esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Terrestrial-Underwater FSO System: Design and Performance Analysis
## 2 System Model and Definitions
Furthermore, in this paper, optical scattering [10; 22; 29] is incorporated into our model in a simple and straightforward manner as in [24]. As explained above, scattering, which depends on the amount of impurities and the turbidity of the water [30], leads to a temporal broadening of the transmitted pulses and, consequently, may produce inter-symbol interference (ISI). The amount of ISI depends, however, on both the bit rate and the propagation environment. In this sense, the effect of ISI is usually greater in shallow water environments, where reflections from the sea surface and seabed are added to reflections from particles in the water. In contrast, in deep oceans, these reflections do not occur and the ISI effect is smaller. Therefore, ISI degradation is more harmful in coastal areas than in the open ocean. Since ISI may limit the performance of any underwater optical communication system, we incorporate its effect in our model by adding to Equation (3) an additional term due to ISI, \(i_{\text{ISI}}\), which is also affected by \(h_{2}\). Therefore, the equation is as follows:  
\[i_{2}=g{i_{1}}R_{2}h_{2}+i_{\text{ISI}}h_{2}+i_{n2}. \tag{4}\]  
For the sake of simplicity, the ISI interference is modeled here by its variance \(\sigma_{s}^{2}\). Taking into account the modification of Equation (4), the signal-to-noise ratio of the underwater link is now defined as follows:  
\[\gamma_{2}=\frac{P_{2}R_{2}h_{2}}{\sigma_{2}}, \tag{5}\]  
where \(P_{2}\) is the average optical power emitted by the transmitter of the underwater link, and \(\sigma_{2}^{2}=\sigma_{n}^{2}+\sigma_{s}^{2}\). We note that \(\sigma_{s}^{2}\) depends on \(h_{2}\). However, averaging the noise and the inter-symbol interference over oceanic turbulence in the way proposed in [24], \(\gamma_{2}\) can be approximated as follows:  
\[\gamma_{2}\simeq\frac{P_{2}R_{2}h_{2}}{\sqrt{<\sigma_{n}^{2}>+<\sigma_{s}^{2}> }}, \tag{6}\]  
where \(<\cdot>\) denotes the average over turbulence. Here, as in the first link, Equation (6) can be expressed as \(\gamma_{2}=\gamma_{u}h_{2}\), assuming that \(\gamma_{u}=P_{2}R_{2}/\sqrt{<\sigma_{n}^{2}>+<\sigma_{s}^{2}>}\).
## 5 Test Results and Analysis  
### Algorithm Evaluation Indicators  
The _precision_ (\(P\)), _recall_ (\(R\)) and _mean average precision_ (\(mAP\)) are used as evaluation indicators [35]. The specific equations are  
\[P=\frac{TP}{TP+FP} \tag{24}\]  
\[R=\frac{TP}{TP+FN} \tag{25}\]  
\[AP_{i}=\int_{0}^{1}P(R)d(R) \tag{26}\]  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{27}\]  
_TP_ is the number of positive samples accurately detected, _FP_ is the number of negative samples incorrectly detected, and _FN_ is the number of positive samples incorrectly detected.
## 3 Experimental Setup  
### Datasets  
In order to validate the method in this paper, two representative datasets (IP102 [31] and Plant Village [32], as shown in Table 1 and Figure 5), in the field of pest and disease classification and detection in agriculture, were selected. IP102 is a large-scale benchmark dataset for pest identification, which contains 75,222 images of eight crops, including rice, corn, wheat, sugar beet, alfalfa, grape, citrus, and mango, with a total of 102 pest categories. The dataset exhibits a diverse array of image forms, encompassing samples from various developmental stages of pest infestation, and demonstrates characteristics of imbalance and high variance, presenting a more challenging scenario. While Plant Village is a dataset of plant leaf images, consisting of 54,306 images of healthy and unhealthy leaves, grouped into 38 categories by species and type of disease (including 14 crops covering 17 fungal diseases, four bacterial diseases, two mycobacterial diseases, two viral diseases, and one disease caused by a mite versus 12 as healthy leaves).  
The IP102 database provides segmentation rules that can be applied to the classification and object detection tasks. In the classification task, the number of training sets is 45,095, the number of validation sets is 7508, and the number of test sets is 22,619. In the object detection task, the number of images containing object annotations is 18,976, the number  
Figure 4: Overview of detection model based on multi-scale attention. Bottom left corner: Fusion of multi-head attention maps. Green part: Attention map fusion network. Yellow part: Target localization network.  
of training sets is 15,178, and the number of test machines is 3798. Plant Village is only suitable for classification tasks, and the ratio of 8:1:1 is used for automatic partitioning in this paper. The number of training sets is 43,429, the number of validation sets is 5417, and the number of test sets is 5459.
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
###### Abstract  
In the present scenario of the fossil fuel crisis, a shift from conventional transportation to electric vehicles (EVs) is the goal, and it is necessary to make it economically feasible. Developing an efficient charger with mid-range power level may successfully resolve this problem. In this direction, an EV charging infrastructure has been proposed to achieve grid-to-vehicle (G2V) charging, with vehicle-to-grid (V2G) capability. In G2V mode, the proposed infrastructure consists of an on-board, single-phase, 7.7 kW totem pole converter in continuous conduction mode to achieve high-power factor correction (PFC). Additionally, instead of conventional Si power MOSFET, an SiC-based converter is introduced to lower the switching losses at high switching frequency with smaller filters. Using an SiC-based converter leads to increased efficiency (more than 98%) and reduced total harmonic distortion (less than 5%), making the system economical. Simultaneously, to make the system more economical, the proposed converter works as an inverter to feedback the power to the grid in V2G mode. Furthermore, to analyse the feasibility, the proposed infrastructure has been simulated and its performance is validated using the simpower tool in MATLAB/Simulink environment.  
power factor correction; totem pole rectifier; grid-to-vehicle transfer; vehicle-to-grid transfer +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: School of Electronic and Electric Engineering, Daegu University, Daegudae-ro 201,  
Gyeongsan-si 38453, Gyeongsangbuk-do, Korea; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-53-850-6652
## 5 Modeling and Simulating Si-SC Structures
### Electrical Modeling and Simulation  
The electrical performance of the optimized ARC designs was assessed using the Lumerical CHARGE platform. By coupling optical generation data with device-level models, the short-circuit current density (\(\text{J}_{sc}\)) was calculated using Equation (9), considering the spatial distribution of carrier generation and local quantum efficiency.  
\[\mathbf{J}_{sc}=\int_{\omega}q\times\text{G}_{v}(x,y,z)\times\text{QE}_{ local}(x,y,z)\,dx\,dy\,dz. \tag{9}\]  
where \(q\) is elementary charge.  
The open-circuit voltage (\(\text{V}_{oc}\)) was estimated using Equation (10), incorporating Boltzmann statistics and the reverse saturation current density.  
\[\mathbf{V}_{oc}=\frac{kT}{q}\ln(\frac{I_{sc}}{I_{o}}+1). \tag{10}\]  
where \(k\) is the Boltzmann constant, \(T\) is the absolute temperature in Kelvin and J\({}_{o}\) is the reverse saturation current density.  
Finally, the power conversion efficiency (PCE) was determined using Equation (11), considering the fill factor (FF) calculated from the maximum power point.  
\[\mathbf{PCE}=\frac{J_{sc}V_{oc}FF}{P_{in1 SG}}\times 100. \tag{11}\]  
To comprehensively assess solar cell performance, both internal quantum efficiency (IQE) and external quantum efficiency (EQE) are essential metrics. EQE is determined by the interplay between optical efficiency (OE) and IQE. OE represents the fraction of incident light that is absorbed by the device, while IQE characterizes the efficiency of converting absorbed photons into collected charge carriers. Mathematically, EQE can be expressed as:  
\[EQE(\lambda)=OE(\lambda)\times IQE(\lambda) \tag{12}\]  
where OE is calculated as:  
\[OE=1-R-T \tag{13}\]  
with R and T representing the total reflected and transmitted power, respectively.  
To accurately capture the optical and electrical behavior of Si-SCs, the FDTD simulations employed a refined mesh with a plane wave incident source covering the wavelength range of 350 nm to 1100 nm. Solar generation calculations were incorporated within the silicon layer to determine the spatial distribution of carrier generation. This comprehensive modeling approach provides a robust foundation for understanding the interplay between ARC design, optical properties, and electrical performance in Si-SCs.
## 3 Methodological Approach, Data Acquisition and Processing
Here, for the first time, the method is applied to process field datasets acquired in urban environments, as its main features fit perfectly with the needs of the case study presented. * For GPR, as the interpretation of each section can lead to underestimation or overestimation of the reflected signals and makes it not easy to identify the effect of lateral bodies present in the subsoil, all sections were processed together using standard methodological approaches in order to obtain 2D horizontal maps at a different range of depth (GPR-SLICE 7.0 software) [74]. Data were converted by subtracting out the dc-drift (wobble) in the data, and at the same time adding a gain with time of 20. A time-zero correction was determined to designate the starting point of the wave and the center frequency of the antenna was matched. Then the bandpass filter and the background removal were respectively applied to reduce noise from oscillating
components that had a regular frequency cycle in the frequency domain and to remove striation noises that occurred at the same time. Processed radargrams were subsequently corrected with an automatic gain function applied to each trace based on the difference between the mean amplitude of the signal in the time window and the maximum amplitude of the trace. Thus, horizontal sections (time slices) were processed considering the whole dataset. Data were gridded using the inverse distance algorithm, which includes a search of all data within a fixed radius of 0.75 m of the desired point to be interpolated on the grid and a smoothing factor of two. Grid cell size was set to 0.01 m to produce high resolution images. * For EMI, during data elaboration the measured values of conductivity were transformed in values of resistivity and visualized in 2D maps through a contouring software.
## 3 Weightless Model Predictive Control Based on ESO Observer  
The improved MPTC block diagram of the AC permanent magnet synchronous motor is shown in Figure 4, and its control flow is as follows. (1) At the moment k, other sampling values such as the motor rotor electric angular velocity \(\omega\), rotor position \(\theta\), and three-phase motor currents \(i_{a}(k),i_{b}(k),i_{c}(k)\) are collected by the encoder, and then the d- and q-axis current and voltage values are obtained by the coordinate transformation. (2) The improved ESO observer is designed to observe and compensate for the current error and other problems caused by external parameter perturbation in real time, and also to compensate for it with a two-step delay to obtain \(i_{d}(k+2)\), \(i_{q}(k+2)\). (3) Three voltage  
Figure 3: The dynamic equivalent circuit of the permanent magnet synchronous motor in the d- and q-axis coordinates.  
vectors are selected according to the new spatial voltage vector sector division diagram and 2-D look-up table, the torque prediction value \(T_{e}(k+2)\) and the magnetic chain prediction value \(\psi_{s}(k+2)\) are calculated, and finally, the torque angle prediction value \(\delta(\text{k}+2)\) is obtained. (4) The electromagnetic torque reference value \(T_{e}^{ref}\) and the stator magnetic chain reference value \(\psi_{s}^{ref}\) are obtained through the PI regulator and MTPA, and then the torque angle reference value \(\delta^{ref}\) is obtained by calculation, which is brought into the reconstructed cost function without weight coefficients to find the voltage vector with the smallest g-value as the optimal vector (5) The output of the optimal voltage vector acts on the inverter to control the AC PM synchronous motor.
## 2 Existing ASs and the Related Market Design
### Categories of ASs
#### 2.1.4 Demand Control  
The foundation of agreements between the SO and consumers lies in active participation in frequency control, achieved through a reduction in electricity demand. This involves alleviating some of the burdens placed on the grid by consumers.  
Instant demand control is facilitated through low-frequency relays, with the relay's setting value designed to automatically reduce demand by 10% to 20% if the system frequency drops to 49.0 Hz. This method is particularly suitable for the immediate demand control needs of industries like cement and iron-steel. Consumers can choose to either defer their demand for electrical energy to a later period or source the required energy from on-site generators or embedded production facilities behind the measurement point.  
Looking ahead, and as the integration of renewable-based distributed resources increases in electrical power systems, we can see that demand control is poised to play a more significant role. Reports from the European Power Exchange (EPEX) highlight instances of negative prices for electrical energy units in Germany on specific dates in 2017, indicating the likelihood of excess production, especially during extended periods like nights, weekends, and holidays [37].
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
###### Abstract  
In cellular networks, device-to-device communications can increase the spectrum efficiency, but some conventional schemes only consider uplink or downlink resource allocation. In this paper, we propose the joint uplink and downlink resource allocation scheme which maximizes the system capacity and guarantees the signal-to-noise-and-interference ratio of both cellular users and device-to-device pairs. The optimization problem is formulated as a mixed integer nonlinear problem that is usually NP hard. To achieve the reasonable resource allocation, the optimization problem is divided into two sub-problems including power allocation and channel assignment. It is proved that the objective function of power control is a convex function, in which the optimal transmission power can be obtained. The Hungarian algorithm is developed to achieve joint uplink and downlink channel assignment. The proposed scheme can improve the system capacity performance and increase the spectrum efficiency. Numerical results reveal that the performance of the proposed scheme of jointly uplink and downlink is better than that of the schemes for independent allocation.  
device-to-device communications; power allocation; channel assignment; uplink and downlink resource allocation; Hungarian algorithm +
Footnote †: journal: Future Internet
## 3 Implementation of the Algorithm  
The original NERA algorithm [5] was implemented as an Excel add-in and is well documented in the associated user manual. However, the source code is not available. The original implementation of the algorithm had a number of disadvantages: the impossibility of running the program simultaneously for a set of initial signals and soil profiles with subsequent averaging of the obtained spectra, the inability to develop the program, for example, for the case of using other geomechanical models, complicating the structure of soils, considering P-wave propagation, etc.  
Therefore, the algorithm presented in this paper was implemented in the Matlab programming environment. The basis is the original NERA algorithm [5]. We added the ability to run calculations both for P- and SH-waves, to calculate the parameters of a porous saturated medium for soil layers and use them in the main algorithm, as well as to obtain constrained moduli reduction curves for the cases above and under the water table. Additionally, we added the ability to run the calculation for a set of input seismic signals, soil profiles, shear and constrained moduli reduction curves with subsequent averaging of the output response spectra. In addition, there is a possibility to obtain the theoretical reduction spectra due to the water column above the sites, simulate this reduction by the set of digital Butterworth bandpass filters and apply them to accelerograms.  
The MatNERApor package consists of a set of Matlab scripts and functions. Figure 6 shows the block scheme of the package containing several subsets:  
Subset 1--a core of earthquake site response computing;  
Subset 2--a toolkit for calculating the bulk density of porous soil and various parameters of porous soil from velocity profiles for the Kik-net sites;  
Figure 5: Target theoretical reduction spectrum due to the water column and frequency response of complex digital filter designed to simulate it.  
Subset 3--a toolkit for calculating constrained moduli degradation curves above and under water table and creating an input file with a set of soil moduli degradation curves;  
Subset 4--a toolkit for calculating damping curves for P- and SH-wave cases;  
Subset 5--a toolkit for visualizing general and supplementary output;  
Subset 6--a toolkit for calculating theoretical reduction spectra for vertical components due to the water column of different thicknesses above the site, and simulating this reduction by a set of digital Butterworth bandpass filters and applying them to accelerograms.  
The MatNERApor package is available in the GitHub repository: [[https://github.com/ArtWings/MatNERApor.git](https://github.com/ArtWings/MatNERApor.git)]([https://github.com/ArtWings/MatNERApor.git](https://github.com/ArtWings/MatNERApor.git)) (Supplementary Materials, accessed on 3 May 2022). In addition, the repository contains a manual describing the purpose of each individual script and function indicated in Figure 6, instructions for running the program, as well as a description of the format of input and output data.
# Biohydrogel Based on Dynamic Covalent Bonds for Wound Healing Applications
## 3 Results and Discussion
### Formulation and Relevant Characterization of Hydrogels
#### 3.1.3 FTIR Spectroscopy  
Protein-polymer interaction can greatly affect healing, clotting, and other bioprocesses. Thus, we performed a series of FTIR measurements to gain insight regarding the compatibility between PBA-Alg gel and biological systems (i.e., extracted collagen). The FTIR spectrum of the PBA-Alg complex (Figure S1) showed a broad band at 3280 cm\({}^{-1}\), which indicates the strong presence of the -OH functional groups of boronic acid and alginate, while the peak at 2929.7 cm\({}^{-1}\) represents the C-H stretch from alginate. In the spectrum of the extracted collagen (Figure S2), the broad band at 3291 cm\({}^{-1}\) shows the presence of -OH, while a small weak peak of amine was observed around 3675.2 cm\({}^{-1}\). Further, C-H bond stretching vibrations were observed at 2918.5 cm\({}^{-1}\) and 2847.7 cm\({}^{-1}\). The band of C=O was also observed around 1606.5 cm\({}^{-1}\); the smaller peaks around it represent C=C bond stretching vibrations. The FTIR spectrum of the hydrogel-collagen mixture (Figure S3)exhibits peaks of the components; those at 3283.8 cm\({}^{-1}\), 2918.5 cm\({}^{-1}\)/2847.7 cm\({}^{-1}\), and 1602.8 cm\({}^{-1}\) represent -OH, C-H, and C=O, respectively, with that at 3675.2 cm\({}^{-1}\) indicating the presence of amines. Observations of only minor shifts indicate that the mixtures are stable and compatible.
## 3 LIB SOH Prediction Case Study and Analysis
### Feature Importance Calculation  
Using a Python program, the processed LIB charge-discharge cycle experimental data were read. A total of 90% of the data was randomly selected as the training sets, while 10% was set aside as the testing sets. Finally, the importance of each feature in the RF model was calculated and sorted using the defined 'importance_scores' code, as shown in Figure 6.  
Initially, a feature set consisting of 13 features was selected, including charge-discharge capacity, median voltage, charge-discharge time, and others, as shown in Table 4. Starting with the feature set containing 13 features, the features were sorted based on their importance. The least important feature was removed, and the importance of the remaining features were recalculated. This process continued iteratively until the number of features reached 7. At this point, no feature in the feature set had an importance lower than the threshold of 0.003 (indicating minimal impact on the model). It was considered that all features in this set had a significant influence on the model. The final selection of features  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Cyde Index**} & \multirow{2}{*}{\begin{tabular}{c} **Charge** \\ **Capacity (Ah)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Discharge** \\ **Capacity (Ah)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Charge** \\ **Discharge** \\ **Efficiency (\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Constant Current** \\ **Charging** \\ **Ratio (\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Constant Current** \\ **Charging** \\ **Capacity (Ah)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Constant Current** \\ **Charging** \\ **Voltage (V)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Mean** \\ **Time** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Charge** \\ **Time** \\ \end{tabular} } \\ \hline
200 & 0.6428 & 0.6434 & 0.525 & 0.81 & 0.6279 & 0.619 & 0.568 & 0.6365 \\
201 & 0.6416 & 0.641 & 0.475 & 0.83 & 0.6479 & 0.616 & 0.556 & 0.6341 \\
202 & 0.6396 & 0.6418 & 0.625 & 0.86 & 0.6479 & 0.607 & 0.564 & 0.6353 \\
203 & 0.64 & 0.6372 & 0.35 & 0.86 & 0.6479 & 0.613 & 0.576 & 0.6318 \\
204 & 0.636 & 0.64 & 0.425 & 0.91 & 0.6552 & 0.6 & 0.572 & 0.6306 \\
205 & 0.634 & 0.632 & 0.5 & 0.96 & 0.6479 & 0.607 & 0.54 & 0.6282 \\
206 & 0.6324 & 0.6352 & 0.65 & 0.99 & 0.6479 & 0.604 & 0.532 & 0.6294 \\
207 & 0.6342 & 0.6374 & 0.675 & 0.96 & 0.6479 & 0.601 & 0.572 & 0.6318 \\
208 & 0.6366 & 0.6374 & 0.55 & 0.92 & 0.6479 & 0.601 & 0.548 & 0.6318 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Partial data after standardized processing.  
Figure 5: The discharge capacity graph of #1 LIB cycle 985 times.  
and their importance rankings are shown in Table 5. From the data in the table, it can be observed that, apart from cycle index and discharge time, the features with the highest importance are discharge capacity and charge capacity.