esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 3 The Proposed Methods
### An Evaluation of the Results of Image Defogging
#### 3.4.2 Objective Evaluation  
Objective evaluation is a quantitative analysis methodology based on specific parameters and metrics, categorized into reference-based and non-reference-based approaches. It evaluates images by analyzing pixel values, structural information, and statistical features to provide empirical data support. Reference-based evaluation requires fog-free images of the same scene as a benchmark, typically utilizing synthetically generated image pairs for assessment. Non-reference evaluation does not require the original clear image; it directly analyzes a single dehazed image. Considering the difficulty of acquiring clear images in real foggy conditions, we adopt the visibility assessment method based on perceptual edges proposed by [PERSON] et al. as an objective criterion [37] for algorithm performance comparison. This method evaluates image visibility through three specific metrics.  
(1) Ratio of visible edges (_e_):  
The visibility of edges in the dehazed image is quantified by the visible edge ratio (_e_), where a higher value of \(e\) indicates a greater number of detectable edges and more detailed information in the dehazed image. Its mathematical expression is as follows:  
\[e=\frac{n_{r}-n_{0}}{n_{0}} \tag{32}\]  
where \(n_{0}\) denotes the number of visible edges in the original foggy image, and \(n_{r}\) denotes the number of visible edges after image defogging.  
(2) Ratio of average gradient (_f_):The mean visibility of the dehazed image is quantified by the average gradient ratio (\(\overline{\tau}\)), where a higher value of \(\overline{\tau}\) indicates a clearer dehazed image. Its mathematical expression is as follows:  
\[\overline{r}=\frac{\overline{g}_{r}}{g_{0}} \tag{33}\]  
where \(\overline{g}_{r}\) denotes the mean gradient of the dehazed image, and \(g_{0}\) denotes the mean gradient of the original foggy image.  
(3) Saturation pixel ratio (\(\sigma\)):  
The number of completely black and completely white pixels in the dehazed image is quantified using the saturation pixel percentage (\(\sigma\)). A lower value of \(\sigma\) indicates higher image contrast and a more effective defogging process, expressed by the following formula:  
\[\sigma=\frac{n_{s}}{\text{dim}_{r}-\text{dim}_{c}} \tag{34}\]  
where \(n_{s}\) denotes the number of completely black or white pixels in the dehazed image, \(\text{dim}_{r}\) denotes the number of rows in the image, and \(\text{dim}_{c}\) denotes the number of columns in the image.
## 2 Results and Discussion
### Perceptual Interactions
#### 2.2.2 Interactions among MeSA and Floral Compounds  
In order to obtain a better understanding of the interactions of MeSA on the floral compounds of tea infusion, the effect of MeSA on each floral compound was explained by comparing the measured threshold with the theoretical value calculated by the [PERSON] additive model; the fitted sigmoid curve was used to evaluate the aroma interaction during binary mixing [22; 26; 27]. The binary mixture was prepared according to the average concentration ratio of MeSA and floral compounds actually detected in the tea infusion.  
Among the 10 groups of binary mixtures tested in this study, five groups showed masking effects, four groups showed synergistic effects, and one group showed additive effects. Among them, the actual threshold was greater than the theoretical threshold obtained by [PERSON]'s additive model: for masking effect, including MeSA/linalool (R = 5.8587), MeSA/phenylacetaldehyde (R = 3.7119), MeSA/geraniol (R = 5.4815), MeSA/phenethyl alcohol (R = 2.7309), MeSA/\(\beta\)-ionone (R = 2.6626); the ratio of the actual threshold to the theoretical threshold was between 0.5 and 1 for addition effect, MeSA/cis-jasmone (R = 0.8140); the ratio of the actual threshold to the theoretical threshold was less than 0.5 for synergistic effect, MeSA/nerol (R = 0.4369), MeSA/benzyl alcohol (R = 0.4360), MeSA/\(\alpha\)-terpineol (R = 0.4736), MeSA/\(\beta\)-ocimene (R = 0.4953). Part of the results are shown in Figure 2.  
Although most of the previous studies have been based on the same or different chemical structures, the chemical complexity of compounds would affect the perception of interaction [28; 29]. Experimental results proved that although the chemical categories of the mixture may be the same, the interaction effects were not exactly identical.
## 4 Experiment and Result
### Multi-GNSS Static PPP Solution
Meanwhile, taking positioning based on GPS/BDS/GLONASS as an example, it can be seen from the Table 2 that the horizontal positioning accuracy of the proposed PPP algorithm for the smartphone has reached the level of single frequency PPP using a geodetic receiver, while the vertical one is relatively lower. It should be further noted that it takes less time for PPP positioning using the Huawei Mate30 to meet different accuracy levels of 0.5, 0.2, and 0. 1m,compared with single frequency PPP positioning using the geodetic receiver. In addition, for the PPP positioning using the Huawei Mate30, 329, 3874, and 4833 epochs are the cost, whereas for the single frequency PPP positioning using the geodetic receiver, 1426, 4031, and 5730 epochs are the cost. However, it can be seen from Table 2 that there is still a large gap in terms of the precision of multi-GNSS dual frequency PPP between the Huawei Mate30 and geodetic receiver. Figure 6: Comparison of the static PPP errors with different combinations of GPS, GPS/BDS, and GPS/BDS/GLONASS observed by the Huawei Mate30 in three directions. (**a**) GPS; (**b**) three-dimensional deviation points of GPS. (**c**) GPS/BDS; (**d**) three-dimensional deviation points of GPS/BDS. (**e**) GPS/BDS/GLONASS; (**f**) three-dimensional deviation points of GPS/BDS/GLONASS. The E and N denote east and north direction. The EK denotes the conventional PPP method with an elevation-dependent stochastic model and Kalman filter estimation model, and the CR denotes the proposed PPP method with a \(C\!N_{0}\)-dependent stochastic model and robust Kalman filter estimation model.
# Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution
## 2 Methodology and Principle
### LSM in Imaging Domain
#### 2.2.2 Global Space-Varying Deconvolution  
Equation (8) shows that the point spread function is exactly the image of the scattered wave radiated from the scattering point, which has a limited distribution in the space. Thus, the image-domain LSM can be based on the framework of the region division strategy, where the global point spread function can be divided into \(n\) blocks and the real impact of each point spread function is constrained within local neighborhoods \(s_{n}\) to the scattering point \(x_{i}\), \(i=1,\ldots,s_{n}\),  
\[K(x_{n},x_{i},\omega)=L(x_{n},\omega;x_{s},x_{r})^{T}[L(x_{i},\omega;x_{s},x_{r} )\delta(x-x_{i})]. \tag{14}\]  
Furthermore, within the neighborhood, the media velocity and illumination conditions are slowly varied, thus the PSFs for each position in \(s_{n}\) can be assumed to be the same, and the reflectivity model for the neighborhood can be expressed as  
\[\mathbf{m}_{n}=\sum_{i\in s_{n}}\mathbf{I}_{i}\otimes\mathbf{K}_{i}=\mathbf{I}_{n}\otimes\mathbf{K }_{i}. \tag{15}\]  
Equation (15) means the regional reflectivity model for the neighborhood \(s_{n}\) can be obtained by performing local deconvolution of the regional RTM image and the point spread function. The entire model can be obtained by stacking all the regional parts, and the global space-varying PSF can be computed using only one-time demigration and migration.
## 4 Numerical Examples  
As numerical examples, three problems were solved, and all solutions were compared with the finite element method (FEM) results. For all problems, the Harmony memory considering rate (HMCR) was taken as 0.5, while PAR is equal to 0.1. These parameters are the best cases for the examples in the study for sensitive final solutions and fast convergence. The HMCR value as 0.5 leads to usage of global and local optimization phases with equal probability, and the PAR value as 0.1 is a suitable value for convergence around existing solutions.
## 4 Signal Preprocessing
### Interference Filtering
#### 4.3.2 MTI Algorithm  
In this step, the MTI algorithm is applied to exclude static clutter points. This process necessitates the use of range and velocity information because it filters out static targets from the FoV and removes points corresponding to static targets (i.e., points that appeared in the previous frame). To remove these points, the static targets are mapped onto a vertical line that corresponds to a velocity of 0 m/s, and the Doppler channels associated with negligible velocities are removed from range-Doppler localization.  
By adjusting the CFAR threshold, most non-target dynamic interference can also be removed. However, the dynamic interference may still be difficult to remove. This is because some distractors are moving at high or low speeds, whereas others are moving at speeds close to drones, such as when humans are walking. A threshold that is too small results in too much dynamic interference, whereas a threshold that is too large results in part of the drone and non-drone targets not being detected.
# Design of Low-Power ECG Sampling and Compression Circuit
## 3 Proposed Scheme
### Sampling and Compression Circuit  
The sampling and compression circuit is realized based on the proposed ADC and CS module, as shown in Figure 6. The CS module comprises a matrix generator, compression and storage, data transmission, and control. The matrix generator has two sets of linear feedback shift registers and \(M\) XOR gates, and the \(M\times N\) measurement matrix is generated according to the CF. The compression and storage module completes the selective accumulation calculation of the high part of the ECG signal based on the measurement matrix and stores the data in pulsed latches after each compression round. The data transmission module converts the compressed parallel data into serial data and transmits them based on the SPI protocol. The control module provides the clock, reset, and enables signals to other modules.  
Figure 5: The storage units: (**a**) master–slave register; (**b**) pulsed latch.  
Figure 6: The structure of the sampling and compression circuit.
# Pothole Classification Model Using Edge Detection in Road Image
## 1 Introduction
The proposed method digitizes the image in the form of binary in pixels. After that, all other objects except the pothole are detected using the object detection algorithm, and the detected objects are removed and filled with 255 to process as a background. Then, the feature is extracted by detecting the edge of the pothole. Lastly, it is a method to detect, classify, and predict potholes based on YOLO. Performance evaluation is conducted in three ways. The first evaluates the distortion rate and restoration rate of image data using MSE, PSNR, and SSIM. The second conducts the accuracy evaluation of the model validity and pothole classification result. Third, we compare the performance of classification accuracy with the existing model. The composition of this paper is as follows: Section 2 describes trends in pothole prediction technology, image object detection and segmentation algorithms. Section 3 describes the pothole classification model using edge detection in road image. Section 4 describes the performance evaluation of the proposed method. Finally, conclusions are given in Section 5.
# Microwave NDT of Smart Composite Structures with Embedded Antennas
## 4 Conclusions  
In this work, the utility of microwave NDT for the structural health monitoring of antenna-embedded composite structures, or smart structures, has been shown. A low-frequency planar resonator probe was employed to generate high-resolution images of the structure under test. The typical advantages of the resonator probe, including high sensitivity to current distributions and low frequency of operation, were deemed favorable for the non-destructive evaluation of antennas embedded in composites. This fact was verified from simulation study and through experimental trials wherein a C-band patch antenna fabricated on a honeycomb substrate sandwiched between a GFRP sheet (top) and a metallic mesh (bottom) was considered. The antenna was constructed from copper foil and, subsequently, from a highly conductive copper mesh. The antenna was intentionally damaged by introducing narrow slits in the patch and a cut on the transformer line, creating two scenarios representing a healthy and a damaged antenna. The aforementioned defects were successfully detected by the planar resonator when imaged. The defects were depicted clearly, with high contrast and distinction from the background. Furthermore, to highlight the effectiveness of the resonator probe, the damaged mesh antenna was imaged using a standard K-band rectangular aperture probe. Not only were the resonator probe images qualitatively better when contrasted to the rectangular aperture probe images, but they also had a relatively very high SNR (>8 times). While the efficacy of the resonator probe was elucidated herein, the significance of the NDT of such composite smart structures was  
\begin{table}
\begin{tabular}{c c} \hline
**Probe Type** & **SNR Value** \\ \hline UHF Probe & 541.17 \\ \hline K-band rectangular aperture probe & 64.52 \\ \hline \end{tabular}
\end{table}
Table 2: The SNR values of damaged patch antenna (mesh) images.  
the primary focus of this work. More importantly, the potential of microwave NDT as an inspection tool for the evaluation of CLAS was demonstrated herein.  
Conceptualization, M.A.A.-K. and W.J.C.; methodology, M.A.A.-K.; software, O.S.H.; validation, M.S.u.R., O.S.H. and A.A.M.; formal analysis, M.S.u.R. and O.S.H.; investigation, M.S.u.R. and O.S.H.; resources, M.A.A.-K.; writing--original draft preparation, M.S.u.R., O.S.H. and A.A.M.; writing--review and editing, M.A.A.-K. and W.J.C.; supervision, M.A.A.-K., W.J.C.; project administration, M.A.A.-K.; funding acquisition, M.A.A.-K. All authors have read and agreed to the published version of the manuscript.  
This paper is based upon work supported by Khalifa University of Science and Technology, Abu Dhabi, UAE, under award No. CIRA-2020-037.  
Not applicable.  
Data available on request due to restrictions.  
The authors declare no conflict of interest.
# Attack Classification Schema for Smart City WSNs
## 4 Attack Classification
### Contingency Plans
#### Medium-term actions:  
**Transmission medium compromised**  
* Data from the compromised service and the nearby services excluded from learning, statistical and analysis processes
**Gateway compromised**
* Data from the compromised service and other services in the compromised gateway excluded from learning, statistical and analysis processes
**Provider's security compromised**
* Data from the services of the compromised provider excluded from learning, statistical and analysis processes  
Putting the measures listed above in place avoids the situations where other services compromise new data and where compromised data are used in an urban operation. For instance, in the short term, we propose establishing exclusion areas when attacks compromise the transmission medium. In this way, mobile devices using WSN technology avoid entering areas where their transmissions can be in danger. In the medium term, we propose that data from compromised devices should be excluded from business analytical tasks to avoid drawing the wrong conclusions. For example, the municipality could decide to expand a parking facility, basing the analysis on compromised WSN data from a parking lot, indicating overuse.  
Depending on the critical nature of the affected services, it is possible that other contingency strategies are required. Hence, administrators have to analyze the details of each use case to figure out the necessary additional countermeasures.
###### Abstract  
To overcome the limitations of rigid body limit equilibrium methods in earth rock dam slope stability analysis, this study develops a system reliability framework using the finite element strength reduction method (FEM-SRM). An elastoplastic finite element model simulates dam construction and impoundment, identifying potential slip pathways. Each pathway, treated as a parallel system of shear-failed elements, is analyzed via the response surface method to derive explicit limit state functions. Reliability indices are computed using an improved first-order second-moment method, while interdependencies are assessed through stepwise equivalent linearization. System reliability is determined using [PERSON]'s narrow bound method. Applied to a 314 m earth rockfill dam, three critical slip pathways were identified: upstream shallow (reliability index is 6.94), upstream deep (reliability index is 6.87), and downstream deep (reliability index is 7.44), with correlation coefficients between 0.26 and 0.89. The system reliability index (6.81) significantly exceeds the code target (4.2), highlighting the method's ability to integrate material randomness, stress-strain nonlinearity, and multi-slip interactions. This framework provides a robust probabilistic approach for high earth rock dam stability assessment, enhancing engineering safety evaluations.  
Keywords:earth rock dam; slope stability; reliability; finite element method; strength reduction method
## 1 Introduction  
Dairy products are popular due to their unique flavor, aroma and health-promoting properties, which are determined by the physicochemical composition of milk [1]. A glass (250 mL) of cow's milk contains on average 250 mg of phosphorus (P), 380 mg of potassium (K), 300 mg of calcium (Ca), 125 mg of sodium (Na), 32 mg of magnesium (Mg) and 0.90 mg of zinc (Zn) [2,3]. The chemical composition of milk may vary depending on various genetic (cattle breed, genotype), environmental (management system, diet, season) and physiological (cow's age, lactation stage, udder health, yield) factors. The profitability of dairy cattle production is largely dependent on milk performance. Selective breeding for increased productivity has compromised the health status of dairy cattle herds, including reducing fertility and an increasing incidences of metabolic diseases, locomotor disorders and inflammations of the mammary gland and udder tissue (mastitis) [4]. The focus on food safety and hygiene, and the quality of dairy products has contributed to the development of milk payment systems where quantity payment (kg) is combined with quality payment based on the chemical composition, cytological and microbiological quality of milk [5,6]. According to Polish Standard (PN-A-86002),the maximum allowable somatic cell count (SCC) per ml of extra class milk is 400,000 and the total bacterial count cannot exceed 100,000. However, udder inflammations remain the greatest problem in herds of high-yielding dairy cows. Mastitis is the second, after infertility, most economically significant disease worldwide [7], which reduces the yield and quality of milk [8; 9]. The relationships between SCC and major milk components have been extensively researched, whereas few studies have investigated the relationships between SCC and the content of mineral elements in milk. In addition, milk from high-producing Polish Holstein-Friesian (PHF) cows may contain insufficient concentrations of selected macronutrients, in particular Ca and Mg, relative to the relevant standards [10; 11].  
The aim of this study was to determine the effects of the month of lactation and udder health of high-yielding PHF cows on daily milk production and the content of K, Ca, Na, Mg and Zn in milk.
# Head Pose Detection for a Wearable Parrot-Inspired Robot Based on Deep Learning
## 3 Learning Model
### Database Generation  
The process of database generation involves acquisition of images required for the classification and training of deep neural networks. Here, our intention was to classify the images captured by the robot into Left, Right, Straight, Left Intermediate, and Right intermediate classes. Our database consists of Left, Right, Straight, Left Intermediate, and Right intermediate as the five classes. We captured 250 images for each class from five subjects, and they were captured in an indoor setting at our university laboratory with sufficient lighting. It took about 15 min to capture 250 images. We mounted the camera on the parrot robot's eye sockets and captured the images of the head pose while a person was wearing the robot. In addition, we chose four different individuals to wear the parrot robot and collect the images of different head poses. The number of classes is always scalable and not limited to the number five. In this case, we chose five different head poses that were visually distinct. We captured 250 images for each class. For generating diversity in the database, for each class we acquired images from four different individuals. Likewise, we have collected a total of 1000 images (labeled as 250 images from each class). The KiliRo robot uses an Ai-Ball camera for capturing the images of the head poses. Having a resolution of 640 \(\times\) 480 (VGA), focal length of 200 mm, frame rate of 30 fps, and a view angle of 60 degrees, this camera met the requirements for our head pose detection needs. As the Ai-Ball camera is compact (diameter of 30 mm and 35 mm in length) and lightweight (less than 100 g), it was a perfect choice for our robot. This compact design makes it more suitable for wearable robots with limited on-board computational power. Figure 6 illustrates the examples of five head poses used in our study.  
Before generating the image database, all the images are resized to a gray scale rectangular matrix of 60 \(\times\) 60. To use during the training phase, we computed the image mean for the created dataset. To boost the performance of the neural network, the calculated mean image was subtracted from the individual images in the dataset during the training phase figure. The calculation of the mean image ensures our data would have a zero mean. For instance, a training dataset \(X\) with five images can be represented as;  
\[X=\{x_{0};\ x_{1};\ x_{2};\ x_{3};\ x_{4}\} \tag{1}\]  
where \(x_{i}\) (\(i\) can take values 0, 1, 2, 3, 4) represents the individual image in the database.  
Figure 5: System flow diagram of the KiliRo robot.  
From (1), the mean of the data is calculated as:  
\[X_{Mean}=\frac{1}{4}\sum_{i=0}^{4}x_{i} \tag{2}\]