esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Electrical Characterization of the Interface Trap Density  
Charge pumping measurements were conducted to accurately evaluate the interface quality of the control and fluorinated MOSFETs. The charge pumping characterization was performed by sweeping the base level voltage (V\({}_{\mathrm{base}}\), -2.3 V to 1 V in a step of 50 mV) of the trapezoidal gate pulse (with a constant-amplitude, 1 V) at 200 KHz. The S/D terminals were grounded. The region of the bandgap probed was from electron emission energy level to hole emission energy level, which was around the midgap. The charge pumping current (I\({}_{\mathrm{cp}}\)) is plotted as a function of V\({}_{\mathrm{base}}\) for equal trapezoidal pulse rise time (t\({}_{\mathrm{R}}\)) and fall time (t\({}_{\mathrm{F}}\)), as shown in Figure 11. t\({}_{\mathrm{R}}\) and t\({}_{\mathrm{F}}\) are varied from 100 to 800 ns. Lower I\({}_{\mathrm{cp}}\) for the fluorinated sample is indicative of reduced D\({}_{\mathrm{it}}\). The mean D\({}_{\mathrm{it}}\) value can be extracted according to the following equation [14,15],  
\[\frac{I_{\mathrm{cp}}}{f}=2 qD_{\mathrm{it}}AkT\left\{\ln\sqrt{t_{R}t_{F}}+\ln \left(\frac{\left|V_{\mathrm{ph}}-V_{\mathrm{r}}\right|}{\left|\Delta V_{g} \right|}V_{\mathrm{it}}n_{\mathrm{r}}\sqrt{\sigma_{\mathrm{s}}\sigma_{p}} \right)\right\}\]  
where \(q\) is the electronic charge, \(A\) is the transistor gate area (2.08 \(\times\) 10\({}^{-4}\) cm\({}^{2}\) in our devices), \(k\) is the Boltzmann constant, \(V_{\mathrm{f}b}\) is the flat band voltage, \(V_{\mathrm{r}}\) is the threshold voltage, \(\Delta V_{g}\) is the gate pulse amplitude, \(V_{\mathrm{it}}\) is the thermal velocity of the carriers, \(n_{i}\) is the surface concentration of minority carriers, and \(\sigma_{n}\) and \(\sigma_{p}\) are the capture cross sections of electrons and holes, respectively. The mean D\({}_{\mathrm{it}}\) values were extracted from the slope of \(I_{\mathrm{cp}}\)_/versus_\(\ln[(t_{R}\times t_{F})^{1/2}]\), as shown in Figure 12. It has been found that the mean D\({}_{\mathrm{it}}\) value was reduced \(\sim\)5\(\times\) from 2.8 \(\times\) 10\({}^{12}\) to 4.9 \(\times\) 10\({}^{11}\) cm\({}^{-2}\)eV\({}^{-1}\) after F plasma  
Figure 10: Effective channel mobility _versus_ inversion charge density as a function of CF\({}_{4}\)/O\({}_{2}\) plasma treatment time. RF power: 30 W.  
treatment. F atoms possibly passivate dangling bonds and oxygen vacancies in the HfO\({}_{2}\)/In\({}_{0.53}\)Ga\({}_{0.47}\)As interface and thereby reduce D\({}_{\rm it}\) value.  
Table 1 compares the device performance and interfacial properties of In\({}_{0.53}\)Ga\({}_{0.47}\)As MOSFETs reported in this paper and other high-k/In\({}_{0.53}\)Ga\({}_{0.47}\)As gate stacks in recent publications. The D\({}_{\rm it}\) value reported in this paper is the lowest value compared to prior reported HfO\({}_{2}\)/In\({}_{0.53}\)Ga\({}_{0.47}\)As gate stacks.  
Figure 11: Charge pumping measurement with rise/fall time dependence. Samples with F incorporation show much smaller I\({}_{\rm cp}\). V\({}_{\rm base}\): –2.3 V to 1 V in a step of 50 mV and the pulse amplitude is 1 V.
## 2 The Experimental Data
### Modeling of a Synthetic Source Gather  
A synthetic source gather represents a modeled field that could be registered by an HLA of hydrophones recording arrivals from a single instant source. It represents the amplitude of the recorded sound as a function of its arrival time (two-way travel time) and offset.  
To model such a source gather, we calculated multiple single-frequency sound fields at the frequency band of the air gun (i.e., wide band) and transformed their sum from the frequency domain into the time domain using the inverse fast Fourier transform. The sound fields were modeled with the range-dependent acoustic modeling software RAM [64], which is based on the split-step Pade solutions of the parabolic equation [65]. The sound pressure was calculated with the aforementioned parabolic equation solution as follows:  
\[p(r+\Delta r,z)=\exp\Bigl{(}ik_{0}\Delta r\sqrt{1+X}\Bigr{)}p(r,z), \tag{10}\]  
where \(X=k_{0}^{-2}\Bigl{(}\rho\frac{\partial}{\partial z}\frac{1}{\rho}\frac{ \partial}{\partial z}+k^{2}-k_{0}^{2}\Bigr{)}\),  
\(k=(1+i\eta\alpha)\frac{\omega}{c}\),  
\(\eta=\left(40\pi\log_{10}e\right)^{-1}\),  
\(\rho\) is the density, \(\alpha\) is the attenuation in dB per wavelength,  
\(\omega\) is the cyclic frequency, \(c\) is the typical phase speed in the media,  
\(k_{0}=\frac{\omega}{c}\) is the wavenumber.  
As above, we show here the modeling results for the case of source gather #2290 in profile ISEM-2073-108. Our geoacoustic model consisted of three layers: (1) a layer of thickness \(H_{1}\) and a sound speed profile \(c_{w}\) that vary with seasonality, (2) a sub-seafloor layer of thickness \(H_{2}\) and the sound speed at the seafloor \(c_{0}\), which increased with depth due to a positive sound-speed gradient \(K\), and (3) a salt layer of infinite thickness with a sound speed of 4000 m s\({}^{-1}\). The density of the second layer at the seafloor was 1250 kg m\({}^{-3}\), according to measurements we made on a sediment core that was obtained in the study area. In our model we assumed that the density in the second layer was increasing linearly according to the equations of [PERSON] (1972), in correlation with the sound speed, and reached 2500 kg m\({}^{-3}\) at the bottom of the second layer (i.e., the depth of the top-salt interface).  
Figure 10 compares the experimental and modeled source gathers. The synthetic gather was modeled for \(c_{0}\) = 1535 m s\({}^{-1}\) and \(K\) = 0.75 s\({}^{-1}\), which were found by the geoacoustic travel-time inversion that best fit the experimental data (Figures 7 and 8). Arrival A is a possible caustic that was found in the experimental data and is matched by our model. Arrival A follows a significant intensity increase (by \(\sim\)30 dB) at the range of \(\sim\)6200 m, which is associated with the arrival of the caustic. B is another high-intensity arrival, which can possibly be another caustic from a sub-bottom layer, or another sort of reflection from sub-bottom layering.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 4 Results and Discussion
### System Evaluation  
#### 4.2.1 Assessment of the Monitoring Stability of the CEMS  
Traditional ECG monitoring devices often impose physical constraints that hinder users' daily activities, significantly compromising user comfort. As shown in Figure 12a, the physical structure of the CEMS addresses this limitation. The STE is sewn onto the inner surface of an elastic band, enabling direct contact with the skin to facilitate ECG signal acquisition. The embedded system circuit board responsible for signal processing is housed in a compact plastic enclosure and is attached to the elastic band via Velcro, as illustrated in Figure 12b. The high flexibility of the STE ensures comfortable wearability, while the miniaturized design of the system allows users to wear it unobtrusively even during exercise. The subject's ECG signals are transmitted from the skin surface to the CEMS via the STE. After passing through the filtering circuitry, the signals are acquired by the MCU and subsequently transmitted to a mobile device via a Bluetooth module (Figure 13), enabling users to observe and analyze the data in real time.  
To evaluate the monitoring stability of the CEMS, a continuous testing experiment was conducted on subjects for over 600 s under a low-activity state. The results are presented in Figure 14. Throughout the test, the circuit operated continuously without interruption. The amplitude decay of the subject's ECG signal was less than 0.08 V, and the standard  
Figure 11: Signal comparison between the STE and Ag/AgCl gel electrodes within a single cycle.  
Figure 12: Photographs of the CEMS hardware. **(a)** The CEMS is in a partially assembled state. **(b)** The CEMS is being worn on the body.  
deviation of the RR interval, a key ECG characteristic parameter, was 0.189 s. Additionally, the system successfully captured complete characteristic waveforms of the subject's ECG signals. These results demonstrate that the CEMS exhibits excellent stability and is capable of providing reliable ECG monitoring during daily activities.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 2 Vehicle Model  
### Lateral Dynamics Modelling  
Several assumptions are put forward as stated in [14]. Specifically, it is presumed that the combination of the car body and suspension constitutes a stiff system, and the vertical motion aspect of the vehicle is disregarded. The present study narrows its focus solely to the lateral movement and yaw motion of the vehicle for the purpose of analysis. Subsequently, a vehicle dynamics model is set up, which is illustrated in Figure 1.  
In the context of planar motion, the dynamics model of the vehicle can be described as follows:  
\[\left\{\begin{aligned} m\dot{y}=F_{yr}+F_{y\prime}cos\delta-mV_{x} \dot{\varphi}\\ I_{z}\ddot{\varphi}=l_{f}F_{y\prime}cos\delta-l_{r}F_{y\prime} \end{aligned}\right. \tag{1}\]  
In the equation, \(\dot{\varphi}\) represents the vehicle's yaw rate, \(I_{z}\) denotes the vehicle's vertical rotational inertia, while \(l_{f}\) and \(l_{r}\) represent the distances from the vehicle's center of gravity to the front axle and rear axle, respectively.  
The lateral force exerted by a tire is directly associated with the tire's slip angle. This relationship is of utmost importance in understanding the vehicle's handling and stability characteristics, as the lateral force significantly influences the vehicle's ability to maintain  
Figure 1: Vehicle dynamics model.  
its intended path during maneuvers. Assuming a small-angle approximation, the formula for the tire's lateral force can be expressed as follows:  
\[\left\{\begin{aligned} F_{yf}&=2C_{f}\bigg{(}\delta- \frac{V_{y}+l_{f}\dot{\varphi}}{V_{x}}\bigg{)}\\ F_{yr}&=2C_{r}\bigg{(}\frac{V_{y}-l_{r}\dot{\varphi}}{V_{x }}\bigg{)}\end{aligned}\right. \tag{2}\]  
In the equation, \(C_{f}\) represents the front wheel camber stiffness, and \(C_{r}\) represents the rear wheel camber stiffness.
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 4 Performance Results
For \(N_{rx}=64\) (Figure 6), the gain of the proposed hybrid equalizer, from the 1 st to 4 th iteration, for a BER target of \(10^{-3}\), is 4.4 dB, and the penalties from hybrid to fully digital case are 6.6 dB and 3.6 dB, for iterations 1 and 4, respectively. For both cases, the performances are closer for iteration 4 than for iteration 1, which means that the iterative approach is very interesting for hybrid systems. A performance comparison for a different number of users (and of RF chains) can be seen in the Figure 7. For iteration 1, we can see that the performance gets worse when the number of users increases. This happens because when the number of users increases, the interference level also increases, and the receiver does not have the ability to remove this interference only with a linear filter, i.e., with only one iteration. However, when the number of iteration increases, this interference is removed. Then, at iteration 4, the performance can even improve when the number of users increases, because we have more degrees of freedom at the digital part, since the number of RF chains also increases. Finally, in the Figure 8, we can see the performance for a different number of receive antennas. This figure proves that there is a gain when we only increase the complexity of analog part, in terms of number of antennas and phase shifters, keeping the same complexity for digital processing. Therefore, the analog part of proposed equalizer is very efficient to improve the system performance, proving that the hybrid analog-digital architecture is a very promising approach. The gain of proposed hybrid equalizer, from 1 st to 4 th iteration, for a BER target of \(10^{-3}\), decreases when the number of receive antennas increases. This happens because a higher level of interference is removed at the analog part, when we increase the number of antennas and phase shifters. At iteration 4, the g  
Figure 5: Performance comparison between the proposed iterative hybrid equalizer and full-digital equalizer, with \(N_{rx}^{RF}=U=8\) and \(N_{rx}=32\). Figure 6: Performance comparison between the proposed iterative hybrid equalizer and full-digital equalizer, with \(N_{rx}^{RF}=U=8\) and \(N_{rx}=64\). to \(N_{rx}=64\), and from \(N_{rx}=64\) to \(N_{rx}=32\) are 2.2 dB and 2.5 dB, respectively, which is a little less than the expected 3 dB, which is the expected for a typical full-digital system.
# DRL-Based Load-Balancing Routing Scheme for 6G Space-Air-Ground Integrated Networks
## 3 System Model  
This section explains the research scenario of the proposed DQN-LLRA algorithm, the basic framework of the DQN algorithm, and the framework and mechanism of the DQN-LLRA algorithm.  
In this paper, the Iridium satellite, a typical polar LEO constellation, is taken as the research scene. The Iridium constellation has six polar orbits, and 12 LEO satellites are distributed on each orbit, including 11 communication satellites and one standby satellite. In total, there are \(6\times(11+1)=72\) satellites in the whole constellation. The interorbit inter-satellite links change periodically with the movement of satellites. The connectivity of the inter-satellite link changes periodically with the motion of the satellite. The satellites in the first orbit move in opposite directions to the satellites in the sixth orbit; so, there is no direct communication between the satellites in the two orbits. As shown in Figure 4, in a typical constellation, there are up to four inter-satellite links around a satellite, connecting it to four satellites, two of which are in the same orbit and two in adjacent orbits. Communication between satellites with an inter-satellite link is possible.  
In order to represent the relevant parameters of the satellite topology, we constructed this topology as an undirected graph \(G=(V,E)\), where \(V=\{v_{1},v_{2},\cdots,v_{n}\}\) represents the set of satellite nodes, and \(n\) is the total number of satellite nodes. We denoted the queue utilization of each node \(v_{i}\) by \(QU_{i}\), which represents the ratio of the number of packets currently existing in the queue of satellite node \(v_{i}\) to the queue capacity of the node \(v_{i}\), and can be expressed mathematically as:  
\[QU_{i}=\frac{\text{Number of packets in queue of node $i$}}{\text{Total queue size of node $i$}}. \tag{1}\]  
As stated earlier \(E=\{e_{(v_{1},v_{2})},e_{(v_{2},v_{3})},\cdots,e_{(v_{i},v_{j})}\}\) represents the set of inter-satellite links, and \(e_{(v_{i},v_{j})}\) represents the inter-satellite link between satellite \(v_{i}\) and satellite \(v_{j}\). We denoted the time delay and bandwidth of each inter-satellite link by \(C_{(v_{i},v_{j})}\) and \(B_{(v_{i},v_{j})}\), respectively. Our DQN-LLRA algorithm was implemented on the basic model described in the next section.
## 3 Methodology: Proposed Prefetcher
### Controller  
Figure 5 describes the overall structure of the controller, and Figure 6 shows the internal operation flow of the prefetch controller. The controller manages all functions of the prefetch buffer. First, in the history table, an update is applied when memory access is requested from the LLC to the prefetch buffer. The controller checks whether the page number of the current demand request's address is stored in the page table and refresh the contents of the table consequently.  
If the page number of the current accessed address is already stored in the page table, then the corresponding correlated line table is also updated as shown in Figure 6. When the number of blocks accessing the correlated line to which the currently accessed address belongs is four or more, the correlated line-based picture table will be updated. Otherwise, we check whether there has been a previously accessed history with four or more access times per page. If so, we update contents of the page-based picture table. In addition, the controller manages a prefetch buffer. In our proposed model, prefetching operations only occurs for DRAM. In other words, only prefetched data are stored in the prefetch buffer. Therefore, the controller that manages the buffer controls the prefetching. To conduct the prefetching, the controller determines whether the current access is on the same correlated line as the previous access. When the accessed address is on the same correlated line, the controller analyzes the access pattern through modules I and II. When it does not exist on the same correlated line, the controller conducts a prefetching aggressively through module III. If the prefetch engine executes the prefetching, the fetched data are stored in the prefetch buffer by the controller. The controller manages the prefetch buffer by applying the LRU replacement policy.
# Numerical Modeling, Analysis, and Optimization of RFID Tags Functioning at Low Frequencies
## 3 Software Program Used for Determining Possible Structures
### Influence of the Shape of the Inductor on the Gain Distribution  
The second step of the study was analyzing the structures obtained from the gain point of view. To achieve this, the structures with an exterior diameter of 13 mm, seven turns, and a distance between the turns equal to the thickness of the turns with a dimension of 0.1 mm were compared. First, a 3D representation of the gain was considered, as shown in Figure 12, and it was determined that the shape of the coil did not significantly influence the shape of the gain, but the values were higher for the square coil.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Shape** & **Inductance** & **Capacitance** \\ \hline Square & 4\%–6\% & 4\%–10\% \\ Hexagonal & 10\%–13\% & 10\%–20\% \\ Octagonal & 10\%–13\% & 10\%–20\% \\ Circle & 2\%–5\% & 1\%–10\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The errors between the results obtained analytically and through numerical modeling.  
Figure 11: Comparison between the numerically and analytically obtained values for the inductance and capacitance required to achieve resonance at 13.56 MHz for the octagonal coil structures: (a) inductance; (b) capacitance.  
In Table 3, the peak values of the gain and directivity of the considered structures are presented. It can be concluded that the square coil inductor radiated more than the others.
###### Abstract  
In this paper, we propose underwater acoustic (UWA) communications using a generalized sinusoidal frequency modulation (GSFM) waveform, which has a distinct ambiguity function (AF) and correlation function characteristic. For these reasons, it is more robust in multipath channels than the conventional chirp spread spectrum (CSS) with a linear frequency modulation (LFM) waveform. Four types of GSFM waveforms that are orthogonal to each other are applied for each symbol in the proposed method. To evaluate the performance of the proposed method, we compared the performances of the proposed method and conventional method by conducting diverse experiments: simulations, lake trials and sea trials. In the simulation results, the proposed method shows better performance than the conventional method. The lake trial was conducted with a distance of 300-400 m between the transmitter and receiver. As a result of the experiment, the average bit error rate (BER) of the proposed method is \(3.52\times 10^{-2}\) and that of the conventional method is \(3.52\times 10^{-1}\), which shows that the proposed method is superior to the conventional method. The sea trial was conducted at a distance of approximately 20 km between the transmitter and receiver at a depth of 1500 m, and the receiver was composed of 16 vertical line arrays (VLAs) with a hydrophone. The proposed method had a BER of \(0.3\times 10^{-2}\) in one channel and was error free in the other.  
underwater acoustic communication; linear frequency modulation; nonlinear frequency modulation; generalized sinusoidal frequency modulation; ambiguity function; correlation function; multipath; Doppler shift; bit error rate +
Footnote †: journal: 2021  
13 August 2021  
Accepted: 27 October 2021  
Published: 29 October 2021  
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**Copyright:** 2021 by the authors. Licence MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Methodology
### Data and Preprocessing  
For our experiments, we used a recent benchmark dataset TwiBot20 [42] that included samples from the Twittersphere to train our model. The authors of TwiBot-20 divided users into four interest domains: politics; business; entertainment; and sports. For each user, TwiBot-20 contains the tweets, profile properties, and neighborhood information of a Twitter user. This dataset includes 229,573 Twitter users, 33,488,192 tweets, and 455,958 following relationship links. TwiBot20 includes 38 features for profile metadata. These features are called the user's metadata, as they represent the profile information. We extracted 18 profile features in addition to the account label, either as a human user or bot, from three files, Train/Test/dev. These profile features are shown in Table 2. The total number of labeled accounts that the three files had was 11,826. According to the data, 55.7% of the samples consisted of bot accounts, and 45.3% were human. Figure 3 depicts the distribution classes of bots and humans in the TwiBot-20 data. Figure 3: Distribution of the popularity of bots (class = 1) and humans (class = 0) in TwiBot-20. Figure 2: Illustration of transfer learning in our case scenario. Twibot20 was used as a source to train our model, Bot-MGAT, and to train target data that were new and unlabeled, to predict the labels. To test our model, we used two different hashtag datasets from NodeXL [43; 44]. The first dataset was for a climate hashtag, \"#tactonclimate\", and we refer to it as \"climate hashtag\". This dataset had a total of 5664 nodes and 15,651 edges. The second dataset was constructed based on the keywords \"#IStandWithPutin\" OR \"#IStandWithRussia\" OR \"#StandWithRussia\" OR \"#NaziUkraine\". This dataset contains 4941 nodes, and 6944 links. We refer to this dataset as \"Russian hashtag\". For both datasets, we extracted the profile features shown in Table 2, in addition to the links of interaction to build the graphs for the attention mechanism. Table 3 provides the summary statistics of the used datasets.
## 3 Implementation of Vehicular VLC System
### Packet Design and Timing Requirements for Vehicular VLC System  
The proposed vehicular VLC system adopts asynchronous serial communication with a baud rate of 1200, and it was designed to transmit one data packet every 50 ms using a timer. As illustrated in Figure 4, each data packet consists of 2 bytes of target speed data, 2 bytes of acceleration data, and 1 byte of end data, respectively. Each byte contains 8 data bits, and they are transmitted with 1 start bit, 1 parity bit, and 2 end bits, resulting in a total of 60 bits transmitted per cycle. Figure 4 presents an illustration of the packet design used in the proposed vehicular VLC system.