esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Geometrical Factors on Torsion in Cold Roll Forming of the Lower Side Beam of a Car
## 3 Results and Discussion
### Plate Thickness  
The strain difference corresponding to different thicknesses is shown in Figure 10a. With the decrease of strip thickness, the strain difference distribution gradually approached y = 0; that is, the torsion was improved. The cross-section was extracted to obtain the torsion angle, as shown in Figure 10b. As the thickness of the strip increased, the torsion angle gradually increased, which was consistent with the results of strain analysis. Thus, it can be seen that thickness has a significant effect on torsion in asymmetric forming.  
The increase of strip thickness indicates that the larger the forming force required for each pass of forming, the larger the corresponding torque, the larger the overall torque difference, and the greater the longitudinal strain, thus aggravating the torsion defect.
## 2 Surface Localisation and Profiling  
Structured light systems emit low-powered Infra-Red (IR) light that are reflected by objects and received by at least two cameras. The combined observations of the projected pattern are used to triangulate and calculate a depth map in the camera's field of view. The key disadvantage of these systems is their sensitivity to ambient light and surface reflectivity. Ambient lighting conditions with wide band-width rays, such as direct sunlight, can hide and distort the projected pattern observed by the RGB/D camera system. For  
Figure 2: Part profiling, path planning, and path execution pipeline requiring minimal and noisy/incomplete visual information.  
highly reflective surfaces, sections of the part may be missing or suffer significant noise as a result.  
Applying traditional robotic-NDT path-planning methods to RGB/D point clouds requires surface position and normal estimation for each of the visited points, so as to align the tool correctly. However, even minor point cloud distortions can effect the numerical accuracy of the extracted vector. An advantage of RGB/D cameras is the ability to apply traditional computer-vision techniques to the point cloud map attained. In segmenting parts, two approaches were used for parts of varying scale. For parts smaller than the field of view of the camera, an initial image of the cell and a subsequent image were used in combination with the structural-similarity indices of the two colour images to locate the part. For larger parts, clustering of points to find the nearest point cloud were used to identify the part.  
Once the part's point cloud position is attained and normals are approximated, a rough path for the robot to follow is extracted from the camera's inherent rasterisation, shown in Figure 3. Though the RGB/D camera is composed of two cameras, their co-planarity allows the field to view to be merged and simplified to a single camera's. Excess information within the point cloud has then been utilised as environmental information to detect collisions.  
An additional method implemented for guided scanning enables the operator to select a bounding polygon on the colour image, specifying a scanning region. A path is planned on the points within the polygon on the colour and depth maps.
# Configuration of Non-Pumping Reactive Wells Considering Minimum Well Spacing
## 3 Results and Discussion
### Prediction of the Contaminant Removal Using Multiple Transect NPRW  
#### 3.3.1 Performance Curve of Multiple Transect NPRW  
Contaminant removal by NPRWs with multi-transects is affected by the design factors for the unit segment, such as d\({}_{1}\), d\({}_{2}\), \(\alpha\), d\({}_{m}\), and S\({}_{i}\), as well as N. Here, d\({}_{2}\) and \(\alpha\) are dependent on the site conditions, such as hydraulic conductivity, and can vary depending on the uncertainties due to the heterogeneity of the geologic media. Considering the dependency between d\({}_{m}\) and S\({}_{i}\), the configuration factors that determine the removal rate can be deduced by d\({}_{1}\), S\({}_{i}\), and N. Then, the performance of NPRW can be calculated using the relationship of removal efficiency with well diameter (d\({}_{1}\)), width of unit segment (S\({}_{i}\)), and number of wells (N). Here, we assumed that the contaminated groundwater with constant concentration, C\({}_{0}\), flowed with a constant flux q in laminar flow. Then, the mass input of contaminant into unit segment for the unit depth, F\({}_{in}\), was as follows:  
\[\text{F}_{in}=\text{S}_{i}\text{-C}_{0}\text{-q} \tag{9}\]  
The reactive material in the well was assumed to remove the contaminant completely and instantaneously. Then, the mass flux removed was:  
\[\text{F}_{in}=\text{N}\text{-d}_{2}\text{-C}_{0}\text{-q} \tag{10}\]  
Then, the ratio of contaminant removal (R\({}_{e}\)) for a unit segment could be expressed as  
\[\text{R}_{e}=\frac{\text{N}\text{-d}_{2}\text{-C}_{0}\text{-q}}{\text{S}_{i} \text{-C}_{0}\text{-q}}=\frac{\text{N}\text{-d}_{2}}{\text{S}_{i}} \tag{11}\]  
By Equation (5),  
\[\text{R}_{e}=\frac{\text{N}\text{-}\alpha}{\text{S}_{i}\text{/d}_{1}} \tag{12}\]  
By setting the width of the unit segment S\({}_{i}\) equal to the minimum well spacing, d\({}_{m}\), R\({}_{e}\) could be expected as follows:  
\[\text{R}_{e}=\frac{\text{N}\text{-}\alpha}{\text{d}_{m}\text{/d}_{1}} \tag{13}\]  
This equation explains the removal efficiency relationship as a function of field constraint (d\({}_{m}\)), geological condition (\(\alpha\)), and well configuration (d\({}_{1}\) and N). Therefore, this equation can be used to construct the performance curve of multiple transect NPRWs.  
Figure 5: Removal efficiency (R\({}_{e}\)) and number of wells in unit segment (N) of NPRWs: (**a**) single transect, (**b**) double transect, (**c**) conventional triple transect, and (**d**) triple transect with sawtooth array of reactive wells.
# Application-Specific Integrated Circuit of an Inter-IC Sound Digital Filter for Audio Systems
## 2 Materials and Methods
### Digital Filter Architecture
#### 2.2.2 Deserializer (M2)  
The deserializer module converts the input serial data into a 16-bit word. Figure 2 shows the block diagram consisting of a shift register with 16 D-type flip-flops.
# Crossover Rate Sorting in Adaptive Differential Evolution
###### Abstract  
Differential evolution (DE) is a popular and efficient heuristic numerical optimization algorithm that has found many applications in various fields. One of the main disadvantages of DE is its sensitivity to parameter values. In this study, we investigate the effect of the previously proposed crossover rate sorting mechanism on modern versions of DE. The sorting of the crossover rates, generated by a parameter adaptation mechanism prior to applying them in the crossover operation, enables the algorithm to make smaller changes to better individuals, and larger changes to worse ones, resulting in better exploration and exploitation. The experiments in this study were performed on several modern algorithms, namely L-SHADE-RSP, NL-SHADE-RSP, NL-SHADE-LBC and L-NTADE and two benchmark suites of test problems, CEC 2017 and CEC 2022. It is shown that crossover rate sorting does not result in significant additional computational efforts, but may improve results in certain scenarios, especially for high-dimensional problems.  
differential evolution; parameter adaptation; crossover +
Footnote †: journal:  
0000-0001-999-22000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000-0  
L-SHADE-RSP [13], NL-SHADE-RSP [14], NL-SHADE-LBC [15] and the recently proposed L-NTADE [16]. Two benchmarks were chosen for testing, namely CEC 2017 [17] and CEC 2022 [18], due to their different number of functions, different set of dimensions and algorithms efficiency comparison methods. The main contributions of this study are the following:  
1. The crossover rate sorting is capable of significantly improving the performance of modern DE algorithms, although there are cases where it decreases the performance;
2. The crossover rate sorting has larger effect on high-dimensional problems, as well as multimodal problems;
3. For relatively simple unimodal problems, sorting the crossover rates significantly increases the convergence speed but at the cost of decreased diversity.
4. The crossover rates saved in the memory cells are much larger if the crossover rate sorting is applied.  
The rest of the paper is organized as follows. Section 2 contains an overview of related work, the third section describes the proposed approach, the fourth section contains the experimental setup and results, then a discussion of the results is provided, and the last section concludes the paper.
# Monitoring the Health and Residence Conditions of Elderly People, Using LoRa and The Things Network
## 3 Research Method  
Portable sensors, such as the accelerometer, small in size, with low energy consumption, and with high precision, have been used in many tests in individuals with diseases that restrict their mobility, allowing to validate, in real time, the occurrence of falls [26]. Several authors have been working with these and other sensors, showing the advantage of using these small devices in tracking and monitoring people. One of the main problems affecting the elderly is the incidence of falls leading to their disability due to fractures. [PERSON] and [PERSON] [27] and [PERSON] et al. [28] showed that the use of accelerometers made it possible to determine the cause of a fall through machine learning algorithms, exploring a hitherto under-worked strand, mainly in the field of adult monitoring. More recently, [PERSON] et al. [29] showed that portable sensors have enormous potential to monitor people's movements and analyze the incidence of falls.  
The current mobile communication devices, which we call smartphones, have several built-in sensors, including an accelerometer, gyroscope, and GPS, to mention a few. However, they are challenging to operate by the elderly population, who are unaccustomed to using this type of technology, sometimes with significant physical limitations, as reported by [PERSON] et al. [30]. Thus, we propose exploring and enhancing the use of these sensors, integrating them into a solution to monitor, in real-time, the movement of older people and  
Figure 1: Example of MQTT connection to TTN using Node-RED (source: author’s elaboration).  
their state of health, within and outside the home, with sensors incorporated in pervasive, non-intrusive devices.  
Some authors have proven that the ESP32 microcontroller associated with the LoRa transceiver is suitable and adds LoRa and LoRaWAN protocol support. It is needed to act as Gateway for The Things Network [31], but also the Dragino Gateway LG01 could be used [32], which is more versatile and has many connection types, such as Wi-Fi, Ethernet, and 3G/4G. Another ESP32 module with LoRa support that includes GPS may be used in outdoor environments [33].  
The low-cost ADXL335 accelerometer sensor [34] is ESP32 compatible and can be included for fall detection and systems driven by anomalous motion detection.  
The body temperature [35], body humidity [36], and pulsation [37] sensors can be interconnected with the ESP32 microcontroller to control vital signs.
## 5 Conclusions  
The main findings of the present study are as follows:  
1. Two novel coordination polymers containing the alkaline-earth metal ions Ca\({}^{2+}\) and Sr\({}^{2+}\) and the anti-osteoporotic drug RIS were synthesized and structurally characterized.
2. Ca-RIS is a 1D coordination polymer, whereas Sr-RIS is a 2D layered coordination polymer.
3. Both Ca- and Sr-RIS were utilized as controlled release systems (excipient-containing tablets) of the active drug RIS in conditions that mimic the human stomach (pH = 1.2). Because RIS is a sparingly-soluble drug in acidic solutions, it was necessary to use drug solubilizers. The ones tested were the PAMAM-2 amine-terminated cationic dendrimer and the LPEI cationic polymer. Both effected smooth RIS release and prevented its precipitation.
4. The drug release profiles Ca- and Sr-RIS (in the presence of LPEI) were compared to that of \"free\" RIS (absence of metals), and it was found that they release RIS 4-5 times faster than the \"free\" RIS system.
5. This behavior was rationalized based on the structural idiosyncrasies of each system. The overall drug release profile for each system was the result of several structural factors, such as presence or absence of \(\pi\)-\(\pi\) interactions between the RIS pyridyl rings, H-bonding interactions and strength of the metal-O(phosphonate) bonds.  
Based on these results, the utility of cationic solubility enhancers is highlighted, which could find application in other poorly-soluble, anionically charged drugs. Finally, the role of the metal cation in such coordination polymers apparently influences both the initial drug release rates and the final plateau release value. Hence, with proper selection of the metal ion, these features can be controlled.  
Further studies are underway in our laboratory that include different types of drug hosts (e.g., hydrogels [31,32]) and the mapping of several other anti-osteoporotic drugs with a variety of structural features.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app112311383/sl](https://www.mdpi.com/article/10.3390/app112311383/sl)]([https://www.mdpi.com/article/10.3390/app112311383/sl](https://www.mdpi.com/article/10.3390/app112311383/sl)), cif files for Ca-RIS and Sr-RIS compounds.  
Conceptualization, K.D.D.; methodology, M.V., P.T. and C.K.; investigation, M.V., P.T. and C.K.; data curation, M.V., P.T. and C.K.; writing--original draft preparation, M.V.; writing--review and editing, K.D.D., M.V., C.K., P.T. and D.C.-L.; supervision, K.D.D., project administration, K.D.D., funding acquisition, M.V. and K.D.D. All authors have read and agreed to the published version of the manuscript.  
The research work at UoC was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number: 258) to [PERSON] acknowledges funding by project no. PGC2018-102047-B-I00 (MCIU/AEI/FEDER, UE).
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
###### Abstract  
Formation damage remains a key challenge in oil and gas exploration and development, requiring effective prediction and diagnostic technologies to mitigate its impact. Despite decades of research, current techniques lack the accuracy and practicality demanded by modern oilfield operations and the future of intelligent oil and gas development. This study systematically reviews advancements in formation damage prediction and diagnostics, focusing on wellsite diagnosis, experimental methods, imaging techniques, analytical approaches, numerical modeling, and artificial intelligence applications. The advantages and limitations of these methods are analyzed to provide a comprehensive understanding of their capabilities. The paper emphasizes the need for further research to develop an intelligent expert system that integrates multiple damage factors and accounts for spatial-temporal evolution, paving the way for improved future hydrocarbon production and sustainable energy development.  
Keywords:formation damage; formation damage control; artificial intelligence; numerical simulation; mathematical modeling; well testing; core samples +
Footnote †: journal: Applied Sciences
## 6 From Single-Point to Multi-Point Equalization
### Average and Weighted Average Methods  
One of the earliest multi-point approaches was proposed by [PERSON] and [PERSON]. In [77], the authors presented a method for designing an equalization filter for sound reproduction systems by adjusting the filter coefficients so as to minimize the sum of squared errors between the equalized responses at multiple points in the room and a delayed version of the original signal. The paper considers both fixed and adaptive equalizers based on filtered-x algorithms. The approach is effective and has also been applied in many other improved techniques [9; 31; 33; 118]. The main limitation is given by the fact that the implicit averaging in the sum of squared errors cannot exploit the similarities in the room responses, nor can favorite equalization at certain positions. In the context of car audio equalization [118], the technique was improved by considering multi-point equalization with a weighted average of the errors. The solution provided improvements in the response at the selected location, _\"without significant degradations at other points\"_[118].
# Controller-Pilot Data Link Communication Security
## 5 Potential Countermeasures
### Protected Aircraft Communications Addressing and Reporting System (PACARS)  
The commercial company Aeronautical Radio, Incorporated (ARINC) supplies a standard for what they call PACARS [2]. Originally developed for the military to protect their communication, ARINC claims to provide encrypted data link traffic for FANS 1/A. It supports data confidentiality, integrity, and authentication using ECC, which is a step in the correct direction towards securing the system. Currently it only supports FANS 1/A and is widely used [16], which does not necessarily guarantee that its implementation would universally secure the CPDLC system as it might not cover the European ATN.  
PACARS is an application that can be added to the aircraft without hardware modifications and without burdening the system with message delays. Using the ARINC 823-defined compression, both message latency and overall message size are reduced. PACARS has been validated in several avionic manufacturers of data link Communication Management Units (CMUs). PACARS can also be added to other peripherals, such as the flight management systems (FMSs) and electronic flight bags (EFBs).  
PACARS implementation seems to be costly and not well documented. We consider it promising in the view of its applicability to aircraft communications addressing and reporting systems (ACARSs) in general, and to CPDLC in particular, as a part of it. However, it is a closed commercial system, and future studies should aim at developing an open expandable architecture based on open standards.
## 4 Experimental Results and Performance Analysis
### Detailed Performance Measure By Sentimental Classes \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **MCC** & **ROC Area** & **PRC Area** & **Class** \\ \hline
0.875 & 0.354 & 0.720 & 0.875 & 0.790 & 0.536 & 0.760 & 0.694 & **Neutral** \\
0.728 & 0.124 & 0.775 & 0.728 & 0.751 & 0.612 & 0.802 & 0.665 & **Happy** \\
0.203 & 0.002 & 0.875 & 0.203 & 0.329 & 0.406 & 0.600 & 0.230 & **NotRelevant** \\
0.182 & 0.003 & 0.571 & 0.182 & 0.276 & 0.315 & 0.589 & 0.121 & **Angry** \\
0.000 & 0.000 & - & 0.000 & - & - & 0.500 & 0.003 & **AngryDisgust** \\ - & 0.004 & 0.000 & - & - & - & - & **Disgust** \\
0.000 & 0.001 & 0.000 & 0.000 & 0.000 & \(-0.002\) & 0.500 & 0.005 & **HappySurprise** \\
0.000 & 0.003 & 0.000 & 0.000 & 0.000 & \(-0.005\) & 0.499 & 0.010 & **Sad** \\
0.000 & 0.000 & - & 0.000 & - & - & 0.500 & 0.011 & **Surprise** \\
0.000 & 0.002 & 0.000 & 0.000 & 0.000 & \(-0.002\) & 0.499 & 0.003 & **HappySad** \\ - & 0.001 & 0.000 & - & - & - & - & **SadDisgust** \\
0.000 & 0.000 & - & 0.000 & - & - & 0.500 & 0.001 & **SadAngry** \\ - & 0.000 & - & - & - & - & - & **SadAngryDisgust** \\
0.733 & 0.227 & - & 0.733 & - & - & 0.753 & 0.618 & **Weighted Average** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detailed accuracy by class (single feature selection technique). Figure 6: Detailed accuracy by class (single feature selection techniques). Table 5 represents the confusion matrix by class using a single feature selection technique in combination with LL, and its graphical representation is shown in Figure 7.  
This section covers the experiments conducted in this research using tetrad configuration (four feature selection procedures), describing the experimental setup in Table 6 and the findings of the model used in all the tests. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline a & b & c & d & e & f & g & h & i & j & k & l & m & **Classified as** \\ \hline
468 & 59 & 2 & 1 & 0 & 2 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & a = Neutral \\
103 & 283 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & b = Happy \\
45 & 8 & 14 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & c = NotRelevant \\
15 & 3 & 0 & 4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & d = Angry \\
1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & e = AngryDisgust \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & f = Disgust \\
1 & 4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & g = HappySurprise \\
5 & 4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & h = Sad \\
11 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & i = Surprise \\
0 & 2 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & j = HappySad \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & k = SadDisgust \\
1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 = SadAngry \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & m = SadAngryDisgust \\ \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix by class (single feature selection technique). Figure 7: Graphical representation of confusion matrix (single feature selection technique).
# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 3 Experiment
### Performance Metrics  
In this study, several metrics were used to evaluate the performance of the semantic segmentation models:  
* Accuracy--It measures the overall correctness of the segmentation by calculating the ratio of correctly predicted pixels to the total number of pixels. It is computed as \[\text{Accuracy}=\frac{\text{True positives}+\text{True Negatives}}{\text{Total Pixels}}\]
* Precision--It quantifies the model's ability to correctly identify positive predictions among all predicted positives. It is computed as  
Figure 8: DeeplabV3 block diagram with backbone encoder–decoder CNN architecture.  
\[\text{Precision}=\frac{\text{True positive}}{\text{True Positive}+\text{False Positive}}\]
* Recall (Sensitivity)--It measures the model's ability to detect all relevant instances of a class in the image. It is computed as \[\text{Recall}=\frac{\text{True positive}}{\text{True Positive}+\text{False Negative}}\]
* F1 Score--It provides a balanced measure of precision and recall. It is calculated as the harmonic means of precision and recall: \[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\]
* Jaccard Index (IoU)--Also referred to as intersection over union (IoU), it measures the overlap between the predicted and ground truth regions: \[\text{IoU}=\frac{\text{Intersection of Predicted and Ground Truth Regions}}{\text{Union of Predicted and Ground Truth Regions}}\]
* Mean IoU (mIoU)--It calculates the average IoU across all classes and provides an overall measure of segmentation accuracy across different classes.  
These metrics were computed using the trained segmentation models to evaluate their performance on the test dataset. Additionally, class weighting based on Dice scores was applied during training to mitigate the impact of class imbalance.