esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results for Dimensions and Tensile Strength for the Lower Level of the Working Chamber
#### 3.2.3 Results for Thickness in the Lower Level of the Working Chamber  
A modified quadratic model was chosen for data analysis for the thickness at the lower level of the working chamber. The combination of factors A\({}^{2}\)B was added to the analysis because, in addition to A, AB, and A\({}^{2}\), they have a great influence on the thickness (Table 16). The factor B itself, including B\({}^{2}\), has no influence on the thickness at the lower level of the chamber, which is correlated with the thickness at the upper level of the chamber.  
Table 17 shows statistics for thickness at the lower part of the working chamber. The ratio of 21.6297 indicates an adequate signal, which means that this model can be used to navigate the design space.  
Thickness can be described by an equation with actual parameters:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sum of** & **Degrees of** & **Mean** & **F Value** & **\(p\)-Value (Risk of** \\  & **Squares** & **Freedom DF** & **Square** & & **Rejection of H\({}_{0}\))** \\ \hline
**Model** & 0.1560 & 6 & 0.0260 & 30.39 & \textless{}0.0001 significant \\ A & 0.0163 & 1 & 0.0163 & 19.09 & 0.0008 \\ B & 0.0014 & 1 & 0.0014 & 1.69 & 0.2161 \\ AB & 0.0124 & 1 & 0.0124 & 14.55 & 0.0021 \\ A\({}^{2}\) & 0.0431 & 1 & 0.0431 & 50.40 & \textless{}0.0001 \\ B\({}^{2}\) & 0.0017 & 1 & 0.0017 & 1.97 & 0.1840 \\ A\({}^{2}\)B & 0.0175 & 1 & 0.0175 & 20.42 & 0.0006 \\
**Residual** & 0.0111 & 13 & 0.0009 & & \\ Lack of fit & 0.0097 & 10 & 0.0010 & 2.08 & 0.2967 not significant \\ Pure error & 0.0014 & 3 & 0.0005 & & \\
**Cor Total** & 0.1671 & 19 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 16: Analysis according to ANOVA for the thickness at the lower level of the working chamber.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Thickness \(h\), mm** \\ \hline Standard deviation & 0.0292 \\ Mean & 4.10 \\ Coefficient of determination (\(R\)-squared (\(R^{2}\))) & 0.9335 \\ Adjusted \(R^{2}\) & 0.9027 \\ Predicted \(R^{2}\) & 0.7128 \\ Adeq Precision & 21.6297 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Fit statistics for the thickness at the lower level of the working chamber.  
Figure 9: Dependence of hatch distance and orientation on the length in the lower part of the working chamber.  
\[h=4.99-4.86\times HD-0.016\times\alpha+0.073\times HD\times\alpha+6.52\times HD ^{2}+0.000026\times\alpha^{2}-0.10009\times HD^{2}\times\alpha \tag{9}\]  
where \(h\) [mm]--thickness, \(HD\) [mm]--hat distance, and \(\alpha\) [\({}^{\circ}\)]--orientation.  
For an approximate hatch distance of 0.37 mm, a thickness value of the nearest 4 mm can be obtained for all orientations, which can be seen in the diagram (Figure 10), that is, the area between the two turquoise lines. In this case, a value of exactly 4 mm cannot be achieved with any combination of parameters.
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
## 3 Materials and Methods
### The Space Segment: The UVSQ-SAT CubeSat Payload
#### 3.2.1 The ERS Sensors
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Uncertainty Sources** & **Absolute Uncertainty** & **Determination Method** \\ \hline Emissivity (\(\varepsilon_{K_{1},i}\), \(\varepsilon_{K_{2},i}\)) & \(\pm 0.25\%\) & Ground based calibration, BRDF \\ Absortivity (\(\alpha_{K_{1},i}\), \(\alpha_{K_{2},i}\)) & \(\pm 0.25\%\) & Ground based calibration, BRDF \\ Temperatures (\(T_{K_{1},i}\), \(T_{K_{2},i}\)) & \(\pm 0.01\) Kelvin & Ground based calibration, in-flight validation \\ Voltage (\(U_{K_{1},i}\), \(U_{K_{2},i}\)) & \(\pm 50\) mV & Ground based calibration, in-flight validation \\ Responsivity (\(S_{K_{1},i}\), \(S_{K_{2},i}\)) & \(\pm 0.25\%\) & Ground based calibration, in-flight validation \\ \hline Satellite altitude (\(z\)) & \(\pm 0.1\%\) & Orbital assessment \\ TSI & \(\pm 0.5\) Wm\({}^{-2}\) & Space based radiometers observations \\ \(d\) & \(\pm 0.05\%\) & IMCCE determinations \\ \(Qr_{i}\) & \(\pm 0.1\%\) & Calculations, IMCCE determinations \\ \hline \(\Theta_{s}\) & \(\pm 1\%\) & Deep learning approach \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error budget of the ERS sensor. From the ERS sensor error budget (Table 3) and from the knowledge of the TSI absolute uncertainty (\(\pm 0.5\) Wm\({}^{-2}\) at 1\(\sigma\)), the expected performances in the absolute determination of OLR and OSR were each \(\pm\) 10 Wm\({}^{-2}\) at 1\(\sigma\) (quadratic summation of uncertainties). Table 5 presents the expected performances from the UVSQ-SAT ERS measurements. The stability per year of the sensors will be demonstrated in orbit and can be compared with other space based measurements.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 7 Conclusions and Future Work  
### Conclusions  
This paper investigates a route selection mechanism using device-to-device (D2D) communication over a testbed based on 5G with heterogeneous devices, which have different transmission power i.e., 20 dBm for macrocell (MC) base station (BS) and 10 dBm for D2D nodes, frequency range (i.e., from 850 MHz to 910 MHz), and processing capability i.e., 1.4 GHz for femtocell (\(fc\)) nodes. In addition to a femtocell \(fc\) source node and a \(fc\) destination node, the testbed consists of six \(fc\) intermediate nodes that forms a D2D routes and a MC BS that forms a backbone route. The \(fc\) source node transmits a data stream (or traffic) towards a \(fc\) destination node through three disjoint D2D routes or the backbone route. The D2D routes have different priority levels given by the central controller based on the level of PUs' activities. In general, the D2D routes have higher priority levels to improve traffic offload from MC to \(fc\), and a D2D route with a higher priority has a lower level of PUs' activities. Nevertheless, the random reappearance of PUs' activities is inevitable, and so the backbone route must be selected when PUs' activities reappear in all the D2D routes. Experimental results show that the overall packet delivery ratio increases from 75.07% in the absence of a backbone route to 89.36% in the presence of a backbone route, giving a 14.29% improvement and a traffic offload of up to 75.05% of the MC BS traffic. The reappearance of PUs' activities increases the cumulative end-to-end delay of 0.048 s due to route reassessment and switches. The cumulative end-to-end delay is expected to further increase with the number of intermediate nodes (or hops) in a route. Compared to packet delivery ratio, the PUs' activities have a greater effect on the cumulative end-to-end delay.
# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 2 Materials and Methods
### Materials
#### 2.1.1 EEG Data  
The EEG data used for the purposes of this work were derived from PhysioNet, an open access database that contains EEG recordings from 23 children diagnosed with epilepsy, admitted and evaluated at Boston Children's Hospital [17]. For each patient, 9-42 one-hour continuous EEG data were used. For data modelling, we randomly selected a 1-h EEG recording from an 11-year-old patient. The sampling rate was 256 Hz, resulting in a total of 23,040 samples for 90 s of EEG data. The proposed approach for encoding EEG-generated data considers the dataset as a whole, without any limitation to the seizure onset area, resulting lower entropy and better power consumption during serial data transmission.
## 3 TMDP-Based Resource Allocation Algorithm and Problem Linearization
### Optimization Problem Linearization
Based on the above discussion, the original optimization problem \(\mathbf{P}_{1}\) can be transformed into a MILP problem \(\mathbf{P}_{2}\) as follows:  
\[\mathbf{P}_{2}: \max_{\omega_{t}^{q}}\sum_{j\in\mathcal{J}}\sum_{t\in\mathcal{J}} \sum_{q\in\mathcal{Q}}\omega_{t}^{q}\psi_{j,t}^{q}\] (26a) s.t. \[\sum_{q\in\mathcal{Q}}\omega_{t}^{q}=1 \tag{26b}\] \[\sum_{t\in\mathcal{T}}\sum_{q\in\mathcal{Q}}\omega_{t}^{q}\rho_{i,t }^{q}\geq D,i\in\mathcal{I}\backslash\mathcal{J}\] (26c) \[\omega_{t}^{q}\chi_{t}^{q}\leq\sqrt{\frac{4\epsilon^{2}}{\delta B}} \tag{26d}\]Compared to the optimization problem \(\mathbf{P}_{1}\), there are no more logarithmic terms in the problem \(\mathbf{P}_{2}\). As both the objective function and constraints of \(\mathbf{P}_{2}\) are linear, the original problem has been transformed into a MILP problem. Subsequently, this transformation allows us to utilize the TMDP method to solve the MILP problem, which demonstrates great performance in the next section.
## 6 Conclusions  
The complexity of the power converter system for a cogenerator and adding a battery may seem unjustified and expensive. However, looking at the whole solution, the built power supply system has advantages that the conventional solution does not have, i.e., the possibility of cooperation with the grid and uninterrupted transition to an off-grid power supply. In a typical solution, additional battery systems (uninterruptible power supplies, UPS) are used. The assumption was to use a battery of small capacity, sufficient for temporary power balancing in transients. At the same time, it is possible to use a high-capacity battery which serves as a guaranteed power supply system when, for example, the generator drive is stopped.  
The use of the power converter system allows the building of a universal and scalable power supply system with an induction generator, because the NC RfG certificate will be issued only for the network converter. Other converters will be adapted to the power of the generator as well as the capacity and power of the battery.  
State of achievements of main assumptions:  
* in on-grid and off-grid modes, mCHP active power is controlled by changing the rotational speed and torque (e.g., fuel dose) of the internal combustion engine.  
This assumption was tested in a simulation with an electric motor instead of an ICE. Based on the obtained results, shown in Sections 4.1 and 4.2, it can be concluded that active power controlled by torque or rotational speed provides sufficient dynamics. Determination of the set active power at the connection point with the grid did not exceed 2 s;  
* in the off-grid mode, ensuring the quality parameters of the electricity in accordance with the PN-EN 50160:2010E standard.  
In this case, voltage stabilization in off-grid mode is important. The values of the phase voltages with an asymmetric load did not exceed the permissible values. The obtained extreme values of phase voltages were about 251 V and 219 V.  
* switching time between modes < 40 ms.  
The switching time between operating modes was approx. 20 ms.  
Feature work: in the next stage of the prototype development of the power supply system, the characteristics of the output power as a function of frequency and grid voltage will be implemented in order to meet the requirements of the NC RfG network code. The use of an additional PC3.2 converter also improves the dynamics of power balance control in the on-grid mode (e.g., limiting the power at the connection to the power grid). In the finished product, a heater is planned to be connected to the heat recovery circuit of the internal combustion engine. The heater will act as an additional resistor, R. The use of a converter system to stabilize the off-grid parameters allows for the connection of other energy sources, e.g., a photovoltaic source, which requires stable voltage parameters in the installation to which it is connected. This functionality will also be tested in the next stage of prototype testing.
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 4 Algorithm Design  
This section describes the environment-aware adaptive reinforcement learning-based routing (EARR) protocol. In EARR, vehicle nodes make optimal routing decisions considering node stability, node duration, received signal strength, and available bandwidth, and the proposed adaptive scheme improves routing performance.
## Abbreviations  
The following abbreviations are used in this manuscript:  
IoV Internet of vehicles  
V2V Vehicle-to-vehicle  
V2I Vehicle-to-infrastructure  
MEC Multiaccess edge computing  
ROI Region of interest  
CPM Cooperative perception message  
C-V2X Cellular-vehicle-to-everything
## 3 System Model
### Network Model: Basic Definitions
Thus, the overall network lifetime will be shortened. In order to prolong the network lifetime, the GNs are thus regularly rotated on the basis of residual energy. Definition 1: A CDS of graph \(G=(V,E)\) is a set of nodes V\({}_{CDS}\subset V\), such that every node \(v\) belongs to \(V\setminus D\); there is at least one node \(u\) in V\({}_{CDS}\) that dominates \(v\). Moreover, nodes in V\({}_{CDS}\) are connected to each other. Let us consider the example in which nodes are randomly deployed in a network field. For the sake of simplicity, we take 15 sensor nodes to establish the CDS. The criterion for CDS construction in our protocol is discussed above. In this example, the highest degree node is 7, and the second highest degree node is 13. These two nodes are called dominators and, thus, included in the CDS. The second step is to check the dominate of the dominator for the above-mentioned conditions. The dominatee nodes of 7 include (4, 2, 5, 6, 1, 9). Nodes 5 and 6 are leaf nodes, so they remain as leaf nodes. Nodes 4 and 7 are not leaf nodes; moreover, they have only leaf neighbours, so one of the two nodes is included with the dominator node. Hence, Node 4 is included in the dominator and also in the CDS. The dominatee Nodes 1 and 9 have no further leaf neighbours, so these two nodes are not included in the CDS. Now, consider the second highest degree node, which is Node 13. Node 13 is included in the dominator, as well as in the CDS. The dominatee nodes of 13 include (10, 12, 11, 14, 9). Nodes 10 and 12 are leaf nodes, and hence, these two nodes remain as dominatee. Node 14 is included in the dominator and the CDS, because it has a leaf neighbour, which is Node 15. Moreover, as we have mentioned, the node that is the dominatee of two different dominators is also included in the CDS. After applying all of the above-mentioned steps on the network, we get the set of CDS nodes, which includes (4, 7, 9, 13, 14, 8), as shown in Figure 2.  
Definition 2: A undirected connected graph \(G=(V,E)\), such that a sub-graph \(S^{\prime}\) of G is called the spanning tree that connects all of the nodes in V. An MST \(S^{\prime\prime}\) is a sub-graph of \(S^{\prime}\), such that the weight of \(S^{\prime\prime}\) is less than or equal to the weight of \(S^{\prime}\) of G. The weight of the spanning tree depends on the Euclidean length of the edge, \(e\left(i,j\right)\). Definition 3: Given a undirected connected graph \(G=(V,E)\) with the formation of CDS and MST, an HC is defined as the path followed by the AUV that is accomplished through the CDS-MST, such that each edge is visited by the AUV once and returns to the starting node. Figure 2: Formation of the CDS. Definition 4: A CDS, V\({}_{CDS}\subset V\) of \(G\), such that HC is transformed to a sub-optimized elliptical path of AUV. Moreover, V\({}_{CDS}\subset\) V, such that the major and minor axis of the elliptical path \(a\) and \(b\) are sub-optimized to achieve: \(Maximize\)\(d_{total}\) = \(\sum_{i=1}^{T_{GN}}\) (\(kr_{i}\) + \(d_{i}\)).
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 3 Instrumentation, Results and Discussion
### Field Tests  
#### 3.3.1 Deployment of the Buoy  
For the field tests, the GNSS buoy was deployed in the waters of Suao (in the northeastern part of Taiwan) from 17 August 2013 to 18 June 2014. The test lasted ten months. Figure 4 shows a snapshot of the deployed buoy. The GNSS buoy is a discus-type with a diameter of 2.5 m. The submerged structure is approximately 1.5 m long. The GNSS buoy was anchored at the seabed with a mooring line longer than the local water depth; therefore, the buoy continued to float and move under the actions of water waves and ocean currents. An ATC sensor was also installed on the buoy to provide wave data for comparison with those obtained from the GNSS receiver.  
Figure 4: Snapshot of the GNSS buoy deployed in the waters of Suao, Taiwan.  
Figure 3: Altitude of the GNSS receiver above the geoid in dynamic test.  
The buoy was deployed with the assistance of the Coastal Ocean Monitoring Center (COMC), National Cheng Kung University (NCKU), Taiwan. The COMC has been responsible for developing, deploying, and operating the long-term real-time ocean monitoring networks around Taiwan since 1998. To date 17 long-term operational data buoys have been deployed in Taiwan waters for collecting the meteo-oceanographic data at water depths ranging from 10 m to 5600 m. Such sustained, long-term in situ observations will provide monitoring data for numerical ocean model validations [25] and oceanographic research [26].  
The locations of the GNSS buoy and nearby Suao tide station are shown in Figure 5. The GNSS buoy was located north of Suao Harbor. The local water depth was approximately 20 m, and the nearest distance to the coast was 2.1 km. The Suao tide station is located inside Suao Harbor and run by the Central Weather Bureau (CWB) of Taiwan. Real-time tide data for comparison were acquired from the CWB website. The distance from the GNSS buoy to the Suao tide station was 4 km, and one GNSS base station is located at the Suao tide station.  
The standard calibration accuracy of the tide gauge in the Suao tide station, is \(\pm\)0.025%, the nonlinearity is \(\pm\)0.02%, and the precision is \(\pm\)0.01%. The standard range of the tide gauge is 10 m; therefore, the standard calibration accuracy is \(\pm\)2.5 mm, the nonlinearity is \(\pm\)2.0 mm, and the precision is \(\pm\)1.0 mm, respectively.  
Based on the working principle illustrated in Figure 1, the GNSS buoy measured the ellipsoidal height, velocities, and quality index hourly. The sampling rate was 1 Hz. The data acquisition system began to acquire samples at the 50 th minute of each hour, and the data were collected for 10 min. The monitored data were transmitted via the GPRS modem to the receiving system for further data processing.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 2 V2X Technology
### Types of V2X Communications  
The advancements in vehicular communication include various types of communication technology. There are currently two underlying technologies that allow V2X communication, namely IEEE 802.11p and cellular technologies, also known as CV2X. Besides the standard V2X communication, there are few other enabling technologies that are in the process of standardization, such as Visible Light Communications (VLC), mm Wave, etc. The IEEE 802.15.7 VLC Task Group has accomplished MAC and PHY standards for VLC for vehicular networks also known as Vehicular Visible Light Communication (VVLC). Some of its features are VLC spectrum is unlicensed, works in between 380 nm to 780 nm, providing 1000 times higher bandwidth, and being economically feasible. However, VVLC has limitations, such as it only works in visible light and it is heavily impacted by poor weather, rain, fog, obstacles, etc. Hence, it can only be considered as a complementary technology for cellular V2X or IEEE 802.11p, which can be useful in dense and urban location [6; 7]. In the recent past, millimeter wave (mmWave) were used for the processing of high-bit rates. The gigabit per second bit rates necessary for the sensor data sharing between the vehicular nodes is not supported in current networks i.e., 802.11p or LTE. The 5G mmWave communications provide higher data transfer rates for a specific vehicle using multiple antenna beamforming. It is also possible to create a new standard or an updated version of the ISO standard [8] with a dedicated spectrum for mmWave V2X communications that is similar to 802.11p, but it may require supplementary equipment to support mmWave V2X communication [9].  
In general, V2X communications can be direct communication, fully network-assisted communication, and hybrid communication, as shown in Figure 2. In direct communication, vehicles connect with neighboring vehicles on a peer-to-peer basis. Communication must be carried out via the RSU or Base Station (BS) in fully network-assisted communications. In hybrid communications, vehicles communicate with each other through a combination of direct communication and network-assisted communication by utilizing 802.11p and cellular networks components, such as RSUs and BSs.
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 4 Practical Challenge--Content Dependency and Possible Approaches to Its Analysis  
There have already been successful experiments with 5G-based radar car detection, described in [9], where the authors outline all the required processing steps. They point out the crucial issue of content-dependent transmission, but have not deeply analyzed this issue. Resource occupancy directly affects the quality or possibility of radar detection. This effect is depicted in Figure 8.  
This practical challenge is even more important when considering the detection of small objects, such as drones, with a small radar cross-section (RCS). There is a clear need for a reliable method allowing the automatic selection of a time interval with sufficient signal filling.
## 3 Results  
### Spatiotemporal Patterns of FDS and LST Differences in Disturbance and Non-Disturbance  
\(\Delta\) FDS and \(\Delta\) LST show opposite changes from 1986 to 2016 (Figure 1). \(\Delta\) FDS exhibited a significant and pronounced decline, decreasing from a mean value of 5.92 \(\pm\) 4.6 in 1986 to 0.35 \(\pm\) 2.36 in 2016 (Figure 1a). This reduction was primarily concentrated between 1986 and 2000 (slope = \(-\)0.549, \(p\) < 0.05), with no significant trend observed thereafter (post-2001, \(p\) > 0.05). Conversely, \(\Delta\) LST showed a significant increase from 1986 (\(-\)27.04 \(\pm\) 10.15 K) to 2016 (16.47 \(\pm\) 10.67 K) (Figure 1c). This warming trend accelerated significantly more during the 2001-2016 period (slope = 0.744) compared to that during 1986-2000 (slope = 0.377). While the air temperatures (Tmax and Tmin) also increased over the entire period, the rate of change did not differ significantly (\(p\) > 0.05) between the two sub-periods (Figure S2). The spatial patterns of \(\Delta_{\mathrm{s}}\) FDS and \(\Delta_{\mathrm{s}}\) LST (Figure 1b,d) showed a high degree of overlap, suggesting a potential link between the evolution of forest disturbance impacts and their thermal signatures.  
Figure 1: Spatiotemporal patterns of \(\Delta\) FDS and \(\Delta\) LST in Europe between 1986 and 2016. (**a**) \(\Delta\) FDS time series. (**b**) Spatial patterns of \(\Delta_{\mathrm{s}}\) FDS. (**c**) \(\Delta\) LST time series. (**d**) Spatial patterns of \(\Delta_{\mathrm{s}}\) LST. Orange and green dashed lines in (**a**,**c**) represent linear regression lines fitted to data for 1986–2000 and 2001–2016 periods, respectively.  
The \(\Delta_{\rm s}\) FDS and \(\Delta_{\rm s}\) LST of the 35 European countries were not spatially homogeneous. The regions with larger \(\Delta_{\rm s}\) FDS values were predominantly located in Western and Northern Europe (Figure S3a). The regions with larger \(\Delta_{\rm s}\) LST values are situated in the southern parts of Europe (Figure S3b). The mean values of the \(\Delta_{\rm s}\) FDS and \(\Delta_{\rm s}\) LST for all the countries were \(-\)3.95 and 39.68 K, respectively (Figure S5; Table S1).