esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance of the Single-Stage FLTD Cavity  
Figure 18 depicts load current generated by the experimental prototype, 3-D field model, and circuit model. In both simulations and experiments, the two capacitors in a discharging brick are charged to \(\pm\)80 kV and the load resistance is set to 0.05 \(\Omega\). The rise time (10% to 90%) and amplitude of the load current is \(\sim\)110 ns and \(\sim\)950 kA, respectively. Thus, the MA-class single stage FLTD with 24-separate columns can generate required output current for the multi-stage series-connected FLTD module. By using the fast Fourier transform (FFT), the load current waveforms can be transformed from time domain to frequency domain, as illustrated in Figure 19. The simulated waveforms are in accordance with the experiment results except for low frequencies between 0-0.5 MHz and high frequencies between 26-35 MHz. Most of the spectral components of load current are in the range of 0 to 5 MHz. The discrepancies between simulations and measurements in the high frequency range are probably the effects of stray capacitances in the FLTD cavity. As shown in Figure 20, bricks' discharge currents demonstrate that brick no. 12 has the smallest rise time and highest peak value, which conform to the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Brick No.** & **Total Inductance (nH)** & **Self-Inductance (nH)** & **Mutual-Inductance (nH)** \\ \hline
1 & 195.8 & 160.1 & 35.7 \\
2 & 194.3 & 160.1 & 34.2 \\
3 & 194.2 & 160.1 & 34.1 \\
4 & 194.2 & 160.1 & 34.1 \\
5 & 194.1 & 160.1 & 34.0 \\
6 & 194.2 & 160.1 & 34.1 \\
7 & 195.6 & 160.1 & 35.5 \\
8 & 194.3 & 1 smallest equivalent inductance of brick no. 12 in Table 2. Moreover, the circuit-field coupled TD-FIT only needs 3 h, 32 min calculation time and 3.7 GB memory consumption to complete the whole simulation. This is efficient to the 3-D time-domain simulation with over 10 million DoFs.  
The cross-sectional view of electric- and magnetic-field intensity distributions at the time instant of maximum load voltage and load current are illustrated in Figures 21 and 22, respectively. The electric field mainly distributes along the axial direction in the primary side and radial direction in the secondary side, whereas the magnetic field distributes along the azimuthal direction in both the primary and secondary sides. The EM wave propagates along the axial direction in the de-ionized water insulated transmission line of the secondary side of FLT cavity, thus it operates in the transverse EM (TEM) wave mode. The highest magnitude of electric field intensity is now located near the narrow  
Figure 19: Load current waveforms of the single-stage FLT in the frequency domain.  
Figure 20: Simulated current waveforms of main discharging bricks of the single-stage FLT.  
Figure 18: Simulated and measured load current waveforms of the single-stage FLT.  
water-insulator interface region. The leakage magnetic flux is located in the middle insulator and the region of deionized water, which conforms to the theoretical analysis.
# Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator
## Materials and Methods  
### CYGNSS Background  
The CYGNSS mission is a Global Navigation Satellite Systems-Reflectometry (GNSS-R) bistatic radar system that was launched on 15 December 2016 and consists of eight microsatellites, each with a four-channel Global Positioning System (GPS) L1 coarse acquisition (C/A) radar receiver. The main goal of the CYGNSS mission is rapid sampling of ocean surface winds in the inner core of tropical cyclones. However, several qualities unique to CYGNSS make its observations highly useful for overland applications as well. Namely, the measurement modality, near-specular bistatic radar using GPS transmissions provides the ability to see through heavy rain and vegetation. The small satellite constellation architecture supports high revisit rates over a large portion of the globe (+/-38 degrees latitude), allowing for frequent updates and tracking of extreme weather events. Initial studies have demonstrated CYGNSS's high sensitivity to land/water boundaries and its ability to delineate river systems with a high degree of detail [4].
## 3 Results  
### Results of Measured Time-Series Data  
Figure 5 shows the results of the measured time-series data using the force plate. The data from the stance phase in the gait cycle are measured during the one-step gait cycle, and those of the swing phase are not recorded in the experiment. The normalized time for 0\(\sim\)25% expresses the initial contact, 25\(\sim\)80% expresses the midstance, and 80\(\sim\)100% expresses the propulsion. The body weight of the participant normalizes the value of GRFs. The x-axis represents the normalized time for the stance phase, and the y-axis represents the measured values. The three plots on the top side are three directional ground reaction forces (GRFs), and the three at the bottom are three directional moments. Blue-colored curves indicate the patient data, and orange-colored indicate those of the control group. Soft shades of blue and orange show the standard deviation distribution, and heavy curves show the mean values.  
By visualizing the data as mean and standard deviation, it was found that there was no significant difference in the measured time-series data between the two groups. However, we could see a tiny difference when the stage in the stance phase was changed around the normalized time of 25% and 80%.  
Figure 4: The warping path to minimize the cumulative distance between two time series is determined by the dynamic time warping (DTW) in the n-by-m matrix, reproduced with permission from [14], copyright from Professor [PERSON] 2023. The dot on \(A_{\pi}\) indicates the optimal alignment of an element in \(x\) with an element in \(x^{\prime}\).
## 3 Results and Discussion
In addition to the validation of the proposed system using fuzzy logic, an estimation of the potential power generated for the base case is provided using the following hydraulic power equation:  
\[\text{P}=\eta\times\rho\times\text{A}\times\text{V}^{3} \tag{1}\]  
where  
P = available hydraulic power (W). \(\eta\) = turbine efficiency (80%). \(\rho\) = water density (kg/m\({}^{3}\)). A = cross-section area of water flow (m\({}^{2}\)). V = water speed (m/s). Then,  
Width of water reaching the turbine (A) = 0.5 m.  
Depth of water reaching the turbine (H) = 0.5 m.  
Water speed (V) = 1.9 m/s. Turbine efficiency (\(\eta\)) = 0.8. Water density (\(\rho\)) \(\approx\) 1000 kg/m\({}^{3}\). Cross-section area of water flow (A) = width \(\times\) depth of water reaching the turbine  
A = 0.5 m \(\times\) 0.5 m = 0.25 m\({}^{2}\). P = 0.8 \(\times\) 1000 kg/m\({}^{3}\)\(\times\) 0.25 m\({}^{2}\)\(\times\) (1.9 m/s)\({}^{3}\)\(\approx\) 1371.8 W.  
Furthermore, once the proposed system has been validated using fuzzy logic, an estimation of the potential power generated for the base case is provided. Considering the diameter of the canal, water depth, and flow velocity, it is calculated that a single axial turbine could generate approximately 1.37 kilowatt-hours (kWh). However, it is important to note that this figure is a preliminary estimate and could vary depending on various factors, such as the number of turbines installed and the specific energy consumption of each lock. The substitution of energy by the turbines will largely depend on the energy demand of the lock infrastructure and the generation capacity of the turbines, which should be carefully evaluated in future feasibility studies.
## 5 Case Study
### Description of the Retaining System  
A retaining system based on the new HRS method was designed to protect the excavation based on the environmental and geological conditions of the site described above (see Figures 23 and 24). The design involved first excavating the soil to a depth of 3.25 m with slope of 40\({}^{\circ}\). Then, the next 6 m of the excavation process (from \(-\)3.25 to \(-\)9.25 m) was to be protected using the HRS approach. The main supports were to be based on bored cast-in-situ vertical piles (\(\Phi\)800 mm) and steel pipe batter piles (\(\Phi\)500 mm). The vertical piles were to have a center-center spacing of 1.5 m. The batter piles were placed into soil by pre-boring with an inclination of 17\({}^{\circ}\). The pre-boring holes for steel pipe batter piles were drilled by an improved machine, which is modified from the machine used to drill holes for anchors (see Figure 25). The drill bit diameter of the machine is 500 mm, and it can drill a hole in ground with an inclination of 0-45\({}^{\circ}\) from a vertical axis. It is the first application of the new machine in this case. The steel pipe piles were filled with cement mortar after they were placed into the ground, in order to increase batter pile stiffness. The gap between the wale and the batter pile head was filled with concrete. An anchor cable inclined at 35\({}^{\circ}\) from a horizontal axis was also incorporated into the design (at the top of the bored piles) to further strengthen the retaining system. High-pressure rotary  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Layers** & **Depth (m)** & \(c\) **(kPa)** & \(\varphi\) (\({}^{\circ}\)) & \(E_{\text{s(I-2)}}\)(MPa)** \\ \hline Miscellaneous fill & 1.0–4.8 & 10.0 & 8.0 & - \\ Silt & 0.4–2.5 & 8.0 & 3.0 & 2.0 \\ Clay & 0.5–4.4 & 22.0 & 11.0 & 5.5 \\ Muddy silty clay & 2.2–8.7 & 9.0 & 3.5 & 2.5 \\ Clay & 1.7–11.0 & 30.0 & 14.0 & 8.0 \\ Clay & 1.4–4.0 & 20.0 & 10.0 & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of the soils encountered in the scope of the excavation.
# High Throughput Data Relay in UAV Wireless Networks
## 4 Proposed Solution
### Overall Algorithm
Based on (11), (12a) and (12b), we obtain \(\eta(\mathcal{A}^{g},\mathcal{C}^{g})\leq\eta(\mathcal{A}^{g+1},\mathcal{C}^{ g+1})\), which means that the objective value of problem (5) is non-decreasing after each iteration of Algorithm 1. As the objective value of problem (5) is upper bounded by a finite value, Algorithm 1 is therefore convergent. The proposed Algorithm 1 is based on the general state-of-the-art optimization framework consisting of BCD and SCA, together with a novel mechanism, named power balance, to handle the feasibility requirement of the DS method.
## 2 System Description
### Transmitting Part  
The function of the transmitting part is generating all kinds of modulated RF waveforms according to a set program. The multi-channel technology and digital beam-forming are applied in the transmitting part. The five-channel transmitting part is based on the AD9911 chip. This chip is a complete direct digital synthesizer with low power dissipation and high performance. The digital data moves to the digital beam-forming computer through the USB bus. The phase and amplitude of the digital waves are adjusted precisely in the digital beam-forming computer and synthesized into five channels for beam formation. A calibration section is designed to calibrate the amplitude-frequency and phase-frequency response in the digital domain for consistency across the five channels. The digital beam-forming computer and the calibration section will be explained in detail in the following section. The generated waveforms are amplified to 50 dBm and then emitted by each log-periodic antenna.
# The Antimicrobial Potential of Hexane Oils and Polyphenols-Rich Extracts from _Pistacia vera_ [PERSON].
## 1 Introduction  
The increased resistance to antimicrobials has brought a more global effort focused on natural drugs, used in combination with existing antibiotics or alone. Plant extracts are a complex mixture with a range of beneficial effects on human health, including antimicrobial potential. With reference to their chemical structure and properties, plant antimicrobial compounds can be classified into the following groups: essential oils, phenolics, alkaloids, saponins, and peptides [1]. Pistachio (_Pistacia vera_ L.) nuts are known to contain nutrients and phytochemicals with beneficial health effects: they are a good protein, fiber, monounsaturated fatty acid, minerals and vitamin source, in addition to carotenoids, flavonoids phenolic acids and anthocyanins [2].  
Previous studies have reported the antiviral [3] and antibacterial [4, 5] effects of pistachio extract, showing that these activies are due to the interactions between bioactive components.  
We have previously shown that pistachio polyphenols have bactericidal effect against _Listeria monocytogenes_, _Staphylococcus aureus_ and MRSA (methicillin-resistant _Staphylococcus aureus_) strains [4, 5]. _L. monocytogenes_ is a pathogen found in moist environments which can even grow at 4 \({}^{\circ}\)C, temperature that would inhibit the growth or kill most pathogenic species. Listeriosis, particularly dangerous during pregnancy, can cause sepsis, meningitisitis,encephalitis, intrauterine infections and spontaneous abortions. _S. aureus_ and MRSA are cause of a range of infections (respiratory, skin and bone joint), bacteremia, endocarditis, and toxic shock syndrome [6]. Furthermore, the increased rate of resistance amongst these strains makes current treatment particularly challenging, especially in immunocompromised patients.  
Several authors investigated the influence of the processes, such as roasting, on the phenolic compound [2,7,8] content. Extracts obtained from pistachio using different methods, may have a different phenolic profile and, given that polyphenol activity is closely related to its chemical structure, a different biological activity.  
The objective of this work was to elucidate the influence of an innovative extraction method on the qualitative and quantitative polyphenic profile and consequently on the bioactivity of the obtained extracts. In particular, the antimicrobial activity of these polyphenols' rich extracts against a number of Gram-positive and Gram-negative bacteria and the yeast _Candida albicans_ was assessed.
## 2 Materials and Methods
### Study Data
#### 2.2.2 LandScan Data  
LandScan is a global high-resolution population distribution dataset developed by the Oak Ridge National Laboratory in the United States. It is generated using a combination of remote sensing, census data, and geographic information, producing a global population distribution map with a resolution of 30 arc seconds (approximately 1 km). This dataset is widely applied for analyzing global population spatial dynamics. Compared to traditional population data, LandScan's advantage lies in its high spatial resolution and ability to capture both daytime and nighttime population activities, making it particularly suitable for studies within the spatial structure of urban agglomerations [65]. In this study, we obtain 2023 population distribution data for the PRD urban agglomeration from the official website ([[https://landscan.ornl.gov/](https://landscan.ornl.gov/)]([https://landscan.ornl.gov/](https://landscan.ornl.gov/)), accessed on 10 November 2024). We preprocess the data by filling in missing values, normalizing population density, and adjusting spatial resolution, resulting in the preprocessed LandScan data (Figure 3).  
Figure 2: Preprocessed NTL Data for the PRD Urban Agglomeration.
## 4 Proposed Lightweight Authentication Based on Multi-Factor Hash Chain with T-OTP in VMF
### Comparison between Overall Proposed Model and Previous Studies  
The existing hash chain-based studies required improvements owing to network operation constraints as a result of the poor communication environment in rapidly changing VMF-based wireless CNR networks. The proposed authentication model addressed these limitations through a four-phase process of initialization and registration, authentication and verification, re-authentication and revocation, and various exception handling flows. The related comparison results and the taxonomy of the 112 bit-based minimum security strengths are presented in Figure 11 and Table 9, respectively.  
Figure 11: Brief comparative result of LSH and SHA hash functions based on each QR code.  
Figure 10: Overview of overall VMF-based CNR testbed architecture with hash chain authentication.  
During the initialization and registration phases, for the conditions of LL (2), the entire initial authentication process of the VMF-based tactical CNR networks based on wireless terminals and squad devices was completed within a short period of time, allowing the operations to be rapidly conducted and the multiple squads to be quickly input into the battlefield environment. In terms of LAO (4), smooth communication and message transmission were ensured for hierarchical low-speed, low-bandwidth networks and the low-spec connected equipment with limited resources. Because the authentication process was an improvement over existing processes in VMF standards, it also secured interoperability, satisfying the DI (6) conditions based on military ad hoc networks, such as MANET and VANET, with pre-built cell planning areas. During the authentication phase, by satisfying the CKP (4) conditions, the authorization of friendly squad nodes in arbitrary tactical networks could be verified at a reliable level, even in a rapidly changing limited network. In addition, to maintaining a regular authentication process and reducing the overhead, the system was configured to exclude the key ownership stage in one of the two terminals from the authentication algorithm, while satisfying the RA (4) conditions. Thus, security against overall wireless attacks in a CNR was achieved by activating various defensive mechanisms, such as preventing the reuse of random keys and exception handling. The RR (4) conditions were satisfied during the verification phase, which ensured that any new nodes continued to participate as legitimate nodes even after the initial authentication was completed. An environment in which theft and damage might occur owing to intrusions from unforeseen hostile enemy forces during combined operations was considered when designing this scheme.
# Real-Time Swarm Search Method for Real-World Quadcopter Drones
## 2 The Proposed Swarm Search Method
### The Drone Position Update Mechanism  
#### 2.1.1 The Swarm Intelligence for the Mechanism  
At each iteration of the proposed method, the update mechanism should calculate the next positions of the drones by obtaining the information of the current positions and sharing them with each other (i.e., swarm intelligence). To implement the swarm intelligence, as a backbone, a particle swarm optimization (PSO) scheme is employed [19; 20]. In the PSO scheme, each drone decides where to go by combining the information of its previous displacement, the personal best position it has ever experienced, and the global best position the entire swarm has ever found. If we denote the displacement, the personal best position, and the global best position at iteration \(t\) as \(\mathbf{v}_{t}\), \({}^{p}\mathbf{x}_{t}\), and \({}^{g}\mathbf{x}_{t}\), respectively, then the next displacement of a drone is determined as:  
\[\mathbf{v}_{t+1}=w\cdot\mathbf{v}_{t}+c\cdot[\phi_{1}(^{p}\mathbf{x}_{t}- \mathbf{x}_{t})+\phi_{2}(^{g}\mathbf{x}_{t}-\mathbf{x}_{t})], \tag{1}\]where \(w=0.7\) and \(c=0.6\) are constants and \(\phi_{1}\) and \(\phi_{2}\) are random real values uniformly distributed in \([0,1]\). Note that the random values are newly generated at each iteration for each particle. Obviously, the next destination \(\mathbf{x}_{t+1}^{*}\) is calculated as:  
\[\mathbf{x}_{t+1}^{*}=\mathbf{x}_{t}+\mathbf{v}_{t+1}. \tag{2}\]  
However, drones in the real world cannot teleport to their destinations. Instead, they gradually approach their destinations following control commands based on their dynamic model. Therefore, the next position \(\mathbf{x}_{t+1}\) at the \((n+1)\)-th control period is determined as:  
\[\mathbf{x}_{t,n+1}=\mathbf{x}_{t,n}+\mathbf{\tilde{v}}(\mathbf{x}_{t+1}^{*}), \tag{3}\]  
where \(\mathbf{\tilde{v}}\) represents the control output according to the input. This control loop is repeated until \(\mathbf{x}_{t,n}=\mathbf{x}_{t+1}^{*}\), and then \(t\) increases by 1.
## 1 Introduction
Therefore, it is possible to predict the displacement of the structure in real-time with only strain data that is relatively easy to measure in the field. Also, if training is done properly, the displacement of the structure can be predicted quickly and accurately without any extra cost. Because of their capabilities, many studies on data prediction using ANNs have been conducted in various fields [27; 28; 29; 30]. In this paper, a method for real-time prediction of the overall vertical displacement distribution of a structure is proposed--particularly, a bridge--from measurements of longitudinal strain, which is relatively easy to measure, using the advantages of ANN. For the training of the ANN, training data was acquired from a finite element model (FEM) verified with experimental values as an alternative to collecting hard-to-obtain actual data. Also, by using various numbers of hidden layers and nodes, the prediction accuracy of ANN by ANN structures was compared.
## 2 Materials and Methods
### Computation of Margin of Stability  
As shown in Figure 2, the MoS is computed using the position vectors of the center of mass (\(\mathbf{x}_{\text{com}}\)) and the edge point in the base of the support area (\(\mathbf{x}_{\text{bos}}\)) in the transverse plane. In this study, \(\mathbf{x}_{\text{com}}\) is the centroid of the right and left anterior superior iliac spines and sacral crest, and \(\mathbf{x}_{\text{bos}}\) is the position of the toe. The distance between these two vectors measures the margin against falling under a still condition. To express the margin during walking, the MoS adopts the concept of an extended center of mass, that is, the position vector of the center of mass prospected in the future [11; 12]. The position vector of the extended center of mass is defined as  
\[\mathbf{x}_{\text{xcom}}=\mathbf{x}_{\text{com}}+\frac{\hat{\mathbf{x}}_{\text{com}}}{ \omega}, \tag{1}\]  
Figure 1: Feature points examined in the study. Open circles indicate the feature points on the frontal side.  
where \(\omega\) is the natural angular frequency of an inverted pendulum with a support length of \(l\). The height of the center of body mass corresponds to \(l\). \(\omega\) is calculated using \(l\) and the gravitational acceleration \(g\):  
\[\omega=\sqrt{\frac{g}{l}}. \tag{2}\]  
The MoS values for the anterior direction (y) and mediolateral direction (x) are computed separately. The anterior MoS is defined as  
\[m_{\text{ant}}=\mathbf{x}_{\text{bos}}^{(y)}-\mathbf{x}_{\text{xcom}}^{(y)}. \tag{3}\]  
Notably, \(\mathbf{x}^{(y)}\) indicates the y component of vector \(\mathbf{x}\). Figure 3a shows an example of a change in the extended center of mass (\(\mathbf{x}_{\text{xcom}}\)) and the endpoint of the base of support (x\({}_{\text{bos}}\)) during a gait cycle. As a representative value, the most critical moment, which is just after the heel contact, is referred to. Hence, the smaller value between the MoS values at 0% (first heel contact) and \(\sim\)50% (second heel contact) is used as the representative MoS value in this study. The anterior MoS value can either be positive or negative. Moreover, the greater the value, the more stable the posture. In the example in the figure, \(\mathbf{x}_{\text{xcom}}\) precedes \(\mathbf{x}_{\text{bos}}\), and the anterior MoS is negative.  
The mediolateral MoS is defined as  
\[m_{\text{lat}}=|\mathbf{x}_{\text{bos}}^{(x)}-\mathbf{x}_{\text{xcom}}^{(x)}|. \tag{4}\]  
The mediolateral MoS assumes positive values during normal walking, indicating that the posture remains stable in the mediolateral direction. The value is at a minimum at either \(\sim\)10% or \(\sim\)60% gait cycle, and the smallest value between them is employed as the most critical value in this study.  
Figure 3: Margin of stability and changes in the extended center of mass and base of support. (**a**) Y-direction (anterior) and (**b**) X-direction (mediolateral).  
Figure 2: Margin of stability. Top view of a walking person.