esa-sceva/satcom-chunk-collection huggingface dataset:
_Review_  
3D-Printed Customized Cages for Foot Arthrodesis  
[PERSON] 1, [PERSON] 1,*, [PERSON] 2,* and [PERSON] 3  
1 Department of Orthopedics, Colentina Clinical Hospital, 020125 Bucharest, Romania; botezatu,[EMAIL_ADDRESS]  
2 Faculty of Industrial Engineering and Robotics, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania  
3 Department of Orthopedics, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (D.L.); [PERSON]S] (D.P.)  
**Featured Application: This systematic review focuses on analyzing the use of 3D-printed cages in foot arthrodesis to address complex bone defects, failed prior fusions, severe bone loss, or deformities. The application of customized Ti-6A1-4V open-structure, porous implants, manufactured through additive manufacturing processes such as electron beam melting (EBM), selective laser melting (SLM), or direct metal laser sintering (DMLS), is demonstrated to ensure osseointegration and provide a proper anatomical fit and mechanical stability.**  
**Abstract: In recent years, the application of 3D-printed implant cages or trusses for foot arthrodesis has emerged as a personalized approach to address complex bone defects and deformities. Twenty studies involving different regions of the foot, such as the ankle and subtalar joints, were reviewed to document the 3D-printed custom solutions. The design of these implants is also discussed, including custom titanium trusses and lattice structures, which can promote osseointegration and fit the bone geometries. From a mechanical perspective, these implants proved to be stable and compatible with natural bone, aiming to reduce stress shielding while offering the mechanical strength needed for optimal outcomes. This systematic survey also addresses the additive manufacturing processes involved, namely EBM, SLM, or DMLS. Clinical cases were focused on patients with large bone loss, failed prior fusions, and deformity corrections, with the follow-up results showing high rates of fusion and functional improvement. Of the analyzed studies, three provide level III evidence, while the rest provide level IV or V, consisting of case series or reports. Since 2015, 148 patients have been reported to receive such implants. This review addresses the question, \"how effective are 3D-printed titanium cage implants in foot arthrodesis in addressing large bone defects and deformities?\" It is the first review to gather data on the use of such customized implants in foot arthrodesis, providing critical insights to enhance their application, including amputation avoidance. This study highlights the advantages of personalized 3D-printed implants in achieving a better anatomical fit, improving clinical outcomes, and ensuring faster recovery times, while also addressing considerations such as the cost and the need for long-term clinical data.**  
**Keywords: additive manufacturing; customized implant; cage; truss; foot; arthrodesis; review; electron beam melting; selective laser melting; direct metal laser sintering**
# Automatic Detection of Construction Workers' Helmet Wear Based on Lightweight Deep Learning
## 2 Related Work
In depth-wise convolution, a single filter to each input channel is applied, and then the pointwise \(1\times 1\) convolution is used to combine the outputs of the depth-wise convolution. The proposed MobileNet (adopted as the backbone) was used for fast multi-scale feature map generation. This means the network can detect small objects and can deal with occlusions. The average precision of the proposed method was up to 89.4%, with the running speed being 62 FPS. In contrast, representative works such as [25] proposed a sampling fusion network, SCRDet, which fuses multi-layer features with adequate anchor sampling to improve small object detection due to the difficulty of detecting objects of small size, any orientation, and dense distribution. Based on this, [26] proposed SCRDet++ to highlight the features of small objects and reduce the interference of the background by designing an instance-level feature map denoising module in the feature map and improving the handling of rotation detection by incorporating the IoU constant factor into SmoothL1 loss. This work provides an excellent idea for dense small-size object detection. In this paper, we propose a new one-stage lightweight automatic helmet wear detection algorithm to address the limitations of previous studies. The method's superiority is demonstrated in the publicly available dataset, contributing to industrial application development and lightweight research on neural networks.
## 3 Materials and Methods
### Determination of Antioxidant Activity  
The ABTS assay followed the method of [PERSON] et al. [52] with some modifications. Briefly, 0.1 mL of extract was mixed with 3 mL of ABTS (7 mM) reagent and left in the dark. After 95 min, absorbance was measured at 734 nm.  
The antioxidant activity of samples was also determined by the radical scavenging activity method using 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) [53]: 0.2 mL of extract was mixed with 3 mL of DPPH (4 mM) solution. After 15 min, absorbance was measured at 517 nm.  
The cupric reducing antioxidant capacity assay was carried out according to the method of [PERSON] et al. [54]. Copper chloride, neocuproine and ammonium acetate buffer (pH 7) solution were mixed in a ratio of 1:1:1. Then, 0.2 mL of extract was added to the obtained mixture, followed by 0.9 mL of distilled water. After 30 min, absorbance was recorded at 450 nm.  
Additionally, the antioxidant capacity of samples was determined according to the Benzie and Strain method [55] with some modifications: 0.2 mL of extract was added to 3 mL of FRAP (10 mM) reagent to form a mixture. The mixture was incubated for 30 min in the dark, and the absorbance was measured at 593 nm.  
For each method for blank, the extract was replaced with water and all measurements were performed in triplicate. Antioxidant activity evaluated by the ABTS, DPPH, FRAP and CUPRAC methods was calculated from the calibration curve with Trolox as a standard and expressed as nmol TE/100 g, except for ABTS (umol TE/100 g).
# A Two-Stage Registration Strategy for Thermal-Visible Images in Substations
## 3 Experimental Results and Discussion
### Quantitative Analyses  
A comparative analysis of matching accuracy among various methods is presented in Figure 7. The experimental results show that the proposed method has the best matching accuracy, the average accuracy is 85.88%, and there are no obvious outliers. The experimental results are stable and robust. In several other methods, the matching accuracy of PSO-SIFT is the lowest, making the registration almost impossible, and the performance of RANSAC is relatively improved. However, there are obvious outliers in these four methods, indicating that some images have not been successfully matched.  
Figure 6: Comparison of test results for different registration methods on image pair.  
Table 1 presents the average and standard deviation of the TRE for different methods. The results demonstrate that the proposed method exhibits a superior performance, with a registration error of less than 5 pixels. The average error and standard error of the target registration for different images are shown in Figure 8. PSO-SIFT and SI-PIIFD manifest significant registration errors. The average TRE value of RANSAC is 6.36 pixels, which is relatively good, but its standard error is 0.97, indicating that its stability is poor. The average TRE value of the proposed method is 4.53 pixels, the standard error is 0.39, and the overall performance is the best.
## 4 Optimization of Channel MIMO Capacity by Combining OSTBC and Polarization-Diversity with XPI
### Diversity Transmission Using OSTBC-MRRC MIMO Technique
Beyond the distinction in improving performance in the system in which we incorporated the proposed feedback, two significant insights can be discerned. The first insight is that the system, without any feedback in the low region of the SNR, starts with a high error threshold and drags this threshold (that is not capable of producing information) to a value of almost \(\sim\)20 dB. (Remind that the decoding threshold value in terms of BER value, in such a case, is \(\sim\)10\({}^{-3}\)). The second insight is a difference of almost \(\sim\)10 dB, about a given BER value of \(\sim\)10\({}^{-4}\), a figure that indicates significant achievement of diversity gain with the feedback, and this is in addition to the fact that the feedback copes very well with the destructive effects of changes in the channel between the symbol times as we described. Figure 9 represents the average capacity under the two scenarios above. As we can see, the capacity performance with the feedback is not significantly better than without the feedback, although the BER performance is much better with the feedback than without it. The reason for this is as follows. While the capacity depends on the the sum of the eigenvalues of \(H_{TR}H_{TR}^{*}\) that can be the same for MIMO with spatial-diversity or without spatial-diversity, the BER depends on the spatial-diversity and on the orthogonality of the  
Figure 8: BER OSTBC With/Without a Feedback. Figure 9: Average Capacity With/Without Feedback. paths. Note that when the spatial-diversity is slight, even if the capacity is high, the system performance is unstable, since any slight distortion can cause a total collapse. We therefore recommend to use the feedback, even when the capacity is the dominant issue, because it decreases significantly the channel uncertainty, as can be seen from the BER performance.
## 4 Numerical Simulation
### Mode Analysis  
As shown in Figure 4a, the FE models of the corrugated composite panels investigated in this research were established by using a commercial software UG NX, and the specific parts of the panel model are shown in Figure 4b. Both the fiber cloth and the resin layer  
Figure 3: (**a**) Schematic diagram of sound insulation measurement by sound intensity tests. (**b**) The size of the tested composite panel.  
were meshed with CQUAD4 elements, whereas the foam strips were meshed with CHEXA8 elements. The components were assembled by using a merge technique which makes the joint surface between components have common nodes. According to the literature [48], at least ten elements per local wavelength were ensured at the highest frequency in the simulation analysis. Therefore, the element size was set to be 2 mm to guarantee the precision of the analysis. Material properties of the considered parts were obtained from the data listed in Tables 1 and 2. Corresponding to the mode tests, a free boundary condition was used for the panel model to calculate the modes.
# Experimental Investigation on Flow-Induced Rotation of Two Mechanically Tandem-Coupled Cylinders
## 2 Theoretical Basis
### Dynamic Model of Flow-Induced Rotation (FIR) System
#### 2.2.3 Transmission System  
The transmission system is the transfer motion part of the FIR system, which includes the left and right two transmission belts and the matching driving wheels. In the process of motion, the force of a single transmission belt and the matching driving wheels are shown in Figure 4a, including the torque, \(M^{\prime}{}_{1}\), provided by the pendulum system and the torque, \(M^{\prime}{}_{2}\), provided by the restoration system.  
Figure 3: Force analysis of restoration system: (**a**) Equilibrium position; (**b**) Force after rotation angle \(\theta_{2}\).  
\[(I_{1}+2J_{a}+J_{2})\ddot{\theta}+(c_{1}+c_{2})\dot{\theta}+2 KB^{2}\tan\theta= \frac{1}{4}\Big{(}c_{\text{tangent,}up}+c_{\text{tangent,}down}\Big{)}\rho U^{ 2}DHL\cos^{2}\theta+\frac{\pi}{8}C_{f}\rho HD^{4}\Big{(}\dot{\theta}\Big{)}^{2} \tag{17}\]  
Definition \(c_{total}=c_{1}+c_{2}\) and \(J_{osc}=J_{1}+J_{2}\), hence, the kinetic equation of the FIR system is further written as:  
\[(J_{osc}+2J_{a})\ddot{\theta}+c_{total}\dot{\theta}+2 KB^{2}\tan\theta=\frac{1}{ 4}\Big{(}c_{\text{tangent,}up}+c_{\text{tangent,}down}\Big{)}\rho U^{2}DHL \cos^{2}\theta+\frac{\pi}{8}C_{f}\rho HD^{4}\Big{(}\dot{\theta}\Big{)}^{2} \tag{18}\]  
Considering the large Reynolds number (\(2.0\,\times\,10^{4}\)\(\,\times\,Re\)\(\,\leq\,9.7\,\times\,10^{4}\)) and small angular displacement (\(\theta\,\leq\,30^{\circ}\)) in this experiment, it can be preliminarily judged that the order of magnitude of the term containing \(\Big{(}\dot{\theta}\Big{)}^{2}\) in Equation (18) is small, so it is not, temporarily, considered in this paper, and the more explicit impact of this term on the FIR system is further analyzed in subsequent research. Therefore, Equation (18) can be further simplified as:  
\[(J_{osc}+2J_{a})\ddot{\theta}+c_{total}\dot{\theta}+2 KB^{2}\tan\theta=\frac{1} {4}\Big{(}c_{\text{tangent,}up}+c_{\text{tangent,}down}\Big{)}\rho U^{2}DHL \cos^{2}\theta \tag{19}\]  
The dimensionless parameter damping ratio (\(\zeta_{total}\)) and the natural frequency (\(f_{n,air}\)) of the system in air are introduced, and the following equations are established:  
\[\zeta_{total}=\frac{c_{total}}{2\sqrt{2 KB^{2}J_{osc}}} \tag{20}\]  
\[f_{n,air}=\frac{1}{2\pi}\sqrt{\frac{2 KB^{2}}{J_{osc}}} \tag{21}\]  
Substituting Equations (20) and (21) into Equation (19) gives the final kinetic equation of the FIR system:  
\[(J_{osc}+2J_{a})\ddot{\theta}+2\zeta_{total}\sqrt{2 KB^{2}J_{osc}}\dot{\theta}+ 4\pi^{2}f_{n,air}^{2}J_{osc}\tan\theta=\frac{1}{4}\Big{(}c_{\text{tangent,}up}+c_{ \text{tangent,}down}\Big{)}\rho U^{2}DHL\cos^{2}\theta \tag{22}\]
## 4 Experimental Results
### Subrate
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Hall Monitor**} \\ \cline{2-9}  & **Subrate** & **0.05** & **0.10** & **0.15** & **0.20** & **0.25** & **0.30** \\ \cline{2-9}  & **BCS-TV-AL3** & 21.70 & 23.53 & 24.76 & 26.44 & 28.16 & 29.77 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm M_{NR}\))** & 22.88 & 25.54 & 27.37 & 29.34 & 30.84 & 32.62 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm 0.5\))** & **28.98** & **30.65** & **31.77** & **32.81** & **33.83** & **34.85** \\ \cline{2-9}  & **Content** & \multicolumn{6}{c}{**Mother Daughter**} \\ \cline{2-9}  & **Video** & **0.05** & **0.10** & **0.15** & **0.20** & **0.25** & **0.30** \\ \cline{2-9}  & **BCS-TV-AL3** & 24.88 & 30.36 & 32.19 & 33.73 & 35.55 & 37.15 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm M_{NR}\))** & 29.02 & 32.15 & 33.81 & 35.57 & 37.83 & 39.15 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm 0.5\))** & **32.00** & **34.26** & **35.81** & **37.65** & **38.63** & **39.61** \\ \hline  & & \multicolumn{6}{c}{**Coast guard**} \\ \cline{2-9}  & **Subrate** & **0.05** & **0.10** & **0.15** & **0.20** & **0.25** & **0.30** \\ \cline{2-9}  & **BCS-TV-AL3** & 21.86 & 23.98 & 25.01 & 26.15 & 27.18 & 27.99 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm M_{NR}\))** & 22.32 & 24.90 & 25.37 & 26.41 & 27.67 & 28.58 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm 0.5\))** & **24.53** & **25.61** & **26.69** & **27.6** & **28.38** & **29.14** \\ \cline{2-9}  & **Content** & \multicolumn{6}{c}{**Foreman**} \\ \cline{2-9}  & **Video** & **0.05** & **0.10** & **0.15** & **0.20** & **0.25** & **0.30** \\ \cline{2-9}  & **BCS-TV-AL3** & 23.66 & 25.38 & 27.05 & 29.0 & 30.70 & 33.83 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm M_{NR}\))** & 24.92 & 26.39 & 28.10 & 29.84 & 31.35 & 33.35 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm 0.5\))** & **26.29** & **27.86** & **30.05** & **31.42** & **32.96** & **34.61** \\ \hline  & & \multicolumn{6}{c}{**Mobile Calendar**} \\ \cline{2-9}  & **Subrate** & **0.05** & **0.10** & **0.15** & **0.20** & **0.25** & **0.30** \\ \cline{2-9}  & **BCS-TV-AL3** & 16.68 & 18.58 & 19.58 & 20.52 & 21.58 & 22.50 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm M_{NR}\))** & 17.99 & 19.43 & 20.65 & 21.80 & 22.88 & 24.03 \\ \cline{2-9}  & **JID-TV (\(\rm M_{R}=\rm 0.5\))** & **20.15** & **21.28** & **22.24** & **22.97** & **23.79** & **24.65** \\ \cline{2-9}  & **Content** & \multicolumn{6}{c}{**Stefan**} \\ \cline{2-9}  & **Video** & **0.05** & **0.10** & **0.15** & **0.20** & **0.25** & **0.30** \\ \cline{2-9}  & **BCS-TV-AL3** & 19.92 & 21.76 & 22.89 & 24.14 & 25.23 & 26.42 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm M_{NR}\))** & 20.23 & 22.65 & 23.85 & 25.09 & 26.12 & 27.50 \\ \cline{2-9}  & **JD-TV (\(\rm M_{R}=\rm 0.5\))** & **23.39** & **25.08** & **26.27** & **27.07** & **27.93** & **29.14** \\ \hline \hline \end{tabular} Note: The bold values represent the highest PSNR (dB) achieved at specific subrate for video sequence.  
\end{table} 
Table 4: Average PNSR evaluation of proposed JD-TV scheme and the independent BCS-TV-AL3 for different low, moderate and high motion content video sequences at GoP = 8.
# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 2 Related Work
### Data Augmentation  
Data augmentation plays a crucial role in the processing of remote sensing image data. Remote sensing images typically contain rich information but can be affected by factors such as noise, varying lighting conditions, different seasons, and viewing angles. Therefore, data augmentation is essential for enhancing the performance and generalization capabilities of deep learning models when dealing with these images. Common traditional data augmentation methods include random rotations, flips, scaling, cropping, adjustments to brightness and contrast, and color space transformations. In addition to these techniques, more recent approaches such as Mixup [36], Cutout [37], and Mosaic [38] have been introduced. Mixup involves blending two or more images in specific proportions to create new images. Cutout randomly erases a portion of an image and fills it with a fixed color to introduce randomness. Mosaic combines multiple images by randomly assembling them into a single larger image, thereby increasing data diversity. Furthermore, empirical evidence has shown that stopping the use of Mosaic during the later stages of training can lead to improved performance in object detection models. This highlights the significance of adapting data augmentation strategies throughout the training process. Multi-scale training [39] is also a commonly used technique in deep learning and object detection. It typically involves using data at different scales (or resolutions) to train a model, enabling the model to better adapt to various input scenarios, with the aim of improving the model's performance and robustness. Therefore, in this paper, the baseline network integrates these data augmentation strategies.
# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 5 Experiment
### Evaluation Metrics  
In this article, we approach 110 Call incident risk prediction as a binary classification problem. Consequently, we utilize four standard evaluation metrics pertinent to classification tasks: accuracy (ACC), F1-score, area under the curve (AUC), and root mean squared error (RMSE).  
ACC is a fundamental evaluation metric used to measure the proportion of correctly classified instances among the total number of instances. ACC provides a straightforward indication of the overall performance of a classification model. It is defined as:  
Figure 6: DSTGAT architecture.  
\[ACC=\frac{Number\ of\ Correct\ Predictions}{Total\ Number\ of\ Predictions} \tag{5}\]  
The F1-score is a metric that combines both precision and recall into a single measure, providing a balance between them. It is defined as the harmonic mean of the precision (the proportion of true positive predictions among all the positive predictions) and recall (the proportion of true positive predictions among all the actual positives). It is calculated as:  
\[F1\ Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{6}\]  
The AUC refers to the area under the receiver operating characteristic (ROC) curve. The AUC quantifies the overall ability of the model to discriminate between positive and negative classes, with values ranging from 0 to 1. An AUC of 1 indicates perfect classification, while an AUC of 0.5 suggests no discrimination ability.  
The RMSE is a widely used metric for assessing the regression accuracy of prediction models. It measures the average magnitude of the prediction errors, giving an indication of how well the model predicts continuous outcomes. The RMSE is defined as:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}} \tag{7}\]
# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 2 Materials and Methods
### Construction and Identification of the Convolutional Neural Network Model  
In this study, CNN [32], which is a widely deployed architecture of the artificial neural network for image-identification tasks, was deployed as the primary identification model. Additionally, Grad-CAM [36] was used to visualize the discriminating evidence of the CNN during the identification. This study was also aimed at investigating personal identifications based on multidirectional ear images and the key feature regions that contribute to the discrimination of the model. The combination of CNN and Grad-CAM was considered an appropriate methodological approach to achieve the objectives.  
Figure 2 shows an overview of the architecture of the CNN model constructed in this study, and Table 1 details its structure. The input of the model comprised the preprocessed ear images obtained as described in Section 2.1. Next, we performed feature extraction through a sequence of seven convolutional layers that were interspersed with three max-pooling layers, a smoothing layer, and three fully connected layers. The final fully connected layer produced a 10-dimensional feature vector, which was subsequently transformed into class probabilities by the Softmax function [37]. Thereafter, the network parameters were optimized using the backpropagation algorithm [38], with the optimization function named Adam [39]. Additionally, the rectified linear unit (ReLU) function was deployed as the activation function for all the layers except the last fully connected layer, which utilized the SoftMax function. The convolutional layers exhibited a 3 \(\times\) 3 filter size, with a stride of 1. The number of filters increased progressively across the layers: 64 filters for the first two layers, 128 filters for the third and fourth, and 256 filters for the fifth and sixth. The three fully connected layers contained 256, 128, and 10 neurons, respectively.  
The categorical cross entropy (CCE) loss was deployed as the loss function in our model training; CCE is expressed, as follows:  
\[\text{CCE}=-\sum_{k=1}^{N}t_{k}\text{-}logy_{k} \tag{1}\]  
Here, \(N\) denotes the number of subject classes, \(t_{k}\) denotes the ground-truth label for class \(k\), and \(y_{k}\) denotes the predicted probability for class \(k\). During the identification, the ear images were fed into the trained model, which generated probability distributions over the subject classes. Thereafter, the input ear image was assigned to the class with the highest probability score.  
Figure 2: Architecture of the CNN model.