption rate values and temperature rises differ across subjects and settings. The variations observed underscore the necessity of examining every possible configuration to draw comprehensive conclusions about EM safety.  
Moreover, the investigation highlights the importance of further exploring the effects of EM exposure in elevator cabins under various occupancy conditions and at different portable device frequencies. Such studies are crucial for developing targeted safety guidelines and protective measures, particularly in environments where RF exposure is compounded by enclosed spaces like elevators. This research not only enhances our understanding of EM field interactions with human tissues but also aids in the formulation of better health and safety standards for vulnerable populations such as children and pregnant women.  
Conceptualization, I.K.; Methodology, I.K.; Software, I.K.; Formal analysis, I.K.; Resources, I.K.; Data curation, I.K.; Writing--original draft preparation, I.K.; Writing--review & editing, S.B. and S.K.; Supervision, S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Research on Construction Sequencing and Deformation Control for Foundation Pit Groups
## 2 Materials and Methods
### Experimental Setup  
#### 2.1.1 Model Test Design  
Indoor physical model tests were first conducted to investigate the excavation sequence and deformation characteristics of foundation pit groups, as shown in Figure 2. Physical model tests were designed and carried out for separated foundation pits and adjacent foundation pits (Figure 3) under various excavation sequences. To accurately capture the deformation patterns of the retaining structures, strain gauges were installed on the surfaces of the diaphragm walls for real-time monitoring during the tests. This approach systematically recorded and analyzed the deformation behavior of the enclosure structures during the excavation process. The test results are valuable for understanding the stress and deformation characteristics of retaining structures under different construction sequences and provide a reliable reference for subsequent theoretical analysis and numerical simulation studies.  
Figure 2: Schematic diagram of the model test (mm).
## 1 Introduction
Furthermore, the policy with single content predication and time-variant characteristics is not suited for LEO-SMC-ICN-IoT. The problems of proactive caching due to its time-variant characteristics can be extensively solved with Machine Learning (ML) [23; 30; 31]. ML can not only infer what to cache but also where to cache by predicting node locations [23; 32; 33; 31]. So, we will apply ML to the proactive caching policy in LEO-SMC-ICN-IoT. Random Forest (RF), a supervised ML algorithm for modeling high-dimensional data, can handle continuous, categorical, and binary data [34]. And the major features of RF that have gained attention are accurate predictions and better generalization due to the use of ensemble strategies and random sampling [35]. In this paper, we propose a novel caching decision policy using RF in LEO-SMC-ICN-IoT and focus on the caching policy of satellite nodes. So, the data of satellite nodes is taken as a resource. Also, the RF is introduced to predict the matching relationship between the satellite nodes and the content. Because of the optimization of the caching policy according to multi-dimensional data using attributes such as content popularity and satellite nodes' caching rate, copies of content with high popularity are stored at locations close to the requesting satellite nodes. Hence, the LEO-SMC-ICN-IoT can provide content to Mobile Users (MU) with lower latency, i.e., better QoE. To our knowledge, past research on ICN caching policy has primarily focusedon traditional or classical caching algorithms such as LCE and LCD [24; 25; 26], and some studies use improved ICN caching algorithms such as OPP, prob 0.5 [24; 27]. While few studies on caching policies adapt to ML for different application scenarios, they do not consider satellite constellations [32; 33]. Based on this, the contributions of this paper are listed as follows:  
1. The framework of LEO-SMC-ICN-IoT is proposed. Using this framework, the caching policy can be dynamically managed, and adaptively optimize the caching performance. 2. We propose a novel caching algorithm of satellite nodes based on random forest denoted by CARF, which combines both content popularity prediction and satellite node location prediction. In CARF policy, using the concept \"viscosity\" [32; 33], we present a cache matching rule: if the viscosity between content and satellite nodes is greater than some threshold, then the content is cached on satellite nodes. 3. Considering the transient and small characteristics of the IoT data [36], the proposed caching policy takes both the IoT data lifetime and the freshness as the important parameters and is more suited for IoT scenarios. We organize the rest of this paper as follows: Section 2 introduces the LEO-SMC-ICN-IoT system model. In Section 3, we present the caching framework in LEO-SMC-ICN-IoT. In Section 4, we use the RF algorithm to design a proactive caching policy in LEO-SMC-ICN-IoT. Section 5 reports a performance evaluation of the proposed caching policy, by using the discrete event Network Simulator 3 (NS-3). Finally, we draw the conclusions.
# Application Research of Negative Pressure Wave Signal Denoising Method Based on VMD
## 2 Methodology  
### The Principle of NPW  
The schematic diagram of leak location based on NPW is shown in Figure 1. When a leakage fault occurs in the pipeline, the NPW caused by the leakage will travel up and down the pipeline. There is an obvious pressure drop inflection point at the leak point. Through the upstream and downstream NPW inflection points, respectively, there is a time difference \(\Delta t\) between them reaching the two sensors. Using this time difference, we can locate the location of the leak point, as shown in Equation (1):  
\[X_{A}=\frac{1}{2}(L+v\Delta t) \tag{1}\]  
where \(L\) is the pipe length; \(t_{1}\) is the time for the negative pressure wave to reach sensor A; \(t_{2}\) is the time for the negative pressure wave to reach sensor B. \(\Delta t=t_{1}-t_{2}\). \(v\) is the NPW velocity, which can be calculated by Equation (2) [19]:  
\[v=\sqrt{\frac{K/\rho}{1+\frac{K}{E}\frac{D}{e}C}} \tag{2}\]  
where  
\(K\)--the bulk modulus of water, Pa;  
\(\rho\)--the density of water, kg/m\({}^{3}\);  
\(E\)--[PERSON]'s modulus of the pipe material, Pa;  
\(D\)--the pipe inner diameter, m;\(e\)--the tube thickness, m;  
C--the correction factor. \(C\) is related to the constraint conditions of the pipeline [20].  
According to [PERSON] [21], the pipeline has three supporting conditions. The first is fixed upstream, the downstream can be expanded at will. The second is that there is no axial movement of the whole pipeline. The third is that the connections between the pipes are expansion joints. The correction coefficients \(C\) of these three cases are: \(C=1-\frac{\mu}{2}\); \(C=1-\mu^{2}\); \(C=1\), where \(\mu\) is the Poisson coefficient of the pipe. Our test pipelines are laid on the ground or in tube Wells, so \(C=1\).  
Under the same pipe condition, \(v\) is a constant. Therefore, the accurate calculation of \(\Delta t\) is key to identifying the leak's location. Obviously, noise will affect the extraction accuracy of inflection point, thus reducing the accuracy of the leakage point location. In order to solve this problem, this paper's extraction method of the inflection point, finally realizes the purpose of reducing the positioning error of the leakage point.