esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 State-of-the-Art Solutions
### Active Front End (AFE) Rectifier  
Another attractive topology candidate is the active front end (AFE) rectifier [29], as depicted in Figure 7. The popular B6 converter can be utilized, which consists of three IGBT half bridges. Under the case where wide-range operation capability is required, another B6 + chopper architecture may be used to achieve the adjustment of output current and voltage, as depicted in Figure 7[37]. To simplify, this work focuses on B6-AFE, and conclusions drawn from B6-AFE can also be extended to B6 + chopper-AFE.  
Due to the current rating limitation of commercial IGBTs [38], multiple (\(\geq\)2) IGBT-based B6 converter stacks configured in parallel are mandatory for a power level of 10 MW.  
Figure 5: Circuit diagram of a 12-pulse diode rectifier with multi-phase chopper and passive trap filter (12-DRMC).  
Figure 6: Circuit diagram of a hybrid system with 12-pulse thyristor rectifier and active shunt power filter (12-TRASPF).  
Figure 7: Circuit diagram of an active front end rectifier (AFE).  
Three-winding LFTs used in 12-TR and 12-DRMC can still be employed in AFE systems to achieve the desired power rating, provide galvanic isolation, and to mitigate the \(6k\pm 1^{\text{th}}\)\((k=1,3,5,\ldots)\) harmonics; although, these harmonics are minor components for a B6-AFE system.  
In the AFE rectifier system, the input-side ac current can be controlled to be sinusoidal by shaping the duty ratio of the Si IGETs, so that much lower current harmonic distortion shall be expected for such AFE rectifier systems. Moreover, the phase shift of ac current with respect to grid voltage can also be manipulated by adjusting the modulation signal of the B6 converter so that a high power factor value can be achieved simultaneously. Therefore, one unique advantage of using the AFE rectifier system is its superior power quality regulation capability. No additional passive/active harmonic filters and VAR compensators will be needed.  
The AFE rectifier system can also be implemented using the emerging SiC MOSFETs as a substitution of Si IGETs. The SiC-based AFE rectifiers are demonstrated in [39; 40]. One unique feature of the SiC-based B6-AFE rectifier is that the SiC MOSFET can be operated under the synchronous rectification mode by its channel reverse conduction, which features low conduction loss. It is reported in [40] that the AFE rectifier achieves 1.2% efficiency promotion by utilizing 1200 V SiC MOSFETs.
## 2 Materials and Methods
### The Principle of the Dual Mode Operation  
Compared with the conventional SIW-fed dipole antenna [8; 9; 10], the proposed folded dipole antenna achieves a wide operating bandwidth owing to dual mode resonance. For a dipole antenna whose diameter is much smaller than the wavelength, current flowing in the arms of dipole has a sinusoidal standing wave pattern with a null at the end. Figure 2 shows the current distribution corresponding to dipoles with different arm lengths. For \(0.5\lambda\) dipole, the currents have the same phase and amplitude in the two arms and the input impedance of \(0.5\lambda\) dipole mode is about \(73~{}\Omega\). For \(1.5\lambda\) dipole, there is opposite-phase currents on the arms at the same time and two current zeros located at \(\pm\lambda/4\) can be observed. The input impedance of the \(1.5\lambda\) dipole is calculated as \(103~{}\Omega\). The input impedance of the two dipoles with different lengths is very close. Thus, if two gaps are cut at the zero current positions of \(1.5\lambda\) dipole to ensure that the current distribution of \(1.5\lambda\) higher-order dipole mode is not destroyed, it is possible to achieve functionality in the \(0.5\lambda\) dipole mode and the \(1.5\lambda\) dipole mode.  
Figure 3 shows the evolution of wideband printed long dipole with dual resonant mode. In order to generate a half-wavelength dipole mode, the gap should be cut at zero currents to avoid destroying the original higher-order mode. By tuning the gaps to the proper position, the \(1.5\lambda\) printed dipole antenna can operate with two resonant modes, i.e., a \(0.5\lambda\) dipole mode and a \(1.5\lambda\) dipole mode. The results have been verified in our previous work and the detailed design for the working mechanism can be seen in [16]. For the sake of brevity, the design process and the analysis of operating principles will not be illustrated here. In this work, the improvement of cross-polarization of the dual mode dipole will be elaborated.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(W_
# Battery Management System Hardware Concepts: An Overview
## 2 Battery Management System Requirements
### Current Acquisition  
In the previous paragraph, the impact of voltage accuracy on SOC determination was discussed. While Open Circuit Voltage (OCV) measurements for SOC determination are of highest precision during stand-still periods, for dynamic SOC determination during operation, an additional method to determine an SOC uses the measured current value: coulomb counting. This method simply integrates the current flowing into or out of a battery. After having calibrated the system to a defined state, say SOC = 100% after a full state detection, a coulomb counter could be used to track the actual SOC alone. However, solely relying on coulomb counting for precise SOC determination appears risky because current sensors are also non-ideal systems that underlie a certain drift-, offset- and temperature-error (see e.g., [10]). In addition, often the used current sensors have to meet oppositional requirements at the same time: it is not rare that current sensors in automotive applications should be able to measure currents starting in milliampere range, while also being able to measure currents up to 1000 Ampere. Depending on the application, the sensor might need to have a high bandwidth to capture fast current changes. Furthermore, the precision of the current sensor and the immunity against EMI noise should be considered [11]. As a result of all these aspects, the determined SOC will be different from the actual SOC--especially, when the battery is used in a low current regime, when noise and small offsets are playing a major role in current acquisition. To overcome the resulting problems for SOC estimation, there are approaches using algorithms and parametrised models to combine the available data and retrieve usable values [12], which however is beyond the scope of this work.  
The implementation details on current data acquisition are handled in the paragraph _Current Measurement_ on page 13 later on.
## 1 Introduction  
Seismic gap images have increasingly attracted the attention of researchers in seismic activity and earthquake prediction theory and methodology research [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. [PERSON] [2] proposed two types of seismic gaps. The first type represents large earthquake rupture gaps in seismic zones along plate boundaries or intra-plate tectonic boundaries, whereas the second represents a phenomenon where minor earthquake activity at and around the hypocenter decreases suddenly or showcases a lull before a strong earthquake. In China, seismologists normally refer to the second type as precursory seismic or seismogenic gaps, and they provide insights that could facilitate medium and short-term prediction of earthquakes [4; 5].  
Based on routine earthquake monitoring, from the \(M4.2\) earthquake in Luhuo, Sichuan on 3 June 2020 to the \(M5.5\) earthquake in Ninglang, Yunnan on 2 January 2022, there was a \(M\geq 4.0\) seismic gap image with a quiet period of approximately 577 days in the Sichuan-Yunnan-Tibet-Qinghai junction area on the southeastern margin of the Tibetan Plateau. The gap was terminated by the \(M5.5\) earthquake in Ninglang on 2 January 2022. In addition, approximately eight months later, on 5 September 2022, a strong earthquake of 6.8 magnitude occurred within the gap in Luding, Sichuan.  
In the present study, attempts are made to answer the following questions. Have similar seismic gap images been observed frequently in the region in the past? In other words, do such images emerge repeatedly and are they statistically significant when all past records of the region are examined? In addition, are there always strong earthquakes after the gap is terminated, and could the images give valid predictions of subsequent strong earthquakes?
## 2 Materials and Methods
### Procedure  
The study was carried out at the Department of Ear, Nose and Throat diseases at Vilnius University Hospital Santaros Clinics. Each study participant underwent an audio-logical assessment by otoscopy, impedance audiometry and pure-tone audiometry. The examination of the external ear canal and tympanic membrane was performed by otoscope. Tympanometry was performed with a Homoth impedance audiometer with a probe-tone frequency of 226 Hz. The audiological tests were performed in a double-walled sound-treated booth using a calibrated clinical audiometer (Interacoustics AC40, Interacoustics, Middelfart, Denmark) meeting the ANSI S3.6-1996 standards. Pure-tone audiometry was carried out at frequencies of 125, 250, 500, 1000, 2000, 4000 and 8000 Hz using both air and bone conduction. The pure-tone stimuli were presented via TDH-39 headphones. The speech stimuli were routed from a CD player (Panasonic DVD-S42, Panasonic, Osaka, Japan) to the clinical audiometer and delivered to the participants via TDH-39 headphones. Prior to each test session, the input of the audiometer was calibrated to a 0 volume unit (VU) using the 1000 Hz calibration tone. The assessment of the WRSs for each of the four 50-word lists was performed at different presentation levels ranging from 0 dB HL to 40 dB HL in 5 dB increments in quiet. The words were presented randomly to each participant monaurally, to either the left or the right ear randomly. The test ear was counterbalanced for the male and female subjects. The WRS was assessed by the percentage of correctly repeated words. The data were statistically analyzed by using Statistical Analysis System software SAS(r) Studio 9.4. A \(p\) value < 0.05 is regarded as statistically significant.  
This study was approved by the Vilnius Regional Biomedical Research Ethics Committee of Lithuania. The participants signed an informed consent form at the beginning of this study.
# Process Optimization and Wear Performance of Plasma-Cladding Fe5 Coatings on Rotary Tillage Blades
## 2 Materials and Methods  
### Experimental Materials  
In this experiment, the cladding substrate consisted of 65 Mn steel plates measuring 55 \(\times\) 28 \(\times\) 5 mm and a 245 mm versatile rotary tiller blade made of the same 65 Mn steel material. The 65 Mn steel plates were used for process optimization and the testing and analysis of microstructure properties, while the rotary tiller blade was employed for field trials. The chemical composition of the materials is presented in Table 1. The cladding layer was fabricated using an iron-based self-fluxing alloy powder, designated as Fe5, with its chemical composition detailed in Table 2. Prior to the experiment, the surfaces of the workpieces were roughened using 120-grit and 240-grit SiC sandpaper. Subsequently, the workpieces were immersed in an ethanol solution and cleaned in an ultrasonic cleaner to remove oil and contaminants from the surface. Finally, the workpieces were removed and dried with cold air.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Mn** & **C** & **Si** & **Ni** & **Cr** & **P** & **S** & **Fe** \\ \hline
0.90–1.20 & 0.62–0.70 & 0.15–0.35 & \(\leq\)0.25 & \(\leq\)0.25 & \(\leq\)0.035 & \(\leq\)0.035 & Bal \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of 65 Mn (wt.%).
## 5 Experiments and Discussions
### Comparison of Feature Combination  
In addition to the classification accuracy evaluation metrics, the other commonly used evaluation metrics include recall, false positive rate (FPR), F-measurement, precision, the area under the receiver operating characteristic (ROC) curve (AUC), etc. [27]. The lower value of the FPR metrics indicates the better feature extraction performance, while the higher value of the other metrics indicates the better feature extraction performance.  
In order to verify the superiority of the proposed scheme DNST-GLCM-KSR, we compared it with the Contourlet-KLPP scheme in reference [1], the DST-KLPP scheme in reference [2], Contourlet-GLCM-KLPP in reference [11], as well as with several similar combination feature schemes. Table 1 lists classification results of some evaluation metrics. From Table 1, the DNST-GLCM-KSR scheme achieves the lowest value of FPR, the highest values of precision, F-measure, AUC, and accuracy, indicating that the proposed scheme obtained the best comprehensive performance in the seven schemes. Besides, when only extracting DNST features, using the KLPP algorithm to reduce dimension can achieve better metrics than that of using KSR except AUC metrics. When extracting DNST-GLCM features, using KSR algorithm to reduce dimension can achieve better metrics than that of using KLPP. The above shows that both dimensionality reduction technologies are effective, and which one is better to use depends on experiments. The principle of the two technologies is the same, but the calculation technic is different.  
Taking the accuracy metrics as an example, DNST-GLCM-KSR scheme achieved the highest accuracy of 96.37%, which is 2.82% higher than that of reference [1] and 2.02% higher than that of [2] and [11], indicating that the proposed scheme is superior to the traditional ones. When using the same dimensionality reduction technology, the accuracy of Contourlet-KLPP is 93.55%, DST-KLPP is 94.35%, and DNST-KLPP is 95.16%, which indicates that the DNST can extract more discriminant  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **FPR (\%)** & **Precision (\%)** & **F-Measure (\%)** & **AUC** & **Accuracy (\%)** \\ \hline Contourlet-KLPP & 6.57 & 92.04 & 92.79 & 0.961 & 93.55 \\ Contourlet-GLCM-KLPP & 5.11 & 93.69 & 94.09 & 0.967 & 94.35 \\ DST-KLPP & 5.11 & 93.69 & 94.09 & 0.972 & 94.35 \\ DNST-KSR & 6.57 & 92.17 & 93.25 & **0.976** & 94.35 \\ DNST-KLPP & 5.84 & 93.04 & 94.09 & **0.973** & 95.16 \\ DNST-GLCM-KLPP & 3.65 & 95.41 & 95.15 & 0.979 & 95.16 \\
**DNST-GLCM-KSR** & **2.92** & **96.36** & **96.37** & **0.980** & **96.37** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of different evaluation metrics.  
Figure 4: Classification results of different features.  
features of continuous casting slabs than that of Contourlet and DST. The above results show that DNST-GLCM-KSR is an excellent feature fusion approach for continuous casting slabs.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Units**} & \multicolumn{2}{c}{**Typical Abattoir**} & \multirow{2}{*}{**King Island**} & \multirow{2}{*}{**Southern Meats**} & \multirow{2}{*}{**Churchill**} & \multirow{2}{*}{**Oakey Beef**} \\  & & **Raw Wastewater** & & **(Beef) b** & & & **DAF (Sheep) c** & \\ \hline BOD & mg-L\({}^{-1}\) & 1600-3000 & 3000 & \(\sim\)1/2 COD & 164–7020 & _n_/a \\ COD & mg-L\({}^{-1}\) & 4200–8500 & 7200 & 3100–11,500 & 1040–12,100 & 9275 \\ FOG & mg-L\({}^{-1}\) & 100–200 & 120 & 290–2670 & 5–2110 & 808 \\ TSS & mg-L\({}^{-1}\) & 1300-3400 & 2000 & 1150–5700 & 457–6870 & _n_/a \\ VSS & mg-L\({}^{-1}\) & _n_/a & _n_/a & 1040–5300 & _n_/a & _n_/a \\ Total \(n\) & mg-L\({}^{-1}\) & 114/148 & 450 & 180–440 & 296–785 & 199 \\ NOx & mg-L\({}^{-1}\) & _n_/a & _n_/a & 0.01–0.12 & _n_/a & _n_/a \\ NH\({}_{4}\)-N & mg-L\({}^{-1}\) & 65–87 & 250 & 18–135 & 23.8–349 & 60 \\ Total P & mg-L\({}^{-1}\) & 20–30 & 45 & 26.4–60 & _n_/a & 43 \\ VFA & mg-L\({}^{-1}\) & 175–400 & _n_/a & 61–600 & 70–906 & 453 \\ Alkalinity & mg-L\({}^{-1}\) & 350–800 & _n_/a & 340–700 & 1020–1980 & _n_/a \\ \hline \hline \end{tabular}  
* [PERSON] [7]; b [PERSON], [PERSON] and [PERSON] [8]; c UNSW [9]; d [PERSON] et al. [10]; e [PERSON], [PERSON] and [PERSON] [11];
* [PERSON], [PERSON] and [PERSON] [12]. BOD = biochemical oxygen demand; TSS = total suspended solids; VSS = volatile suspended solids; N = nitrogen; NOx = oxides of nitrogen; NH\({}_{4}\)-N = ammonium as nitrogen; P = phosphorus; VFA = volatile fatty acids; DAF = dissolved air flotation.  
\end{table} 
Table 1: Concentrations of parameters of high-strength wastewaters produced by abattoirs amended and adapted from [PERSON] and [PERSON] [6]. While fat recovery to produce tallow carries the added benefits of reducing the problematic impacts of fat on the wastewater treatment system, no solution is 100% effective and digesters must be appropriately designed and managed to accommodate fat loads. As the industry's attention has turned from simple wastewater treatment to bioenergy capture, interest in maintaining and optimising these systems has grown. Consequently, this review aims to summarise a compilation of studies conducted in the period 2010-2017 which investigate the performance of CALs treating high-fat RMP wastewater. The review explores opportunities to improve digester functionality, including those for co-digestion, which enhance biogas production and thereby increase the profitability of a facility through displacement of energy costs. Recommendations are made to inform future decision making regarding anaerobic logoons for treating high-fat RMP waste. Figure 1: Equipment used to remove solids from wastewater include, but are not limited to: (**a**) Rotary screens; (**b**) Save-alls and; (**c**) Dissolved-air flotation (DAF).
# A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
## 3 Collaborative Domain Adversarial Network
### Fault Diagnosis Process  
The optimization objectives of the CDAN are as follows: (1) minimize the identification error of fault samples; (2) minimize the inter-domain discrepancy of sample distributions.  
Firstly, the original signals undergo preprocessing such as denoising, outlier removal, and completion. Then, the signals are segmented into source domain and target domain datasets using a signal sliding window approach. The data collected in the laboratory are defined as the source domain data, while the data from real industrial environments are defined as the target domain data. Comprehensive source domain data are collected in the laboratory, while a small amount of target domain data are collected in industrial environments. The second step involves updating and optimizing network parameters using the Stochastic Gradient Descent (SGD) backpropagation algorithm. According to Algorithm 1, the model parameters are iteratively updated to train the CDAN model and obtain the optimal network parameters. Finally, the unlabeled target domain fault samples (distinct from the target domain samples in the test data) are input into the optimized domain adversarial network model to predict fault types. The model's performance is assessed by calculating the predicted fault diagnosis accuracy. The formula for predicting bearing fault types is as follows:  
\[\hat{y}_{test}^{j}\left(x_{test}^{j},\hat{\theta}_{G}^{T},\hat{\theta}_{C} \right)=\max\left(C\left(flatten\left(G_{S}\left(x_{test}^{j},\hat{\theta}_{G }^{T}\right)\right),\hat{\theta}_{C}\right)\right) \tag{22}\]  
where \(\max(\cdot)\) extracts the label with the highest fault probability.  
```
1:Labeled source domain dataset \(\Psi_{S\_train}\), unlabeled target domain dataset \(\Psi_{T\_train}\), learning \(\lambda\), \(\mu\).
2:Initialize the all network parameters.
3:for\(\left(x_{i}^{S},y_{i}^{S}\right)\subset\Psi_{S\_train}\), \(x_{i}^{T}\subset\Psi_{T\_train}\)do
4:for\(L_{D},L_{C}<\epsilon\)do
5:for not reaching Nash equilibrium do
6:Forward propagation:
7:Calculate the features \(f_{S}\) and \(f_{T}\) using Formula (6);
8: Calculate the features \(L_{D}\) using Formula (10);
9:Calculate the features \(L_{C}\) using Formula (13);
10:Network parameter optimization:
11:Calculate the features \(\theta_{G}^{S}\) using Formula (15);
12:Calculate the features \(\theta_{G}^{S}\) using Formula (16);
13:end
14: Update parameters by Formula (18) to (21)
15:end
16:end
17:Optimal network parameters: \(\hat{\theta}_{D}\), \(\hat{\theta}_{G}^{S}\), \(\hat{\theta}_{G}^{T}\), \(\hat{\theta}_{C}\);
```  
**Algorithm 1**Domain adversarial network training process
## 3 Methodology
### Pipeline Overview  
This section presents a novel two-stage methodology designed for detecting and categorizing anomalous events found in inertial data acquired from wearable sensors wore by patients diagnosed with PD, as shown in Figure 1.  
The proposed pipeline processes the captured inertial data on a segment-by-segment basis. First, the pipeline utilizes binary classification to discriminate between normal and anomalous signal segments. To accomplish this, the three anomalies (tremor, dyskinesia, and bradykinesia) from the dataset are consolidated into a unified binary label. Here, the signal segments are treated anomalously upon the detection of any anomaly, and they are classified as normal, when devoid of any anomalies. Following this, if a segment is identified as anomalous, it proceeds to the second stage of multi-label classification to categorize the specific types of anomalies present. In contrast to past studies, which involved dealing with only a single anomaly, like FOG or dyskinesia, our approach addresses multiple mutually inclusive classes. This implies that a single segment may be classified ashaving both tremor and dyskinesia simultaneously. Such an approach proves valuable for quantifying the severity of symptoms; for instance, an individual exhibiting both tremor and dyskinesia would receive a higher severity score compared to someone experiencing only dyskinesia. Conversely, if a signal segment is categorized as normal during the initial stage, the subsequent stage is bypassed, as no anomalies are detected. This study focuses on the role of data collected from the IMU attached to the most affected limb; thus, all experiments and results are derived from the GeneActiv IMU.
# Photodynamic Inactivation of Human Coronaviruses
## 2 Materials and Methods
### AlamarBlue Cytotoxicity Assay  
BHK-21 cells and Huh7.5 cells were seeded in 96-well plates at 1 \(\times\) 10\({}^{4}\) cells/well in 5% FBS/DMEM + Pen/Strep/Gln or 10% FBS/DMEM + Pen/Strep/Gln, respectively. The following day, the media were removed and 2-fold dilutions of PhytoQuin (from 2.4 to 78 ug/mL for BHK-21; 0.125 to 64 ug/mL for Huh7.5) or equivalent DMSO concentrations (from 0.024 to 0.78% for BHK-21; 0.016 to 8% for Huh7.5) diluted in medium (1% FBS/DMEM + Pen/Strep/Gln, BHK-21; 2.5% FBS/DMEM + Pen/Strep/Gln, Huh7.5) were added to the cell monolayers and incubated 20 cm beneath a 30 W light-emitting diode (LED) visible light lamp (HC-GTG21-30W, SOLLA) for up to 30 min at room temperature. \"Dark\" and \"Light\" treatments were performed with 96-well plates wrapped or not wrapped in aluminum foil, respectively. The total fluence delivered during the 15 min or 30 min light treatments were 49.2 J/cm\({}^{2}\) or 98.5 J/cm\({}^{2}\), respectively, at a rate of 54.7 mW/cm\({}^{2}\). After the incubation beneath the lamp, the cells were left for 44 h. At 44 h post-treatment, the media were replaced with fresh low-serum media containing10% alamarBlue and placed back in the incubator for 3.5 h. Fluorescence was recorded using a FLUOstar Omega microplate reader (BMG Labtech, Ortenberg, BW, DE; ex/em: 544/590 nm) and normalized to fluorescence in the live cell control. The phototoxic concentration (CC\({}_{50}\)) for each cell line was calculated using GraphPad Prism 9.2.0 with a non-linear fit of [inhibitor] vs. response (variable slope, four parameters).