esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, recent advances in deep learning-based techniques of denoising have revolutionized the domain of image noise reduction, where we use convolutional neural networks that help the model clear out as much noise as possible while preserving all the minute details of the image. These techniques have significantly increased the effectiveness of image denoising, but a lot of work still needs to be done in this domain. In the next phase, we focus on the subfield of super-resolution. Super-resolution is a task that aims to restore image resolution beyond its original extent. In other words, this process \"enhances\" the quality of image resolution such that extra details are revealed, resulting in clearer visual appearances. We investigate the top-notch deep learning frameworks that are sketched specifically for super-resolution. These frameworks utilize neural networks to build up high-quality images from their low-quality equivalents, requiring intricate network designs and training schemes to improve image resolution while maintaining important structural attributes. This approach aims to create a model that provides better visual results with more improved images and fewer computational complexities. In each resolution stage, the information has been exchanged hierarchically across all the scales. In contrast, traditional methods isolate each scale and process them in a top-down order, making this approach different from conventional methods. The processing of information exchange has been carried out via a kernel fusion process per stream. In addition, ELEF uses a self-attention mechanism after picking a useful set of kernels from each stream. The most significant part is the fusion of traits from varying receptive domains through a fusion block, keeping distinct features.
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 3 Experimental Results
### Experimental Results for Scanning Operating Mode
#### 3.2.4 The Multistorey Building Experiment  
Another practical experiment was carried out in a multistorey office building to test the performance of the designed radar system. The two Golay-coded radars are placed on the ground of the fifth floor of a laboratory building (Figure 18a) and the tested objects are located at the ground of the fourth floor (Figure 18b). The height between floors of this building is 5 m. The tested people lay still on the fourth floor, breathing normally.  
Figure 17: Measurement results for ruins scene. (**a**, **b**) The output range-frequency images using the normal FFT-based respiration detection methods using the echoes from a single radar. (**c**) The output cross-correlated range-frequency three-dimensional (3D) image, with the vital sign feature (VSF) marked in green. (**d**) The output two-dimensional (2D) slice (range \(\times\) range) image for the specific respiration frequency.  
The distances between the target and the radar No. 1 and No. 2 are 5.35 and 5.41 m, respectively.  
Figure 19a,b shows the output range-frequency images of the normal FFT-based respiration detection methods using the echoes from a single radar. Due to the low SNR in this experimental scene, the output range-frequency images are too noisy to distinguish the VSF. According to our proposed \"scanning operating mode\", the cross-correlated range \(\times\) range 2D image is shown in Figure 19c. The resultant VSF is remarkable, showing that the distances between the target and the radar No. 1 and No. 2 are D1 = 5.30 m and D2 = 5.34 m, respectively.
## 2 Method and Principle  
Aiming at the problem of anchor-rod identification and positioning in coal-mine tunneling, we constructed a YOLOv7 + CLAHE + BiFormer + CBAM fusion model and proposed a bolt identification and positioning method based on this fusion model. The principle of identification and positioning is shown in Figure 1.  
The complex environment of the coal-mine roadway has a significant influence on anchor-rod recognition and positioning. Under the influence of complex environmental factors, the YOLOv7 model has no noticeable improvement effect on image processing. To effectively improve the accuracy of anchor-rod recognition, the contrast-limited adaptive histogram equalization algorithm (CLAHE) is used to enhance the original data and the image quality. Combined with the BiFormer and the Convolutional Block Attention Module (CBAM) to strengthen the ability of feature extraction, the anchor-rod target-detection and recognition model of YOLOv7 + CLAHE + BiFormer + CBAM fusion is constructed. The positioning method of the supported anchor rod based on the target anchor-rod detection and recognition model is established. The method includes the supported anchor-rod detection and recognition model, the perception system calibration and target-solving model, target coordinate transformation, and the positioning information solution model.  
(1) For the supported anchor-rod detection and recognition model, collect anchor sample data in the roadway environment by simulating the roadway and combining with the image data of the actual coal-mine roadway to establish a supported anchor-rod dataset; an improved YOLOv7 target-detection model is built to improve the detection performance of the model. The obtained anchor-rod dataset is used to train the visual recognition model, and the camera is used to detect the real-time image. The detection box and confidence of the target are identified, and the detection box is used as the region of interest (ROI) in the image;  
(2) For perception system calibration and the target-solving model, the camera is calibrated to obtain the internal and external parameters of the camera. The depth map and RGB image of the roadway anchor rod are captured, and the corresponding ROI in the depth image is processed by median filtering and bilateral filtering to restore the practical information in the depth map. The center point of the target-detection frame is used as the positioning target point, and the depth map is aligned with the RGB image to obtain the depth value of the target point and the two-dimensional coordinates in the image;  
(3) For target coordinate transformation and the positioning information solution model, the obtained depth value and the two-dimensional coordinate of the target point are transformed to obtain the three-dimensional coordinate information of the target point in the camera coordinate system. Aiming at the problem of large \(X\)-axis coordinate deviation, the coordinate value is corrected by least squares linear fitting to realize the visual positioning of the bolt.
# Analysis of Drone Flight Stability for Building a Korean Urban Air Traffic (K-UAM) Delivery System
## 5 Drone Flight Data Analysis--Hovering
Nevertheless, the drone's hovering radius error satisfied all requirements within 3 m.  
The second rule for the hovering test is that the altitude error must be within 3 m. The hovering maintenance altitude measurement data collected at Yeosu Gaedo show that it is almost identical to the test procedure process in Figure 15, and the actual numerical error is also stably satisfactory within 3 m. On the other hand, the hovering data collected in Yeosu Jakgeum in Figure 16 show that the drone's altitude is maintained at an average of 2 m or lower, and sometimes even momentarily exceeds the numerical error limit of 3 m. Fortunately, the error rate analysis of Equation (1) is 1.2195%, which does not exceed 3%, so it is considered to have passed the gas stability category. Figure 12: Yeosu, where the drone’s hovering test was conducted. Figure 13: Good case of hovering radius error in Yeosu Gaedo. Finally, the results of analyzing the hovering left/right errors and altitude errors in the Yeosu area are as shown in Table 5. It can be seen that the hovering test, which evaluates the stability of the drone according to regulations, was passed. Figure 14: Bad case of hovering radius error in Yeosu Jakgeum. Figure 15: Good example of hovering radius error in Yeosu Gaedo. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region** & **Distance Error (\%)** & **Altitude Error (\%)** & **Evaluation** \\ \hline (a) Gaedo & 0 & 0 & Stable \\ (b) Jakgeum & 0 & 1.2195 & (Pass) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of drone hovering data errors in Yeosu.
# Performance Analysis of Physical Layer-Based Multiple-Input Multiple-Output on WiMAX (MIMO-WiMAX) +
## 2 WiMAX Physical Layer Model
### Cyclic Prefixes  
To avoid the guard interval of analog transmission, a cyclic prefix is added to the time domain samples, and used in time-defined multicarrier transmitter implementations.  
The cyclic prefix is first removed at the receiver in a manner similar to the guard interval removal. At the same time, the channel effect is transformed into a cyclic convolution of the discrete-time channel with the IDFT of the data symbol. Figure 4 shows the cyclic prefix.  
Figure 4: Cyclic prefix.  
Figure 3: Convolutional encoder of code rate, 1/2.
## 2 Theoretical Model of Loss Calculation and Thermal Analysis
### Electromagnetic Principles of Magnetic Field-Modulated Motors  
Magnetic field-modulated motors represent an advanced class of electrical machines that leverage magnetic field modulation effects to achieve enhanced performance characteristics particularly suitable for flywheel energy storage systems [13]. The fundamental operational principle, as illustrated in Figure 1, involves the strategic interaction between permanent magnets, flux modulators, and stator windings to create effective electromagnetic coupling [14]. Unlike conventional permanent magnet synchronous machines, these motors employ a distinct configuration where flux modulators--typically ferromagnetic pole pieces--are positioned between permanent magnet arrays and armature windings [15].  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Rated power & 5 kW \\ Rated speed & 30,000 rpm \\ Stator outer diameter & 120 mm \\ Rotor outer diameter & 60 mm \\ Active length & 85 mm \\ Air gap length & 0.8 mm \\ Number of stator slots & 12 \\ Number of rotor pole pairs & 4 \\ Number of modulators & 5 \\ Permanent magnet material & N425H \\ Core material & M250-35A \\ Maximum flux density & 1.6 T \\ \hline \end{tabular}
\end{table}
Table 1: Key parameters of the high-speed magnetic field-modulated motor.  
Figure 1: A schematic representation of the magnetic field modulation principle in a high-speed motor, showing permanent magnet array, flux modulators, and stator winding configuration.  
This arrangement facilitates harmonic modulation of the magnetic field, enabling effective energy conversion at variable speeds while maintaining high torque density.  
The magnetic field distribution can be mathematically described using Fourier series analysis, where the air gap flux density contains both fundamental and modulated harmonic components [16]. These modulated harmonics are responsible for the electromechanical energy conversion through magnetic coupling, with the modulation ratio directly influencing the torque transmission capability. As depicted in Figure 1, the pole pair relationship between modulated flux components and stator windings establishes a stable electromagnetic torque production mechanism that maintains operational stability under varying load conditions. This principle is particularly valuable in flywheel applications where wide-range speed variation necessitates consistent torque production across different operational states [17]. The modulated field distribution creates a magnetic gear effect, enabling speed adaptation without mechanical transmission components, thereby reducing system complexity and potential failure points.
# The Effect of Nerolidol Renal Dysfunction following Ischemia-Reperfusion Injury in the Rat
## 4 Results  
As demonstrated in Tables 2 and 3, the basal serum creatinine, serum urea, creatinine clearance, 24 h urinary albumin and albumin/creatinine ratio were similar in all groups (\(p>0.05\) for all variables). Similarly, there was no difference in any of these variables between the Pre-IRI and Basal values in any of the groups (\(p>0.05\) for all variables).  
As expected, in the G-Sham, there was no difference in any variable post manipulation compared to the Pre-IRI value (\(p>0.05\) for all variables). In the G-IRI, there was a significant deterioration in renal parameters following IRI (Table 2). For instance, IRI caused an increase in serum creatinine from 0.32 \(\pm\) 0.05 to 1.51 \(\pm\) 0.48 mg/dL (\(p<0.05\)). In contrast, creatinine clearance decreased to 33.5 \(\pm\) 7.8 compared to 69.9 \(\pm\) 6.6 mL/min before IRI (\(p<0.01\)). In addition, IRI caused an increase in urinary albumin leakage. The 24 h urinary albumin and albumin/creatinine ratio increased to 0.783 \(\pm\) 0.111 \(\upmu\)g (vs. 0.079 \(\pm\) 0.009, \(p<0\)-001) and 166.1 \(\pm\) 27.0 (vs. 13.7 \(\pm\) 2.2, \(p<0\)-001), respectively (Table 3).  
As shown in Tables 2 and 3, the administration of nerolidol significantly attenuated IRI-induced alterations in these parameters (\(p<0.05\) for all variables).
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 2 Construction of Longitudinal PID Controller for a Vehicle  
The longitudinal motion model of an autonomous vehicle, which refers to the movement in the forward and backward directions, is crucial for describing the vehicle's dynamic behavior during various driving conditions such as acceleration, deceleration, and constant-speed cruising. This model serves as a foundation for controlling and tracking the vehicle's speed, enabling the vehicle to respond appropriately to the environment and driver commands.  
The model is based on [PERSON]'s second law of motion, which states that the acceleration of an object is determined by the net forces acting upon it. In the context of vehicle dynamics, the model considers the balance of various forces, including driving force, resistance from the air, rolling friction from the tires, and braking forces. By analyzing these forces, the model predicts changes in the vehicle's acceleration, speed, and displacement over time. This makes the longitudinal motion model essential for implementing precise speed control in autonomous driving systems, ensuring the vehicle can adjust smoothly and safely during different driving scenarios.
## 2 Materials and Methods
### Samples Preparation  
Six samples were synthetized by different industrial by-products (silica fume or bottom ash). The composition of each sample, expressed in g except for hydrogen peroxide expressed in mL, is reported in Table 1.  
First, BA was dried for 1 h at 100 \({}^{\circ}\)C to eliminate moisture and then sieved to obtain a fine powder with a size dimension lower than 300 \(\upmu\)m. Sample 1 and sample 2 were synthetized according to [20]. Sodium alginate and calcium iodate (used as a cross linker) were mixed vigorously with MilliQ water. Then, silica fume (sample 1) or bottom ash and calcium hydroxide, in the same proportion (sample 2), were added to the compound. Finally, sodium bicarbonate (as pore former) was added. The slurry obtained was put on a heating plate for 1 h at a low temperature (70/80 \({}^{\circ}\)C) to allow for pore generation due to sodium bicarbonate decomposition.  
To avoid the thermal treatment, sodium bicarbonate was replaced with hydrogen peroxide in the synthesis process of samples 3, 4, 5 and 6. Sample 3 was performed by mixing 9 g of bottom ash, 9 g of calcium hydroxide, 7.2 mL of hydrogen peroxide and 9 mL of MilliQ water. The amorphous phase of BA is expected to react with hydrogen peroxide to promote pozzolanic reaction [33], which favors the formation of a stable material. Sample 4 is characterized by a lower quantity of hydrogen peroxide (5.4 mL) compared to sample 3. Sample 5 was synthetized by mixing vigorously 20 mL of MilliQ water, 0.6 g of sodium alginate and 1 g of calcium iodate until a gel is formed. Then, 17.88 g of silica fume and 7.2 mL of hydrogen peroxide were added. Sample 6 contains a lower quantity of hydrogen peroxide (5.4 mL). After the synthesis, all samples were left at room temperature for 1 week to reach the final consolidation. Figure 1 shows the images of all samples after consolidation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Silica Fume (SF)** & **Bottom Ash (BA)** & **Ca(OH)\({}_{2}\)** & **NaC\({}_{6}\)H\({}_{7}\)O\({}_{6}\)** & **Ca(IO)\({}_{2}\)** & **NaHCO\({}_{3}\)** & **H\({}_{2}\)O\({}_{2}\)** \\  & **(g)** & **(g)** & **(g)** & **(g)** & **(g)** & **(g)** & **(mL)** \\ \hline Sample 1 & 17.88 & - & - & 0.6 & 1 & 5 & - \\ Sample 2 & - & 9 & 9 & 0.6 & 1 & 5 & - \\ Sample 3 & - & 9 & 9 & - & - & - & 7.2 \\ Sample 4 & - & 9 & 9 & - & - & - & 5.4 \\ Sample 5 & 17.88 & - & - & 0.6 & 1 & - & 7.2 \\ Sample 6 & 17.88 & - & - & 0.6 & 1 & - & 5.4 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of porous samples. Samples 1, 5 and 6 were synthetized by silica fume, while samples 2, 3 and 4 were synthesized by bottom ash.  
Figure 1: Images of samples synthetized with silica fume (**a**,**e**,**f**) and with bottom ash (**b**–**d**).
## 3 Results  
Once the SMD parameters have been optimized in the machine learning stage, its performance is tested on three datasets. The first one is a synthetic dataset similar to one from [17]. Structure-oriented SVD [17] was tested on several datasets and while it was successful at reducing noise in all examples, it produced numerical artifacts in one of them. In this work we reproduce that challenging dataset in order to demonstrate the improvements achieved with SMD. The other synthetic data test cases from [17] contain fewer coherent waves and don't show interference between waves of different slopes and are not as difficult for reliable compression and analysis. For that reason, the second and third data sets on which SMD is tested are from field data obtained during marine seismic gathers. The second dataset has many, interfering, strong arrivals which can together create a complicated signal. The third dataset has fewer arrivals, but the coherent waves are much weaker and difficult to differentiate from noise.
## 4 Oil Film Layer Thickness Estimation
The respective optimum parameters for different criteria application to measure individual deviations from average mass values of oil films in the 25-mm pipes are stated in the \[m(x)=\pi\varrho\int_{0}^{x}\left[r^{2}-(r-h(\xi))^{2}\right]\,d\xi=\pi\varrho \int_{0}^{x}h(\xi)\big{[}2r-h(\xi)\big{]}\,d\xi, \tag{1}\]1  
\[m(x)=\pi\varrho\int_{0}^{x}\left[r^{2}-(r-h(\xi))^{2}\right]\,d\xi=\pi\varrho \int_{0}^{x}h(\xi)\big{[}2r-h(\xi)\big{]}\,d\xi, \tag{1}\]2 (a) theoretical values of the logarithms \(m_{i}\) (asterisk * marks measured values) describing average values of 25-mm pipes; (b) deviations of theoretical logarithms of the values of \(m_{i}\) from logarithms of the values \(m_{e_{i}}\) obtained experimentally, \(i=1,\ldots,6\) for criteria 1–4. \[m(x)=\pi\varrho\int_{0}^{x}\left[r^{2}-(r-h(\xi))^{2}\right]\,d\xi=\pi\varrho \int_{0}^{x}h(\xi)\big{[}2r-h(\xi)\big{]}\,d\xi, \tag{1}\]3 (a) theoretical values of the logarithms \(m_{i}\) (asterisk * marks measured values) describing average values of 35-mm pipes; (b) deviations of theoretical logarithms of the values of \(m_{i}\) from logarithms of the values \(m_{e_{i}}\) obtained experimentally, \(i=1,\ldots,6\) for criteria 1–4. Figure 9 shows the minimization results for 35 mm pipes. Only the criterion number 3 yields appropriate results, with theoretical mass values higher in the 2 nd and the 3 rd section, respectively. This is why we determined the appropriate parameters manually also in this case--the result is marked by number 5. Figure 10 (left) shows oil film thickness dependencies on the distance \(x\)--the position of the point from the place of the oil mist injection--for optimal parameter values for the exponential model (3). In pipes of 15 mm in diameter, the \"sufficient film thickness\" stretches only half way through. The suitable layer in the 25 mm diameter pipes is along the whole length. The tubes of the largest diameter 35 mm have a much larger thickness at their start (we cannot see the corresponding parts of the curves because we chose a common vertical scale for better comparison); however, only about 2 m have the sufficient oil film thickness. Figure 10 (right) shows the situation for the \"exponential\" model (6). It seems that we can expect that, at the given oil mist injection mode, the thickness of the film of a 25 mm pipe will be everywhere above the critical value of \(h_{c}=1.68675\)\(\upmu\)m. In the case of a 15 mm pipe, we can expect that up to the intersection point \(x_{15}^{*}\doteq 3.11\) m, the oil film thickness will be greater than critical; therefore, upon the oil mist injection from both sides, a sufficient thick film should form alongside the entire pipe. In the case of 35 mm pipes, the \"critical\" value is \(x_{35}^{*}\doteq 2.48\) m, i.e., the injection mode needs adjusting.
# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 1 Introduction
The hybrid models in this review are three classes: model-intensive, technique-intensive, and data-intensive [27]. The emerging frameworks, such as Convolutional Neural Network (CNN) [28], widely used in the field of the image, are also included in this review. In this review, ANN models for water quality variables prediction are summarized. Previous reviews [20; 27; 29] about ANNs are more concerned about the water quantity (e.g., flow and rainfall-runoff) prediction, while less attention has been paid to water quality prediction (e.g., Suspended solids (SS)), and the major scenarios they investigated are river systems. At the same time, previous reviews care about the development of the model while ignoring the output strategies between input(s) and output(s) in a given prediction task. To overcome the limitations above, this review focuses on the use of ANNs methods for water quality prediction, with more water quality variables investigated than previous reviews, which are mainly divided into three categories, namely chemical, biological and physical variables [30]. The research scenarios include not only the river system that was the focus of the previous review, but also reservoirs, lakes, wastewater treatment plant (WWTP), groundwater, etc. It must be pointed out that the review did not consider drinking water systems. The reason for this is that drinking water is a system that includes source, treatment, and distribution, and should be considered as an independent branch or subject for systematic research [30]. In addition to the increased number of water quality variables reviewed and broader research scenarios, this review also summarizes five output strategies. The period of the investigated papers covered was from 2008 to 2019. This period was chosen as it follows on from the period covered in the review by [27] (i.e., 1999-2007). The review is organized as follows. Section 2 presents the process of the paper collection. Section 3 describes three basic model structures in water quality prediction. In Section 4, the applications of artificial neural networks in water quality are surveyed. Then, Section 5 represents the results of this review. Finally, the discussions are given in Section 6. All the abbreviations are mentioned in Table 1.