esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Model Architecture
#### 3.3.1 Convolutional Neural Network  
The proposed model synergistically combines essential components from the CNN architecture, including three convolutional layers, a pooling layer, and a BN layer. These components work together to capture meaningful patterns and extract significant features from the input data. Figure 2 depicts a representative CNN structure. The initial three layers of the model consist of convolutional layers, with 64, 128, and 256 respective convolutional kernels. Each kernel possesses a size of 2 \(\times\) 2, and a rectified linear unit (ReLU) is employed as the activation function. Convolutional layers play a crucial role in this model by extracting features through kernels. The kernel can be regarded as a moving matrix that traverses the input feature matrix both horizontally and vertically [41]. During this movement, dot product calculations are performed between the input feature matrix and the corresponding kernel weights. This process continues until the kernel's movement stops, resulting in a new output feature matrix. Figure 3 provides a depiction of the convolution operation between the input features and the kernel. The presence of three convolutional layers enhances the model's hierarchical feature extraction capability, enabling it to extract more abstract and higher-level feature representations. Furthermore, the varying numbers of convolutional kernels aid the model in capturing features across multiple scales, thereby further improving the model's feature representation capacity [42].  
After the convolutional layers, we performed downsampling on the input feature maps using a 2 \(\times\) 2 pooling layer. This process reduces the spatial dimension and computational complexity of the features [43]. Moreover, this downsampling process helps reduce the number of network parameters, which mitigates the risk of overfitting [44]. Max pooling and average pooling are two widely adopted methods. Max pooling divides the input feature matrix into distinct regions and retains the maximum value from each region. In contrast, average pooling calculates the mean value within each region. In this study, we choose to use the max pooling method because it can emphasize outstanding features. Figure 4 presents the pooling operation.  
Following three convolutional layers and a pooling layer, the distribution changes in the input data pose challenges such as internal covariate shift, gradient vanishing, and reduced training efficiency. In order to overcome these challenges, BN is employed during the training process to adjust the mean and variance of each data batch [44]. A reshape layer is introduced to meet the input requirements of the subsequent BiLSTM layer by reshaping the output feature tensor. Through the integration of these fundamental components of CNN, the proposed model achieves efficient feature extraction, dimension reduction, normalization, and tensor adjustment. This integration significantly enhances the model's performance and generalization capability.  
Figure 4: Pooling operation.  
Figure 3: Convolution operation.  
Figure 2: Typical CNN architecture.
## 5 Experiments and Analysis
### Feature Engineering for Classification  
Random Forest Importance (RFI)  
The RFI technique is utilized to determine the crucial features in a classification problem that predicts the \"final_result\" target class. The RFI analysis reveals that \"total_click\" is the most significant feature, with an importance score of 0.44. This indicates that the number of clicks a student makes in a course is the most influential predictor of their final result. Additionally, the RFI analysis identifies other important features, including \"date_registration\", \"region\", and \"imd_band\", with importance scores of 0.13, 0.08, and 0.07, respectively. These results offer valuable insights into the key factors that contribute to predicting a student's ultimate outcome in a course. The results and ranking as per the score as shown below in Figure 3.  
Figure 3: Feature importance illustration using RFI.
# Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels
## 3 Results
### Transducer Characterization  
The ability of the system for acquisition of the pre-beamformed channel data was used for characterization of the developed matrix array transducer. For an initial performance assessment of the 26 \(\times\) 39 element matrix array transducer described above, pulse-echo measurements were performed against a steel reflector in a water tank. The system was programmed for generating 1014 transmit evens, in which only one channel was active, resulting in the excitation of all transducer elements individually with one single cycle square burst at a transmit frequency of 4 MHz. The received echoes are sampled at 80 MSA/s prior to any analog signal processing.  
A total of nine elements (0.89%) were found to be inactive. The maximum signal amplitudes for each element of the aperture are color-coded in Figure 12a.  
The whole aperture shows a high signal amplitude homogeneity with a standard deviation of 11%. Bandwidth and center frequency were assessed based on the one-way spectrum, extrapolated from the acquired data by taking the square root of the amplitude spectrum. This was done because of a misleading high sensitivity peak at the lower frequency edge that disturbs the overall bandwidth behavior. The resulting average center frequency is 3.65 MHz with a very high uniformity over the aperture (2.3% standard deviation). The average 6 dB one-way bandwidth is 104%.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 5 LoRa under Noisy Channel
### One to Many Mode  
In OttM communication mode, as shown in Figure 9, a transmitter sends a series of 1000 packets to four non-coherent receivers. Each packet contains data to be transmitted via LoRa. The transmitter initiates transmission by modulating this data into LoRa signals and transmitting them over an AWGN or industrial transmission channel. During propagation, these signals encounter obstacles and other environmental factors that can affect their power and quality in the case of an NLOS configuration. Each receiver detects and receives these signals, then uses LoRa demodulation techniques to extract the transmitted data. Once the data has been successfully demodulated, it can be processed by each receiver independently of the others. This process is repeated for each packet until all 1000 packets have been transmitted and received by all four LoRa receivers.  
Figure 10 illustrates that signal quality is disturbed in terms of PER due to Gaussian noise in the LOS configuration in addition to the fading effect in the NLOS configuration. However, in the LOS configuration, the signal remains detectable, and packets are received correctly for negative SNR values ranging from \(-19\) dB to \(-6\) dB for spreading factors  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline Communication & MtO and OUM \\ Transmission configurations & LOS and NLOS \\ Frequency & 868 MHz \\ Bandwidth & 125 kHz \\ Spreading factor & 7 and 12 \\ Coding rate & 4/5 and 4/8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: General simulation parameters.  
Figure 9: LOS and NLOS configurations for OUM mode.  
12 and 7, respectively. In the NLOS configuration, the signal undergoes strong attenuation, resulting in a gain loss of over 28 dB in SNR, which means that packets are only received correctly for SNR values ranging from 9 up to 21 dB for spreading factors 12 and 7, respectively.  
In Figure 11, by adding industrial noise composed of Gaussian noise and impulse noise, the packet error rate was measured. Unlike Gaussian noise, industrial noise is highly disruptive to OtM communication in both LOS and NLOS configurations, resulting in rapid convergence of the communication architecture and reduced performance in terms of packet error rate for the spreading factors considered. However, packets are correctly detected with SNR equal to \(-2\) dB and 9 dB for spreading factors 12 and 7, respectively, in the LOS configuration. Packets are received without error for SNR exceeding 19 and 30 dB in the NLOS configuration for the same spreading factors.
## 2 Materials and Methods
### Determination of Anthocyanins, Flavonoids, Limonin, and Total Phenols  
One gram of peel or pulp was cut into 2-3 cm sections. Anthocyanins were extracted with 0.1 mol/L of HCl for 4 h in the dark. The optical density (OD) value of the filtrate was measured with a visible light spectrophotometer [41]. Total flavonoids in the peel or pulp were determined by the rutin colorimetric method [42]. Total phenolic compound content in the peel or pulp was determined via the Folin-Ciocalteu colorimetric method [43]. Total phenolic content was expressed in gallic acid equivalents.  
The kinds and contents of flavonoids in the peel and pulp were determined by HPLC [41]. Flavonoid derivatives in the peel and pulp were extracted by grinding them into powder with liquid nitrogen and adding 5 mL of extraction solution (CH\({}_{3}\)OH:H\({}_{2}\)O:HCOOH:C\({}_{2}\)HF\({}_{3}\)O\({}_{2}\) = 70:27:2:1). The extraction was performed at low temperature (4 \({}^{\circ}\)C) in the dark for 24 h, and the extract was filtered through a filter membrane (0.22 \(\upmu\)m) and analyzed by HPLC.  
The content of flavonoid derivatives was determined using an HPLC system (1260, Agilent) equipped with a Phenomenex Luna C18 column (4.6 mm \(\times\) 150 mm, 5 \(\upmu\)m). HPLC analysis was performed using 10 \(\upmu\)L of the diluted supernatant [44]. The mobile phases consisted of 1% formic acid-water (A) and acetonitrile (B). The flow rate was 1.0 mL/min, and the column temperature was 30 \({}^{\circ}\)C. The linear gradient elution conditions were as follows: 0-42 min, 4-60% (B); 42-43 min, 60-4% (B). The content of flavonoid derivatives was determined at 280 nm by a diode array detector.  
Dilhydroflavonol-4-Reductase (DFR), Anthocyanidin Synthes (ANS), Chalcone Synthase (CHS), and Chalcone Isomerase (CHI) Activity Assay  
DFR activity was measured according to a method reported by [PERSON] et al. [45]. One unit of DFR activity was defined as the change in the absorbance of the mixed solution at550 nm per unit time, measured with a UV-Vis Spectrophotometer (Drawell DU-8600R), multiplied by 0.01, and the dihydroflavonol-4-reductase activity is expressed as U\(\cdot\)g\({}^{-1}\cdot\)FW (fresh weight). ANS activity was determined via the method published by [PERSON] et al. [46]. ANS enzyme activity was calculated by measuring the absorbance value at 340 nm, and the anthocyanin synthase activity is expressed as U\(\cdot\)g\({}^{-1}\cdot\)FW.  
CHS and CHI enzyme activities were determined via an ELISA kit (Shanghai Enzyme-linked Biotechnology Co., Ltd., Shanghai, China). The absorbance was measured at 450 nm with a UV-Vis Spectrophotometer (Drawell DU-8600R). One unit was defined as the amount of enzyme that catalyzed 1 mM substrate per hour.
## 2 System Model  
A signal model is formulated for point-to-point MIMO systems equipped with one-bit ADCs. Based on this model, a likelihood-based decision rule is developed to optimize the detection error probability under quantized observations. Subsequently, a cooperative DF MIMO system that exploits spatial diversity is introduced. The signal model and detection strategy serve as an analytical basis to extend the performance analysis to cooperative relay scenarios.
# DIVA Meets EEG: Model Validation Using Formant-Shift Reflex
## 2 Materials and Methods
### DIVA Model Simulation
#### 2.1.2 Generation and Source Localization of Synthetic EEG  
During simulation, the output of each DIVA node is associated with the computational load (denoted L in [3]), a term that represents the instantaneous neural activity of the node. These neural activities served as input for the EEG generative model. Therefore, point sources for the DIVA-EGG generation were seeded in brain locations that match the different nodes in the original DIVA model [3]. Table S1 shows the brain coordinates for the centroids of the seeds Traces of the synthetic EEG are displayed in Figure S1. A full-brain activity pattern was then constructed by treating the electrical activity of the seeds as Gaussian activity sources (_J_DIVA_) that added-up together at each brain location. The standard deviation of the normal distribution was 2. Voxels with amplitudes lower than 0.01 times the maximum amplitude were deemed inactive. The synthetic EEG (_DIVA_EEG_) was obtained by multiplying the simulated brain activity (_J_DIVA_) and the lead field \(K\). The lead field \(K\) was computed by using a head model of three concentric, piece-wise homogeneous, and isotropic spheres [49]. Voltages (_DIVA_EEG_) were obtained in 64-scale locations (a 64-electrode layout that followed the 10/20 international system for electrode placement). The DIVA-EEG is expressed by the following equation:  
\[\textit{DIVA\_EEG}=K\cdot\textit{J\_DIVA}, \tag{2}\]  
where the matrix _DIVA_EEG_ has one row for each EEG sensor and one column for each time (size _Nsen_\(\times\)_Nt_), \(K\) has the number of DIVA model components as columns and is of size _Nsen_\(\times\)_Nc_, and _J_DIVA_ contains the time series of the different seeds of the model and is of (size _Nc_\(\times\)_Nt_).  
Brain source localizations were estimated using the standardized Low-Resolution Electromagnetic Tomography method (sLORETA, [50]; for a review, see [51]). sLORETA is based on an appropriately standardized version of the minimum norm current density estimation which overcomes problems intrinsic to the estimation of deep sources of EEG.
# The Development of a Soft Robot Hand with Pin-Array Structure
## 3 Design of CTSA-II Hand
### Grasping Principle  
The CTSA-II hand was used to grasping an object. When grasping an object, it is mainly divided into two steps: a self-adaptive step and a grabbing step, as shown in Figure 7.  
Figure 6: The design of the CTSA-II hand: (**a**) the explosion view, (**b**) the overall appearance.  
Figure 7: Grasping process of the robot hand: (**a**) before grasping, (**b**) self-adaptive step, (**c**) self-adaptive step hiding membrane, (**d**) gripping step, and (**e**) gripping step hiding membrane.  
In the self-adaptive step, the CTSA-II hand approaches the object placed on the support surface. Depending on the shape of the object, different pins slide to varying degrees to adapt to the shape of the object from the vertical direction. Since the pin array is covered with the membrane, the membrane is attached to the surface of the object under the action of the pin array and the object, as shown in Figure 7b,c.  
In the grasping step, the negative pressure source outside the CTSA-II hand starts working through the connection hole, so that the pressure of the internal cavity of the CTSA-II hand is smaller than the pressure of external atmospheric so that the membrane shrinks and the pin array is inwardly pressed. Since the pins are bendable, the pins will be deformed under the action of the film, as shown in Figure 7d,e, and then the membrane is driven to more closely wrap the object from the side, and the grasping force is generated. If a closed cavity is formed between the membrane and the object during this process, the cavity will generate negative pressure-assisted grip when the CTSA-II hand moves to enhance the reliability of grasping.  
When the object needs to be released, the CTSA-II hand is stopped from connecting to the negative pressure and is connected its internal cavity to the atmosphere. Because of the existence of the springs, the pins will return to the initial position in the vertical direction. Since the pins have elasticity, the pins will return to the initial position in the lateral direction, thereby returning the membrane to the initial position and stopping the grasping force on the object.
# Re-Optimized Design for the ADR-Based Dual-Loop Controller of 100 m Class Aperture Radio Telescope
## 4 Simulation Results and Analysis
### Frequency-Domain Analysis  
The Bode diagram of the antenna's 4 DOF model used for the controller algorithm test, shown in Figure 10, indicates obvious anti-resonance (at 0.25 Hz, 5 Hz) and multiple resonance points in the antenna 4 DOF model, suggesting the antenna is a rigid-flexible coupled system. It is the flexible structure that increases the difficulty of high-precision control for the antenna. Figure 11 presents the frequency-domain responses of the motor, the base, and the dish of the system with RPID, LADRC, and DLADRC control in terms of the Bode diagram, respectively.  
Comparing Figures 10 and 11, smaller depth and narrower width at the anti-resonance frequency points, and little magnitudes at other resonance points of the DLADRC-controlled motor system can be seen. The structure resonance at about 1.5 Hz is suppressed by the DLADRC rather than RPID or LADRC. The three methods suppress the motor system's first-order resonance frequency (0.68 Hz) due to a notch filter. The changes in phase curves in the DLADRC-controlled system are more monotonous than others, which also indicates DLADRC's excellent control performance. These response characteristics indicate that DLADRC has better flexibility suppression performance than the other two algorithms.  
Compared with RPID, the DLADRC enhances the bandwidth of the motor's and the dish's transfer function, thus reaching the desired position faster. Figure 11b presents the bandwidth of DLADRC as a little smaller than the LADRC, suggesting a slower response of DLADRC. The smaller magnitudes of the base and the dish in the middle frequencies of the DLADRC-controlled system make the response curves smoother than those of RPID and LADRC. Figure 11 also presents the DLADRC's higher stability margin over the other two approaches, thus giving smoother response curves.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **RPID** & **LADRC** & **DLADRC** \\ \hline Mean & 0.0046 & 0.0007 & 0.0006 \\ Max. & 0.0384 & 0.0185 & 0.0153 \\ Median & 0.0018 & 0.0000 & 0.0000 \\ RMS & 0.0090 & 0.0032 & 0.0025 \\ \hline \end{tabular}
\end{table}
Table 4: Ramp-tracking errors (in deg) of the base under the three control methods in an average wind of V = 6 m/s.  
Figure 10: Bode diagram of the antenna’s 4 DOF velocity model.
###### Abstract  
Due to the negative effects that derive from large impervious surfaces in urban areas, pervious concrete has been developed, and has become an environmentally friendly pavement material. As a porous and permeable material, pervious concrete presents an overwhelming advantage in solving urban problems, such as flooding, groundwater decline, urban heat island phenomena, etc. Waste crumb rubber has been verified as a feasible modifier for pavement material. The objective of this paper is to explore the effects of rubber particle size and incorporation level on the permeability, mechanical properties, and freeze-thaw resistance of pervious concrete. Two kinds of rubbers (fine and coarse) with four incorporation levels (2%, 4%, 6%, and 8%) are used in the experiment. Permeability, compressive strength, flexural strength, flexural strain, and freeze-thaw resistance are tested. The results indicate that the addition of rubber slightly decreases strength and permeability, but significantly enhances ductility and freeze-thaw resistance. Fine crumb rubber with a suitable incorporation level could remarkably improve the ductility and freeze-thaw resistance of pervious concrete without sacrificing excessively strength and permeability.  
pervious concrete; waste rubber; permeability; flexural strain; ductility; freeze-thaw durability +
Footnote †: journal: Applied Sciences
## 4 Discussion
### NIRSpec Use Case  
After the successful training of the ANN models, they were then applied to the NIRSpec use case. The test data with varied stiffness were generated by reducing the Young's modulus of the material of the optical bench plate by 5% of the initial value in the FE model. Figure 11 shows the prediction of the four considered models against the calculated stress with FEA with respect to the frequency steps. The frequency steps divide the considered frequency range (5-200 Hz) into equal steps, the steps and the corresponding normalized stress values both resulting from the FEA.  
As can be seen from Figure 11b, the NARX model predicts the element stress curve without major deviation. It only seems to struggle slightly with the first frequency step, which can be ascribed to the use of that first step as delay in the model's architecture. The frequency-dependent ANN in Figure 11a struggles to predict the peak stress values, for instance, around frequency step 400 and also with the shapes of a few peaks, mainly at the last frequency steps from steps 500 to 600. The pretrained ANN, as seen in Figure 11d, also seems to struggle with the shapes of a few peaks, especially at the end of the  
Figure 11: Stress calculated by FEA and predicted stress by ANN over the frequency steps for the NIRSpec use case: (**a**) frequency-dependent ANN, (**b**) NARX, (**c**) ANN with biLSTM layer, and (**d**) pretrained ANN.  
frequency range. The fact that both these models struggle at the end of the frequency range can be ascribed to the fact that this represents the mode that was shifted the most by changing the material properties. The NARX model, however, does not face any difficulties with this. Figure 11c shows that the biLSTM also makes more or less accurate predictions, while it was the most delicate to train. However, it also struggles with some peak stress values and completely omits the mode at frequency step 500. Figure 12 shows the resulting regression plots.  
Table 4 makes clear that both recurrent ANNs (NARX and biLSTM) perform the best, the NARX model having the best regression coefficient of 0.9936 and the smallest error of 0.0454 MPa. In contrast, the pretrained ANN makes the largest error of 0.1219 MPa and has the poorest regression coefficient of 0.9724, which is still a high accuracy of prediction.  
Figure 12: Regression plots of predictions for the NIRSpec use case with 35 sensors: (**a**) frequency-dependent ANN, (**b**) NARX, (**c**) ANN with biLSTM layer, and (**d**) pretrained ANN. The output values and the target values are normalized.