esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Quantitative Analysis of Soil Microstructure
#### 3.2.4 Distribution of Pores  
The curve of pore distribution can intuitively and quantitatively reflect the composition and distribution of pores. In this study, for the convenience of analysis, the pores of the soil samples were divided into the following categories: large pores, with diameters of \(d>1.0\)\(\upmu\)m; medium pores, with diameters of \(0.5\)\(\upmu\)m \(<d<1.0\)\(\upmu\)m; medium and small pores, with diameters of \(0.3\)\(\upmu\)m \(<d<0.5\)\(\upmu\)m; small pores, with diameters of \(0.2\)\(\upmu\)m \(<d<0.3\)\(\upmu\)m; and micropores, with diameters of \(0.1\)\(\upmu\)m \(<d<0.2\)\(\upmu\)m. Figure 9 shows the curve of soil pore diameter under different consolidation times.  
The pore components with pore sizes ranging from \(0.3\)\(\upmu\)m to \(1.0\)\(\upmu\)m completely dominated the soil samples, accounting for roughly 65 percent of the total area of the pore. During the initial stage of consolidation, the composition of each pore size did not vary much, and only a portion of the content of large pores decreased. Since large pores were more easily crushed into tiny pores than small pores, as consolidation time increased, the components of large pores decreased dramatically, whereas the components of small holes grew significantly. Furthermore, the area of the micropore group was almost within a certain range, and the proportion was quite little. This is because the micropores were not sufficiently compressed under the experimental circumstances. Only a small  
Figure 8: The curve of the circularity of pores under different consolidation time.  
percentage of medium or small pores were broken up, resulting in a slight increase in micropore proportion.  
It can be seen that the changes in pore size caused by consolidation were not equal for each pore group, and only visible pores made the corresponding adjustments. This adjustment broke the original form of the distribution of particle size and replaced it with a new structural form that was compatible with consolidation compression [44].
## 4 Results of the Experiment  
A view of the finished station used for conducting experiments is shown in Figure 13. The experimental parameters are presented in the Table 1.  
Figure 11: Gas installation of an integrated platform for automatic measuring surface tension (1—MFC mass flow controllers, 2—accumulation tank, 3—solenoid, 4—bleed valve, 5—adjustable needle valve, 6—pressure sensor) [4].  
Figure 12: Handle to the capillary and optical sensors used to detect the base position of the sample at the furnace entrance (1—ceramic capillary, 2—electrodes, 3—gas connection, 4—electrical connection, 5—optical sensors to detect the base position of the sample) [4].  
A ceramic capillary is used in the bubble experiment. The solenoid valve is opened after activating the capillary surface, and the gas mixture is pumped through the capillary. This is accompanied by a move towards the solder, where the capillary is placed at a preset depth after detecting contact with the solder surface. A part of the recorded course of pressure changes as a function of time for the bubble experiment is presented in Figure 14.  
During the bubble stage, the maximum recorded pressure obtained was P\({}_{max}\) = 11.83 [mbar] and the generation time of a single bubble was about 0.9 [s]. After the bubbling stage in the solder is completed, the capillary is pulled out above the solder mirror. The experimental results are presented in Table 2. The surface tension value determined according to the relation in (9) is \(\sigma\) = 565.55 [mN/m]; after considering the correction according to Formula (10), the surface tension is \(\sigma_{k}\) = 458.13 [mN/m].  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Temperature & 250 [\({}^{\circ}\)C] \\ Capillary material & Al\({}_{2}\)O\({}_{3}\) \\ Surface activation time & 60 [s] \\ Sample immersion time & 120 [s] \\ Sample Standstill Time After Experiment & 30 [s] \\ Immersion depth & 6 [mm] \\ Capillary diameter & 3 [mm] \\ Crucible Filling & S-Sn97 Cu3 \\ Solder Density & 7300 [kg/m\({}^{3}\)] \\ \hline \end{tabular}
\end{table}
Table 1: The bubble experiment settings in solder (S-Sn97 Cu3) for a ceramic capillary Al\({}_{2}\)O\({}_{3}\).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Maximum Pressure & 11.83 [mbar] \\ Surface tension \(\sigma\) & 565.55 [mN/m] \\ Corrected surface tension \(\sigma_{k}\) & 458.13 [mN/m] \\ \hline \end{tabular}
\end{table}
Table 2: The bubble experiment results in solder (S-Sn97 Cu3) for a ceramic capillary Al\({}_{2}\)O\({}_{3}\).  
Figure 13: View of the bubble experiment handle [4].
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
## 3 RUDP Implementation in the Transport Layer to Handle Latency
### A Design of RUDP Architecture
The variable 'next' will be increased by one when a single packet has been sent from the sender and the same increase will be observed for the variable 'base' when a single Ack (Acknowledgement) packet has been received by the sender. This process is repeated continuously to check the number of packets contained in the buffer. The timeout is re-adjusted once the packet has been transmitted by using the timer. To test the performance of the implemented R UDP model, several packet loss and network delay scenarios have been simulated. The packets assigned with the value corresponding to the current time of the system plus the value of the network delay were aligned in a queue before being carried over to the socket. When the current time matches the value of a certain packet, that packet will be removed from the queue and transmitted after some delay. The packets also bear sequentially-assigned numbers so that the packets will be transmitted sequentially according the corresponding acknowledgement numbers.
###### Abstract  
In recent years, vision-based vehicle detection has received considerable attention in the literature. Depending on the ambient illuminance, vehicle detection methods are classified as daytime and nighttime detection methods. In this paper, we propose a nighttime vehicle detection and tracking method with occlusion handling based on vehicle lights. First, bright blobs that may be vehicle lights are segmented in the captured image. Then, a machine learning-based method is proposed to classify whether the bright blobs are headlights, taillights, or other illuminant objects. Subsequently, the detected vehicle lights are tracked to further facilitate the determination of the vehicle position. As one vehicle is indicated by one or two light pairs, a light pairing process using spatiotemporal features is applied to pair vehicle lights. Finally, vehicle tracking with occlusion handling is applied to refine incorrect detections under various traffic situations. Experiments on two-lane and four-lane urban roads are conducted, and a quantitative evaluation of the results shows the effectiveness of the proposed method.  
vehicle detection; headlight; taillight; vehicle tracking; occlusion handling +
Footnote †: journal: Applied Sciences
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 4 Countermeasures against Covert Channel Attacks
### Jamming  
Another type of countermeasure is jamming, which involves introducing noise to a system in order to sufficiently increase the BER of the channel, thus making the transmitted data useless. Thermal noise is introduced in TCCs where the frequency band of the noise overlaps with the covert channel data transmission frequency [58]. However, this broad spectrum jamming fails to interfere with a channel that is enhanced by exploiting frequency-hopping spread spectrum (FHSS) [59]. FHSS is a technique where the frequency of the transmitted signal changes over time in order to avoid interference. Both the transmitter's and receiver's frequency hopping pattern are synchronized. An enhanced jamming model that periodically scans the frequency spectrum for an attack and injects noise corresponding to that frequency band, in order to thwart TCCs enhanced by FHSS, was introduced in [58]. Compared to other countermeasures jamming is highly inefficient, because it requires high intensity instructions to be executed for the CPU to generate these thermal waves, thus wasting significant power [58]. Furthermore, jamming implies that the core is not able to perform normal tasks during this time. Similarly, software can either execute power intensive instructions on the electronic device to introduce noise to power-based covert channels [32], or perform irregular memory accesses to increase error rates for electromagnetic covert channels [31].
## 4 Experimental Setup and Results
### Experimental Configuration  
This section provides a comprehensive overview of the experiment, including the dataset, experimental environment setup, and results. The experimental setup involved training and testing models on NVIDIA GTX 1070 Ti GPUs with 16 GB of DDR3 RAM, running on a 64-bit Windows 10 operating system. The study utilized an ensemble model implemented with the Hugging Face library. The coding environment was configured in Python 3.60, utilizing the PyTorch 2.7.0 framework, and employed the cross-entropy improver technique after pre-processing the data.
## 2 Numerical Calculation Model of Four-Tooth Three-Stage Brush Seal
### Calculation Conclusions and Parameter Improvements  
#### 3.3.1 Calculation Conclusions  
In Section 3.2, the leakage rate and interstage pressure drop distribution characteristics of four-tooth three-stage brush combination seals are systematically calculated under varying structural parameters and operational conditions. The computational results demonstrate that: (1) The axial distance of brush bundles exhibits no significant impact on either the leakage rate or the unevenness characteristics of interstage pressure drop distribution. (2) While increased brush bundle thickness (including cumulative thickness across all stages) reduces leakage rate, only gradient-configured thickness across stages demonstrates marginal adjustment capability for interstage pressure drop distribution. (3) Radial clearance adjustment between brush bundles serves as an effective method for improving pressure drop unevenness characteristics, though such clearance configurations inevitably increase leakage rate--notably, first- and second-stage clearance adjustments induce smaller leakage increments compared to third-stage modifications. (4) The backplate height elevation effectively reduces localized brush bundle pressure drop percentages, albeit with concurrent leakage rate elevation. (5) The grate teeth component contributes to fluid energy dissipation and enhances the overall seal pressure-bearing capacity. (6) A reduction in tooth quantity and radial clearance proves beneficial for pressure drop uniformity optimization, though tooth reduction inversely elevates leakage rate. (7) The grate teeth inclination angle demonstrates negligible influence on both pressure drop equalization characteristics and leakage rate performance.  
In conclusion, the radial clearance of the brush bundle and the height of the backplane are effective parameters to improve the unevenness characteristic of the pressure drop of the multi-stage brush bundle, but the evenness characteristic needs to be reduced at the cost of the sealing performance.  
The calculation results show the structural parameters that can be used to regulate the interstage pressure drop unevenness characteristic of the combined seal, elaborate the adjustment effectiveness of the parameters, explain the mechanism of parameter adjustment, and point out that the leakage rate is the concomitant condition of adjustment, which  
Figure 34: The effect of the number of grate teeth on the unevenness characteristic of pressure drop between combined seal stages.  
provides a reference basis for improving the structural parameters of the multi-stage brush seal and improving the interstage pressure drop unevenness characteristic.
## 2 Principle of DASH Interferometer
### DASH Interferometer Configuration  
The DASH interferometer is a monolithic interferometer consisting of cube beam splitters (BS1 and BS2), prisms (P1 and P2), wedged spacers (S1 and S2), parallel spacers (PPS), and gratings (G1 and G2), as shown in Figure 2. The material and thickness of the wedged spacers S1 and S2 are selected to compensate for the phase shift at the fringes as the arms expand with temperature [23]. The interferometer design parameters are listed in Table 2.
# Automatic Detection of Ballast Unevenness Using Deep Neural Network
## 3 Experimental Results
### Training and Testing Mask R-CNN Network
The _AP_ metric is defined as:  
\[AP=\int_{r=0}^{r=1}p(r)dr \tag{9}\]  
where \(p\) and \(r\) denote precision and recall, respectively. The precision-recall curve is usually approximated with rectangles and the integral in (9) is calculated as a sum of rectangles [38]. Table 1 shows the AP, IoU, Dice coefficient and Pixel Accuracy calculated for the networks with Resnet51 and Resnet101 backbones. Additionally, the mean processing time is also shown. An IoU threshold of 0.5 was used for all _AP_ metric calculations. As can be seen from Table 1, the highest values of the metrics were obtained for the Mask R-CNN network with Resnet101, while the shortest processing time was obtained for the network with Resnet51. This is due to the difference in complexity of the Resnet101 and the Resnet51 network structures. The complexity of the Mask R-CNN network with Resnet101 is higher, which results in its higher accuracy (higher values of metrics) at the expense of increased processing time. Figure 8 shows examples of detection and segmentation of the sleepers from the intensity images of the track obtained for Mask R-CNN with the Resnet101 backbone. The first column shows the image of the track, the second the image with the predicted bounding box and mask overlaid on it, the third the predicted bounding box and mask and the last the target (truth) bounding box and mask. If we compare the predicted bounding boxes (dashed lines) and predicted masks (green areas) in column three with the corresponding truth bounding boxes (dashed lines) and truth masks (green areas) in column four, we can see negligible differences between them. The exception is the last lower sleeper found in the image from row three. Due to its very small size, it was not detected by the Mask R-CNN network. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Mean Average** & **IoU** & **Dice** & **Pixel** & **Mean Processing** \\  & **Precision** & & **Coefficient** & **ACCURACY** & **Time [ms]** \\ \hline Resnet51 & 0.8571 & 0.7602 & 0.8046 & 0.7845 & 125 \\ Resnet101 & 0.8901 & 0.78004 & 0.8304 & 0.8063 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection and segmentation metrics for Mask R-CNN with two different backbones. Figure 8: _Cont_. Figure 8: Examples of detection and segmentation of sleepers from intensity images of the track obtained for Mask R-CNN with Resnet101 backbone. The first column corresponds to the image of the track, the second the image with the predicted bounding box and mask overlaid on it, the third the predicted bounding box and mask and the fourth the target (truth) bounding box and mask.
# Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons
## 3 Results
### Round Static Obstacle Placed So It Covers a Quarter of the Path of the Prototype's Detection Cone  
The prototypes were moved to all the already marked testing distances and a log file was saved. Analyzing all the log files for those prototypes, the accuracy of the prototypes can be seen in Figures 18 and 19.  
Figure 19: WAVE prototype accuracy.  
Figure 17: WAVE prototype accuracy after correction.  
The data behind these images are presented in Appendix E, Table A5.  
Examining these latest results reveals that the PCM prototype maintains an average success rate of 89.3%. As seen in the prior test set, the precision of messages for critical distances remains at 97.6%.  
It is noticeable that the recent correction implemented in the algorithm has been successful, ensuring the accuracy of the message transmitted for the 70 cm distance. Apart from the challenges encountered at 140 cm and 210 cm, the WAVE prototype maintains a success rate exceeding 90%. For shorter distances, this success rate reaches 100%. Repeated tests for 140 cm revealed an increased success rate of 98%. The potential cause of this error could be linked to the heating of the prototype during prolonged usage. This issue was observed when the prototype was used for more then 9 h without powering it down.  
To sum up, the accuracy of the prototype is significantly influenced by the shape of the obstacle and its placement relative to the prototype in this scenario. Despite occasional deviations, the accuracy consistently remains above 80%, and for crucial distances, it surpasses 90%. Additionally, there is a pattern where erroneous messages correspond to a shorter distance than the actual one, making them more alarming. This feature serves as a protective measure for the user against potential hazards.
# Information Relaying Methods in VANET: Algorithms, Standards and Tests
###### Abstract
The test result shows that the packet reception rate remains highly stable (e.g., 95% or above) within the transmission range of 50 ms, and gradually declines to 80% and even worse with transmission range growing up to 400 m. As observed in the bird's eye view Figure 1a, the road (i.e., path of B to D) is curved at a certain level, trees and bushes in the middle of the road happen to be a natural fence for long transmission between the sender and the receiver along the path of B to D, and the rough relief terrain between B and C also leads to typical NLOS transmission along the path of B to C. For such NLOS transmission, the worst test result shows that a data packet reception rate of 70% or even 60% may be achieved based on current 3 GPP Rel-14 C-V2X chipset OBU.
## 4 Network Layer: The Main Building Block of Fixed Networks
### _Ofdm-Pon_
Various works of \"multi-parallel\" oDAC functionality have been proposed, arraying a pair of 1-bit (OOK) [59] or 2-bit (QPSK) [60, 61, 62, 63] modulation gates in parallel, fed by splitting a common optical source, with their outputs superposed in an optical combiner. The latest and state-of-the-art multi-parallel oDAC structure has been introduced in [57], where the authors have demonstrated an architecture which, with the combination of identical parallel paths and a variable splitter and combiner, is able to generate higher-order m-PAM/m-QAM constellations, suitable for operation with either direct or coherent detection receivers (Figure 20). The basic goal of a parallel design is to generate optical multilevel PAM signals using only binary driving digital signals. As shown in Figure 20, 1-bit intensity or phase gates are the fundamental building blocks that are required at each one of the oDAC paths. These gates are practically implemented using MZMs that are properly biased and driven by electrical signals [57]. Then, by adding all the optical paths via a variable combiner, one can produce optical signals with different amplitude/phase values (and thus multi-level  
Figure 19: Segmented MZM (SEMZM) seven segments of the oDAC architecture-optimized thermometer weighted DAC structure for generation of pulse amplitude modulation (PAM)8 [57]. m-PAM/m-QAM). Moreover, extensive analysis has been performed in [57] to demonstrate the significance of variable, controllable splitting and combine ratios to realize such a design in an operationally efficient way. To be able to achieve proper operation under dynamically changing operational conditions, the authors added control and calibration functionalities to their architecture along the optical paths by utilizing slow phase-modulators. With this type of treatment of the possible mismatches in splitting or combine ratios that can occur through the manufacturing processes or due to temperatures/environmental changes, their design becomes far more robust compared with other works of the literature. Overall, we can conclude that the discussed optical DAC advantages in the relevant articles available in the literature, point in the direction that the future of digital to analogue conversion at optical transmitters will be mainly relying on optical processing based on photonic integrated circuits in order to break-through the bandwidth and energy efficiency limitations of electronic-DAC-based transmitters. In the next sub-section we analyze the backbone part of fixed networks.
# Structured Urban Airspace Capacity Analysis: Four Drone Delivery Cases
## 3 Flight Planning Algorithm
### Four Metrics for the Capacity Estimation  
In the ATM system, typically, the airspace capacity is defined as the maximum number of aircraft that can be accommodated by the air traffic controllers in a given airspace at any point of time, while throughput is defined as the number of aircraft that land at an airport over a specific time window. In the SUASs' flights in high-density VLL urban airspace, however, it is expected that it might be difficult to construct and negotiate multiple SUASs' flight plans a few days in advance, such as the flight plans in the ATM system, and it might be complicated to conduct separation management through conventional human air traffic controllers due to the large number of sUASs. In addition, compared with commercial aircraft, very short flight times, many landing points, many flights, and fewer operators are expected. Thus, we can expect that one option is to analyse the urban airspace capacity based on the detailed flight plans with regard to the flight time to complete the given mission, the total flight time, the total flight distance, and the number of conflicts. We utilise such detailed flight plans to define four metrics to analyse the urban airspace capacity.
## 2 Proposed Simulation Environment
### Geolocation and Target-Tracking Architecture  
In the Orolia Skydel simulator, setting up LEO satellite simulation concerning tracking UAV is possible, which spuriously generates the RFI source of interference. With the help of \"Orolia\", an advanced software-defined GNSS simulator installed in the LASSENA laboratory, we can generate the simulation for different instances localization algorithms are recorded in raw I/Q files [44]. Moreover, the information of transmitter and receiver can also be downloaded using logging files for each epoch as a reference file, which will be useful to fix the parameters of UAV and LEO satellites coordinates and velocity, etc. in the source localization algorithm implemented in MATLAB. Initially, the number of sources of RFI in the form of UAV and the LEO satellites are set in separate instances by assigning a window to a Radio 0, 1, 2, etc. To the best of our knowledge, this is the first time, this simulator is used to simulate and record RFI signal geolocation and emitter-tracking problems.