esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correlation Analysis Between 18F-FDG Parameters  
A significant strong correlation was found between FDG\({}_{\text{Cortex}}\) and FDG\({}_{\text{Ventricles}}\) in the CTRL group (r = 0.516; \(p\) = 0.003). When considering AD patients, we retrieved a less strong but still significant correlation between the two parameters (r = 0.358; \(p\) < 0.001) (Figure 3). No significant differences were observed between the two correlations (z-score 1.038; \(p\) = 0.299).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**CTRL (n = 35)**} & \multicolumn{2}{c}{**All AD (n = 224)**} & \(p\) \\ \hline  & **Mean \(\pm\) SD** & **Median (IQR)** & **Mean \(\pm\) SD** & **Median (IQR)** & \\ \hline MMSE & 28.60 \(\pm\) 1.22 & 29.00 (2.22) & 22.73 \(\pm\) 2.79 & 23.50 (3.00) & **<0.001** \\ CSF A\(\beta\)42 (pg/mL) & 1028.22 \(\pm\) 290.34 & 1005.00 (334.00) & 391.57 \(\pm\) 140.25 & 390.50 (182.20) & **<0.001** \\ CSF p-tau (pg/mL) & 35.75 \(\pm\) 15.19 & 32.35 (23.00) & 84.57 \(\pm\) 44.37 & 73.50 (53.00) & **<0.001** \\ CSF t-tau (pg/mL) & 212.05 \(\pm\) 133.80 & 169.00 (158.95) & 616.98 \(\pm\) 351.76 & 555.81 (457.65) & **<0.001** \\ CSF p-tau/A\(\beta\)42 & 0.036 \(\pm\) 0.018 & 0.034 (0.020) & 0.252 \(\pm\) 0.201 & 0.200 (0.147) & **<0.001** \\ Qalb & 7.21 \(\pm\) 2.15 & 6.98 (2.42) & 6.72 \(\pm\) 2.87 & 6.05 (3.54) & **<0.001** \\ CSF lactates (pg/mL) & 1.63 \(\pm\) 0.40 & 1.50 (0.69) & 1.79 \(\pm\) 0.35 & 1.80 (0.40) & **<0.001** \\ Age (y) & 67.09 \(\pm\) 7.33 & 67.00 (11.50) & 70.25 \(\pm\) 6.83 & 71.00 (9.00) & 0.085 \\ \hline  & \multicolumn{2}{c}{\%} & \multicolumn{2}{c}{\%} & \multicolumn{2}{c}{\%} \\ \hline Sex (F) & 40.0 & & 53.5 & **0.007** \\ APOE (\(\epsilon\)4) & 14.3 & & 44.3 & **0.014** \\ \hline  & mean \(\pm\) SD & & mean \(\pm\) SD & \\ \hline FDG\({}_{\text{Ventricles}}\) & 0.94 \(\pm\) 0.22 & & 0.72 \(\pm\) 0.13 & **<0.001** \\ FDG\({}_{\text{Cortex}}\) & 1.28 \(\pm\) 0.15 & 1.20 \(\pm\) 0.11 & **<0.001** \\ FDG\({}_{\text{Ventricles}}\)/FDG\({}_{\text{Cortex}}\) & 0.71 \(\pm\) 0.11 & 0.60 \(\pm\) 0.10 & **<0.001** \\ \hline  & CTRL: control; AD: Alzheimer’s disease; n: number; \(p\): p-values; SD: standard deviation; IQR: interquartile range; y: years; F: female; CSF: cerebrospinal fluid; APOE: Apolipoprotein E; Qalb: Albumin Quotient; FDG: fluorodeoxyglucose. Continuous variables are presented as mean \(\pm\) SD if normally distributed and as median (IQR) if not normally distributed. Categorical variables are expressed as percentages (%). Bold \(p\)-values denote statistical significance. \\ \hline \end{tabular}
\end{table}
Table 1: Comparisons of demographic features and CSF dosages between CTRLs and patients with AD.  
Figure 3: Scatter plots depicting the correlation between FDG\({}_{\text{Cortex}}\) and FDG\({}_{\text{Ventricles}}\) in the CTRL (blue dots) and AD (red dots) groups. Colored areas represent 95% confidence intervals.
## 2 Developed Methodology
### Instance-Level Feature Importance Analysis for DSA-DL Model
#### 2.2.4 Instance-Level Feature Importance Analysis  
For the insecure OC/instance, SHAP is utilized to obtain the top influential variables on the prediction of the DSA-DL model by calculating the FIA on the specific instance, which is called local interpretation. The units corresponding to the top influential variables are recognized as the critical units in the power system under the current OC. Besides,  
Figure 1: Overall flowchart of I-FIA for DSA-DL model.  
through the global interpretation of multiple typical OCs/instances, the qualitative relationship of the importance and value of features is explicitly demonstrated, further providing a strategy of control direction of units for SC. Based on the strategy, SC can finally be achieved through step-by-step adjustment.  
The flow chart of online SC is illustrated as Figure 2. The online OC is detected in real-time by online TSA using the offline-trained model. Once the OC is determined to be insecure, SC is activated. SC is conducted in a limited number of iterations. Local interpretation by SHAP is applied to the critical unit detection. In SC, the balance of power generation or adjustment must be considered. Therefore, critical unit pairs are determined, in which the positive critical unit (\(Gp\)) has the most influence on the secure prediction, while the negative critical unit (\(Gn\)) has the most significant impact on the insecure prediction. Global interpretation globally analyzes the importance of features and the correlation between features and predictions, thus adjunctively judging the directional correctness of SC. Then, the adjustment amount for critical unit pairs is calculated according to a given adjustment rate (\(a\)). After that, the load flow of the system is analyzed to check the static security and output a new OC. Then, online TSA will be executed again until the secure SC is determined or the maximum number of iterations is reached.
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 6 Discussion  
### Summary and Analysis of the Results  
The comparison between the proposed method and the baseline confirms the significant advantages of the proposed approach. Under varying traffic demands, the benefits of the proposed method include a reduction in carbon emissions by 5.24% to 17.60%, a decrease in stop frequency by 14.8% to 75.4%, and a reduction in delay by 22.82% to 52.62%. Moreover, under conditions of unbalanced traffic demand, the proposed method achieves a reduction in carbon emissions by 8.92% to 14.51%, a decrease in stop frequency by 41.58% to 47.19%, and a reduction in delay by 41.10% to 43.38%.  
Figure 11: Carbon benefits of CBCVs under different v/c. (**a**) v/c = 0.2; (**b**) v/c = 0.5; (**c**) v/c = 0.8.  
A notable highlight of the proposed method is the integration of CIM with traffic control. Specifically, the approach leverages carbon pricing from the carbon trading market to calculate the emission reductions achieved by CBCVs that follow intersection speed guidance instructions and subsequently provide financial rewards. This study represents the first exploration of such a method. Figure 12 provides a comprehensive illustration of the carbon reduction costs associated with the proposed method at intersections: the rewards provided to CBCVs for each ton of carbon emission reduction at the intersection range from RMB 7.5 to 31.5. This cost is lower than the carbon trading market price of 90 RMB/t of carbon dioxide, indicating that the trading price in the carbon market can fully cover the financial rewards given to CBCVs. The difference arises because the emission reductions at the intersection are due to both signal control and vehicle speed guidance. The emission reduction benefits from signal optimization are not used for reward distribution. In practical applications, this portion of funds could be allocated to cover the costs of signal optimization. Another viable approach could be to increase the financial rewards given to CBCVs, enhancing economic incentives for drivers to adopt eco-driving habits.
## 4 Results
### Model Parameter Optimization  
The grid search algorithm was applied by using various hidden layers and unit sizes, learning rates, loss functions, and optimization functions for hyperparameter optimization. The number of hidden layers for LSTM was tested by gradually increasing from a single layer to three stacked layers. The unit size of these stacked layers was tested for 32, 64, and 128. The tested learning rates were \(10^{-2}\), \(10^{-3}\), and \(10^{-4}\). For the optimization function, we tested Adam, Adamax, and SGD [70]. For epoch number, the test was for values between 1000 and 1500 with 100 steps. Lastly, the dropout rate was between 0 and 0.5 with 0.05 increments.  
The performances of the trained models with setups having different window sizes are presented in Table 3. We can see that the window size of five days is performing better than other window sizes, with the overall MAE reduced to \(\sim\)0.03 for both training and testing. Out of these four different window sizes, the one-day window size showed the worst prediction results with \(R^{2}\) values of \(\sim\)0.70 for both training and testing. Following the window size of five days, 10, and 30 days gave comparable results.  
Focusing on the window size of the last five days, which performed better than the other tested cases, we found that LSTM with two hidden layers and 32 unit sizes followed by a one-dimensional dense layer having a learning rate of \(10^{-3}\), an epoch number of 1000, and a dropout rate of 0.25, and Adamax as the activation function gave the best accuracy for SM prediction. The summary of the grid search is given in Table 4.
## 2 Methods
### Array Design  
The positions of the microphones have an influence both on the filter \(\mathbf{w}_{n}(f_{p})\) and the transfer function \(\mathbf{G}_{um}(f_{p})\), and thus, on the directivity pattern itself. The optimal microphone positions selected for this paper maximize the spatial aliasing frequency and, at the same time, minimize the frequency from which beamforming is effectively possible. The spatial aliasing frequency describes the lowest frequency \(f_{al}\) for which aliasing effects occur, which is caused by a spatial undersampling of the array for sound waves at high frequencies. The aliasing leads to side lobes with the same amplitude as the main lobe. The spatial aliasing frequency of an array with linear microphone spacing is usually given in the literature as:\[f_{al}=\frac{c}{2\triangle x} \tag{12}\]  
with \(\triangle x\) as the space between the microphones [10].  
A small microphone spacing sets an upper limit to the spatial aliasing frequency. In contrast, a large microphone spacing sets a lower limit to the frequency from which beamforming is effectively possible. In order to have good directional properties of the microphone array across a wide frequency range, an irregularly-spaced microphone array is used in which both kinds of spacing can occur. A linear-shaped, logarithmically-spaced, to the reference microphone (\(n=0\)), symmetrical array is used in this paper. Consequential, the number of the used microphones \(N\) has to be uneven \((N\in\mathbb{N}_{U})\). The symmetry around one central microphone ensures a purely real directivity. The microphone positions are calculated as follows [11].  
\[\begin{split}(x_{n+1}-x_{n})&=(x_{n}-x_{n-1})\xi \quad\text{if }n>0\\ (x_{n-1}-x_{n})&=(x_{n}-x_{n+1})\xi\quad\text{if }n<0 \end{split} \tag{13}\]  
with:  
\[\begin{split} x_{0}&=0\\ \xi&=\left(l_{spread}\right)^{\frac{2}{N-3}}\\ (x_{1}-x_{0})&=(x_{0}-x_{-1})=\frac{Length}{2\sum_{n =1}^{\frac{N-1}{2}}\xi^{n-1}}\end{split}\]  
where \(Length\) is the total length of the array. The array parameter \(l_{spread}\in\mathbb{R}^{>0}\) is a free variable describing the ratio between the spacing of the microphones at the extremities of the array and the spacing of the microphones at the center of the array. Linear microphone spacings are archived with \(l_{spread}=1\). If \(l_{spread}<1\), the spacing of the microphones at the extremities of the array is smaller than the one at the center of the array. In the case of \(l_{spread}>1\), it is the opposite.
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 6 Fuzzy Logic Control of the Hirata Cartesian Robot Servomotor  
The speed and torque control of Hirata Cartesian robot servomotor is based on the frequency and amplitude of the vibration signal that occurs because of mechanical damage in the robot's transmissions. This correlation is based on several reasons, namely the detection of damage in the transmissions and the robot's operation under conditions of damaged transmission.  
Control based on fuzzy logic operates by defining linguistic variables and constructing a fuzzy set of data. Therefore, the fuzzy logic algorithm does not require the determination of an exact mathematical model of the robot for servomotor control.  
Fuzzy sets for linguistic input variables related to the amplitude and frequency of vibrations are illustrated in Figure 6.  
The shape of the membership function is chosen to align with the logic of the robot's operation under specified conditions. For instance, the nominal vibration of the robot during work operations is 0.3 g for the nominal motor rotation speed at which the maximum speed of the robot's working element is achieved. Therefore, for the fuzzy set of amplitude, trapezoidal and triangular functions are chosen, while only trapezoidal functions are selected for frequency. Gaussian functions are chosen for the output parameters of the speed and torque of the servomotor to ensure smoother regulation of these parameters.  
Fuzzy sets for linguistic output variables related to the speed and torque of the servomotor are presented in Figure 7.  
Figure 6: Fuzzy sets for linguistic input variables: (**a**) vibration amplitude, where AL—low amplitude, AM—medium amplitude, AH—high amplitude; (**b**) vibration frequency, where FL—low frequency, FH—high frequency.  
As a result, based on the presented fuzzy sets and the derived fuzzy rule base, patterns for the speed and torque of the servomotor can be obtained. The simulation results are illustrated in Figure 8.  
As seen from the simulation results, the speed parameter is inversely proportional to the torque of the servomotor. This is implemented to overcome the consequences of mechanical damage to the robot's transmission. When a fault is detected, the robot's movement speed decreases, and the torque increases to mitigate the impact of undesirable consequences on other parts of the robot. This also helps reduce the influence on its fundamental characteristics, such as positioning accuracy, performance, and energy efficiency.  
Figure 8: Patterns for the speed (**a**) and torque (**b**) of the servomotor.  
Figure 7: Fuzzy sets for linguistic output variables: (**a**) servomotor speed, where SLL—minimum speed, SL—low speed, SM—medium speed, SH—high speed; (**b**) servomotor torque, where TLL—minimum torque, TL—low torque, TM—medium torque, TH—high torque.
# A Spatial 4-DOF Laser Collimation Measurement System
## 2 Optical Principle  
As mentioned earlier, the laser collimation method was used for the design. The principal diagram of the four-degree-of-freedom measurement system is shown in Figure 1. Since the laser diode is small in size and has a long life, it is suitable for high-altitude space environments; therefore, the laser diode was used. Since the actual laser will have a divergence angle, it is necessary to use a collimator to convert it to parallel light to improve the performance and accuracy of the optical system. Two corner cone prisms were used: one was the integrated angle measurement and ranging of the light path, while the other was used to eliminate the effect of the pitch angle and yaw angle on the straightness measurements and reduce the influence of crosstalk. These will be described in detail in Section 2.3 CMOS was used to calculate the change in the position of the moving end by measuring the moving position of the laser on the target surface; this is described in detail in Sections 2.1 and 2.2. The reason other photodetectors were not used is that, with a four-quadrant detector, the angle measurement range is not large enough; therefore, small CCD or CMOS photodetectors should be used. Compared with CCD, CMOS has lower power consumption and can achieve a faster image reading rate. The polarization beam splitter (PBS2) and the quarter wave plate QWP were combined to form the optical isolator. The light intensity regulator was composed of a half-wave plate HWP and polarization beam-splitter (PBS1). Other optical lenses, such as spectroscope BS2, were used to change the light path, so that the ideas discussed in Sections 2.1 and 2.2 could be realized; otherwise, the laser would be covered and could not reach the CMOS.
## 1 Introduction
RVC has been selected in most studies as the cathode material, mainly because of its higher surface area and its porous structure that creates local turbulence, improving mass transfer [22]. Although the passivation of the RVC electrode in contact with chromate ions has been reported [23], it was found elsewhere that the direct Cr(VI) reduction in both synthetic and field samples can be practically 100%, by using the RVC cathode in a parallel-plate flow-by reactor and applying a relatively low voltage value [20]. Most of the aforementioned electrochemical treatment studies deal with effluents, containing relatively small amounts of Cr(VI) (typically less than 1000 mg/L) and aim to reduce its levels below 0.5 mg/L [20], so that it can be safely discharged, according to environmental regulations. A typical application, similar to the previous conditions, is the treatment of rinse wastewaters, originating from the Cr plating industry. As far as we know, we are not aware of any study, dealing with the treatment of concentrated Cr(VI) solutions, such as those originating from chromium leaching from thermally treated tannery sludge by acid extraction, aiming at electrochemical Cr(III) regeneration and potential reuse in tanneries. The aim of this study is to optimize and maximize the recovery of chromium from tannery sludge by a sequence of thermal, hydrometallurgical and electrochemical treatment techniques. The specific objectives have been the following: (a) the establishment of appropriate thermal treatment conditions for the quantitative oxidation of Cr(III) to Cr(VI) in the resulting solid waste; (b) the establishment of appropriate acid extraction conditions (acid kind and concentration) for the quantitative Cr(VI) leaching from the solid waste; and (c) the optimization of main experimental conditions (i.e., acid concentration, electrodes, operation of reactor) for the quantitative regeneration of Cr(III) by the application of direct electrochemical reduction of Cr(VI), using RVC electrodes in prototype batch and batch-recirculation type reactors.
# A Rectangular Notch-Band UWB Antenna with Controllable Notched Bandwidth and Centre Frequency
## 2 Antenna Design
### Controllable Notch Bandwidth and Frequency  
The bandwidth of the rejection band is an important characteristic of the design of UWB antennas. The notch bandwidth and frequency should be adjusted according to the requirements of the wireless communication system. In the proposed antenna, the length and width of both EBG structures are key parameters that affect the notch bandwidth of the antenna. To make it clear, we are studying the parametric study. It is to be noted that during the parametric study, only one parameter is changed, and all other parameters are unchanged. Figure 10a shows the \(|\mathbb{S}_{11}|\) of the antenna according to different lengths (\(l_{1}\)) of EBG1. The upper frequency of the notch band shifted linearly toward the left side, increasing the \(l_{1}\) without affecting the passband of the antenna. Because EBG1 controls the upper frequency of the notch, the lower frequency showed almost no change in the \(l_{1}\). However, the length (\(l_{2}\)) of EBG2 affects the lower frequency of the notch band, as shown in Figure 10b. The lower frequency of the notch shifted toward the left side, increasing (\(l_{2}\)) while showing a negligible change in the upper frequency of the notch band.  
Figure 11 shows the effect of changing the EBG widths on the \(|\mathbb{S}_{11}|\) of the antenna. The variations in the width of EBG1 (\(w_{1}\)) and EBG2 (\(w_{2}\)) show a response similar to the change in the length of the EBG. The upper frequency of the notch band moved to the left side, increasing the values of \(w_{1}\), and  
Figure 10: \(|\mathbb{S}_{11}|\) of the antenna for different lengths of: (a) EBG1 \(l_{1}\), and (b) EBG2 \(l_{2}\).  
Figure 9: Design procedure of the proposed antenna with rectangular notch-band characteristics.  
changed the lower frequency of the notch band in the same way as \(w_{1}\). These observations indicate that by tuning the dimensions of the EBGs, we can control the bandwidth and notch frequency of the proposed antenna according to the demands of the application. In the demonstration, the bandwidth of the notch band was reduced from 5-6 to 5.2-5.8 GHz, and the notch band was shifted to the X-band satellite downlink (7-8 GHz), as shown in Figure 12. For the reduction (5.2-5.8 GHz) in the bandwidth of the notch band \(l_{1}\) was increased by 2 mm, while \(l_{2}\) was decreased by 2 mm from the optimized values. Moreover, to shift the notch from WLAN to the X-band, the width, length, and spacing between the EBGs were reoptimized. The optimized parameters of the X-band notch antenna are as follows: \(A=16\) mm, \(L=24\) mm, \(h=1.52\) mm, \(g=1\) mm, \(Lg=7\) mm, \(Lp=\)12 mm, \(Sw=3.2\) mm, \(Wd=0.6\) mm, \(r=0.3\) mm, \(l_{1}=8.5\) mm, \(w_{1}=2\) mm, \(s=3.5\) mm, and \(p=0.2\) mm, \(l_{2}=8.5\) mm, \(w_{2}=2\), and \(s_{1}=11.3\) mm.
## 3 Results and Discussion
### Variations in the Ratio of GAN Generated Images in the Training Dataset

Table 1: Different variations of the GAN generated images ratio in the data augmentation of the original RGB training set and the respective mean IoU values (%) of the validation process for each instance. Figure 8: Detection of defects using two instances of the DeepLabv3+ semantic segmentation neural network. (**a**) photometric-stereo texture channel images; (**b**) ground truth masks; (**c**) predictions with the original RGB photometric-stereo database; and (**d**) predictions adding GAN-generated RGB photometric-stereo images. Figure 11 shows that we get 70% of IoU thanks to the data augmentation done by GAN, compared with 68.3% IoU using only real images. Similarly, it is observed that the IoU curve shown in Figure 10 with the increase of data and the 38% ratio is placed in a higher position compared to the IoU curve with the original dataset. It should be noted that the training set is unbalanced in terms of the type of surface defect of the galvanized steel. It is observed that defects that alter the base color of the material (such as oxidation) are more abundant in the dataset than defects with dimensional affection (such as scratches or impact marks). These images with color based alteration are those included in the range between image 19 and image 35. It can be seen in Figure 10 that there are segmentation results in some images, specifically between images 3 and 15 of the test set that obtains more than 6% improvement in the IoU when using a dataset augmented with GAN generated images. We realized that images exhibiting that improvement are those with more severe surface dimensional affection. On the other hand, images that obtain almost the same results are those related with changes only in the color of the material. We argue that segmenting images with surface dimensional affection requires segmentation models with higher information. Figure 10: Mean of IoU (%)result with original dataset augmented with a conventional method and the original augmented with synthetic images generated by the GAN with a 38% ratio. Figure 9: Confusion matrices of the test set, where label 0 represents background and label 1 means defect. (**a**) Confusion matrix of DeepLabV3+ trained only with real images and (**b**) confusion matrix of DeepLabV3+ trained with the original dataset augmented with synthetic images generated by the GAN with a 38% ratio. Therefore, models that were trained with an augmented dataset using GAN generated images obtain better results compared with those trained with original real images only, due to the addition of new images. Thanks to these new images, we are adding more information to the segmentation model for better distinguishing between affected and non-affected surface regions.