esa-sceva/satcom-chunk-collection huggingface dataset:
# Generation of Pedestrian Crossing Scenarios Using Ped-Cross Generative Adversarial Network
## 2 Related Work
### Pedestrian Datasets  
Pedestrian datasets have been an area of interest for more than 15 years. One of the first examples was published by [PERSON] and [PERSON] (2004) [12]. Their INRIA dataset contained 1805 64 \(\times\) 128 images of people in various orientations. However, this dataset contained only individual images of people, and not sequences/videos, and therefore is not capable of understanding the context of a pedestrian's movements, such as intention to cross or looking at the ego vehicle.  
The datasets used in this work are the Caltech pedestrian dataset [7], the Joint Attention for Autonomous Driving (JAAD) [13], and the Daimler pedestrian dataset [14]. Caltech was one of the earliest comprehensive pedestrian datasets aimed to improve the detection of pedestrians. Subsequently, it has been used extensively as a benchmark for machine vision tasks and pedestrian detection [15; 16; 17]. It comprises approximately 10 h of 640 \(\times\) 480 30 fps driving footage. A total of 250,000 frames were annotated, with more than 350,000 bounding boxes and 2300 unique pedestrians.  
JAAD is a relatively new pedestrian dataset and is different from others as it goes a step beyond simply labeling the pedestrian [13]. This dataset labels the behavior of the pedestrian, such as 'Looking', and their speed. JAAD has been used to instantaneously predict if a pedestrian is about to cross the road [18]. However, what the dataset lacks is specific information regarding the movement action, such as their movement direction or their speed. What is presented in Section 3.2, shows that this research solves this issue. The JAAD dataset contains 346 videos between 5 and 10 s in length, their ground truth contains approximately 82,000 frames and 2200 unique pedestrians, resulting in 337,000 bounding boxes.  
The final dataset used was the Daimler pedestrian dataset which was collected from moving or stationary vehicles [14]. The 68 video clips contain 4 key movement types, which were crossing, stopping, starting to walk, and bending-in. Of these 68 video clips, there were more than 12,000 images containing pedestrians.
## 7 Discussion  
This section puts together the obtained results for those various multi-frequency, multi-constellation GNSS signal groupings; jamming signal type and jamming power level; and trajectory dynamics. Looking at Figures 32 and 33 (the low power case for both simulated and actual jamming), it is obvious that the performance of the GNSS receiver in the second trajectory is much better than the first trajectory, especially for the actual jamming, due to the lower dynamics compared to the first trajectory. Additionally, the multi-frequency GPS signal-based positioning solution is barely affected by the applied low-power jamming signal. As an example, the maximum RMSE is about 6 m in the vertical positioning component for the first trajectory under an actual jamming attack. By combining multi-frequency signals from both GPS and Galileo, the positioning shows better performance in both horizontal and vertical positioning errors. However, the RMSE of the GPS TF + Galileo TF-based solution in the vertical direction is slightly worse than the GPS TF counterpart in the first trajectory. This is because the number of Galileo satellites decreased suddenly, as shown in Figure 22, especially during jamming, affecting the resultant solution performance in the vertical direction, as presented before in Figure 21. Moreover, the more constellations incorporated in the positioning solution, the less the RMSE is, leading to the best solution derived from the GPS, Galileo, and GLONASS constellations combined. In the high jamming case, as shown in Figures 34 and 35, the GPS-only solution was the worst case, which accumulated position errors during the jamming period that grew up to about 45 m in the horizontal component and about 50 m in the vertical component in the simulated jamming case. The RMSE of the same signal combination was better with the real jamming case because the GPS TF-based solution suffered from discontinuities during the jamming interval compared to the full availability of both the GPS + Galileo and GPS + Galileo + GLONASS counterparts during the jamming interval. As a result, the GPS + Galileo and the GPS + Galileo + GLONASS-based solutions have some spikes under high jamming, leading to higher RMS error values compared to the GPS-only solution. The multi-constellation-based positioning solutions maintained better positioning performance even under the high-power jamming signal, which reflects the importance of using a multi-frequency multi-constellation GNSS receiver, especially with high jamming attacks.  
Figure 32: RMS of horizontal errors for the simulated and actual jamming scenarios (low jamming).  
Figure 34: RMS of horizontal errors for the simulated and actual jamming scenarios (high jamming).  
Figure 35: RMS of vertical errors for the simulated and actual jamming scenarios (high jamming).  
Figure 33: RMS of vertical errors for the simulated and actual jamming scenarios (low jamming).
# Investigating the Effect of Motion Capture Suits on the Test-Retest Reliability of Gait Parameters
## 4 Discussion
### Alternative Foot Definition  
The source of error resulting in PiG poor reliability of the ankle angle and the large MDC in the suit was identified as inconsistent placement of heel and toe markers. The PiG relies on careful alignment of these markers with the AJC. If toe and heel markers are not aligned with the AJC, incorrect inversion/eversion of the foot segment will ensue (Figure 4). This altered measured ankle angles by up to 80\({}^{\circ}\), reducing reliability. This error was observed in both conditions, but was more common in suited trials. The alignment of the heel, AJC and toe becomes particularly difficult when using the MOCAP suit slippers which are bulky and poorly fitting, making it hard to consistently mark-up toe and heel markers.  
An alternative foot segment definition, which we believe has not yet been presented in other literature, in conjunction with the rest of the PiG model, improved ankle angle reliability and reduced the MDC in both conditions. This alternative foot definition presented in this paper (Figure 1b) uses easily identifiable landmarks (lateral malleolus) not affected by the suit slippers. We see reliability of the ankle angle increase from poor to moderate in the suited trials and moderate to good in the non-suited condition. There is also a large decrease in the suited MDC to 6.51\({}^{\circ}\), much closer to the established maximum acceptable error, and a decrease in the ankle angle MDC in the non-suited condition (1.12\({}^{\circ}\)). We propose, when small changes in ankle angle are being measured, using the PiG marker set;  
Figure 4: Example of rotation of the foot segment due to the incorrect placement of the heel and toe markers.  
the alternative foot segment definition may provide a more accurate method of estimating ankle angles when compared to the typical PiG foot segment definition.
# The Law and Mechanism of the Effect of Surface Roughness on Microwave-Assisted Rock Breaking
## 1 Introduction
### Results
#### 2.5.2 Crack Growth  
The dielectric properties and thermal expansion coefficients of each mineral in basalt are different; therefore, basalt expands unevenly after microwave irradiation, resulting in cracks. Before irradiation, there were no macroscopic cracks on the surface of the flat specimen, and no macroscopic cracks were observed on the surface of the specimen after microwave irradiation. However, there was significant crack propagation on the surface of the rough specimen. During the microwave irradiation, the local temperature of the rough surface was higher, and the temperature distribution was uneven, so the local position generated large thermal stress; then, new cracks were generated when the rock strength limit was exceeded. The crack of the rough surface specimen is shown in Figure 7. The above shows that the microwave irradiation effect of a rough rock surface is better, which is beneficial to improve the efficiency of microwave-assisted rock breaking.
# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 3 CI-Based Power Allocation Schemes  
This section explains the CI-based power allocation schemes to solve the mathematical optimization problem in (11). First, the proposed EABC-based power allocation scheme is presented, followed by implementations based on PSO and GA, respectively.
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 4 Discussion
Based on the data in Figure 12, it can be concluded that the accuracy of the localization in certain segments always differed from the required accuracy regardless of the number of neurons. Almost all segments with high error values were also present if the localization was performed by using WkNN algorithm. By using WkNN algorithms, the performances of the localization was better, but there were segments in which the localization error differed from the majority of the assembling line.
# Quantification of the Benefits for Power System of Resilience Boosting Measures
## 4 Modeling Framework for the Measures
### Modeling of Threat-Specific Active Measures for Wet Snow Events
#### 4.2.1 Security-Constrained Generation Redispatching  
An active measure modelled within the RELIEF tool in engineering and operational planning modes consists in the redispatching of generation aimed to assure a minimum anti-icing current on the branches most subject to wet snow sleeve accretion, both in transmission system and in distribution networks [29; 30], where it is possible and effective.  
In particular the following functions are implemented:  
* A security constrained redispatching (SC-R) [29] of the active powers injected by conventional dispatchable generators in the EHV/HV transmission system: the redispatching costs of generators are minimized while assuring (1) the fulfillment of N and N-1 security constraints, and (2) a minimum current on the HV branches which present a high probability of failure in the pre-redispatch condition, due to the potential formation of wet snow sleeves. This SC-R accounts for the process of wet snow sleeve formation during the whole forecasted duration of the event, considering the changes in load/generation patterns and the minimum current values which are updated via advanced NWP models [11]. The minimum current is set with the aim to prevent ice formation thanks to the heat dissipated by Joule effect in the conductor.
* An OPF-based measure [30] which sets the generators' active and reactive power setpoints with minimum and maximum current constraints on the branches of the MV feeders, considering the availability of some active measures (re-dispatching of generation and voltage regulation by acting on the tap position of On-Load Tap Changers, OLTC, at HV/MV substation transformers). More details about the mathematical formulations of these measures can be found in [29; 30].
## 4 Results and Discussion
### Identification and Categorization of Factors
#### 4.2.3 Technological Factors  
Technology attributes, such as complexity in handling equipment and data, costs of technology implementation, and trialability, are important determinants of DAT adoption. Low-cost equipment could motivate farmers to adopt this new technology, especially among smallholder farmers [35]. [PERSON] et al. [31] found that access to digital information plays a positive role in precision pesticide technology adoption through services and equipment acquisition. Farmers using the Internet to acquire timely agricultural technology information are more likely to adopt novel technologies. The adoption rate might be further accelerated through improved training and the availability of digital infrastructure [41].
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 3 Experimental Results and Discussions
### Measurement Setup  
The R&S ZVA110 Vector Network Analyzer (VNA) (Rohde & Schwarz, Munich, Germany) is utilized to investigate and verify the proposed tri-band antenna's frequency domain reflection coefficient. Far-field radiation parameters of the proposed design are analyzed using a shielded RF anechoic chamber. A standard 24 dBi horn antenna (SGH-series) is utilized as a transmitter while the proposed antenna design is used as a receiver. Transmission losses and open air may affect the signal produced by a voltage-controlled oscillator (VCO). Measurement set-up employed high-gain power amplifiers, which can boost the produced signal and efficiently transmit the radio signal from transmitter to receiver.
# Electroless Copper Patterning on TiO\({}_{2}\)-Functionalized Mica for Flexible Electronics
## 3 Results and Discussion
### Electroless Copper Deposition on Ag-Activated TiO2/Mica Substrates
Figure 9a-c shows AFM images at the interface between the Cu-ELD coated track and the uncoated TiO\({}_{2}\)/Mica substrate, with markers indicating the areas used to determine the height cross-section plots shown in Figure 9d-f. These data revealed that the Cu track heights were 71 nm, 106 nm, and 132 nm for the Cu-ELD layers grown over 1, 1.5, and 2 min, respectively. Although based on a limited dataset, these results suggest a linear growth rate for Cu-ELD layers of approximately 68 nm min\({}^{-1}\). Finally, the effect of the Cu-ELD plating duration on the electrical conductivity of the copper layers was evaluated. Table 1 summarizes the data for the sheet resistance (R\({}_{\rm s}\)) versus Cu-ELD duration and AFM-measured thickness (d) for the three layers formed during  
Figure 8: XRD data showing the Cu (111) reflection, consistent with metallic Cu, for Cu-ELD layers grown on the Ag/TiO\({}_{2}\)/mica substrate after 1, 1.5, and 2 min of deposition. Figure 9: AFM topography images at (**a–c**) a 100 \(\times\) 100 μm field of view, and corresponding cross-section plots (**d–f**) for determining Cu-ELD layer thickness after (**a,d**) 1 min, (**b,e**) 1.5 min, and (**c,f**) 2 min in the Cu-ELD bath. The plots, displayed in panes (**d–f**), are obtained according to the position and scan direction indicated via yellow arrows in panes (**a–c**). the 1-2 min of Cu deposition. As expected, the increased thickness led to a corresponding decrease in Rs. The specific resistivity (\(\rho\)) of the Cu-ELD layers, calculated using \(\rho\) = d \(\times\) Rs, was found to be 4.32 \(\pm\) 0.21 \(\upmu\Omega\) cm\({}^{-1}\), which is about 2.5 times higher than the bulk Cu value (\(\rho\infty\) = 1.7 \(\upmu\Omega\) cm\({}^{-1}\)) but is still within an acceptable range for \(\sim\)100 nm thick Cu-ELD layers, where values below 5 \(\upmu\Omega\) cm\({}^{-1}\) are considered a benchmark and typical values for such layers can often exceed 12-300 \(\upmu\Omega\) cm\({}^{-1}\)[42, 43].
# Plume Divergence and Discharge Oscillations of an Accessible Low-Power Hall Effect Thruster
###### Abstract  
Hall effect thrusters (HETs) are an increasingly utilized proportion of electric propulsion devices due to their high thrust-to-power ratio. To enable an accessible research thruster, our team used inexpensive materials and simplified structures to fabricate the 44-mm-diameter Western Hall Thruster (WHT44). Anode flow, discharge voltage, magnet current, and cathode flow fraction (CFF) were independently swept while keeping all other parameters constant. Simultaneously, a Faraday probe was used to test plume properties at a variety of polar coordinate distances, and an oscilloscope was used to capture discharge oscillation behavior. Plasma plume divergence angle at a fixed probe distance of 4.5 thruster diameters increased with increasing anode flow, varying from 36.7\({}^{\circ}\) to 37.4\({}^{\circ}\). Moreover, divergence angle decreased with increasing discharge voltage, magnet current, and CFF, by 0.3\({}^{\circ}\), 0.2\({}^{\circ}\), and 8\({}^{\circ}\), respectively, over the span of the swept parameters. Generally, the thruster exhibited a strong oscillation near 90 kHz, which is higher than a similarly sized HET (20-60 kHz). The WHT44 noise frequency spectra became more broadband and the amplitude increased at a CFF of less than 1.5% and greater than 26%. Only the low flow and low voltage operating conditions showed a quiescent sinusoidal discharge current; otherwise, the discharge current probability distribution was Gaussian. This work demonstrates that the WHT44 thruster, designed for simplicity of fabrication, is a viable tool for research and academic purposes.  
hall effect thruster; discharge oscillation; faraday probe; cathode flow fraction Article
## 2 Site Investigations and Laboratory Tests
### Laboratory Testing
#### 2.3.2 Frost Heave Testing  
Using a one-dimensional temperature controllable cell, frost heave expansion of the two soils at the soil slope were investigated, namely ML and SW. The experimental setup used in this study consisted of a walk-in environmental room, a temperature-controllable cell for inducing one-dimensional freezing, and three independently working thermostats for imposing temperatures to the top, bottom, and periphery of the soil inside the cell (see Figure 6a,b). A linear variable differential transformer (LVDT) was used to measure the change in specimen height induced by freezing. The temperature controllable cell was developed by the Korean Geotechnical Research Institute (KICT, Korea) and slightly modified by NGI. The soil was reconstituted following [PERSON] [15] air pluviation by funneling. The specimen height target was 100 mm and the diameter of the cell was 99.72 mm. A relative density of -80% on SW soil, and a unit weight of 18 kN/m\({}^{3}\) on ML was achieved by tamping. The specimens were then saturated with de-aired water using a small hydrostatic gradient. Finally, freezing was achieved by setting the temperature of the thermostat controlling the bottom cap at \(-\)10 \({}^{\circ}\)C, the thermostat controlling the temperature of the cell at 1 \({}^{\circ}\)C, and the temperature of the top cap at 3 \({}^{\circ}\)C. Hence the temperature gradient imposed was 0.13 \({}^{\circ}\)C/mm, which is in agreement with the recommendation with the Transport and Road Research Laboratory standard (TRRL) in the UK [16]. The vertical effective stresses \(\sigma_{\text{v}}^{\text{ }}\) applied to the soil (due to the own weight of the top cap and half of the soil) was 2 kPa.  
Figure 5: Representative grain size distributions from the slope surface.  
Figure 6: (**a**) Soil heave test apparatus with temperature controller (NGI and KICT); (**b**) Sketch of temperature-controllable cell (modified from [PERSON] et al.) [17]; (**c**) Frost heave results on ML and SW soils.  
The experimental results are presented in Figure 6c in terms of the elapsed time after the start of freezing versus the frost heave expansion as axial strain (\(\epsilon_{\text{a}}\)). Experimental results confirmed the suspicion that Oysand soils on the slope face are frost susceptible. The ML soil expansion is about \(\epsilon_{\text{a}}=12\%\), and four times higher than the frost heave expansion of the SW soil (\(\epsilon_{\text{a}}=3\%\)).
## 2 Slope Survey and Monitoring
### Slope Monitoring Arrangement  
The slope has been experiencing prolonged sliding. In 2019, the local government designated this area as a geological hazard-prone area and installed warning signs to inform the public of the potential danger. Due to heavy rainfall, this slope began to slide again in September 2020. A certain section of the road subgrade was damaged by traction, resulting in large-scale cracking and collapse, which disrupted the road and seriously affected the transportation of villagers. This situation necessitates urgent engineering treatment. To dynamically analyze the deformation characteristics of this landslide, invert the geotechnical mechanical parameters of the landslide, and provide a parameter basis for subsequent emergency rescue work, surface displacement monitoring, deep displacement monitoring, and rainfall monitoring were conducted on this landslide. The detailed monitoring layout is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Stratum** & **Rock Type** & **Thickness (m)** \\ \hline
1 & \(Q_{1}^{m}\) & Quaternary artificial fill & 2–6.5 \\
2 & \(Q_{1}^{m}\) & Quaternary collapse accumulation layer block stone soil & 3.1–23 \\
3 & \(Q_{1}^{m+d}\) & Quaternary residual slope deposit silty clay & 1.7–9.3 \\
4 & \(P_{3}l\) & Sandstone with mudstone & 3.3–10.5 \\
5 & \(P_{3}\beta\) & Basalt & 9.2–20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test table of rock and soil mechanical parameters.  
Figure 3: Landslide map.  
During the monitoring period, ground surface inspections were conducted once a month. In the rainy season, or when abnormalities occurred or deformation intensified, the monitoring frequency increased based on the actual situation. Manual monitoring was conducted 1-4 times per month, with increased frequency during the rainy season, or when abnormalities occurred or deformation intensified. Automatic monitoring was dynamically adjusted in real time based on construction and geological disaster site conditions. The schematic diagram of the monitoring plan is shown in Figure 4.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 2 Literature Review
### Semi-Supervised Anomaly Detection  
Technique patterns that operate in semi-supervised mode have only labeled instances of normal classes in the training data. Since they do not require labels of anomaly classes, they are more widely applicable than supervised techniques than supervised ones [30]. For example, in spacecraft fault detection, anomalies imply accidents, which are not easy to model [31]. The typical approach used in such techniques is to build a model normal behavior for the class corresponding to normal behavior and use that model to identify anomalies in the test data [32].  
It has been previously proposed to use only anomalous instances for training [33]. This technique is not commonly used, mainly because it is difficult to obtain training datasetsthat cover all possible anomalous behaviors in the data [34]. Some algorithms are the fuzzy c-means and probabilistic c-means [35]. Other algorithms include Self-Organizing.  
Maps (SOM), Expectation-Maximization, Find Out, CLAD and CBLOF [36] belong to this category. However, based on the present large data situation, anomalies are not difficult to obtain, so this provides a new opportunity for semi-supervised learning.