esa-sceva/satcom-chunk-collection huggingface dataset:
# MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12
## 3 Experiment and Analysis
### Experimental Process
#### 3.3.3 Performance Evaluation and Comparative Experiments of AHFIN  
In the YOLOv12 model architecture, the AHFIN feature fusion network is embedded into the Neck part of the network, where features from different scales are weighted and fused. A performance comparison is made among the base YOLOv12 model, the model using the classical FPN feature fusion method, and the model incorporating the AHFIN feature fusion network. The experimental results on the two datasets are shown in Tables 5 and 6, respectively.  
The experimental results for the Kaggle PCB Defects dataset indicate that incorporating the AHFIN module enhanced the model's mean average precision from 85.3% to 89.4%, reflecting a 4.1% increase. Additionally, compared to the YOLOv12 + FPN model, the mAP achieved an improvement of 2.2 percentage points. Moreover, the detection speed also saw a corresponding increase.  
For the Peking University PCB Defects dataset, compared to the base YOLOv12 model, after integrating the AHFIN module, the model's mAP increased from 84.2% to 89.0%, an increase of 4.8 percentage points, and the FPS improved by 1.2 points, showing a notable improvement in both detection accuracy and speed. Compared to the YOLOv12 + FPN model, the mAP also improved by 0.9 percentage points.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **mAP/\%** & **FPS** \\ \hline YOLOv12 & 84.2 & 48.0 \\ YOLOv12 + FPN & 88.1 & 48.4 \\ YOLOv12 + AHFIN & 89.0 & 49.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Impact of AHFIN on performance (Peking University PCB Defect dataset).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **mAP/\%** & **FPS** \\ \hline YOLOv12 & 85.3 & 47.2 \\ YOLOv12 + FPN & 87.2 & 49.1 \\ YOLOv12 + AHFIN & 89.4 & 49.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of AHFIN on performance (Kaggle PCBA Defects dataset).  
Compared to the FPN structure, AHFIN's lightweight Deformable Convolution performs real-time spatial correction on low-resolution features in PCB defect images and then applies adaptive weighted fusion to features from different scales. This efficient and accurate feature space processing method leads to a higher mAP value. In addition, its unique gradient compression module significantly enhances the detection speed. When handling multi-class and multi-size targets in PCB defect detection, it demonstrates superior performance.
# Matheuristics and Column Generation for a Basic Technician Routing Problem
## 5 Dantzig-Wolfe Reformulation and CG Matheuristics
### How to Select Partitions of Sub-Problems?
#### 5.4.2 MILP Formulation with Two Technician by Cluster  
In the case where technicians are grouped by at most two, the clustering optimization can be formulated with an MILP formulation similar to assignment problems. By defining binary variables \(x_{i,i^{\prime}}\in\{0,1\}\) for all \(i<i^{\prime}\)\(x_{i,i^{\prime}}=1\) if and only if technicians \(i,i^{\prime}\) are grouped in the same cluster, the clustering optimization can be formulated as following MILP.  
\[\min_{x_{i,i^{\prime}}} \sum_{i<i^{\prime}}d_{i,i^{\prime}}x_{i,i^{\prime}} \tag{38}\] \[\sum_{i^{\prime}>i}x_{i,i^{\prime}}+\sum_{i^{\prime}<i}x_{i^{ \prime},i}\leqslant 1 \forall i\in\mathcal{I}\] (39) \[\sum_{i\in\mathcal{I}}\left(1-\sum_{i^{\prime}>i}x_{i,i^{\prime}} -\sum_{i^{\prime}<i}x_{i^{\prime},i}\right)\leqslant 1\] (40) \[x_{i,i^{\prime}}\in\{0,1\} \forall i<i^{\prime}\in\mathcal{I} \tag{41}\]  
Constraints (39) enforce that each technician is associated to at most one cluster so that clusters are of size at most two. Constraint (40) enforces that at most one technician is not related to another one. Note that such situation is unavoidable and happens when the number of technician is odd. In the case of even numbers of technician, one can remove constraint (40) and consider an equality in (39). In this case, the problem is very similar with assignment problems, which ensures that the MILP formulation is efficient for large values of \(\mathcal{I}\). Even in the general case, the small values render solving ILP easy. One can also consider an extension with at most three technicians by cluster; the bottleneck is the resolution of Constraint (36) here and not optimal computations of such partitions.  
As previously mentioned, it is useful in Algorithm 6 to have diversified partitions. By solving the previous MILP formulation, it is easy to have one optimal solution, and many solutions with similar costs can be generated using local moves with 2-opt exchanges. Indeed, considering only competences, there exist many symmetries with 2-opt exchanges with technicians having the same skill sets.
# Influence of Stray Current on Fastening System Components in Urban Railway Tracks
## 4 Results
### Visual Inspection and Corrosion Classification on Fastening System Components
#### 4.2.3 Steel Plate  
After the samples were disassembled, a massive deterioration of the steel plate at the edges was also noted. The conditions of the steel plate after the completion of the test are shown in Figure 14. The dimensions of the corroded areas are defined for each steel plate. Corrosion of the steel plate was detected at the edge of the plate for all test samples. The percentage of corroded surfaces was calculated for each test sample (Table 6). Owing to the large dimensions of the plate, the corroded area was very small. Thus, the corrosion was classified as follows:  
- Low corrosion level: from 0.00% to 1.99% of corroded area  
- Medium corrosion level: from 2.00% to 3.99% of corroded area  
- High corrosion level: more than 4.00% of corroded area.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fastering System**} & \multirow{2}{*}{**Clip**} & \multirow{2}{*}{**Measured Height of the Cross Section**} & \multicolumn{2}{c}{**Thinning of the Cross Section**} & \multirow{2}{*}{**Corrosion Level**} \\ \cline{3-4} \cline{6-6}  & & **Cross Section [mm]** & & **mm** & **\%** \\ \hline \multirow{2}{*}{ZG 3/2} & SKL-2 (1) & 13 & 0 & 0 & Low \\ \cline{2-6}  & SKL-2 (2) & 13 & 0 & 0 & Low \\ \hline \multirow{2}{*}{PPE} & SKL-1 (1) & 5.5 & 7.5 & 58\% & High \\ \cline{2-6}  & SKL-1 (2) & 7 & 6 & 46\% & Medium \\ \hline \multirow{2}{*}{DEPP} & SKL-1 (1) & 10 & 3 & 23\% & Low \\ \cline{2-6}  & SKL-1 (2) & 6.5 & 6.5 & 50\% & High \\ \hline \multirow{2}{*}{Zagreb 21-CTT} & SKL-12 (1) & 11 & 2 & 15\% & Low \\ \cline{2-6}  & SKL-12 (2) & 9 & 4 & 31\% & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 5: Corrosion level classification for the clips in all testing samples.  
Figure 13: Condition of clips and steel rib after testing was completed: (**a**) ZG 3/2-clip, (**b**) ZG 3/2-steel rib, (**c**) PPE, (**d**) DEPP, (**e**) Zagreb 21-CTT.
# Evaluation Method for Rock Burst Hazards in Strip Filling of Working Faces in Deep Coal Mines
## 4 Case Investigations
### Impact Hazard Evaluation  
#### 4.2.1 Overall Impact Risk  
According to the previous analysis, the elastic energy index of coal and the stress state of the strip coal pillar are the factors affecting the overall impact risk of the strip filling of the working face. The weight coefficient of the influence of two parameters on the overall burst risk is \(w_{1}=w_{2}=0.5\); the evaluation equation of the overall burst risk of the strip filling of the working face is established as follows:  
\[U_{2}=0.5\varphi_{W_{\rm{ET}}}+0.5\varphi_{L_{2}} \tag{14}\]  
According to the mining technical conditions of the C8301 working face, \(L_{1}=80\) m, \(L_{2}=130\) m, \(L_{\rm{p}}=8\) m, \(l_{\rm{d}}=15\) m, \(l_{\rm{r}}=4\) m, \(\sigma_{\rm{d}}=[\sigma_{\rm{c}}]=11.09\) MPa, \(\lambda=4\), and \(\gamma=25\) kN/m\({}^{3}\). Putting the relevant parameters into Equation (14), the relationship between the elastic energy index and the width of the strip coal pillar on the overall impact risk of the working face is shown in Figure 6.  
Figure 5: Roof and floor strata histogram of the C8301 working face.  
Figure 6: The variation of elastic energy index and strip coal pillar width on the overall impact risk.  
Therefore, under the condition of the symmetrical hanging roof structure, the overall impact risk coefficient of the strip coal pillar gradually decreases with the increase in the width of the strip coal pillar. It shows that the increase in the width of the coal body in the elastic bearing area can not only share more load but also helps to reduce the average stress level of the coal body in the elastic area after stress averaging, which is beneficial to reducing the overall impact hazard level of the strip working face. In addition, with the increase in the elastic energy index, the critical width of the strip coal pillar gradually increases when the overall impact risk occurs. It shows that the increase in elastic energy of the coal seam has a significant effect on the overall impact risk of the strip working face.  
The width of the strip coal pillar in the C8301 working face is 120 m. The original elastic energy index of the coal body is \(W_{\text{ETO}}\) = 3.52, and \(U_{2}\) = 0.9 is calculated. The overall impact risk of the strip coal pillar is strong. It means that the possibility of the overall impact of the strip coal pillar is very high; measures must be taken to weaken the ability of coal to gather elastic energy and reduce the stress concentration degree of the strip coal pillar before mining.
## 2 Results and Discussion
### Analysis of the Volatile Fraction
#### 2.1.2 GC-FID Quantitative Determination of Hemp EO Main Constituents  
The quantitative GC-FID analysis results for the nine EOs, shown in Table 1, highlight the presence of \(\alpha\)-pinene, myrcene, terpinolene, and (\(E\))-caryophyllene as the major terpenes. The highest content of \(\alpha\)-pinene was detected in Venom OG (21.2 g/100 g), while Amnesia Cookies proved to be the richest in myrcene (29.2 g/100 g). The best concentration value of terpinolene was found in Lemon Conti Kush New (30.5 g/100 g), while (\(E\))-caryophyllene reached the greatest amount in Fresh Mountain (19.7 g/100 g). The limonene, \(\alpha\)-humulene, and caryophyllene oxide percentages were lower than those of the above compounds (for a total of 9.2 g/100 g in White Shark, 8.6 g/100 g in Amnesia Cookies and 8.0 g/100 g in 24 K). The other quantified terpenes, namely, 1,8-cineole and (\(E\))-\(\beta\)-ocimene, were detected at a very low level in all the investigated EOs. CBD content ranged from 2.3 g/100 g in Amnesia Cookies to 5.5 g/100 g in White Shark EOs. It is worth noting that in all the analyzed EOs, the THC content was within the limit of 0.2%, which is far below that stated by EU regulation for industrial hemp biomass (Table 1).
## 3 Results  
### Simulated 3D Deformation Results of the Proposed Chamber Using Three Different Materials  
Pressure variation was employed to investigate the associated mechanisms of the SPA performance design parameter during the inflation-deflation cycle. The pressure variation can also be utilized to evaluate the developed prototype model. Figure 6 shows the simulation result of the SPA chamber's detected operating rhythms using the three silicone rubber materials. This result demonstrates the applicability of the developed 3D model in the prediction of the pneumatic pressure and its variation during the inflation  
Figure 5: Experimental setup: (**a**) real-time arrangement and (**b**) schematic diagram for measuring the inflation height.  
deflation cycles of the SPA system for the three different materials. The variation profile was based on the pressure supply at the inner wall of the SPA chamber.  
The SPA was simulated from 0 to 120 mmHg. The deformation result of the SPA system during inflation-holding-deflation was obtained, and the simulation of the three different silicone-based materials was compared. The contour of deformation during inflation at 27 s of the three different materials is shown in Figure 7. Comparatively, the deformation in the y direction was considered. At the initial state, the deformation started at approximately 14 to 16 mm for each material. The deformation of each chamber is represented by the color map. A red indication represents high pressure and blue low pressure. The top part of the chamber revealed significant deformation compared to the bottom part. This finding is due to the different thickness inputs for the upper and lower part. The maximum pressure appears at the center of the chamber compared to the area near the surrounding edge.  
The average deformation varied between 0 to 23 mm: A10 Shore silicone had the highest inflation with a maximum deformation of 25.34 mm, followed by A15 Shore with a maximum deformation at 24.68 mm, and Sylgard 184 with a maximum deformation of 21.38 mm. Figure 8 indicates that A10 Shore silicone could result in higher deformation under the same air pressure as compared to A15 Shore and Sylgard 184. The material with the higher deformation is used to produce the main part of the SPA. [PERSON] et al. [28], in an overview of the recent development of soft actuators, reported that low silicone hardness causes greater deformation at lower pressure values. This will reduce the time required to achieve the shape transition and provide the desired force toward the leg.  
Figure 6: Detected pneumatic pressure during simulated inflation–holding–deflation cycle for three different materials by the SPA system.  
Figure 7: Deformation comparison of three different silicone rubber of the SPA chamber from the FEM simulation models. Slygard 184: (**a**) side view and (**b**) bottom view. Food-grade silicone (A15 Shore): (**c**) side view and (**d**) bottom view. Food-grade silicone (A10 Shore): (**e**) side view and (**f**) bottom view.
## 3 Deep Neural Networks Assisted Nanophotonics Design for Silicon Platform  
Traditionally, the optical response of a given silicon geometry is fulfilled via EM simulations, which are accurate but time-consuming. If more than one device with similar structures and optical responses is needed for a specific scenario like arbitrary power splitter, different mode converter, wavelength filter or vertical grating for different wavelength bands, methods for iterative optimization in Section 2 will need to be performed multiple times for different target responses. It is important to find the relationship between optical response and geometric parameters instead of optimizing one by one. However, this implicit relationship is hard to find. Fortunately, DNNs open up a new way for silicon photonics design. DNNs have gained great enthusiasm from research community for their ability to find the complex and nonlinear relationships between input and output data [82].  
Recently, DNNs have been introduced to accelerate the design process of silicon photonics. As shown in Figure 5, designer abstracts geometric parameters \(\mathbf{x}\) from a certain device structure like empirical structure, QR code-like structure or irregular structure. Optical response is sampled as \(\mathbf{y}\) like EM field distribution or optical spectrum which describes the designer's interested phenomena. Usually several groups of device geometric parameters \([\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x_{n}}]\) are corresponding to a certain optical response \(\mathbf{y}\)[83]. The mapping from device geometric parameters \(\mathbf{x}\) to optical response \(\mathbf{y}\) is called a forward model [84; 85; 86; 87; 88; 89; 90], while the inverse model describes the mapping from optical response to device geometric parameters [91; 92; 93]. Both of above-mentioned mapping types have been widely performed through DNNs.  
Many advanced DNNs have already been considered for the design of metasurface [94; 95; 96; 97; 98; 99; 100; 101; 102; 103] since many similar components with different diffraction angles are needed in this case. Advanced DNN architectures may also enable the design of integrated silicon photonic devices. In this section, we will discuss how DNNs can be trained as forward models and inverse models to assist the design of nanophotonics on silicon platform. For each kind of DNNs, we will demonstrate a few examples as well as give basic mathematical background of some fundamental DNN types.
## 3 Results and Analysis
### Uniaxial Compression Test  
#### 3.2.1 Uniaxial Compressive Strength  
The values of UCS for tonalite specimens which were only subjected to freeze-thaw cycles are presented in Figure 4. The average UCS of tonalite specimens with freezing temperatures of \(-\)30 \({}^{\circ}\)C, \(-\)40 \({}^{\circ}\)C and \(-\)50 \({}^{\circ}\)C are 66.73%, 66.43% and 60.64% of the original value. The UCS of tonalite specimens subjected to the combined effect of chemical corrosion and freeze-thaw cycles is shown in Figure 5. The average UCS of tonalite specimens subjected to 60 days of chemical corrosion (G2) is 76.02% of that for the control group. The average UCS values for tonalite specimens subjected to combined effect (G4) with freezing temperatures of \(-\)30 \({}^{\circ}\)C, \(-\)40 \({}^{\circ}\)C and \(-\)50 \({}^{\circ}\)C are 65.69%, 74.45% and 59.20% of the original value. It can be seen that the UCS of tonalite specimens subjected to combined effect did not decrease distinctly with decreasing freezing temperature. The uniaxial compressive strength should have declined after freeze-thaw weathering. However, it did not decrease after chemical corrosion.
# Improving the Structural Behavior of Tied-Arch Bridges by Doubling the Set of Hangers
## 7 Buckling  
This section studies the effect of the hanger arrangement on the critical buckling load. In addition to the bridges V1, V2, N1, and N2, the bridges FV2 and FN2 have also been studied. The bridges FV2 and FN2 are obtained, respectively, by forking the arches and by splitting the central hollow-box cross-section of the deck of the V2 and N2 bridges into two hollow-box rectangular cross-sections of half the width, each one at an edge of the deck, as shown in Figure 30.  
In this study, the critical out-of-plane buckling load has been obtained, which is the governing buckling mode. The geometric imperfections have been considered according to the Spanish Structural Steel Instruction EAE [25], which coincides with the values defined in Annex D to EC 3-2 [16]. All models have been analyzed with a linear buckling analysis in SAP2000 [18], which considers geometrical nonlinearity and postprocessed in MATLAB [26] under the Q\({}_{\text{b}}\) loads combination:  
\[\text{Q}_{\text{b}}=1.35\cdot(SW+DL)+0,\!6\cdot 1.5\cdot W+\lambda_{u}\cdot(q_{1}+ q_{2}) \tag{3}\]  
where \(\lambda_{u}\) is the load factor that multiplies the live load \(q_{1}+q_{2}\) (i.e., the whole deck loaded) at the moment of buckling (Manterola-Armisen [8]). The values of \(\lambda_{u}\) for all cases are shown in Table 2.  
Figure 28: Montigny-Les-Cormeilles footbridge, France. Photo: Google Maps, Street View.  
Figure 29: (**a**) La Barqueta Bridge, Seville, Spain. Photo: [PERSON], Wikimedia Commons.  
(**b**) Third Millennium Bridge, Saragossa, Spain. Photo: edu1975, Wikimedia Commons.  
As can be seen in Table 2 and Figure 31, the models with a central set of hangers (V1 and N1) present the lowest values of \(\lambda_{u}\), with hardly any difference between them. In the case of the models with two sets of lateral cables (V2 and N2), \(\lambda_{u}\) increases notably, becoming, in the bridges studied, at least twice as much as in the case of V1 and N1. This increment appears because the cables, inclined and attached to a deck with high transversal stiffness, tend to prevent the arch from displacing transversally. The highest values of \(\lambda_{u}\) correspond to the forked arch bridges (FV2 and FN2) because of a double effect: on the one hand, the transversal stiffness of the deck is higher because the cross-section of the deck is split into two separate halves, and, on the other hand, the triangular cells at the ends reduce the buckling length of the arch.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & \(\Lambda_{u}\) \\ \hline V1 & 11.2 \\ N1 & 11.2 \\ V2 & 24.1 \\ N2 & 28.0 \\ FV2 & 37.5 \\ FN2 & 42.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Minimum factors of \(q_{1}+q_{2}\) to achieve out-of-plane buckling.  
Figure 30: Definition of (**a**) FV2 and (**b**) FN2 bridges. (**c**) Cross-section for FV2 and FN2 bridges.
## 2 Methods  
### Proposed Coherent Plane-Wave Compounding  
In CPWC, data are acquired by transmitting multiple tilted plane-waves and compounding tilted receive-signals over the entire imaging region. The process is shown in Figure 1. The x-direction is parallel to the transducer array, and z is the depth direction of the imaging medium.  
We defined \(\alpha_{i}\) as the inclination angles of a set of \(N\) plane waves and each point (\(x\),\(z\)) of every transmitted-angle (\(\alpha_{i}\)) image can be expressed as [5]  
\[X_{c}^{i}(x,z)=\sum_{\mathrm{j=1}}^{M}RF(x_{j},\tau(x,z,x_{j})), \tag{1}\]  
where \(M\) is the element number of the aperture which depends on the \(F\)-number and \(x_{j}\) is element position. \(X_{c}^{i}\) is the ultrasound image of \(\alpha_{i}\) (\(i\) = 1,2,3... \(N\)). The delay \(\tau\) changes with different angles and it can be expressed as:  
\[\tau(x,z,x_{j})=(zcos\alpha_{i}+xsin\alpha_{i})/c+\sqrt{(z^{2}+(x-x_{j})^{2})} /c. \tag{2}\]  
Let \(X_{c}\) be the CPWC image of the point (\(x\),\(z\)). It can be given as [2]:  
\[X_{c}(x,z)=\frac{1}{N}\sum_{\mathrm{i=1}}^{N}X_{c}^{i}(x,z)=\frac{1}{N}\sum_{ \mathrm{i=1}}^{N}\sum_{\mathrm{j=1}}^{M}RF(x_{j},\tau(x,z,x_{j})), \tag{3}\]  
where \(N\) is the number of tilted-angles.  
Figure 1: Schematic diagrams of tilted plane-wave transmit and receive in CPWC. The inclination angle of the plane wave is \(\alpha_{i}\).  
We can replace the signal position index (\(x\),\(z\)) to the discrete-time index k. After distance transform, the RF signals for a specific point in (3) can be shown in a 2D matrix \(X\)(k) [23]  
\[X(\text{k})=\left(\begin{array}{ccc}x_{1,1}(\text{k})&\ldots&x_{1,M}(\text{k })\\ \vdots&\ddots&\vdots\\ x_{N,1}(\text{k})&\cdots&x_{N,M}(\text{k})\end{array}\right) \tag{4}\]  
where \(x_{i,j}(\text{k})\) is the RF signal received by the element \(j\) at the tilted angle \(\alpha_{i}\). In the DAS method, we could obtain the \(X_{c}^{i}\) (1) and \(X_{c}\) (3) in the discrete form:  
\[X_{c}^{i}(\text{k})=\frac{1}{M}{\sum_{j=1}^{M}x_{i,j}(\text{k})}, \tag{5}\]  
\[X_{c}(\text{k})=\frac{1}{N}{\sum_{i=1}^{N}X_{c}^{i}(\text{k})}=\frac{1}{MN}{ \sum_{i=1}^{N}\sum_{j=1}^{M}x_{i,j}(\text{k})}. \tag{6}\]  
More details of CPWC can be found in [2]. [PERSON] et al. applied the DAS to CPWC, and it was accepted as a standard method. The framework is shown in Figure 2a.  
Conventional coherence beamformers processes to the rows or the columns of \(X_{c}\) in Equation (6). The operations in the rows and columns respectively focus on the signals within each plane-wave transmission and superposition signals at different angles. To differentiate the two conventional ways, we name them spatial beamformer and angular beamformer. In Section 2.2, we will describe the proposed spatial and angular SCF beamformer applied to CPWC.  
Figure 2: Framework schematic diagrams of CF-based beamformer frameworks. (**a**) DAS, (**b**) the spatial SCF method, (**c**) the angular SCF method, (**d**) the united SCF method. The symbol \(\sum\) represents the process of summing and averaging data. The symbol \(\times\) represents the process of multiplication.