esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design and Analysis of the Birdcage RF coil
### Resonance Frequency  
The proposed analytical solution given in Section 1 does also provide the information about the resonance frequency modes for all three configurations (low pass, high pass and band pass) of the birdcage RF coil by plotting its input impedance \(Z_{ER,p}\) against frequency. We assumed the case where the receiver port is connected across the half end-ring segment and then determined the resonance frequencies spectrum of all three coil configurations using Equation (21). The effective inductance values of the leg and end-ring segments along with their corresponding effective capacitance values (with respect to the coil configuration) were used to compute the impedances \(Z_{l}\) and \(Z_{er}\). A comparison between the input impedance obtained though the analytical solution proposed in this paper and the input impedance plot obtained through 2D circuit simulation with effective inductance and their corresponding capacitance values in ANSYS [26] for each configuration of 1.5 T and 3 T birdcage RF coils are given in the Figure 8 below.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & & \multicolumn{2}{c}{**Low Pass**} & \multicolumn{2}{c}{**High Pass**} & \multicolumn{2}{c}{**Band Pass (\(C_{er}\) = 50)**} \\ \hline  & & \(C_{l}\) & **\%Error** & \(C_{er}\) & \(Error\) & \(C_{l}\) & \(Error\) \\ \hline Proposed & \(C_{cal}\) & \(8.1\) & \(-0.01\) & \(32.4\) & \(-0.034\) & \(22.9\) & \(-0.023\) \\  & \(C_{off}\) & \(7.8\) & \(-0.007\) & \(31.3\) & \(-0.023\) & \(20.9\) & \(-0.003\) \\ Conventional & \(C_{cal}\) & \(8.35\) & \(-0.0125\) & \(28.5\) & \(0.005\) & \(19.4\) & \(0.012\) \\  & \(C_{off}\) & \(8.11\) & \(-0.0101\) & \(27.7\) & \(0.013\) & \(18.2\) & \(0.024\) \\
2D Simulation & \(C_{cal}\) & \(8.35\) & \(-0.0125\) & \(27.7\) & \(0.013\) & \(18.8\) & \(0.018\) \\
2D Simulation & \(C_{off}\) & \(8.12\) & \(-0.0102\) & \(26\) & \(0.03\) & \(17.8\) & \(0.028\) \\
3D Simulation & \(C_{3D}\) & \(7\) & \(0.001\) & \(29.5\) & \(-0.005\) & \(20.5\) & \(0.001\) \\ Measured & \(C_{mes}\) & \(7.1\) & & \(29\) & & \(20.6\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lumped capacitor values in pF for 3 T birdcage coils.  
Figure 8: \(Cont.\)
## 7 Conclusions and Future Work
Currently, the maximum point-to-point transmission distance achievable by the DIFRT method is almost 1.7 m, therefore, the maximum distance can reach a total of 5.1 m with two relay nodes in the pipe. In theory, the transmission range can be expanded by increasing the number of relay nodes (follower robots). However, as the BER increases, the DIFRT system will be restricted in a certain distance. The limiting number of relay nodes should be further further investigated. In addition, increasing the number of relay nodes will enhance the risk of forming a robot chain system. Since optical signal distortion and attenuation such as signal amplitude and noise occur during the transmission, the utilization of the suitable wave-shaping module and signal amplifier for each relay node can assist the whole system to achieve longer transmission with fewer follower robots. If the above deficiencies can be solved, a high-quality, long-range, high-efficiency image transmission technique can be implemented on pipe inspection robots. The RCS is capable of operating at much longer distances and sending back much higher quality image information from the pipes. This image information will help operators analyze the internal damage of the pipeline. It will be also conducive to pipes' timely maintenance, repair, and other regular operations. Combined with the previous technique, the possible architecture of RCS is illustrated in Figure 12, in which multiple relay nodes (from to '1 st follower robot' to '_n th_ follower robot') are connected in a 'chain' and establish a bidirectional relay system. In the bidirectional transmission mechanism, the sensor data (e.g., air pressure, concentration, humidity, and temperature) and image data are transmitted to the external display terminal through the 'follower robots'. The control commands are sent back to the 'leader robot' from the external control terminal. In the future, different types of robot chain systems will be developed and finally applied to some other complex environments such as tunnels, mines and underwater, etc. **Author Contributions:** [PERSON]. ([PERSON]) and [PERSON] from Waseda. Univ. managed the research project and revised the manuscript; [PERSON] from Waseda. Univ. developed the system, conducted the laboratory test and wrote the manuscript; [PERSON] from Waseda. Univ. conducted the laboratory test; [PERSON]. ([PERSON]) and [PERSON] from Tokyo Gas Co. Ltd. provided some technical information and guidance. **Acknowledgments:** This research is supported in part by Tokyo Gas Co., Ltd. and in part by the Research Institute for Science and Engineering, Waseda University. **Conflicts of Interest:** The authors declare no conflict of interest.
# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 4 Experiments  
### Dataset Collection and Pre-Processing  
In this paper, based on the Chinese standard GB2894-2008 [51], \"Safety signs and guideline for the use\", 10 types of safety signs (see Figure 4 and Table 1) were collected from the complex environment under the mine and close to the mine environment. The reasons for considering these 10 types of signs were attributed to their wide usage in coal mines. Data augmentation was performed, including cropping, random rotation, and changing brightness and contrast (see Figure 5 and Table 1), which aims to improve the generalization ability of the model. The whole dataset was divided into training set, validation set, and test set according to the ratio of (training set + validation set):test set = 9:1 and training set: validation set = 9:1. The selected images were labeled with safetysigns targets using LabelImg software 1.8.6 (cmd>conda activate pytorch>LabelImg) and annotated with XML files in PASCAL VOC [52] format.  
Figure 4: Image comparison before and after data augmentation.
# High-Accuracy Clock Offsets Estimation Strategy of BDS-3 Using Using Multi-Source Observations
## 3 Results
\begin{table}
\begin{tabular}{c c c} \hline
**PRN** & **RMS (ns)** & **95\% Error (ns)** \\ \hline C19 & 0.27 & 0.55 \\ C20 & 0.27 & 0.58 \\ C21 & 0.31 & 0.66 \\ C22 & 0.31 & 0.67 \\ C23 & 0.30 & 0.61 \\ C24 & 0.29 & 0.62 \\ C25 & 0.28 & 0.59 \\ C26 & 0.29 & 0.65 \\ C27 & 0.26 & 0.56 \\ C28 & 0.28 & 0.62 \\ C29 & 0.30 & 0.64 \\ C30 & 0.27 & 0.59 \\ C32 & 0.27 & 0.57 \\ C33 & 0.28 & 0.59 \\ C34 & 0.28 & 0.59 \\ C35 & 0.28 & 0.59 \\ C36 & 0.27 & 0.56 \\ C37 & 0.25 & 0.53 \\ C38 & 0.28 & 0.62 \\ C39 & 0.26 & 0.53 \\ C40 & 0.26 & 0.56 \\ C41 & 0.29 & 0.65 \\ C42 & 0.26 & 0.54 \\ C43 & 0.28 & 0.57 \\ C44 & 0.37 & 0.77 \\ C45 & 0.27 & 0.58 \\ C46 & 0.3 & 0.62 \\ C59 & 0.33 & 0.71 \\ C60 & 0.36 & 0.86 \\ C61 & 0.38 & 0.66 \\ \hline \multicolumn{3}{c}{Average of all satellites} & 0.29 & 0.61 \\ \end{tabular}
\end{table} 
Table 2: Statistical results of 2-h prediction errors of two-way estimated clock offsets for 10 days.
# Photodynamic Inactivation of Human Coronaviruses
## 2 Materials and Methods
### RNase-Protection Assays and qPCR  
HCov-229E without treatment or HCov-229E treated with 4 \(\mu\)g/mL PhytoQuin or 0.5% DMSO and LED light treatment at 49 J/cm\({}^{2}\) as described in Section 2.3 were used for RNA extractions. Each sample contained 25 \(\mu\)L of virus, 1 ng in vitro transcribed _GFP_ RNA (prepared from a T7-driven _GFP_ plasmid using the HiSeqTM T7 High Yield RNA Synthesis Kit (E2040S, New England BioLabs)), and either serum-free DMEM only (\"\(-\)\"), serum-free DMEM with 500 \(\mu\)g RNase A/T1 (EN0551, Thermo Fisher Scientific) (\"+R\"), or serum-free DMEM with 500 \(\mu\)g RNase A/T1 and 0.1% Triton X-100 (\"+T/R\"). All samples were incubated at 37 \({}^{\circ}\)C for 20 min, after which TRIzol(r) reagent (15596026, Thermo Fisher Scientific) was immediately added to arrest RNase activity. Then, 10 \(\mu\)g of 293T cell RNA (used as a carrier) and 1 ng in vitro transcribed firefly luciferase (_Luc_) RNA (prepared from a T7-driven _Luc_ plasmid using the HiScribe(tm) T7 High Yield RNA Synthesis Kit) dissolved in RLT Plus Buffer (QIAGEN, Hilden, NRW, DE) were added to each sample prior to RNA isolation. Chloroform was added to the TRIzol containing samples, and the aqueous phase was used for RNA extractions using the RNeasy Mini Kit (74104, QIAGEN). Reverse transcription reactions were performed using the Maxima H Minus First Strand cDNA Synthesis Kit using random hexamers and oligo(dT)\({}_{18}\) (K1652, Thermo Fisher Scientific) using a modified \"RT-qPCR--First Strand cDNA Synthesis\" protocol with optional 65 \({}^{\circ}\)C incubation, 0.5 \(\mu\)L Maxima RT per reaction, and a 30 min (50 \({}^{\circ}\)C) reaction time. Quantitative PCR was performed using GoTaq(r) qPCR Master Mix (A6002, Promega, Madison, WI, USA) with 200 nM primers in 10 \(\mu\)L reactions, using primers to amplify portions of: HCov-229E _ORF1_ (229E-_ORF1_-F 5\({}^{\prime}\)-ATGCTCCGACGTTTGACAT, 229E-_ORF1_-R 5\({}^{\prime}\)-GTACTACGACGACGCTG), HCov-229E \(N\) (229E-_N_-F 5\({}^{\prime}\)-GGCAAACGGTGATTGTC, 229E-N_-R 5\({}^{\prime}\)-CCCAGACGACCTT CAACA), _GFP_ (_GFP_-F 5\({}^{\prime}\)-CTTTCCTCAAGTCCGACGT, _GFP_-R 5\({}^{\prime}\)-GGTCTTGTGTCC GTGT), and _Luc_ (_Luc_-F 5\({}^{\prime}\)-GGCCAATTATGGTCAGAG, _Luc_-R 5\({}^{\prime}\)-TCCCAGTAAGCTAT GTCTC). Samples were run in triplicate in a CFX Connect Real-Time PCR Detection System (Bio-Rad) with the following two-step protocol: 3 min at 95 \({}^{\circ}\)C, 40 cycles of 10 s at 95 \({}^{\circ}\)C and 30 s at 60 \({}^{\circ}\)C. Data were analyzed using a \(\Delta\)Ct method; gene expression was normalized to \"DMSO/Dark/Untreated\" and expressed as fold change.
## 1 Introduction
### Literature Review
#### 1.2.3 Vehicle Control  
The role of vehicle control in the autonomous driving architecture is to track the theoretical trajectory computed by the planning algorithm. Models of vehicle kinematics, like the bicycle model [55], are used to translate this trajectory \((x(t),y(t))\) into a series of commands, usually steering angle and acceleration. Different controllers have been used for autonomous driving at relatively low speed: Proportional Integral Derivative (PID) controllers [56], pure pursuit controllers [57], Stanley controllers [58], among others. Dynamic model-based control methods have better performance at high vehicle speed or with a large curvature change rate. Nonlinear control [59], Model Predictive Control (MPC) implementations [60; 61; 62] and feedback-feedforward control [63] can increase the stability of the vehicle at high speeds.
# Slow Pyrolysis as a Method of Treating Household Biowaste for Biochar Production
## 3 Results and Discussion
### Properties of the Biowaste
#### 3.1.3 Results of Other Biowaste Measurements  
The Higher Heating Value (HHV) of kitchen biowaste was 17.24 MJ/kg, higher than the 15.08 MJ/kg recorded for canteen food waste [49]. Spring and autumn garden waste had HHVs of 10.60 MJ/kg and 11.16 MJ/kg, respectively--much lower than the 18.12 MJ/kg reported for shredded green waste [51].  
The LHVs for kitchen, spring garden, and autumn garden biowaste were 15.69 MJ/kg, 9.60 MJ/kg, and 10.27 MJ/kg, respectively. These values, derived from HHVs, are lower due to the exclusion of the latent heat from water vapor condensation during combustion [40].  
The combustible compound content was highest in kitchen biowaste (93.19%), followed by spring garden waste (82.25%) and autumn garden waste (56.17%). This indicates that kitchen waste ignites most easily, while autumn garden waste is most resistant. Similarly, the total organic matter content was 90.06% for kitchen, 81.77% for spring garden, and 55.32% for autumn garden waste, suggesting that kitchen waste is the most biodegradable. The organic content also influences waste collection and storage methods [1].  
The pH of kitchen biowaste was acidic at 5.18, while spring and autumn garden biowaste samples were alkaline, with values of 9.20 and 8.62, respectively.
## 2 Materials and Methods
### Radar Data Processing
The data were also subjected to different SNR filters, illustrated in Figure 5b, but the filter was not constricted further, as doing so caused removal of data points with distance values around 40 m, likely corresponding to the preceding truck. Since the radar sensors were able to measure up to 5 detections simultaneously, the next filter (filter 4) involved selecting only one desired target in multi-target instances: The one most likely corresponding to the preceding truck. The distance value of each detection was compared with the average distance values of the previous 10 measurements (moving average). The detection with the smallest difference was chosen. However, occasional single-object detections had distances which were quite different from the general trend. In such cases, the algorithm had no choice but to select the only detection available. This produced spikes or drops in distance which affected the moving average. This problem was subsequently minimized by filter 5, which downsampled the data to 1 Hz (one measurement per second) by averaging all distance measurements within each second. This temporal resolution was considered sufficient, and also allowed for direct coupling between curated radar data and event durations from video footage. Inspecting plots of distance versus time for downsampled radar data revealed the presence of sporadic periods entirely void of points, and also to periods when points were scattered (i.e., having varying distance values following no obvious trend). To understand these detections, video footage was coded for _LWRR_ and _LBRR_. Filter 6 used these codes to include only data collected in _LWRR_ periods, and to exclude data collected during _LBRR_ periods. Cameras malfunctioned at times, during which _LWRR_ and _LBRR_ could not be coded. Associated radar data were discarded, ensuring methodological consistency. In brief, filters 1 and 6 were based on manual video codes, while filters 2 and 3 were based on recorded radar metrics, making them the most interesting ones in terms of radar operation. Filters 4 and 5 were computational heuristics. See Appendix A.3 for more details.
## 3 Results and Discussion  
### Construction of Datasets  
Machine learning models were built using the results of oxyacetylene ablation performed on as-prepared samples. Input variables and the single output variable (mass ablation rate, MAR (g/s)) are given in Table 1. The content of SiO\({}_{2}\), SiC, and CF varies in the range of 5-50 phr, 2-20 phr, and 2-20 phr, respectively. The intervals of the fillers' content are evenly divided into steps of 2.5, 1, and 1 phr within their respective ranges, which generates 6859 points in the original unlabeled sample pool.  
According to the Supplementary Material Tables S1 and S2, ten points evenly distributed in the pool were selected firstly to prepare the corresponding materials, and the MAR of the materials was obtained by an oxyacetylene ablation test. Ten generated samples formed the initial dataset used for modeling. Thus, 10 points are removed from the pool, leaving 6849 unlabeled samples in the pool.  
Based on the regression analysis of the results of the uniform design and exploitation strategy, an additional 10 samples were selected from the sample pool to be labeled to form the validation set for the subsequent model. The remaining 6839 unlabeled samples became candidates for subsequent active learning.
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 3 Edge Computing and Deep Learning-Based Malware Detection Method
### Main Functions of the Edge Computing Deep Learning-Based Malware Detection System
#### 3.4.2 Model Deployment  
After evaluating the malware detection rate (accuracy) of the completed deep learning model and inference program, a global (integrated) deep learning model is deployed from an edge server to each edge device and periodically optimized. It is also updated and deployed from the cloud server to each edge server and managed. In other words, the global deep learning model is updated by receiving local information generated from multiple edge server networks, and the optimized security model can be shared and applied on the local edge network. Older models with poor accuracy are replaced with new, trained models. This global model-sharing method improves the accuracy of the corresponding traffic and reduces overfitting, enabling efficient attack detection within the network with the optimized global model.  
Figure 6: Primary functions of the proposed edge computing deep learning-based malware detection system.
# A Robust Conformal Framework for IoT-Based Predictive Maintenance
## 6 Experimental Results and Performance Evaluation: A Case Study on CMAPSS Datasets
### Aggregate Performance Evaluation  
Table 5 presents a comprehensive view of both the point estimate metrics and the conformal metrics across all four FD subsets. These results highlight the variability in model accuracy under different operating conditions and fault modes while also illustrating the effectiveness of conformal calibration in quantifying predictive uncertainty. The highest \(R^{2}\) values and the lowest error metrics (MSE, MAE, RMSE) for the FD1-FD3 dataset class are achieved using the N-BEATS model, suggesting that the chosen architectures can capture sensor behavior more effectively when fewer operating regimes and clearly defined fault modes are involved. Conversely, FD4 poses the most significant challenge due to its complexity and broader operational variability, as evidenced by higher S-scores and slightly inflated error metrics, proposing more complex architectures as the best option for the RUL estimation.  
The CP patterns are consistent with Section 3, where the weighted approach proved more adaptive by assigning larger weights to recent residuals in exchangeable conditions, whereas bootstrap instead seems to be more suitable for datasets with slight drifts and a noticeable amount of noise. The metrics underline the difficulty of maintaining highly precise uncertainty intervals in non-stationary environments, with the robust conformal framework helping to keep deviations within a useful range for practical maintenance actions.
## 3 Result and Discussion
### Modified Asphalt Performance at High Temperature before and after RTFOT
#### 3.5.2 Phase Angle (\(\delta\))  
The phase angle (\(\delta\)) shows the strain versus stress results in the viscoelasticity properties of asphalt. The phase angle reflects the proportion between the elasticity and viscous in the asphalt. The phase angle value can be 0 (\(\delta\) = 0\({}^{\circ}\)) if the material is fully elastic and has a value of 90\({}^{\circ}\) (\(\delta\) = 90\({}^{\circ}\)) if the material is fully viscous [35; 37]. However, if the material is viscoelastic, the phase angle value will be 0\({}^{\circ}\)-90\({}^{\circ}\). Figure 10 shows the asphalt pen grade 60/70 samples' phase angles, B2 non-TMQ, and B2 before and after RTFOT.  
Figure 10 shows that the phase angle decreases with the addition of crepe rubber and TMQ both before and after aging. The decrease in phase angle after the addition of crepe rubber and TMQ (Sample B2 non-TMQ and B2) was due to the increase in the elastic proportion after crepe rubber and TMQ. At 58 \({}^{\circ}\)C, the phase angle of asphalt pen grade 60/70 was 86.6\({}^{\circ}\), while the non-TMQ and B2 samples had 81.4\({}^{\circ}\) and 80.5\({}^{\circ}\) phase angles before RTFOT. However, the phase angle of all samples increases with increasing temperature.  
After RTFOT, all samples experienced a decrease in phase angle and increased with increasing temperature. The reduction in phase angle after aging is due to the increased proportion of elasticity after RTFOT. This has a good correlation with decreasing asphalt penetration after RTFOT. Visually, the decrease in phase angle is due to the stronger asphalt binder network, indicating that the asphalt is getting harder. In general, the reduction in phase angle suggests that the addition of crepe rubber and TMQ plays a vital role in the viscoelasticity of the asphalt [38].  
The phase angle of sample B2 decreased due to the addition of TMQ, which increased the elastic proportion in the asphalt-rubber mixture. Comparing the phase angles of the asphalt pen grade 60/70 samples. B2 non-TMQ, and B2, the phase angle increases with crepe rubber and TMQ. The addition of crepe rubber increases the elastic proportion, while the increase in temperature affects the viscous ratio.
## 3 Results and Discussion
### Manufacturing Flow  
Once the component was designed in detail, the respective manufacturing flow had to be set up, according to the Medical Device Regulation (EU) 2017/745 for medical devices. Its main steps were:  
* Step 1: Sourcing of raw materials;
* Step 2: Machine processing: folding, milling, drilling, threading, wire erosion; roughing, burr removal; washing; packaging in labelled bags;
* Step 3: Acceptance check;
* Step 4: Acceptance; belt sanding; polishing; washing; final check; packaging, shipping;
* Step 5: Solvent washing; sandblasting; quality control (QC), single packaging in plastic trays;
* Step 6: Acceptance check;
* Step 7: Marking; washing; preliminary packaging (Clean Room ISO 7 Operational);
* Step 8: Sterilization;
* Step 9: Acceptance check;
* Step 10: Final packaging and labelling; release.  
At this stage, experimental and numerical tests demonstrated that the new dynamizable plate behaved adequately from a mechanical point of view compared to other devices already on the market. Ongoing tests are focusing on the evaluation of the locking/unlocking mechanism, as well as on the transmission system design. To allow marketing of the plate, the preliminary analysis presented here will be followed by a rigorous campaign of mechanical tests on a number of replicas able to ensure statistical consistency. According to ASTM F382, both static and fatigue testing will be performed and a rationale will be built for the device certification request to the notified body.