esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Middlebury Dataset
Red and green pixels are mismatched pixels in occluded and non-occluded regions, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Initial Disparity**} & \multicolumn{2}{c}{**Final Disparity**} \\ \cline{2-5}  & **O\_noc** & **O\_all** & **O\_noc** & **O\_all** \\ \hline NL [7] & 6.57\% & **17.20**\% & 6.64\% & 12.67\% \\ ST [16] & 6.15\% & 17.34\% & 6.27\% & 12.30\% \\ SGM4 [22] & 5.56\% & 18.34\% & 4.59\% & 11.05\% \\ SGM8 [22] & 5.42\% & 18.17\% & 4.78\% & 11.21\% \\ MGM [30] & 5.24\% & 17.62\% & 4.47\% & 11.21\% \\ Our method & **4.10**\% & 17.43\% & **3.71**\% & **10.57**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with state-of-the-art non-local cost aggregation approaches on Middlebury dataset [9]. O\({}_{-}\)all: percentage of erroneous pixels in all region. O\({}_{-}\)noc: percentage of erroneous pixels in non-occluded region. obvious on stereo pairs with large weakly textured area, such as the second and the ninth testing images. The accuracy gains come from our omni-directional SGM tries to aggregate information from all pixels along all directions and the successive cost volume update scheme. GF incorporates costs in a local window to suppress noise. However, most stereo pairs in this dataset contain large low textural area, since merely taking pixels in a local window into account cannot solve the ambiguity in these areas, resulting in the worst performance among these methods. MGM produces poor results for testing images with large weakly textured regions as it enforces strong local smoothness constraint which inhibits informative messages from being propagated in wide range. Figure 7 presents examples of disparity images for GF, SGM, MGM and our method. It can be seen that our method generates high quality disparity images on these challenging stereo pairs. Our method successfully recovers the disparities of pixels not only in major structures and fine-scale details, but also in large homogeneous area, such as ground.
# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 2 Materials and Methods
### Materials and Samples
#### 2.3.2 Etched Surfaces  
A Ti-6 Al-2 Sn-4 Zr-2 Mo-Si specimen (82 \(\times\) 32 \(\times\) 2 mm) was prepared by standard metallographic preparation for titanium and etched with Kroll's reagent to reveal the grain structure through optical macrography. The Quartet detector was used to scan the sample, with 16 averages per point and a pixel size of 100 \(\times\) 200 \(\upmu\)m.
# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 3 Results
### Object Detection Improvement  
In rescue scenarios, accurately locating and identifying individuals is critical. However, when a scene is not properly exposed, certain features of its objects can be obscured or washed out, making it difficult for the detector to identify them. To test and evaluate the pipeline's effectiveness in identifying people, we used the YOLOv7 [39] object detector, which is a state-of-the-art deep learning model for object detection. Specifically, we used the pre-trained weights of YOLOv7 on the Microsoft COCO: Common Objects in Context data set [40], which contains a large number of annotated images of objects belonging to 80 different categories, including people. We applied the detector to both the original and corrected images, to assess its performance in identifying people under different lighting conditions.  
The detector performances were evaluated using: Precision (P), Recall (R), F1-score (F1), and Average Precision (AP). Precision computes the proportion of true positives (correctly detected people) among the total number of people detected. Recall is a measure of the proportion of true positives among the total number of actual people. F1-score is the harmonic mean of precision and recall. Average Precision (AP) evaluates the accuracy of the detector based on its precision and recall and it is calculated as the area under the Precision-Recall (PR) curve, which plots the precision values against the corresponding recall values at different detection thresholds.  
The performances are summarised through the Precision-Recall (PR) and F1-Confidence curves shown in Figure 8(a,b). Additionally, the Average Precision (AP) is reported in Table 3 to provide a comprehensive evaluation of our model's performance. In Figure 10, we also provide a visual comparison of the predicted bounding boxes.
## 6 Mammary Tissue Modeling
### Forward Modeling
Moreover, the thermal properties (blood perfusion and metabolic heat generation rate) of both healthy and cancerous breast tissues were obtained by choosing those that best matched the surface temperature distributions recorded by an IR camera. According to the authors, the results obtained may be applied to cases with similar breast cancer features. Unlike the previous authors, [PERSON] et al. [53] considered a simplified 3D breast model that consists of a two-layer finite plate representing a fat layer and the gland region. Also, a spherical-shaped lesion was placed inside the deeper layer. The bio-heat transfer problem was solved numerically by means of the transmission line matrix technique [81]. Both steady-state and dynamic conditions were simulated by the authors. They reported that steady-state thermal contrast depends both on the depth and size of the lesion. It is also affected by breast density; fatty breasts lead to lower thermal contrasts; on the contrary, dense breasts result in increased thermal contrasts. Finally, in dynamic conditions, they found that after the application of a cold stress, the consequent transient thermal contrast may potentially provide valuable information about the tumor depth when it is analyzed using signal processing techniques. The effect of breast density on the surface temperature distribution was further investigated by [PERSON] et al. [82], who developed four different 3D breast models with different compositions. All the models were based on a three-layer configuration consisting of a muscle layer, a glandular layer, and a subcutaneous fat layer. In particular, the breast geometry was represented by a hemispherical shape with non-concentric internal layers (as in Figure 8b). On the contrary, the tumor was modeled with a perfect spherical shape embedded in the breast model. The authors concluded that breast density may affect the surface temperature distribution and should be considered in breast cancer detection when using infrared thermography. Finally, the effects of tumor depth, tumor size, metabolic heat generation rate, blood perfusion, and thermal conductivity on the breast surface temperature distribution in steady-state conditions were analyzed by [PERSON] et al. [83]. These authors employed a 3D finite element hemispherical breast model consisting of four layers (muscle, gland, fat, and skin). Different breast sizes were also considered in their work. The results obtained showed that larger breasts consistently reduce surface temperature variations, leading to some difficulties in observing significant thermal contrasts between cancerous and healthy breasts. Additionally, tumors smaller than 0.5 cm and located in smaller breasts may be difficult to detect, as they could not yield observable temperature variations. The same also occurs for tumors located deep within the breast layers.
## 2 Methods
### Model Based Methods
#### 2.1.3 SupReME  
The SupReME (SUPer-Resolution for multispectral Multiresolution Estimation) method proposed in [4] relies strongly on the observation model of the imaging process that generates the low-resolution images (blurring and downsampling). It exploits the fact that spectral bands are correlated and can be represented in a lower-dimensional subspace, learned from the input data. The textural information from the high-resolution bands is utilized by encoding the discontinuities of the data and propagating the spatial information to the lower resolution bands.  
The vectorized sharpened image at band \(i\) has a low-rank representation \(\mathbf{x}_{i}=\mathbf{Z}\mathbf{u}_{(i)}\), where \(\mathbf{Z}\) is \(n_{i}\times p\) and \(p=7\). The sharpened image is estimated by minimizing  
\[J(\mathbf{Z})=\sum_{i=1}^{L}\frac{1}{2}\Big{\|}\mathbf{y}_{i}-M_{i}B_{i}\mathbf{Z}\mathbf{u}_{( i)}\Big{\|}^{2}+\lambda\sum_{j=1}^{p}q_{j}\phi_{w}(\mathbf{z}_{j}). \tag{3}\]Here \(y_{i}\) is the vectorized observed image at band \(i\), \(M_{i}\) is a downsampling operator, and \(B_{i}\) is blurring. The regularization term is a weighted roughness penalty given by  
\[\phi_{w}(\mathbf{z}_{j})=\mathbf{z}_{j}^{T}\Big{(}H_{h}^{T}\mathbf{W}\mathbf{H}_{h}+\mathbf{H}_{v}^{T }\mathbf{W}\mathbf{H}_{v}\Big{)}\mathbf{z}_{j}. \tag{4}\]  
\(\mathbf{W}=[w_{ij}]\) is a diagonal spatial weighting matrix and \(\mathbf{H}_{v}\) and \(\mathbf{H}_{h}\) are \(n\times n\) finite difference matrices that approximate vertical and horizontal derivatives of the images in the subspace, respectively [5].  
An important part of the method is the estimation of \(\mathbf{U}=[\mathbf{u}_{(i)}^{T}]\). It is estimated using \(\mathbf{X}\mathbf{Q}\), where \(\mathbf{Q}\in\mathbb{R}^{n\times n}\) is a blurring matrix representing a 2D cyclic convolution. As \(\mathbf{X}\), the \(L\times n\) super-resolved image, is not known, \(\mathbf{X}\mathbf{Q}\) is approximated by upsampling all the bands of \(\mathbf{y}\) to the same high-resolution using bicubic interpolation and blurring each band such that the blur of all the bands is equivalent to the strongest blur of the PSF. Singular value decomposition of this approximation of \(\mathbf{X}\mathbf{Q}\) yields \(\mathbf{U}\).  
SupReME fixes \(\mathbf{q}=[1\;1.5\;4\;8\;15\;15\;20]\) since the first few dimensions are expected to be less affected by noise. Per pixel weights, \(\mathbf{w}\), are for quadratic smoothing as a spatial regulariser. They are calculated from the Prewitt image gradient magnitude [35] and set to a value between 0.5 and 1. The minimization is then solved using the Constrained Split Augmented Lagrangian Shrinkage Algorithm (C-SALSA) [36; 37; 38] and the augmented Lagrangian as detailed in [4].
## 6 Simulation Results
### Voltage Evaluations  
In this subsection, the upper and lower SM voltages and the output voltages due to the three proposed control strategies in phase \"a\" are evaluated. Figure 11 exhibits the upper SM capacitor voltages of phase \"a\" for the three proposed controllers.  
As can be seen from this figure, all the controllers have a good steady-state response with a similar steady error. However, after the load change at t = 0.2 s, the third controller shows a better response with a smaller error, as depicted in Figure 11c.  
This better response occurs due to the consideration of more harmonic components of circulating currents in the structure of the third proposed control technique. The same scenario happens for the lower SM capacitor voltages of phase \"a\" according to Figure 12.  
However, in comparison with the upper SM capacitor voltage in Figure 11a which is achieved by applying the first proposed controller, the lower SM capacitor voltage shown in Figure 12a experiences higher error after the load alteration. To complete this discussion, analysis of both Figures 11 and 12 determined that the minimum and maximum SM voltage errors were 0.25% and 1.25%, respectively. These errors verify the precision of the three proposed control strategies in keeping the SM voltages balanced. Another aim of the proposed control strategies was to reach the desired waveforms for the MMC output voltages.  
Figure 11: The upper submodule (SM) capacitor voltages of phase “a” for the (**a**) first controller, (**b**) second controller, and (**c**) third controller.  
Figure 13 demonstrates the MMC output voltages driven by the three proposed control strategies. As can be seen from Figure 13b,c, the second and third control strategies led to responses with a higher quality for the MMC output voltage when compared with the first controller in Figure 13a. The main reason for this result is the incorporation of the second-order harmonic component of circulating currents in both the second and third proposed control techniques. This means that the second-order harmonic component of the circulating currents can have a large impact on reaching the desired waveforms for MMC output voltages under both steady and dynamic state changes.  
Figure 12: The lower SM capacitor voltages of phase “a” for the (**a**) first controller, (**b**) second controller, and (**c**) third controller.  
Figure 13: _Cont._
## 4 Experiment and Results Analysis
### Database  
The CU-ECG database was constructed by Chosun University and includes a total of 100 subjects (89 male and 11 female) aged between 23 and 34. Each subject was seated comfortably in a chair for a 1-lead ECG, which is the potential difference between the right arm and left arm. The signals were recorded for 10 s, once per subject, and a total of 60 times, obtained consecutively. The sample speed of the acquired signal was 500 kHz. An analog-digital converter Keysight MSO9104 was used for acquiring the ECG data. Here, ATmega8 was the processor, and wet electrodes were attached [33]. The number of data points for each subject is 60. The ECG signals of multiple cycles were divided uniformly with respect to the R-peak point. The number of data points after the division was 16,930. Of these, 80% (13,546 data points) were used as training data whereas 20% (3384 data points) were used as validation data.
# Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography
## 3 Principle of the Proposed Method
### Determine Mini-Crack  
The damage of conveyor belt is divided into mini-crack and crack in this paper. A defect with a width less than 10 mm is defined as a mini-crack, and a defect with a width greater than 10 mm is defined as a crack. This paper mainly focuses on mini-crack detection. Hough line detection method [45] is used to detect mini-crack and crack. The mini-crack detection process of the conveyor belt is shown in Figure 7. With the above image processing, it can be found that the boundary of the wear area in the binarization image is continuous and parallel. Therefore, Hough transform is used to extract parallel lines as the basis for mini-crack interrogation of conveyor belt. The steps of mini-crack interrogation are developed as follows:  
Figure 5: Schematic of weighted template operator for image enhancement.  
Figure 6: The image processing results of threshold segmentation. (**a**) Gray mini-crack feature image; (**b**) Image enhancement; (**c**) Threshold segmentation; (**d**) Morphological denoising.  
Suppose there exists a point (\(x\), \(y\)) on the mini-crack line in Cartesian coordinate system, which can be expressed by polar coordinates as \[r=x\cos\theta+y\sin\theta,\tan\theta=\frac{y}{x}\] (7) where \(r\) is the distance from the origin to the line, and \(\theta\) is the vector angle from the origin to the perpendicular of the line.
2. For each group (\(r\), \(\theta\)) in polar coordinates corresponds to a line that passing through the point (\(x,y\)) in rectangular coordinates. For a given point (\(x,y\)), draw all the lines that passing through the point in polar coordinates, and obtain the sine curve; 3. Repeat the above steps for all points in the original image. Set the threshold and calculate the number of curves corresponding to each intersection point in the image. If the number of curves that intersecting at one point exceeds the threshold, the pair  
Figure 7: Flow chart of mini-crack detection process of the conveyor belt.  
of parameters (\(r\), \(\theta\)) represented by the intersection point is considered a line in the original intersection point.
4. The equation of the line is obtained through Hough line detection, then the distance \(X\) between the two lines is calculated by the linear distance function. According to the camera calibration results, an appropriate threshold \(D\) is set to discriminate mini-crack and crack. When \(X\) = 0, the test result is normal; When \(X\) < \(D\), the detected damage is considered as a mini-crack; When \(X\) > D, the damage is considered as crack.
# Prediction of Deflection of Shear-Critical RC Beams Using Compatibility-Aided Truss Model
## 4 Verification of Proposed Method
### Verification of Proposed Method
This is because shear deflection was not considered in Equation (3). Figure 8 shows the experimental and analytical results for the load-deflection relationships of the six specimens. In the figure, the solid line indicates the experimental result, and the circular symbol indicates the calculated result by the proposed analytical method. As shown in Figure 8, the predicted behavior was similar to the experimental one. Since the proposed shear model could be analyzed after the occurrence of shear cracks, the analytical results, marked below 140 kN in Figure 8, were calculated using only the flexural deflection represented in Equation (3). The analytical results marked at 140 kN or higher is the result calculated by Equation (1). To examine the accuracy of the proposed method for the load-deflection relationship after shear cracking, Table 4 compares the analytical and experimental results at the same load. It is noted that since the deflections at the same load are compared, Table 4 deals with arbitrary loads lower than the experimental peak load of each specimen. As shown in Table 4, when only flexural deflection was considered, the experimental result was greatly underestimated at 0.65, whereas the proposed method considering both flexural and shear deflections predicted the experimental results more reasonably with an average of 1.12 and a COV of 13.2%. It is noted that time-dependent deflection due to creep and shrinkage was not considered in this study. As a result of comparing the experimental results with the analysis results, the proposed method can be used to calculate the deflection of shear-critical RC beams. Figure 7: Loading history of a typical shear-critical RC beam (S1-3.0). Figure 8: Prediction results for load-deflection relationship of shear-critical RC beams: (**a**) S1-3.0; (**b**) S1-3.5; (**c**) S1-4.0; (**d**) S2-2.5; (**e**) S2-3.0; (**f**) S2-3.5.
## 6 Findings
### An Exploration of the 20 Central-Authority Blogs
Deliberation/criticism: To support virtual, small and large-group discussions, to express thoughts or ideas, to comment on or criticize, allowing reflection and consideration of issues. 4. Discourse: To support analysis and representation of discourse. 5. Electioneering: To support politicians, political parties and lobbyists in the context of election campaigns. 6. Information provision: To structure, represent and manage information in participation contexts. 7. Polling: To measure public opinion and sentiment. 8. Concern creation: To make an impression about a fact/set of ideas and cause further uncertainty or suspicion. 9. Media and book reference: References to websites, magazines, newspapers _etc._ as well as books. 10. Environmental issues: References to environmental topics. 11. News. Table 8 presents the percentages of blogs within the 20 central blogs and within the total 127 blogs, which make reference to the eParticipation areas. In general we can observe that in almost all eParticipation areas, the central blogs are more active than the overall Greek political blogosphere. To verify which are the main topics for which central blogs are most active compared to the blogosphere, chi-square tests are used. Statistically significant differences are found for the eParticipation areas to which central blogs also make references with the highest frequencies: information provision, media references, criticism, news, discourse and concern creation. Also there is a difference for environmental issues. These eParticipation areas may be used to describe a general profile of the central blogs. Thus, the central blogs can be characterized as being critical, informative, promoting discourse and concern. They are not only heavily linked but they also are active and very much involved in the political discourse. \begin{table}
\begin{tabular}{|c|c|c|c|} \hline  & **20 Central blogs** & **127 blogs (Total)** & **Chi-square significance** \\ \hline Information Provision & 95 & 54 & * \\ \hline Media references, publications & 85 & 52 & * \\ \hline Deliberation, criticism & 85 & 57 & * \\ \hline News & 80 & 50 & * \\ \hline Discourse & 60 & 32 & * \\ \hline Concern creation & 55 & 33 & * \\ \hline Campaigning & 40 & 32 & \\ \hline Environmental issues, concern & 25 & 10 & * \\ \hline \end{tabular}
\end{table} 
Table 8: Percentages of central blogs, which make references to the eParticipation areas.
# An Adaptive Procedure for the Global Minimization of a Class of Polynomial Functions
## 5 Experimentation
### The Nonnegative Factorization of a 3 rd-Order Tensor
Given two matrices \(A\in\mathbb{R}^{m\times h}\) and \(B\in\mathbb{R}^{n\times h}\), the Khatri-Rao product \(A\odot B\) is the \(m\,n\times h\) matrix  
\[A\odot B=[\mathbf{a}_{1}\otimes\mathbf{b}_{1}\mid\mathbf{a}_{2}\otimes \mathbf{b}_{2}\mid\dots\mid\mathbf{a}_{n}\otimes\mathbf{b}_{h}],\]  
where \(\mathbf{a}_{i}\) and \(\mathbf{b}_{i}\) are the \(i\)th columns of \(A\) and \(B\) respectively, and \(\otimes\) denotes the Kronecker product of two vectors. With this notation (8) becomes  
\[T^{(1)}\approx U(Z\odot V)^{T},\quad T^{(2)}\approx V(Z\odot U)^{T},\quad T^{ (3)}\approx Z(V\odot U)^{T}. \tag{10}\]  
In the experimentation we consider the nonnegatively constrained CP decomposition and minimize (9) by applying ANLS as the local minimizer to the three matrices given in (10). Having fixed nonnegative \(U_{0}\) and \(V_{0}\), the computation proceeds alternating on three combinations as follows  
\[Z_{\
u}=\operatorname*{argmin}_{Z\geq 0}\,f(U_{\
u-1},V_{\
u-1},Z)= \operatorname*{argmin}_{Z\geq 0}\,\|T^{(3)T}-\big{(}V_{\
u-1}\odot U_{\
u-1} \big{)}Z^{T}\|_{F}^{2}\] \[U_{\
u}=\operatorname*{argmin}_{U\geq 0}\,f(U,V_{\
u-1},Z_{\
u})= \operatorname*{argmin}_{U\geq 0}\,\|T^{(1)T}-\big{(}Z_{\
u}\odot V_{\
u-1} \big{)}U^{T}\|_{F}^{2}\] \[V_{\
u}=\operatorname*{argmin}_{V\geq 0}\,f(U_{\
u},V,Z_{\
u})= \operatorname*{argmin}_{V\geq 0}\,\|T^{(2)T}-\big{(}Z_{\
u}\odot U_{\
u} \big{)}V^{T}\|_{F}^{2}\]  
for \(\
u=1,2,\dots\)  
For the experimentation, given the integers \(m\), \(n\), \(\ell\) and \(h\), three matrices \(A\) with columns \(\mathbf{a}_{s}\in\mathbb{R}_{+}^{m}\), \(B\) with columns \(\mathbf{b}_{s}\in\mathbb{R}_{+}^{n}\) and \(C\) with columns \(\mathbf{c}_{s}\in\mathbb{R}_{+}^{\ell}\) are generated for \(s=1,\dots,h\), with random elements uniformly distributed over \([0,1]\). The tensor \(T=\sum_{s=1}^{h}\mathbf{a}_{s}\circ\mathbf{b}_{s}\circ\mathbf{c}_{s}\) is then constructed. Given an integer \(\rho\), the proposed problem requires computing the CP decomposition (8) of \(T\) into the sum of \(\rho\) triads under nonnegative constraints. The problem is dealt with for the two cases \(\rho=h\) and \(\rho<h\). The following cases are considered  
\[ \begin{array}{l}m=10,\;n=5,\;\ell=15,\;h=5,\;\rho=3,\\ m=10,\;n=5,\;\ell=15,\;h=5,\;\rho=5,\\ m=10,\;n=20,\;\ell=30,\;h=8,\;\rho=4,\\ m=10,\;n=20,\;\ell=30,\;h=5,\;\rho=5.\end{array} \]  
The dimension of the solution space is \(N=(m+n+\ell)\;\rho\). In the cases \(\rho=h\) we expect \(f^{*}=0\). The results are shown in Table 3. From the point of view of the computational time, the performances of Class 1 and Class 2 methods agree with those seen in the other tables. From the point of view of the accuracy, Class 2 methods with the stronger request \(\epsilon_{2}\) for the tolerance are competitive with Class 1 methods.