esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
These layers help to extract a set of features that are insensitive to translational shifts and minor distortions. Reduced feature map size not only regulates network complexity but also aids in increasing generalization by reducing overfitting. Max pooling employs a kernel size of \(2\times 2\) to lessen the spatial dimension and, thus, the computation time. It is used to shrink the dimensions of the feature maps in the suggested model. Thus, it greatly reduces the number of learnable parameters and the amount of computation performed in the network. The max pooling operation is performed by applying a max filter to non-overlapping subregions of the input representation. For a feature map having dimensions \(n_{H_{0}}\times n_{W_{0}}\times n_{C_{0}}\), the dimension of output feature map is \(n_{Hop}\times n_{Wop}\times n_{Cop}\) and is represented by Equations (6) and (7), respectively. \[n_{Hop}=\left[\frac{n_{H_{0}}-f}{S}+1\right], \tag{6}\]  
\[n_{Wop}=\left[\frac{n_{W_{0}}-f}{S}+1\right], \tag{7}\]  
where \(f\) is the pool size and \(S\) indicates the stride. Fully connected layer: The fully connected layer combines the low- and high-level features which are extracted from the hierarchical arrangement of convolutional and max pooling layers. In general, it is a feed forward network, where the neurons from each stage connect to every neuron in the next stage. The output of the fully connected layer denotes the number of classes. Softmax layer: This layer incorporates the softmax activation function. It normalizes the output of the fully connected layer. It is used to map the output of a neuron to a probability distribution over predicted output classes. Mathematically, it can be represented by Equation (8)  
\[Sof(Z)_{i}=\frac{e^{Z_{i}}}{\sum_{k=1}^{c}e^{Z_{k}}}, \tag{8}\]  
where \(c\) denotes number of classes, \(Z\) is the vector, and \(Sof(Z)_{i}\) indicates class probabilities. Classification layer: The final layer in the CNN model is the classification layer. It computes the cross entropy loss between the predicted and actual output values using Equation (9). \[loss=-\sum_{i=1}^{S}\sum_{j=1}^{C}t_{ij}ln(y_{ij}), \tag{9}\]  
where \(S\) is the number of samples, \(C\) is the number of classes, \(t_{ij}\) indicates the \(i^{th}\) sample belongs to \(j^{th}\) class, and \(y_{ij}\) gives the output for sample \(i\) for class \(j\).
## 3 Results and Discussion
### Storage Time of Laboratory-Made Wine  
Filtered and pasteurized laboratory-made wines were stored for a month in darkness at 12 \({}^{\circ}\)C. Although the aging time of commercial wines differs from those produced in  
Figure 3: (**a**) Total phenolic content (TPC) expressed as mg of gallic acid equivalent per liter. (**b**) Antioxidant capacity (AC) as mmol of Trolox equivalent during fermentation of Cabernet Sauvignon grapes harvested in Queretaro (QRO) and Baja California (BC1 and BC2). Each point is the average of \(n=3\). (**c**,**d**) Graph of boxes of the content of phenolic compounds and antioxidant activity respectively in lab-made red wine from different regions (one month of storage) and commercial wines (CBC and CQRO).  
this work, a comparison of the bioactive compounds present in each sample was made. Figure 3c,d show the total phenolic compounds and antioxidant capacity, respectively, of the laboratory-made wines (QRO, BC1, and BC2) and the commercial CS red wines (CQRO and CBC). The laboratory-made Queretaro wine did not show changes in TPC after one month of storage; however, its antioxidant capacity was reduced after this time. Laboratory-made wines showed a significant difference (\(p<0.05\)) from the commercial wines, which presented higher values. This difference can be explained by the harvest year, the storage time, and the process conditions [30]. Some winemakers consider the mixture of grapes from different vineyards to equilibrate acidity and taste. However, there was no significant difference (\(p>0.05\)) in the antioxidant capacity or total phenolic compounds among the commercial wines from the different zones studied. The BC2 wine had the highest phenolic content and antioxidant capacity. According to our previous work [15], this geographic region is associated with higher Cu, Fe, Al, and Mg contents in grapes, as well as higher soil electrical conductivity and Fe content. Thus, these characteristics are related to higher phenolic compound contents and antioxidant activity in the wines produced here. Nonetheless, in grapes, many factors during the winemaking process can increase the phenolic content, such as the relationship between peel and pulp, pH, and the presence of yeast favoring the extraction of phenolics from the peel of the grape into the wine [18].
## 6 Video Technology in M-Health Systems  
Video technology continues to enhance safety and effectiveness in medical care. For emergency cases, the use of wireless video transmission or video conferencing is expected to have a significant impact on the procedures to be taken by the paramedics during the first hour (golden hour) after the accident. The specialist can provide instructions to paramedics based on video images of the victim and his physiological parameters, to sustain a better treatment that can have positive implications for the patient's survival [50,51].  
Furthermore, it can be of critical importance to the specialist to be aware of the situation of the victim prior to arrival at the hospital. Video transmission continues to be more efficient thanks to the advances in wireless networks and video compression technology. One of the emerging video coding standards is H.264/Advanced Video Coding (AVC) which has been developed and standardized collaboratively by both the International Telecommunication Union (ITU-T) and the International Organization for Standardization/ International Electrotechnical Commission (ISO/IEC) [52].  
The H.264/AVC standard is built on the concepts of MPEG-2 and MPEG-4, and it offers the potential for better efficiency, _i.e._, better quality compressed video, and greater flexibility in compressing, transmitting and storing video. The fact that the specialist from the hospital could have a video image of the situation of the victim from the accident, e.g., to see his face color, his reactions, his injuries, helps to a faster treatment when the patient arrives to the hospital, where the specialist is already prepared. Thus, the use of video is a significant step for the emergency medical care scenario.
## 2 Experimental Section
### Method Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & **LOQ (ug. L\({}^{-1}\))** & **DL (ug. L\({}^{-1}\))** & **Calibration Equation** & \(R^{2}\) \\ \hline \multicolumn{6}{c}{**HS-SPME Extraction\({}^{\text{a}}\)**} \\ \hline Chloroform & 12 & 3.6 & \(y=20.14x+2.8\) & 0.988 \\ Bromoform & 12 & 3.7 & \(y=21.29x+2.46\) & 0.989 \\ Bromodichloromethane & 12 & 3.7 & \(y=30.53x+0.06\) & 0.975 \\ Dbromochloromethane & 12 & 3.5 & \(y=131x+1109\) & 0.993 \\
1,2-dibromoethane & 13 & 3.8 & \(y=30.54x+0.4\) & 0.998 \\
1,2-dichloroethane & 13 & 3.9 & \(y=20.27x+0.4\) & 0.966 \\ \hline \multicolumn{6}{c}{**DI-SPME Extraction\({}^{\text{a}}\)**} \\ \hline Chloroform & 9 & 2.6 & \(y=35.9x+277\) & 0.998 \\ Bromoform & 9 & 2.7 & \(y=8.9x+2549\) & 0.989 \\ Bromodichloromethane & 9 & 2.7 & \(y=19.84x+3.12\) & 0.985 \\ Dbromochloromethane & 8 & 2.5 & \(y=17.2x+3.1\) & 0.993 \\
1,2-dibromoethane & 11 & 3.3 & \(y=307.1x+1132\) & 0.998 \\
1,2-dichloroethane & 12 & 3.6 & \(y=13.58x+4.02\) & 0.978 \\ \hline \multicolumn{6}{c}{**HS-TWA Extraction\({}^{\text{a}}\)**} \\ \hline Chloroform & 12 & 3.5 & \(y=10.97x+3.62\) & 0.989 \\ Bromoform & 12 & 3.5 & \(y=11.78x+3.84\) & 0.988 \\ Bromodichloromethane & 11 & 3.2 & \(y=11.89x+3.82\) & 0.984 \\ Dbromochloromethane & 12 & 3.4 & \(y=11.54x+4.78\) & 0.990 \\
1,2-dibromoethane & 13 & 3.8 & \(y=128x+593\) & 0.997 \\
1,2-dichloroethane & 12 & 3.7 & \(y=10.24x+4.28\) & 0.968 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Headspace (HS-), Direct (DI-) and Headspace-Time Weighted Averaging (HS-TWA) SPME-GC/MS Sampling for Target THM Standards from Aqueous Samples.
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
## 7 Attitude and Orbit Control System of the FireBIRD Satellites
### The Attitude and Orbit Control Subsystem of the FireBIRD Satellites
\begin{table}
\begin{tabular}{c c} \hline \hline BOSS: BIROS Operating System & MCS: Magnetic Coil System \\ \hline CMD: Command & MFS: Magnetic Field Sensor \\ \hline CSS: Cors Sun Sensor System & ONS+: on-board Navigation System \\  & (+ stands for extended version, comp. with TET-1) \\ \hline DTA: Data & QTS: Quadrant Sensor \\ \hline IMU: Inertial Measurement System & RW: Reaction Wheel \\ (Gyro System only) & \\ \hline GPS: Global Positioning System & Rx: Receiver Unit \\ \hline HTW: High Torque Wheels & THR: Thruster Unit \\ \hline IF: Interface & Tx: Transmitter Channel \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Abbreviations of Figure 16.  
it is possible to generate a direct geo-coding of the FireBIRD multi-spectral camera data during the off-line data processing on the ground.
## 5 Attributes and Characteristics of FL
### Secure Multi-Party Computation (SMPC or SMC)  
Secure multi-party computation (SMPC or SMC) is a critical concept within federated learning. With FL's decentralized approach, which involves training models directly at the data sources (such as edge devices or nodes), there is a growing demand for advanced techniques that can safeguard data privacy while maintaining model efficiency. SMPC directly meets this requirement. In essence, SMPC allows multiple participants, each with their private inputs, to collaboratively conduct computations and obtain a result without revealing their individual inputs to each other. In the context of FL, where data remain decentralized, SMPC's importance cannot be overstated. It allows for the collaborative training of models across multiple nodes, without the necessity to expose individual datasets [37; 68]. In FL, models are trained at the data's source, ensuring minimal risks associated with data breaches and exposure. Within this framework, SMPC operates by facilitating collaborative computation across these decentralized nodes. Rather than sharing raw data, which could compromise privacy, each participant or node shares encryptedfragments of computations. These fragments, when combined, can provide a result (like a model update) without ever revealing individual data points.  
While secure aggregation focuses on a mixture of updates, SMPC takes this one step further by allowing entire computations to be jointly performed, yet without the nodes having to reveal their proprietary datasets to one another. Given FL's application in data-sensitive domains like healthcare and finance, the utility of SMPC becomes even more relevant. These sectors can use SMPC within the FL framework to derive insights collaboratively from multiple sources, all while ensuring that individual data remain confidential [69]. However, SMPC, despite its strengths, is not without limitations. The complexity of collaborative computation across multiple nodes can introduce latency, especially as the number of participating nodes increases. Moreover, the cryptographic techniques underlying SMPC, while ensuring privacy, can sometimes be computationally intensive, demanding more resources and potentially affecting the efficiency of the FL process.
# Experimental Study on Mechanical Properties of Highway
## 1 Introduction  
In recent years, infrastructure investment in western China has increased, and the construction of railway and road projects in cold regions has been increasing [1]. Currently, most of the cold regions in China are still dominated by railway construction. However, compared to railways, roads have a shorter construction period and a more flexible budget. Therefore, the future development potential of roads in cold regions should not be underestimated [2, 3]. The rapid development of China's transport infrastructure construction, the growth in demand for road transport, and the increasing number of passenger and freight vehicles, along with the continuous improvement of road design and construction standards, have placed strict requirements on the service level of the road system [4, 5, 6].  
As the main stress-bearing part of the road, the strength, stiffness, and long-term stability of the roadbed will directly affect the road's performance and service life. Under traffic load, the roadbed soil is directly subjected to large weekly cyclic shear action transferred from the upper layers. As a result, the roadbed strength continues to deteriorate and permanent deformation continues to accumulate. This leads to the pavement structure being very prone to folding, cracking, and various other issues, which seriously affect the safety, comfort, and durability of the highway [6, 7]. Roadbeds in cold regions are even more susceptible to damage of road pavements due to cyclic temperature changes duringthe freezing and thawing process [8; 9].As shown in Table 1, scholars around the world have studied the mechanical properties and deformation mechanism of railway roadbeds in cold regions, and have made certain academic achievements [10; 11; 12].  
However, there have been fewer studies on roadbeds in cold regions, especially regarding the mechanics and deformation mechanism of thawed soil after freeze-thaw cycles. The current research mainly focuses on the thermal insulation properties of roadbeds [19], changes in moisture content [20; 21], vehicle loading [22; 23], improvement treatments [24; 25; 26], and the effects of multi-field coupling [27].  
Therefore, this paper aims to conduct indoor mechanical tests to measure the mechanical property laws of thawed soil in the roadbed of cold regions under different conditions. The research object includes seven different freeze-thaw cycles (0, 1, 3, 5, 7), three freezing temperatures (\(-5\,^{\circ}\)C, \(-10\,^{\circ}\)C, \(-15\,^{\circ}\)C), four water contents (6%, 8%, 10%, 12%), and three peripheral pressures (50 kPa, 100 kPa, 150 kPa) of the thawed soil. Triaaxial shear tests were conducted on the soil samples. By analyzing the test results, the changes in the mechanical properties of the thawed soil in the roadbed under different conditions were determined. This provides a valuable reference for the study of mechanical properties of thawed soil in roadbeds in cold regions, and offers guidance for the design and construction of highways in cold regions.
## 4 Discussion
### Evaluation of Tested Biopolymers as Dust Suppressants  
This study found that the tested biopolymers formed crusts with significantly increased penetration resistances relative to the water-treated control and, in part, enhanced moisture retention. As penetration resistance [13; 17; 26] and moisture retention [28] are commonly used indicators for assessing potential dust suppressants, it is concluded that most tested polysaccharides and proteins show potential as dust suppressants. XG was incorporated as a benchmark in the study, as it is the most widely studied biopolymer in soil stabilisation and dust control [22; 69]. The results on both tested soil types showed that some polysaccharides (CMC, CS, and WS) and proteins (FBPC, PP, and WP) achieved similar crust penetration resistances as XG (Figures 7 and 8). However, it must be considered that XG was only tested at a quarter of the concentration of the other biopolymers.  
Commercially available dust control products, such as chloride salts, petroleum-based products, and synthetic polymers, are effective dust suppressants but remain costly and can have adverse environmental effects [12; 70]. Thus, there is a need for affordable, environmentally friendly alternatives that are easy to apply. While approaches such as microbially induced carbonate precipitation show potential to mitigate wind erosion [71; 72; 73], their cultivation and application are challenging [23], rendering them not easy-to-apply off-the-shelf solutions. Alternatively, by-products and wastes from food processing have shown potential as dust suppressants [74; 75], but their inconsistent composition and dry-matter content may limit their application potential. By contrast, biopolymers show potential to meet the needs mentioned above.  
This study and previous research [13; 14; 15; 16] have shown that biopolymers have potential as dust suppressants. Moreover, they are biodegradable [76], well-studied, and frequently applied in the food industry and medical applications [40; 66; 77]. The tested biopolymers have relatively low cost, are easily dissolvable in water, and can be applied using conventional water-spraying equipment. In addition, the tested biopolymers originate from botanical (corn, pea, wheat, cellulose, potato, and fava bean) and animal (pig, chicken, and cow) sources that are widely cultivated and bred. Consequently, biopolymers show potential as environmentally benign, readily available, low-cost, and easy-to-use alternatives to traditional dust suppressants. Further laboratory and field studies are needed to investigate their potential at different dosages and under realistic operating conditions to raise industry awareness.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 2 Intrinsic Height Errors and Location-Induced Errors  
Error sources can exert their influence on InSAR measurements through two primary pathways. The first pathway results in direct inaccuracies in the derived output InSAR measurements. In specific terms, this pertains to errors in estimating either the surface height or the displacements, depending on whether the InSAR technique is employed to derive digital elevation models (DEMs) or assess surface deformation. This first set of error sources is referred to as intrinsic height errors [30]. On the other hand, the second error pathway does not alter the fundamental value of the output InSAR measurement itself. Instead, it leads to the misrepresentation of the actual position of the true topographic features within the radar image. This second set of errors is known as location-induced errors [30].
## 5 Conclusions  
Monitoring the walking environment is essential for pedestrians. Conventional approaches to identify environment barriers have several limitations: time-consuming, labor-intensive, and discontinuous inspection. This study suggests a cascaded unidirectional and bidirectional LSTM-based DRNN model using pedestrians' gait data collected from a wearable sensor to overcome these limitations. To determine a model and investigate the feasibility for a real-world application, this study uses two gait datasets sourced from two data collections. A total of 132,749 gaits were used in this study. The suggested model was trained using 81,286 gaits and tested twice using the first dataset (20,321 gaits) and the second dataset (31,142 gaits). The results of the model tests showed high accuracy, precision, recall, and F1 score. Even more, abnormal gaits (ground truth labels, the first dataset, and the second dataset) and the presence of environmental barriers are highly correlated. The high correlation coefficients indicate the potential of the suggested method to identify environmental barriers. In summary, the suggested method shows the ability to classify normal and abnormal gaits of 84 participants and the feasibility to derive reasonable outputs for newly added data.  
Figure 9: Comparison of Effects by Size and Location of Environmental Barriers: (**a**) Comparison of Location of Environmental Barrier; (**b**) Comparison of Width of Environmental Barrier; and (**c**) Comparison of Depth of Environmental Barrier.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of DANKOOK UNIVERSITY (DKU 2020-09-027, date of approval 14 September 2020).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Some or all data, or code generated during the study, are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data).  
**Acknowledgments:** The author wishes to acknowledge [PERSON], director of Jungja Running Club, [PERSON], director of Jungja 2 Dong Gateball Club, and all the participants for their help in data collection.  
**Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 3 Approach
### Workflows  
The system consists of two main processing workflows. The initialization phase shown in Figure 4a is comprised of two main processes: Detection and classification of objects as well as adaptation of the prediction model. The main goal of this phase is to gather information about the current traffic and road conditions. Depending on the view angle of the camera and the current traffic condition, the initialization phase needs more or less time to build up a sufficient model for later predictions. If the model has enough valid data the system changes over to the actual processing workflow. Moreover, sharing the first two components with the initialization workflow, the processing workflow consists of two additional components as depicted in Figure 4b: The tracking and counting parts. It is defined as an iterative process and analyzes frames as long as an input feed is available to finally count vehicles passing by the scene. The tracking component tries to find a set of detected objects in all the past frames and assigns random identifiers. Based on the identifier, already tracked and therefore unique vehicles are counted after leaving the scene.  
Figure 3: Basic network architecture of the YOLO framework. It has a Convolutional Neural Network (CNN) with over 20 connected layers. Source [24].
# Low-Temperature and Low-Pressure Silicon Nitride Deposition by ECR-PECVD for Optical Waveguides
## 3 Characterization and Results
### Stoichiometry
Additionally, longer pump-down times after sample transfer before deposition may achieve a lower concentration of water vapour in the chamber, as well as a chamber bake step, which will both be considered in the future. The recipes used have shown that a non-stoichiometric and medium hydrogenated film leads to thicknesses and optical losses acceptable for integrated photonic devices for the visible and NIR wavelength regions. Some recipes show promising losses in the important S, C and L communications bands even in unannealed films, and further investigation of deposition conditions and recipes can lead to reduced hydrogen incorporation and losses in that spectral region.