esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
We installed an app that periodically collected GPS, Wi-Fi, and Bluetooth low energy (BLE) beacon information on the smartphones of participants for one month, with their agreement (the experiment was conducted in compliance with research ethics procedures with prior consent for the collection of personal information of participants). To prevent the excessive collection of personal information, we set up a geofence around the university campus so that personal information was collected only when the participants were on campus. The experiment was approved by the Institutional Review Board (IRB). We followed the guidelines of the IRB to protect personal information and personally identifiable information of the research participants. The contributions of this study are as follows: We propose a novel contact tracing method using exposure detection based on machine learning, considering that a virus has different transmission characteristics, depending on indoor and outdoor conditions. For an indoor space, we propose space-based indoor Wi-Fi contact tracing to detect exposure by determining the co-location status of people in the same space. For an outdoor space, trajectory-based outdoor GPS contact tracing is used to detect exposure by determining the companion status of people, such as the same movement in their trajectory. We evaluated the performance of the proposed method using real data collected from 28 people for a month. This study can also be used as guidance for future research works in the development of effective contact tracing methods for COVID-19 and other infectious diseases by considering the transmission characteristics. The structure of this paper is organized as follows. Section 2 reviews research works related to contact tracing. Section 3 proposes indoor and outdoor contact tracing methods of intelligent contact tracing based on transmission characteristics of COVID-19 (IntelliTrace). Section 4 introduces the experimental design. Section 5 discusses the analysis of exposure detection based on data obtained from actual experimental results. Section 6 presents the conclusions of this study.
## 3 Results and Analysis
### Experimental Results  
On the test set, we compared our final model with the current state-of-the-art convolutional neural network-based super-resolution networks, including SRCNN [27], EDSR [28], RCAN [29], SRGAN [30], ESRGAN, and MSRResNet [31]. As shown in Table 1, our experimental results surpassed other models on multiple image quality metrics, particularly standing out in the aspect of perceptual quality of images. Our super-resolution experimental results are more in line with human visual perception. Compared with the baseline model ESRGAN, our model achieved an 11.92% improvement in the PSNR index and a 4.8% increase in the SSIM index. On 2 perceptual quality assessment metrics, NIQE and LPIPS were improved by 10.78% and 1.25%, respectively. Moreover, our model reduced the number of parameters by 21%, achieving higher efficiency. The results show that GAN-type networks tend to outperform other networks in terms of perceptual quality, although other types of models might achieve better results in PSNR and SSIM, their perceived quality usually does not match that of GAN-type networks.  
We compared the performance of several super-resolution models on the details of coronal images, as presented in Figure 8. The EDSR model achieved excellent results on the PSNR metric; however, its performance on the LPIPS index was not as good as the model we proposed. As can be seen from Figure 8c, the MSRResNet model produced severe anomalous dark spots in the coronal images after super-resolution processing. Similarly, the super-resolution image generated by the EDSR model exhibited haze-like artifacts. Despite this, both models scored higher on the SSIM index than our model. This presents a clear inconsistency with people's intuitive understanding of image quality.
###### Abstract  
This study assessed the between-session reliability of the FysioMeter H-station when measuring isometric quadriceps and hamstrings strength and H/Q (hamstring/quadriceps) ratio, and the concurrent validity when compared to an isokinetic dynamometer (ID). Sixteen young males and three females (25.6 \(\pm\) 1.7 years old) performed maximum quadriceps and hamstring contractions using the H-station and the Humac NORM ID over two sessions. Between-session reliability was evaluated by comparing scores from the first and the second session. Concurrent validity was assessed by comparing scores from the H-station with the Humac NORM ID. The H-station showed excellent relative reliability for the quadriceps, hamstring, and the H/Q ratio with ICC2.1 ranging from 0.89 to 0.91. The relative reliability of the Humac NORM was good to moderate for the quadriceps, hamstring, and the H/Q ratio with ICC2.1 ranging from 0.89 to 0.91. Acceptable absolute reliability of the H-station was found across quadriceps, hamstring, and H/Q measures (coefficient of variance (CV) = 7.2 to 9.3%, limit of agreement (LOA) = 20.0 to 24.3%). The validity of the H-station was large for hamstring strength (r = 0.79) and moderate for quadriceps strength (r = 0.69) and H/Q ratio (r = 0.39) compared to the Humac NORM ID. Bland-Altman plots showed LOAs ranging from 37.9 to 59.5%. The results indicate that the H-station can be used to make reliable assessments of relative changes in maximum isometric quadriceps and hamstring strength.  
Revised: 9 December 2023  
3 January 2024  
17 January 2024  
H/Q ratio; knee peak torque; hamstring injuries; validity
###### Abstract
However, their most prevalent application is in generating new images. Comprising two Artificial Neural Networks (ANNs), the generator and the discriminator, GANs operate by pitting these networks against each other: the generator generates new data instances, while the discriminator assesses their authenticity [24]. WGANs enhance traditional GANs by utilizing the Wasserstein distance, also recognized as the earth mover's distance, as the objective function during training. Wasserstein distance improves model stability, while mitigating issues like mode collapse and convergence. This distance metric offers a more insightful evaluation of the resemblance between real and generated data distributions compared with the probability-based metrics utilized in conventional GANs. In WGANs, the discriminator is called a \"critic\" because its role goes beyond binary classification but evaluates the quality of generated samples relative to real ones [25; 26]. In this study, we present a novel approach aiming to reduce large sample size needs in clinical trials. The proposed approach suggests training an artificial neural network, specifically a WGAN, on a limited dataset (the \"sample\") and then utilizing a WGAN to generate virtual subjects. The proposed approach suggests that only the small subset (i.e., the \"sample\") of the true population can be used along with WGANs to create a virtual population (the \"generated\" dataset). To demonstrate the applicability of the proposed approach, Monte Carlo simulations of clinical studies were performed to compare the performance of the WGAN-synthesized virtual subjects (i.e., \"generated\" dataset) against both the entire population (the so-called \"original\" dataset) and a subset of it, namely, the \"sample\". The ultimate objective of this work is to illustrate the following:  
* How AI techniques can be applied to generate virtual volunteers/patients, as an effort to reduce the need for large numbers of real patient data. * The introduction of \"virtual patients\" to decrease the costs and times of clinical studies as well as reduce the human exposure in clinical studies. Overall, it aims to offer a potential solution to the challenges associated with small sample sizes and human exposure in clinical research.
## 2 Security Considerations for Marine Network Protocols
### Confidentiality  
Manufacturers must ensure that only authorized parties are allowed to modify given data. Access control mechanisms and authentication procedures are implemented in the software to control access to resources. Further, encryption protocols are applied to enhance data protection. Additional measures include administrative solutions, such as policies and training, as well as physical controls that prevent people from accessing facilities and equipment. Examples of NMEA training include installation of marine computers, data and ethernet, VHF, physical planning and documentation, and connecting to other data sources, as well as network configuration and troubleshooting [10].  
Due to the limitation of bandwidth in many marine network protocols [4; 5], encryption mechanisms are not always implemented. As such, the main approach to ensure confidentiality for these systems is primarily through access control.
###### Abstract  
Biomass gasification technology is a clean and renewable energy utilization approach. Understanding the evolution of gasification product properties is crucial to achieving carbon neutrality goals. A fixed-bed reactor is employed for the biomass gasification with CO\({}_{2}\) in this study. Various methodologies have been conducted to characterize the syngas, tar, and biochar produced from the electric gasification (EG) and microwave gasification (MG) of oak and corn stalk samples at different temperatures. When gasifying the same biomass at the same temperature, the syngas yield of MG is generally 3-7% higher than that of EG, and the difference increases with decreasing temperature. The biochar yield of MG is 3-6% higher than that of EG. EG produces more tar at 600-800 \({}^{\circ}\)C. The yield of syngas increases as the gasification temperature rises from 600 to 1000 \({}^{\circ}\)C, but that of tar and biochar falls. The syngas mainly comprises H\({}_{2}\), CH\({}_{4}\), CO, and CO\({}_{2}\). MG produces 8-15% higher CO content and 2.5-3.5% higher H\({}_{2}\) content than EG. This is due to different heating mechanisms. The net calorific value of syngas increases with temperature, reaching a maximum of 11.61 MJ m\({}^{-3}\) at 1000 \({}^{\circ}\)C for syngas from corn stalk MG. When the temperature rises from 600 to 1000 \({}^{\circ}\)C, more primary tars are converted into polycyclic aromatic hydrocarbons (PAHs). At 900 \({}^{\circ}\)C, corn stalk biochar from MG has a maximum specific total pore volume (0.62 cm\({}^{3}\) g\({}^{-1}\)), surface area (525.87 m\({}^{2}\) g\({}^{-1}\)), and average pore diameter (4.18 nm). The intensities of the characteristic peaks of biochar functional groups decrease gradually. The heating method has little effect on the types of functional groups.  
biomass gasification; electric heating; microwave heating; syngas; tar; biochar +
Footnote †: journal: Physics Letters B
## 3 Results and Discussion
### Gas seepage Regularity in Different Structural Bituminous Coal Samples
These enlarged pores, significantly exceeding the mean free path of gas molecules, result in Darcy-dominated flow even at low pressures. As is shown, permeability in all four experimental conditions increased rapidly with gas pressure before stabilizing. Figure 7: Gas seepage characteristics of raw coal samples. (**a**) Gas seepage regularity of briquette coal samples under different axial stress; (**b**) gas seepage regularity of briquette coal samples under different confining pressures; (**c**) gas seepage regularity of briquette coal samples with different gases; (**d**) gas seepage regularity of briquette coal samples with different sample forming pressure. Gas seepage regularity of briquette coal samples under different axial stress and confining pressures were demonstrated in Figure 7a,b. Raised axial and confining pressures compress the briquette matrix, reducing pore connectivity and increasing flow resistance, thereby decreasing permeability. This trend aligned with observations in raw coal but occurred at a higher baseline due to briquette coal's inherent structural homogeneity. From Figure 7c, briquette coal replicated the gas-specific permeability hierarchy of raw coal, also governed by molecular kinetic diameters and adsorption interactions. Additionally, from Figure 7d, higher forming pressures enhance particle cementation, reducing porosity and permeability. For instance, briquettes formed under a pressure of 130 MPa exhibited a permeability approximately one-tenth of those prepared at 50 MPa. It follows that the migration dynamics of gas within coal seams are fundamentally affected by the geological formation processes and in situ geo-mechanical conditions of the seam. Moreover, permeability increases nonlinearly with gas pressure due to the asynchronous growth of gas velocity and pressure gradients. This behavior persists across both raw and briquette coal, emphasizing the universal influence of gas dynamics on flow regimes. According to the above test results, gas transport in coal is controlled by stress conditions, structural heterogeneity, gas type, and pressure gradients. Specifically, permeability scales with the ratio of gas molecular size to pore channel dimensions and the magnitude of pressure gradients. When the gas species remains constant, permeability increases proportionally with both the relative dimensions of the flow pathways and the magnitude of the gas pressure gradient. Under low-pressure conditions, the Klinkenberg effect amplifies permeability for small-pore systems (e.g., raw coal), while briquette coal's larger pores miss out on this phenomenon. Thus, in outburst scenarios, where coal is highly fragmented, permeability increases dramatically due to enhanced pore connectivity. This accelerates gas flow velocity and volume, intensifying coal mass failure and its ejection--a critical feedback mechanism in outburst dynamics.
# Mechanical Properties and Microstructure of Polyvinyl Alcohol (PVA) Modified Cement Mortar
###### Abstract  
The mechanical properties of cement mortars with 0-2.0% (by mass) polyvinyl alcohol (PVA) were experimentally studied, and the effects of PVA incorporation on the hydration products and microstructure of the cement mortar were determined with differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The results show that the rational content of PVA formed evenly dispersed network-like thin films within the cement matrix, and these network-like films can bridge cracks in the cement matrix and improve the mechanical properties of the cement mortar. Over- incorporation of PVA may result in the formation of large piece polymer films that coat the cement particles, delay the hydration of the cement mortar and adversely affect its performance. The mechanical properties of the cement mortar show a significant increase and then decrease with a change in the PVA incorporation. When the PVA content was 0.6% and 1.0%, the mortar had the best compressive and flexural strengths, respectively. The compressive strength of the cement mortar increased by 12.15% for a PVA content of 0.6%, and the flexural strength of the cement mortar increased by 24.83% for a PVA content of 1.0%.  
polyvinyl alcohol; cement mortar; mechanical properties; microstructure +
Footnote †: journal: Applied Physics Letters
# Kinetics and Thermodynamics of Efficient Phosphorus Removal by a Composite Fiber
## 2 Materials and Methods
### Static Adsorption and Phosphorus Removal Experiment of ZSFB Composite Fiber  
#### 2.1.1 Adsorption Isotherm Model  
Langmuir Adsorption Isotherm Model  
The model assumes that the adsorption is a monolayer adsorption and the adsorption sites on the surface have the same force on the adsorbate. If a site is occupied by the adsorbate, the site no longer adsorbs other adsorbates. In the process of adsorption, as the adsorption sites are occupied, it is more and more difficult for the adsorbates to find available adsorption sites, so the adsorption rate slows down until the adsorption equilibrium is reached [10], as shown in Equation (1):  
\[Q_{e}=Q_{m}K_{L}C_{e}/(1+K_{L}C_{e}). \tag{1}\]  
Converted to a linear form:  
\[C_{e}/Q_{e}=C_{e}/Q_{m}+1/Q_{m}K_{L}. \tag{2}\]  
In the equations above, \(Q_{e}\) indicates the adsorbed amount (mg/g) of the adsorbate per unit mass of adsorbent until adsorption equilibrium, \(Q_{m}\) represents the maximum adsorbed amount in the saturated state (mg/g), \(Ce\) indicates the concentration of the adsorbate in the solution at adsorption equilibrium (mg/L) and \(K_{L}\) is the Langmuir constant (L/mg), which represents the affinity of the adsorbent for the adsorbate. \(C_{e}/Q_{e}\) is plotted against \(C_{e}\) and linearly fitted, \(Q_{m}\) can be obtained from the slope of the line and \(K_{L}\) can be obtained from the intercept.  
Freundlich Adsorption Isotherm Model  
The Freundlich model is an empirical model that assumes that the adsorption process is a monolayer adsorption that occurs on an uneven surface and is suitable for lower concentration. The disadvantage is that the maximum adsorption capacity cannot be estimated [11]. The equation is as follows:  
\[Q_{e}=\mathrm{K_{F}C_{e}}^{1/n}. \tag{3}\]  
Converted to a linear form:  
\[\log Q_{e}\ =\ \log K_{F}\ +\ \frac{1}{n}\log C_{e}. \tag{4}\]  
Among these equations, \(K_{F}\) and n are Freundlich constants. The value of \(K_{F}\) can reflect the adsorption affinity and the maximum adsorption capacity of the adsorbent to some extent. The larger the \(K_{F}\), the larger the maximum adsorption capacity. The value of n can reflect the difficulty of adsorption, and the larger the value of n, the easier the adsorption occurs. It is generally considered that the value of n of the effective adsorbent is between 2 and 10 [12]. \(\log Q_{e}\) is plotted against \(\log C_{e}\) and linearly fitted, n can be obtained from the slope, and \(K_{F}\) can be obtained from the intercept [13].
## 4 Experimental Results  
During the experiment, test data is used to evaluate the performance of the proposed technique. RIS-DUGL method does not use any prior knowledge such as labels (except those used for the fine-tuning) and other metadata of images. Results related to WANG, IAS- RGB, and Merchant datasets are shown in Figures 13-15, respectively, along with their cosine similarity scores. With various conditional query images such as left, down, and right side poses of faces. It also covers the image's sufficient depth to recognize the subject lying far from the sample image for the retrieval of its more visible version. In  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Layers** & **1** & **2** \\ \hline No. of Neurons Per Layer & 100 & 80 \\ \hline L2 Weight Regularization & 0.001 & 0.001 \\ \hline Sparsity Regularization & 6 & 5 \\ \hline Sparsity Proportion & 0.1 & 0.1 \\ \hline Scale & True & True \\ \hline Epochs & 50 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values of Sparse Auto encoder used in the proposed RIS-DUGL.  
Figure 13, some of the results collected on the WANG dataset and the proposed framework have recognized the desired subject on the sample image having multiple subjects. It also covers color manipulation and recognizes the desired subject with background conditions. In Figure 14, it can be seen that some of the query images have equal participation in the desired object and the background information. Here, the proposed RIS-DUGL technique gives more value to the content of the desired subject and finds out its more visible version, which is a prominent feature for the sake of person identification. Figure 15 shows results using the Merchant dataset where the retrieved scale and rotation invariant best match. By using precision as a performance measure, the proposed RIS-DUGL technique has achieved 98.46% accuracy.
## 2 Materials and Methods
### Hyperspectral Imaging System  
The HSI system comprised two main components: the HSI camera and the illumination unit. This study's hyperspectral camera was a line-scan hyperspectral camera (TIVITA Tissue Camera, Diaspective Vision GmbH, Am Salzhaff, Germany), based on the push broom scanning method. This camera contained a CMOS sensor (AR0130 CS, On Semiconductor Corp., Phoenix, AZ, USA) with a resolution of 1280 pixels \(\times\) 960 pixels and was equipped with an AZURE Photonics 2/3\" 12 mm objective lens (AZURE Photonics U.S.A., Inc., San Ramon, CA, USA). In order to acquire a focused image, the distance between objective and kidney sample surface was adjusted to 46 cm. The HSI system covers the spectral range of 500-995 nm with a spectral resolution of 5 nm. The illumination unit contained six 20 W quartz-tungsten-halogen spots (OSRAM 41,861 Decostar 51 ALU, Osram GmbH, Munchen, Germany) with an aluminum reflector for homogeneous illumination of the field of view (FOV). A laptop (ThinkPad W530, Lenovo GmbH, Stuttgart, Germany) with the TIVITA Suite software 0.6.1.4 (Diaspective Vision GmbH, Am Salzhaff, Germany) was used exclusively for image acquisition. This software reduced the hyperspectral data cube to 640 px (x-dimension) \(\times\) 480 px (y-dimension) \(\times\) 100 wavelengths (\(\lambda\)-dimension). Further technical information of the HSI camera system can be found in previously published literature [24].  
A custom written MATLAB code (MATLAB R2018b, The MathWorks, Inc., Natick, MA, USA) was used to analyze the HSI data. The workflow of the main steps in the current research is illustrated in Figure 1.
# Evaluation on Improvement Zone of Foundation after Dynamic Compaction
## 3 Parametric Studies
### Effect of Tamper Radius  
The numerical simulation of DC is performed with a tamping distance \(S=6.0\) m, drop energy \(MH=4000\) kN-m, tamper radius (\(R\)) ranging from 0.6 to 3.0 m, and the blows of 10 at each impact point to investigate the influence of the tamper radius on the soil improvement.  
As is expected in Figure 16a, for the case of the single-point tamping, a larger radius leads to a smaller improvement depth due mainly to the lower impact stress. Besides, a wider improvement zone can be achieved by using a tamper with a larger radius. It can also be seen in Figure 16b, for the case of the multipoint tamping, that an increase of the tamper radius leads to a wider improvement area, while a slightly less improvement depth between impact points was observed.  
Figure 16: The zone of \(I_{x}=7.7\%\) for tamper radius: (**a**) impact at point 1; (**b**) impact at point 2.  
Closer inspection of Figure 17 indicates that the smaller the tamper radius, the better the improvement effect, and the more pronounced the effect of the tamper on the location of point 1. This is consistent with the results obtained by [PERSON] [32]. However, a less improvement effect was observed between the impact points. The results indicated that DC can be effectively employed to improve local reinforcement effects as the dimension of the tamper reduces. Besides, \(I_{\mathrm{r}}\) below point 3 is little affected by the tamper radius. This is consistent with the observation of \(\delta_{z,2}\), and \(\delta_{\mathrm{U,2}}\) in Figure 22.  
Figure 18a shows that \(\triangle\delta_{z,\mathrm{i}}\) (\(i=1,2\)) below points 1 and 3 decreases slightly with tamper radius, indicating that the soil improvement affected by the tamper radius is so little that it can be negligible. Figure 18b shows that \(\triangle\delta_{\mathrm{U,1}}\) increases from 70% when \(R=0.5\) m to about 10% when \(R=1.6\) m and then decreases from 106% when \(R=1.6\) m to about 94% when \(R=3.0\) m during impact at point 1, while \(\triangle\delta_{\mathrm{U,2}}\) changes little with the increase in tamper radius during impact at point 2. This suggests that for a given weight of tamper, there is an optimum tamper radius to optimize soil improvement between impact points during impact at point 1, which is consistent with [PERSON] and [PERSON]'s findings [10]. Figure 18b also shows that there exists the attenuation of the compaction effect between impact at points 1 and 2.  
Figure 17: \(I_{\mathrm{r}}\) of soil versus depth for tamper radius: (**a**) below point 1; (**b**) below point 3.  
Figure 18: Normalized indicators versus tamper radius: (**a**) \(\triangle\delta_{z,\mathrm{i}}\); (**b**) \(\triangle\delta_{\mathrm{U,i}}\).