esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 1 Introduction
The filter-and-sum network (FaSNet) by [PERSON] et al., performs time-domain filter-and-sum beamforming at the frame level and directly estimates beamforming filters with a neural network [17]. The beamforming filters are then applied to the time-domain input signal of each frame, similar to the beamforming method. In this study, we propose a dual-channel speech enhancement technique based on complex time-domain mapping inspired by adaptive neural network beamforming and complex time-domain operations. To fully utilize the information from the two speech channels in the time domain, we developed a complex neural network adaptive beamformer (CNAB) approach to predict complex time-domain beamforming filter coefficients. During the training process, the coefficients are updated according to changes in the noisy dataset, which differs from the fixed-filter approach of previous studies [18; 19]. Additionally, the real and imaginary (RI) parts of the beamforming filter coefficients estimated by the CNAB perform complex convolution and summation operations on the input to each channel. The resulting complex time-domain beamforming output is then used as input to the local complex full convolutional network (CFCN) to predict the complex time-domain information of the enhanced speech. The primary contributions of this study are as follows:  
1. To enable time-domain complex operations and prepare the input of the network, we utilized the Hilbert transform to construct a complex signal. 2. We developed a complex neural network adaptive beamforming technique that utilizes complex inputs to perform speech enhancement. 3. We developed a complex full convolutional network that takes the output of the CNAB as input to enhance speech. By combining all of these components, we designed and tested a dual-channel complex enhancement network that performs speech denoising in the time domain. The rest of the paper is structured as follows: Section 2 presents the proposed algorithm, which covers the preparation of complex inputs, the loss function, the structure of the CNAB, and the structure of the CFCN. In Section 3, we describe the experimental settings, including dataset creation, different scenarios, and network parameters. Section 4 presents the experimental results and associated discussions on the performance of the proposed network compared with the baseline and different models in various scenarios. Finally, we draw conclusions and suggest topics for future research in Section 5.
# An Image Processing Method for Measuring the Surface Area of Rapeseed Pods
## 3 Results and Analysis
### Analysis of the Accuracy of Measurement
#### 3.1.1 Comparison of the Measurement Results between the Image Processing and 3-D Methods  
The measurement results of the modeling samples obtained by the image processing and the 3-D methods were significantly and linearly correlated; the average correlation coefficient \(R\) of the three replicates was 0.92 ** (** \(p<0.01\)), which was the same as those used in the following contents. Before the correction, the average area of the modeled sample measured using the image processing method was 1.23-fold larger than that of the 3-D method; this is consistent with the previous proposition that it is essential to correct the measurement results of the image processing method. A univariate linear regression model established using the measurement results of the modeling sample (which utilized the image processing and 3-D methods) was used to correct the measurement results of samples measured using the image processing method. The average relative error of the corrected value compared with that of the 3-D method data was 2.46%, while the average RMSE was 0.92 cm\({}^{2}\). An analysis of the accuracy of the image processing methods is shown in Table 1.
## 2 Proposed SALDL Based on GCM  
### Motivations  
Image decomposition is an approach for analyzing and reconstructing images. Image transformation (e.g., wavelet transformation), structure-adaptive filtering, and sparse coding have been considered as effective tools for realizing image decomposition. However, DCNNs have recently demonstrated excellent performance in image enhancement and restoration. Therefore, this study focuses on incorporating image decomposition into a deep learning framework for inverse halftoning. In particular, a new deep learning model to enable the residual learning of both the base and detail layers is introduced. As discussed in the Introduction, residual learning that directly maps an input image into the residual layer is not applicable to inverse halftoning because the additive model is no longer valid. Moreover, the output range of the residual layer cannot be narrowed, owing to the black-and-white dot patterns. PRL can be considered as an alternative for realizing image decomposition. However, the PRL that has hitherto been developed for inverse halftoning merely applies inverse halftoning twice in succession, since the quality level of the restored base layer is similar to that of the original image. In addition, the PRL merely uses initially reconstructed images through a DCNN for base layer generation; hence, the design of the base layer lacks novelty. Furthermore, existing PRL cannot recover textures and fine details from low-quality base layers. Hence, a new SALDL based on GCM is proposed herein.  
Figure 1 shows the concept of image decomposition based on the proposed SALDL for inverse halftoning. Unlike traditional approaches such as wavelet transform and image pyramids, residual-learning-based image decomposition is proposed. In particular, novel GCM-based residual learning and structure-aware residual deblurring are introduced for base and detail layer generation, respectively. By adding the predicted base and detail layers, a continuous-tone image can be reconstructed from the input halftoned image. Details regarding the generation of the base and detail layers are provided below.
## 3 Results and Discussion
### Effect of Mineral-Impregnated PSC on the Decontamination of Paddy Soil  
Compared to a positive decontamination degree of 3.8% of the blank PSC, those of the PSCs impregnated with cesium chloride, barium chloride, calcium chloride, and magnesium chloride were \begin{table}
\begin{tabular}{c c c} \hline \hline
**Original PSC** & **Contaminated PSC** & **BaCO\({}_{3}\) Reagent Grade** \\ \hline  & & 615 (weak) \\  & 680 & 690 \\  & 885 & 850 \\  & 960 & 1030 \\
1020 & 1030 & 1050 \\  & 1058 (shoulder) & \\
1255 (weak) & & \\  & 1425 & 1425 \\  & 1530 & \\  & 1610 & 1625 (weak) \\  & & 1750 \\
1975 & & \\
2035 & & \\
2170 & & \\  & 2325 & \\  & 2370 & \\
2455 & & 2450 \\
2535 & & \\
2855 & & \\
2930 & & \\
3225 & 3010 (weak) & \\  & & 3410 \\
3650 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: IR absorption bands (cm\({}^{-1}\)) of the original PSC, radiocontaminated PSC, and literature barium carbonate [24]. Figure 4: FT-IR spectra of the original PSC and radiocontaminated PSC. negative (Table 7). One exception was potassium chloride, of which the decontamination degree was the highest, 27.6%, or more than 7 times that of the untreated PSC. In order to confirm the effect of the impregnated chlorides, free cesium and potassium chlorides were separately mixed with the contaminated soil at a dosage of 1%, based on the soil weight. These two free chlorides, similar to PSC impregnated with cesium chloride, barium chloride, calcium chloride, and magnesium chloride, only negated the decontamination treatment. The reason for the negative impact of the chlorides mentioned above is unknown. Because barium sulfate and calcium sulfate are insoluble in water, they could not be tested. Among the sulfates under consideration, the decontamination degree of the cesium sulfate-impregnated PSC was negative. The decontamination degrees of the iron (II) and zinc sulfate-impregnated PSCs were similar and higher than that of the untreated PSC. The decontamination degree was greatly improved when the PSC was impregnated with potassium, copper, or magnesium sulfates. However, the capability to decontaminate the soil by potassium sulfate was inferior to that of the copper and magnesium sulfates. [PERSON] and co-workers [25] asserted that the ionic cesium strongly bound to South African vermiculite is desorbed by magnesium nitrate. Furthermore, [PERSON] [26] found that carrier-free \({}^{134}\)Cs in soil is highly and significantly correlated with the \({}^{134}\)Cs in soil, which is extractable by magnesium nitrate. These results confirmed the present finding that radiocesium in soil does exchange cations with magnesium ions in PSC. As indicated in the periodic table, cesium is grouped with potassium and sodium in Column 1. Thus, it is expected that potassium in potassium chloride and potassium sulfate impregnated on PSC would exchange with radioactive cesium in the contaminated soil, resulting in the improvement of the PSC's decontamination degree.
# A Survey of Low-Cost 3D Laser Scanning Technology
## 4 Problems of a Moving 2D LiDAR
### Problem I: Accuracy
#### 4.1.1 Problem 1.1--For Categories 1-6: The Measurement Error of the 2D LiDAR  
When it comes to the measurement error of the 2D LiDAR, some literatures specifically study the characteristics of 2D LiDAR and calibration methods. For example, in [90,91], various factors affecting the measurement accuracy of 2D LiDAR are explored, including the distance of the object which is measured, the incident angle of the laser beam, the material and surface reflectivity of the object that is measured, the environmental brightness, and etc. A mathematical model showing the error of the 2D LiDAR is proposed to calibrate it. There are two points worth noting, one of which is the time instability of the measurement data. When the 2D LiDAR is used to measure a static target, the measured value will change over time and will not stabilize until a period of time (usually about an hour). This phenomenon is called drift effect, which may be linked to the operating temperature of the 2D LiDAR. Drift effect affects the accuracy of the 2D LiDAR very slightly, for most applications of the 2D LiDAR, the error caused by the drift effect can be ignored. The other point worth noting is that there is a slight deviation of the angle range of the 2D LiDAR. Experimental tests show that the serial number of the front-most beam of the 2D LiDAR may not be consistent with the nominal value. The nominal value is the middlemost serial number, and the experimental tests show that there is a deviation between the nominal value and the true value. This will cause a slight deviation of the angle range of the 2D LiDAR.  
By Equation (1) in Section 2, we can calculate the coordinates of the sampling point relative to the coordinate frame \(L\) of the 2D LiDAR, and the accuracy of the calculation depends on the measurement accuracy of the 2D LiDAR. The studies on problem 1.1 focus on the measurement accuracy of 2D LiDAR.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 4 Experimental Implementation  
This section provides a detailed explanation of the implementation of the wireless fire monitoring system configuration. MQTT is the messaging protocol ensuring that data can be transmitted to the central server in real-time. The system operates autonomously, with each sensor acting as both a data collector and relay node in the multi-hop network.
## 2 Modulation Transfer Function (MTF) Calculation Method  
In this section, we propose a general MTF expression for the mixed image motion of linear motion and sinusoidal motion. In order to obtain the analytical expression of MTF for the mixed image motion of linear motion and sinusoidal motion, the summation of infinitely many terms involving Bessel function is introduced in order to replace the integral of the trigonometric functions.
## 3 Data and Methodology
### Methodology  
This study integrated airborne LiDAR data and multi-temporal UAV photogrammetry data and proposed a method for monitoring mining subsidence featuring co-registration of UAV LiDAR data and photogrammetry. The process consists of four steps: first, the airborne LiDAR point cloud was used as the reference data to co-register the multi- temporal UAV dense matching point cloud, and then the performance of the co-registration was evaluated through the calculated M3C2 distance. Further, a dynamic subsidence basin for surface subsidence monitoring was constructed based on the DoD obtained from the UAV dense matching point cloud filtering and interpolation. Finally, the development process and subsidence law of the subsidence basin was studied. On this basis, we analyzed the accuracy of the dynamic subsidence basin by comparing it with the measured points in field investigation, and also quantified the uncertainty of the DoD. The flow chart of this study is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**D-CAM2000 Aerial Module**} & \multicolumn{2}{c}{**D-LiDAR2000 LiDAR Module**} \\ \hline Camera & SONY a6000 & Ranging & 190 m@10\%Reflectivity@100 klx \\  & & 450 m@80\%Reflectivity@0 klx \\  & 24.3 million & Scanning frequency & 240 kHz \\  & 23.5 \(\times\) 15.6 mm (aps-c) & Ranging accuracy & \(\pm\)2 cm \\  & 25 mm & Horizontal positioning accuracy & 0.02 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters configuration of aerial survey module and LiDAR module.  
Figure 2: The flow chart for subsidence monitoring via UAV.
# Potential Role of Vitamins A, B, C, D and E in TB Treatment and Prevention: A Narrative Review
###### Abstract  
(1) Background: Tuberculosis (TB) is one of the world's top infectious killers, in fact every year 10 million people fall ill with TB and 1.5 million people die from TB. Vitamins have an important role in vital functions, due to their anti-oxidant, pro-oxidant, anti-inflammatory effects and to metabolic functions. The aim of this review is to discuss and summarize the evidence and still open questions regarding vitamin supplementation as a prophylactic measure in those who are at high risk of _Mycobacterium tuberculosis_ (MTB) infection and active TB; (2) Methods: We conducted a search on PubMed, Scopus, Google Scholar, EMBASE, Cochrane Library and WHO websites starting from March 1950 to September 2021, in order to identify articles discussing the role of Vitamins A, B, C, D and E and Tuberculosis; (3) Results: Supplementation with multiple micronutrients (including zinc) rather than vitamin A alone may be more beneficial in TB. The WHO recommend Pyridoxine (vitamin B6) when high-dose isoniazid is administered. High concentrations of vitamin C sterilize drug-susceptible, MDR and extensively drug-resistant MTB cultures and prevent the emergence of drug persisters; Vitamin D suppresses the replication of mycobacterium in vitro while VE showed a promising role in TB management as a result of its connection with oxidative balance; (4) Conclusions: Our review suggests and encourages the use of vitamins in TB patients. In fact, their use may improve outcomes by helping both nutritionally and by interacting directly and/or indirectly with MTB. Several and more comprehensive trials are needed to reinforce these suggestions.  
tuberculosis; vitamin A; vitamin B; vitamin C; vitamin D; vitamin E; _M. tuberculosis_
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 3 The Simplified Stability Analysis Method Based on the GNC and Verification
### Experimental Verification of the Stability Analysis Results
The main oscillation frequencies of 34 Hz and 66 Hz are very close to the predicted oscillation frequencies of 36.5 Hz and 63.5 Hz. Hence, the experimental results are consistent with the theoretical stability analysis results, which further proves the accuracy of the proposed impedance model and stability analysis method for the inverter under the three-phase unbalanced condition.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 2 Classification and Progress of Power Industry Optical Wireless Communications
### Channel Characteristics between Power Line and Indoor Wireless Optical Communications
#### 2.1.1 Channel Characteristics of Indoor Wireless Optical Communications  
The channel frequency response (CFR) of PLC is as shown in the following [20; 23]:  
\[H_{PLC}(x)=\sum_{l=1}^{N}g_{l}e^{-(\alpha_{0}+\alpha_{1}x)\tau_{l}\tau_{p}}e^{- \beta nx\tau_{l}} \tag{1}\]  
where \(g_{l}\) is for the weighting factor of the \(l\)th path, which includes the reflection factor and transmission factor along the path, attenuation parameters are represented by \(\alpha_{0}\) and \(\alpha_{1}\), the exponent of the attenuation factor is expressed in terms of \(x\) (usually 0.2 to 1), \(v_{p}\) is for the group velocity and \(\tau_{l}\) stands for the \(l\)th path delay, and the \(N\) denotes path number.  
The received signal in the IWOC part can be expressed as:  
\[H_{VLC}(x)=Sh_{LOS}e^{-\beta nx\tau_{LOS}} \tag{2}\]  
Figure 4: PLC to IWOC module [19].  
Figure 3: The block diagram of the integrated indoor wireless optical communications-power line communications (IWOC-PLC) system [19].  
where the discrete Fourier transform of the \(h_{VLC}(x)\) is \(H_{VLC}(x)\), \(S\) is the photosensitivity, the gain is expressed in terms of \(h_{LOS}\), and \(t_{LOS}\) represents the delay of the LOS signal, which is line of sight signal. In other words, there are no obstructions in the signal transmission process from the transmitter to the receiver.
## 2 Materials and Methods  
### Design of Experiment  
The toxicity tests were carried out according to the Organisation for European Economic Cooperation (OECD) Guideline No. 215 Fish, Juvenile Growth Test [28]. Zebrafish (_Danio rerio_),at the age of 30 days, were chosen as the model organism for our study. This model organism belongs to the one of the most commonly used model species [29; 30; 31; 32; 33].  
Moreover, the zebrafish is among the recommend organisms to perform Fish Juvenile Growth Test [28]. The fish used for the experiment were at the age of 30 days, as at this period of time, they were all considered to be at the juvenile stage [34].  
Experimental organisms were exposed to a range of concentrations of tramadol hydrochloride (0.2, 2, 20, 200 and 600 \(\upmu\)g/L). The duration of the subchronic toxicity test was 28 days. According to OECD guideline No. 215, five concentrations of the test substance are required. The two lowest concentrations were established with respect to the reported environmentally relevant concentration of this substance in surface water [7; 23]. Additionally, 10\(\times\), 100\(\times\) and 300\(\times\) higher concentrations were also tested, in order to better understand the toxic effects of the substance tested.  
The control and all experimental concentrations were tested in duplicate. The experiment was approved by the ethical committee of University of Veterinary and Pharmaceutical Sciences Brno (Czech Republic) and was in compliance with national legislation (Act No. 246/1992 Coll., on the Protection of Animals Against Cruelty, as amended, and Decree No. 419/2012 Coll., on the Protection, Breeding and Use of Experimental Animals, as amended).  
A total of 600 fish were randomly divided into twelve 30-litre glass aquaria in dechlorinated tap water (50 in each aquarium). A toxicity test was performed in a flow-through system, with bath exchange every 12 h. Testing solutions of tramadol hydrochloride (chemical purity \(\geq\) 99%, Sigma-Aldrich, Czech Republic) were prepared every day, and an ultrasonic bath was used to dissolve the substance. The initial body weight and total length were \(11.34\pm 1.53\) mg and \(10.60\pm 4.83\) mm (mean \(\pm\) standard error of the mean), respectively. The fish were fed with commercial dried _Artemia salina_, without nutshells, at the total rate of 8% body weight. After fourteen days of toxicity test, the fish were weighed again, and the feed amount was recalculated. At the end of our experiment, the fish were euthanized (MS 222) and weighted, and their total lengths and tank-average specific growth rates (r) were determined.  
During the toxicity tests, conditions, water quality and the number of dead fish were recorded in each aquarium, at 12-hour intervals. The values of water quality were as follows: temperature \(25\pm 1\)\({}^{\circ}\)C; oxygen saturation above 84 %; and pH from 7.94 to 8.24.
## 3 Experiments and Analysis  
### Experimental Setup  
We finally validated the proposed algorithm through a hardware experimental platform built by ourselves. The ICM-42688-P was the IMU that integrated a three-axis gyroscope and a three-axis accelerometer. The IST8310 was a three-axis magnetometer. SPL06-001 was selected as a digital barometric air pressure sensor. The SKG122S was selected as the GNSS module owing to its multi-system dual-band performance. The DW1000 chip was selected as the UWB because it has low power and can be compliant with the IEEE 802.15.4-2011 UWB standard. The STM32F407 VET6 was selected as the microcontroller of the system. The sensors were mounted as illustrated in Figure 5, where the IMU, the magnetometer, and the barometer were placed on a shoe, and the GNSS and UWB modules were fixed on the helmet. The parameters of the sensors are listed in Table 1.
## Materials and Methods
### Selection of Quality Control Tests
#### 2.1.1 Fresh SCC Quality Control Tests  
The following quality control tests were chosen to be performed on the fresh SCC samples. Photographs of the testing procedure are shown in Figure 1.  
1. **L-Box Test:** This test demonstrates the SCC's flowability through tight sections and restricted reinforcement arrangements. For a sample of concrete to be considered SCC, the ratio _H2/H1_ (with _H2_ being measured at the end of the open channel and _H1_ being measured at the vertical hopper) should preferably be 1.0. However, the standard allows for a minimum ratio of 0.8 [16].
2. **J-Ring Test:** This test demonstrates the SCC's flowability around reinforcements. This test also gives an indication of its workability and exposure to segregation. SCC is defined as having an average slump flow diameter of at least 500 mm, which was used as a cutoff to determine whether the sample of concrete is SCC or not. A difference of 25 mm between the J-Ring flow and slump flow test results is acceptable [17].
3. **V-Funnel Test:** This test was used to get an idea of the SCC's viscosity. The SCC's viscosity was measured immediately after placing it in the V-Funnel and after leaving it in the V-Funnel for 5 min. The timings of the two iterations are then compared [18].