esa-sceva/satcom-chunk-collection huggingface dataset:
# Situationally Sensitive Path Planning
## 7 Discussion
### Hardware Stress Testing  
Our idea is founded on algorithmic modification. Therefore, we tested the simulation's performance under two different scenarios. The first scenario featured a simulation variant that continuously increased the number of active pedestrians until the flow rate of the simulation environment reached a consistent equilibrium. In this scenario, two separate hardware setups were compared.  
The first setup was a desktop PC with an NVIDIA GeForce RTX 4070 GPU, an AMD Ryzen 5 5600X 6-Core processor, and 16 Gb of RAM. The second setup was an MSI Gs75 Stealth gaming laptop with an NVIDIA GeForce RTX 2080 with Max-Q Design GPU, an Intel i7 6-Core processor, and 32 Gb of RAM. Broadly, the purpose of this scenario was to identify how the number of active agents and the choice of hardware would scale with simulation performance. This in turn may provide performance guidelines to simulation engineers regarding the appropriate crowd scale required to maintain optimal performance.  
The second stressor was used to identify the maximum number of possible agents that could be active on the field while maintaining at least 30 frames per second (FPS). This scenario was tested only on the 4070 desktop PC. Though similar to the first scenario, this set of simulation runs specifically considers possible case situations where a lower bound of performance is expected. To ensure consistent metrics, twenty distinct two-minute sessions were conducted on each computer for each scenario. The simulation was set up to record, per every frame, the current number of active agents, the delta time difference between the previous frame and the current frame, the estimated raw framerate at that current frame, and an interpolated \"smoothed\" frame rate calculated from the mean FPS across the last three frames. The smoothed frame rate was purely to reduce noisy FPS measurements caused by occasional stutters in data output from Unity. The results are illustrated by timing and computational load in Figure 6.  
For the first scenario, we used data from both hardware setups and compared the number of active agents to FPS performance (see Figure 6, at left). The 4070 and 2070 both adhere to a similar decreasing logarithmic curve whereby FPS decreases as the number of agents increases. Perhaps unsurprisingly, for any number of active agents, the 4070 is noted to always have a higher FPS. Interestingly, the disparity between the performance curves generated by the 4070 and 2070 seems to diminish as the number of agents increases. This indicates that further stressing the simulation may allow for more agents with relatively minor decreases in FPS. For the second scenario (see Figure 6, at right), we similarly compared the number of active agents and the FPS but solely with the 4070. When fitted with a logarithmic regression model, the curve predicts that 406.6 agents (approximately 406-407 agents) is the maximum number of active agents that the 4070 could theoretically handle while staying above 30 FPS.
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 4 Results
### Analyzing the Image Quality of the Proposed System Under Different Channel SNRs  
PSNR between the received and transmitted images is used as an objective metric to evaluate the performance of decoders in the proposed autoencoder-based M2M communication system. Figure 7 illustrates how PSNR varies across different channel SNR levels under various trained decoders. The simulation considered SNR levels of 2.5, 3.0, 3.5, 4.0, and 4.5 dB, representing different qualities of the transmitted signal. At an SNR of 2.5 dB,  
Figure 6: The error correction performance of \(\mathcal{PC}\) (\(2048,1600\)).  
Figure 7: SNR against PSNR.  
the simulation recorded a very high BER. As the SNR increases to 3.0 dB and 3.5 dB, the BER reduces, indicating more reliable transmission. At an SNR of 4 dB or higher, the BER becomes negligible, indicating error-free transmission.  
The results show that each decoder reaches its saturation point at high SNRs. When the SNR is low, each decoder shows low PSNR levels, indicating that the reconstructed signal differs significantly from the original signal. This is expected because a low SNR implies a high noise level, making it difficult for the decoder to accurately reconstruct the original signal. When the SNR is moderate, the decoder trained with zero BER has a lower PSNR level compared to other decoders. This shows that, despite performing well at high SNR values, the decoder trained with zero BER may not operate well in a low noise-level channel.  
Figure 7 also shows that the channel decoder successfully recovers nearly all the bits transmitted through the channel at an SNR of 4 dB or above. However, the receiver PSNR saturates at 22 dB at high SNRs because the AE cannot be trained to predict the exact image, resulting in a residual error. This residual error is the semantic noise carried forward at the receiver. Therefore, the maximum achievable quality is 22 dB PSNR. While 22 dB PSNR might seem low for human perception, it is sufficient for machine perception. As demonstrated later, the image classification model can still accurately identify the intended meaning of the message, indicating that this level of quality is adequate for machine vision applications.
## 3 Proposed Methodology
### Combination and Classification Module
#### 3.3.2 Multi-Region Ensemble  
N frames after being synthesized will be put into multi-region ensemble for synthesis and evaluation, region information is also collected from feature sets and then pooled to derive video classification results while evaluating if the video is real or fake.  
The majority of prior research has relied on a threshold parameter, which represents the probability of fake video to synthesize information between frames in a video in order to make a final decision about whether the video is real or fake. This threshold parameter is generally set to a fixed value, which might make the model more sensitive to fake or real video, lowering the overall model quality. In our proposal, this threshold for distinguishing real and fake will be dynamically changed to match the trained parameters to solve this problem, enhancing the capacity to properly predict the outcome.  
In this section we use a simple logistic regression model with the input and where the input is portrayed by a value for each video along with ground truth label of video. It can be represented as \(X=[x_{1},x_{2},\dots,x_{M}]\in\mathbb{R}^{d\ x\ M}=\big{[}\frac{a_{1}}{N}, \frac{a_{2}}{N},\dots,\frac{a_{M}}{N}\big{]}\) and \(Y=[y_{1},y_{2},\dots,y_{M}]\), where \(M\) is number of samples; \(N\) is selected frames per video for combination and classification module, \(a_{i}\) is representation of the probability of fake video value.  
The output of logistic regression is written in the following format (2):  
\[f(x)\ =\theta\Big{(}w^{T}X\Big{)} \tag{2}\]  
where \(\theta\) is called logistic function, \(w\) is weight value.  
We assume that the probability that a data \(x\) falls into class 1 is \(f(w^{T}x)\), \(x\) falls into class 0 is \(1-f\big{(}w^{T}x\big{)}\), we can represent it as follows (3):  
\[P(y_{i}|x_{i};w)\ =z_{i}^{y_{i}}(1-z_{i})^{1-y_{i}} \tag{3}\]  
\(P(y_{i}|x_{i};w)\ =z_{i}^{y_{i}}(1-z_{i})^{1-y_{i}}\) where \(z_{i}=f\big{(}w^{T}x_{i}\big{)}\).  
In this case, we use the Stochastic Gradient Descent (SGD). Loss function with \((x_{i},\ y_{i})\) is (4):  
\[J(w;x_{i},y_{i})\ =-(y_{i}\log z_{i}+(1-y_{i})\log(1-z_{i})) \tag{4}\]
## 3 Results
### The Effect of Storage on the Sensory Quality of Very-Low-Sugar Apple Jams with Added SGs  
Selected apple jams (VL30, VL50, and VL80) with sugar and substitution SGs were evaluated at the beginning and after 3 and 6 months of storage. The results are presented in Figure 4.  
Figure 4: Changes in the sensory quality of very-low-sugar apple jams with steviol glycosides after storage for 3 and 6 months: t—taste; f—flavor; and o.—odor. Figure 3: Consumer evaluation of very-low-sugar apple jams (\(n=120\)). a–e—samples marked with the same letters do not significantly differ statistically; and c.u.—conventional units. Jams with increased SG addition were rated slightly lower regarding their sweet taste (except for the 80% level), apple flavor, color, and consistency than jams with only sugar. The sweet taste ratings varied from 6.4 to 7.6 c.u., and the apple flavor was between 7.0 and 8.5. A higher variation was found between jams with different levels (0-80%) of SG substitution for apple odor (6.1-8.7), color (3.8-5.3), and consistency (5.5-8.5 c.u.). The perception of the bitter taste and metallic odor of apple jams was significantly higher with increasing SG substitution levels (\(p<0.001\)) and a storage time of the jams of up to 3 months (\(p<0.05\)). In the case of metallic odor, this relationship was not found for the 80% SG addition. The overall quality of very-low-sugar apple jams also decreased with the increase in SG addition from 8.5 (0% SGs) to 5.8 (80% SGs) and storage time from 0 to 6 months. This could be due to a higher negative note, i.e., bitter taste and metallic odor. The very-low-sugar apple jams with SGs had a relatively light color, which darkened significantly after 3 and 6 months of storage (Figure 4). The lighter color of the jams with additional steviol glycosides compared to conventional jams with sugar was due to the caramelization of the sugar during the technological process. The additives used, such as pectin and citric acid, also impacted the lightening of the color. With the addition of steviol glycosides to the jams and, thus, a reduction in the sugar content, the jam samples were lighter (Table 4). They were characterized by a yellow color, which became more visible with an increasing SG content. It can be stated that the addition of citric acid stabilized the color, and the observed changes in the \(\Delta\)E parameter ranged from 1.33 to 2.18 depending on the addition of SGs (Table 4). All the tested jams with steviol glycosides were initially characterized by yellow (positive value of the \(\Delta\)b parameter) and low color saturations (\(\Delta\)C parameter).
# Wheeler Method for Evaluation of Antennas Submerged in Lossy Media
## 3 Analysis of the Wheeler Cap Method of Antenna Surrounded by the Lossy Media  
### Simulation  
#### 3.1.1 Simulation Setup  
The radiation efficiency evaluation method is validated by stepwise analysis according to the change in the dielectric constant and conductivity of the medium encompassing the antenna. The radiation efficiency analysis using the Wheeler method is conducted through ANSYS HFSS. A normal-mode helical antenna (NMHA) resonating at a frequency of 172 MHz in air and an insulation layer are designed using copper wire and Teflon (\(\epsilon_{r}=2.1\), tan\(\delta\) = 0.001), respectively. The detailed dimensions of the antenna are illustrated (Figure 4a). The metallic cavity is designed as a hemisphere consisting of PEC (Figure 4b). The space inside the insulation layer is modeled as air and remained identical across all experiments. The simulation setup is presented in Figure 4c. The size of the lossy medium is 1000 mm \(\times\) 1000 mm \(\times\) 500 mm.
# Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators
## 4 Experimental Characterization  
This section reports the experimental results obtained with the two integrated reconfigurable linear optical processors: one device per platform was available for testing. A first step consisted of the characterization of all thermal phase shifters within MZI elements to find their actual phase-current relation, using the procedure described in [5]. Then, the two PICs have been validated for photonic analog processing. To this aim, the impact of the realized devices on the bit resolution of input signals was evaluated through the effective number of bits (ENOB) reduction. The ENOB is a standard figure of merit for the resolution of analog signals, reflecting the number of bits necessary to represent those signals in the digital domain [39]. This is particularly relevant in the context of reconfigurable linear optical processors as they emulate a single DNN layer; signals are then undergo electrical and analog-to-digital conversion for successive use in the emulation of a successive layer.  
The ENOB can be derived from the signal to noise ratio (SNR) and the total harmonic distortion (THD) at the output of an analog device when a reference sinusoidal optical signal crosses the device under test. For this characterization, as reported in Figure 1, a LiNbO\({}_{3}\) Mach-Zehnder modulator (MZM) has been used in its linear region to impress a 10 GHz sinusoidal signal to the amplitude of an incoming lightwave at 1550 nm. The spectrum of this reference signal has been acquired using a broadband photodiode (bandwidth 50 GHz) connected to an electrical spectrum analyzer (ESA, bandwidth 45 GHz). The spectrum has then been compared to the spectra obtained after crossing the two linear optical processors in order to evaluate their impact on the SNR and THD, and thus on the data resolution through the ENOB.
## 3 Experimental Setup
### Data Acquisition  
In order to capture the structure-borne sound signal, a piezoelectric sensor from the company QASS is used. This sensor records the high-frequency vibrations generated during hobbing and converts them into an electrical signal. This signal is then routed through a cable to the preamplifier and then to the Optimizer4D, an evaluation unit. A study by [PERSON] et al. has demonstrated that microgear measurements using focus-variation technology can achieve low, single-digit micrometer measurement uncertainties [29]. Based on the findings of [PERSON] et al., the geometric reference measurements are performed analogously using an optical coordinate measuring machine \"\(\mu\)CMM\" from Bruker Alicona. To do this, the manufactured microgear is placed in ethanol and cleaned to exclude any measurement deviations caused by contamination. The coordinate measuring machine works on the principle of focus variation. Light is projected onto the measurement object and reflected from its surface. The reflected light is focused by precision optics and hits a  
Figure 4: Sensor integration in the machine.  
Figure 3: CAD layout of the sensor integration (sensor in blue on the **left**, sensor with protecting housing on the **right**).  
light-sensitive sensor. Depending on the distance of the sensor from the component surface, only certain areas of the component are in focus. By moving the sensor vertically and analyzing the sharpness of different areas, the topography of the component surface can be reconstructed. In this way, point clouds of the gear geometry are created in a four-minute measurement program, from which the gear characteristics such as profile deviations, flank line deviations, and transmission errors, are calculated using commercial gear inspection software \"Reany\".  
The structure-borne sound and geometry data, as well as the specified process parameters of the experiment, are fused together and evaluated in Matlab (see Figure 5).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Units** & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**Case**} & \multicolumn{1}{c}{\multirow{2}{*}{**MD \(\pm\) SD**}} \\  & & **Group** & **Group** & **Group** & \multicolumn{1}{c}{\multirow{2}{*}{**PD**}} & \multicolumn{1}{c}{\multirow{2}{*}{**MD \(\pm\) SD**}} \\ \hline Knee Valgus at IC & deg & \(-0.69\pm 3.98\) & \(1.36\pm 3.87\) & \(0.198\) & \(-2.05\pm 1.54\) \\ Peak Foot/Pelvis Angle & deg & \(-7.97\pm 12.63\) & \(-9.17\pm 4.72\) & \(0.745\) & \(1.20\pm 3.65\) \\  & & At Peak Knee Valgus & & & \\ Max Knee Valgus & deg & \(1.86\pm 3.97\) & \(4.00\pm 3.84\) & \(0.187\) & \(-2.14\pm 1.57\) \\ Max Knee Flexion & deg & \(42.31\pm 32.80\) & \(52.60\pm 26.38\) & \(0.393\) & \(-10.29\pm 11.82\) \\ Max Knee Rotation & deg & \(-11.11\pm 4.79\) & \(-12.15\pm 4.95\) & \(0.601\) & \(1.04\pm 1.97\) \\  & & Angular Moments & & & \\ Knee Extension & Nm/kg & \(2.05\pm 0.43\) & \(1.57\pm 0.30\) & \(0.003\) * & \(0.48\pm 0.14\) \\ Knee Flexion & Nm/kg & \(-0.26\pm 0.19\) & \(-0.14\pm 0.12\) & \(0.067\) & \(-0.12\pm 0.06\) \\ Knee Valgus & Nm/kg & \(0.29\pm 0.22\) & \(0.24\pm 0.12\) & \(0.413\) & \(0.06\pm 0.07\) \\ Knee Varus & Nm/kg & \(-0.23\pm 0.20\) & \(-0.19\pm 0.08\) & \(0.459\) & \(-0.04\pm 0.06\) \\  & & Angular Velocities & & & \\ Knee Extension & deg/s & \(-701.06\pm 80.94\) & \(-619.22\pm 72.91\) & \(0.014\) * & \(-81.84\pm 30.82\) \\ Knee Flexion & deg/s & \(517.84\pm 78.64\) & \(498.83\pm 96.79\) & \(0.601\) & \(19.01\) * \\ \hline \end{tabular} Data are presented for the reconstructed leg of case participants along with one (randomly selected) leg of the control participants as mean \(\pm\) standard deviation. The between-group comparisons show \(p\)-values, non-standardized mean differences and standard deviations. Significant differences (\(p<0.05\)) are identified with an asterisk (*). IC, initial contact with ground. Max, maximum. MD, mean difference. \(p\), \(p\)-value. SD, standard deviation. vGRE, vertical ground reaction forces.  
\end{table} 
Table 2: _Cont._  
Figure 2: Comparison of the statistically significant differences between the control, bone–pstellar tendon–bone (PT) and four-strand hamstring tendon (HT) autographs in terms of the knee extension moment and angular velocity during the drop vertical jump. Significant differences (\(p<0.05\)) are identified with an asterisk (*).
## 2 Data and Methods  
### Study Area and Data Collection  
In this study, open-source airborne LiDAR data collected by the U.S. Geological Survey were used together with high-resolution optical satellite images from the Google Maps platform. While the Google Map API service is available to gather satellite images everywhere on Earth, airborne LiDAR is a valuable (and expensive) data source. The U.S. Geological Survey initiated the National Geospatial Program with airborne LiDAR campaigns that cover the United States to improve and deliver topographic information. This program contains different quality completed products from quality Level-0 to Level-3. In order to test the performance of the feature-wise fusion of optical images and LiDAR, only level-1 quality data that are located over major cities are taken into account. In this context, the Florida southeast project that falls within the Florida counties of Broward, Collier, Hendry, Miami-Dade, Monroe, and Palm Beach was chosen as the study area [35].  
This LiDAR data were collected on June 2018 and published by U.S. Geological Survey with the name of Florida Southeast LiDAR-Block 1. The point cloud was generated at level 1 quality with a source DEM of 0.5 m/pixel. The vertical accuracy of the point cloud is \(\pm\)10 cm in terms of root mean square error. Nominal pulse spacing is smaller than 0.35 m. The point density is 14.87 points/m\({}^{2}\). It has seven classes, namely ground, low noise, water, bridge decks, high noise, ignored ground, and unclassified. The dataset consists of 526 tiles, each of them covering a 1 km \(\times\) 1 km area. Concerning data size, only 393 tiles, which cover the majority of the road network in the area, were used in the study.  
After the LiDAR data were obtained, the required satellite images were generated using the Google Map Static API-based tool. This tool was created by [36] and it generates satellite and corresponding mask images randomly or in sequence in the defined region based on latitude and longitude. It also produces a metadata file with which to rectify these satellite images, if required. In this way, the registration of the LiDAR data and the satellite images could be performed. The Google Maps Static API provides images at various levels which correspond to different scales and resolutions on the Earth's surface. To generate images, zoom level 17 was chosen because of its ideal coverage area and pixel resolution. The satellite and mask images were extracted with a dimension of 512 \(\times\) 512 pixels. This resulted in an image with a spatial resolution of 1.07 m \(\times\) 0.96 m. Consequently, a total of 1426 images were generated, which cover the LiDAR dataset and contain roads.  
The optical satellite images and their corresponding masks are geo-referenced in the WGS4 datum. This enables the inter-usability of these images together with other geospatial datasets; in this case, the LiDAR point cloud. As shown in a sample overlay presented in Figure 1, the horizontal coordinates of these datasets match with each other harmoniously for use in road segmentation studies.  
Figure 1: \(Cont.\)
## 5 Conclusions  
A novel UME wire rope detection method was proposed in this paper. According to the MFL production principle, a prototype was manufactured with small volume, light weight, and convenient installation. We detected enough broken wire defects in the experiment, and the baseline signal was estimated by the subsection mean method. Then digital image processing was adopted to filter the quantification noise. To improve the image circumferential resolution, an MFL image SR method was proposed with Tikhonov regular. Finally, the most sensitive characteristics were selected through the variable control method, and an RBF neural network was designed to quantitatively classify broken wires. The experimental work can be summarized as follows:  
Figure 13: Recognition distribution of error when “spread” was 0.145.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Spread** & **Maximum Error** & **Average Error** & **Training Accuracy** & **Recognition Accuracy** & **One Error Accuracy** \\ \hline
0.11 & 4 & 0.6714 & 99.53\% & 52.86\% & 87.14\% \\
0.145 & 3 & 0.5429 & 95.26\% & 55.71\% & 91.43\% \\
0.18 & 3 & 0.5143 & 91.00\% & 58.57\% & 91.43\% \\
0.215 & 3 & 0.5 & 86.73\% & 60\% & 91.43\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the designed RBF neural network.  
1. The proposed detection method could effectively test various types of defects of wire rope, even abrasion and small gaps.
2. Based on Tikhonov regular SR reconstruction methods, image features of defects could effectively remain while the axial resolution was reduced and circumferential resolution was increased.
3. The proposed detection system, signal processing, and image processing method were applicable and general to other testing conditions. The accuracy of the RBF neural network was high, and it was easy to achieve online testing with the system algorithm. The testing results could provide reliable information for estimation of the lifespan of wire rope.  
[PERSON] designed and performed the experiments, then analyzed the data and wrote the paper; [PERSON] provided the experimental platform, supported funding, and corrected the paper.  
This work is partially supported by the National Natural Science Foundation of China (Grant No. 61040010, 61304144), Key Technologies R&D Program of Henan Province (Grant No. 152102210284), the science and technology program of the Henan Education Department (Grant No.17A510009), and the Technology Development Cooperation Project of Henan Province (182106000026).  
The authors declare no conflicts of interest.