esa-sceva/satcom-chunk-collection huggingface dataset:
# Hand Gesture Recognition on Edge Devices:
## 5 Pre-Processing/Signal Processing
### Phased Array/Beamforming  
The beamforming effect is caused by overlapping signals interfering constructively or destructively. This interference is either used to focus and steer an emitted beam or to determine the direction of arrival. For ultrasound HGR the far field assumption applies. Most cases rely on delay and sum (DaS) beamforming but MVDR is also possible. Figures 8-11 show the simulated steering capability of a linear array of four and eight ultrasound transducers at 0\({}^{\circ}\) and 45\({}^{\circ}\) with DaS beamforming. The steering capability is dependent on the number of transducers in the array, as Figures 8 and 10 show. The fewer tranducers are used the wider the main beam.  
Figure 7: Spectrogram (in radar often called micro-doppler), calculated by 1D Fourier transform in fast-time direction, over the entire sample in a frequency band around the sent single frequency.  
For DaS beamsteering, the direction of a beam is determined by delaying the signal of the senders such that the signal positively interferes in the desired direction while other directions, especially surrounding the desired direction, are locations of destructive interference such that the actual signal is only sent in the desired direction. Through constructive interference the signal is multiplied which leads to higher possible distances and stronger received echoes. A drawback of this method is, that a smaller area can be evaluated one at a time. If beamforming is applied on the receiver side, the signals of the senders are not received by the signal.  
Figure 8: 4 ultrasound sender at \(\frac{\lambda}{2}\) spacing, 24.5 kHz frequency, 0\({}^{\circ}\) steering.  
Figure 10: 8 ultrasound sender at \(\frac{\lambda}{2}\) spacing, 24.5 kHz frequency, 0\({}^{\circ}\) steering.  
Figure 9: 4 ultrasound sender at \(\frac{\lambda}{2}\) spacing, 24.5 kHz frequency, 45\({}^{\circ}\) steering.  
several receivers are shifted in time and summed to cause the same effect. Thanks to parallel and multi core processing many directions can be evaluated at the same time. DaS beamforming is a form of fixed beamforming. It has no feedback loop to take the current environment into account. MVDR beamforming by contrast is an adaptive beamforming method. That means it depends on weights that are adjusted depending on the noise environment. If unwanted noise is detected weights that correspond to that directions are lowered to disregard the information from that direction in further processing of the signal.
## 1 Introduction
### SAR Interferometry  
SAR interferometry uses two SAR acquisitions recorded at slightly different positions [28, 29, 30, 31, 32]. The phase \(\phi\) of the chromatic radar wave is directly proportional to the \(2R\) travel way of the signal (double travel path from the sensor to the ground and back). Both sensors receive slightly different phase signals as the distance from the second SAR sensor to the object on the ground is by the amount of \(\Delta R\) larger than the corresponding distance from the first SAR (Figure 2):  
\[\phi_{1}=-\frac{4\pi}{\lambda}\,R_{s}\,+\,\phi_{scatt\,1} \tag{1}\]  
\[\phi_{2}=-\frac{4\pi}{\lambda}\,(R_{s}\,+\,\Delta R)\,+\,\phi_{scatt\,2} \tag{2}\]  
\(\Delta R\) depends on the perpendicular baseline \(B\bot\) between the two SAR sensors and on the angle \(\varphi\), which is the angle between the absolute and perpendicular baseline (Figure 2):  
\[\Delta R=B_{\bot}\,\tan\varphi \tag{3}\]  
Figure 1: **First row**: Intact building (**left**) and destroyed building with roof still intact (**right**); **Second row**: Using optical imagery it is very difficult to distinguish between the intact and the destroyed building with still intact roof (especially in the near nadir region); **Third row**: Interferometric analysis uses the phase difference between the pre- and post-disaster SAR acquisition enabling the distinction between both buildings.  
Assuming constant backscattering properties at both acquisition times (\(\phi_{scart1}=\phi_{scart2}\)), the interferometric phase \(\phi_{int}\) is:  
\[\phi_{int}=\phi_{1}-\phi_{2}=\frac{4\pi}{\lambda}\Delta R \tag{4}\]  
The temporal baseline \(B_{t}\), the time delay between both SAR acquisitions, is an important factor for successful InSAR applications, as with increasing of \(B_{t}\) the probability of backscattering property changes due to wind induced movement of trees or growth of vegetation increases [33].  
Besides the use for damage assessment--which will be described in detail in Section 2--SAR interferometry (InSAR) is used for two main applications: (I) generation of digital elevation models (DEM) of the Earth's surface [28, 29, 30, 31, 32] and (II) measurement and analysis of terrain motion (differential SAR interferometry--DInSAR). For DEM generation \(\phi_{int}\) is used to derive the terrain height of the object on the ground as represented in Figure 2. DInSAR measures the deformation in the time between the SAR acquisitions with sub-centimeter accuracy [34, 35, 36, 37]. It has been applied to study earthquakes [38, 39, 40, 41], volcanoes [42, 43, 44], subsidence [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55], landslides [54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), soil consolidation [70], and tectonic deformation [71, 72].
# A Multi-Domain Model for Variable Gap Iron-Cored Wireless Power Transmission System
###### Abstract  
Wireless power transfer (WPT) devices represent one of the most efficient and increasingly used technologies for the transfer of data and power in the near-field range. This work analyzes and describes a new type of device: a ferrite-cored, variable gap, high-frequency power and data transfer system. The classic theoretical models existing in the literature for near-field communication (NFC) and WPT devices have foreseen a lumped-parameters characterization based on the representation of an equivalent circuit model (ECM). The strict interdependence between the different physical domains has clearly increased the difficulty in predicting the behavior of the device, due to the unwanted continuous and chaotic variation of the parameters. The proposed paper aims to provide a general and reliable multi-physics model based on the co-simulation of a Spice(r)-based ECM analysis and the ESRF Radia(r)-based 3D finite volume methodology (3 DFVM), placing particular emphasis on the intrinsic sensitivity with respect to variables that cannot be directly controlled, such as the variation of the air gap between the coupled coils interfaces. Furthermore, this work outlines a detailed and effective experimental methodology for the estimation of static and dynamic electromagnetic parameters and the validation of the numerical models in both the time and frequency domain, through the analysis of a real coupled WPT device.  
wireless power transfer; coupled inductors; SPICE; multi-physics; co-simulation; cored inductances; variable air gap; analog electronics; Radia
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 2 Background Theory
### Wave Formation
#### 2.1.2. Defect  
In cases (d)-(g) of Figure 1, the received waves is generated from a non-axisymmetric defect with a cross-sectional area size of 3%. The shown signals are different sequences from various times with respect to the temporal domain signal. The capture times are shown in Figure 2b where the red line is representing case (d), the blue line is representing case (e), and the green line is representing case (f). Case (g) is captured at the same time as case (e), but the generated wave is from a defect located at 0\({}^{\circ}\) (the right side of the pipe), while the other cases were from a defect angled at 90\({}^{\circ}\) (top side). Four important points are illustrated from the figures:  
1. Unlike the pipe end, defects that are non-axisymmetric features will inherently generate flexural waves. Nonetheless, the strongest energy will be associated with the T(0,1) wave.
2. The end of the signal envelope will contain more flexural waves compared to the start of the envelope. At the start of the envelope, at most one cyclic variation will exist in the spatial domain, which would represent a low-order flexural wave mode. Nonetheless, near the end of the envelope, higher order variations (three or more) are starting to increase. This is because after the center of the envelope, the power of the T(0,1) wave is starting to decrease, and the higher order flexural with a lower travel speed is beginning to arrive.
3. The strongest energy of the T(0,1) wave is associated with the peak of each cycle in the temporal domain, where most of the transducers will have the same phase.
4. When the angle of the defect is changed, the direction of the arrival changes, but the phases and the overall formation of the wave remain the same.  
Figure 2: Temporal domain of cases (d)–(i) from Figure 1
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
## 3 Problem Definition
\begin{table}
\begin{tabular}{l l} \hline
**Not.** & **Definition and Description** \\ \(G=(V,E)\) & Unweighted undirected graph, with \(V\) as nodes and \(E\) as edges. \\ \(N[u]\) & Structural neighbors of node \(u\), including \(u\) itself. \\ \(\deg[u]\) & Degree of node \(u\), i.e., the size of \(N[u]\). \\ \(N(u)\) & Open neighborhood of \(u\), i.e., its direct neighbors. \\ \(\sigma(u,v)\) & Structural similarity between nodes \(u\) and \(v\). \\ \(\mu_{uv}\) & Relative similarity between \(u\) and its neighbor \(v\). \\ \(\rho_{u}\) & Local density of \(u\), summing \(\sigma(u,v)\) values under a normal distribution. \\ \(\delta_{u}\) & Dependency similarity of \(u\), the max similarity to neighbors with higher density. \\ DirREACH\((u,v)\) & Direct density reachability from \(u\) to \(v\) if \(u\) is a peak. \\ REACH\((u,v)\) & Density reachability if there is a path of peaks between \(u\) and \(v\). \\ \hline \end{tabular}
\end{table} 
Table 1: The Summary Of Key Notations. **Definition 5** (Direct Density Reachability). : _If a vertex is a density peak, then it is directly density-reachable from all its structural neighbors. For instance, if vertex \(u\) is a density peak and vertex \(v\) is a structural neighbor of vertex \(u\), then vertex \(u\) is directly density-reachable to vertex \(v\), which is formally defined as follows:_  
\[\text{DirREACH}(u,v)\Leftrightarrow\text{DensityPeak}(u)\wedge v\in N(u) \tag{4}\]  
For example, in Figure 0(b), node \(u\) is a density peak, and node \(u\) is directly density-reachable to all its first-order neighbors. **Definition 6** (Density Reachability). : _If there exists a path between two vertices such that every vertex along the path is a density peak, then these two vertices are considered density-reachable. Vertices that are density-reachable are grouped into the same cluster. For instance, if vertex \(u\) is a density peak and there is a path from vertex \(u\) to vertex \(v\) where every vertex on the path is a density peak, then vertex \(u\) is density-reachable to vertex \(v\), which is formally defined as follows:_  
\[REACH(u,v)\Leftrightarrow\exists\,path\,P(u\to v):\forall w\in P,\text{Density Peak}(w) \tag{5}\]  
Here, \(w\) represents each vertex \(w\) along the path \(P(u\to v)\), emphasizing that each of these vertices is a density peak. For example, in Figure 0(c), there is a path from \(u\) to \(v_{1}\) that passes through \(w_{1}\) and \(w_{2}\), where \(u\), \(w_{1}\), \(w_{2}\), and \(v_{1}\) are all density peaks. Therefore, \(u\) is density-reachable to \(v_{1}\).
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 4 Security Evaluation of the Proposed Methodology
### Informal Analysis
#### 4.1.5 Sensor Node Impersonation Attack (IA)  
Suppose an attacker \(\mathcal{A}\) appears to be trying to portray a communication on behalf of a \(SN\) to a trusted authority \(TA\). \(\mathcal{A}\) attains \(\{PR_{ID_{n}},SN_{ID_{n}},SKEY_{TA,n}\}\) from the sensor node memory and \(\langle RND_{3}^{\prime},AUTH_{n},PR_{ID_{n}},T_{SN}\rangle\) while communicating. At this instant, \(\mathcal{A}\) attempts to create a response but cannot, since it is unaware of these variables \(V_{temp1},R_{3}\), and \(RND_{3}\); therefore, it is difficult for the adversary to manufacture them. Similarly, trusted authority and server impersonation are impossible due to the secret parameters.
# Experimental Study of Perfobond Rib Shear Connector under Lateral Force
## 1 Introduction  
Steel-concrete composite girders are widely used because of their superior durability and accelerated construction time. The design and analysis of the composite girders are closely dependent on the composite action through the steel and concrete components. Therefore, the capacity and behavior of the shear connection become essential in composite girders. In the composite girder, slip between the concrete top and steel girder is almost unavoidable. However, repeated and excessive slip must be prevented, as it may not only cause the fatigue at the end of the shear connector itself, but also tear up the concrete around the shear connector and lead to connection issues.  
The Perfobond rib (Perfobond Leiste in German [1]) shear connector represents a type of shear connector that is perforated and reinforced with steel bars to utilize the dowel action to provide more reliable shear resistance than the traditional stud shear connectors in composite construction. The Perfobond rib shear connector was introduced in 1987 by a German consulting firm as the result of the construction of the 3 rd Caroni River Bridge in Venezuela [1]. The Caroni River Bridge was designed to take a heavy live load and that live load over time may result in connection fatigue issues if traditional shear studs were to be used. However, the Perfobond rib shear connector serves as an option because it has advantages over the traditional shear studs of very minimal slip under service condition and being able to maintain the connection between the slip and the live load under the associated fatigue threshold.  
For steel-concrete composite beam bridges, the distribution length of the shear connectors at the steel-concrete connection is relatively large, and the end resistance of the concrete is relatively small, which can be ignored. The main feature of the steel-concretecomposite beam bridge is that transverse prestressed steel bars are set in the concrete deck. The pre-compression stress of the transverse prestressed steel bars will cause the concrete around the shear connectors to be under compressive stress. The confinement restraint will increase the friction between the steel plate and the concrete, so it is necessary to consider the influence of the confinement restraint caused by the transverse prestress on the bearing capacity of the shear connector. In addition, under the working load of the composite beam bridge, the concrete around the shear connectors may be under tensile stress. This article comprehensively considers the mechanical characteristics of the steel-concrete composite beam bridge, and analyzes the bearing capacity and the mechanical mechanism of the shear connector considering the transverse prestressed effect.
# Evaluation of the Mid-Latitude Ionospheric trough Using GRACE Data
## 3 Results and Discussion
### Dependence on the Hp30 Index  
The Hp30 index is a high-cadence index that is similar to the Kp index, although its resolution is 30 min [25]. The Kp index has been used in several MIT model approaches, for example, by [PERSON] and [PERSON] (2018) [29] and [PERSON] et al. (2016) [26]. We examined the dependence on a high-cadence Hp30 index of trough minimum location, width, and depth. The results are presented in Figure 19. The trough moves equatorward with an increasing Hp30 index. In the northern hemisphere, this dependence is more prominent than in the southern hemisphere. Furthermore, the northern hemisphere has pronounced dependence on the Hp30 index for trough width. The trough widens with a decreasing Hp30 index. The depth has an opposite dependence, i.e., trough deepens with an increasing Hp30 index.  
The observed correlation between the geomagnetic activity level and the trough position is in agreement with results of previous studies [9; 16]. This relationship can be partly explained by storm time subauroral polarization streams (SAPS) or subauroral ion drift (SAID) flow channels, which also clearly depend on geomagnetic activity levels [18].
# Autonomous Environment Generator for UAV-Based Simulation
## 3 Experimental Setup
### UAV Design  
This section will describe the HEIFU hardware used in this paper to represent the real UAV in the Gazebo simulation. HEIFU is a custom-made solution aimed at the surveillance sector. The HEIFU is equipped with an onboard computer running Ubuntu and Robot Operating System (ROS). This is an open platform that allows the integration of different inputs and runs multiple tasks, such as image processing, data relaying, and remote control of a drone. HEIFU can be used with different communication systems, such as a mobile network or Wi-Fi connection. HEIFU is a Hexacopter, as can be seen in Figure 7, with a diagonal wheelbase of 1.4 m. The drone weight is around 6.2 kg, including battery, and the hovering time is about 30 min with a battery of 22 Ah.  
The following describes additional HEIFU specifications:  
* Pixhawk hardware (running ArduCopter) is used to control low-level operation; this hardware contains the IMU and GPS to provide the UAV's position and orientation; the Pixhawk connects to the Jetson Nano embedded system via a MAVlink protocol and the UAV's battery; lastly, to control the UAV's motors it is connected to a UHF receiver;
* a Jetson Nano is used to control the high-level operation; it receives data from the distance sensor (depth cameras), the RGB camera, and communicates with external devices via a Wi-Fi or 4G link;
* an RGB camera with a gimbal stabilizer is installed capturing onboard images at a resolution of \(1920\times 1080\) pixels; and
* the camera and lens specifications are known, allowing the field of view (FOV) and the pixel size in meters to be computed.  
Figure 6: Close-up of drone simulation over Las Vegas, Nevada.
## 3 Distributed Cognition-Based Data Analysis and Results
### The Test Result of Data Normality  
According to the results of the normality test presented in Table 3 (n = 500), the mean values (M) of all variables range from 3.281 to 3.866, suggesting that users generally exhibit a positive attitude toward digital interactive installations in museums (mean value \(>\) 3). The standard deviation (SD) varies between 0.972 and 1.339, indicating a moderate level of data dispersion. Notably, the variable \"satisfaction (SA2)\" demonstrates the highest fluctuation (SD = 1.339).  
The Skewness values fall within the interval of \(-\)1.122 to \(-\)0.243, all of which are negative, implying left-skewed distributions and suggesting that user ratings tend to cluster at higher levels. Additionally, the absolute values of Kurtosis range from \(-\)0.862 to 0.406, all of which are less than 1, satisfying the criterion for approximate normal distribution (\(\mid\)Kurtosis \(\mid\) < 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Attribute** & **Classification** & **Number of People** & **Percentage \%** \\ \hline \multirow{2}{*}{Gender} & Male & 256 & 51.2 \\  & Female & 244 & 48.8 \\ \hline \multirow{4}{*}{Age} & Under 18 years old & 28 & 5.6 \\  & 18–24 years old & 32 & 6.4 \\  & 25–34 years old & 224 & 44.8 \\  & 35–44 years old & 146 & 29.2 \\  & 45–54 years old & 47 & 9.4 \\  & 55 years old and above & 23 & 4.6 \\ \hline \multirow{4}{*}{Occupation} & Student & 58 & 11.6 \\  & Freelancer & 39 & 7.8 \\ \cline{1-1}  & Enterpyine employee & 345 & 49 \\ \cline{1-1}  & Government/Institution personnel & 45 & 9 \\ \cline{1-1}  & Others & 13 & 2.6 \\ \hline \multirow{2}{*}{Duration of stay inside the Capital Museum} & Within 1 h & 1 & 23.6 \\ \cline{1-1}  & 2 h & 170 & 34 \\ \cline{1-1}  & More than 4 h & 167 & 33.4 \\ \cline{1-1}  & Others & 45 & 9 \\ \hline \end{tabular}
\end{table}
Table 2: Descriptive Statistical Analysis of Basic Information of Survey Respondents.  
According to [PERSON]'s (2015) multivariate normality assessment criteria (absolute skewness \(<\) 3, absolute kurtosis \(<\) 10) [35], all the measurement indicators in this study have skewness and kurtosis significantly lower than the critical threshold (\(\,\)I Skewness \(\mid\)\(\,\)\(<\) 1.12, \(\mid\) Kurtosis \(\mid\)\(\,\)\(<\) 0.86), indicating that the data distribution conforms to the multivariate normality assumption and supports the applicability of subsequent parametric statistical analysis. Specifically, the skewness of each item under the latent variables tends to be symmetrically distributed (\(\,\)I Skewness \(\mid\)\(\,\)\(\leq\) 1.21), and the kurtosis values do not show extreme peak or low peak characteristics (\(\,\)I Kurtosis \(\mid\)\(\,\)\(\leq\) 0.86), further verifying the statistical robustness of the measurement model. Therefore, the data meet the basic normality assumption of BSEM and are suitable for further analysis.
# Dynamic Quality Monitoring System to Assess the Quality of Asphal Concrete Payement
## 2 Development of the Dynamic Monitoring System for Asphalt Concrete  
### _Structure Design of Information Monitoring System_  
The dynamic monitoring system for asphalt concrete pavement quality is composed of four subsystems, namely: Asphalt mixture production (1), transportation (2), paving (3) and rolling (4), which collectively integrates to form the whole process of asphalt mixture production and construction. The key parameters for the asphalt mixture mixing station, raw material transport vehicle, asphalt mixture paver and roller are extracted and collected through the Internet of Things, intelligent sensing and BDS systems. Compared with GPS, which can only accept location information, BDS can realize two-way communication and therefore offers significant benefits [19]. Table 1 provides details on the key monitoring contents of the digital monitoring system. The construction quality data is collected on site and is transmitted to the cloud data server in real time through 5G wireless network transmission technology for storage and management. Through real-time and dynamic analysis of the collected parameters, the system feeds back the abnormal data to the managers in the form of an SMS alarm [20; 21]. The overall architecture for pavement construction quality digital monitoring system is shown in Figure 1.