esa-sceva/satcom-chunk-collection huggingface dataset:
# FD-YOLO: A YOLO Network Optimized for Fall Detection
###### Abstract  
Falls are defined by the World Health Organization (WHO) as incidents in which an individual unintentionally falls to the ground or a lower level. Falls represent a serious public health issue, ranking as the second leading cause of death from unintentional injuries, following traffic accidents. While fall prevention is crucial, prompt intervention after a fall is equally necessary. Delayed responses can result in severe complications, reduced recovery potential, and a negative impact on quality of life. This study focuses on detecting fall situations using image-based methods. The fall images utilized in this research were created by combining three open-source datasets to enhance generalization and adaptability across diverse scenarios. Because falls must be detected promptly, the YOLO (You Only Look Once) network, known for its effectiveness in real-time detection, was applied. To better capture the complex body structures and interactions with the floor during a fall, two key techniques were integrated. First, a global attention module (GAM) based on the Convolutional Block Attention Module (CBAM) was employed to improve detection performance. Second, a Transformer-based Swin Transformer module was added to effectively learn global spatial information and enable a more detailed analysis of body movements. This study prioritized minimizing missed fall detections (false negatives, FN) as the key performance metric, since undetected falls pose greater risks than false detections. The proposed Fall Detection YOLO (FD-YOLO) network, developed by integrating the Swin Transformer and GAM into YOLOv9, achieved a high [EMAIL_ADDRESS] score of 0.982 and recorded only 134 missed fall incidents, demonstrating optimal performance. When implemented in environments equipped with standard camera systems, the proposed FD-YOLO network is expected to enable real-time fall detection and prompt post-fall responses. This technology has the potential to significantly improve public health and safety by preventing fall-related injuries and facilitating rapid interventions.  
f +
Footnote †: journal: Applied Sciences
# Quantitative Modeling of Near-Field Interactions in Terahertz Near-Field Microscopy
## 2 Methods  
The probe's operational schematic in s-SNOM is shown in Figure 1a. \(E_{i}\) represents the incident electric field, \(E_{r}\) represents the sample reflected electric field, and \(E_{s}(\Omega)\) is the electric field containing the near-field interactions information of the sample and tip (\(\Omega\)) is the frequency of tip vibration). All of the FEM simulation software, such as ANSYS, ABAQUS, and COMSOL, are appropriate for the numerical calculation models.  
To construct the model, we need to extract the longitudinal profile of the probe axis, as [PERSON]'s study has shown that the decrease in dimensions resulted in less information loss [17]. To cut down the calculation consumption, the x-direction range should not be excessively broad, and the y-direction range should completely cover the length of the modified probe so as to provide adequate space for tip. This reduces the amount of space for detailed calculations. The near-field scattering simulation model is seen in Figure 1b.
## 3 Analysis of Key Technologies
### Extraction Algorithm of Twist Angular Position of the Profile Based on Geometric Analysis  
As shown in Figure 8, O\({}_{1}(x_{1},y_{1})\) and O\({}_{2}(x_{2},y_{2})\) represent the centers of the leading and trailing edges respectively, \(R_{q}\) and \(R_{h}\) represent the radius of the leading and trailing edges respectively, and the line of EF is the profile chord. The axial line of engine is parallel to \(X\) axis after registration, thus the mathematical model of twist angular position of the profile can be defined as follows:  
\[\psi=\beta+\theta \tag{3}\]  
where \(\beta\) is the angle between the center line O\({}_{1}\)O\({}_{2}\) of the leading and trailing edges and \(X\) axis, \(\theta\) is the angle between the profile chord and the center line O\({}_{1}\)O\({}_{2}\). By geometric analysis, we can get the mathematical models of parameters \(\theta\) and \(\beta\) as follows:  
\[\theta =\arcsin\ \frac{\left|R_{q}-R_{h}\right|}{\sqrt{\left(x_{2}-x_{1} \right)^{2}+\left(y_{2}-y_{1}\right)^{2}}} \tag{4}\] \[\beta =\arctan\ \frac{y_{2}-y_{1}}{x_{2}-x_{1}} \tag{5}\]  
Figure 7: Curves of the leading and trailing edges extracted based on a priori planning technique.  
Based on the least squares fitting technology, the center coordinates and radius of the blade edges are solved. Then the twist angular position of the profile can be calculated using Equations (3)-(5).
## 5 Conclusions  
This study proposed a novel interlocking screw guidance system that enables cost-effective and accurate drill guidance for distal screwing procedures of a femur fracture reduction surgery. In the conventional operation, a lot of X-ray images are required in finding the direction and position of the hole, and therefore our guidance process was divided into a parallelization step and a fine-positioning step. A parallel guidance system was developed for the parallelization step, and a mechanical fine-adjustment device was developed for the fine-positioning step. The experiment results using the real C-arm devices showed that the parallelization step took 41.1 \(\pm\) 19.0 s, and the tilted angle error was measured as 1.24 \(\pm\) 0.715\({}^{\circ}\). The fine-positioning step took 207 \(\pm\) 107 s and was possible only using X-ray imaging 6.67 \(\pm\) 2.65 times. The translation error was measured as 0.378 \(\pm\) 0.120 mm. These values are considered sufficient for the system to be used in real operations.  
In future work, evaluation on the drilling process using a real C-arm will be performed. It was verified that the tilting angle error and the translation error are sufficiently small using X-ray-based analysis, but the actual drilling guidance experiment is required for final validation. Moreover, an ex vivo experiment using animal bones will be performed after the drilling evaluation.  
Conceptualization, S.Y.K.; methodology, L.A.; validation, B.X. and L.A.; formal analysis, L.A.; writing--original draft preparation, L.A.; writing--review and editing, B.X. and S.Y.K.; supervision, S.Y.K.; funding acquisition, S.Y.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health and Welfare, the Ministry of Food and Drug Safety) (project number: KMDP_PR_2020901_0122, 9991007296) and was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (no. 2019R1A2C2091005).  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors thank the medical microrobot center, Chonnam National University in Korea for their support for the use of C-arm device and X-ray shielding room. The authors declare no conflict of interest.
# Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network
## 2 Materials and Methods
### Mathematical Models
#### 2.1.7 Focal Loss  
With a large number of classes, true negatives can easily overwhelm training and the loss might become unrealistically small for false negatives. Likewise, if the dimension number of the output is large (there are many classes of labels) and each training sample has only few cases of some classes, the loss would encourage ignoring these few cases. Nonetheless, those cases might be extremely important for the model's predictive performance.  
One way to solve this is by using focal loss. Focal loss was originally proposed as an effective loss function for the image processing-object detection problem. The problem with object detection might occur in cases when rare objects are missed because the background occupies a large part of the image. Thus, detection fails because extreme foreground/background class imbalance prevents learning. Yet research shows that focal loss is useful not only for visual analysis but also in general multiclass classification tasks of imbalanced class conditions [15].  
Focal loss improves the classifier by configuring the loss function with modulating factor. Focal loss can be defined as:  
\[\mathcal{L}_{F}(y^{(i)},\hat{y}^{(i)})=-\sum_{c=1}^{d_{y}}\alpha_{c}(1-\hat{y}^{ (i)})^{\gamma}y_{c}^{(i)}\log(\hat{y}_{c}^{(i)}) \tag{13}\]  
where \(y_{c}^{(i)}\in\{0,1\}\) is a binary class label, \(\hat{y}_{c}^{(i)}\in(0,1)\) is an estimate of the probability of the positive class, \(\gamma\) is the focusing parameter, \(\alpha\) is a hyperparameter that weights the positive class up or down \(\alpha\in[0,1]\)[11, 15, 16].  
The hyperparameter \(\gamma\) prevents overwhelming loss with frequent cases focusing the model on instances with fewer classes. The higher the \(\gamma\), the higher the rate at which easy-to-classify examples are down-weighted. The weighted binary cross-entropy loss is special case of focal loss when \(\gamma=0\). Criteria to choose \(\gamma\) value is the degree of data imbalance [15, 16].
# Shape Memory Alloy Reinforced Self-Healing Metal Matrix Composites
## 3 Synthesis of Shape Memory Alloy Reinforced Self-Healing Metal Matrix Composites
### Reinforcement-Metal Wettability  
Wettability is a critical factor in the development of SMA-reinforced SHMMCs as it directly influences the interaction between the liquid matrix and SMA reinforcements. It determines the matrix's ability to penetrate and wet the SMA reinforcements, which significantly affects the quality and performance of the composite. Achieving good wettability is crucial as it promotes effective bonding and facilitates load transfer between the matrix and reinforcements. However, it is important to note that wettability and bonding are not synonymous terms. While excellent wettability, characterized by a low contact angle, is an essential requirement, it does not guarantee a strong bond at the interface. Good wettability can coexist with a weak van der Waals-type low-energy bond [79; 80]. Therefore, additional factors such as surface chemistry, interfacial reactions, and mechanical interlocking contribute to the overall bonding strength in SHMMCs.  
The poor wettability of metal matrix with NiTi SMA is a significant concern in the development of SHMMCs, which has been addressed by various researchers through different approaches. [PERSON] [38; 39] proposed electrochemical etching of the SMA followed by sputter coating it with gold (Au) to improve wettability. Electroless coating with copper (Cu) [81] or removal of the oxide with an etchant or a flux [82; 83; 84] have also been explored to enhance wettability. [PERSON] [85] investigated pressure infiltration with an electroless copper coating to improve the wetting behavior of the NiTi surface. However, coating-based approaches have shown limitations because of diffusion of the metal coating into the alloy matrix, resulting in interface deterioration with aging and thermal cycling, making them less popular for solder matrices. [PERSON] [85] reported that electroless Cu-coated SMAs exhibit persistent fiber pullout rather than crack bridging in the solder-SMA composite, indicating poor interfacial strength. Pressure infiltration alone can partially overcome non-wetting forces, but because of a lack of bonding, a good fiber-matrix interface cannot be obtained. Good bonding between the matrix and SMA reinforcement was achieved by [PERSON] et al. [50; 69], [PERSON] et al. [51; 70], and [PERSON] et al. [71] using an etchant (4.8%HF-10.5%HNO\({}_{3}\)) to remove the oxide layer from SMA reinforcement. [PERSON] et al. [50; 69], and [PERSON] et al. [71] conducted an additional step after etching the SMA reinforcement that further enhanced the wettability of the reinforcement with the metal matrix. A phosphoric acid-based flux was applied after etching the SMA reinforcement to remove surface oxides, perform pickling, and enhance surface energy. This flux aimed to prepare the reinforcement for subsequent processing and enhance bonding with the matrix material. The metal matrix and reinforcement bond most effectively when both etching and fluxing have been applied.
## 5 Hardware Experiment  
To demonstrate the effectiveness of the proposed algorithm as well as its hardware feasibility, we constructed a hardware platform for testing. The topology and the photograph of this platform are presented in Figures 11 and 12. The main components of this platform are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Identifier** & **Component** & **Model Specification** \\ \hline
1 & Multi-user transmitters FPGA & Xilinx xc72020 \\
2 & Power combiner & Mini-Circuits ZB8 PD-362-S+ \\  & & Mini-Circuits ZB8 PD-30-S+ \\
3 & Noise generator & Noise/com NC6110 \\
4 & Spectrum analyzer & Agilent 8563 EC \\
5 & Receiver FPGA & Xilinx xczu27 dr \\
6 & RF chip & AD9361 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main components of the hardware platform.  
Figure 10: \(K=6\), BER comparison of the proposed acquisition algorithm over AWGN channel.  
We employed ZYNQ-7000 integrated with AD9361 to construct our hardware transmitter. For this particular test, we utilized a total of seven transmitters, with one dedicated to transmitting the test signal and the remaining six for transmitting strong interference signals. To ensure compatibility, we developed transmission programs in the Verilog Hardware Description Language (Verilog HDL) on the aforementioned field programmable gate arrays (FPGAs). The transmitters accept I/Q baseband signals via a joint test action group (JTAG) table from our PC, subsequently modulating the baseband signal for transmission at a frequency of 2.25 GHz.  
Then, the transmitting signals are merged with the output of a wide-band noise generator in consideration of the impact of AWGN. After passing through the filter, one of the output paths of the aliasing signal is directed towards a spectrum analyzer. The purpose of this analyzer is to quantify the power of the signal as well as the in-band noise, enabling the measurement of the SNR of the received signals.  
The RF receiver is also constructed with ZYNQ-7000 and AD9361. To enhance the acquisition process, improved Verilog HDL acquisition programs are developed. With the assistance of ChipScope, we gather real-time processing outcomes from the acquisition program via a JTAG cable and utilize MATLAB for statistical analysis. To optimize data efficiency, we observe a substantial number of independent acquisition events, reaching up to \(10^{6}\) in total.  
The modulation mode of the test signal and primary parameters are listed in Table 2, while the performance of BER is shown in Figure 13. The solid line represents the BER results of theoretical simulation, while the dashed line represents the results of hardware platform testing. The hardware implementation of the improved algorithm suffers a loss of 1.5 dB compared to the theoretical simulation, which is caused by the circuit chip process and AD chips. In addition, the hardware platform exhibits performance similar to simulation. The feasibility of the enhanced algorithm on hardware is established.  
Figure 11: Topology of the hardware system.  
Figure 12: Photograph of the hardware system.
# Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones
## 3 Materials and Methods
### Calculating Required Voice Characteristics
```
1:\(sound\), \(pitch\_floor\), \(time\_step\), \(max\_freq\), \(pre\_emphasis\_from\)
2:PowerCepstrogram
3:functionPowerCepstrogram(\(sound\), \(pitch\_floor\), \(time\_step\), \(max\_freq\), \(pre\_emphasis\_from\))
4:\(sound\leftarrow\textsc{Resample}(sound,2\times max\_freq)\)
5:Pre-emphasize(\(sound\), \(pre\_emphasis\_from\))
6:\(window\_length\leftarrow\frac{3}{pitch\_floor}\)
7:\(window\leftarrow\) Gaussian window with length window length
8:\(frame\_length\leftarrow\) length of window
9:\(hop\_length\leftarrow\)\(time\_step\times 2\times max\_freq\)
10:\(num\_frames\leftarrow\lfloor\frac{\text{length of sound}-frame\_length}{ hop\_length}\rfloor\)
11:\(power\_cepstrogram\leftarrow\) empty matrix with dimensions \((\lfloor\frac{max\_freq}{pitch\_floor}\rfloor,num\_frames)\)
12:for\(i\gets 1\) to \(num\_frames\)do
13:\(start\leftarrow(i-1)\times hop\_length+1\)
14:\(end\leftarrowstart+frame\_length-1\)
15:\(frame\gets sound[start:end]\timeswindow\)
16:\(spectrum\leftarrow\textsc{Spectrum}(frame)\)
17:\(power\_spectrum\leftarrow|spectrum|^{2}\)
18:\(power\_cepstrum\leftarrow\textsc{PowerCepstrum}(power\_spectrum)\)
19:\(power\_cepstrogram[:,i]\leftarrow\) values from \(power\_cepstrum\) up to \(\lfloor\frac{max\_freq}{pitch\_floor}\rfloor\)
20:endfor
21:return\(power\_cepstrogram\)
22:endfunction
```  
**Algorithm 1** PowerCepstogram algorithm. The second step is to perform pitch analysis and calculate harmonicity (see Figure 7 and Algorithm 2). Figure 6: Calculation of the CPPS. Algorithm 2 shows an implementation of the pitch analysis method that uses the cross-correlation technique to determine the pitch of a sound signal. The input parameters are the sound signal, the time step, the pitch floor and ceiling, as well as various thresholds and costs that affect the pitch analysis. The algorithm returns a pitch object that contains the pitch measurements and other pitch-related information. The formant calculation algorithm is presented in Algorithm 3. This algorithm is a partial implementation of a function called \"getFormantMean\" that calculates the average value of a specified formant for a given time range. The function takes four arguments: \"formantNum\" specifies which formant to calculate the mean for, \"fromTime\" and \"toTime\" specify the time range to consider, and \"units\" specifies the units of the time range. Algorithm 4 presents an algorithm to calculate harmonicity using Praat's harmonicity object. This algorithm takes a Praat sound object, a start and end time for the analysis, a time step, a silence threshold, and the number of periods per analysis window. It first creates a Praat harmonicity object from the input sound, with the given time step, silence threshold, and periods per window. It then selects the portion of the harmonicity object corresponding to the specified time range and returns the mean value of the selected portion. Aproach is explained in the Algorithm 4.
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 1 Introduction
### Related Work and Motivation
End-to-end object detection (DETR) with the Transformer [12] first involves using the Transformer in advanced vision, which adds location information to complement image features and feeds them into the Transformer structure to obtain predicted class labels and bounding boxes. However, there is still low detection of small targets and weak local information acquisition. In this paper, image features are extracted using the Swin Transformer [13] as the backbone network. The Swin Transformer constructs a hierarchical Transformer and performs self-attention calculations within non-overlapping window regions. Rather than performing self-attention computation on the entire set of patch markers, it uses window partitioning as an attention mechanism to apply constraints on the position and allows communication between windows by using a shift configuration. Through the ensemble learning strategy, the feature extraction capabilities of different deep learning network frameworks in different pixel size datasets are integrated to give full play to the advantages of each model. In this way, it can improve the generalization ability of the model, and then improve the overall prediction accuracy.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
###### Abstract  
Ship detection, a crucial task, relies on the traditional CFAR (Constant False Alarm Rate) algorithm. However, this algorithm is not without its limitations. Noise and clutter in radar images introduce significant variability, hampering the detection of objects on the sea surface. The algorithm's theoretically Constant False Alarm Rates are not upheld in practice, particularly when conditions change abruptly, such as with Beaufort wind strength. Moreover, the high computational cost of signal processing adversely affects the detection process's efficiency. In previous work, a four-stage methodology was designed: The first preprocessing stage consisted of image enhancement by applying convolutions. Labeling and training were performed in the second stage using the Faster R-CNN architecture. In the third stage, model tuning was accomplished by adjusting the weight initialization and optimizer hyperparameters. Finally, object filtering was performed to retrieve only persistent objects. This work focuses on designing a specific methodology for ship detection in the Peruvian coast using commercial radar images. We introduce two key improvements: automatic cropping and a labeling interface. Using artificial intelligence techniques in automatic cropping leads to more precise edge extraction, improving the accuracy of object cropping. On the other hand, the developed labeling interface facilitates a comparative analysis of persistence in three consecutive rounds, significantly reducing the labeling times. These enhancements increase the labeling efficiency and enhance the learning of the detection model. A dataset consisting of 60 radar images is used for the experiments. Two classes of objects are considered, and cross-validation is applied in the training and validation models. The results yield a value of 0.0372 for the cost function, a recovery rate of 94.5%, and an accuracy rate of 95.1%, respectively. This work demonstrates that the proposed methodology can generate a high-performance model for contact detection in commercial radar images.  
Keywords: radar images; automatic cropping; deep learning; contact detection +
Footnote †: journal: Medical Imaging
## 1 Introduction
Specifically, we combine the statistical features and the cumulative load length sampling of messages in the DTLS data transmission phase, which in line with the difference in circuit establishment between Tor normal web access and hidden service access. Then, a classifier identifies if a Tor user is accessing a hidden service. * Our experiments show that our proposed method can accurately identify Snowflake traffic with little time consumption, and efficiently identify Tor-Snowflake hidden services traffic. The remainder of the paper is organized as follows. Section 2 discusses related work in Tor and Tor PT traffic identification, whereas Section 3 introduces our threat model and Snowflake communication principle. Architecture of the Tor-Snowflake traffic identification framework are described in Section 4, and a more detailed experimental evaluation is reported in Section 5. Section 6 concludes the paper.