esa-sceva/satcom-chunk-collection huggingface dataset:
# Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm
## 2 Materials and Methods  
Faster-RCNN [12] is the third generation of RCNN series algorithm, which was proposed by [PERSON] in 2016. In this algorithm, a regional proposal network (RPN) is proposed for the first time to replace the selective search used in the previous two versions to obtain proposals. Then proposals fused with the feature map extracted by convolution-rectified linear unit (ReLU)-pooling network are sent into the full connection network for target classification and location. The proposed method raised the target detection efficiency to 17 fps, and the network accuracy obtained from Visual Object Classes 2012  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Defect Type** & **Description** \\ \hline Multi-tin & The solder joint is too large, exceeding the distance between adjacent pins by 50\% \\ Less-tin & The solder joint is too small, without covering more than 75\% of the pin cup \\ Connected & The solder is connected to the adjacent pin, causing a short circuit \\ welding & The surface of the solder joint has a sharp thorn, easy to cause short circuit \\ Tin tip & The surface of the solder joint has a sharp thorn, easy to cause short circuit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed descriptions of solder joints defects.  
Figure 1: Examples of five connector solder joints. (**a**) the qualified solder joint, (**b**) the multi-tin solder joint, (**c**) the less-tin solder joint, (**d**) the connected welding solder joint and (**e**) the tin tip solder joint.  
(VOC 2012) data set to 75.9% at that time. First, we will introduce the key technology of Faster-RCNN, and then we will elaborate on the important improvement points of the algorithm in this paper.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 5 Discussion
### Model Component Contribution Analysis  
The proposed CNN-LSTM-SAM framework integrates three core modules to address the limitations of conventional landslide prediction models. Based on the experimental results and architectural mechanisms, the specific contributions of each component are analyzed as follows:  
The CNN module processes the 16-dimensional tabular feature vectors by reorganizing them into spatial representations through feature embedding layers. This transformation enables convolutional operations to extract local feature interactions and nonlinear patterns within the 16 geospatial factors. As shown in Table 3, the standalone CNN achieves 88.14% accuracy and 84.94% recall--significantly outperforming logistic regression and SVM models. This demonstrates CNN's capacity to capture complex relationships within tabular data, particularly for terrain-related features like slope gradient and soil composition.  
The LSTM module handles sequential dependencies within the feature set. By processing any inherent temporal patterns or ordered dependencies within the 16 feature dimensions, it captures dynamic interactions and temporal evolution patterns among factors such as vegetation index changes and soil moisture variations. The 3.52% accuracy gain of the full framework (91.66%) over standalone CNN (88.14%) validates LSTM's critical role in modeling feature dynamics, which is further evidenced by the 2.13% recall improvement (87.64% vs. 85.51% for RF).  
The SAM (Spatial Attention Module) dynamically recalibrates feature importance across the spatial dimensions derived from CNN outputs. This mechanism focuses computational resources on the most discriminative features while suppressing noise from less relevant factors. As demonstrated in Figure (a)a, the framework maintains precision >90% across recall levels >0.8--a capability directly attributable to SAM's selective weighting of high-impact spatial patterns in complex terrain environments.  
Collectively, these components establish a synergistic processing chain: CNN extracts localized feature representations, LSTM models dynamic interactions, and SAM optimizes feature weighting. This integrated architecture overcomes the feature integration limitations of conventional approaches while achieving state-of-the-art performance.
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 5 Spacecraft-Relative Navigation Model  
### Reference Frames  
To describe the spacecraft's relative motion and sensor measurement model, three related coordinate frames are defined in this section, as shown in Figure 5.  
1. Earth-centered inertial (ECI) frame (\(O_{I}-X_{I}Y_{I}Z_{I}\) ): The origin \(O_{I}\) is located at the center of Earth, the \(X_{I}\)-axis points to the vernal equinox, the \(Z_{I}\)-axis points to the North Pole, and the \(Y_{I}\)-axis forms a right-handed system with the \(X_{I}\)-axis and the \(Z_{I}\)-axis.
2. Local-vertical-local-horizontal (LVLH) frame (\(O_{L}-X_{L}Y_{L}Z_{L}\) ): The origin \(O_{L}\) is located at the mass center of the chief spacecraft, the \(X_{L}\) -axis points from the center of Earth to the center of the chief spacecraft, the \(Z_{L}\) -axis points in the same direction as the orbital angular velocity, and the \(Y_{L}\)-axis forms a right-handed system with the \(X_{L}\) -axis and the \(Z_{L}\) -axis.
3. Spacecraft body coordinate frame (\(O_{b}-X_{b}Y_{b}Z_{b}\) ): The chief body and deputy body are denoted as \(O_{c}-X_{c}Y_{c}Z_{c}\) and \(O_{d}-X_{d}Y_{d}Z_{d}\), respectively. The \(O_{b}-X_{b}Y_{b}Z_{b}\) is fixed to the spacecraft and its origin \(O_{b}\) is located at the mass center of the spacecraft. The three axes of \(O_{b}-X_{b}Y_{b}Z_{b}\), a right-handed system, coincide with the inertial axes of spacecraft. When \(O_{c}-X_{c}Y_{c}Z_{c}\) is coincident with \(O_{L}-X_{L}Y_{L}Z_{L}\), the \(X_{c}\)-axis points outward radially along the orbit (yaw axis), the \(Y_{c}\)-axis points toward the direction of flight (roll axis), and the \(Z_{c}\)-axis forms a right-handed system with the \(X_{c}\)-axis and the \(Y_{c}\)-axis (pitch axis).
## 3 Model Architecture
### Semantic Interaction Layer  
Although in general the context in reading comprehension tasks can be very long, the answer often exists in a short segment related to the query. Hence, the semantic interaction layer can be used to correlate information of the words in the context and the query to capture the interactive information. This layer uses the bi-directional attention mechanism to calculate the attention of both context-to-query (Context2 Query) and query-to-context (Query2 Context), respectively. The input is the contextual vector representation of the context \(H\in R^{d\times n}\) and the query \(V\in R^{d\times m}\) both from the previous layer. The shared similarity matrix \(S\in R^{n\times m}\) between the contextual embedding of \(H\) and \(V\) can be calculated as follows:  
\[\delta(h,v)=w^{n}_{(S)}[h;v;h\odot v], \tag{5}\]  
\[S_{nm}=\delta(H_{:n},V_{:m})\in R^{n\times m}, \tag{6}\]  
where \(S_{nm}\) is computed by the similarity of the n-th context word and the m-th query word; \(\delta\) is a numerical function that encodes the similarity of the two input functions; \(w_{(S)}\) is a trainable weight vector; \([:]\) represents the concatenation of matrix vectors on rows; \(H_{:n}\) is the n-th column vector of \(H\); and \(V_{:m}\) is the m-th column vector of \(V\). Then, the attention value in both directions can be calculated by the similarity matrix \(S\).  
Context-to-query Attention. Context2 Query attention indicates which query word is the most relevant to each context word. First, the attention weight \(\alpha_{n}\) is calculated by row normalization of the matrix \(S\) using softmax. Then, the context-to-query vector \(\vartheta\) is obtained by calculating the weighted summation of the query vectors, and the calculation process is as follows:  
\[\alpha_{n}=softmax(S_{n:})\in R^{m}, \tag{7}\]\[\vartheta=\sum_{m}\alpha_{nm}V_{:m}\in R^{d}, \tag{8}\]  
where the \(\alpha_{n}\) is the attention weight of the n-th context word to the query words; \(\sum\alpha_{nm}=1\) for all \(n\); the query representation of the whole context is \(\tilde{V}\in R^{d\times n}\).  
Query-to-context Attention. Query2 Context attention indicates which context word has the closest similarity to the word in the query. First, we take the maximum value of each column of the similarity matrix \(S\); then, the attention weight \(b_{n}\) is obtained through softmax normalization, and, finally, the query-to-context vector \(\tilde{h}\) is obtained after the weighted summation of the context vectors. The context representation of the query is \(\tilde{H}\in R^{d\times n}\), and the calculation process is as follows:  
\[b_{n}=softmax(max_{col}(S))\in R^{n} \tag{9}\]  
\[\tilde{h}=\sum_{m}b_{nm}H_{:n}\in R^{d} \tag{10}\]
# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
## 2 Motivation and Main Methodology
### Overview of Flight Decision Algorithms  
Real-time decision-making methods for multirotor flight strategies based on TOPISIS are shown in Figure 1. These methods include the construction of a condition assessment information source, the construction of a failure level classification model of a multirotor based on the random forest algorithm, and the construction of a real-time attribute decision model of a multirotor based on TOPSIS. Firstly, based on the RHysim UAV flight simulation platform developed by the Reliable Flight Control Research Group, Beihang University, the multirotor flight was simulated, the fault injection module was constructed, different types of fault injection were simulated, the multirotor flight status was monitored in real time, and the condition assessment information source was established by collecting the flight data under each fault condition. Then, based on the random forest algorithm, the failure level classification model of the multirotor was constructed, and the flight data of three types of safety faults were input to train and verify the model. This model can effectively classify the fault levels of the multirotor. At the same time, this paper constructed failure-level classification models of the multirotor through various machine learning methods and compared the classification effects of different classification models. Finally, under the framework of the failure level classification model, a real-time attribute decision-making model of the multirotor based on TOPSIS was constructed for the flight decision of the multirotor under different fault conditions.  
Figure 1: Algorithm flow chart.
## 3 Results and Discussion
For example, the move away from drought (the 2009 to 2010 years) is illustrated by the directions B+ G+ W+ and B- G+ W+, which represent changes towards green vegetation-mostly increases in agriculture and forest. Again, this is more apparent for the Houston site where proximity to an urban area would mean higher levels of irrigation (Figure 4a). In contrast, the more rural South Texas site is indicative of B+ G- W-, which represents a decrease in vegetation and a trend towards drying conditions. The level is above 90% for the severe drought event years of 2010 to 2011, compared to 65% for the Huston site for the same period. Incidentally, B+ G- W+ and B- G- W+ represent changes towards urbanization, which ideally should be masked out for an analysis of changes caused by drought. It is also interesting to see that B- G+ W- mostly affects urban classes. For the remaining change directions however, the type of change that occurs is less clear. \begin{table}
\begin{tabular}{c c c c c} \hline
**Land Use/Cover** & **2009–2010 (H)** & **2009–2010 (ST)** & **2010–2011 (H)** & **2010–2011 (ST)** \\ \hline Urban (U) & 18.7 & 9.9 & 9.2 & 3.3 \\ Rocks/Soil (R) & 18.2 & 12.9 & 9.4 & 3.3 \\ Agriculture (A) & 41.8 & 76.2 & 50.6 & 85.9 \\ Forest (F) & 21.1 & 1.0 & 30.4 & 7.1 \\ Water (W) & 0.2 & 0.0 & 0.4 & 0.4 \\ \hline \end{tabular}
\end{table} 
Table 3: Land use/cover (%) from threshold TCT: Houston (H), South Texas (ST).
_Article_  
Eco-Structured Biosorptive Removal of Basic Fuchsin Using Pistachio Nutshells: A Definitive Screening Design--Based Approach  
[PERSON]*(r), [PERSON](r), [PERSON] and [PERSON] Department of Chemistry and Earth Sciences, College of Arts and Sciences, Qatar University, Doha 2713, Qatar; [EMAIL_ADDRESS] (A.S.E-S.); [EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (A.A.I)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +974-4403-4675; Fax: +974-4403-4651 check for updates  
Received: 22 October 2019; Accepted: 7 November 2019; Published: 13 November 2019  
Biosorptive removal of basic fuchsin (BF) from wastewater samples was achieved using the recycled agro-wastes of pistachio nut shells (PNS). Seven adsorbents were developed; raw shells (RPNS) and the thermally activated biomasses at six different temperatures (250-500 \({}^{\circ}\)C). Two measures were implemented to assess the performance of utilized adsorbents; %removal (%R) and adsorption capacity (\(q_{c}\)). RPNS proved to be the best among the tested adsorbents. A smart approach, definitive-screening design (DSD) was operated to test the impact of independent variables on the adsorption capacity of RPNS. pH, adsorbent dose (AD), dye concentration (DC), and stirring time (ST), were the tested variables. Analysis of variance (ANOVA), control, and quality charts helped establishing regression model. Characterization was performed using Fourier- transform infrared (FT-IR)/Raman spectroscopies together with thermogravimetric analysis (TGA) and scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDX) analyses. The surface area and other textural properties were determined using the Brunauer Emmett-Teller (BET) analysis. Removal of 99.71% of BF with an adsorption capacity of 118.2 mg/g could be achieved using a factorial blend of pH 12, 100 mg/50 mL of RPNS, and 250 ppm BF for 20 min. Equilibrium studies reveal that the adsorption is physisorption with adsorption energy of 7.45 kJ/mol as indicated by Dubinin-Radushkevich (DR) and Langmuir isotherms. Moreover, adsorption follows pseudo-second-order kinetics with respect to BF and is controlled by the adsorption rate.  
**Keywords:** pistachio nutshells (PNS); definitive-screening design (DSD); basic fuchsin (BF); adsorption capacity (\(q_{c}\)); characterization; equilibrium; kinetics
## 1 Introduction
To test this hypothesis, we used COMSOL to simulate artificial muscle structures at a 100 \(\upmu\)m scale, constructed as N \(\times\) N \(\times\) 10 arrays of microcapacitors, with N varying from 1 to 13, while also varying tendon thickness within each array. The maximal force density for each configuration was plotted against N, revealing saturation around and beyond N = 10, thus confirming our hypothesis. Consequently, a 10 \(\times\) 10 \(\times\) 10 array was found to be representative and predictive of much larger arrays of artificial muscle devices. This result is significant for advancing research and developing practical artificial muscles. The saturation level was approximately 9 kPa at the 100 \(\upmu\)m scale, suggesting a force density of about 1 MPa at the 10 \(\upmu\)m scale, indicating the feasibility of strong, energy-efficient, low-density muscles for various applications [27]. While force density saturation for the current design was determined, there is still room for optimization of the actuator design. For example, introducing air gaps in the bulk dielectric or ligament of muscle fibers offers several notable advantages. First, air gaps could significantly reduce the overall weight of the artificial muscle, enhancing its force-to-weight ratio and making it more efficient and responsive. This reduction in weight would be beneficial for applications where minimizing mass is crucial, such as in wearable exoskeletons or aerial drones. Additionally, air gaps could improve the flexibility and compliance of the muscle fibers, allowing for more natural and adaptive movements that better mimic biological muscles. This would be essential for applications in prosthetics and soft robotics, where fine motion control and adaptability are required. Moreover, the inclusion of air gaps could increase actuation efficiency, as the reduced material density can lessen the dampening effect the bulk dielectric has on the current electrostatic actuator design. Finally, longitudinal air gaps should allow for the muscle fibers to expand laterally more easily, which should increase the contraction, decrease the plate separation, and thus non-linearly increase the output actuation force. Herein, we report on the results of COMSOL simulations of air gaps inside the microfluidic artificial muscles. These results show that the air gaps lead to a significant gain in the overall output force density compared to the traditional non-gap architecture. Hence,this is a theoretical proof of principle for this new approach. This design innovation could thus contribute to the development of stronger, lighter, and more energy-efficient artificial muscles suitable for a wide range of practical applications.
## 4 Experimental Test Results
### Effect of Different Parameters  
Figure 6 shows the effect of different parameters on the resistance of the RC T-section beams strengthened with CFRP. The cracking resistance \(\mathrm{Pcr}\) was not significantly affected by the number of CFRP layers. However, increasing the flexural reinforcing ratio \(\rho\)f \(\rho_{\mathrm{fl}}\) from 0.95% to 1.29%, decreasing U-wrap spacing from 150 mm to 100 mm, and increasing the compressive strength of concrete from 20 MPa to 40 MPa increased the \(\mathrm{Pcr}\) value by 16%, 19%, and 23%, respectively. Using higher strength concrete leads to higher cracking tensile strength of the beam as expected. Also, increasing the flexural reinforcing ratio offers larger equivalent cross-section area; and decreasing the spacing of the CFRP U-rap increases the stiffness of the beam, which delays the cracking of the concrete. As the number of CFRP sheets increased from 0 to 1 and 2, the \(Py\) (or \(Pu\)) values increased by 42% (or 6%) and 62% (or 48%), respectively. This is because introducing CFRP sheets to RC T-section beams increases the equivalent reinforcing ratio of the beam, which contributes to their \(Py\) and \(Pu\) resistance. However, the CFRP-concrete bonding failure limits the full utilization of the CFRP, which is reflected in the much lower than the strain at failure of the T-section beams. Hence, peeling of the CFRP from the strengthened RC T-section beams needs further study. Increasing the flexural reinforcing ratio of the T-section beam, \(\rho_{\mathrm{fl}}\) from 0.95% to 1.29% led to 13% and 48% increments in \(Py\) and \(Pu\), respectively. Increasing the flexural reinforcing ratio of the T-section beams increases the bending moment resistance of the cross-section. Due to premature failure of the CFRP-concrete bonding, the contribution of the CFRP to the bending resistance is minimized.  
Figure 6: Effects of different parameters on resistance of RC T-beam strengthened by CFRP.  
Increasing the spacing of the CFRP U-wrap, S, had limited effect on \(Py\) and \(Pu\). As the spacing of the CFRP U-wrap increased from 100 mm to 150 mm, the \(Py\) and Pu values only changed by -1% and 7%, respectively. This is likely because all tested specimens failed in a flexural mode, and the spacing of the U-wrap only increases the transverse shear resistance of the T-section beam without improvement on its flexural bending resistance. The test results also show that using higher strength concrete had limited influence on the flexural resistance of the RC T-section beams strengthened with CFRP. This may be caused by the CFRP-concrete bonding failure of the beam.
## 4 Results
### Experiment I
We applied the trained networks on the second NIDDK dataset. The proposed multistage network yielded transformations with significantly (\(p<0.05\)) less image folding than the benchmark (\(\lvert\mathrm{J}\rvert\leq 0\) (mean \(\pm\) std): multistage network: \(4.8\pm 2.1\), benchmark: \(6.1\pm 2.1\)). Figure 3: Example results of the registration of two volumes from the first NIDDK dataset. The images show central slices of the coronal plane. The fixed images, moving images and result (moved) images are overlaid by the kidney segmentations (column 1–3). The resulting composites for image and segmentation show the fixed data in blue and the moving/moved data in red. The deformation field is visualized in RGB channels. Examples of registration results of the second NIDDK dataset can be seen in Figure 4. Before the registration, the liver especially is not well aligned in the fixed and moving image (white arrow in Figure 4). Registration with the baseline did not improve the spatial alignment the corresponding structures. The overlap of the liver in the fixed and moved image did not increase. The benchmark and multistage CNN produced registration results with good alignment. Within the organs, the deformations were smooth.
## 2 Effectiveness of Eddy Current Damping in Seawater
### Free Vibration Tests of a Eddy Current Damper in Seawater Environment  
As mentioned before, eddy current dampers have been successfully applied in TMD systems as a linear damping source. The key issue for applying ECTMD to control vibrations occurring in subsea pipelines is the validation of the damping effect of eddy currents in a seawater environment.  
Studies worked on the application of eddy current damping in a seawater environment have not been reported on before. In this paper, to verify the feasibility of eddy current damping in a seawater environment, the free vibration responses of a cantilever beam are measured in four cases and the damping ratios of each case are calculated.  
The experimental setup is shown in Figure 2a-c. A laser displacement sensor is used to collect the response of the cantilever beam and a seawater container is used to carry seawater to simulate the seawater environment. Moreover, an eddy current damping device, as shown in Figure 2d, with two permanent magnets is fixed on the bottom of the water container to provide extra damping force for the cantilever beam. The sample frequency of the laser displacement sensor is set to 100 Hz and the gap between the magnets and the aluminum plate is set to 5 mm.  
Figure 1: Generation of eddy current damping in a conductive plate.  
Figure 2: _Cont._