esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining Higher-Order Statistics and Array Techniques to Pick Low-Energy P-Seismic Arrivals
###### Abstract
This is due, on one side, to the low amplitude of the events they try to catch and, on the other side, to the data reduction that is involved in these techniques to identify beforehand some effective fingerprints that implicitly lead to an undersampling of the original signal and thus to a reduction in the resolution. Furthermore, in complicated geological settings or if multiple seismic phases reach a receiver at close times, automated systems may become ambiguous, making it difficult to precisely detect individual wave arrivals. Addressing these problems is critical for expanding the capabilities of autonomous seismic picking approaches and increasing their utility in diverse seismological applications. Here, we propose an approach that is able to provide reliable P-wave arrival times picking by combining single-station and multi-channel numerical techniques to identify the first seismic arrival, hereinafter also denoted as onsets. Specifically, on individual traces, we analyze signal segments to detect changes in statistical properties (e.g., variance and higher-order statistics) compared to the background noise. We fix in this way the onset of an earthquake at one receiver; eventually, these picks are validated by evaluating the consistency of these solutions within an array of receivers, grouping them by a clustering approach. The proposed technique requires two inputs: a list of earthquakes whose origin time is required and the seismograms of those earthquakes, recorded at one or multiple arrays. Our algorithm comprises parameters that need to be fixed based on the features of the specific dataset under analysis. In this study, we choose to use the phase arrival times obtained from the automated method proposed by [17] and adopted in [18] as an initial reference for calibrating our method. Our method is particularly well-suited as an extra module that can complement automatic workflows for earthquake detection (f.i., machine learning or amplitude-based approaches) with the goals of automatically discarding false earthquake detection and incorrect picks, refining any eventual existing onset and integrating the catalog with new reliable picks.
## 2 Materials and Methods
### Virtual Inertia Boundary Evaluation Based on RBF Neural Network
#### 2.1.4 Evaluation Process and Principles  
The virtual inertia boundary evaluation process based on RBFNN is shown in Figure 2, which includes database construction, offline training, and generalization applications. In this paper, a simulation model of the example system is established, and the expected disturbances are set in advance. A large amount of data is obtained through multiple simulations for the offline training of the RBFNN model. The input data of the RBFNN offline training database should include parameters closely related to the system's operating state and the available inertia of the system. Active power disturbance \(\Delta P\) directly reflects the power shortage of the system and directly affects the system frequency change; temperature and light intensity directly affect the output of the PV system; and the battery charge level directly affects the discharge capacity of ESS, which plays an important role in the system frequency recovery. In this paper, the active power disturbance \(\Delta P\) of the PV/ESS load, the temperature \(T\) of the PV system, the light intensity \(E\), and the battery charge level \(Q\) of ESS are taken as input variables; the maximum available virtual inertia of the system is used as the output variable of the offline training database, which can comprehensively evaluate the boundary of the available virtual inertia of the system after active disturbance occurs when there is a high amount of new energy access.
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 7 Conclusions  
Many activities, such as parameter evaluation, monitoring of undersea resources, and military operations planning, occur concurrently while investigating an underwater environment. Because the UWSN can only perform a limited number of tasks, the network's focus is on the capacity of the network's batteries. A number of routing protocols, including AODV, LAR1, OLSR, Fisheye, and STAR-LORA, are employed in this study in UWSN networks with a variety of deployment applications, including Telnet and Superframe. These protocols' performance is examined and contrasted. One of the many statistics measured was the amount of energy utilized during transmit, idle, and receive modes. During the receive mode in the Telnet deployment application, the STAR-LORA routing protocol consumed 0.1 mWh of energy, whereas the LAR1 routing protocol consumed 0.021 mWh of energy in the Superframe deployment application. LAR1 used 0.009 mWh of energy in transmit mode when running the Superframe deployment application, whereas STAR-LORA used 0.05 mWh of energy while running the Telnet deployment application. Both of these measurements were taken using a relatively comparable manner. The application Superframe outperforms the Telnet software in terms of performance. We have limited the study to only energy parameters, and in the future, we will extend our research to other parameters, such as the transmission delay, Percentage of Utilization, End to End delay, BER, throughput, etc.  
Conceptualization, M.N.A.W.; Methodology, K.S. and R.C.; Formal analysis, R.A.; Investigation, R.A.; Resources, G.P.; Writing--original draft, K.S. and R.C.; Writing--review & editing, M.N.A.W. and M.F.A.; Visualization, M.F.A.; Supervision, R.C.; Project administration, G.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 4 Results
### Interpretable Analysis  
Figures 15 and 16 illustrate the attention weights and feature attribution map between various features, respectively. These visuals provide an interpretative analysis of how the proposed model incorporates attention mechanisms through GAT. Figure 15 shows the attention weight distribution across two layers of a graph attention network (GAT). The nodes labeled 2, 3, 4, and 6 represent the corresponding monitoring points. The weights on the edges indicate the strength of the attention between these nodes. For example, in both layers, node 2 shows a strong connection to node 3 (weights of 0.53 and 0.5), while node 6 has weaker connections to the other nodes, such as 0.31 and 0.35. These weights reflect how the GAT assigns importance to different monitoring points when predicting wall deflection based on spatial relationships.  
Figure 16 represents a feature attribution map between various input features. The importance of input features is evaluated using integrated gradients. This method attributes feature importance by calculating the cumulative change in the model output as each feature transitions from a baseline to its actual value, offering a more reliable explanation of the deflection predictions. On the x-axis, features such as \"Wall deflection\" and \"Axial force\" are plotted, while the height of each bar shows the importance of each feature to the target feature. For \"Wall deflection 2\" and \"Wall deflection 8,\" as highlighted by the red circles, the most important features are their own lagged values from the previous step. For the other points, neighboring monitoring points play a more significant role. This demonstrates that wall deflection at a given location is influenced by multiple surrounding monitoring points, highlighting the need to consider spatial relationships between monitoring points  
Figure 14: Comparison of the prediction results between the proposed ST-GAT model and other alternative models.  
for accurate prediction. Axial forces affect different wall deflections to varying degrees, but their overall impact is substantial. This suggests the potential for developing a model that controls wall deflection by adjusting axial forces.
# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 3 Experimental Setup  
### Hardware Setup and Experimental Scenario  
In this paper, we use Texas Instruments IWR 1843 boost [29], operating at 78-81 GHz, which integrates four receiving antennas (RX) and three transmitting antennas (TX) to track multiple objects with distance and angle information, as shown in Figure 6. This antenna design can estimate the distance and the pitch angle, enabling object detection in a three-dimensional plane. The FMCW radar first transmits a linear FM signal from the TX antennas, and then the RX antennas capture the reflection of the linear FM signal generated by the object. After that, an IF signal is generated by an electronic component called a mixer [30]. The FMCW radar system estimates the angle of the reflected signal concerning the horizontal plane, also known as the angle of arrival. The angle estimation is obtained by the phase change in the peak of the Doppler Fast Fourier Transform due to a slight change in the object's distance. The radar point cloud data can be converted using the angle, distance, velocity, and signal-to-noise ratio information [31]. A CSV file of the point cloud is exported and contains the number of frames, the x-y-z coordinates, Doppler (velocity), and the signal-to-noise ratio.  
The radar board's power connector is powered up at startup and the data exchange interface is connected to the PC. In the configuration area, the distance resolution is set to 4.0 cm, the maximum detection range is 9 m, the maximum measurable radial velocity is 1.5 m/s, the radial velocity resolution is 0.13 m/s, and 15 data frames are collected per second. The radar height is set to 0.8 m, the distance between the experimenter and the radar is set to 4 m, and the distance between the two experimenters is set to 0.5 m, which is usually considered the average social distance. The experiment recorded the following five actions (punching, kicking, bowing, going from standing to crouching, and going from sitting to standing, as shown in Figure 7). Nine volunteers ranged from 155 cm to 185 cm in height and from 42 kg to 91 kg in weight. These volunteers are grouped in pairs and performed different actions facing the radar at an average period of 4 s. Eventually, 21,600 data frames are measured for each action, and 108,000 frames are captured for the five actions. Stacking 6 frames gave 3600 point cloud images for each action and 18,000 point cloud images for the 5 actions.  
Figure 6: IWR1843 boost.
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 10 Conclusions  
This work presents a novel solution for the privacy-preserving EU Digital Vaccine Certificates. Our solution solves the privacy and security shortcomings of the current solution such as ineffective certificate holder identification and a high violation of user privacy with the disclosure of sensitive information. Furthermore, our solution is in line with current EU legislation, i.e., GDPR and eIDAS 2.0 regulations. The core of our proposal is built on our novel attribute-based credential scheme, which can be easily implemented on various handheld devices, especially on Android smartphones and smartwatches. However, due to the lightweight nature of our scheme, it can also be implemented on constrained devices such as smart cards. Our experimental results show that even if we use certificates with a theoretical number of 500 attributes (which is a highly inflated value), the required time would reach less than 300 ms on current smartphones. However, implementation of our solution on a smart card platform is also possible. In the case of a certificate containing 20 attributes, the verification of completed vaccination would take approximately 3 s. This would allow European Health Insurance Cards to be used as a repository for privacy-protecting digital vaccine certifications.  
**Author Contributions:** Conceptualization, P.D.; methodology, P.D. and S.R.; software, P.D. and P.I.; validation, P.D., S.R., P.I., L.M. and C.A.-T.; formal analysis, P.D. and S.R.; investigation, P.D., S.R., P.I., L.M. and C.A.-T.; resources, P.D.; data curation, P.D. and P.I.; writing--original draft preparation, P.D., S.R., P.I. and L.M.; writing--review and editing, P.D., S.R., P.I., L.M. and C.A.-T.; visualization, P.D. and P.I.; supervision, P.D.; project administration, P.D.; funding acquisition, P.D., L.M. and C.A.-T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministry of the Interior of the Czech Republic under grant VJ03030014 (Development of International Collaboration in Cryptography and Cybersecurity Research). It is also supported by the PECT \"Cuidem el que ens uniex\" project, Operation Sensorica [PR15-020174], within the frame of the RIS3 CAT and ERDF Catalonia Operational Programme 2014-2020, co-financed by the Catalan Government and the Provincial Council of Tarragona; and by the Grant PID2021-125962 OB-C32 \"SECURING/DATA\" funded by MCIN/AEI/10.13039/501100011033 and by \"ERDF A way of making Europe\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures
## 2 Problem Formulation and Simulation Methodology
### 5G System Module  
The rate requirements set at each CN are to be met by 5G system options [16; 17; 18], hence, a generic 5G rate calculation module was used in order to associate SINR calculations at each CN with the maximum throughput offered by the system. As previously mentioned, the system module that was originally designed upon the LTE-A system's specifications [7] is now extended to consider 5G system options in FR2 bands, i.e., n257 in the mmWave range around the 28 GHz. The system module considers a wideband channel of 100 MHz with subcarrier spacing of 60 KHz, a combination that leads to 132 resource blocks [19]. The carrier aggregation technique was also considered with 16 New Radio (NR) carriers [20], the maximum allowable by the 5G NR Radio Access Technology (RAT). The total bandwidth increased to 1.6 GHz, while the total number of available RBs was 2112 (16 NR carriers \(\times\) 132 RBs per carrier). The module used the measured SINR at the CN side to estimate the required number of RBs to offer the capacity requirement.  
Figure 3 provides an insight on the rate offered from a 5G system as configured herein, when compared to a typical LTE-A system with 20 MHz of bandwidth and five component carriers leading to 100 MHz of aggregated bandwidth. Both systems were considered without MIMO spatial-multiplexing schemes since an eight-layer transmission can be supported by both.
## 2 Materials and Methods
### Selected AM Technology and Material  
The chosen AM technology in this study is the PBF-LB/M, one of the most often used techniques of the powder-bed fusion category for metals processing [49; 50]. It uses the thermal energy provided by a laser source to selectively melt and consolidate regions of a powder bed. This way it is possible to fabricate layer by layer full dense metallic components characterized by very complex geometries [26; 51].  
When considering small spacecraft constructions, material selection is critical. Physical qualities (density, thermal expansion, and radiation resistance) as well as mechanical properties (modulus, strength, and toughness) must be met. Aluminum alloys have been used in the aircraft industry since its commencement. Because of its low cost, light weight, high strength-to-weight ratio, and simplicity of manufacture, aluminum was the most widely used material in aircraft until recent developments in composite technologies [13]. For the specific case of this work, the choice of material was constrained by several factors, both technological and design. The need for lightness steered the choice toward light alloys; the specification that the structure also has a heat exchange function ruled out titanium alloys, focusing the choice on aluminum ones. Needing to have the highest possible Technology Readiness Level, the choice was oriented toward the AlSi10 Mg, processable with all industrial-grade metal PBF-LB/M machines. The chemical composition of the employed powder supplied by EOS is reported in Table 1.  
The particle size distribution is measured by the D50, D10 and D90 parameters which are 29.2 \(\upmu\)m, 16.4 \(\upmu\)m and 40.3 \(\upmu\)m, respectively.
# UV-Induced Aging in Thermochromic Pigment-Integrated Food-Grade Polymers: A Performance Assessment
## 3 Results
### Tensile Properties After UV Exposure
One-way ANOVA was conducted separately for each formulation (PP98/TP2 and PP92/TP8) to evaluate the statistical significance of changes in the tensile strain at break, the maximum tensile stress, and Young's modulus across 0, 500, and 1000 h of indoor UVA exposure (RS 559-934, 360 nm). The results showed a statistically significant effect of exposure duration on all three mechanical properties (\(p\) < 0.001). Post hoc Tukey HSD analysis confirmed that for PP92/TP8, all the timepoints differed significantly from one another (\(p\) < 0.05). To quantify the statistical reliability of the observed mechanical degradation, 95% confidence intervals (CIs) were calculated for each tensile strength value. For example, PP98/TP2 declined from 25.30 \(\pm\) 1.49 MPa (95% CI) at 500 h to 23.50 \(\pm\) 1.37 MPa at 1000 h under the RS 559-934 exposure. Under the QUV/Solar Eye exposure, the same blend exhibited greater degradation, dropping from 22.10 \(\pm\) 1.61 MPa to 20.70 \(\pm\) 1.24 MPa over the same period. The PP92/TP8 formulation displayed steeper losses, declining to 17.10 \(\pm\) 1.12 MPa at 1000 h under the QUV exposure. These confidence ranges confirm the increased susceptibility of PP92/TP8 to UVA-induced mechanical deterioration, which also  
Figure 7: (**a**) Tensile strain at break, (**b**) maximum tensile stress, and (**c**) Young’s modulus for thermochromic polypropylene blends PP98/TP2 and PP92/TP8 after 0, 500, and 1000 h of UVA exposure (RS 559-934, 360 nm). Error bars represent standard deviation (\(n\) = 5). supports the statistical significance of the performance differences across pigment loadings and exposure circumstances.
### Review Objectives  
This article aims to provide the reader with a summary of the trends and techniques used by the scientific community in the use of wearable technologies to acquire data and define digital biomarkers for motor symptoms, thus providing a review of the current status of the defined digital biomarkers.  
Recent reviews have investigated the potential of digital biomarkers in PD, providing useful insights from different perspectives. A summary of biomarkers (speech, gait, handwriting) with potential clinical applications in PD was provided in [22], with a particular focus on validation, regulatory approval, ethical, legal, and social aspects. In [23], the potential of biomarkers extracted from facial expression analysis and eye tracking were investigated for early diagnosis and assessment of cognitive decline. Nevertheless, to the best of our knowledge, no review studies have focused on digital biomarkers for motor symptoms of PD (DB-MS-PD) obtained through wearable devices, as well as their key features, encompassing sensor settings, experimental procedures, and data analysis methods.  
This study reviews relevant research articles published between 2012 and 2024, focusing on digital biomarkers defined for PD through motion analysis using data collected through wearable devices. In specific, the objective of this study is to provide a comprehensive overview of sensors' settings, experimental procedures and data analysis methods that allow the extraction of relevant DB-MS-PD. Ultimately, comparative analysis of various studies can facilitate the identification of promising and minimally invasive systems (both hardware and software) for the supervised and unsupervised assessment of PD.  
The remainder of the document is organized as follows. Section 2 presents an overview of the wearable devices and digital biomarkers. Section 3 describes the methodology used for the systematic review, including the search strategy used to find relevant articles. Section 4 presents the results of this literature review. Section 5 provides a discussion of the findings in the analyzed articles. Finally, the conclusions of this work are given in Section 6.
## 2 Modern Physical Layer
### Underwater Acoustic Channel Characterization  
#### 2.1.1 Attenuation and Noise  
The path loss of the signal can be modeled as Equation (1), where \(f\) is the signal frequency and \(l\) is the transmission distance. The path-loss component, \(k\), models the spreading loss and is usually between one and two. The absorption coefficient can be obtained using Thorp empirical equation, as described in [8], and is expressed in Equation (2).  
\[A(l,f)=l^{k}a(f)^{l} \tag{1}\]  
\[10\log a(f)=\frac{0.11f^{2}}{(1+f^{2})}+\frac{44f^{2}}{(4,100+f^{2})}+0.000275f ^{2}+0.0003 \tag{2}\]  
The ambient noise (\(N\)) is dependent on the deployment environment. For ocean environments, empirical formulas exist that model the noise from four sources: turbulence (\(N_{t}\)), shipping (\(N_{s}\)), waves (\(N_{w}\)) and thermal noise (\(N_{th}\)) [9]. Formula Equation (3) gives the power spectral density of the four noise components in decibels re\(\mu\) Pa per Hz as a function of frequency (\(f\)) in kHz. In addition, \(s\) is the shipping factor, which ranges from zero to one for low and high activities, respectively, and \(w\) is the wind speed in \(m/s\):  
\[\begin{array}{rl}10\log N_{t}(f)=&17-30 logf\\ 10\log N_{s}(f)=&40+20(s-0.5)+26\log(f)-60\log(f+0.03)\\ 10\log N_{w}(f)=&50+7.5w^{1/2}+20\log f-40\log(f+0.4)\\ 10\log N_{th}(f)=&-15+20\log f\\ N(f)=&N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f)\end{array} \tag{3}\]  
As introduced in previous works [3, 10], the overall effect of the transmission loss (\(A(l,f)\)) and the noise density (\(N(f)\)) are evaluated as the narrow-band signal-to-noise ratio (SNR) for different distances and different frequencies. The results shown in Figure 1 are useful for estimating the optimal frequency band, attending to the area that the network should cover. For example, larger areas should be covered using low-frequency acoustics (below 10 kHz, as expressed in [3]), but for medium or short distances in high-density networks, frequencies around 80 kHz are preferred.