esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Signal transmission path of human body communication from a wearable transmitter worn on the wrist to an off-body stationary receiver touched by the user's finger was investigated. Numerical simulation results of electric field strength distribution, electric field vector distribution, and electric current in the human body were compared for one-electrode transmitter and two-electrode transmitter. When the signal and ground electrodes of the transmitter were in contact with the human arm, i.e., the two-electrode transmitter, capacitive coupling between the receiver ground and the human torso was much stronger than that of the one-electrode transmitter. As a result, there was electric current flow inside the torso, upper arm, elbow, and to the ground electrode of the transmitter. The increased capacitive coupling and the current flow inside the human body contributed to stronger return path. In the case of one-electrode transmitter, the only return path was through the capacitive coupling of the floating ground. This difference in the transmission path explains why the received signal was larger when two-electrode transmitter was used. Transmission path analysis in this study contributes to the  
Figure 8: Transmission signal path: (**a**) Signal path for the one-electrode transmitter. (**b**) Signal path for the two-electrode transmitter.  
better understanding of signal transmission mechanism of HBC and will be useful for developing HBC applications.  
Conceptualization, K.Y.; Data curation, K.Y. and Y.N.; Formal analysis, K.Y., Y.N., K.S., D.M. and F.K.; Funding acquisition, K.S.; Investigation, K.Y.; Methodology, K.Y.; Project administration, K.S.; Supervision, K.S.; Writing--Original draft, K.Y.; Writing--Review & editing, Y.N., K.S., D.M. and F.K.  
This research was partly funded by Japan Science and Technology Agency A-STEP Program AS272S001a.  
The authors declare no conflict of interest.
# A High Throughput Hardware Architecture for Parallel Recursive Systematic Convolutional Encoders
## 4 Discussion  
Data presented in Section 3.1 complete the results shown in our previous work [16], by providing information about the implementations of the various RSC encoders on FPGA. This provides an additional characterization of such encoders in terms of their speed performances and their source occupation.  
The most important contribution of this work is linked to the analysis performed in Sections 3.2 and 3.3 which permitted to extrapolate the dependency of the input data rate and the FPGA slice LUTs on the parallelism degree.  
Figure 8: Estimated trend of the number of lookup tables (LUTs) as function of \(k\).  
\begin{table}
\begin{tabular}{c c} \hline \hline \(N_{0}\) & 1.91672252010724 \\ \hline \(N_{1}\) & 0.655328418230563 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(N_{0}\) and \(N_{1}\) values derived through a MSE interpolation.  
Such analysis, even if approximated, provides a description complete of the most important merit parameters of the implementation, allowing to choose the values of \(k\) depending on the different application requirements.  
In particular, it is useful to notice that an increment of \(k\) leads to a less than proportional improvement of the \(R_{IN}[k]\) value but requires a more than proportional increment of the number of used sources.  
In addition, even if this analysis is performed for a specific RSC encoder, the methodology applied and the results is general are valid. In fact, the trends estimated do not depend on the polynomials \(h_{k}(x)\) and \(g(x)\) but only on the topology of the network used.  
The validity of such results might be compromised by modifications of the topology of the network, e.g., an insertion of pipeline registers would lead to a different \(R_{IN}[k]\) trend. Nevertheless, such optimization is linked to the single implementation and of difficult generalization.
# An In-Depth Review on Blockchain Simulators for IoT Environments
## 4 A Systematic Review of Blockchain Simulators in IoT
### Comparative Analysis
#### 4.3.3 Ns3  
NS3 is a simulator that has the ability to simulate the network and consensus layer and can be used in both C++ and python. This simulator is quite interesting as it has a scalability feature in which packets can have virtually zero bytes or \"dummy bytes\". Also, the nodes have optional features, which allow for minimal memory wasted in IPv4 stacks that nodes do not need. These features are incredibly useful as they save memory which can directly lead to shorter simulation times. However, the NS3 simulator does lack a GUI and has weak visualization that is still experimental [46].  
In terms of simulating blockchain within IoT, it is being tested in IoT security and anonymity as well as building trust within IoT networks. NS3 was used in simulation to evaluate latency, processing time, and resilience against cyberattacks. In this simulation, it was used to simulate up to 50 nodes as well as transactions between these nodes. It has been noted that NS3 is widely used for peer-to-peer networks [47]. Similarly, in the second simulation, NS3 was used to simulate 48 nodes, in which three gateway nodes generated blocks every 4.5 s, while others did not generate any blocks [27]. Finally, in the third simulation, both the network and consensus layer were simulated. For their initial simulation, the raft consensus was chosen, while in their network simulation, a PoW consensus was simulated [48].
# Neural Network Approach to MPPT Control and Irradiance Estimation
## 2 Theoretical Background  
### Equivalent Electrical Circuit of PV Module  
The photovoltaic module is commonly modeled in literature with a single-diode circuit shown in Figure 1[1]. The relation between the module terminal current and voltage is given by:  
\[I=I_{pv}-I_{s}\left[\exp\left(\frac{V+IR_{s}}{V_{t}}\right)-1\right]-\frac{V+ IR_{s}}{R_{p}}. \tag{1}\]  
where \(I\) is the output current of the module, \(V\) is the output voltage, \(I_{pv}\) is the photocurrent, \(R_{p}\) is a shunt resistance, \(R_{s}\) is a series resistance, \(I_{s}\) is the diode saturation current, and \(V_{t}\) is the thermal voltage. The thermal voltage is equal to \(V_{t}=nkTN_{s}/q\), where \(q\) is the electron's elementary charge [\(=1.602\times 10^{-19}\) C], \(k\) is the Boltzman constant [\(=1.381\times 10^{-23}\) J/K], \(T\) is the temperature of the cell [40], and \(N_{s}\) is the number of series solar cells in a PV module.  
The equivalent circuit has five unknown parameters that need to be determined. Generally, the parameters of the PV module depend on the environmental conditions, especially on the irradiance (\(G\)) and module temperature (\(T\)). The parameters \(I_{pv}\), \(I_{s}\), \(V_{t}\) and \(R_{p}\) for different working conditions can be expressed as [40]:  
\[I_{pv}=\left(I_{pv,ref}+\alpha(T-T_{ref})\right)\frac{G}{G_{ref}}, \tag{2}\]  
\[I_{s}=I_{s,ref}\left(\frac{T}{T_{ref}}\right)^{3}\exp\left(\frac{qE_{q}}{nT} \left(\frac{1}{T_{ref}}-\frac{1}{T}\right)\right) \tag{3}\]  
\[V_{t}=V_{t,ref}\left(\frac{T}{T_{ref}}\right) \tag{4}\]  
\[R_{p}=R_{p,ref}\left(\frac{G_{ref}}{G}\right) \tag{5}\]  
where index \(ref\) denotes parameters values measured under standard test conditions (STC), defined at ambient temperature 25 \({}^{\circ}\)C and irradiance 1000 W/m\({}^{2}\). The short-circuit current temperature coefficient is denoted by \(\alpha\), whereas \(E_{g}\) is the bandgap energy of the semiconductor.  
Figure 2 shows the typical \(P\)-\(V\) characteristic of the PV module for different values of temperature and irradiance. It can be seen that the position of the MPP changes as a function of irradiance and temperature.  
Figure 1: Equivalent electrical circuit of a PV module.  
Figure 2: \(P\)-\(V\) curve for different values of irradiance and temperature.
## 3 Aerial Survey by UAS  
### Review of Commercial UAS  
Today more and more high-performance UASs are available on the market. In addition, very competitive prices and the improving aerial survey performance of UASs have increased user accessibility. Table 4 shows some of the UASs used in the field of photogrammetry; in particular, for each of them, the type, manufacturer, weight, maximum flight time for each battery, maximum speed, type of navigation, and the camera supplied with the drone are reported.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Manufacturer** & **Photo** & **Type** & **Weight** & **Endurance** & **Max Speed** & **Navigation** & **Camera** \\ \hline SwitchBlade-Elite + Sony RX R II & Vision Aerial & & & Multicopter & 2900 & 40 & 27 & RTK/PPK & Sensor: Full frame BSI & Sensor: Full frame BSI \\  & Sony RX R II & & & Tricopter & & GPS + GLONASS & Resolution: 42 MP \\ \hline Phantom 4 Pro v2.0 & DJI & & & Multicopter & 1388 & 30 & 20 & \begin{tabular}{c} SPP/PPK \\ GPS + GLONASS \\ \end{tabular} & \begin{tabular}{c} Sensor: CMOS 1\({}^{*}\) \\ Resolution: 20 MP \\ \end{tabular} \\ \hline PHANTOM 4 RTK & DJI & & & Multicopter & 1391 & 30 & 13.9 & \begin{tabular}{c} RTK \\ (Asia) \\ GPS + GLONASS + \\ \end{tabular} & \begin{tabular}{c} Sensor: CMOS 1\({}^{*}\) \\ Resolution: 20 MP \\ \end{tabular} \\ \hline Parrot Anafi & Parrot SA & & & Multicopter & 320 & 25 & 15 & \begin{tabular}{c} SPP \\ GPS + GLONASS \\ \end{tabular} & \begin{tabular}{c} Sensor: CMOS 1\({}^{*}\) \\ Resolution: 20 MP \\ \end{tabular} \\ \hline EVO II RTK & Autel Robotics & & & Multicopter & 1250 & 36 & 12.5 & \begin{tabular}{c} RTK \\ GPS + GLONASS+ \\ BelOne + Galileo \\ \end{tabular} & \begin{tabular}{c} Sensor: CMOS 1\({}^{*}\) \\ Resolution: 20 MP \\ \end{tabular} \\ \hline TYPHOON H3 & Yunec and Leica & & & Multicopter & 1985 & 25 & 20 & \begin{tabular}{c} SPP \\ GPS + GLONASS + \\ Galileo \\ \end{tabular} & \begin{tabular}{c} Sensor: CMOS 1\({}^{*}\) \\ Resolution: 20 MP \\ \end{tabular} \\ \hline Leica Abbot (customized & & & & & & & & \\ DJI Matrice 600–Sony & Leica Coosystems & & & & & & \\ aRTiJ) & & & & & & & \\ \hline UJS HP & Trimble & & & Fixed-Wing & 2500 & 35 & 23.5 & \
\begin{tabular}{c} TPK \\ GPS + GLONASS \\ \end{tabular} &
\begin{tabular}{c} Sensor: CMOS 1\({}^{*}\) \\ Resolution: 36.4 MP \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Commercial UASs.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Manufacturer** & **Photo** & **Type** & **Weight** & **Endurance** & **Max Speed** & **Navigation** & **Camera** \\ \hline eBee X + SenseFly & SenseFly, an & & & & & & & & RTK /PPK & Sensor: CMOS 1\({}^{*}\) \\ S.O.D.A. 3D & Agele company & & Fixed-Wing & 1600 & 90 & 12.8 & GPS + GLONASS & Resolution: 20 MP \\ \hline WingtraOne + Sony & & & & & & & & \\ RXIR II & WingtraO & & & & & & & \\ RXIR II & WingtraO & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cont.
## 3 Results and Discussion
### Differential Scanning Calorimetry (DSC)
Therefore, the peculiar effect of TPI can be traced back not only to its peculiar structure, but also to the combined action with the other stabilizing amphiphiles naturally present in the bitumens. Looking at the trend of the calorimetric curve of the asphaltenes derived from TPI-regenerated bitumen, it results as an intermediate between the one obtained for pristine bitumen and that obtained after aging with RTFOT for 75 min. In fact, it is noted that the RTFOT aging process for 75 min simulates the mixing process of the conglomerate and so reproduces a binder with the real chemico-physical characteristics of those present in the road pavement after having been put to use. Therefore, through these calorimetric measurements, it was possible to distinguish between the regenerating effect from the fluxing effect. Figure 1: (a) DSC of asphaltenes bitumen Type 1 Non-Aged, RTFOT 75 and 225 min, showing the effect of aging, as compared to the RTFOT 225 after addition of TPI and Soy Oil; (b) DSC of asphaltenes bitumen Type 2 Non-Aged and PAV aged, as compared to the PAV aged after addition of Agent 3 and 4. In Figure 1b, the calorimetric curves are reported for the asphaltenes obtained from the second type of bitumen (Type 2) used in this study, which was aged by PAV. In this case we also observed changes in the DSC line in accordance with those obtained for the first type of bitumen: the inflection point positions are influenced by the level of oxidation of the bitumen. In fact, the more oxidized the bitumen is, the more these inflection points are positioned towards higher temperatures. The same effect holds for the first signal of the calorimetric curves, where the characteristic marks of the bitumen aged by PAV occur at higher temperatures compared to those observed for the non-aged bitumen. In addition, with this type of bitumen it can be confirmed that the use of a regenerating additive, and specifically Agent 3 (green line), brings back the TG values of the PAV-aged bitumen to those obtained for non-aged bitumen. On the other hand, an additive without a regenerating capacity but simply with fluxing characteristics such as Agent 4 (purple line) leaves the TG values just as they were. For both types of bitumen, we can observe that the calorimetric curves obtained for the non-aged bitumen are very linear and they show almost exclusively the inflection points of the material. On the other hand, the calorimetric curves derived from the aged samples (RTFOT or PAV) are more undulating and they present, in addition to the inflection point, other small peaks that are most probably due to the presence of molecules with different melting points inside the sample.
## 3 Methodology
### Index for Interferometric Performance Evaluation  
The coherence of InSAR depicts changes in backscattering characteristics on the scale of the radar wavelength. It measures the quality of an interferogram, provides information about the types of the ground surface, and detects the small-scale changes in the scatter distribution. The coherence of InSAR is generally decreased by long baselines between satellites, long-time lags between image acquisitions, and random motions of scatters in resolution cells. Several sources of decorrelation could be expressed by correlation terms [37]:  
\[\gamma=\gamma_{thermal}\cdot\gamma_{baseline}\cdot\gamma_{rotation}\cdot\gamma_{ volume}\cdot\gamma_{temporal}\cdot\gamma_{other} \tag{1}\]  
Thermal decorrelation, denoted by \(\gamma_{thermal}\), is due to the thermal noise induced by the characteristics of the \(SNR\) of the radar system [38].  
\[\gamma_{thermal}=\frac{1}{1+SNR^{-1}} \tag{2}\]  
\[SNR=10 lg(P_{s}/P_{n}) \tag{3}\]  
\(P_{s}\) is the average received signal power, \(P_{N}\) is the power of thermal noise in the receiver system, and \(SNR\) is the signal-to-noise of the radar system.  
Baseline decorrelation is a geometric decorrelation related to the baseline; the larger the perpendicular baseline, the greater differences between the primer and secondary SAR images. The baseline decorrelation could be simply defined as,  
\[\gamma_{baseline}=\begin{cases}1-\frac{B_{\perp}}{B_{c}}&B_{\perp}<B_{c}\\ 0&B_{\perp}>B_{c}\end{cases} \tag{4}\]  
\[B_{c}=\frac{RB_{w}tan(\theta_{inc}-\alpha)}{f} \tag{5}\]  
where \(B_{c}\) is the critical baseline of the InSAR system, it depends on radar frequency (\(f\)), bandwidth (\(B_{w}\)), and topographic slope (\(\alpha\)).  
Figure 2: The flowchart reveals the deceleration of Beijing land subsidence by Gaofen-3 SAR images. It consists of the workflow of Gaofen-3 SAR images interferometry, interferometry analysis, time-series processing of Gaofen-3 SAR images, and the deceleration of Beijing land subsidence with Gaofen-3 results.  
The difference in Doppler centroid frequencies (\(\Delta f_{DC}\)) induced the geometric decorrelation in the azimuth direction, the coherence factor \(\gamma_{DC}\) decreases linearly with increasing differences in the Doppler centroid frequencies, as follows:  
\[\gamma_{DC}=\begin{cases}1-\frac{\Delta f_{DC}}{B_{A}}&\left|\Delta f_{DC}\right| \leq B_{A}\\ 0&\left|\Delta f_{DC}\right|>B_{A}\end{cases} \tag{6}\]  
where \(B_{A}\) is the bandwidth in the azimuth direction [37].
# Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm
## 4 Proposed Algorithms
### Fbs-Cnc  
We present the FBS algorithm to solve the aforementioned model (11). The specific iterative steps are illustrated below.  
1. For the data-fidelity term: \[v^{k+1} =x^{k}-\alpha\
abla\bigg{(}\frac{1}{2}\|y-F^{-1}\Lambda Fx^{k}\|_ {2}^{2}\bigg{)}\] \[=x^{k}-\alpha\Big{[}F^{T}\Lambda^{T}(F^{-1})^{T}\Big{(}F^{-1} \Lambda Fx^{k}-y\Big{)}\Big{]}\] \[=x^{k}-\alpha\Big{(}F^{T}\Lambda^{T}\Lambda Fx^{k}-F^{T}\Lambda^ {T}Fy\Big{)}.\] (20)
2. For the regularization term: \[x^{k+1} =\text{prox}_{\alpha\lambda\phi_{b}}(v^{k+1})\] \[=\text{prox}_{\alpha\lambda\|\cdot\|_{1}}\bigg{[}v^{k+1}+\alpha \lambda b^{2}\bigg{(}x^{k}-\text{prox}_{\frac{1}{b^{2}}\|\cdot\|_{1}}(x^{k}) \bigg{)}\bigg{]}.\] (21)  
Finally, after further refinement of the previous two steps, we have developed the FBS-CNC algorithm for image deblurring, as outlined in Algorithm 1.  
```
0:\(x^{0},y,F,\Lambda,\alpha>0,\lambda>0,b>0\).
0:\(x\). while \"stopping criterion is not met\" do \(v^{k+1}=x^{k}-\alpha\Big{(}F^{T}\Lambda^{T}\Lambda Fx^{k}-F^{T}\Lambda^{T}Fy \Big{)}\); \(u^{k+1}=\text{prox}_{\frac{1}{b^{2}}\|\cdot\|_{1}}\Big{(}x^{k}\Big{)}\); \(w^{k+1}=v^{k+1}+\alpha\lambda b^{2}\Big{(}x^{k}-u^{k+1}\Big{)}\); \(x^{k+1}=\text{prox}_{\alpha\lambda\|\cdot\|_{1}}\Big{(}w^{k+1}\Big{)}\). endwhile
```  
**Algorithm 1** FBS-CNC for image deblurring
## 4 Sehaa Tool: Methodology and Design
### Data Collection Module
#### 4.2.1 Keywords and Geolocation  
Firstly, we collected the tweets using a filter based on a list of keywords. These keywords were partly taken from the latest statistics published by the Saudi Ministry of Health (MOH) on their official website [80]. Some of the other keywords were acquired from the domain experts through personal discussions, and from common knowledge and vocabularies in the Saudi Dialect. Table 1. lists common vocabularies for most common symptoms and diseases in Saudi Arabia. Column 1 lists the set of keywords that could be used by Tweeters to mention symptoms, diseases, or medications and hence were included in the search keywords. These keywords are grouped based on particular representative diseases in English and Arabic, and are listed in Column 2. The abbreviations for the diseases are provided in Column 3. The next three columns provide similar details for other diseases.  
Secondly, we filtered the tweets based on the geographical location of the tweets to ensure that the tweets are generated from Saudi Arabia. This was achieved by specifying the top-right and bottom-left geographical coordinates and defining a bounding square box around the country. The tweets that originated from within the defined bounding box were extracted by the stream listener. This is different from the paid Twitter Search API, where the user's location field is used to filter tweets. A Python function using the Tweepy library was written that incorporated the two filters described above into the stream listener.  
We understand that some tweets originating outside Saudi Arabia could also be relevant to this work and should have been collected. However, we used a free streaming Twitter API and were limited in resources. Moreover, the tweets originating from outside Saudi Arabia would form a small proportion of the overall tweets and we plan to consider this in future work.
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
## 2 Experimental Method
### Resistance Measurement  
FSR sensors function as variable resistors, with resistance values that change based on the applied load. Direct measurement of these variable resistance values in a circuit is challenging; hence, a voltage divider method was employed to measure the voltage changes corresponding to different loads. Figure 3 illustrates the circuit configuration used to measure the output voltage of the FSR sensor as the load changes.  
The fixed resistor in the circuit was initially set to 10 k\(\Omega\), as recommended by the manufacturers. To analyze the output characteristics of the FSR sensors under various conditions, we tested the sensors with fixed resistances of 1 k\(\Omega\), 4.7 k\(\Omega\), 5.6 k\(\Omega\), 10 k\(\Omega\), 47 k\(\Omega\), 100 k\(\Omega\), and 200 k\(\Omega\). In the case of commercial FSR sensors, the applicable model is determined according to the load. However, FSR sensors can be used beyond the load limits specified by the manufacturers, depending on the resistance value. Under this premise, it is necessary to verify whether the sensors exhibit the same linearity across different resistance values.
## 2 Related Work  
A dc-dc converter works in continuous conduction mode (CCM) for most applications, although occasionally it may operate in discontinuous conduction mode (DCM) [25; 26; 27]. The operation of a dc-dc converter in DCM is primarily due to low loading conditions; however, in a PV system environmental conditions may force the system to shift into DCM. Moreover, PV systems are designed to operate in MPPT mode using a dc-dc converter, sometimes called as regulator. Based on an algorithm, the regulator accepts inputs such as photovoltaic voltage (\(V_{pv}\)) and current (\(I_{pv}\)) and sets the desired duty cycle (D) to ensure that the PV array delivers the maximum power. The underlying principle of MPPT is the implementation of maximum power transfer theorem, wherein the dc-dc converter matches the source and load impedance to maximize the power transfer.  
The impact of the operating mode of a dc-dc converter on system performance has always been an area of interest for researchers [28]. In this regard, our literature review revealed that the transition from CCM to DCM for a PV-fed pumping station was studied in [29]. Similarly, the transition of a buck-boost dc-dc converter between CCM and DCM was expounded in [30]. The authors in [31] argued that the system can be made to work smoothly in a mixed conduction mode with a fuzzy controller and a fixed switching frequency. Other researchers have included the converter operation in the modeling phase of the converter [32]. However, all of the presented works have only discussed CCM and DCM in terms of the load. In this paper, we suggest the theory explaining the impact of irradiance on the dc-dc converter has not been fully developed, and that the converter may operate in DCM mode even if the load is kept constant. Therefore, we propose a solution wherein the impact of irradiance on the dc-dc converter is first evaluated, then added to the maximum power point tracking algorithm.