esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution towards Smart and Software-Defined Internet of Things
## 7 IoT Challenges and Future Directions
### Mobility and Dynamic Environment  
It is challenging for IoT devices to communicate in a dynamic environment, where devices continuously change their positions. People, devices, and vehicles are carrying those IoT sensors or devices and communicating in the real world. This has shifted the traditional static networks to highly dynamic networks, where mobility is an important factor, which was not in the case of traditional computer networks. In IoV, the network topology is continuously changing and vehicles are communicating with each other to enhance passenger safety. This type of dynamic communication has brought a revolution in the field of intelligent transport systems [98; 99].  
IoT must support connecting applications and devices to the requisite services for the mobile users [11]. A service provider often finds itself connecting to devices or providing service to nodes that are constantly on the move or to mobile edge devices. A mobile user may face service disruption when the device switches from one zone to another or a handover takes place between gateways. This disruption of service reduces the QoS and degrades the system as a whole. An IoT framework demands the architecture to be flexible, scalable, and efficient enough to provide uninterrupted service to mobile devices on the network. Therefore, developing some robust and efficient algorithms for mobility management and coping with a dynamic environment is essential. In IoT networks, machine learning can help predict the dynamically changing environment.
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 7 Automatic Classification Algorithms for EEG Signals  
As the nature of EEG signals is oscillating and continuous (EEG is a representation of a mental state composed of electrical activity) [40]), it is complex to deal with the raw information of these signals. Such conditions motivate the design of information classification techniques to extract relevant information or features that could allow using EEG in BCI and RR. These techniques extract features or characteristics of the signals and are parts of the branch of a discipline called \"signal processing\".  
Classification is a process that assigns a category (or \"class\") to an object (or information) [63]. This class is an attribute that contains a group of information with similarities yielding an intrinsic description of their relationship. Usually, to get the precise classification, the classes must be labeled. A label is a name that characterizes the input data associated with a certain class. This process depends on the information that the study pretends to highlight and categorize, such as the movement or the conscious state of a person. The extracted features are different for each BCI application. However, many studies focus on the relationship between movement and imagination because this feature could improve the accuracy and application of BCI, which directly impacts the rehabilitation process. Figure 9 reflects the BCI implementation processes; Table 3 shows different ML techniques for EEG classification.  
Machine learning is a form of applied statistics using computational power to estimate future values of a function [63]. This estimation is known as \"learning\". This process involves from the categorization of certain data. As mentioned before, machine learning algorithms can be divided into supervised, unsupervised, and reinforcement learning [52]. The different machine learning algorithms are classified into \"families\". These families are different in the way they rely on their mathematical methods for classifying data. The next section describes some of the families and the algorithms found in that category.
## 3 Our Proposed Method
### Model Configuration  
Although the proposed decoding scheme can be applied to larger SCMA systems, it is important to note that the design of codebooks for such systems is beyond the scope of our study. Additionally, channel estimation is not the primary focus of this research. Therefore, in this paper, we consider a basic downlink SCMA system model with \(J=6\), \(K=4\)\(M=4\) and, perfect CSI, which is consistent with the settings in [15; 16]. The \(A\) and \(\left(X_{O_{k}},Y_{O_{k}}\right)\) in Equation (4) and the \(s\), which governs the range of diagram output, are set as  
\[A=1,\] \[\left(X_{O_{k}},Y_{O_{k}}\right)=\left\{\begin{array}{l}(+s,+s),k=1\\ (-s,+s),k=2\\ (-s,-s),k=3\\ (+s,-s),k=4\end{array}\right., \tag{8}\] \[s=5.\]  
The colormap, designated as \"gray\", is set to determine the functional relationship between the floating-point number and color value of \(K\) points in the \(K\)-Polygon diagram. The classification network utilizes a modified MobileNet model, with the comprehensive structure illustrated in Table 2.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 2 Literature Review and Applications in Disaster Management
### Applications
#### 2.2.6 Communication Infrastructure  
In a disaster scenario, the conventional communication infrastructure may get destroyed. In this case, UAVs can be incorporated to fill the coverage holes. Drone-based small cell deployment is investigated to fill the coverage holes in a cellular network post-disaster in [13]. The authors also proposed a clustered deployment of drone-based small cells in the disaster-hit area. In [14], the location of the UAV-mounted BS is optimized to improve throughput and coverage performance. The energy efficient path for the UAV to serve a maximum number of users in post-disaster communication under limited battery capacity is investigated in [16]. The authors of [15] proposed a distributed NOMA scheme for emergency communication using UAVs. It jointly optimizes the channel assignment, hovering time, and power budget of UAVs to maximize the access ratio and sum rate of IoT devices in disaster-affected areas. UAV-mounted base stations are adopted in [12] to facilitate M2M communication between human wearable IoT devices for disaster rescue. A resource allocation scheme is proposed to improve user scheduling and energy efficiency of UAV mounted BSs. The flow of critical information using UAVs in the absence of a legacy communication network in the event of a disaster is investigated in [50]. The authors employed multiple UAVs with specialized payloads to scan the affected area for survivors and take images for decision support scenarios.
# A Balanced Symmetrical Branch-Line Microstrip Coupler for 5G Applications
###### Abstract
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Refs.** & **Advantages** & **Disadvantages** & **Design Method** & **Applications** \\ \hline
[5] & Balanced phase & No filtering response &
\begin{tabular}{c} Calculation of the \\ reflection coefficient \\ \end{tabular} & WLAN-5G \\ \hline
[6] & \(--\) & No filtering response, &
\begin{tabular}{c} Analysis of the \\ ABCD matrix of a \\ transmission line \\ \end{tabular} & 5G \\ \hline
[7] & \
\begin{tabular}{c} Filtering response, \\ Compact size \\ \end{tabular} & High losses &
\begin{tabular}{c} No mathematical \\ design \\ \end{tabular} & GSM \\ \hline
[8] & Novel structure & Phase unbalance &
\begin{tabular}{c} No mathematical \\ design \\ \end{tabular} & 5G \\ \hline
[9] & \(--\) & \
\begin{tabular}{c} Phase unbalance, \\ Magnitude unbalance, \\ No filtering response \\ \end{tabular} &
\begin{tabular}{c} Calculation of the \\ line impedance and \\ power ratio between \\ output ports \\ \end{tabular} & 5G \\ \hline
[10] & \(--\) &
\begin{tabular}{c} No filtering response, \\ High loss, \\ Phase unbalance, \\ Magnitude unbalance \\ \end{tabular} & No mathematical & WiMAX-5G \\ \hline
[12] & \(--\) & No filtering response &
\begin{tabular}{c} No mathematical \\ design \\ \end{tabular} & 5G \\ \hline
[13] & Low losses & \
\begin{tabular}{c} No filtering response, \\ Phase unbalance \\ \end{tabular} &
\begin{tabular}{c} Calculation of the \\ input impedance of \\ the basic resonator \\ \end{tabular} & WLAN-5G \\ \hline
[16] & Filtering response, \\ Novel structure & Large size, Phase &
\begin{tabular}{c} Obtaining the \\ output-input \\ voltage ratio \\ \end{tabular} & 5G \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The specifications of the previous couplers. the simulation and measurement results, description of the measurements setup and comparison with the previous works. Finally, Section 4 concludes the paper.
# Study of Effective Length Factor of Frame-Core Wall Structure with Cross-Layer Columns
## 2 Effective Length Modification for Frame-Core Wall Structures with Cross-Layer Columns
### Effective Length Factor in GB 50017-2017  
GB 50017-2017 Steel Structure Design Standard provides the effective length factor of frame column with lateral shift, which is given by Equation (5).  
\[\mu_{i}=\sqrt{\frac{7.5K_{1}K_{2}\ +\ 4(K_{1}\ +\ K_{2})\ +\ 1.52}{7.5K_{1}K_{2} \ +\ K_{1}\ +\ K_{2}}} \tag{5}\]  
where \(K_{1}\) and \(K_{2}\) are the ratio of the sum of the beam line stiffness and the sum of the column line stiffness intersecting the upper and lower ends of the column, respectively.  
The buckling load \(P_{cr}\) of the columns can be obtained from [PERSON]'s formula, as shown in Equation (6), where \(m\) denotes the number of short-sided frame columns; \(i\)th denotes the \(i\)-th layer; \(EI_{cj}\) is the flexural stiffness of the column; \(\mu_{i}\) denotes the effective length factor of the \(j\)-th column in the \(i\)-th layer obtained according to the GB 50017-2017.  
\[P_{cr}=\sum_{j=1}^{m}\frac{\pi^{2}EI_{cj}}{\left(\mu_{j}h_{i}\right)^{2}} \tag{6}\]  
The effective length factor method in GB50017-2017 considers the restraint strength of the upper and lower end of the column and this method is suitable for plane frames. For the frame-core structure, the side columns experience strong restraint from the frame beams within the frame plane. Outside the frame plane, the side columns are often connected to the core via the floor beams with small cross-sectional dimensions. Since the floor beams are usually hinged with the columns, the side columns are subjected to little out-of-plane restraint. The application of the effective length factor method is challenging since it predicts that the stable bearing capacity of out-of-plane frame columns is nearly zero, which does not align with the actual situation.  
The location of cross-layer columns in the structural layout can cause an abruptly change in the lateral stiffness resistance of the floor. In GB 50017-2017, the lateral stiffness of a structure is determined based on the surrounding restraint of individual members, and it does not consider the torsional effect produced by vertical irregular structures, as shown in Figure 2. Furthermore, this design standard only considers weak direction members in the overall lateral stiffness of the structure, and does not include the influence of the interaction between multiple members. Additionally, different distribution forms of the cross-layer columns are also not considered.
# First Earth-Imaging CubeSat with Harmonic Diffractive Lens
## 2 A CubeSX-HSE Nanosatellite-Based Remote Sensing Platform
### Nanosatellite Design
#### 2.1.1 Data Transmission System  
The OrbiCraft-Pro ultrashort-wave transducer SXC-UHF-02 is implemented on a separate board connected to the satellite's main electronic module board with a PC/104 connector [22]. The transducer operational frequency can be tuned within a 434-436 MHz range, with a 20 kHz passband. This enables a data transmission rate of 9600 haud in the Gaussian minimum-shift keying (GMSK) modulation mode, sufficient for exchanging telemetry, commands, and the payload data. The maximum output power of 1 W and the sensitivity of 119 dB are optimized for the data transmission from a low Earth orbit (LEO). The ultrashort-wave transducer operates in the temperature range of \(-\)40 to +85 \({}^{\circ}\)C and weighs \(\sim\)40 g. The transducer is compatible with the OrbiCraft-Pro PC/104 interface, provides an additional universal asynchronous receiver-transmitter (UART), and enables the transmission of heartbeat packages and onboard time signals to the onboard CAN bus.  
The onboard high-speed radio transmitter SXC-XTX-01 can send data to the Earth at a rate of 10 Mbit in the X-band (8175-8215 MHz) [22] and is also connected to the onboard bus CAN2.0B with a unified connector PC/104.
# Use of Vine Shoot Waste for Manufacturing Innovative Reinforced Cement Composites
## 3 Results and Discussion
### Nuclear Magnetic Resonance (NMR) Spectroscopy  
The NMR spectra of raw materials (CEM and VSW), CP and CP10 VSW samples are presented in Figures 10 and 11. NMR spectroscopy allows recording signals for both amorphous and crystalline parts of the investigated samples. In hydrated cement, the amorphous part consists mainly of the CSH phase, which gives the hardened cement paste mechanical strength. The \({}^{27}\)Al and \({}^{29}\)Si MAS NMR spectra for raw materials (CEM and VSW), CP, and CP10 VSW samples are presented in Figures 10 and 11, respectively. The \({}^{27}\)Al MAS NMR spectra recorded for the CEM sample show two spectral lines: at 80 ppm, attributed to the aluminate (C\({}_{3}\)A) and at 10 ppm, attributed to the aluminate-ferrities (C\({}_{4}\)AF) [48; 49]. The signal from 80 ppm corresponds to the Al atoms present in the tetrahedral environment of oxygen atoms Al\({}^{(\rm IV)}\). At about 10 ppm, the signal corresponds to the aluminum atoms present in the octahedral environment of oxygen atoms Al\({}^{(\rm VI)}\)[48]. VSW does not show any peak using \({}^{27}\)Al MAS NMR spectroscopy. The \({}^{27}\)Al MAS NMR spectra of the CP sample show the absence of aluminate and the presence of trisulphoaluminate at 13 ppm and monosulphoaluminate at 10 ppm [50]. In the presence of vine shoot waste in cement paste, the peak attributed to trisulphoaluminate does not appear and the peak attributed to monosulphoaluminate is present.  
Figure 9: FT-IR spectra of (**a**) CEM, (**b**) CP, (**c**) CP10 VSW, (**d**) VSW samples.  
The \({}^{29}\)Si MAS NMR spectra recorded for the CEM sample show a broad spectral line between the \(-66\) to \(-77\) ppm attributed to the alite (C\({}_{3}\)S) and belite (C\({}_{2}\)S) primary phase [41]. VSW does not show any peak in the \({}^{29}\)Si MAS spectra. The different structures of the CSH from CP and CP10 VSW are associated with broad spectral lines ranging between \(-75\) to \(-95\) ppm and \(-66\) to \(-78\) ppm corresponding to the unhydrated clinker (alite and belite) [51,52]. CP and CP10 VSW samples contain Si atoms in a tetrahedral environment of oxygen atoms. The spectral line from \(-75\) to \(-90\) ppm corresponds with one to two linked  
Figure 11: \({}^{29}\)Si MAS NMR spectra of (a) CEM, (b) CP, (c) CP10 VSW and (d) VSW samples.  
Figure 10: \({}^{27}\)Al MAS NMR spectra of (a) CEM, (b) CP, (c) CP10 VSW and (d) VSW samples.  
silicon tetrahedra Q\({}^{1}\) and Q\({}^{2}\) structural elements [52]. The presence of vine shoot waste results in a higher number of different structures of CSH.
# Feasibility of a Quasi-Static Approach in Assessing Side-Wind Hazards for Running Vehicles
## 4 Feasibility of the Quasi-Static Approach Under Turbulent Winds
### Comparison of Tire Forces Under Various Mean Wind Speeds
The differences between the vertical reaction forces estimated using the  
Figure 16: Normalized friction force on front axle of the van according to mean wind speed. quasi-static approach and those estimated using the dynamic approach were a result of the additional rolling moment. Consequently, the critical wind speeds differed by approximately 1.3 m/s between the two approaches, as shown in Table 2. On the other hand, the enhanced quasi-static approach, which considers the additional roll moment, provided reaction forces that approximated those of the dynamic approach with a critical wind speed difference of only 0.2 m/s. According to the findings from the above two case studies, the feasibilities of the quasi-static and the enhanced quasi-static model were confirmed for their use in estimating the tire forces and, as a result, predicting the critical wind speed, even under turbulent wind conditions. The proposed trailer model, however, had one limitation in that the resisting rolling moment at the ball hitch connection was not reflected. Since some large trailers are equipped with moment-resisting hitch models, it would be acceptable to underestimate the overturning safety using the proposed trailer model. This could be considered a conservative assessment of vehicle safety. One can also further develop the trailer model to accommodate a hitch model with high roll-stiffness based on the formulation done for the two-axle vehicle in the present study by assuming that the tractor and the trailer rotate together.
## 2 Materials and Methods
### Survey and Data Processing Methods
In this regard, there is a third, necessary factor that allows formation of robust cartographic representations that reflect the distribution of individual radionuclides in the studied area. It consists of overlapping the effective radii between adjacent survey profiles in combination with interpolation methods with a high smoothing coefficient and an interpolation radius similar to the effective radius. When surveying at a 45 m altitude, the effective radius reaches 80 m. In this case, when the distance between the surveying profiles is 25-50 m, data from 7-4 neighbouring routes enter the interpolation window. Using local polynomial interpolation (LPI) to construct geostatistical surfaces, which selects a smoothed surface for the data, high data noise is coped with. Simultaneously, as shown in the next section, all the main geological structures and the epicentres of the main anomalies remain in their places, and the results are reproducible. When surveying is finished, the separate energy line counts in the channels of uranium, thorium, and potassium in the given time intervals are summed up. A layer with spatially linked measurement points is formed, including three attributes (the number of quanta in each radionuclide channel or their conversion to concentration, taking into account the coefficient matrix). Carrying out an LPI interpolation and forming a cartographic representation, allows the study of the integral radioactivity and the distribution of its components on the area. The integral channel can be represented in pulses per time unit or \begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **Energies. MeV** &
\begin{tabular}{c} **Line Intensity.** \\ **Relative Units** \\ \end{tabular} \\ \hline \multirow{4}{*}{Uran—\({}^{238}\)U} & 2.198 & 0.28 \\  & **1.762 * & **1.00** \\  & 1.12 * & 0.9 \\  & 0.609 * & 2.56 \\  & 0.350 & 1.74 \\ \hline \multirow{4}{*}{Thorium—\({}^{232}\)Th} & **2.62 * & **1.00** \\  & 0.91 * & 0.74 \\ \cline{1-1}  & 0.58 & 0.80 \\ \cline{1-1}  & 0.238 & 1.31 \\ \hline Potassium—\({}^{40}\)K & **1.460 * & **1.00** \\ \hline \hline \end{tabular}  
* Energy lines used in our system. Bold—energy lines used in the classical approach to gamma data processing.  
\end{table} 
Table 2: The main energy lines of gamma radiation are thorium, uranium, and potassium-40. in gamma radiation dose rate units, taking into account gamma quanta with energies from 0.4 to 2.81 MeV.
## 4 Discussion
### Sensitivity of Measure Outcomes to Motor Function  
The sensitivity of outcome measures for motor function is a seemingly important factor in determining whether NMES is effective for UE function. Which of the outcome measures are the most sensitive to changes in UE function? It is likely that FMA-UE features almost the same sensitivity to severe UE paresis as WMFT-FAS (Figure 6a). BBT seemed less sensitive to UE function than FMA and WMIFT (Figure 6b,c). More specifically, BBT marked zero even when the FMA scores and WMFT were estimated as 40 and 35, respectively. Accordingly, it is plausible that the included studies used FMA most frequently as outcome measures for motor recovery when applying NMES. It is notable, however, that full FMA-UE scores do not mean full recovery from paresis. For patients with mild paresis, FMA is saturated. Instead, BBT is available. This means that the sensitivity and suitability of outcome measures may depend on stroke severity. In the near future, a new universal outcome measure, independent of the severity of UE paresis, is expected to be established.
## 4 Discussion  
Figures 13 and 14 report the frequency responses of the rear wheel and the sprung mass under different constant acceleration frequency sweeps.  
Figure 13 shows the influence of unsprung mass, front wheel, and pitch movement natural frequencies on the rear wheel frequency response. The low-frequency contribution to the response comes from the unsprung mass (A) with the highest module, as expected. Respectively, (B) and (C) are the frequency contribution due to front wheel and rear wheel natural frequencies while (D) is the pitch movement natural frequency.  
In Figure 14 the sprung mass natural frequency is clearly visible at the same frequency of the one reported in Figure 13 (again, A). In this case, the frequency contribution coming from the front wheel and rear wheel natural frequencies are much less visible due to the filtering action achieved by the respective suspension systems.  
All these results are in line with the expected frequency response characteristics computed with vehicle mass, inertia, and wheel rate data as input. They are also congruent with the theory of motorcycle vertical dynamics as outlined in [17], chapter 5 (In-Plane Dynamics), proving the ability of the system to reproduce the vertical response of an unrestrained bike during its straight-line motion. Similar considerations are also reported in [22].  
Figures 15 and 16 report comparisons between real-world and rig-generated road profiles. It is worth highlighting that the difference between time histories of real-world road and rig-generated profiles is not visible when overlapping the two data streams since it is very small. Therefore, the absolute error has been reported for each sensor as well. This is an indication of the effectiveness achieved with the system.  
A test performed on good quality, smooth road pavement at 100 km/h is reported in Figure 15. It shows very low acceleration and wheel displacement levels. Under these conditions the system can replicate real-world road data very well, with a maximum absolute error value (MAE) of 0.32% for unsprung mass acceleration and 0.25% for suspension displacement. The RMS error is 0.20% in both cases.  
Figure 16: Test performed on a speed hump at 30 km/h.  
Results of a test performed on a speed hump are shown in Figure 16. The road input is akin to an impulse. Under this condition the system is still able to simulate motorcycle dynamics accurately since the acceleration MAE is 0.95%, the suspension displacement MAE is 0.98%, the pitch angle MAE is 0.98% while the RMS error is 0.45%. It is worth noticing that the pitch angle does not return to zero after the speed hump because the motorcycle is accelerating.