esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Leading Process
#### 3.1.4 Effect of Reaction Time  
Figure 7 shows the leaching efficiency of vanadium and chromium by varying the reaction time from 30 min to 180 min under selected conditions: reaction temperature of 90 \({}^{\circ}\)C, liquid-to-solid ratio of 5:1 mL/g, dosage of MnO\({}_{2}\) at 50 wt.%, and concentration of H\({}_{2}\)SO\({}_{4}\) at 30 wt.%. Increasing of reaction time could promote the reaction between H\({}_{2}\)SO\({}_{4}\) and vanadium-chromium reducing residue and achieve high leaching efficiency. Almost all chromium was leached out at a reaction time of 180 min, and the leaching efficiency of vanadium was increased from 86.43% at 30 min to 97.93% at 180 min, which indicated that 180 min was enough to leach out chromium and vanadium.
## 2 Antenna Design
### Single Communication Mode  
#### 3.3.1 Communication Mode #1  
NB Antenna #1, located at the upper right corner of the ground, is responsible for communication in the low-frequency band (Communication Mode #1). Figure 5 depicts the simulated S-parameters of the proposed antenna in Mode #1. NB Antenna #1 has a wide bandwidth, from 2.96 GHz to 5.38 GHz (fractional bandwidth: 58.8%), which is achieved by combining resonances at 3.5 GHz (first resonance) and 4.9 GHz (second resonance). In addition, the isolation between the UWB antenna and NB Antenna #1 is greater than 17.3 dB, with a peak value of 39.4 dB at 2.96 GHz.  
To understand the operating principle of NB antenna #1, the surface current distributions at resonant frequencies are analyzed in Figure 6. As shown, the loop-type current is distributed on NB Antenna #1 at the first resonance frequency of 3.5 GHz from Figure 6a. In Figure 6b, it is noted that the second resonance at 4.9 GHz is excited by the U-shaped current distribution formed by the slot between the loop and left ground.  
Because the UWB antenna is an electric dipole type, the resonances on the UWB antenna are mainly determined by the vertical length of the ground plane [30]. Due to the orthogonal current modes from the ground plane, high isolation characteristics can be achieved without applying any additional decoupling structures in this design.  
To further understand the high isolation mechanism of NB Antenna #1, parametric studies for the key parameter D are analyzed in Figure 7. The parameter D represents the distance NB Antenna #1 moves down toward the wide slot. D = 0.8 mm is the maximum value to obtain the complete  
Figure 4: Simulated surface current distributions of the proposed antenna for spectrum sensing mode (P1 ON): (**a**) low frequency at 3.1 GHz, (**b**) mid frequency at 6.52 GHz, and (**c**) high frequency at 9.5 GHz.  
Figure 5: Simulated S-parameters of the proposed antennas for communication mode #1.  
wide-slot UWB antenna structure. From Figure 7, it can be observed that the antenna achieves high isolation performance (greater than 17 dB) in spite of the close proximity of the UWB antenna and NB Antenna #1, without a noticeable change in the reflection coefficient characteristics. To attain optimal antenna performance, D = 0 mm is chosen as the proposed design parameter for NB Antenna #1.
## 2 Materials and Methods  
As mentioned, an alternative to census data is the application of machine learning and, in particular, deep learning using EO data. Using several cases, we will highlight advancements of EO to fill existing data gaps for NSO.  
The first step in any machine learning application is to construct a training dataset. That is a set of examples of the objects we want the algorithm to make predictions about. These objects have to be labelled, which means that we know a priori the value of the characteristic that we want the algorithm to predict. No matter the machine learning method used, representative and vast training datasets are required to obtain accurate results, particularly when predicting complex classes (e.g., socio-economic conditions), which are not as evident as the presence of vegetation, for example.  
The use of deep learning algorithms to predict not only the location of slums but also the slum severity (or deprivation level) with EO data (which is an open problem) will be highlighted by selected cases. There is relevant progress in this particular use of EO, e.g., [24] showed that deep learning and transfer learning techniques allow for processing VHR images, reaching an overall accuracy of 94.2; nevertheless, very few studies analyse physical differences between slums [25; 26]; most studies assume that slums are homogenous (e.g., [27; 28]). One exciting and challenging approach to improve model results is to combine distinct types of images [29]. Additionally, the analysis of dynamics of slums is still challenging, hindered by limited access to commercial VHR images (e.g., around $20 per km\({}^{2}\)) [30]. It is also worth noting that even when data and computational challenges are addressed, it is not clear to most EO experts how data on slums can be made available without putting the privacy and security of slum residents at risk.
## 1 Introduction
Due to this, the agent calculates an action-value function \(Q(\cdot,\ \cdot)\) that maps state-action pairs into the corresponding expected return. DRL has broadly been used for various applications, including but not limited to computer vision [6; 7], robotics [8], natural language processing, video games [9], education, transportation, finance, and healthcare. [PERSON] et al. proposed a novel danger-aware adaptive composition framework method for self-navigation [10] and a map-less navigation method for robot navigation [11]. [PERSON], [PERSON] et al. solved a robot navigation problem with the novel method of DRL with a graph attention network (GAT) among external autonomous agents [12]. This paper proposes a complete controlling strategy to assist the teleoperator in an unknown dynamic healthcare environment during delayed operating signals from the operator. The proposed model is tested in the gmapping environment using interfaces based on a robot operating system (ROS), and the outcome shows that the technique is workable and effective. The primary purpose of our research is to demonstrate a novel hybrid approach consisting of gated recurrent units (GRU) integrated with a double deep Q-learning network (DDQN) algorithm. Then we used it to control differentially driven telepresence robots. With two different input signals, we define the kinematics of the mobile robot. We consider the definition of reward as a variable, the kind of control input, and the random exploratory process to demonstrate the impact on the learning process. This proposed study presents a new method for controlling a telepresence robot with the ability to make autonomous decisions by learning from ongoing controlling signals provided by the teleoperator during delayed communication. The remaining article is organized as follows: Section 2 discusses the related work and literature review. An overall system overview is presented in Section 3. Section 4 presents the proposed model for controlling the telepresence robot. We discuss the experimental analysis in Section 5, and the Section 6 concludes the article.
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
## 9 The Effect of Ohmic Heating on the Bioactive Compounds in Food  
Ohmic heating is an alternative thermal treatment, which is considered to have less impact on biologically active compounds compared to traditional thermal processes. Bioactive compounds are highly valuable components because of their nutritional importance and their relation to human health. Therefore, preservation of these compounds during thermal treatment such as boiling, pasteurization, and sterilization is one of the most difficult challenges in the food industry [87]. High temperature leads to the loss of some active ingredients. For the purpose of determining the appropriate temperature for a process, the highest temperature that maintains these bioactive compounds is highly recommended [88].  
[PERSON] [82] studied the effect of ohmic heating on carotenoids (\(\beta\)-carotene and lycopene), phenolic compounds (phenolic acids, narringin, quercetin, and total flavonoids), and ascorbic acid in fresh tomato juice. The results showed that ohmic heating did not affect the amount of carotenoids and phenolic compounds in tomato juice. Ohmic heating also did not cause any decrease in the amount of the \(\beta\)-carotene, lycopene, phenolic acids, and quercetin at temperatures of 95, 100, 105, and 110 \({}^{\circ}\)C, and confirmed the stability of lycopene at pH 3.9 and temperatures of 90, 95, 100, and 105 \({}^{\circ}\)C. Moreover, ohmic heating can successfully retain the values of ascorbic acid and dehydroxy-ascorbic acid when treated at a temperature of 90 \({}^{\circ}\)C. The study also confirmed the ability of ohmic heating to increase the total content of naringenin compared to untreated samples [82].  
[PERSON] et al. [89] mentioned that the use of a low-frequency for the electric field (10 Hz) led to the highest value of ascorbic acid deterioration and an increase in the color changes due to the electrochemical reactions, while the interactions at (100 Hz) became less. As a result, the use of high electric field frequencies did not affect the dissociation of ascorbic acid, indicating that the oxidation reaction in a molecule of ascorbic acid was not affected by rapid changes in the electric field values. [PERSON] et al. [90] used ohmic heating to produce tomato pulp powder. The study showed that the hydrophilic part was not significantly affected, while lycopene was positively affected with significantly increased antioxidant efficacy. This study demonstrated the importance of ohmic heating in maintaining appearance and nutritional value [90]. The effect of ohmic heating on bioactive compounds content, phenolic content, and antioxidant activity are shown in the Table 4.
## 3 Electothermal Modeling of the HEMT  
We will detail the electrothermal modeling approach in this part, which allows us to study the thermal behavior of the high electron mobility transistor as a function of operating conditions. During its operation, the transistor dissipated power at the active area at the output of the gate [22]. The dissipated power is related to the current flowing in this transistor and the voltage applied to its terminals such as [19,21]:  
\[P_{\textit{Diss}}=I_{\textit{DS}}*V_{\textit{DS}}, \tag{1}\]  
where \(V_{\textit{DS}}\) and \(I_{\textit{DS}}\) are, respectively, the voltage and the current flowing between its terminals: drain and source. For studying the electro-thermal behavior of the HEMT, the approach that we will use is based on injecting the dissipated power at its active zone. Then we will study the heat distribution at its structure. This transfer is principally done by conduction mode; the other modes of transfer (convection and radiation) are negligible because they represent just less than 1.5% of the global transfer [21,23]. Based on the principal transfer mode, the heat equation will be [24]:  
\[k\
abla^{2}T=\rho C_{p}\frac{\partial T}{\partial t}+Q, \tag{2}\]  
where:  
* [label=-]
* \(Q:\) dissipated power (J);
* K : thermal conductivity \(\left(\mathrm{W}\cdot\mathrm{m}^{-1}\cdot\mathrm{K}^{-1}\right)\)
* \(\rho:\) density \(\left(\mathrm{Kg}\cdot\mathrm{m}^{-3}\right)\)
* \(C_{p}:\) thermal mass capacity \(\left(\mathrm{J}\cdot\mathrm{Kg}^{-1}\cdot\mathrm{K}^{-1}\right)\)
* \(T:\) the temperature \(\left(\mathrm{K}\right)\)  
Equation (2) describes the HEMT's thermal behavior; for better modeling, we will use the finite element method that allows us to solve this problem using Comsol multiphysics software [25]. The electrothermal modeling of the transistor is done in the form of a 3D finite element model with Comsol multiphysics software, to observe the influence of the  
Figure 1: HEMT structure.  
dissipated power in the active area of the transistor during its operation, and that of the reference temperature at the substrate. The system is composed of several materials whose properties are shown in Table 1[26]. Using these properties, numerical simulations of the HEMT were performed, then the variation of the operating temperature as a function of the dissipated power \(P_{diss}\) and the reference temperature \(T_{Ref}\) was studied.
## 3 Methods  
The existence of pre-earthquake thermal infrared anomalies has been demonstrated in many studies. Most traditional studies were based on different algorithms to extract the intensity and distribution range of thermal infrared anomalies in pre-earthquake time and spatial dimensions, such as the RST algorithm, wavelet vorticity method, and eddy field method [2; 3; 4]. The experiment was based on time series data of outgoing longwave infrared radiation and used a time series prediction model to predict the values of longwave infrared radiation at different time ranges before the earthquake within a specific spatial coverage of the epicenter. Data preparation involved downloading the data and selecting seismic examples and spatial and temporal scales. The data were cropped to the corresponding temporal and spatial scales to generate the dataset. By comparing and analyzing the prediction performance of different time series prediction models for different prediction periods, we selected the best performing model for the earthquake example study in the algorithm evaluation phase. In the anomaly extraction phase, the confidence interval was used as the evaluation criterion to extract the magnitude of the range outside the confidence interval and the corresponding date. Finally, the anomalies were analyzed based on the spatiotemporal dimension. Figure 3 shows the entire process of this experiment.  
Figure 2: (**a**) OLR values in China in the 30 days before Yangbi _M_s6.4 earthquake (from 22 April 2021 to 21 May 2021). The red box indicates the study area of the Yangbi earthquake. (**b**) The Yangbi _M_s6.4 earthquake’s OLR values within the \(5^{\circ}\times 5^{\circ}\) range of the epicenter (from 22 April 2021 to 21 May 2021).
# Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images
## 3 Research Methodology
### Feature Merge Module  
The FFM connects the transformer and CNN semantically. The feature maps of CNN and transformer PE are different in size, so a direct conversion between them causes a loss of meaningful details.  
The FFM receives two inputs, \(F_{input}^{c}\) from CNN and \(F_{input}^{T}\) from the transformer route. The \(F_{input}^{c}\) has B \(\times\) C \(\times\) H \(\times\) W size, while \(F_{input}^{T}\) has a size of B \(\times\) (1 + H \(\times\) W) \(\times\) C and 1 for the class token. Before being converted to PE, \(F_{input}^{c}\) is subjected to a global pooling (11 convolutions with BN and ReLU) and has its channel number altered to 768. This GP is viewed as a gate to choose instructive channels and increase the input's sensitivity to class. The pooled is then down-sampled using average pooling with stride 2. By calculating the average value across adjacent pixels, average pooling can aid in the dissemination of global information. The CNN input is consequently closer to the transformer features. Feature maps are modified to PE \(F_{patch}^{C}\) following average pooling. Transformer input (\(F_{input}^{T}\)) and output (\(F_{patch}^{C}\)) are fused to create \(F_{output}^{T}\). The proposed model has four FFMs all have \(F_{output}^{T}\). As depicted in Figure 6. The output from the previous transformer stage is added to each \(F_{output}^{T}\) of the FFM, and their sum is input to the following stage. All PE of \(F_{input}^{T}\) except the class token, are abandoned throughout the fusion process. To obtain \(F_{output}^{T}\), \(F_{patch}^{C}\) is linked to the class token and local information abounds in \(F_{patch}^{C}\) features, which also assists in CNN transformation. The attention mechanism in Transformer investigates global features on the basis of local feature information to improve the representation capabilities once \(F_{output}^{T}\) is input to the following transformer stage.  
Figure 5: Transformer block structure.  
Only the FFM-2 and FFM-4 include the output for the CNN feature extractor, or \(F^{c}_{output}\), as seen in Figure 6. Here, IMM-1 and IMM-2 receive \(F^{c}_{output}\) from FFM -2 and FFM-4. Transformer input (\(F^{T}_{input}\)) in the FFM is scaled to the picture space to produce \(F^{T}_{image}\). The equation for \(F^{c}_{output}\) is as follows:  
\[F^{c}_{output}=\theta(F^{c}_{input})\ \otimes\ F^{T}_{image} \tag{4}\]  
In this equation the sigmoid function is denoted by \(\theta\) and the elementwise multiplication is represented by \(\otimes\). To give \(F^{T}_{image}\) appropriate spatial support, the \(\theta\) (\(F^{c}_{input}\)) serves as a mask. Transformer embeddings are turned into the global feature-rich \(F^{T}_{image}\). Global features that are passed from \(F^{T}_{input}\) to \(F^{c}_{output}\) can be given to the CNN feature extractor. FFM preserves global features from Transformer pathways and local features from CNN more effectively.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
### Example Lab Deployment  
We deployed WISCANet on a network of Ettus X310s connected to Dell Precision Tower 3630s and Ettus B210s connected to Intel Skull Canyon NUCs [27]. This deployment is depicted in Figure 8. This particular implementation features combined X310s on each tower, providing a maximum of 4 transmit or 4 receive channels per radio. We use a GPS repeater to lock the radios in this indoor environment.  
To achieve greater than \(2\times 2\) MIMO, multiple X310's must be used in tandem. Two X310s can be combined by sharing one unit's 10 MHz and PPS signals to enable a \(4\times 4\) MIMO device. This method can be extended to create larger platforms or replaced with an Octoclock-G to distribute the 10 MHz and PPS signals to all X310's in a single edge node \(N\times N\) MIMO configuration. Either of these combinations can achieve the same phase-stable behavior demonstrated in the previous results.  
Figure 8: This WISCANet deployment features four Ettus X310s with UBX-160s sharing 10 MHz and PPS signals disciplined by the internal GPSDO. Each edge node uses two independent X310s for higher bandwidth transmit and receive. This deployment also features four Ettus B210s as independent edge nodes. One Ettus N320 is also included for embedded processor experiments. This deployment can be used for a variety of experiments in sub-6 GHz bands, emphasizing disparate processing capability, and adaptive waveform employment.
## 2 Hardware of Transient Luminous Event (TLE) Observation System
### Board Configuration  
The TLE observation system communicates with the command and data handling system (C&DHS) via an RS-422 universal asynchronous receiver/transmitter (UART) interface. For compatibility with the electrical interface standards for bus interconnection between all modules in the VisionCube, the TLE observation board has a PC/104 stack-up connector. The board is powered by 3.3 V and 5 V supply voltage provided via the PC/104 connector. The simplified board configuration for the TLE observation system is shown in Figure 4. It has an extra 10 pin header for programming the SoC configuration data to a flash memory device EPCS16. The CMOS image sensor is configured via I2C (inter-integrated circuit) serial bus from the FPGA logic to set the effective pixel area, exposure time, gain, and other parameters. The detailed hardware specifications are summarized in Table 2.  
Figure 5 shows the 8-layer printed circuit board (PCB) artwork design for fabrication. The size of the board is 90.5 mm \(\times\) 95.5 mm, which is specified by the CubeSat electrical board standard. The MaPMT and the CMOS image sensor are carefully placed in consideration of the mechanical design of lens adapter and housing fixture. The trace wire from the DDC264 is directly connected to the MaPMT while minimizing its lengths to prevent signal pollution. Figure 3: Multi-anode photon-multiplier tube (MaPMT) by [PERSON]. (**a**) H7546B MaPMT [30]; (**b**) Sensing plane containing 8 \(\times\) 8 anode array. Figure 4: Board configuration of TLE observation system. It is composed of six main components: an SoC chip, an MaPMT, a high voltage supply, a DDC264 analog-to-digital converter, CMOS image sensor, and two SDRAMs. Figure 5: Design of PCB artwork of the TLE observation system.
## 1 Introduction
This has led to the development of two-phase (or two-stage) AD, in which the processes are divided into two separate BRs with different working volumes, as the rapid processes of hydrolysis and acidogenesis are in the first BR and are accompanied by hydrogen evolution, and more slow-moving processes of acetogenesis and methanogenesis--in the second BR (with a larger working volume), which releases methane [4,8,9]. It is known that this division of the processes into two consecutive BRs leads to significantly higher energy yields (by over 40%) for the two-phase system (H2 + CH4), compared to the traditional single-stage CH4 production process [4,8,10]. Lately, an idea to divide the processes into three separate BRs (hydrolytic, acidogenic, and methanogenic) has occurred [11]. At the same time, in the two-phase anaerobic biodegradation (TPAD) with CSTR, the waste organic substances are 9-16 times lower than the incoming ones, i.e., the degree of biodegradation (DBD = 89-93%). In our laboratory experimental studies for AD of mixtures of milk whey and waste-activated sludge from a wastewater treatment plant in a two-stage process (with biomethane production only), a DBD of 95% was obtained [12]. In the case of TPAD, the waste obtained can also be used as natural fertilizer in agriculture. The corn steep liquor (CSL) selected as the raw material was used (alone or in mixtures) to produce biohydrogen or biomethane [13,14]. Corn steep liquor is a cheap alternative to yeast extract for numerous biotechnological industrial processes. At the same time, because of the rapid development of the production of corn starch, substantial amounts of CSL have been produced. Many starch manufacturers discharge corn soak water directly into the environment, which has a negative impact on the environment [15]. According to our known information, it is not used in TPAD for simultaneous production of hydrogen and methane. The aim of this paper is to investigate and present some of our results on the simultaneous production of hydrogen and methane in a pilot-scale automated system for TPAD of CSL with CSTRs.
## 2 Error Analysis of Grating Ruling Machine
### Effect of Screw Error on Grating Spectral Performance
#### 2.3.1 Influence of Lead Screw Error on Grating Performance  
Assuming that the lead of the lead screw is \(1\) mm, the error period is \(1\) mm, and according to the sine function distribution, the amplitude limit is \(500\) nm. For gratings in Table 1, the period error distribution function of the groove is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Groove Density** & **Wavelength** & **Blaze Angle** & **Blaze Level** & **Error Distribution** \\ \hline
600 g/mm & 632.8 nm & 22\({}^{\circ}\) & \(-2\) & \(-10\) nm, \(10\) nm, \(\cdots\) \\ \hline \end{tabular}
\end{table}
Table 2: Diffraction grating parameters.  
Figure 5: The relative intensity distribution of diffracted light.  
Figure 6: The distribution function of the period of the unit ghost line in the groove.  
According to Equation (19) and based on MathWorks MATLAB R2019b for Linux (MathWorks, Natick, MA, USA) software, the diffracted light distribution can be calculated, as shown in Figure 7.  
It can be observed from Figure 7 that the periodic error of the screw does not produce obvious ghost lines, but only enhances the stray background of the diffracted light distribution. Therefore, the screw error is one of the causes of the grating stray light.