esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel SIW Leaky-Wave Antenna for Continuous Beam Scanning from Backward to Forward
## 2 Antenna Design
### Unit Cell in Ideal Waveguide
The effective attenuation constant \(\alpha_{eff}\) and the effective phase constant \(\beta_{eff}\) can be calculated using the S-parameters as follows [38]:  
\[\alpha_{eff}=\frac{1}{p}\text{Im}\bigg{[}\text{cos}^{-1}\bigg{(}\frac{A+D}{2} \bigg{)}\bigg{]} \tag{6}\]  
\[\bigg{|}\beta_{eff}\bigg{|}=\frac{1}{p}\text{Re}\bigg{[}\text{cos}^{-1}\bigg{(} \frac{A+D}{2}\bigg{)}\bigg{]} \tag{7}\]  
where \(A\) and \(D\) are the elements of the ABCD matrix of the unit cell, which can be calculated with Equation (2) using the classic conversion formulas. Figure 15 shows the effective phase constant and the attenuation constant for a unit cell with a longitudinal slot. It can easily be seen that the attenuation constant is maximum at the broadside frequency, which can result in gain deterioration occurring in OSB. Figure 16 depicts the effective phase constant and attenuation constant of the proposed unit cell, which consists of a longitudinal slot and a step discontinuity. Results confirm that the effective attenuation constant is approximately near zero and that OSB suppression is achieved. Moreover, the dispersion diagram confirms that the slope of the effective phase constant versus frequency continuously varies from negative to positive, covering the zero value at broadside frequency. Therefore, the LWA consisting of an array of the proposed unit cells can provide the continuous beam-frequency beam scanning from backward to forward through the broadside. Figure 14: Imaginary part of the shunt admittance in the circuit equivalent for longitude slot, rectangular-shaped h-plane discontinuity, and proposed unit cell. Figure 13: Schematic of SIW modeling of (**a**) longitude slot, (**b**) rectangular-shaped h-plane discontinuity, and (**c**) proposed unit cell.
# Gazing Characteristics of a Driver during Vehicle Backing
## 5 Conclusions  
In this study, we investigated the drivers' gazing time during a backing maneuver in the 90-degree reverse parking system. We used a Japanese SUV with the steering wheel at the right-hand side. We modified the vehicle with the following four technological conditions: no-tech vehicle (NT-V), sonar vehicle (S-V), backup-camera vehicle (BC-V), and backup camera with sonar vehicle (BCS-V). The gazing time of the drivers was investigated from start to the 5-m point (start phase) and the phase from backing from the 5-m point to stop (stop phase) to elucidate the driver's gaze while approaching the parking area and in the parking area during backing. The following conclusions are drawn from the findings of this study.  
1. In the comparison between NT-V and S-V, the results indicated that the average total gazing time of each mirror was similar, and there was no significant difference between them for the start phase and stop phase. The same trend was seen in the comparison between BC-V and BCS-V. Thus, the gazing time during backing maneuvers was not influenced by the presence or absence of sonars.
2. For the start phase, the average gazing time of the left-side mirror was the longest among the AOIs when drivers drove all types of vehicle technologies. For the stop phase, when drivers drove a vehicle without a backup camera (NT-V and S-V), the gazing time on the right-side mirror was the longest, and when they drove vehicles with a backup camera (BC-V and BCS-V), the gazing time on the monitor was the longest among the AOIs.
3. For the start phase, there were no significant differences in the total gazing time of all AOIs in the four technologies. For the stop phase, however, the total gazing time of all AOIs for the vehicle with a backup camera (BC-V and BCS-V) was significantly longer than that of the vehicle without a backup camera (NT-V and S-V) owing to the increase in the gazing time of the monitor.
4. The results obtained from the present experiments can contribute to the development of a vehicle camera system that combines the left- and right-side mirror cameras and the rear camera into one screen.  
Conceptualization, Y.M.; investigation, S.O.; writing-original draft, Y.M.; writing-review and editing, S.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Japan in 2017.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
None.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Evaluation Metrics  
#### 3.1.1 Evaluation of Detection  
The detection methods generate bounding boxes to identify the detected region. There are four classification categories, true positive (\(TP\)), true negative (\(TN\)), false positive (\(FP\)), false negative (\(FN\)). Precision can be also called positive predictive values (PPVs), as it represents the ratio of positive results that are \(TP\) (Equation (2)). Moreover, recall corresponds to the ratio of positive results that are correctly predicted to be positive (Equation (3))  
\[Precision=\frac{TP}{TP+FP}, \tag{2}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{3}\]  
Moreover, intersection over union (IoU) is an evaluation metric which is used to compare the predicted bounding box (detection output area) with the ground truth annotated area. It is defined in Equation (4):  
\[IoU=\frac{A_{gt}\cap\ A_{P}}{A_{gt}\cup\ A_{P}}, \tag{4}\]  
where \(A_{gt}\) is the area of the ground-truth of experts annotation and \(A_{p}\) is the predicted bounding box from the detetcion method.
# Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme
## 4 Simulation Results
### Performance Comparison
Theorem 2 in reference [6] proposes that for an \(l\) by \(m\) square area, the upper limit of pad usage is given by \(\left\lceil\frac{2l}{\sqrt{2}d_{maxpl}}\right\rceil\left\lceil\frac{2m}{ \sqrt{2}d_{maxpl}}\right\rceil\). Furthermore, since the base station is located at the center of the map in this study, the upper limit of pad usage should be revised to \((2\left\lceil\frac{l/2}{d_{maxpl}/\sqrt{2}}\right\rceil)(2\left\lceil\frac{m/ 2}{d_{maxpl}/\sqrt{2}}\right\rceil)\). For the small, medium, large, and xlarge maps mentioned above, the upper limits of pad usage are 16, 16, 36, and 100, respectively. The number of pads used by each method for various sensor counts is also below these upper limits, which can indeed serve as a reference upper limit calculation formula. For the proposed method, QT&MSC, when the number of sensors in the network is increased to 5000, the number of pads used for the small, medium, large, and extremely large maps is only 46.25%, 81.88%, 71.67%, and 77.30% of the aforementioned upper limits, respectively. Therefore, finding a more precise upper limit will be a direction for future research.
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 4 Relationships Necessary for the Application of the Method
### Solar EV Charging
#### 4.4.2 EV Charging Strategies  
The charging power profiles for individual vehicles were not recorded during the period considered. Thus, we conduct simulations with different EV charging strategies based on charging session history to reconstruct these profiles. As the charging records  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Periods**} & \multirow{2}{*}{**Duration (Months)**} & \multicolumn{2}{c}{**Annual PV**} & \multirow{2}{*}{**Annual EV**} & \multirow{2}{*}{**Annual _PTC Ratio_**} \\  & & **Production (100 kW)** & & **Consumption (MWh)** & \\ \hline
1 June 2016–31 May 2017 & 12 & 178 & 24 & 7.21 \\
1 June 2017–31 May 2018 & 12 & 171 & 76 & 2.25 \\
1 June 2018–31 May 2019 & 12 & 180 & 109 & 1.66 \\
1 June 2019–31 May 2020 & 12 & 173 & 76 & 2.28 \\
1 June 2020–31 May 2021 & 12 & 171 & 77 & 2.21 \\
1 June 2021–31 August 2022 & 15 & 238 & 178 & 1.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sum-up of the PV production, the EV consumption and the _PTC_ ratio over the 6-year period.  
Figure 9: Raw production forecast (**left**) and corrected values (**right**).  
contain the energy withdrawn by the vehicle \(i\) (\(E^{i}\)) and the transaction duration DT, we suppose that this data are used by the CPO for controlling the charges. In practice, this data are not known, but they could be statistically estimated by the CPO. In this work, we consider three charging strategies with different complexity.  
The first strategy is the Plug and Charge (PC) strategy described in Section 3.1. In that case, the charging power is limited by the nominal charging power of the embedded charger of the vehicle, noted \(P_{max}^{i}\). The effective duration of the charging session is then equal to \(E^{i}/P_{max}^{i}\).  
The second strategy is called 'Mean Power' (MP). In that case, the vehicle charges at constant power from the beginning to the end of the charging session. The duration of the charge is thus equal to \(DT\) and the constant charging power for vehicle \(i\) is \(E^{i}/DT\).  
The third strategy is a Smart Charging (SC) strategy, whose main objective is to increase the self-production rate. The detailed algorithm is out of the scope of the article but can be briefly explained as follows. The PV production forecast of the PV plant is considered as the power that is \"available\" to charge the EVs. The planning algorithm then sorts the EVs according to the alphabetical order of the badges and allocates a part of this available power to each car. The principle of this allocation is as follows. The setpoints of a car are constituted of a constant power part added to a part that is proportional to the available power. Two constraints have been taken into account when choosing the setpoints: the sum of the power has to be less than the maximum power of the car (\(P_{max}^{i}\)) and the integrate of the setpoints has to be equal to the energy withdrawn by the car ( \(E^{i}\)).
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 4 Results
### Data Analysis Results
#### 4.1.2 Results of Acquisition of Turning Circle Radius  
The radius of the turning circle was derived using the diameters of the measured OS trajectories. Table 1 shows the diameters of the OS trajectories, their ratios, and the radius R acquired using them. \(D_{x}\) and \(D_{y}\) represent \(x\)-axis diameter and \(y\)-axis diameter, respectively, \(D_{y}/D_{x}\) represents their ratios, and R represents their radii. Diameter decreases with increasing rudder angle, and the ratio is shown to be similar to approximately 1.0 in the second decimal place. Therefore, it was confirmed that the measured OS trajectory is similar to a circle with radius R. The acquired radius R was shown to be a maximum of 1210.95 m (minimum 574.32 m) at a given rudder angle.
# Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging
## 1 Introduction
It has two main structural forms: the first structure is the up- and down-sampling layer used to learn the degradation features from high-resolution images to low-resolution images and the second structural is the residual layer used to learn the basic features of the image. The proposed model provides the following unique advantages:  
1. The deep learning super-resolution model is applied to the field of laser ultrasound image signal enhancement, providing a new solution for enhancing laser ultrasound images. 2. A new end-to-end ultrasonic image super-resolution model is proposed, which combines up and down projection layers, deep residual network, and Charbonnier loss to solve the problem of ultrasonic image super-resolution data enhancement. The model does not require manual feature extraction or annotation of ultrasound images based on a large amount of prior knowledge. 3. Super-resolution data enhancement of ultrasound images can be directly realized without any modification to the existing laser ultrasound equipment. Compared with the existing method of increasing image resolution through hardware, the cost of industrial applications is reduced. 4. The model was compared with the classic super-resolution imaging model in the actual ultrasonic nondestructive testing data set under the indicators of peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM). The model showed better imaging results and provided a better detection signal for subsequent defect identification.
## 6 Discussion  
In this section, we will delve into the analysis of significant differences in the fracture morphology and mineral composition between marine shale and continental shale, and dissect how these variations may impact geological properties and hydraulic fracturing. We employed advanced QEMSCAN technology, which offers high-resolution determination of mineral composition, providing detailed insights into the microscopic structure of both rock types (Figure 13).  
Through QEMSCAN analysis, we observed that continental shale exhibits a lower quartz content compared with marine shale. This finding aligns with our understanding of the geological background, as continental environments are typically associated with freshwater lakes and river deposits, differing in sedimentary material from marine environments. Marine environments often feature more silicate precipitation, contributing to the formation of quartz-rich rocks. This discovery provides preliminary clues for understanding the behavior of these two types of shale in hydraulic fracturing.  
Figure 12: Fractal dimensions of hydraulic fracture surfaces in marine and continental shale.  
Figure 13: Shale mineral composition maps for (**a**) marine shale and (**b**) continental shale.  
The lower quartz content in continental shale may result in reduced overall rock brittleness. Brittleness refers to a rock's ability to fracture under external forces, and lower quartz content generally indicates relatively lower brittleness. Simultaneously, we observed a higher occurrence of bedding planes or laminations in continental shale. With lower overall rock brittleness, fractures are more likely to develop along bedding planes, forming laminated fractures. This can significantly influence the spatial distribution and orientation of the fracture network.  
Marine shale, on the other hand, exhibits a more complex fracture morphology. This difference can be attributed to the higher quartz content in marine shale, as quartz is a brittle mineral, and its presence may lead to more intricate fracture patterns. The elevated quartz content constrains the development of fractures, resulting in a greater prevalence of intersecting, twisting, and branching fractures. This has important implications for predicting oil and gas migration paths and designing hydraulic fracturing operations.  
In conclusion, our study, utilizing QEMSCAN technology, comprehensively analyzed the disparities between marine and continental shale, focusing on their mineral composition and fracture morphology. These findings not only enhance our understanding of the geological characteristics of these shale types but also provide substantive references for the exploration and development of shale oil and gas resources. We anticipate that these research results will offer valuable insights for future studies and exploration efforts, facilitating more effective resource development in the oil and gas industry.
# A Vision-Based Approach for Autonomous Motion in Cluttered Environments
## 4 Experiment and Results
### Robot Experiment
#### 4.2.2 Path Planning  
Based on the grid map established by the mapping algorithm, the path planning was further conducted, and the result is presented in Figure 11. The red path is searched by the A* algorithm, the blue path is searched by the Path Smooth algorithm, and the green one is the path searched by the proposed algorithm. Obviously, the path searched by the A* algorithm is close to the obstacle and has multiple corners, which is not suitable for robot execution. The path searched by the Path Smooth algorithm collides with obstacles. By contrast, our proposed algorithm plan keeps a safe distance from obstacles and is relatively smooth. Moreover, in the actual test, the robot reached the target point safely along the path planned by our algorithm.
## 2 Background
### Design Rationales  
This section sets the design settings based on the underlying principles, justifications, and explanations that tell the designers about the decisions (that we made) to implement this crypto processor for PM computation of ECC. Therefore, multiple architectural styles have been employed in literature to implement ECC's PM operation, including crypto processors, crypto co-processors, and multicore crypto processors [2]. Comparatively, multicore processors are beneficial to maximize the performance of PM computation; conversely, the co-processors offer higher flexibility--where a host processor (such as microcontroller or microprocessor) needs to integrate with the crypto processor design [26; 27]. The host processor controls the integrated crypto processor, and the crypto processor implements the cryptographic operations for higher speedups. Therefore, rather than the crypto co-processors and multicore crypto processors, a crypto processor implementation style is selected in this work to accelerate the PM operation of ECC on FPGA. The arithmetic operations in ECC can be employed differently to compute PA and PD operations. For example, multiple finite field arithmetic operators are used in [28; 29] to maximize the performance of ECC; conversely, single finite field arithmetic operators are utilized in [12; 13; 16] to minimize the hardware resources. Hence, our objective is to realize area-efficient PM computation of ECC; we preferred single arithmetic operators. Fewer ECC designs [12; 13] even adopted one adder and one modular multiplier, then use multiplier for both square and inversion computations. We also utilize the same strategy but with a different modular multiplier.
# An Edible, Decellularized Plant Derived Cell Carrier for Lab Grown Meat
## 3 Results
### Cell Viability and Cell Density  
Primary bovine satellite cells were inoculated into each bioreactor tube containing florets in suspension. After 120 h, individual florets were removed from the bioreactor and observed for cell viability and cell dispersion. An average cell death of \(2.55+/-1.09\%\) was observed across all replicates. There was no significant difference between the different animals of origin (n = 9 with three biological replicates). There was an average of \(2818+/-1062\) cells/mm\({}^{2}\) for all samples. There was no significant difference in cell density between the three different animals of origin (Figure 5).  
Figure 4: Primary bovine satellite cells cultured on decellularized florets for three days following the dynamic inoculation method. DNA (blue), cytoskeletal actin (purple) (n = 3).  
Figure 5: Cell viability and cell density analysis (n = 9). (**a**) Live (green)/dead (red) florescent stain of primary bovine satellite cells cultured on decellularized florets. (**b**) Comparison of cell viability between three different animals. The machine learning algorithm built into the Trainable Weka Segmentation Plugin for FJJI software was used to classify positive signals for dead cells amongstscaffold autofluorescence. Dead signals colocalized with nuclei were considered dead cells. (**c**) Visualization of cell dispersion on florets. White signals reflected nuclei from seeded primary bovine satellite cells. (**d**) Comparison of cell densities per whole floret between three different animals. _ns_ indicates no significance.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 2 General Setting and Definitions
### Mathematical Setting
The Fourier transform of wave Equation (4) leads to the Helmholtz equation for the spatial part \(u(\mathbf{r})\) of the scattered pressure field:  
\[\
abla^{2}u_{\text{sc}}(\mathbf{r})+k^{2}u_{\text{sc}}(\mathbf{r})=0, \tag{8}\]  
where \(k=\frac{\omega}{c}\) is the wave number. The Neumann and the Sommerfeld radiation conditions of Equations (6) and (7) become, respectively,  
\[\frac{\partial u_{\text{in}}(\mathbf{r})}{\partial\mathbf{n}}=-\frac{ \partial u_{\text{sc}}(\mathbf{r})}{\partial\mathbf{n}},\quad\mathbf{r}\in\Sigma, \tag{9}\]  
\[\lim_{r\rightarrow\infty}\left[r\Big{(}\frac{\partial u_{\text{sc}}(\mathbf{r })}{\partial r}-iku_{\text{sc}}(\mathbf{r},t)\Big{)}\right]=0. \tag{10}\]  
The incoming wave for a source placed at a finite position is given, in the frequency domain, by the following:  
\[p(\mathbf{r},t)=p_{\mathrm{in}}(\mathbf{r},t)+p_{\mathrm{sc}}(\mathbf{r},t). \tag{3}\]0  
where  
\[p(\mathbf{r},t)=p_{\mathrm{in}}(\mathbf{r},t)+p_{\mathrm{sc}}(\mathbf{r},t). \tag{3}\]1  
is the Green function for the wave equation, and \(\mathbf{r_{S}}\) is the position of the source. For sources at infinity, the incoming wave becomes a plane wave of wave number \(k\):  
\[p(\mathbf{r},t)=p_{\mathrm{in}}(\mathbf{r},t)+p_{\mathrm{sc}}(\mathbf{r},t). \tag{3}\]2where \(\mathbf{k}\) is the wave vector. Once the scattering problem of Equations (8)-(10) has been solved in the frequency domain, the pressure field \(p_{R}(\omega)\) at the receiver \(R\) is obtained by adding the solution of the scattering problem with the incoming wave of Equations (11) or (13) at \(R\). The pressure field \(p_{\mathrm{O}}(\omega)\) at \(O\) is obtained by evaluating the incoming wave in \(O\). These data enable us to compute the HRTF via Equation (1).
# Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization
###### Abstract  
Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique.  
Keywords:underwater sensor networks; acoustic-optical communication; energy harvesting; localization +
Footnote †: journal: Sensors