esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning in the Biomedical Applications: Recent and Future Status
## 6 Discussions and Outlooks  
### Overview of the Reviewed Papers  
A total of 158 papers are reviewed in this paper: 64 papers for the Omics and 94 for the BBMIs. Figure 11 gives the distribution of these papers. Most of the reviewed papers (more than 50%) were published in 2017 or the first months of 2018, which shows the popularity of deep learning in the biomedical applications during the two last years. Much of the Omics research concerns the proteins, precisely protein structure prediction and protein binding prediction (see Figure 12). In the BBMI, 37% of the papers deals with the disease diagnosis and 60% concerns the brain decoding. Most of these papers use the EEG input signal (see Figure 13 for a complete overview).  
Figure 11: Distribution of papers reviewed in this survey.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Purpose** & **Signal** & **NN Type** & **Ref.** \\ \hline Automated detection of arrhythmia and cardiac & EEG & DBN & [240] \\ abnormalities & & DNN & [241, 242] \\  & ECG & CNN & [243, 244, 245, 246] \\  & & DNN & [247, 248, 249] \\  & & DBN & [250, 251, 252] \\ Automated detection and diagnosis of seizure & EEG & DBN & [88] \\  & & CNN & [253, 254, 255, 256, 257, 258, 259] \\ Localization of epileptogenicity & EEG & CNN & [260] \\ Epileptiform discharge anomaly detection & EEG & DBN & [261] \\ [PERSON]’s disease diagnosis & EEG & DBN & [89] \\ Neurophysiological clinical monitoring & EEG & DBN & [262] \\ Detection of hypoglycemic episodes in Children & ECG & DBN & [263] \\ Automated detection of myocardial infarction & EEG & CNN & [87] \\ Screening of depression & EEG & CNN & [238] \\ Prediction of post anoxic coma & EEG & CNN & [264] \\ Coronary artery disease (CAD) Diagnosis & EEG & CNN & [86] \\ Screening paroxysmal atrial fibrillation & ECG & CNN & [265] \\ Detection sleep apnea & ECG & DNN & [266] \\ Detecting atrial fibrillation & ECG & CNN & [267] \\ Fetal electrocardiogram (FECG) monitoring & ECG & DNN & [268] \\ Drowsiness detection & EOG & CNN & [269] \\ Neonatal Sleep State Identification & EEG & DNN & [239] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overview of papers using deep learning techniques for Diseases Diagnosis.
## 3 Materials and Methods
### Sample Preparation  
To investigate the mechanical characteristics of the water-saturated sandstone in the roof under true triaxial stress conditions, core samples were drilled onsite, sealed in a plastic film to preserve their original structure and moisture, and transported back to the laboratory in foam-padded wooden boxes, as shown in Figure 7. Following the national standard \"Standard Test Methods for Engineering Rock\" (GB/T 50266-2013), the sandstone core samples were processed into standard cubic specimens with dimensions of 50 mm \(\times\) 50 mm \(\times\) 100 mm (length \(\times\) width \(\times\) height). The end face of the sample was perpendicular to the sample axis (with an error within 0.001 radians), and the unevenness did not exceed 0.02 mm.  
The sandstone samples possessed gentle wavy bedding, were calcareously cemented, and had a compact internal structure. To minimize experimental variability, samples were selected and cut to exclude areas with visible fractures. During sample preparation, the surface flatness was maintained within 0.04 mm. Additionally, preliminary testing was conducted using an ultrasonic velocity tester to identify significant deviations, with outliers excluded. The samples were then grouped according to their wave number, as shown in Figure 8, with the grouping results presented in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Group** & **Wave Speeds (m/s)** \\ \hline Group 1 & 3788, 3861, 4049, 4065, 4098 \\ Group 2 & 3906, 3984, 4016, 4032, 4082 \\ Group 3 & 3906, 3984, 4000, 4032, 4096 \\ Group 4 & 3861, 4032, 4032, 4082, 4098 \\ \hline \end{tabular}
\end{table}
Table 1: Sample grouping.  
Figure 8: Pre-treatment of samples: (**a**) parent rock sample—part; (**b**) wave velocity test; (**c**) wave velocity results.  
Figure 7: Sampling location.
## 4 Methods  
We present a maintenance interval optimization model that considers equipment reliability and imperfect maintenance. The overall framework of our method is illustrated in Figure 1 and consists of the following key steps:  
1. Distribution selection and parameter estimation: Based on our investigation, we determined that the two-parameter Weibull distribution is most suitable for modeling transformer defects. Given that some defect patterns in the dataset have limited samples, we employ small sample parameter estimation methods to fit the failure rate function accurately.
2. Imperfect maintenance modeling: We make necessary assumptions and design a time-varying imperfect maintenance improvement factor. This factor is incorporated into the maintenance cost calculation, allowing the cost to dynamically change as the maintenance cycle progresses. This approach forms the foundation for our periodic imperfect maintenance strategy.
3. Optimization model development: We establish an optimization model with the primary objective of minimizing maintenance costs while maintaining the failure rate within acceptable constraints. To solve this model, we utilize the SLSQP (Sequential Least Squares Programming) method, a robust optimization algorithm well-suited for this type of problem.
4. Results and analysis: Through this process, we obtain the optimal maintenance intervals for each defect pattern. We then conduct further analysis of these results to derive insights and recommendations for practical implementation.  
This comprehensive approach allows for a more realistic and dynamic representation of maintenance processes, taking into account the complexities of equipment reliability and the imperfect nature of maintenance activities. By considering these factors, our model provides a more accurate and practical tool for optimizing maintenance intervals in transformer systems.  
Figure 1: Overall framework.
## 1 Introduction
Reference [18] fully considered the impact of the energy storage life on the operation strategy. The data in the study came from a real island demonstration project. Most of the research on PV and storage microgrid systems applied in rural communities is at the simulation research stage, and some studies have verified the correctness of their algorithms using a laboratory experimental platform. The rural community PV and storage microgrid system studied in this paper is a real community microgrid demonstration project in northwestern China. The proposed energy management strategy has been implemented in the demonstration system project. This paper mainly studies the comprehensive economic optimization of rural community microgrid systems, proposes a real-time control strategy considering the cost of the ESS, and adds a priority charging strategy to increase the energy storage life, thereby realizing the real-time optimization control of the community microgrid system. An annual time-scale parameter optimization method based on particle swarm optimization (PSO) is proposed. Two important parameters in the real-time control strategy-the preset energy storage charge value, SOCpro, and the minimum energy storage discharge capacity, SOCmin-are optimized. The objective function comprehensively considers the electricity bill and ESS life loss. Combining the operating characteristics of a typical rural community microgrid system in western China, four types of operating scenarios are summarized, and the optimal setting scheme of operating parameters is studied under these scenarios. The simulation results at the annual time scale prove the correctness of the proposed strategy. This method can adjust the operating parameters to adapt to changes in different seasons and weather to obtain better operating results. The target comprehensively considers the combined impact of energy storage battery life and electricity costs, and economic performance optimization is improved. At the same time, the approach improves the actual application performance of the method. Relying on the demonstration project system of a real community microgrid in western China, a community-level microgrid energy management monitoring system is built, and the multi-scenario variable parameter energy management strategy proposed in this paper is applied to the demonstration system for experimental verification.
# Experimental Study and Parameter Optimization of a Magnetic Coupled Piezoelectric Energy Harvester
## 5 Conclusions  
A high performance magnetic coupled piezoelectric energy harvester is studied in this paper. The process of parameter optimization is discussed in detail and confirmed by experiments. A lumped parameter piezoelectric coupling model considering the magnetic static position is established for this structure. The four configurations of the structure are studied through simulation and \"Low beam repulsion mode\" is selected due to its high performance potential. The performance of the harvester is evaluated via both analytical simulation and experiments. After parameter optimization, it is found that the optimal beams spacing value exists under different magnet flux densities. As the flux density increases, the optimum spacing gradually increases. With the optimized parameters, the structure of this paper can obtain 3.28 mW harvested power and 3.5 Hz bandwidth, with 5 V as the standard, under harmonic excitation of 4 m/s\({}^{2}\). Compared with the conventional single cantilever harvester, the power is increased by 35.0% and the bandwidth is increased by 66.7% experimentally. The harvester has a high performance by enhanced output power in wide operating frequency range and hence can be applied to power the low power consumption devices, such as wireless sensor networks, etc.  
**Author Contributions:** [PERSON] established the theoretical model and performed the simulation; [PERSON], [PERSON] ([PERSON]) and [PERSON] designed the experiments; [PERSON], [PERSON] ([PERSON]) and [PERSON] performed the experiment; [PERSON] and [PERSON] analyzed the data. All authors reviewed the manuscript.  
This research was funded by National Key R&D Program of China (No. 2018 YFF0212201 & No. 2016 YFF0101802) and Natural Science Foundation of Tianjin (No. 17)CYBJC19300).  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
In this study, a novel improved ultra-wideband (UWB) antipodal Vivaldi antenna suitable for breast cancer detection via microwave imaging was designed. The antenna was made more directional by adding three pairs of nestings to the antenna fins by adding elliptical patches. The frequency operating range of the proposed antenna is UWB 3.6-13 GHz, its directivity is 11 dB, and its gain is 9.27 dB. The antenna is designed with FR4 dielectric material and dimensions of 34.6 mm \(\times\) 33 mm \(\times\) 1.6 mm. It was demonstrated that the bandwidth, gain, and directivity of the proposed antenna meet the requirements for UWB radar applications. The Vivaldi antenna was tested on an imaging system developed using the CST Microwave Studio (CST MWS) program. In CST MWS, a hemispherical heterogeneous breast model with a radius of 50 mm was created and a spherical tumor with a diameter of 0.9 mm was placed inside. A Gaussian pulse was sent through Vivaldi antennas and the scattered signals were collected. Then, adaptive Wiener filter and image formation algorithm delay-multiply-sum (DMAS) steps were applied to the reflected signals. Using these steps, the tumor in the breast model was scanned at high resolution. In the simulation application, the tumor in the heterogeneous phantom was detected and imaged in the correct position. A monostatic radar-based system was implemented for scanning a breast phantom in the prone position in an experimental setting. For experimental measurements, homogeneous (fat and tumor) and heterogeneous (skin, fat, glandular, and tumor) breast phantoms were produced according to the electrical properties of the tissues. The phantoms were designed as hemispherical with a diameter of 100 mm. A spherical tumor tissue with a diameter of 16 mm was placed in the phantoms produced in the experimental environment. The dynamic range of the VNA device used allowed us to image a 16 mm diameter tumor in the experimental setting. The developed microwave imaging system shows that it is suitable for the early-stage detection of breast cancer by scanning the tumor in the correct location in breast phantoms.  
radar; microwave imaging; breast cancer; Vivaldi antenna; breast phantom; biophysics (cST); 1511605
## 2 Methodology
### DT and Optimization System
#### 2.2.1 Digital Twins  
To enable a comprehensive analysis and management of the industrial systems, the most critical energy assets are individually examined. Based on the characteristics of the waste management facility, the availability of data, and the impact on overall energy consumption, four DTs are developed and analyzed in this study:  
* DT-PV: A virtual replica of the photovoltaic system, designed to predict its power generation using weather forecast and other relevant variables.
* DT-IAS: Responsible for evaluating and forecasting grid energy prices and power consumption of the Industrial Asset System (IAS), including offices, mechanical workshops, and non-DT-related activities.
* DT-EV: Each EV within the facility is modeled with a dedicated DT, capable of estimating current SOC and SOH, and predicting future performance under varying usage patterns.
* DT-SESS: This DT monitors the SESS and provides its SOC under operation.  
The outputs generated by each DT are then consumed by the optimization system, reducing in this way its computational complexity.
## 3 Results and Discussion
### Characterization of Potatoes Using ATR-FTIR Spectroscopy  
Figure 5a displays the ATR-FTIR spectra in the range 4000-940 cm\({}^{-1}\) acquired from representative samples of the eight potato accessions, which reflected the typical tuber flesh composition [32; 49], mainly consisting of water (77%-80%) and carbohydrates (9%-19%, predominantly starch), followed by minor components, such as proteins (\(\approx\)2%), fibers (0.4%-0.8%), lipids (0.1%), and organic acids (0.4%-1%). Absorption bands in the 1800-940 cm\({}^{-1}\) range are shown in Figure 5c. The spectral region below 940 cm\({}^{-1}\), showing a continuous absorption band with no fine structure, was not used in the classification analysis. The broad absorption band at 3750-2800 cm\({}^{-1}\) was associated with the O-H stretching vibrations of carbohydrates and water [50]. This band strongly overlapped with the signals ascribed to the symmetric and asymmetric stretching modes of the C-H bond that appeared as a single shoulder in the region near 2930 cm\({}^{-1}\). The broad band centered at about 2100 cm\({}^{-1}\) was ascribed to the rocking and scissoring vibrations of water molecules not directly bound to starch. The relatively sharp signal at 1800-1500 cm\({}^{-1}\) was associated with the vibrations of water molecules adsorbed in the amorphous regions of starch [31]; however, the amide I and amide II peaks of proteins also fall in this spectral range [26]. The shoulder at about 1742 cm\({}^{-1}\) was assigned to the C=O stretching of lipids and organic acids [51]. Previous studies revealed that the intensity of the absorption bands at 3750-2800, 2100, and 1800-1500 cm\({}^{-1}\) is directly related to the hydration degree of the potato starch [31]. The weak and partially superimposed bands in the spectral range between 1500 and 1200 cm\({}^{-1}\) were predominantly due to the deformational modes of the CH/CH\({}_{2}\) groups [50]. The absorption bands between 1150 and 940 cm\({}^{-1}\) arose from the coupling of C-O, C-C and C-O-H stretching, and the C-O-H bending of starch. Despite the poor resolution and overlapping of the related signals, which did not allow for an unequivocal attribution, changes in this region were ascribed to the differences in the relative amounts of amorphous and crystalline starch and hydration of the crystalline form [26].  
Figure 4: Dendrogram of the eight potato genotypes created using unweighted pair group method with arithmetic mean (UPGMA) cluster analysis of microsatellite (SSR) marker data; the Dice coefficient was used to estimate the degree of similarity among genotypes. Asterisks identify the accessions from Majella National Park.
## 5 System Modeling and Simulation
### System Simulation  
The simulation test is necessary to verify the feasibility of the scheme and the accuracy of the dynamic model. The system simulation model was built according to the structure shown in Figure 18, and the relevant parameters of the dynamics model are shown in Tables 2 and 3. The specific simulation test includes the following two parts. Figure 18: System simulation model. \begin{table}
\begin{tabular}{l l l c c} \hline \hline
**Parts** & **Physical Meaning** & **Character** & **Value** & **Units** \\ \hline Asynchronous motor & Driving torque & Potential Source & – & – \\ \hline \multirow{3}{*}{Reducer} & The moment of inertia & Inertia Element & \(5.7\times 10^{-3}\) & kg\(\cdot\)m\({}^{2}\) \\  & Rotational damping & Resistive Element & \(11.4\times 10^{-4}\) & N\(\cdot\)m\(\cdot\)s\(/\)rad \\  & Reduction ratio & Converter & 50 & – \\ \hline \multirow{2}{*}{Steel cable reel} & The moment of inertia & Inertia Element & \(1.721\) & kg\(\cdot\)m\({}^{2}\) \\  & Rotational damping & Resistive Element & \(7.9\times 10^{-3}\) & N\(\cdot\)m\(\cdot\)s\(/\)rad \\ \hline \multirow{3}{*}{Steel cable} & Rotational motion to Straight motion & Converter & \(1/0.3\) & rad/m \\  & Mass & Inertia Element & \(57\) & kg \\  & Movement damping & Resistive Element & \(18.12\) & N\(\cdot\)s\(/\)m \\  & Elastic coefficient & Capacitive Element & \(0.3\times 10^{-10}\) & m/N \\ \hline \multirow{2}{*}{ERSD and Power cable} & Mass & Inertia Element & \(1500\) & kg \\  & Movement damping & Resistive Element & \(88.1\) & N\(\cdot\)s\(/\)m \\ \hline \multirow{3}{*}{Chain} & Rotational motion to Straight motion & Converter & \(1/(72\times 10^{-3})\) & rad/m \\  & Mass & Inertia Element & \(3.7\) & kg \\  & Movement damping & Resistive Element & \(11.7\) & N\(\cdot\)s\(/\)m \\  & Elastic coefficient & Capacitive Element & \(0.5\times 10^{-7}\) & m/N \\ \hline \multirow{2}{*}{Power cable reel} & Straight motion to rotational motion & Converter & \(72\times 10^{-3}\) & m/rad \\  & The moment of inertia & Inertia Element & \(21\) & kg \\  & Rotational damping & Resistive Element & \(7.4\) & N\(\cdot\)s\(/\)m \\ \hline \multirow{2}{*}{Helicopter} & Motion resistance & Potential Source & – & – \\  & Mass & Inertia Element & \(10^{4}\) & kg \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of EMPU transmission system.
## 2 Protein Extraction
### Novel Methods
#### 2.5.1 Pulsed Electric Field Extraction  
Pulsed Electric Field Extraction (PEFE) is used as a cell disruption method, particularly in microalgae. It involves the application of short, high-voltage electric current pulses to perforate a cell wall or membrane. Depending on the intensity, amplitude, duration, number and repetition frequency of the external electric pulses, reversible or irreversible pores (electroporation) are formed in the membranes. Irreversible pore formation is of particular importance for extraction of bioactive compounds from natural matrices [14]. Treatments with an electric field strength from 0.7 to 3 kV/cm, a specific energy of 1-20 kJ/kg, a couple of hundred of pulses and a total time duration lower than 1 s are usually used for natural products extraction [80]. When applied to _U. lactuca_, a higher protein content was observed, namely, 59 \(\upmu\)g mL\({}^{-1}\) against the 23 \(\upmu\)g mL\({}^{-1}\) obtained in the control [84]. When 50 pulses of 50 kV, applied at a 70.3 mm electrode gap, were used in the extraction of proteins from _Ulva_ spp., a sevenfold increase in total protein content was obtained when compared to the use of osmotic shock [86]. The applied conditions can possibly limit the scale up of the method.
## 2 Data and Methodology  
### Study Area  
The study area is situated within the North China Plain, spanning from 114\({}^{\circ}\) E to 121\({}^{\circ}\) E and from 32\({}^{\circ}\) N to 40\({}^{\circ}\) N. Characterized by low terrain, this region is conducive to the growth of agricultural crops, rendering it one of the primary grain-producing areas in China. Six primary garlic-producing regions were selected for this study: Jinxiang County, Laiwu District, Lanling County, Pizhou City, Qixian County, and Zhongmu County. These areas span a geographical range from 113.5\({}^{\circ}\) E to 118.5\({}^{\circ}\) E and 33.7\({}^{\circ}\) N to 36.6\({}^{\circ}\) N, covering a total area of 8597 km\({}^{2}\). These regions experience a temperate monsoon continental climate and typically engage in a two-season crop rotation system. The predominant crops cultivated include garlic, winter wheat, vegetables, and peanuts. Among these, garlic cultivation encompasses 228,500 ha, representing 26.58% of the total area. Garlic planting commences in early October each year, with growth accelerating in April of the following year as temperatures rise, culminating in harvesting in June. Conversely, winter wheat is sown in mid-October, exhibiting slightly slower growth than garlic, and harvested in June as well.  
Mixed cropping of the two is widespread in the study area, with most areas featuring a blend of the two crops in smaller units. Given the study's focus on garlic distribution and acreage, other ground cover types are not meticulously distinguished. Vegetation other than garlic (such as winter wheat, vegetables, peanuts, peppers, or other crops) is uniformly classified as \"other\". The fertility period of garlic in the study area is detailed in Figure 1. The distribution of the study area is shown in Figure 2.
## 3 Simulation and Analysis
### Two Satellites under Synchronous Scenarios  
In the synchronous circumstance, we assume that the distance between the MS and SS is 10 km and the two satellites are relatively static, which also can be extended as a relative movement state, and the data rate of uplink and downlink are 20.46 kHz and 20 MHz, respectively. The carrier frequencies of two links are on S-band and C-band, as well as the bandwidths of two links are 20.46 and 40.96 MHz, respectively. We set the CNR in uplink and downlink as 45 and 75 dB\(\cdot\)Hz, respectively, and the clock error is set as 0 ns in the simulation. The parameters of the algorithm are listed in Table 2.
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 3 Preliminaries and Background  
A new route discovery for uplink and downlink direction has been determined using the Java macaque algorithm. Furthermore, the routing protocol incorporates three important mechanisms--the initialization phase, new route discovery, and the route maintenance phase.
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 5 Simulation Results  
An event-driven network simulator is used which was developed in our laboratory based on MATLAB (R2013a) and has been used to evaluate many schemes of medium access control and networking for underwater communication [27; 28; 29; 30; 31]. We compare the proposed SOUNET with Seaweb and S\({}_{13}\). To the best of our knowledge, Seaweb and S\({}_{13}\) are only ones that employ self-organization techniques under the same conditions as ours, i.e., one sink and multiple nodes are randomly distributed, and they all have the same transmission coverage.