esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model of Four-Plate Parallel DCPT Coupler  
### Theoretical Design Method of Equivalent Circuit  
A four-plate CPT system using electric resonance is typically configured as shown in Figure 2. It consists of four metal plates for wireless power transfer, which are electrically connected to the transmitter and receiver circuits on either side. A DC voltage source is converted into an AC voltage at the transmitter, passing through an impedance-matching circuit. The AC voltage source generates electric field coupling between the metal plates, enabling power transfer. To achieve maximum coupling at the desired operating frequency, the impedance-matching circuits on both the transmitter and receiver sides must be accurately designed. This ensures high wireless power transfer efficiency by minimizing the reflected power at the receiver through the alignment of source and load impedances. Therefore, a vector network analysis of the coupler, including impedance matching, is essential. This study aimed to analyze the four-plate parallel DCPT coupler, incorporating an impedance-matching circuit.  
The equivalent circuit of the coupler structure, where mutual coupling occurs, is shown in Figure 3, as derived from the structure in Figure 2. In Figure 3a, self-capacitance C\({}_{1}\) is formed between the two metal plates on the transmitter side, while self-capacitanceC\({}_{2}\) is formed between the two metal plates on the receiver side. Since the transmitter and receiver are not electrically connected in the circuit, mutual capacitance C\({}_{\text{M}}\) is established between them. When this equivalent circuit is transformed into a T-model, it is represented as shown in Figure 3b. In this configuration, the metal plates on the transmitter and receiver sides can be expressed as being connected in the circuit, where C\({}_{\text{M}}\) appears in parallel with C\({}_{1}\) and C\({}_{2}\). Consequently, the combined capacitance can be calculated as a parallel sum and represented in a T-type equivalent circuit, as shown in Figure 3c.  
This study provides a theoretical design of the practical equivalent circuit (PEC) by incorporating a lumped inductor for resonance and an impedance-matching circuit. The S-parameter matrix is derived by multiplying the transmission matrices of each circuit network. Among the parameters of the equivalent circuit, the self-capacitances C\({}_{1}\) and C\({}_{2}\) and the mutual capacitance C\({}_{\text{M}}\) are obtained using the 3D electromagnetic simulation tool Ansys Q3D. The parameters of the impedance-matching circuit are determined based on the impedance of the coupler designed in the 3D full-wave simulation tool ANSYS HFSS.  
The transmission coefficient of the coupler is compared between the theoretical calculations of the equivalent circuit model and the results of 3D full-wave analysis using ANSYS HFSS. According to previous studies, equivalent circuit modeling and simulation validation alone can yield results consistent with experimental measurements [43]. Therefore, this study focuses on providing equivalent circuit analysis through simulation-based validation, excluding experimental measurements.
## 2 Materials and Methods  
### Materials  
Cowpea husks (CPH) were collected from a farm in Nassarawa State, North central, Nigeria right after cowpea harvest. They were first cleaned of dust and dirt and sun dried for 24 h. They were crushed in an electric grinder and classified in a horizontal screen shaker of different sizes. The particles that remained in the 45 um sieves were used for the composite production. Clay was obtained from Galadimaw area in Federal Capital Territory; Abuja, Nigeria with a Global Positioning System (GPS) coordinates of 9\({}^{\circ}\)4\({}^{\prime}\)0.0012\({}^{\prime\prime}\) N and 7\({}^{\circ}\)28\({}^{\prime}\)59.9988\({}^{\prime\prime}\) E at 491 m above sea level. The clay used in this study has been previously characterised in an earlier work by [PERSON] et al. [14], where it was shown that the mineral composition is dominated by kaolinite (Al2 Si2O5(OH)4), muscovite (KF)2(Al2O3)3 (SiO2)6, phenigetic (K(AlMg)2(OH)2(SiAl)10O10) group of micas and haematite (Fe2O3). While the chemical composition of the clay soil mainly consists of 66.10-67.12% silica (SiO2) and alumina (Al2O3) between 14.93-19.45. The mined clay lumps were pulverized, milled, sieved and soaked in water to remove impurities such as stones and decaying organic matter. The mixture was then rinsed severally with distilled water. The obtained clay particles were sun dried for 24 h before drying in a laboratory oven at a temperature of 90 \({}^{\circ}\)C for 4 h. Natural clay powder of <45 um was used to produce the composite samples.  
Unsaturated polyester resin with the cobalt naphthenate (accelerator) and methyl ethyl ketone peroxide (MEKP) (catalyst) was obtained from a local vendor in Kaduna state, Nigeria. The choice of UPT resin is due to its easy handling, low cost, good physical and mechanical properties, high transparency, fast curing at room temperature with no gas generation and also because the individual polymer chains of the UPT cross-links during curing, it can be cast and laminated at a temperature and pressure lower than other thermosetting resin [48].
# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## 5 Results and Analysis
### Subjective Evaluation  
To evaluate speech quality of the proposed generalized loss (GL) function, a subjective evaluation test is conducted among GL and baselines, where we follow the subjective testing procedures of [40]. In this comparison, we choose the parameter configuration (2, \(-\)20 dB, 1) and \(\alpha_{0}=1\) for the proposed GL function. The experiment is conducted in a standard listening room with the size (5 m \(\times\) 4 m \(\times\) 3 m), where 10 listeners participate. The listening material consists of 20 utterances, each of which includes one male and female utterance selected from TIMIT corpus and is mixed with one of five noises including aircraft, babble, bus, cafeteria, and car. Four SNR conditions are selected for mixing, i.e., \(-\)5 dB, 0 dB, 5 dB, 10 dB. A speech pause of 3s duration is specifically inserted before each utterance. Then, the duration of each listening utterance is about 13s. Each listener needs to write down the utterance index that they prefer considering both noise naturalness and speech quality. The same as [40], \"Equal\" option is also provided if no subjective preference can be given. To overcome inertia, the utterance index in each pair is shuffled. The averaged subjective results are presented in Table 2. From this table, one can observe that the proposed GL function with residual noise control achieves better performance in subjective testing, which can be explained as the proposed GL method can effectively recover speech components while preserving the characteristic of background noise to some extent compared with all the baselines.  
Figure 3: Metric scores with the increase of \(\alpha_{0}\). (**a**) NA scores with the increase of \(\alpha_{0}\). (**b**) SA scores with the increase of \(\alpha_{0}\). (**c**) PESQ scores with the increase of \(\alpha_{0}\). (**d**) SDR scores with the increase of \(\alpha_{0}\).
## 2 Materials and Methods
### Laboratory Analysis
#### 2.1.1 Materials and Instrument
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PCM Type** & **Test** & **Heat Flux Direction** & \(\mathbf{T_{up}}\) [\({}^{\circ}\)**C]** & \(\mathbf{T_{low}}\) [\({}^{\circ}\)**C]** & \(\mathbf{\Delta T}\) [\({}^{\circ}\)**C]** & \(\mathbf{T_{average}}\) [\({}^{\circ}\)**C]** \\ \hline \multirow{4}{*}{X} & Test 1 (solid) & upward & 10 & 20 & 10 & 15 \\  & & downward & 20 & 10 & 10 & 15 \\  & & upward & 26 & 36 & 10 & 31 \\  & & downward & 36 & 26 & 10 & 31 \\ \hline \multirow{4}{*}{Y} & Test 1 (solid) & upward & 14 & 24 & 10 & 19 \\  & & downward & 24 & 14 & 10 & 19 \\  & & upward & 30 & 40 & 10 & 35 \\  & & downward & 40 & 30 & 10 & 35 \\ \hline \multirow{4}{*}{Z} & Test 1 (solid) & upward & 14 & 24 & 10 & 19 \\  & & downward & 24 & 14 & 10 & 19 \\ \cline{1-1}  & & upward & 30 & 40 & 10 & 35 \\ \cline{1-1}  & & downward & 40 & 30 & 10 & 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Set-point temperatures used for the X, Y and Z samples. Figure 1: (**a**) The Infinite R\({}^{\text{TM}}\) panel; (**b**) Sample dimension and metering area; (**c**) The DHFM apparatus. A preliminary conditioning was performed, through the DHFM, to ensure that the samples were in either a completely solid or liquid state (depending on the test). The samples were conditioned, for 1 h, at constant temperature of:  
* 7.5 \({}^{\circ}\)C before the melting test (heating test)
* 40 \({}^{\circ}\)C before the solidification test (cooling test)  
The areal enthalpy, \(h_{A}\) [k]/m\({}^{2}\)], was determined according to the ASTM C1784:2020 standard [28]. These values were calculated as a sum of the single enthalpy values measured for each set-point, \(h_{n}\) (1 K), used for the enthalpy vs the temperature test (Equation (2)):  
\[h_{A}=\sum_{n=1}^{17}h_{n}(1\;K)\bigg{[}\frac{kJ}{m^{2}}\bigg{]} \tag{2}\]  
The areal latent enthalpy, \(h_{A,lat}\), was calculated according to Equation (3):  
\[h_{A,lat}=h_{A}-h_{A,sens}\bigg{[}\frac{KJ}{m^{2}}\bigg{]} \tag{3}\]  
where: \(h_{A,sens}\) [k]/m\({}^{2}\)] is the areal sensible enthalpy determined according to Equation (4):  
\[h_{A,sens}=\frac{c_{pF,A}+c_{pM,A}}{2}\cdot\Delta T\bigg{[}\frac{kJ}{m^{2}} \bigg{]} \tag{4}\]  
where: \(c_{pF,A}\) (kJ/m\({}^{2}\)K) is the areal specific heat of the solidified PCM measured over the 15.5-16.5 \({}^{\circ}\)C range; \(c_{pM,A}\) (kJ/m\({}^{2}\)K) is the areal specific heat of the melted PCM measured over the 31.5-32.5 \({}^{\circ}\)C range; \(\Delta T\) is the total temperature variation of the test (17 \({}^{\circ}\)C in this specific case).
###### Abstract  
The safe and efficient function of critical national infrastructure (CNI) relies on the accurate demand forecast. Cyber-physical system-based demand forecasting systems (CDFS), typically found in CNI (such as energy, water, and transport), are highly vulnerable to being compromised under false data injection attacks (FDIAs). The problem is that the majority of existing CDFS employ anomaly-based intrusion detection systems (AIDS) to combat FDIAs. Since the distribution of demand time series keeps changing naturally with time, AIDS treat a major change in the distribution as an attack, but this approach is not effective against colluding FDIAs. To overcome this problem, we propose a novel resilient CDFS called PRDFS (Proposed Resilient Demand Forecasting System). The primary novelty of PRDFS is that it uses signature-based intrusion detection systems (SIDS) that automatically generate attack signatures through the game-theoretic approach for the early detection of malicious nodes. We simulate the performance of PRDFS under colluding FDIA on High Performance Computing (HPC). The simulation results show that the demand forecasting resilience of PRDFS never goes below 80%, regardless of the percentage of malicious nodes. In contrast, the resilience of the benchmark system decreases sharply from about 99% to less than 30%, over the simulation period as the percentage of malicious nodes increases.  
resilient demand forecasting system; false data injection; intrusion detection systems; data trustworthiness; node reputation  
2022120093103390/app121910093
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 4 Computational Conditions
### Grid Convergence Calculation  
In the stage of model verification, the calculation regions were divided by 520,800; 1,223,768; 1,978,886 and 2,864,400 grid numbers to verify the effect of grid density on scouring depth. In the simulation process, a grid convergence index (GCI) [23] was computed to obtain an optimal solution in four grid resolutions. The results of the GCI calculations for each grid solution are listed in the following Table 2.  
As shown in Table 2, the calculation results using a grid size of 0.5 cm and a grid size of 0.4 cm (the number of grids is 1,978,886 and 2,864,400 respectively) are analogues so that the grid size of 0.5 cm is sufficient to provide representative results. The analysis was based on scouring depth and yielded GCI values less than 2% for all simulated discharges.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Flow Discharge (l/s)** & **9.62** \\ \hline Channel slop & 1/1000 \\ Channel width (cm) & 40 \\ Flow depth (cm) & 9 \\ Flow velocity (m/s) & 0.267 \\ Sediment grain size D50 (mm) & 0.473 \\ Sediment density (g/cm\({}^{3}\)) & 1.9 \\ Reynolds number, \(\text{Re}=vR/\
u\) & 16,585 \\ Froude number, \(Fr=v/\sqrt{gh}\) & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hydraulic conditions.  
Figure 3: Computational domain (units: cm).  
A non-uniform structured grid was used in the simulation. The plan view of the computational grid in the vicinity of the spur-dikes group is shown in Figure 4. Due to the complexity of the flow field around the spur dikes, the size of the grid directly affects the accuracy of the calculation results. The surrounding of the spur-dikes group was partially encrypted in the horizontal direction, and the grid was evenly distributed in the vertical direction. The minimum grid size was 0.5 mm, the total grid number was 1,978,886, and the effective computing region of the grid was 1,541,800. The upstream inlet was the flow inlet with a discharge of 9620 cm\({}^{3}\)/s and a water depth of 9 cm, whereas the outlet was simply a free outflow. The VOF method was adopted for analysis of the upper surface to capture the free surface. All other boundaries were solid walls, and the standard wall function approach was employed in the near-wall area.
## 5 Improved Performance of Harmonic Injection by the Phase Perturbation Method  
The AI method suggested in this paper is based on a combination of some existing solutions [29; 38; 39], which have been selected and adapted to fit in an experimental platform with low availability of computational time. These high computational requirements are typical in the case of GSCI due to the extra control needs (more control loops) and extra secondary/tertiary loops to enhance droop control [4; 5; 9; 10]. This situation makes it especially important not to overload the inverter source code with excessive computational burden to add an AIDM. The method, presented in Figure 12, is based on different steps classified into sections for better understanding.  
Figure 11: Bode diagram of the impedance at the PCC (magnitude plot). (**a**) Weak grid effect. (**b**) \(q\) factor effect (increase value).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**AIDM Category** & **AIDM Type** & **GSCI Interactions Issues** & **Valid?** \\ \hline \multirow{2}{*}{Passive method (PM)} & Voltage (\(V\)) & \(V\) and \(f\) indirectly & \multirow{2}{*}{\(\times\)} \\  & Frequency (\(f\)) & controlled by the inverter & \\ \hline \multirow{2}{*}{Active method-positive feedback (AM-PF)} & Slip mode frequency shift (SMS) & Must be implemented in & \multirow{2}{*}{\(\times\)} \\  & Active frequency drift (AFD) & current & \\ \cline{2-3}  & Sandia voltage shift (SVS) & Too slow and not & \\ \cline{2-3}  & Sandia frequency shift (SFS) & suitable proportional gains & \\ \hline \multirow{2}{*}{Active method-impedance measurement (AM-IM)} & Low frequency & Out of detection frequency & \\ \cline{2-3}  & Low frequency & bandwidth (DFBW) & \\ \cline{2-3}  & High frequency & Islanding detection feasible & ok \\ \hline \hline \end{tabular}
\end{table}
Table 3: Anti-islanding detection method (AIDM) versus grid support and constitution inverter (GSCI) interactions.  
It should be remarked that the main idea of the proposal is to detect a change in terms of impedance, and not to be accurate in the exact value of the PCC impedance. Thus, the improvement is aligned with reducing computation burden and sharing calculations at different rates.
## 4 Experiment Verification
In both the CC and CV charging stages, \(U_{\text{IN}}\) was ahead of \(I_{\text{IN}}\) by a small phase angle to achieve the ZVS of FBI, which reduces reactive power loss of the WPT system. Figure 16: Experimental waveforms of \(U_{\text{BAT}}\), \(I_{\text{BAT}}\), \(U_{\text{IN}}\), and \(I_{\text{IN}}\) in the DS-LCC topology. Figure 15: Measured charging process under dynamic loads. (**a**) Charging diagram; (**b**) charging efficiency and output power. The output performance of the CC and CV modes under dynamic loads were shown in Figures 18 and 19. When the WPT system worked in the CC charging stage, \(U_{\text{BAT}}\) decreased from 81 to 47 V and then increased to 81 V and \(R_{\text{L}}\) varied between 4 and 7 \(\Omega\), as shown in Figure 18. The results indicated that \(I_{\text{BAT}}\) was stable at 11.8 A, satisfying the CC-output requirements. Figure 19 presented the waveforms for the CV charging mode. As shown, the output current increased from 7.9 to 10.7 A and then decreased to 7.9 A, and \(R_{\text{L}}\) varied from 8 to 11 \(\Omega\). The output voltage was stable at 88.3 V, satisfying the CV output requirements. The experimental results are consistent with the theoretical analysis results. Figure 19: Experimental dynamic response waveforms of multi-load regulation in the CV mode. Figure 17: Experimental waveforms of \(U_{\text{BAT}}\), \(I_{\text{BAT}}\), \(U_{\text{IN}}\), and \(I_{\text{IN}}\) in the LCC-S topology. It is necessary to evaluate the transient response of the proposed WPT charging systems throughout the switching process. Figure 20 depicts the transient waveforms of \(U_{\text{IN}}\), \(I_{\text{IN}}\), \(U_{\text{BAT}}\), and \(I_{\text{BAT}}\) when ACSs \(S_{1}\) was turned off and \(S_{2}\) was turned on. As shown in Figure 20, the throughout transition process from the CC to CV charging mode, \(U_{\text{IN}}\) always remained unchanged, whereas the \(I_{\text{IN}}\) in the CV mode was slightly larger than that in the CC mode. Throughout the charging process of the battery, \(U_{\text{BAT}}\) and \(I_{\text{BAT}}\) remained essentially constant. The whole switching process took approximately 80 ms to restore stability, which is short compared with entire charging process of few hours, and the impact of mode switching on the whole charging process can be ignored. The foregoing experimental results verify the feasibility of the proposed hybrid wireless power transfer charger.
# A Novel Benthic Phage Infecting _Shewanella_ with Strong Replication Ability
###### Abstract  
The coastal sediments were considered to contain diverse phages playing important roles in driving biogeochemical cycles based on genetic analysis. However, till now, benthic phages in coastal sediments were very rarely isolated, which largely limits our understanding of their biological characteristics. Here, we describe a novel lytic phage (named _Shewanella_ phage S0112) isolated from the coastal sediments of the Yellow Sea infecting a sediment bacterium of the genus _Shewanella_. The phage has a very high replication capability, with the burst size of ca. 1170 phage particles per infected cell, which is 5-10 times higher than that of most phages isolated before. Meanwhile, the latent period of this phage is relatively longer, which might ensure adequate time for phage replication. The phage has a double-stranded DNA genome comprising 62,286 bp with 102 ORFs, ca. 60% of which are functionally unknown. The expression products of 16 ORF genes, mainly structural proteins, were identified by LC-MS/MS analysis. Besides the general DNA metabolism and structure assembly genes in the phage genome, there is a cluster of auxiliary metabolic genes that may be involved in 7-cyano-7-deazaguanine (preQ\({}_{0}\)) biosynthesis. Meanwhile, a pyrophosphohydrolase (MazG) gene being considered as a regulator of programmed cell death or involving in host stringer responses is inserted in this gene cluster. Comparative genomic and phylogenetic analysis both revealed a great novelty of phage S0112. This study represents the first report of a benthic phage infecting _Shewanella_, which also sheds light on the phage-host interactions in coastal sediments.  
coastal sediments; benthic phage; _Shewanella_; phage genome; phylogenetic analysis +
Footnote †: journal: Physics Letters B
## 4 Simulation Results and Analyses
### Comparison between Different Channel Estimation
In Figure 7, the numbers of updates for channel coefficients is reduced intensively, where the abscissa and ordinate correspond to the SNR and the number of updates, respectively. At the SNR of 1 dB, the numbers of updates are reduced by four-fifths. With SNR increasing, the numbers of updates for the channel  
Figure 4: Time evolution of the magnitude impulse response for (**a**) Space\({}^{\prime}\)08 and (**b**) MACE\({}^{\prime}\)10.
## 4 Cluster-Based Distributed Compressed Sensing for QoS Routing  
This section presents a cluster-based distributed compressed sensing approach for QoS routing, the primary mechanism of which is comprised of the following parts (shown in Figure 3):  
1. Event-driven clustering: A video sensor is triggered, and the clustering process is generated when an event is detected within their vicinity. The cluster consisting of several member nodes is formed in the dashed circles.
2. Collaborative node selection based on SCS: After a cluster is formed, the sink sends a message to the cluster head to inform the reconstruction error. On this basis, the cluster head uses the SCS approach to determine how many collaborative nodes are selected to participate in DCS to meet the requirement of reconstruction error rate.
3. QoS-aware routing selection: Each node respectively selects the optimal next hop with the objective of minimizing energy consumption and satisfying QoS requirements in delay and reliability. Afterward, compressed data is transmitted to the sink along the chosen path.  
Figure 2: Sparsity coefficient of signals observed by adjacent sensors.