esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Features of Infrasonic Waves of Sandstone under Shear Load
## 2 Experiment to Acquire Infrasonic Signals during Sandstone Shear Destruction
### Rock Sample Preparation and Experiment Process  
As a kind of sedimentary rock, sandstone plays an important role in many faults or weak layers in geotechnical engineering, especially as the stability of slip and weak zones in many slope projects is closely related to sandstone [24; 25; 26]. Sandstone is widely distributed in the area south of the Yangtze River. When constructing expressways, it appears in large quantities on the slopes in the area south of the Yangtze River. For example, the mileage of the Hunan Hengzao highway [27] through sandstone areas exceeds 100 km, and the maximum slope height is about 70 m. In the entire slope, landslides of different scales have developed, which have seriously affected the safety of the expressway's operation. Therefore, this article chooses sandstone as the experimental object. At present, the monitoring and early warning of sandstone slopes mainly use methods based on displacement, hydrological parameters, and mechanical parameters. These methods have a smaller monitoring range, while infrasound monitoring has a wider range [7]. The authors want to use this research to explore the possibility of infrasound methods for monitoring and early warning of sandstone landslides.
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 5 Conclusions
\begin{tabular}{l l} \(\lambda,A,H\) & Wavelength, amplitude and height (m) \\ \(h,z\) & Sea depth and wave surface elevation (m) \\ \(T_{w},\omega\) & Wave period (s) and wave frequency (rad/s) \\ \(g\) & Acceleration gravity (m/s\({}^{2}\)) \\ \(P_{r},dP\) & Capture chamber pressure and Pressure drop (Pa) \\ \(w_{c},l_{c}\) & Capture chamber inner width and length (m) \\ \(V,Q\) & Capture chamber volume (m\({}^{3}\)) and flow rate (m\({}^{3}\)/s) \\ \(\rho,v_{x}\) & Atmospheric density (kg/m\({}^{3}\)) and airflow speed (m/s) \\ \(l,b,D\) & Blade chord length, blade span and turbine diameter (m) \\ \(n,p,k,K\) & Blade number, pole number, wave number and turbine constant \\ \(T_{e},T_{t}\) & Electromagnetic and turbine torques (N.m) \\ \(J\) & Turbo-generator inertia (kg.m\({}^{2}\)) \\ \(C_{t},C_{a},\phi\) & Torque, power and flow coefficients \\ \(R_{s},R_{r}\) & Stator and rotor resistances (\(\Omega\)) \\ \(L_{s},L_{r}\) & Stator and rotor inductances (H) \\ \(i_{s},i_{r}\) & Stator and rotor currents (A) \\ \(\psi_{s},\psi_{r}\) & Stator and rotor flux (Wb) \\ \(\omega_{s},\omega_{r}\) & Stator and rotor rotational speed (rad/s) \\ \(S_{j},\varphi_{j},b_{j}\) & Sum function, activation function, bias and output of the \(j\)th neuron \\ \(y_{i},y_{j}\) & output of \(i\)th neuron from previous layer and output of the \(j\)th neuron from current layer \\ \(w_{ji}\) & Weight of signal connecting \(i\)th neuron from previous layer to \(j\)th neuron of current layer \\ \end{tabular}
# Research on Automated Fiber Placement Surface Defect Detection Based on Improved YOLOv7
###### Abstract  
Due to the black and glossy appearance of the carbon fiber prepreg bundle surface, the accurate identification of surface defects in automated fiber placement (AFP) presents a high level of difficulty. Currently, the enhanced YOLOv7 algorithm demonstrates certain performance advantages in this detection task, yet issues with missed detections, false alarms, and low confidence levels persist. Therefore, this study proposes an improved YOLOv7 algorithm to further enhance the performance and generalization of surface defect detection in AFP. Firstly, to enhance the model's feature extraction capability, the BiFormer attention mechanism is introduced to make the model pay more attention to small target defects, thereby improving feature discriminability. Next, the AFPN structure is used to replace the PAFPN at the neck layer to strengthen feature fusion, preserve semantic information to a greater extent, and finely integrate multi-scale features. Finally, WIoU is adopted to replace CiU as the bounding box regression loss function, making it more sensitive to small targets, enabling more accurate prediction of object bounding boxes, and enhancing the model's detection accuracy and generalization capability. Through a series of ablation experiments, the improved YOLOv7 shows a 10.5% increase in mAP and a 14 FPS increase in frame rate, with a maximum detection speed of 35 m/min during the AFP process, meeting the requirements of online detection and thus being able to be applied to surface defect detection in AFP operations.  
automated fiber placement; deep learning; defect detection; YOLOv7; attention mechanism +
Footnote †: journal: Applied sciences
# Temperature Condition Analysis for Freezing on Tunnel Lining Back Side According to Tunnel Length
## 2 Background  
### The Process of Freezing inside a Tunnel  
The air temperature inside the tunnel is warmer than the outside in winter due to ground heat. The ground temperature rises as it goes deeper from the surface and maintains a constant temperature at a depth of 5 to 10 m [8; 9; 10]. The tunnels in mountainous areas have a ground depth of 20 m or more, and the air temperature inside the tunnels remains constant due to the surrounding ground depth [4; 11]. The railway tunnels in the mountainous areas in Korea are surrounded by rock with a small permeability coefficient and are designed with a waterproofing membrane to prevent the groundwater from flowing into the tunnel. The hydrogeological conditions can be assumed to be fully saturated and have no head loss conditions. Existing studies have measured the air temperature inside the tunnels, which rises nonlinearly from the entrance to the midpoint along the tunnel length [5; 12]. Therefore, it is reasonable to analyze the tunnel half-length, assuming that the weather conditions are identical at both entrances, as shown in Figure 1a. The air temperature changes inside the tunnel irregularly according to the change in the airflow motion. The inside air temperature changes by the intermittent traffic winds or natural winds blowing into the tunnel are transient [5]. The ventilation systems of railway tunnels are generally not operated unless it is an emergency. The lengths of mountain tunnels in Korea are mostly over 1 km, and the inlet wind speed is generally very low at the tunnel entrance in the winter. The ground temperature flowing inside a tunnel is constant due to geothermal heat, so the air temperature inside the tunnel predominantly influences the outside air temperature and the tunnel length. The inside air temperature variation by the tunnel length is transferred to the back side of the tunnel lining differently, and then freezing and thawing are repeated (Figure 1b). Therefore, the tunnel backside temperature was estimated through thermal analysis, after the daily inside air temperature variation by the tunnel length was analyzed using the actual measurements.  
The minimum temperature that freezes the groundwater on the back side of the tunnel waterproofing membrane was predicted based on Fourier's law for searching for freezing damage inside a tunnel. In the typical design of a drainage tunnel, a waterproofing membrane should be installed between the concrete lining and the shotcrete to prevent groundwater leakage [13]. If the groundwater on the back of the waterproofing membrane becomes frozen in winter, it causes damage due to the volume expansion of the frozen water and the flow obstruction in the drainage system. The minimum temperature that freezes the groundwater behind the waterproofing membrane was predicted based on thermal conduction to estimate the freezing damage inside a tunnel.
## 2 Materials and Methods
### Methods
#### Extraction of Spectral Indices  
Spectral indices are combinations of spectral reflectance from two or more wavelengths, which can enhance the spectral difference between infested and healthy cotton. The biophysical and biochemical crop variables, such as canopy structure, leaf pigment, and canopy water content are changed when cotton is infested with root rot. In this study, 10 spectral indices (Table 2) related to different physiological parameters were selected from the literature on crop diseases and they were further tested for their ability to detect cotton root rot infestation in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spectral Indices** & **Calculation** & **Related to** & **Reference** \\ \hline Normalized Difference Vegetation & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & Vegetation status, & [38] \\ Index (NDVI) & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & canopy structure & [39] \\ Renormalized Difference & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & Canopy structure; & [24] \\ Vegetation Index (GNDVI) & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & \multicolumn{1}{c}{} & \\ Moisture Stressed Index (MSI) & \(R_{1614}/R_{\text{RS}35}\) & Canopy water content & [40] \\ Normalized Difference Water & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & Canopy water content & [41; 42] \\ Index (NDVI1) & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & Canopy water content & [43] \\ Disease Stress Water Index & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & Canopy water content & [43] \\ (DSWI5) & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & Canopy water content & [42; 44] \\ Normalized Burn Ratio (NBR) & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & Pigment content; & [45] \\ Plant Senescence Reflectance & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{R_{\text{RS}}}\) & leaf senescence and ripening & [45] \\ Index (PSRI) & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & Canopy foliage content; & [38] \\ Normalized Difference Vegetation & \(\frac{(R_{\text{RS}}-R_{\text{RS}})}{(R_{\text{RS}}+R_{\text{RS}})}\) & gap fraction; senescence. & [38] \\ Index Red-edge 1 (NDVI1) & \(R_{782}/R_{704}-1\) & Chlorophyll content & [46] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spectral indices used for the identification of cotton root rot in this study.  
Figure 4: Box-scatterplots of reflectance of healthy cotton and root rot-infested cotton.
## 2 LS-SVM Upstream Pressure Model of Control Valve  
### Physical Model of the Upstream Pressure of Control Valve  
The upstream pressure adjusting equation of the control valve can be deduced from the physical properties of the fluid and the restriction characteristics [28], that is:  
\[p_{1}=p_{2}+\frac{q^{2}\rho(1-\beta^{4})}{2\varepsilon^{2}{A_{r}}^{2}E^{2}} \tag{1}\]  
where \(p_{1}\) is the upstream pressure, \(p_{2}\) is the downstream pressure, \(q\) is the flow rate, \(\rho\) is the mean density of fluid, \(\beta\) is ratio of contractive section diameter to the pipe diameter, \(\varepsilon\) is the expansion factor which is influenced by the temperature \(T\), \(A_{r}\) is the area of contractive section which is the function of the opening of the control valve (\(\%\)) and \(E\) is velocity coefficient.  
From Equation (1), the upstream pressure can be written as a function \(f\) of the physical properties of the fluid and control valve in Equation (2):  
\[p_{1}=f(p_{2},q,T,\%) \tag{2}\]  
Now, if the non-linear relation in Equation (2) is known, the upstream pressure of control valve can be obtained by simply measuring the downstream pressure, the flow rate, temperature and the percentage of opening of the control valve. However, the computational calculations of the upstream pressure from Equation (2) may not be practical for the real-time control of the upstream-pressure-control valve due to the strong non-linearity and complexity. To overcome this problem, the LS-SVM is used to capture the relationship between the upstream pressure and the properties of the fluid and the control valve.
## Materials and Methods  
### Material Used and Sample Preparation  
Biomass fuels/residues of comhusk, corncob (_Zea mays_), yam peelings (_Dioscorea spec._), cassava peelings (_Manihot esculenta_) and coconut shell (_Cocos nucifera_) were obtained from farmers in the Ashanti region, Ghana. Upon collection, they were washed in tap water to remove adhered soil particles and dried in an air oven at 105 \({}^{\circ}\)C. The biomass fuels were cut into smaller particles (<1 mm) using the hammer mill (Netzsch-Condux, Hanau, Germany) and then stored in airtight plastic containers until further use.  
Using 3000 mL of tap water and 150 g of the solid residues, a solid-(dry mass) to-liquid ratio of 1:20 (g/mL), the biomass fuels were leached in tap water dissolved in 1 or 5 wt.% citric acid (CA) (Sigma-Aldrich, Steinheim, Germany, purity of >99.99%) at 50 \({}^{\circ}\)C for 2 h. The leaching conditions follow the experimental protocol of [PERSON] et al. [55], and this allowed the comparison of the behaviour of the resulting ashes (biogenic silica powders) regarding purity and textural properties at different citric acid concentrations. The leached samples were rinsed with tap water and dried overnight at 105 \({}^{\circ}\)C.  
For the combustion process, weighed amount of the untreated and acid-leached biomass fuels were combusted at 600 \({}^{\circ}\)C in a lab-scale muffle furnace (Nabether therm 1185H66 EA, Lilienthal, Germany) under air atmosphere for 2 h at a heating rate of 6 \({}^{\circ}\)C/min. The resulting ashes were then cooled to room temperature in the furnace. The combustion temperature was chosen based on literature data [14].  
The biomass fuels were abbreviated as CasP, YamP, CoCH, CorC, and CorH to represent cassava peelings, yam peelings, coconut husks, corncobs, and cornhucks, respectively. Furthermore, the resulting ashes of the biomass fuels were identified as X-U-A-Y, where X is the name of the biomass fuels, U is untreated, A represents the ashes, and Y is the acid concentration used for leaching (1 and 5 \(w/\
u\%\)). Figure 1 illustrates the sample preparation and the extraction process to prepare the silica-rich ashes.  
Figure 1: Extraction of biogenic silica from agricultural biomass residues to produce silica-rich ash.
## 5 Results
### Comparative Study
#### 5.2.3 Experimental Verification on Datasets  
In this section, we conducted comparative experiments using LGAR-Net2 LGAR-Net [23] and GRCNN networks on the Cornell dataset and Jacquard dataset, as depicted in Figure 11. Herein, rectangular boxes are employed to represent the grasping pose of network output.  
As depicted in Figure 11, the baseline network exhibits issues of semantic ambiguity and feature confusion when processing interpretable region features, leading to inadequate attention towards these regions. For instance, during the comparison of the razor (4), interference from the uncatchable area can impact the results. The absence of an attention mechanism in the baseline network restricts proper emphasis on the central position of the cylinder; however, LGAR-Net2 effectively addresses this problem by considering catchable areas on both sides of the razor handle while avoiding uncatchable regions at its top. Furthermore, when there is a color similarity between the cylinder (1) and its background, relying solely on LGAR-Net2 may allocate excessive attention to the background. Nevertheless, LGAR-Net2 not only suppresses background features but also significantly prioritizes catchable areas. In comparison to its counterpart network, LGAR-Net2 with an attention mechanism demonstrates an exceptional level of focus on pixels within catchable areas.  
The findings from Table 1 indicate that the grasping detection algorithm introduced in this paper achieves a better balance between accuracy and speed without relying on depth information, leading to improvements in real-time performance and accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Method** & **Input** & **Accuracy (\%)** & **Times (ms)** \\ \hline [PERSON] [2] & Fast Search & RGB-D & 60.5 & 5000 \\ [PERSON] [3] & SAE, struct. reg. & RGB-D & 73.9 & 1350 \\ [PERSON] [18] & Two-stage & RGB-D & 85.3 & 140 \\ [PERSON] [4] & AlexNet, MultiGrasp & RGB-D & 88.0 & 76 \\ [PERSON] [5] & GG-CNN & D & 73.0 & - \\ [PERSON] [25] & GRPN & RGB & 88.7 & 200 \\ [PERSON] [6] & GRCNN & RGB & 96.6 & 19 \\ \hline Ours & LGAR-Net2 & RGB & _97.7_ & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cornell dataset experimental results.  
Figure 10: Comparison of the checkerboard artifacts: columns 2 and 5 show feature images obtained by ordinary DW convolution, columns 3 and 6 show feature images obtained by BIC block convolution.  
The network proposed in this paper was utilized to conduct experiments on the Cornell dataset and compare with the existing research methods. The results are presented in Table 2, indicating that when using RGB data, the method proposed in this paper achieved a capture detection accuracy of 94.4% on the jacquard dataset.
## 3 Results
### Mendelian Randomization  
In the association between MDD (exposure) and insomnia (outcome), no significant evidence of horizonal pleiotropy was detected based on MR Egger intercept (egger intercept = 0.00, \(p\) = 0.67). Four of the tested methods (weighted median, inverse variance weighted, MR-PRESSO raw and MR-PRESSO outlier corrected) suggested the existence of a causal association between MDD and insomnia (Table 3). Conversely, only a trend was detected based on the simple mode and no significant association based on MR Egger (Table 3).  
In the association between insomnia (exposure) and MDD (outcome), no significant evidence of horizonal pleiotropy was detected based on MR Egger intercept (egger intercept = 0.02, \(p\) = 0.07). All methods except MR Egger suggested the existence of a causal association between MDD and insomnia (Table 3). As regards to other psychiatric traits part of the same cluster, no significant horizontal pleiotropy was detected between insomnia (exposure) and either ADHD (Egger intercept = 0.04, \(p\) = 0.16), ASD (Egger intercept = 0.00, \(p\) = 0.89) or PTSD (Egger intercept = 0.03, \(p\) = 0.07). We observed no significant causal effect of insomnia on ADHD and ASD (Table S3) and very limited evidence for a potential effect of insomnia on PTSD, with only the inverse variance weighted method suggesting a significant association (Table S4). In addition, we observed no evidence of horizontal pleiotropy (Egger intercept = 0.02, \(p\) = 0.41) and no significant causal effect of ADHD on insomnia (Table S3), while the causal effect of PTSD and ASD on insomnia could not be tested due to the limited number of significant and independent SNPs associated with the two psychiatric disorders in the original datasets. The significant bidirectional association observed between MDD and insomnia was further confirmed with additional analyses conducted using the top 10,000 genetic variants of the datasets (including data from 23 andMe participants) or the independent significant loci reported by the MDD (Tables S5-S8) or the insomnia GWAS (Tables S9-S12).  
Figure 3: Cluster plot. Abbreviations: ADHD, attention deficit/hyperactivity disorder; AN, anorexia nervosa; ASD, autism spectrum disorders; BD, bipolar disorder; BMI, body mass index; CAD, coronary artery disease; CRP, C-reactive protein; MDD, major depressive disorder; OCD, obsessive-compulsive disorder; SCZ, schizophrenia; PTSD, post-traumatic stress disorder; T2D, type 2 diabetes; TS, Tourette syndrome.  
Since the most convincing results were obtained in the MR analysis investigating the association between MDD and insomnia, we chose these traits for further analyses. The forest plots in which we compared the MR estimates using different MR methods against single SNP tests are shown in Figures 4 and 5. Ten and eight SNPs were significant using MR single-SNP tests when evaluating the effect of MDD on insomnia (Figure 4) or the effect of insomnia on MDD (Figure 5), respectively. These SNPs were further investigated to obtain information on their potential functional effect.
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 3 Neural Network (NN) Based Elasticity
### Representative Numerical Examples
#### 3.2.3 Discussion  
From the above-detailed studies, it can be concluded that NN-formulation works well for predicting linear elastic material behavior under different loading conditions and geometries. For a better understanding of the computational efficiency of the neural network model incorporated inside the finite element formulations, a comparison is made with that of finite element analysis based on AceGen generated c code; see Table 2.  
The evaluation time for the NN embedded model is longer due to the size of the AceGen file (which includes the neural network formulation) and the functions necessary for the computation and normalization of the data required by the NN-model. The positive aspect is that this has to be done once, and after a successful execution, the generated file can be used for finite element simulations. Similarly, Table 3 provides the simulation report obtained using AceFEM for the 2D plate (Section 3.2.1) and A-notched bar (Section 3.2.2), respectively. Note that the computational effort heavily depends on the machine on which the simulations are running. Therefore, here, only the computation time is compared between the FEM and NN simulations. It can be concluded from the representative examples that although NN accurately predicts the stress-strain relationship, it has no superiority when it comes to the computational effort for the problems in elasticity.
# Combined Use of 3D and HSI for the Classification of Printed Circuit Board Components
## 2 Materials and Methods
### Combining Spectral and Spatial Data Sets  
The first step of the proposed framework is the fusion of the datasets (shown in Figure 3) consisting of HSI and 3D point cloud. This is achieved by using tie points. Due to the technical setup of the sensors (fixed sensors looking vertically downwards), the objects are successively captured during one measuring process in both hyperspectral as well as three-dimensional. The transformation parameters are estimated by using tie points that are existing in both datasets and allow the fusion in x-y plane. Due to the different resolutions of the datasets, the assignment between 3D point and spectral signature is made pixel by pixel checking the neighborhood. The result is a 3D point cloud presented in Figure 4 with a spectral signature for each individual 3D point. Even though the sensor technology presented in Figure 2 would not be readily applicable in an industrial context due to the 3D sensor technology used, the potential for the overall concept of the measurement system and fusion strategy for use in industrial practice is given.  
Figure 2: Measuring setup consisting of hyperspectral cameras, linear stage, illumination unit and 3D structured light scanner.