esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parallel Programming and Implementations of GRICS
### Complexity Analysis of Algorithm 2  
In this section, we present an analysis of the computational complexity of the reconstruction kernel (Algorithm 2) to identify the reconstruction parameters that contribute most significantly to the computational costs. Let us break the application of the operator \(E^{H}E\) into simple multiplication steps:  
* Applying \(T(u_{t_{i}})\): Multiplication of a vector by a sparse matrix of size \(N_{\rho}\times N_{\rho}\) has a complexity of \(\mathcal{O}(\gamma_{1}N_{\rho})\), where \(\gamma_{1}\) is the size of the interpolation kernel (in our case, we use a linear interpolation kernel \(\gamma_{1}=8\)).
* Applying \(T(u_{t_{i}})^{H}\): Same as the application of \(T(u_{t_{i}})\).
* \(C_{j}\) and its \(C_{j}^{H}\) are diagonal matrices, each with a complexity of \(\mathcal{O}(N_{\rho})\).
* FFT and its inverse are performed using FFTW implementation, each with a complexity of \(\mathcal{O}(N_{\rho}\log N_{\rho})\).
* Assuming that the maximal number of non-zero elements per row in the matrices \(S_{t_{i}}\) and \(S_{t_{i}}^{H}\) is \(k\), where \(k\ll N_{\rho}\), the complexity of applying \(S_{t_{i}}\) and \(S_{t_{i}}^{H}\) is \(\mathcal{O}(N\kappa_{\rho})\).  
The dominant term for each inner loop of Algorithm 2 is \(\mathcal{O}(N_{\rho}\log N_{\rho})\). Hence, each outer loop iteration of Algorithm 2 requires \(\mathcal{O}(N_{C}N_{\rho}\log N_{\rho})\). Finally, the overall complexity is provided by  
\[C=\mathcal{O}(N_{t}N_{C}N_{\rho}\log N_{\rho}). \tag{5}\]  
\(C\) can be expressed using the number of \(k\)-space samples in frequency and phase-encoding directions, \(N_{x}\), \(N_{y}\), and \(N_{z}\) (see Table 1) since \(N_{\rho}=N_{x}N_{y}N_{z}\).  
\[C=\mathcal{O}\big{(}N_{t}N_{C}N_{x}N_{y}N_{z}\log\big{(}N_{x}N_{y}N_{z}\big{)} \big{)}. \tag{6}\]  
In our case, the FFT is performed only on the phase-encoding directions; hence, \(C\) reads as follows:  
\[C=\mathcal{O}\big{(}N_{t}N_{C}N_{x}N_{y}N_{z}\log\big{(}N_{y}N_{z}\big{)} \big{)}. \tag{7}\]  
Using similar reasoning, we can compute the memory complexity (the amount of memory a program requires to execute relative to the size of the input data) \(C_{m}\) too; \(C_{m}\) reads as follows:  
\[C_{m}=\mathcal{O}\big{(}N_{t}N_{C}N_{x}N_{y}N_{z}\big{)}. \tag{8}\]
# Ecotoxicological Evaluation of Methiocarb Electrochemical Oxidation
## 2 Materials and Methods  
### Methiocarb Aqueous Solutions Composition and Characterization  
The composition and characterization of the methiocarb aqueous solutions used in this study are described in Table 1. MC solutions were prepared in ultrapure water obtained with a Milli-Q system using the methiocarb PESTANAL(r), analytical standard (CAS Number 2032-65-7), purchased from Sigma-Aldrich. As the MC aqueous solution did not have enough electrical conductivity to pursue with the electrochemical experiments at the applied current densities, the addition of a background electrolyte was required. Two different salts were studied as background electrolytes, sodium sulfate anhydrous (CAS Number 7757-82-6) of 99.7% purity and sodium chloride ACS reagent (CAS Number 7647-14-5), purity \(\geq\) 99.0%. Both were purchased from Sigma-Aldrich. The concentration of added salt was set as the minimum required to perform the EO assays at the highest applied current density studied (300 A m\({}^{-2}\)), which was found to be 250 mg L\({}^{-1}\), corresponding to an increase of the solution's electrical conductivity to approximately 500 \(\upmu\)S cm\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Solution** & **MC + Na\({}_{2}\)SO\({}_{4}\)** & **MC + NaCl** \\ \hline Composition & Methiocarb & 20 & 20 \\ mg L\({}^{-1}\) & Na\({}_{2}\)SO\({}_{4}\) & 250 & \\  & NaCl & – & 250 \\ Chemical oxygen demand/mg L\({}^{-1}\) & 43 \(\pm\) 1 & 43 \(\pm\) 1 \\ Total organic carbon/mg L\({}^{-1}\) & 11.6 \(\pm\) 0.4 & 11.8 \(\pm\) 0.5 \\ Total nitrogen/mg L\({}^{-1}\) & 1.23 \(\pm\) 0.03 & 1.24 \(\pm\) 0.04 \\ pH & 5.45 \(\pm\) 0.03 & 5.28 \(\pm\) 0.03 \\ Conductivity/\(\upmu\)S cm\({}^{-1}\) & 437 \(\pm\) 5 & 514 \(\pm\) 5 \\ Acute toxicity/ & EC\({}_{50}\) (48 h) & 0.21\% & 0.27\% \\ TU 1  & 470.4 & 370.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition and characterization of the methiocarb aqueous solutions used in the experiments.
## 2 Materials and Methods
### Experimental Setup
A sinusoidal modulation was superimposed on the low-frequency voltage ramp via the driver's external input, producing rapid optical frequency modulation. The slow voltage signal was generated by directly changing the current parameters of the driver using software developed in LabView 2020, while the sinusoidal signal was obtained at one of the output ports of the lock-in amplifier generator. For both sensing detection modes, the signal was sent to the lock-in amplifier and demodulated at the 1 st and 2 nd harmonic, respectively. The integration time was set to 10 ms with an 8 th order low pass filter. The lock-in amplifier was interfaced directly with a computer, and the signal was recorded and processed using custom software developed in LabView.
## 4 Implementation  
### Experiment Environment  
#### 4.1.1 Datasets  
The dataset used for this study is \"The CCTV traffic images (in the highways) for solving traffic problems\" provided by AI Hub [22]. This dataset was constructed by sampling every minute the CCTV images monitoring highways in South Korea. The dataset consists of 21,302 images for model training and 2627 images for validation. The size of an image is 1920 \(\times\) 1080 pixels. Each image shows a two-way road with multiple carriageways, and the number of vehicles in the image is provided as an annotation. The annotation format is CVAT image 1.1, and the bounding boxes of vehicles in the image are given with labels such as 'car', 'truck' and 'bus'. The site providing this data reported that mAP@IoU 0.5 achieved 68.2% when object detection was performed using YOLO v4 [22].  
Figure 3 illustrates an example image of training data, which its annotation has been visualized.  
As shown in Figure 3, the dataset content depicts highway scenarios, with moving vehicles labeled in bounding box format. Additionally, there are background elements that move or sway, such as trees, which could potentially generate noise during the DSSIM-MAP creation process.
# Evaluation of Non-_Saccharomyces_ Yeast for Low-Alcohol Beer Production
## 3 Results and Discussion
### Microbial Analyses
#### 3.1.1 Sugar Utilization
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Strain**} & \multirow{2}{*}{**Glucose**} & \multirow{2}{*}{**Sucrose**} & \multirow{2}{*}{**Maltose**} & \multirow{2}{*}{**Lactose**} & \multirow{2}{*}{\(\beta\)**-Lysase**} & \multicolumn{2}{c}{**POF+ Ferulic**} & \multicolumn{2}{c}{**POF+**} \\  & & & & & & & Acid & p-Coumaric Acid \\ \hline HU1 & + & +/- & - & - & - & ++ & - & - \\ \hline HU2 & ++ & - & - & - & ++ & - & - \\ \hline KL1 & + & + & - & - & + & +/- & - \\ \hline MP1 & + & - & - & - & + & - & - \\ \hline MP2 & ++ & + & - & - & +/- & - & - \\ \hline MP3 & + & - & - & - & - & - & - \\ \hline MP4 & ++ & + & - & - & - & - & - \\ \hline MP5 & + & - & - & - & ++ & - & - \\ \hline MP6 & + & - & - & - & ++ & - & - \\ \hline MP7 & + & ++ & - & - & - & - & - \\ \hline MP8 & +/- & ++ & - & - & ++ & - & - \\ \hline MP9 & ++ & +/- & - & - & - & - & - \\ \hline NI1 & ++ & - & - & - & + & +/- & - \\ \hline TD1 & + & + & - & - & ++ & - & - \\ \hline WO1 & + & + & - & - & ++ & + & + \\ \hline WA1 & + & + & ++ & - & ++ & + & + \\ \hline ZM1 & + & ++ & - & - & ++ & - & - \\ \hline ZM2 & + & ++ & - & - & ++ & - & - \\ \hline S.C. & ++ & + & ++ & - & ++ & + & - \\ \hline \hline \end{tabular} T-58 \(\mathrm{POF}\)+ + -  
Interpretation of results: Sugars (glucose, sucrose, maltose): - lack of fermentation; +/- inconclusive; +: gas bubble filling less than a half of Durham tube; ++: gas bubble filling more than a half of Durham tube; \(\beta\)-Lysase activity: - lack of growth on the medium +: growth of individual colonies; ++: growth of many colonies; POF+ (Ferulic and p-Coumaric Acid): - lack of the activity; +/-: inconclusive; +: presence of the activity.  
\end{table} 
Table 1: Sugar utilization across the yeasts, phenolic off-flavors, and \(\beta\)-lysase screening results.
## 2 Model Preparation and Testing Procedure
### Structural Models  
To investigate the effects of the ground layer on SFSI for different structures with various masses and foundation widths, three single-degree-of-freedom (SDOF) structures, namely S1, S2, and S3, were designed with various dynamic structural characteristics. Note, the structures used in this study were made of aluminum. Table 1 indicates the dimensions and dynamic properties of the three structures. The foundation width (B) was 5.2 m for both S1 and S2, and 1.2 m for S3. The heights of the three structures were 9.4 m for S1, 8.25 m for S2, and 2.97 m for S3. Furthermore, impact hammer tests were performed to identify the fixed-base natural frequency (\(f_{n}\)) of the three small-scale structural models with the help of an acceleration attached to the lumped mass of the structure. The signal was recorded by using an oscilloscope. Using the scaling law and fast Fourier transform (FFT) analysis for the recorded signal, the \(f_{n}\) was found to be 2.19 Hz, 1.82 Hz, and 7.05 Hz for S1, S2, and S3 structures, respectively.
# Reinforcement Learning Approach for Adaptive C-V2X Resource Management
## 2 C-V2X Mode 4  
### Physical Layer  
C-V2X employs single carrier frequency division multiple access (SC-FDMA) as its underlying physical layer. Both time division duplex (TDD) and frequency division duplex (FDD) are supported by this system. The LTE frame structure encompasses many temporal units, including the frame, subframe, slot, and symbol. The duration of a single time frame is ten milliseconds, whereas a subframe has a width of one millisecond. Each time slot inside the subframe occupies 0.5 milliseconds. The smallest LTE radio resource unit given to the user equipment is known as a resource block (RB). Each radio band has a minimum frequency width of 180 kHz. In the temporal dimension, an RB spans a duration of one time slot and accommodates a total of seven symbols. Within the area of frequency analysis, an RB is partitioned into subcarriers measuring 12 \(\times\) 15 kHz. The detailed illustration of an LTE frame can be seen in Figure 1.  
The quantity of resource blocks (RBs) is determined by the channel bandwidth within which the system is operational. The channel bandwidth refers to the officially used channel quantified in megahertz (MHz). It is, therefore, synonymous with the channel spacing. The transmission bandwidth configuration, expressed in RB units, indicates the maximum number of RBs that may be sent over any given channel bandwidth. The transmission bandwidth, sometimes referred to as RBs, is the aggregate num  
Figure 1: LTE frame [32].  
single transmission. The range of acceptable RBs for a given channel bandwidth might vary from one RB to the maximum allowable RBs [33].
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 3 Cross-Guide-UNet (CG-UNet)
### Cross-Guide Module
#### 3.1.1 Channel Attention  
Channel attention assesses the relation between feature maps. Figure 3 illustrates the diagram of channel attention. The channel attention utilized by the CGM differs from that of ECA-Net [42]. Initially, the feature maps undergo max and average pooling based on the spatial dimension to acquire spatial aggregation information for each channel. Max pooling tends to capture more prominent information particularly evident at pixel changes, such as edges. Subsequently, a one-dimensional convolution kernel is employed to compute the channel attention. Upon completion of the calculations, the results are summed to obtain complete channel attention and multiplied by the original feature map.  
Equations (3) and (4) outline the calculation methods for channel attention.  
\[W_{ca}^{i}=\sigma\Sigma_{i=1}^{n}\big{(}C\big{(}M\big{(}X^{i}\big{)}\big{)}+C \big{(}A\big{(}X^{i}\big{)}\big{)}\big{)}, \tag{3}\]  
\[Y_{ca}^{i}=X^{i}\cdot W_{ca}^{i}+X^{i}, \tag{4}\]  
\(X^{i}\) is the input feature map. \(M\big{(}X^{i}\big{)}\) represents applying max pooling operation to the input feature map, and \(A\big{(}X^{i}\big{)}\) represents applying the average pooling operation to the input feature map. \(C\) represents convolution operation, and \(\sigma\) is the Sigmoid activation function.\(W_{ca}^{i}\) is the calculated attention weight. Equation (4) multiplies \(W_{ca}^{i}\) with point \(X^{i}\) and performs residual connections to obtain the output, \(Y_{ca}^{i}\).
# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 4 Pre-Processing
### Image Processing
#### 4.3.1 Smoothing  
Smoothing in image processing is often necessary. By smoothing an image, one can capture relevant patterns while filtering the noise. That way, smoothing can provide robustness to the data that is going to be analyzed. There are several ways to smooth an image, but the most popular ones in the reviewed papers are through a bilateral filter [73] or a Gaussian filter [74].  
A bilateral filter is effective in noise removal while keeping edges sharp, since it uses a gaussian function of space for smoothing only the nearby pixels and a gaussian function of intensity for smoothing pixels that have a similar intensity to the central pixel. That way, it can preserve the edges, since they usually have high intensity variations.  
A Gaussian filter is effective in removing Gaussian noise from an image. It takes the neighborhood around the central pixel and find its Gaussian weighted average. This Gaussian filter is a function of space alone; therefore, it will also smooth the edges.  
In a conventional FER system, a bilateral filter has the upper hand when smoothing a face, since it keeps the edges from being blurred. Edges in a face normally delimit facial features such as eyes, nose, eyebrows and mouth. Smoothing these critical features for FER might be something undesirable.
# Fatigue Behavior of Prestressed Concrete Beam for Straddle-Type Monorail Tracks
## 2 Experimental Program
### Instrumentation  
(1) Displacement Measurement: For obtaining the degradation of the beam's stiffness, the deflection at cross sections A-A, C-C, and E-E were measured using dial gauges with a measurement range of \(\pm\)50 mm and a precision of 0.01 mm. To balance the settlement caused by the supports, dial gauges were placed at the supports. There were two deflection measurement points at each section, totaling 10 over the beam. The schematic of the deflection test is shown in Figure 6a, represented in blue.  
Figure 5: Loading setup: (**a**) specimen and device; (**b**) detail of Material Testing System (MTS) electrohydraulic servo actuator.  
Figure 6: _Cont._(2) Rotation Measurement: Two dial gauges positioned horizontally were set at each end of the beam to measure the rotation angle of the beam at the fixed and movable hinge supports. There were four horizontal dial gauges, as shown in Figure 6a.  
(3) Concrete Strain Measurement: The longitudinal strain of concrete was measured using resistance strain gauges (section A-A, B-B, C-C, D-D, and E-E). The strain gauges were attached on the surface of concrete along the longitudinal direction of the beam. There were three measuring points on the top side at each section, eight on the web, and three on the bottom. A total of 14 points was arranged at each section, and 80 points over the beam, as shown in Figure 6a.  
(4) Reinforcement Strain Measurement: The reinforcement strain was determined by resistance strain gauge located on the corresponding position on section F-F, G-G, and H-H. There were seven outer longitudinal bars (N3) in the top and bottom slab. The measurement points were arranged on them longitudinally in the interval of one, as shown in Figure 6b. The number of strain measurement points of the bars in top slab was 4, as well as the bars in bottom slab. There were totally 24 measurement points for obtaining the strain of reinforcement.  
(5) Measurement of Shear Strain: The principal tensile strains of concrete in the shear-bending zone were measured by three rosette strain gauges located on section I-I and section J-J, as detailed in Figure 6c. There were 12 rosette strain gauges on the test beam totally.
## 5 Results and Discussion
### DELM-Based Alpha-Beta Filter Results  
We implemented a deep-extreme-learning-machine-based \(\alpha\)-\(\beta\) filter; here, the same training and test set was used: 30/70. The input to the DELM was composed of temperature sensor values and humidity sensor values, there were five hidden layers, and each hidden layer was composed of 12 neurons in the hidden layer and a sigmoid activation function (AF); the linear AF was used by the output layer. In our research, we used two output layers and named them alpha and beta in the final prediction results; these two values were input to the alpha-beta filter to compute the final output results. To evaluate the performance of the DELM-based alpha-beta filter, the root mean square error and mean absolute error were estimated regarding the typical alpha-beta filter and the newly proposed DELM-based \(\alpha\)-\(\beta\) filter. When we applied the DELM, the result was overwhelmingly positive. The three-fold cross-validation method was used to comprehensively evaluate the performance. The best results were an RMSE of 3.901 and an MAE of 2.811. The DELM-based results are shown in Figure 9.  
Figure 8: Three different cross-validation paradigms used in training and testing.
## 4 Results
On the contrary, if the model updating algorithm takes a high number of previous HI values into account, it takes long time to recognize a fast degradation behavior. Now, suppose that the nominal condition is restored. The online procedure is implemented again. The model updating and RUL prediction are triggered as the system achieves the AT determined in the previous step. Here, different parameter settings are tested, and the results are shown in Table 2.  
The best performance, in terms of prediction accuracy and RUL, is provided by the parameter set in the second row of Table 2. Figure 13 shows the results, where the blue line shows the HI values over time. The dashed red line represents the FT and the red segments represent the predicted HI values every time the degradation model is updated. In this case, the algorithm starts to predict the HI values and the RUL after 32.8299 s, the time at which the AT is reached. Hence, each t = 20 s, the model is updated and 20 HI values are predicted. At the time instant t = 46.2299, the model predicts that the HI value will reach the FT after 2.1441 s. This corresponds to a pessimistic prediction, since the fault will actually occur after 2.68 s, thus losing 0.5359 s of the residual life. In the other three cases, the model predicts Fault 2 will occur after 0.1341 s, while actually the FT at the detection time has been already reached (RUL = 0). In Figure 14, the confusion matrix of the ADP algorithm that is applied to the new dataset is illustrated. We know that two different conditions are included in the generated dataset until the FT is reached. However, the ADP forms four different data clouds, which may represent four different HS of the degradation process. Data cloud 2 is created when the AT is reached. This means that the first data cloud can be labelled \"normal\" and the HI values in cloud 2 can be used for a better determination of the AT. In addition, further offline analysis can highlight that there is another \"normal\" HS before the fault condition. Subsequently, the computational efficiency of the algorithm can be improved by moving the time to start the prediction forward. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SL** & **HI\({}_{\text{f}}\)** & **TI** & **Detection Time (s)** & **Predicted RUL (s)** & **Real RUL (s)** & **Run Time (s)** \\ \hline
100 & 10 & 10 & 48.9099 & 0.1341 & 0 & 45.445 \\
100 & 20 & 20 & 46.2299 & 2.1441 & 2.68 & 23.882 \\
50 & 10 & 20 & 47.5699 & 0.1341 & 0 & 24.304 \\
30 & 20 & 20 & 48.9099 & 0.1341 & 0 & 22.585 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the state space model (SSM) performance with different input parameter sets. Note that [PERSON] et al. provided the original code for anomaly detection in [23], while the download of the code of the ADP algorithm is available at the website in [22]. Here, an integration of the two algorithms was performed, and the online time feature extraction was added.
## 4 Electrodes and Electrolytes for SSTFBs
### Thin-film Electrodes
#### 4.1.1 Anodes
According to the authors, the thin-film electrodes showed a 410 \(\upmu\)Ah/cm\({}^{2}\)\(\upmu\)m after 50 cycles. Lee et al. [359] evaluated the effect of sputtering target compositions on battery performance. Using RF sputtering, the authors grew amorphous LNVO thin films with two different target compositions, namely LNVO and L\({}_{1.1}\)NiVO\({}_{4}\) (L\({}_{1.1}\)NVO), representing that the thin-film electrode deposited with the L\({}_{1.1}\)NVO target showed the higher reversible capacity of 871 \(\upmu\)Ah/cm\({}^{2}\)\(\upmu\)m compared with the reversible capacity of 787 \(\upmu\)Ah/cm\({}^{2}\)\(\upmu\)m for the thin films deposited with the LNVO target. The post-annealing process may also influence the battery performance. Reddy et al. [358; 360] tested how the post-annealing temperature affects the discharge capacity of LNVO thin films sputtered on stainless steel. The authors applied four different post-annealing temperatures to the LNVO thin films, demonstrating that the as-deposited film and the film post-annealed at 300 \({}^{\circ}\)C were amorphous while post-annealed samples at higher temperatures such as 450, 600 and 700 \({}^{\circ}\)C showed a polycrystalline structure. According to the discharge capacity measurements, the discharge capacity of the as-deposited film was found to be \(\sim\)800 mAh/g and increase with the post-annealing at 300 \({}^{\circ}\)C up to \(\sim\)1100 mAh/g. However, further increases in the post-annealing temperature resulted in a decrease in the discharge capacity of the films. For example, the film post-annealed at 600 \({}^{\circ}\)C showed a decreased discharge capacity of \(\sim\)850 mAh/g. TNO thin films can be deposited via PLD [361; 362; 363] and sol-gel method [364]. Using PLD, Daramalla et al. [361] deposited polycrystalline TNO thin films, which showed a discharge capacity of 176 and 143 \(\upmu\)Ah/cm\({}^{2}\)\(\upmu\)m at 35 and 50 \(\upmu\)A/cm\({}^{2}\) current density, respectively. Chang et al. [364] deposited TNO on ITO and Si substrates via sol-gel method. According to this work, the XRD result showed that TNO thin films on ITO substrates stay amorphous even after the post-annealing treatment at 500 \({}^{\circ}\)C. On the contrary, the TNO thin films on Si substrates started to crystalize after the post-annealing above 600 \({}^{\circ}\)C and the TNO films post annealed at 900 \({}^{\circ}\)C were found to have the best crystallinity and a (110) preferred orientation. Table 6 summarizes the discharge capacities of different thin-film anodes. Among LTO, LNVO and TNO, the LNVO thin-film anode showed a better performance with a discharge capacity of 800-100 mAh/g for both polycrystalline and amorphous thin films.