esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Selected Elements of IoT System Architecture
### Edge and Fog Computing  
Today, the amount of data that are generated by IoT systems and then transmitted over the network is increasing at a very high rate. Therefore, it is important to use the throughput of IoT systems as efficiently as possible. The concepts of edge computing and fog computing [53] can be used to optimize this problem. One of the main features of edge computing is the clear separation of the physical devices that make up the edge infrastructure from the core network devices [54]. Edge devices can perform data preprocessing functions at the network edges, which significantly improves the ability to maintain the required transmission time parameters in the context of real-time systems. If the tasks performed by edge devices are subject to time constraints, then these devices are implemented as real-time embedded systems enabling real-time data processing [55; 56]. Therefore, it is becoming increasingly desirable to use edge computing for smart data processing and performing specific optimization tasks [57; 58]. The partial offloading of centralized cloud systems is also possible through fog computing mechanisms [59]. Solutions of this class can perform services by computation at the level of a distributed, decentralized network infrastructure (e.g., using local IoT networks/systems) [60]. Fog computing and edge computing also provide some opportunity to apply function virtualization techniques on a massive scale [61; 62; 63; 64; 65], which consequently reduces the demand for resources and computing power of a data center. Both of the solutions under consideration significantly reduce the amount of data that must be transmitted and processed by the core network/server infrastructure, thus relieving the burden on traditional cloud computing and data center systems. If the target resources are less loaded, then it is easier to meet the assumptions that guarantee throughput and latency at levels appropriate for real-time solutions [66]. The primary purpose of edge infrastructure and the use of distributed computing is to solve a number of critical tasks affecting the latency, throughput, energy consumption, and scalability of IoT systems [67]. Moreover, these solutions allow for a high degree of autonomy in managing the optimal configuration of a system [68; 69] and increasing the level of security [70]. All of the above aspects favorably affect the ability to implement IoT services with time requirements.
# A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks
## 3 Cluster-Based Channel Assignment  
### AIIM Technique  
In this work, we propose a heuristic channel assignment technique based in the proposal made in [27] (from now on, we will call this technique AIIM). In the following, we first describe the original proposal in [27] and then we describe our proposal.  
AIIM is a network-controlled channel assignment technique based on a central coordinator, so it can be considered a heuristic technique useful for coordinated settings, as the one we are using in Section 4.1.  
The final objective of AIIM is to maximize the utility of the AP with the least utility. Note that we are using the definition of utility described in this manuscript. AIIM starts by selecting a random AP and assigning the channel with the best utility (breaking ties randomly), and this process is repeated for all the APs. After this first assignment, we obtain what is called the temporary channel assignment. After this, we compute the utility of this temporary channel assignment, called \(S\). The next step of the procedure is to select APs with utility lower than 1 and try to assign them the respective channels with the highest values for utility that do not degrade \(S\), i.e., without degrading the sum of utilities gotten so far. This process is repeated for all the APs \(R\) times.
## 3 The Proposed Downloading Scheme
### Iterating the Algorithm  
After finishing a round of computations in the preceding sections, it is necessary to update and iterate the network topology because the link status changes, as shown in Figure 5. In order to reduce the iteration rounds and speed up the convergence speed, if the satellites in the \(V_{I}\) set \(l_{I}<\alpha\), then the satellites exit the set \(V_{I}\), changing from a red node to a white node. If the satellites in the \(V_{O}\) set \(l_{O}=0\), they will also exit the set \(V_{O}\), changing from a blue node to a white node. The rest of the nodes of set \(V_{I}\) and \(V_{O}\) repeat the preceding calculation procedure until the number of nodes in set \(V_{O}\) or \(V_{I}\) is NULL, that means, the feeder resources are completely filled and the throughput reaches its maximum. The algorithm terminates the iterative process until the next time slot \(\tau\), at which point it will begin again.  
The flow chart of this algorithm is shown in Algorithm 1.  
Figure 5: Schematic diagram of the network iteration.  
```
1 while\(t=k\tau\)do
2 for\(I_{n}\in E\)do
3 Calculate \(l_{ij},b_{i},L_{i,j},B_{i}\);
4 end for
5 for\(v_{i}\in V_{ES}\)do
6\(l_{I}=\min\{B_{i}-b_{i},\Sigma(L_{ij}-l_{ji})\}\);
7\(l_{O}=\min\{b_{i}-B_{i},\Sigma(L_{ij}-l_{ij})\}\);
8\(D(i,j)=\min(l_{O},l_{I})\);
9 Get the demand traffic matrix \(D\);
10 end for
11 for\(D_{c}\in D\)do
12 Determine the subgraph constrain;
13 Calculate matrix \(R\) by solving two LPs with the constrain;
14 Base matrix \(R\) to update \(l_{ij},b_{i}\);
15 end for
16 for\(v_{i}\in V_{I}\cup V_{O}\)do
17 if\(l_{O}\leq\alpha\)then
18  Delate \(v_{i}\) from \(V_{O}\);
19 end for
20 if\(l_{I}=0\)then
21  Delate \(v_{i}\) from \(V_{I}\);
22 end if
23
24 end for
25
26 end for
27 Until \(V_{I}=NULL\) or \(V_{O}=NULL\);
```  
**Algorithm 1**Downloading Algorithm
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures  
Research on AE source localization in planar materials was initiated as early as the last century. AE source localization for 2D planar surfaces is often applied in the aerospace industry for wing fuselage damage detection, and damage localization in civil and marine structures. Nowadays, acoustic emission damage localization is applied to many industries, such as the construction industry, geotechnical industry and materials industry. The main methods for 2D sound source localization (Figure 2) can be classified into the triangulation method, beam forming, strain rosette technique, modal AE (MAE), artificial neural networks, optimization and the time reversal technique. Many proposed new positioning methods are based on the above methods.
# Audio-Visual Tensor Fusion Network for Piano Player Posture Classification
## 3 Proposed Audio-Visual Tensor Fusion Network
### Feature Extraction  
The feature extraction step for visual information proceeds as follows. The visual information of the skeleton is extracted using OpenPose, which enables us to extract skeleton information of the human body. According to previous research on piano playing, this study focused on the joints that should be viewed while playing piano rather than extracting all joints. [PERSON] noted that the angle of the head and neck is related to the injury posture [20]. In [21], the angle of the head and neck is measured to analyze the level of improvement in piano performance. The authors of [5; 22; 23; 24] measured the coordinates, angles, and silhouettes of the shoulders, spine, elbows, wrists, and fingers. The experimental results of [25] demonstrated that the pinky movements of amateur and professional pianists differed. Hence, this study used videos of pianists playing piano filmed from the right side  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Measure** \\ \hline Number of amateur pianists & 8 \\ Number of professional pianists & 5 \\ Number of music piece & 17 \\ Total sequence & 11,850 \\ Total length & 1506 s \\ Mean clip length & 4.18 s \\ Min clip length & 1 s \\ Max clip length & 14 s \\ Frame rate & 29.97 fps \\ Audio & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of Classic Piano Performance Postures of Amateur and Professionals (C3 Pap) dataset.  
Figure 5: Advantage of the C3 Pap dataset over existing datasets [5; 11; 12; 13].  
to observe the joints that are important in playing the piano. There is a total of 10 skeleton types to extract, including the head, neck, right shoulder, right elbow, right wrist, and four joints of the right pinky. Using OpenPose, the two-dimensional position information of each joint, and the attribute values of each coordinate can be extracted [18].  
The feature extraction step for audio information proceeds as follows. After extracting the audio from the videos, we used the MFCC technique to extract the features of the audio information. MFCC is a state-of-the-art technique for extracting features from speech and is utilized in a variety of speech processing fields, such as instrument classification and speech classification. In the context of the proposed method, MFCC divides the audio information into certain sections and analyzes the spectrum for each section to extract the features. Once features for audio information are extracted, these are normalized to a value between \(-1\) and \(1\).
## 4 Results and Discussion
### Revealed Preferences: Nighttime RP Usage and Perceptions  
Survey-based insights into actual use patterns of the local RP nighttime service indicate currently low adoption rates, as illustrated in Figure 3. Among all respondents, who have ever used the RP service fips (n = 228), only 40% (n = 91) had used the service during nighttime. Of these, 15.4% reported using the service nearly every weekend, another 15.4% used it 2-3 times per month, and 27.5% had tried it only once.  
Since the nighttime service is not widely used, it is important to understand the barriers to its adoption (see Figure 4). Among both daytime-only RP users (n = 138) and non-users with RP awareness (n = 271), the most frequently cited reason for non-use was simply not traveling at night (67% and 55%, respectively). However, 24% of daytime-only users and over 40% of non-users reported being unaware of the nighttime service, indicating substantial information deficits. Additionally, RP non-users show a significantly stronger preference for using the private car compared to daytime-only RP users, aligning with findings by [PERSON] [24] and [PERSON] et al. [25], who documented increased nighttime car usage. Furthermore, operational barriers such as limited service areas ( 20% of RP non-users) and failed booking attempts (8% of RP daytime-only users) point to functional shortcomings or a limited number of operating vehicles during nighttime. Notably, safety concerns were rarely reported as a barrier to nighttime RP use. Taken together, these findings suggest that addressing both informational and operational barriers offers considerable potential to increase awareness and, ultimately, adoption of RP nighttime services.  
The majority of nighttime RP trips (70%) were for returning home, often following leisure-related activities (21%). Only a minority used the service for accessing or leaving PT stations (4%) or for other purposes (4%), as depicted in Figure 5. This usage pattern underscores the critical role of flexible demand-responsive services in bridging accessibility gaps during off-peak hours, especially when traditional PT networks operate at reduced frequencies and limited coverage.  
Figure 4: Reasons for the previous non-use of the RP nighttime service (n = 138).  
Figure 3: Frequency of usage of the RP nighttime service by all RP users (n = 228).  
The analysis of modal combinations shows that many users combine RP with PT: 44% used PT (tram/train or bus) for either the outbound or return trip while using RP for the other. Additionally, 30% used RP for both directions, highlighting its key role in complementing PT, especially during off-peak hours. Figure 6 illustrates the transport modes used before and after nighttime RP trips.  
Overall, the analysis of revealed preferences indicates low current usage of the service during nighttime hours, primarily due to low awareness among potential users. However, the service is particularly valued for convenient return trips. Enhancing user awareness and expanding service coverage could significantly improve nighttime mobility.
## 4 Designing Sensors for Soft Robots
### Modelling
The centre of the circle assumed by the constant-curvature bend is at point \(c_{i}\) (**B**) The planes, \(p_{1}p_{2}p_{3}\) and \(q_{1}q_{2}q_{3}\), form a pentahedron from which the bending radius and the centre point \(c\) can be computed; (**C**) The coordinates of the three conduits at the base of the flexible module; points \(p_{1}\), \(p_{2}\) and \(p_{3}\) are assumed to be located in the centre of each conduit [38]. Defining \(n_{2}=[n_{x},\,n_{y},\,n_{z}]^{T}\), and introducing angle \(\Delta\theta\) as the angle between the two planes and angle \(\beta\) as the angle that describes by how much the flexible module is turned away from the \(x\)-axis, it follows that  
\[ \begin{array}{c}\Delta\theta=\cos^{-1}(n_{1}^{T}n_{2})\\ \beta=tan2^{-1}(\frac{n_{y}}{n_{z}})\end{array}  \tag{3}\]  
Referring to Figure 10B, points \(p_{0},\,q_{0}\) and \(c\) define a triangle whose parameters can be computed using the law of cosines:  
\[2R^{2}(1-\cos\Delta\theta)=|p_{0}q_{0}|^{2} \tag{4}\]  
The above equation can be solved for radius \(R\) and bending angle \(\theta\), as follows:  
\[R\ =\frac{|p_{0}q_{0}|}{\sqrt{2(1-\cos\Delta\theta)}} \tag{5}\]  
\[\theta=l/R \tag{6}\]  
The centre of the circle around which bending takes place is then:  
\[c=(R\cos\beta,R\sin\beta,\ 0) \tag{7}\]  
Since the distances between the two planes (in Figure 10B) are considered small, the three points \(q_{1},\ q_{2}\) and \(q_{3}\) on the \(q_{1}q_{2}q_{3}\) plane can be approximated as follows:  
\[ \begin{array}{c}q_{1}\approx\ p_{1}+\Delta s_{1}n_{1}\\ q_{2}\approx\ p_{2}+\Delta s_{2}n_{1}\\ q_{3}\approx\ p_{3}+\Delta s_{3}n_{1}\end{array}  \tag{8}\]  
Hence, a good approximation of normal vector \(n^{2}\) can be obtained from Equation (2), using Equation (7). Further, it is noted that for our calculations \(n_{1}\) is [0 0 1] and that the following simplification has been used:  
\[|p_{0}q_{0}|\approx\frac{\Delta s_{1}+\Delta s_{2}+\Delta s_{3}}{3} \tag{9}\]  
The origin of the world frame is placed at the centre of the cylindrical modules where the rigid section meets the flexible module (\(z=0\)). The unactuated length of the flexible module is \(l=55\) mm. Due to the constant curvature assumption, coordinates \(p_{1},\,p_{2}\) and \(p_{3}\) can be placed anywhere along the three wires of the flexible module; here, for simplicity, at \(z=0\). The complete definition of \(p_{1},\,p_{2}\) and \(p_{3}\) is [38]:  
\[ \begin{array}{c}p_{1}=[\frac{-\sqrt{3}}{2}d\ \ \frac{-1}{2}d\ \ 0]^{T}\\ p_{2}=[\frac{\sqrt{3}}{2}d\ \ \frac{-1}{2}d\ \ 0]^{T}\\ p_{3}=[0\ \ d\ \ 0]^{T}\end{array}  \tag{10}\]  
where \(d\) is the offset of the wires from the world frame origin (in our test rig: \(d=7.75\) mm). The three \"delta\"-distances (\(\Delta s_{1},\,\Delta s_{2},\,\Delta s_{3}\)) between the vertices of the two planes can be determined from Equation (1), by inserting the wire length changes, \(l_{1},\,l_{2},\) and \(l_{3}\), as obtained from the optic fibre measurements.
## 9 Electronics
### Power Board
#### 9.1.1 Power Circuit  
The power circuit is responsible for charging the battery. To achieve this, the battery, which is composed of 10 cells, is divided into two blocks of 5 cells each. This arrangement enables parallel charging and allows connections in series through relays to power the laser. This design facilitates battery charging without requiring an increase in the input voltage. Furthermore, the use of relays enhances robustness and resilience against overload or current spikes in the circuit's power supply.  
Block A: The input supply provides power to a 12 to 5 V DC/DC converter and another 12 to \(\pm\)12 V DC/DC converter with dual outputs. The latter converter is dedicated solely to the attenuator. A transistor governs the supply of voltage generated by this DC/DC converter to the attenuator.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Property** & **Value** & **Units** \\ \hline Density & 4.43 & \(\times\)1000 kg/m\({}^{3}\) \\ Poisson’s Ratio & 0.342 & \\ Elastic Modulus (GPa) & 113.8 & GPa \\ Tensile Strength & 993 & Mpa \\ Yield Strength & 924 & GPa \\ Elongation & 14 & \({}^{\%}\) \\ Reduction in Area & 30 & \(\%\) \\ Hardness & 36 & HRC \\ Impact Strength & 19 & J \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of grade 5 titanium (T16 Al4V).  
Figure 7: (**Left**) Glass rod next to the top cap where it is inserted. (**Right**) Bottom cap with the connector of the laser beacon.  
Block B: Four double relays manage the parallel charging, series connection for laser activation, and disconnection during _standby_ mode. These relays feature a single coil and two sets of contacts, simultaneously cutting power and ground.  
Block C: This block consists of two transistors delivering enough current to energize the relay coils. Additionally, an AND-type logic gate safeguards the circuit and batteries, allowing operation only in the three designated modes: _Charge_, _Laser_, and _Standby_.  
Block D: In this block, a 150 mA current limiter is incorporated using an LM317T device. This setup ensures simplicity and versatility, requiring only three components: the LM317T, a resistor, and a capacitor. It maintains a constant current limit, adjustable via resistance variation. The current limit is determined by the following formula:  
\[I_{max}=\frac{V_{\text{ref}}}{R1}=\frac{1.25}{R1} \tag{8}\]  
For a desired limit of 150 mA to charge the batteries, [PERSON]'s law yields:  
\[R1=\frac{V_{\text{ref}}}{150\,\text{mA}}=\frac{1.25}{150\,\text{mA}}=8.33\,\Omega \tag{9}\]  
Thus, the closest standard resistor value is 8 \(\Omega\). This limits the current to 156 mA, divided evenly between the parallel batteries, resulting in about 78 mA per battery.  
Block E: This block encompasses the components providing power to the laser beacon. A 9 to 12 V DC/DC converter supplies the laser with power. The DC/DC converter also offers protection against potential power outages or short circuits.
## 4 Simulation Experiments and Result Analysis
### Simulation Environment  
A classical dumbbell-type network is built in the ns-3 platform as the simulation experimental topology shown in Figure 6.  
Assuming that R1 and R2 are connected through WAN links, the bottleneck link bandwidth varies randomly according to a uniform distribution, and the end-to-end aggregated data flows of the traversal topology are long life flows and short life flows, adapting to various different applications of heterogeneous vehicular networking. R1 is wired to the server, and R2 is wirelessly or 5G-connected to the vehicle node.  
The simulation parameters are set according to the actual measurement data of the Telematics environment to reflect the real network as much as possible. The simulation parameters are shown in Table 1.  
Figure 5: Implementation Process of The Experiment.  
SUMO, a micro traffic simulation software, is used to generate traffic flow. Two scenarios are simulated: a static scenario and a high-speed moving scenario. Considering that the transmission range can cover the width of the road, the simulation is simplified to a two-way single-lane scenario with a total length of 1 km, where the nodes are randomly distributed on the road and always move within that range. Moreover, vehicle speed and direction (lane) are also selected randomly, but the range of moving speed is limited to 40-60 km/h, and the ratio of vehicles in both directions is about 1:1. Due to the high packet-sending frequency of the Telematics communication, the relative position of the vehicle changes less during the packet sending time, so the relative position of the vehicle does not have an excessive impact on the sending and receiving of messages. Therefore, the simulation uses a vehicle with a uniform motion model.  
The parameters of the PPO2 algorithm used are set as follows: the number of updated rounds is 200, the discount factor \(\gamma\) is 0.99, the learning rate \(\delta\) is 0.0025, the greedy coefficient \(\varepsilon\) is 0.3, and the maximum number of steps per training round is 150.
## 3 Materials  
In the framework of this study, we used three satellite images from three different multispectral sensors, specifically one ASTER L1T image (acquired on 19 August 2003), one Landsat-8/OLI L1T image (acquired on 25 July 2017), and one Sentinel-2A MSI L1C image (acquired on 22 July 2017).  
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which is aboard the Earth Observing System (EOS) TERRA platform, has 14 spectral bands and measures reflected radiation in three bands between 0.52 and 0.86 um in the visible near infrared (VNIR) and in six bands from 1.6 to 2.43 um in the shortwave infrared (SWIR), with 15- and 30-m resolution, respectively [94; 95; 96]. In addition, emitted radiation is measured at 90-m resolution in five bands between 8.125-11.65 um (TIR). The swath width is 60 km. In this study, we used the nine VNIR-SWIR bands.  
The Operational Land Imager (OLI) and Thermal Infrared Scanner (TIRS) instruments onboard the Landsat-8/OLI satellite collect image data for nine visible, near infrared (VNIR), and shortwave infrared (SWIR) bands and two longwave thermal (LWIR) bands [97; 98]. In this study, we used the seven VNIR-SWIR spectral bands.  
Sentinel-2 is a twin satellite constellation consisting of Sentinal-2A launched on 23 June 2015 and Sentinel-2B, which followed on 7 March 2017 [99]. The onboard Multispectral Imager Instruments (MSI) cover a field of view of 290 km and provide 13 VNIR-SWIR spectral bands with the ground sampling distance (GSD) of four bands at 10 m, six bands at 20 m, and three bands at 60 m [100]. The coastal band (band 1), water vapor band (band 9), and cirrus band (band 10) are at the same GSD of 60 m. Three red edge bands (i.e., bands 5, 6, and 7), one NIR band (band 8a), and two SWIR bands (bands 11 and 12) are the same at 20 m. Bands 2, 3, and 4 are three visible bands at a 10-m GSD as the shorter wavelength NIR band (band 8) [99].  
Furthermore, for the purpose of this study, we digitized and utilized in the form of a classification map the most recent geological map of Nisyros, the \"Nisyros Dodecanese Archipelago\" sheet at a scale of 1:15,000 (digitized extract to the study area is shown in Figure 2) [68]. The geological map synthesizes information from several previous geological maps of the area [71; 72; 76; 77; 78; 79; 80] and presents the main hydrothermal alteration area as debris flow and lacustrine deposits related to hydrothermal explosions (yellow color in Figure 2).
## 2 Data and Methods
### Analysis and Processing of SWOT-Derived SSH Data  
#### 2.3.1. Geostrophic Velocity and Vorticity  
Zonal (\(u_{g}\)) and meridional (\(v_{g}\)) (with respect to the SWOT grid) surface geostrophic velocity components are calculated as:  
\[u_{g}=-\frac{g}{f}\frac{\partial\eta}{\partial y}, \tag{1}\]  
\[v_{g}=\frac{g}{f}\frac{\partial\eta}{\partial x}, \tag{2}\]  
where \(g\) is the gravitational acceleration, \(f\) the Coriolis parameter and \(\eta\) the sea level elevation.  
The absolute geostrophic velocity (\(V_{g}\)) is obtained with:  
\[V_{g}=\sqrt{u_{g}+{v_{g}}^{2}}. \tag{3}\]  
Geostrophic relative vorticity, \(\zeta\), is calculated from the zonal and meridional velocities:  
\[\zeta=\frac{\partial v_{g}}{\partial x}-\frac{\partial u_{g}}{\partial y}. \tag{4}\]