esa-sceva/satcom-chunk-collection huggingface dataset:
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 4 Control Methods of Servomotors
### Current Control  
Current control, also known as torque control, is based on changing the current in the windings of the servomotor. This method allows precise regulation of the torque on the motor shaft because the current magnitude is proportional to the torque. Current control, which provides high accuracy in torque control, is suitable for applications where load control is required, as well as the need for overload protection. Examples of such applications include systems where maintaining stable control is important, such as industrial robots and autonomous motion systems [78; 79].  
The basis of current control is the principle of feedback and regulation. Feedback on the current is applied to the servomotor, the current is measured in the windings, and the value is sent to the controller for comparison with the set value (Equation (5)). When the measured value deviates from the set value, the regulator generates a control signal for the power amplifier. The power amplifier, in turn, adjusts the voltage on the motor windings to bring the current value to the desired level [80; 81].  
\[\mathrm{e}=\mathrm{I}_{\mathrm{s}}-\mathrm{I}_{\mathrm{m}}, \tag{5}\]  
where \(\mathrm{e}\)--deviation error, and \(\mathrm{I}_{\mathrm{s}}\) and \(\mathrm{I}_{\mathrm{m}}\)--the set and measured value of the current.  
The regulator in this type of control method consists of three parts and their combinations: proportional, integral, and differential.  
The proportional component uses the following equation for increasing performance:  
\[\mathrm{P}\mathrm{=e}\mathrm{K}_{\mathrm{p}}, \tag{6}\]  
where \(\mathrm{P}\)--proportional control signal, and \(\mathrm{K}_{\mathrm{p}}\)--proportional coefficient.  
The integral component uses the following equation for error elimination:  
\[\mathrm{I}\mathrm{=K}_{\mathrm{i}}\int\mathrm{edt}, \tag{7}\]  
where \(\mathrm{I}\)--integral control signal, and \(\mathrm{K}_{\mathrm{i}}\)--integral coefficient.  
The differential component uses the following equation for reducing the amount of overshoot:  
\[\mathrm{D}\mathrm{=K}_{\mathrm{d}}\frac{\mathrm{de}}{\mathrm{dt}}, \tag{8}\]  
where \(\mathrm{D}\)--differential control signal, and \(\mathrm{K}_{\mathrm{d}}\)--differential coefficient.  
The main advantages of this method include the following [82; 83]:  
1. High precision in regulating torque.
2. Quick response to changes. Current control allows easy adaptation to external changes.
3. Energy savings. Efficient energy use is due to the adaptive properties of current control.  
Impact on positioning accuracy. Strict control of torque allows controlling the position of the shaft in the final position.  
The main drawbacks of the method, which differ from other methods and negatively affect further development, are the requirements for the accurate measurement of torque and flux parameters and sensitivity to changes in motor parameters, namely inductance and resistance [82; 83].
## 3 Taxonomy
### Non-Al-Based Methods
#### 3.2.4 System Development  
This sub-section discusses the articles that developed IoT-based water monitoring systems. In this sub-section, twenty-eight (\(n=28\)) are included and the articles are divided into three categories based on the way of transferring the data, and these categories are GSM, wireless sensor node (WSN), and Wi-Fi.  
GSM: Three articles (\(n=3\)) are included in this category. In the first article, a real-time IoT-based water quality monitoring system was developed using temperature, pH, turbidity, ultrasonic, and flow sensors [44]. In this system, Data are sent to a cloud via GSM installed in the Arduino in a text-based notification. In the second article, integration between a Supervisory Control and Data Acquisition system and the IoT was proposed for real-time water quality monitoring to identify contaminated water and water leakage in pipelines [45]. In this system, temperature, pH, flow, and color sensors were utilized and data were sent using the GSM module. In a study, a real-time IoT-based portable water quality monitoring and notification system were developed using pH, temperature, conductivity, TDS, turbidity, dissolved oxygen, and salinity sensors [46]. In the system, the data are transferred to a cloud server \"thingspeak\" via GSM.  
WSN: In this category, thirteen (\(n=13\)) are included. In the literature, different studies integrated WSN with IoT to transfer the collected data from pH, temperature, turbidity and dissolved oxygen sensors to monitor the quality of water in real time [47; 48; 49; 50; 51; 52]. In some articles, IoT-based real-time water quality monitoring systems were designed and developed using the Zigbee module to transmit the data via WSN [2; 4; 19; 53; 54; 55]. Also, blockchain technology was integrated with IoT, WSN, and GIS technologies for real-time water pollution source tracing [56].  
Wi-Fi: In this category, twelve (\(n=12\)) are included. In the literature, various studies developed water quality monitoring systems based on IoT attached with sensors such as pH, temperature, and dissolved oxygen and send the readings of these data via Wi-Fi in real-time to ensure the quality of water is maintained and early detect any changes in the water [17; 57; 58; 59; 60; 61; 62; 63; 64; 65]. A study has developed a real-time automated IoT-based system for water quality monitoring using pH, temperature, ammonia, and nitrate level of water sensors, and the data are transferred with the help of Wi-Fi for aquonics farmers [66]. In another study, a real-time web-based water turbidity monitoring system was developed based on IoT to measure the water's cloudiness in pipes and the data were transferred via WiFi [5].
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 2 Requirement Analysis
### Dimension  
A position in space is a three-dimensional component, so it is a relative position coordinate. However, many relative positioning systems are only able to measure the relative position in one or two dimensions. A laser scanner uses a rotating mirror around the vertical axis to scan the environment in the azimuth angle. Consequently, it can output a two-dimensional relative position while disregarding the vertical component. An Ultra-Wideband (UWB) system, on the other hand, is able to measure the Round-Trip Delay (RTD) of signals emitted from the ego vehicle and returned by other vehicles. This system is only able to estimate the range between the vehicles, but not the exact two- or three-dimensional position. Cooperative solutions based on GNSS are able to deliver a full 3D relative position between vehicles.
# Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features
## 4 Experimental Results and Analysis
Moreover, it could also be argued here that the matching performance (in terms of EER) can be greatly affected by a large variation in true matches against the false matches. A comparison of the matching performance in terms of EER (%) between the proposed framework and some closely related works is presented in Table 1.  
From the figures presented in Table 1, one can see that the proposed framework performs effectively in comparison with other techniques, achieving the best EER value of 2.01%. In addition, there is a close matching performance (in terms of EER) between our framework and those previously presented in [42; 43; 45; 46]. The average execution time of the fingerprint recognition algorithm, including enhancement, feature extraction and matching, is about 674 ms, so that it can run sufficiently fast for real-time operation, since the additional computational costs for the fingerprint enrollment are negligible besides the realtime SIFT feature extraction and matching. The average recognition time taken by the proposed technique has been compared with that of several closely related techniques, as shown in Table 2. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Work** & **Techniques** & **EER (\%)** \\ \hline Proposed method & Improve SIFT Features & 02.01 \\ [PERSON] and [PERSON] [41] & Fingerprint Shell & 02.02 \\ [PERSON] et al. [42] & Dictionary Learning & 02.04 \\ [PERSON] et al. [43] & Orientation Extraction & 02.06 \\ [PERSON] et al. [44] & Fingerprint template & 02.07 \\ [PERSON] et al. [45] & Two-Stage Enhancement Scheme & 02.19 \\ [PERSON] et al. [46] & Pre-processing & 02.40 \\ [PERSON] [47] & Curved Gabor Filters & 11.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the performance comparison between the proposed framework and most closely related techniques. Figure 11: A plot of the ROC curve between FMR and FNMR for the FVC 2004 fingerprint database. The presented fingerprint recognition system is implemented for much of its framework using Microsoft Visual Studio 2017 development tools and OpenCV Vision Library to realize real-time digital image processing and automatic object recognition. All tests and evaluations were preformed on a PC with an Intel(r) Core(TM) i7 CPU--2.60 GHz with Turbo Boost Technology, 8 GB RAM, and running Windows 10 Pro (64-bit) as the operational system.
## 3 Experiments and Analysis
### Qualitative Comparison
#### 3.4.2 LCM-Based Methods (DNGM, TLLCM)  
The LCM-based methods of DNGM (Figure 8(b1-b4)) and TLLCM (Figure 8(c1-c4)), which are based on local contrast, show better visual performance under simple backgrounds. However, in complex scenarios, such as those shown in Figures 7(b1-b6,c1-c6,d1-d6) and 8(b5,b6,c5,c6,d5,d6), they suffer from missed detections due to background interference.
# A Geometric Error Measurement System for Linear Guideway Assembly and Calibration
## 2 Measurement Principles
### Parallelism Measurement Module  
The measurement and setup principle of the parallelism is almost the same as that of perpendicularity described in the previous section; the only difference is the beam path utilized in the measurement, as shown in Figure 7.  
The deviation angles of the refracted beam \(\theta_{21}\) and \(\theta_{22}\) were also calculated via the least-squares analysis of the measured points detected by the PSD set upon the linear guideway, respectively. The perpendicularity error \(\epsilon_{para}\) can be obtained by the arctangent relation of \(\theta_{21}\), \(\theta_{22}\), and the measured distance \(D\), as shown in Figure 8:  
\[\epsilon_{para}\ =\ D\ \tan^{-1}[(\theta_{22}-\theta_{21})] \tag{4}\]  
Figure 6: Beam path of perpendicularity measurement.  
Figure 7: Schematic diagram of the parallelism module.
###### Abstract  
In the large inversion range, the wavelet-regularization inversion method (WRIM) is an effective method for improving the inversion accuracy of dynamic light scattering (DLS) data. However, the initial decomposition scale (IDS) of this method has a great effect on the inversion accuracy. The particle size distribution (PSD) obtained from inappropriate IDS is not optimal. We analyze the effect of the different IDS on the inversion result in this paper. The results show that IDS of the smallest relative error should be chosen as the optimal IDS. However, because the true PSD is unknown in the practical measurements, this optimal IDS criterion is infeasible. Therefore, we propose an application criterion determining the optimal IDS. Based on this criterion, an improved WRIM with the optimal IDS is established. By the improved WRIM, high accuracy inversion PSD is obtained from DLS data. The simulated and experimental data demonstrate the effectiveness of this algorithm. Besides, we also further study the effect of the data noise on the optimal IDS. These studies indicate that the optimal IDS usually shows a downward trend with an increase of noise level.  
Keywords:dynamic light scattering; wavelet-regularization inversion method; particle size distribution; initial decomposition scale; inversion +
Footnote †: journal: Applied Sciences
# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 3 Results
### Comparison Experiment with SOTA Deep Learning Methods
The resulting segmentation was more detailed, with clearer and more continuous edges, effectively reducing information loss. On the Kvasir dataset, the original model's segmentation results, while capturing the general outline of the target, suffered from inaccuracies in edge and shape details. Specifically, the target area edges were not precisely segmented, leading to \"gaps\" or \"breaks\" in some structures (Table 2, ao), particularly in irregularly shaped areas. When dealing with more complex and curved shapes, the original model often missed edges and failed to capture the full target shape. In contrast, the AdaptiveConv2d-enhanced model effectively managed irregularly shaped features, accurately capturing every curve and protrusion. The segmentation results were smoother and more continuous, avoiding breaks and capturing the overall shape of the target more completely. In addition to the improvements observed across different datasets, we further compare the performance of AdaptiveConv2d against other state-of-the-art methods, such as UNet, UNet++, nn-UNet, and Attention-UNet, using the same set of evaluation metrics. As shown in Tables 1 and 2, AdaptiveConv2d consistently outperforms these methods in terms of key metrics, including the Dice coefficient, IoU, and accuracy. This reinforces the advantage of AdaptiveConv2d in handling complex medical segmentation tasks, particularly in extracting fine-grained details and segmenting irregular structures. For instance, on the HAM10000 dataset, AdaptiveConv2d achieves a 1.21% improvement in Dice and a 2.07% improvement in IoU compared to the baseline UNet. This enhancement becomes more prominent when compared with other advanced models like UNet++ and nn-UNet, which demonstrates the superior performance of AdaptiveConv2d in complex segmentation scenarios. Moreover, the small but consistent improvements in segmentation metrics, even on datasets with high baseline performance like the Blood Cell dataset, demonstrate the robustness and reliability of the method. These results highlight the adaptability and efficacy of AdaptiveConv2d in improving segmentation accuracy, particularly when dealing with intricate features, complex backgrounds, and subtle details in medical images. Therefore, the consistently superior performance of AdaptiveConv2d further confirms its potential to improve medical image segmentation tasks across a variety of datasets and network architectures.
## 4 Conclusions  
In the present work, a low-cost 3D printer for printing hydrogels composed of biopolymers was constructed by modification of a conventional FFF 3D printer. Hydrogel inks comprising alginate/methyl cellulose/trimethyl chitosan (TMC) showed significant higher stability in comparison with alginate/methyl cellulose inks. This can be attributed to electrostatic interactions between alginate and TMC alginate polymeric chains. The incorporation of pure silicate or bioactive glasses containing 30% mole CaO retained the stability of the alginate/MC/TMC scaffolds in normal saline solutions. Immersion in 2 xSBF solution stabilized the structure for at least two weeks. The possible mechanism leading to this stability is based on the formation of apatite crystals in the polymeric network. Pre-osteoblastic cells seeded on the scaffolds with different compositions revealed a good cell adhesion, an increased proliferation up to seven days in culture, and a similar ALP activity. The production of total collagen was found to be significantly higher in scaffolds containing silica and bioactive glasses, demonstrating their ability to promote extracellular matrix formation. In conclusion, 3D-printed porous scaffolds containing alginate/methyl cellulose/trimethyl chitosan reinforced with pure silica or bioactive glasses are promising candidates for bone tissue engineering applications.  
Conceptualization, N.B.; methodology, experimental work, and data analysis, N.B., M.F., C.K., P.Z., D.F., V.P., R.-M.K.; writing--original draft preparation, N.B., V.P.; writing--review and editing, N.B., D.F., M.C.; visualization, N.B., D.F., C.K., M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
The authors declare no conflict of interest.
## 3 Experiments
### Remote Sensing Investigations of Large Rivers Deltas
#### 3.5.2 The Pearl River Delta  
The Pearl River Delta is the third-largest delta in China, located on the southern coast of mainland China on the South China Sea and is one of the world's most urbanized and industrialized regions [422]. The river delta and many surrounding areas are below local high tide storm surge levels. Over the last 50 years, reclaimed lands have been merged into over one hundred enclosures protected by flood defense measures. However, many coastal areas remain under threat from natural hazards, such as river flooding, waterlogging, typhoons, and tidal-induced flooding [423; 424; 425; 426; 427]. Over recent decades, extensive land reclamation projects have been carried out to keep up with urbanization and industrial development. The delta region is exposed to natural disasters, such as groundsubsidence caused by soft soil compaction, Karst geomorphology, and regional tectonic effects [257; 400; 428]. Guangzhou, one of the major metropolitan regions in the Pearl River Delta, has continuously experienced ground subsidence and ground collapse accidents in recent decades. The increase in geological hazards has also caused casualties [429]. Sudden ground collapse accidents occurred near metro lines, which were under construction at the time, due to over-pumping of underground water, compaction of soft soil, concentrated underground construction, and Karst geomorphology. Noticeable ground subsidence has also been observed in the other mega-cities of the delta, such as Macao, Shenzhen, and Hong Kong [430; 431; 240].  
The Pearl River Delta is also facing increased inundation threats due to SLR and has experienced a high frequency of flooding disasters in the last century [401; 432]. Long time series tidal records and long-term geological subsidence suggest that relative sea level may experience a 30 cm rise at the mouth of the estuary by 2030, and extensive coastal areas will be heavily and severely affected by the potential risk of tidal inundation [433]. The tidal range has also been observed to have a strong sensitivity to sea level variability in this region, as tide gauges in Hong Kong show that the increase in the tidal range may increase by up to 0.6 m for every 1 m of SLR [352]. Additionally, indiscriminate sand excavation throughout the Pearl River region since the 1980s directly affects annual extreme water levels, further increasing the risk of flooding [434].
# A Novel Method of Calibrating Micro-Scale Parameters of PFC Model and Experimental Validation
## 5 Application of Calibrated Model to Rock Fracture and Experimental Validation
### Crack Development around Openings
In other words, the fracture process of the specimen with a prefabricated opening in uniaxial loading starts from the initial-tensile crack, via exfoliation crack and secondary-tensile crack to shear crack, and the specimen eventually suffers from shear-induced failure. The main difference between the numerical and experimental results is that the length of the tensile crack is different. The reason may be that the secondary-tensile crack will propagate to the end of the specimen when the specimen fails. Also, rock heterogeneity may have an influence on it. Besides, provided that the brittleness of the specimen is exceedingly remarkable, rock burst instead of spalling failure emerges on both sides of the opening. It should be noted that the initial-tensile cracks gradually disappear when the secondary-tensile cracks occur and propagate. The closure of the initial cracks is caused by the lateral extrusion resulting from the propagation of the secondary-tensile cracks. However, this behavior cannot be reflected in the numerical modeling. To sum up, there is good consistency in fracture evolution between the numerical and experimental  
Figure 12: Crack development around openings of different shapes under uniaxial loading obtained by numerical method: (**a**) model SC, (**b**) model SS, and (**c**) model SD. investigations. This proves that the calibrated PFC model for study of brittle rock fracturing is reliable, i.e., the above optimized micro-scale parameters obtained by the proposed method are reasonable.
# Research of Adaptive Extended Kalman Filter-Based SOC Estimator for Frequency Regulation ESS
## 3 Performance Verification of SOC Estimation Using Adaptive Extended Kalman Filter
### Extended Kalman Filter Algorithm Design
Since the SOC estimate is obtained based on the voltage, all estimates of the algorithm are inaccurate unless the algorithm correctly estimates the actual voltage. Therefore, it is very important to accurately estimate the voltage in the EKF algorithm in order to estimate the state of the battery. Figure 5: Voltage estimation of battery using EKF. (**a**) Voltage estimation results of EKF; (**b**) Voltage estimation error of EKF; and (**c**) Changes in Kalman gain according to voltage estimation.
## 2 Materials and Methods
### Validation and Interpretation of SAR Shorelines and Change Rates
#### 2.5.1 Bull Island  
The foredune system presents a semicontinuous vegetation cover along the seaward front. The coastal extent of terrestrial vegetation is a commonly accepted proxy used to represent the shoreline for coastal change assessments in soft coasts [35]. In this assessment, SL annual change rates (CRs) were calculated using six very-high-resolution satellite images acquired on dates between 01 June 2016 and 01 March 2022 (see Table 3 for details).  
The vegetation line (VL) for each available image was manually digitized in Google Earth Pro. The methodological workflow can be summarized in three steps:  
1. VL digitizing for each available image.
2. Position and measurement uncertainty calculation.
3. Quantifying SL change employing a dedicated software tool.  
A dedicated tool, Digital Shoreline Analysis System (DSAS) [36], designed by the United States Geological Survey (USGS) is used to quantify SL change. A baseline is drawn parallel to the SL, along which perpendicular transects are derived spaced at regular intervals of 50 m. Specific SL change statistics are then calculated along these transects where they intersect with the VL. These rates, expressed in m/year, are calculated using several statistical methods based on measured differences between SL positions. The primary index rate used in this analysis is weighted linear regression (WLR), whereby the more reliable data are given greater emphasis or weight in determining the average yearly SL CRs [37]. The overall shoreline uncertainty accounts for image position and measurement uncertainties, which are factored into the model calculations. Image positional uncertainties in this research range are below 2 m based on the geo-accuracy assessment. Measurement uncertainties, fundamentally derived from digitizing the vegetation line, have been estimated to be 1 m. The resulting shoreline change rate uncertainty, using a 90% confidence interval, has been estimated to be in the range of \(\pm\)4 m/year [38].