esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The 400 m Sprint Analysis Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subject** & **Leg** & **Method** & _RMSE_ & _bias_ & **SD** & **ICC [95\% CI]** & \(R\) \\ \hline \multirow{4}{*}{A} & \multirow{4}{*}{Both} & Method 1 & 0.397 & 0.058 & 0.393 & 0.569 [0.482, 0.655] & 0.762 ** \\  & & Method 2 & 0.232 & 0.016 & 0.232 & 0.711 [0.652, 0.769] & 0.748 ** \\ \cline{2-7}  & \multirow{2}{*}{Left} & Method 1 & 0.348 & \(-\)0.167 & 0.307 & 0.306 [0.109, 0.503] & 0.544 ** \\  & & Method 2 & 0.237 & \(-\)0.005 & 0.238 & 0.600 [0.486, 0.714] & 0.661 ** \\ \cline{2-7}  & \multirow{2}{*}{Right} & Method 1 & 0.440 & 0.284 & 0.337 & 0.115 [\(-\)0.136, 0.367] & 0.544 ** \\  & & Method 2 & 0.227 & 0.037 & 0.225 & 0.489 [0.343, 0.634] & 0.545 ** \\ \hline \multirow{4}{*}{B} & \multirow{4}{*}{Both} & Method 1 & 0.219 & 0.022 & 0.218 & 0.809 [0.774, 0.843] & 0.845 ** \\  & & Method 2 & 0.197 & 0.017 & 0.196 & 0.833 [0.803, 0.863] & 0.855 ** \\ \cline{1-1} \cline{2-7}  & \multirow{2}{*}{Left} & Method 1 & 0.241 & 0.085 & 0.227 & 0.639 [0.548, 0.731] & 0.705 ** \\ \cline{1-1}  & & Method 2 & 0.210 & 0.066 & 0.200 & 0.712 [0.638, 0.785] & 0.754 ** \\ \cline{1-1} \cline{2-7}  & \multirow{2}{*}{Right} & Method 1 & 0.193 & \(-\)0.040 & 0.190 & 0.800 [0.749, 0.851] & 0.878 ** \\ \cline{1-1}  & & Method 2 & 0.183 & \(-\)0.031 & 0.181 & 0.807 [0.758, 0.856] & 0.862 ** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of _SF_ (Hz) estimates from GNSS-based methods with IMU data by subject and leg. Note: A double asterisk (**) denotes statistical significance at \(p\) < 0.01. Figure 6: Step-by-step analysis of running parameters using RTK GNSS (Method 2) in a 400 m sprint. Panels show step-by-step changes in _running velocity_, _SF_, _SL_, and _elapsed time_ for (**A**) Subject A (200 steps) and (**B**) Subject B (242 steps). The right steps are represented by filled circles (\(\bullet\)), and the left steps are represented by open circles (\(\Circle\)). Shaded areas for Subject A indicate periods in ‘Float’ solution, highlighting reduced GNSS accuracy.
# Online Monitoring of Power Converter Degradation Using Deep Neural Network
## 1 Introduction
A hybrid generalization network called IEDGNet which addresses the diagnosis problem under unseen working conditions is proposed [22]; the method tackles the challenging problem, i.e., the discrepancy between the training data and the test data, by regularizing the discriminant structure of the network with intrinsic and extrinsic generalization objectives. A review of the recent developments of induction motor drives fault diagnosis using expert systems, artificial neural networks, and fuzzy logic systems has been presented [23], the authors utilize the instantaneous voltages and currents as input signals for the discussed methodologies. In this paper, a deep learning-based online classification scheme to identify which Insulated Gate Bipolar Transistors (IGBT) has degraded snubber resistor in the power converter for a three-phase BLDC motor drive is proposed. It is crucial to monitor the health states of the power devices to prevent the possible damage to the system caused by the failure of the power devices. By monitoring the voltage waveform of the shunt resistor in each phase, the degradation of the snubber resistor in IGBTs can be detected. The off-the-shelf deep network ResNet50 is used to perform the classification task for normal and abnormal conditions of the snubber resistors. For generating the dataset, two approaches are used. The first is to encode the voltage waveforms of the shunt resistors into grayscale images. The other is to perform a Short-Time Fourier Transform (STFT) that obtains the spectrogram of the voltage waveform for each phase and then encodes the spectrograms into RGB images. Three different ranges of the snubber resistors are compared. Experiments are carried out to evaluate the classification performance of the proposed scheme. The results using greyscale images and RGB images are compared, and the range of the resistance of the snubber resistor in which the proposed scheme can take effect is also discussed.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 6 Performance Results
### Average Energy Consumption  
Energy consumption is critical in industrial WSN. As stated in the introduction, our hypothesis is that RPE reduces energy consumption. The energy consumption is found from the current consumption, since the voltage is constant. Different WSN designs produce different average current consumption as topology influences energy consumption. The average accumulated current consumption is shown in Figure 15. The effects of packet replication are clear, as shown in Figure 15, Single Path utilizes 159 mAh less than second-place \(\tau=1624\). This illustrates the energy cost of replication in general.  
The effects of overprovisioning are seen with the highest accumulated current consumption at 1225 mAh. This increase is due to the extra cells, which causes more idle listening than the other scenarios.  
Another key point is that the RPE scenarios have a lower average current consumption compared to Dual Path. A decrease in average current consumption is important as this topology does not represent a complete network. In fact, real network data traverse from other paths as well. Hence, decreased current consumption decreases due to reduced load on certain nodes due to funneling effects.
# Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling
###### Abstract  
Signed network embedding concentrates on learning fixed-length representations for nodes in signed networks with positive and negative links, which contributes to many downstream tasks in social media, such as link prediction. However, most signed network embedding approaches neglect hierarchical graph pooling in the networks, limiting the capacity to learn genuine signed graph topology. To overcome this limitation, this paper presents a unique deep learning-based Signed network embedding model with Hierarchical Graph Pooling (SHGP). To be more explicit, a hierarchical pooling mechanism has been developed to encode the high-level features of the networks. Moreover, a graph convolution layer is introduced to aggregate both positive and negative information from neighbor nodes, and the concatenation of two parts generates the final embedding of the nodes. Extensive experiments on three large real-world signed network datasets demonstrate the effectiveness and excellence of the proposed method.  
signed network; network representation learning; balance theory; hierarchical graph pooling
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 1 Introduction  
The firewall is an access control device that separates internal and external networks, which are typically installed at their intersection points. The most common technology used in firewalls is packet filtering, which involves examining network packets, discarding those that do not comply with the settings, and allowing those that do to pass through. Intruders must first breach the firewall's security measures in order to gain access to the target computer. By implementing such measures, strict network security policies can be enforced to prevent unauthorized data access. However, despite advancements in new breakthrough technologies and emerging firewalls, they cannot completely eliminate an unauthorized user's ability to access computers and data [1].  
Sensitive and important data should be protected by more secure tools to ensure that internal network data are not leaked or compromised. Many organizations adopt air-gap isolation to improve security, which involves establishing no physical connection between the internal network containing crucial user data and the external Internet, as shown in Figure 1. These works effectively isolate important information from potential threats andsignificantly enhance security measures. Physical isolation is commonly implemented in government departments, security agencies, business operations planning units, vital scientific research divisions, and similar scenarios.  
In the past ten years, it has been proved that hackers' destructive behavior cannot be completely blocked by firewalls and air-gap isolation. They can still complete the destruction through sophisticated technology, and malware can still infect the organization's internal network. Computer users' intentional or unintentional network crossing behavior, as well as the pollution in the software and hardware supply chain, may introduce malicious programs, which may lead to the infection of the entire organization's intranet [2].
## 4 Results and Discussion
### Baseline Performance Analysis and Bottleneck Identification (Pre-Optimization)
#### 4.2.1 Downtime Analysis  
Figure 6 illustrates the monthly downtime trends. Monthly-aggregated downtime reveals a highly skewed distribution: the Production Line persistently accounts for the largest share of lost productive time, confirming its role as the system's critical bottleneck [7]. The Large Printing Unit follows as the second-largest source but displays sharper peaks, hinting at sporadic, event-driven stoppages that could be mitigated through targeted maintenance. Mid-tier assets Packing Unit, Extruder, Capping Machine, and Packaging Machine cluster far below the top two, indicating routine wearand-tear rather than systemic failure. The Small Printing Unit shows minimal downtime, suggesting either lower utilisation or better control. This profile mirrors observations in other discrete-manufacturing studies, where a single high-capacity line and one specialised machine dominate downtime while secondary equipment exerts only incremental influence [8]. Consequently, dedicating resources first to the Production Line and then to stabilising the Large Printing Unit should deliver the greatest uptime gains.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Caseid** & **Activity** & **Timestamp** & **Resource** & **Event Type** & **Value** \\ \hline Station\_207 & Sensor\_6\_Start & 1/4/2021 6:00 & S6\_207 & Start & 1 \\ Station\_205 & Sensor\_4\_Start & 1/4/2021 7:00 & S4\_205 & Start & 1 \\ Station\_207 & Sensor\_6\_Complete & 1/4/2021 7:00 & S6\_207 & Complete & 0 \\ Station\_203 & Sensor\_4\_Start & 1/4/2021 8:00 & S4\_203 & Start & 1 \\ Station\_207 & Sensor\_7\_Start & 1/4/2021 8:00 & S7\_207 & Start & 1 \\ Station\_202 & Sensor\_7\_Start & 1/4/2021 9:00 & S7\_202 & Start & 107 \\ Station\_203 & Sensor\_4\_Complete & 1/4/2021 9:00 & S4\_203 & Complete & 0 \\ Station\_205 & Sensor\_1\_Start & 1/4/2021 9:00 & S1\_205 & Start & 78 \\ Station\_205 & Sensor\_2\_Start & 1/4/2021 9:00 & S2\_205 & Start & 2 \\ Station\_205 & Sensor\_4\_Complete & 1/4/2021 9:00 & S4\_205 & Complete & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Event log (first 10 events).  
Figure 6: Monthly downtime trends by equipment type. The lines and markers highlight each month’s total downtime seconds. The Production Line (blue) consistently exhibits the highest downtime, while the Large Printing Unit (green) shows substantial but volatile peaks. Mid-tier machines Packing Unit (yellow), Extruder (red), Capping Machine (orange), Packaging Machine (brown) occupy the middle range, and the Small Printing Unit (purple) records the least downtime.
# Machine Learning Applications for Short Reach Optical Communication
## 2 Machine Learning for OPM and MFI in Short Reach Optical Communications
### Coherent Detection System
Furthermore, some of these studies are limited to single-channel systems [49; 101]. Nonlinear distortion will accumulate as the fiber transmission distance increases, which is usually manifested as the crosstalk between symbols in the time domain and the spectrum broadening in the frequency domain. A method based on the LSTM network to estimate OSNR and nonlinear noise power simultaneously by using the strong long-term dependency capturing and learning capabilities of LSTM is reported, which is capable of extracting the relationship between historical and current data [97]. The proposed LSTM network has two hidden layers, each of which has double LSTM memory cells, and each cell contains 15 nodes. The coherent detected signal is translated to the frequency domain by fast Fourier transform (FFT) and then fed to the LSTM network to account for the interdependence of frequency components in a signal with nonlinear distortion. The linear function is employed as the activation function in the output layer of the LSTM network to produce the estimated OSNR and nonlinear noise power. The 5-channel system is simulated through VPI software, and results show that the OSNR estimation error is less than 1.0 dB, with the reference OSNR ranging from 15 to 30 dB for QPSK, 16-QAM and 64-QAM signals [97].
# A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
The safety of the wearable antenna is ensured by evaluating SAR on the multi-layer cuboid phantom body model, which is within the safety limits as per the FCC and ICNIRP. To experimentally verify the behavior of the designed wearable antenna on the human body, its performance on different body parts such as the chest, arm, and wrist is checked. The antenna's performance in all the wearable conditions is found satisfactory as it covers all the frequency bands even after little detuning due to bending.
# Image Inpainting Based on Multi-Patch Match with Adaptive Size
## 4 Experimental Results
### Object Removal  
Object removal is another typical application of image inpainting, by replacing the unwanted object with a plausible background in an image. Figure 15 shows examples where we attempt to remove unwanted objects from the existing images by using our method. Different from damaged image restoration, this task does not have ground truth as the reference, and there also might be multiple possible filling solutions. Thus, the objective quality evaluation is no longer applicable, here, we only provide the visual results. Note that as the masked area increases, some artifacts may still occur, such as the unnatural water texture in Figure 15f, and the discontinuity of the curved edge of the lawn in Figure 15g.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Mean PSNR (dB)** & **Mean SSIM** & **Mean Running Time (s)** \\ \hline CDD [4] & 26.5482 & 0.9218 & 152.81 \\ [PERSON]’s [10] & 28.9099 & 0.9321 & 324.79 \\ Liu’s [17] & 28.4467 & 0.9290 & 229.21 \\ [PERSON]’s [18] & 27.5921 & 0.9193 & 591.42 \\ Proposed & 30.3950 & 0.9408 & 117.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average values in Figures 12–14.  
Figure 15: Examples of object removal using our method. Top row—original images; middle row—masks for the unwanted part; bottom row—results for object removal.
# Spatiotemporal Analysis of Urban Expansion in Beijing, China
## 3 Results
### Rationality of Urban Expansion in Beijing  
The elasticity coefficient of urban expansion, which describes the relationship between the rate of urban expansion and the rate of population growth, was used to measure the rationality of urban expansion. The China Urban Planning and Design Research Institute has analyzed the urbanization process over the years and concluded that a coefficient of elasticity for urban expansion of 1.12 was more reasonable [22]. According to Table 2, the elasticity coefficient of urban expansion in the study area from 1995 to 2000 was 1.67 times the reasonable coefficient. The elasticity coefficient of urban expansion in the research area from 2000 to 2005 was 1.83 times the reasonable coefficient. The elasticity coefficient of urban expansion in the research area from 2005 to 2010 was 0.22 times the reasonable coefficient. The elasticity coefficient of urban expansion in the study area from 2010 to 2015 was a reasonable value. The elasticity coefficient of urban expansion in the research area from 2015 to 2020 was 0.08 times the reasonable coefficient. From 1995 to 2005, the urban expansion rate in the research area was much greater than the population growth rate, and a large amount of land resources were converted to urban construction land, while the population had not yet begun to flow in. From 2005 to 2010, the urban expansion rate of the research area was lower than the population growth rate, which provided a large number of spatial resources for the development of the research area in the early stage. The population began to gradually flow in to accelerate the development of Beijing. The population growth rate from 2010 to 2015 matched the urban expansion rate, and the city was in a coordinated development stage. The urban expansion rate from 2015 to 2020 was much lower than the population growth rate, and a large volume of talent continued to flow into Beijing. However, the urban construction land area did not respond in a timely manner.  
Figure 6: Dynamic changes in the urban edge and center of gravity of Beijing from 1995 to 2020.
# Design of Man-Machine Synergic Lunar Coring Device and Its Coring Dynamic Analysis
###### Abstract  
The Moon is the closest extraterrestrial celestial body to the Earth. Sampling and analysis of lunar regolith or rocks can pave the way for the development and utilization of lunar resources. The acquisition of lunar regolith samples with original stratigraphic information by astronauts on the lunar surface is one of the essential missions in the manned lunar landing project. Therefore, to maintain the original stratigraphic information of the lunar samples during the sampling process while further improving the coring rate and sampling depth, a handheld dual-mode lunar regolith coring device is proposed in this paper. The device innovatively combines impact penetration and rotary drilling sampling, which allows the selection of a suitable sampling method according to the environment. In addition, this study designs a synergic coring device that can be operated by the astronaut and carried on the lander or rover based on the handheld coring device, which can ensure safe and stable coring mission. The mechanical analysis is carried out for the key properties in the coring device, the corresponding mechanical model is established, the structural parameters are optimally designed, and the performance analysis is carried out accordingly. Finally, the impact and drilling process of the coring device is simulated in explicit dynamics, and the results show that the optimized impact module can effectively penetrate the lunar rocks. The research work will provide technical reference and theoretical support for the design of human-machine synergic coring devices in manned lunar exploration missions.  
manned lunar landing; high-frequency impact; man-machine synergic; coring device +
Footnote †: journal: Applied Sciences
## 7 Results and Discussions
### Various Configurations Using Battery Technologies and Renewable Energy Sources  
For this study, models of three different lithium-ion battery configurations (\"PV/BMG/Li-Ion@50% DOD, PV/BMG/Li-Ion@70% DOD, and PV/BMG/Li-Ion@80% DOD\") have been created due to the versatility of lithium-ion batteries which can operate at various depths of discharge (DODs). Additionally, two lead-acid (LA) battery configurations, \"PV/BMG/LA@70% DOD and PV/BMG/LA@80% DOD\", have also been modeled in order to demonstrate the versatility of this technology. Additionally, the PV/BMG/Ni-Fe@80% DOD configuration was taken into account due to the fact that Ni-Fe batteries can operate at both @50% and @80% DOD with a 30-year or longer lifespan at both.  
Using an HPRALC-based scenario (Without DSM) as a starting point, six different topologies are evaluated and compared in order to find the optimal configuration for bringing electricity to the area under investigation. This optimal configuration is then further evaluated by taking into account the demand side management (With DSM) in the MPRAMC and LPRAHC-based scenarios. Through this process, the most cost-effective and practicable configuration can be determined and implemented.  
The Supplemental File includes information about the technical specifications and costs of this study's components, as well as the control parameters of the proposed algorithms.
## 5 Conclusions  
In the present study, a failure situation was investigated upon TKA, in a rapid screening, by combining the clinical evidences with confocal Raman spectroscopy, with high sensitivity and nondestructive measurements. The retrieved components of knee prosthesis (both titanium and UHMWPE component), after 10 years use, were jointly investigated with the assessment of the chemical composition of synovial fluid accumulated in the stem of the tibial component during implantation period. From the material point of view, the metallic component (titanium) did not show any structural modification, as revealed from the Raman spectra. Using confocal micro-Raman spectroscopy, the ten years aged polyethylene showed slight structural changes in terms of Raman signature. Additional changes were noted in the Raman spectroscopy of aged acrylic cement, based on the comparison with the data from pristine PMMA. Metal-plastic aging and the possible scenario of failure due to combined effect of mechanical and biochemical processes that may affect the longevity of knee prostheses are discussed and interpreted in the context of proper functioning and life span of knee prosthesis. The UHMWPE component presents an insignificant level of oxidation (OI \(<\) 1), while small modifications of crystallinity and amorphous phase were noticed, as demonstrated by spectral deconvolution and curve fitting procedure. By analyzing the acrylic cement debris and synovial fluid composition we may assume that lipidomic profile and third body wear are cumulative factors of failure in this case. The results are also supported by the clinical and radiographic examination, which evidenced a drastic deterioration of the surrounding tissue. The cement debris affected the biologic response, resulting in an osteolysis and finally an aseptic loosening.  
Conceptualization, C.T.H. and S.C.; methodology, S.C., S.C.P. and A.G.M.; software, S.C. and S.C.P.; validation, S.C.P., S.C. and F.B.; formal analysis, G.M.; investigation, C.T.H., S.C., S.C.P., A.G.M., F.B., G.M.; writing--original draft preparation, S.C., S.C.P. and G.M.; writing--review and editing, C.T.H. and S.C.; supervision, C.T.H. and S.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.