esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 2 Related Works
### Object Detection  
Object detection is an essential computer vision task, which is considered the cornerstone of many advanced artificial intelligence tasks. Many object detection algorithms based on deep learning have been applied to the field of ships. Common deep learning detection algorithms include Faster R-CNN [3], MASK-RCNN [4; 5], SSD (Single Shot Multi-Box Detector) [6], and YOLO (You Only Look Once) [7]. However, surveillance images (such as photographs or videos) for ships are rare, while Synthetic Aperture Radar (SAR) is available all day under all weather. Thus, for ship object detection, methods for detection with SAR images are proposed, such as DDNet [8], Saliency-Based Centernet [8], and Expansion Pyramid Network (SEPN) [9]. For general ship surveillance images, K-means clustering prior box combined with the yolov network [10] and SSD_MobilenetV2 [11] have been applied to improve ship detection performance. In these studies, the NMS (Non-Maximum Suppression) effect and detection speed of the network for rectangular boxes are improved, but the detection accuracy and accuracy are decreased. In one study [12], the authors constructed ship datasets in the form of COCO datasets and adopted YOLOX, which introduced residual structure and CIoU loss function. The performance of the algorithm is significantly better than the original YOLOX algorithm. In ref. [13], an improved CenterNet network for ship detection in scale-changing images has been proposed, making the network more sensitive to small objects.
# Reducing System Load of Effective Video Using a Network Model
## 3 System Model or Proposal Method
### Investigation of Loss Comparison  
We decided to use mean opinion score (MOS) testing to determine the super-resolution network to be applied to FoV.  
Figure 16 shows the ability of SRGAN according to MOS testing. The MOS test is the evaluation of 12 versions of each image on the BSD100. The networks used for evaluation were nearest, bicubic, SRCNN, SelfExSR [15], DRCN [16], ESPCN [17], SRResNet, and SRGAN. Based on this, SRGAN was selected, and SRCNN, which is used in many environments, was applied to the experiment to compare the proposed architecture.  
First of all, SRCNN has the advantage of having a very simple structure and a high level of accuracy. Its speed is fast, even if only the CPU is used. In addition, it can make the model bigger and deeper, and better performance can be expected as the data set is diversified. It also has the advantage of processing three RGB channels and obtaining high-resolution images at the same time, so it can be used in many places. SRGAN's strength, meanwhile, is that it handles texture well and has a fast speed. By actively utilizing ResNet and skip-connection in the generator network, it shows good performance in saving time and memory. The following Table 1 shows the performance.  
After learning using the DIV2K data set [18], we performed standard quantitative measurements using PSNR and SSIM. The focus of this work was the perceptual quality of super-resolution images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**SRCNN**} & \multicolumn{2}{c}{**SRGAN**} \\ \cline{2-5}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline Ch. 1 & 43.25 & 0.9869 & 85.16 & 0.99 \\ \hline Ch. 2 & 43.08 & 0.9829 & 84.83 & 0.98 \\ \hline Ch. 3 & 58.11 & 0.9767 & 114.43 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative measures with Ch. 1–3.  
Figure 16: Color-coded distribution of MOS scores on BSD100. For each method 2600 samples (100 images \(\times\) 26 raters) were assessed. The mean is shown by the red markers, where the bins are centered around value \(i\) (4\(\times\) upscaling).  
Figure 15: Hierarchy of m3u8.
# Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
## 3 Materials and Methods
### Pixel Alignment Network  
#### 3.3.1 Precision Registration  
We obtain a spatial transformation through the pixel alignment network to align the ST1 image with the \(\underset{\text{T1}}{\rightarrow}\) image. The spatial transformation determines the pixel-to-pixel correspondence [38].  
\[\omega=argmin_{\varphi}\;reduce\bigg{(}\underset{\text{T1}}{\rightarrow},ST1 \circ\varphi\bigg{)}+\lambda reg(\varphi) \tag{3}\]  
where image similarity \(reduce\bigg{(}\underset{\text{T1}}{\rightarrow},ST1\circ\varphi\bigg{)}\) can be described as a combination of mean square (MSE) error and structural similarity (SSIM). \(reg(\varphi)\) is the regularization, whose objective is to ensure the smoothness of the deformation field. \(\lambda\) is the regularization parameter that balances similarity and smoothness.  
We concatenated the ST1 and \(\underset{\text{T1}}{\rightarrow}\) images generated by the border enhancement network and input them into the CNN to obtain the deformation field \(\varphi\). We put the ST1 and segmented label images into the deformation field to obtain the warped image (image A) and the warped segmented label. Through this method, the warped segmented labeled images are rich in structural information, and they will be the quality data for  
Figure 4: T1 segmentation label. A freehand spline drawing technique was used to segment all of the structures in the brain. The outline of each structure was delineated, starting with the innermost structures. By iteratively subtracting delineations to create holes, binary images were created for each structure. Segmentation was performed in a darkened room with optimal viewing conditions. The entire segmentation was inspected for correctness by an expert not involved in the segmentation procedure and corrections were made if needed. A third expert approved the entirety of final segmentation.  
subsequent reverse teaching and learning. Our whole process can be expressed by the following formula:  
\[w(t)^{\prime}=w+\varphi(\mathbf{w}) \tag{4}\]  
\(\varphi\) is the deformation field and \(\varphi(\mathbf{w})\) indicates the offset distance. Each pixel \(w\) in the moving image after transformation can be defined as \(w(t)^{\prime}\).  
Since the pixels are not necessarily integer after transformation, linear interpolation is used to avoid discontinuity in the transformed image. The formula is as follows:  
\[S\circ\varphi(\mathbf{w})=\sum_{q\in Z(\varphi(\mathbf{w}))}N(q)\prod_{d\in(x,y,z)}(1-|\varphi_{d}(w)-q_{d}|) \tag{5}\]  
where \(Z\) stands for the areas made up of nearby pixels. The projected results are smoother and more realistic as a result of this differentiable interpolation process.
## 2 Analysis Method
### Comparison of On-Site Monitoring Results  
In order to verify the accuracy of the numerical model and parameter selection, two representative sections (K1+809 section and K1+846 section) were selected for surface subsidence monitoring. The development curve of the subsidence slot curves of the two sections with the construction excavation sequence and the final settlement curve of the numerical simulation are shown in Figure 5.  
Comparing the on-site monitoring data and the finite element analysis results, the surface subsidence at a distance of one-hole diameter from the station center line is about 60% of that at the center line. The third step of excavation caused a larger surface subsidence. The maximum value is less than 3 mm, the actual final settlement curve is close to the numerical simulation results, and the maximum error is less than 8%. This underlines the robustness of the proposed model because there are no fitted parameters involved in the model.
# Applications of Cold Atmospheric Pressure Plasma in Density
## 7 CAPP for the Treatment of Oral Candidiasis  
Oral candidiasis is an opportunistic disease with high prevalence among immunocompromised patients [129]. Lately, reports on refractory cases of oropharyngeal candidiasis are increasing [130, 131] and the treatment of these cases has faced considerable challenges due to the increasing occurrence of antifungal resistance and low number of new antifungal molecules [132].  
Proton ATPases, efflux pumps, adherence, morphogenesis, and resistance to oxidative stress have posed as new targets to the development of novel antifungal agents [133, 134]. In this context, CAPP antifungal effect has been studied.  
Anti-_Candida albicans_ effect was reported by some studies [21, 135, 136, 137]. Additionally, CAPP showed modulatory effects on _C. albicans_ virulence factors, such as adhesion and filamentation [24, 136]. Suppression of ergosterol biosynthesis has been observed [138].  
Antibiofilm effect is considered a key factor to superficial candidiasis treatment. Exposure to He-CAPP for 150 s reduced significantly the viability of _C. albicans_ biofilms, with low cytotoxicity to Vero cells [24]. The same effect was detected when Ar-CAPP [28; 139; 140] and a microwave-Induced Plasma Torch were used [141]. Recently, [PERSON] et al. [142] reported that RONS from CAPP inhibited _C. albicans_ biofilms by affecting the fungal cell wall.  
Murine experimental model of oral candidiasis treated with CAPP displayed marked reduction in inflammation and fungal tissue invasion [47].
# Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things
## 4 EMLVA for Blind Channel Equalization
### VA and Its Variants  
The Viterbi algorithm (VA) was proposed in 1967 for maximum likelihood (ML) detection of convolutional codes, searching for the ML paths by dynamic programming. At each symbol duration, the path metrics of all paths entering the current state are compared by the VA, and the path with the largest metric is selected for that state, called the survivor path. Finally, the VA outputs the information bits corresponding to the survivor path, which is named the ML path. Thus, the computational complexity of the VA is \(O(M^{L}N)\), where \(M\) is the number of states of the finite-state machine, \(L\) is the memory length, and \(N\) is the length of the sequence. When the number of states in the trellis is too large, searching on the original full trellis for ML paths requires a large number of computational resources. It is not always necessary to traverse the full trellis when only the ML path is searched, especially in the case of a high signal-to-noise ratio.  
Before the VA, sequential decoding algorithms are used in decoding the convolutional code, which are essentially greedy algorithms. They only search for branches that are possible to become the ML path, which has low complexity, but cannot guarantee finding the ML path. Therefore, a variant of the VA algorithm, the Lazy VA algorithm [35; 36], was developed. A priority queue (PQ) data structure is introduced to ensure finding ML paths. Compared to the standard VA, the Lazy VA is far better than the Viterbi algorithm in complexity and no worse than the VA in the worst case. For the ease of writing, the VA and Lazy VA are denoted as Viterbi-like algorithms in this paper, and the Lazy Viterbi algorithm is summarized in Algorithm 1.  
```
1: The trellis is set to empty, and the PQ contains the initial node \(n_{s}\) with initialized cumulative metric \(acc(n_{s})=0\).
2: Pop the top node \(n_{1}\) of PQ.
3:if\(n_{1}\) is the same as some node of the trellis then
4: Discard \(n_{1}\)
5:elseif\(n_{1}\) with the smallest metric in the PQ then
6: Output as the current node
7:elseif\(n_{1}\) is not the last node then
8: Insert its successors into PQ, and return to Step 2
9:else
10: Trace back the ML path and output hard/soft decision bits
11:endif
```  
**Algorithm 1** Lazy Viterbi algorithm [36].
## 1 Introduction
Through intensive simulation, we evaluate the data gathering performances of the proposed protocol and several existing schemes, and the results show that DRADG protocol achieves comparable or higher message delivery ratios at the cost of the lower transmission overhead than the existing schemes. The rest of the paper is organized as follows: we review the related work in Section 2 and identify the problems in the existing works. In Section 3, we present the DRADG protocol in detail. The simulation is carried out, and the performance is evaluated in Section 4. Finally, we conclude this work in Section 5.
## 4 Experimental Validations
### Compliant Path Generator
#### 4.1.1 Validity of the Hinge-Free Design Scheme  
For comparison purposes, the traditional optimization design model of compliant mechanisms is first provided as follows:  
\[\begin{array}{ll}\text{Minimize}:&J=-\sum\limits_{i=1}^{n}\omega_{i}u_{OI}\\ \text{Subject to}:&V/V_{0}\leq f\\ &\mathbf{KU}=F\\ &0<x_{\min}\leq x_{e}\leq 1\\ &e=1,2,\ldots,N\\ &i=1,2,\ldots,n\end{array} \tag{17}\]  
where \(\mathbf{K}\) differs from that in Equation (13) due to the adoption of the spring model and is expressed as  
\[\mathbf{K}=\sum\limits_{e=1}^{N}E_{e}\mathbf{k}+\sum\limits_{i=1}^{n}\left(\mathbf{K}_{li} +\mathbf{K}_{OI}\right) \tag{18}\]  
in which \(\mathbf{K}_{li}\) and \(\mathbf{K}_{OI}\) denote the global stiffness matrices related to the input spring at input point \(li\) and output spring at output point \(OI\), respectively. In this test, the stiffness coefficients of input and output springs are all set to 0.1.  
To verify the effectiveness of the objective function defined by Equation (8) in eliminating de facto hinges, the proposed optimization model (13) without considering coupling constraints is adopted here as  
\[\begin{array}{ll}\text{Minimize}:&J=-\sum\limits_{i=1}^{n}\omega_{i}u_{OI} +\sum\limits_{i=1}^{n}w_{li}C_{li}+\sum\limits_{i=1}^{n}w_{OI}C_{OI}\\ \text{Subject to}:&\frac{V}{V_{0}}=\sum\limits_{e=1}^{n}v_{e}x_{e}/V_{0}\leq f \\ &\mathbf{KU}_{I}=\mathbf{F}_{I}\), \(\mathbf{KV}_{I}=f_{I}\), \(\mathbf{KV}_{O}=f_{O}\\ &0<x_{\min}\leq x_{e}\leq 1;\,e=1,2,\ldots,N\\ &i=1,2,\ldots,n\\ &j=1,2,\ldots,i-1,i+1,\ldots,n\end{array} \tag{19}\]  
The design results of the path generator related to optimization models (17) and (19) are shown in Figure 5a,b, respectively. Both results have reasonable load paths for this compliant mechanism with two inputs and two outputs, even if their topologies are significantly different. Figure 6 plots the corresponding strain energy density distributions.  
Figure 4: Design domain and boundary conditions of the compliant path generator.  
The optimized path generator in Figure 5a is the lumped compliant mechanism as it contains several de facto hinges, around which the values of strain energy density are pretty large, as shown in Figure 6a. It can be seen from Figures 5b and 6b that there are no slender hinges with high strain energy density in the design result, which means that the path generator optimized by using the objective function (8) is the hinge-free compliant mechanism (i.e., the distributed compliant mechanism).
# Probabilistic Models for the Shear Strength of RC Deep Beams
## 4 Probabilistic Shear Strength Models
### Operation of Probabilistic Models
#### 4.1.2 Parameter Removal Process  
The parameter with the maximum value of the Coefficient of Variation (CoV) was regarded as causing little influence on the shear strength of deep beams and would be removed [11]. The parameter deletion process is as follows:  
1. Calculate the posterior estimation values of parameters \(\mathbf{\theta}=[\theta_{1},\,\theta_{2},\,\ldots,\,\theta_{\rm p}]\) and \(\sigma\).
2. Calculate the coefficient of variation COV for \(\theta_{i}\): The Coefficient of Variation of each parameter is calculated as \[\text{COV}=\frac{\sigma_{i}}{\mu_{i}},\] (15) where \(\sigma_{i}\) and \(\mu_{i}\) are the standard deviations and the mean value of the \(i\)th parameter, respectively.
3. Remove \(h_{i}(x)\) with the highest coefficient of variation COV. If the COV for the \(\theta_{i}\) is the largest, it is considered that the \(h_{i}(x)\) has the most negligible impact and is removed. It is continuing the adjustment with the remaining term \(h(x)\).
4. Repeat the parameter removal process until a significant increase occurs in the prediction model's standard deviation (SD).
## 3 Materials and Methods
### Classic Machine Learning Pipeline
#### 3.2.2 Radiomic Feature Extraction  
In this work, the feature extraction was performed automatically using an open-source Python package, the _PyRadiomics_[40]. This package allows the extraction of radiomic features from medical images in accordance with the Image Biomarker Standardization Initiative (IBSI) [41].  
The extracted features can be grouped into three sub-groups:  
1. First order statistical features--describe the variations of pixel's intensity inside the defined ROI, and include metrics such as the energy, entropy or median of the pixel intensities;
2. Textural features--can describe the tumour heterogeneity and the inter-voxel relations in the image ROI. These relations can be quantified using: the Grey Level Co-occurrence Matrix (GLCM), the Grey Level Run Length Matrix (GLRLM), the Grey Level Size Zone (GLSZM), the Grey Level Dependence Matrix (GLDM) and the Neighbouring Grey Tone Difference Matrix (NGTDM).
3. Shape-based--describe the tumour size and shape in 2D or 3D.  
Prior to the feature extraction, six different filters were applied to the original image: (1) square, (2) square root, (3) logarithm, (4) exponential, (5) gradient (all computed according to the absolute intensity of each pixel), and (6) local binary pattern. The wavelets of the image were also computed, resulting in 8 levels of decomposition: HHH, HHL, HLH, LHH, LLL, LHL, HLL, and LLH. Moreover, for the feature extraction in the 3D volume, different Laplacian Gaussian filters (LOG) were applied. The filters were applied with sigma values from 0.5 to 5, with a step of 0.5. With the exception of the shape-based features that are not intensity-dependent and are only dependent on the ROI shape, all feature classes were extracted from the original image and in the derived images. The derived images allow the reduction of noise and the enhancement of edges to improve the representation of different structures in the tumours ROI [5].  
With the purpose of understanding, if radiomic features extracted from a single tumour slice can have the same importance as features extracted from all tumour volumes, different models were constructed using just 2D features or just 3D features. For the case of a single tumour slice, the slice with a larger tumour area was chosen. For the feature extraction in 2D and 3D, a total number of 1032 and 2525 features were extracted, respectively. In both  
Figure 3: Pre-processing of the 2D slices and the 3D CT scans: first a voxel resampling and discretization is applied, followed by image cropping. For the images and volumes used for the DL pipeline, image resampling and normalization are also applied.  
situations, after the feature extraction, all the radiomic features were normalized to values from 0 to 1.
## 4 Experimental Results  
In this section, the defect detection performance of the improved YOLOV8 algorithm for subtle defects in industrial product packaging bags is systematically analyzed and evaluated. The detection results of the proposed algorithm for various subtle defects are summarized. To validate the detection performance of the FSNB_YOLOV8 network structure in scenarios such as fine black spots and damage, trained weights are first used to verify the model. Evaluation metrics, including accuracy (Equation (14)), recall (Equation (15)), mean average precision (Equations (16) and (17)), and FPS are employed to assess the model's performance. Precision refers to the proportion of truly positive samples among the outcomes predicted by the model as positive and measures the accuracy of the model's predictions for positive samples. Recall represents the proportion of actual positive samples that are correctly predicted as positive by the model, evaluating the model's ability to identify all positive samples. The area under the precision-recall curve is the average precision (AP) for that particular category. Subsequently, to explore the impact of each module on the model, ablation experiments are conducted for each improvement item using the YOLOv8s model as the baseline. The same training data and parameter settings are applied for these experiments. Finally, comparisons are made with existing detection methods, including Faster R-CNN, SSD, RetinaNet, YOLOv5, and YOLOv8. All the aforementioned experiments are conducted on a system equipped with an NVIDIA GeForce GTX 4090.  
Modeled as follows:  
\[P=\frac{TP}{TP+FP}\times 100\% \tag{14}\]  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{15}\]  
\(P\) represents accuracy, \(R\) represents recall rate, \(TP\) indicates marking positive samples as positive samples, \(FP\) indicates marking negative samples as positive samples, \(FN\) indicates marking positive samples as negative samples, \(TN\) indicates labeling negative samples as negative samples.  
\[AP=\int_{0}^{1}P(R)dR\times 100\% \tag{16}\]  
\[mAP=\frac{1}{c}\sum_{i=1}^{c}AP_{i}=\frac{1}{C}\int_{0}^{1}P(R)dR \tag{17}\]  
\(c\) is the number of categories, \(AP\) is the average accuracy, \(mAP\) is the mean average accuracy.
## 3 Key Findings
### What Security Enhancement Could the Quantum Mechanism Offer the IIoT Ecosystem?
#### 3.3.3 Quantum Deployment in IIoT
[89], in recognition of the sensitivity and heterogeneity of large voluminous health-related data, implemented quantum machine learning (QML) to detect and assess security loopholes in the Internet of Medical Things (IoMT) for accurate predictions. The authors proposed an innovative fused semi-supervised learning model, compared QML-based results with the other five ML algorithms, and highlighted the merits of QML for a veritable security assessment in IoMT. In addition, [PERSON] et al. [4] discovered operational flaws in the finance sector, and therefore implemented a QNN to actualize an optimized scalable and smart transparent financial sector. Furthermore, the quantum capability to remedy the longer training time [35], computational complexity in CLAs using different approaches [7; 93; 111] and inelasticity of classical bits promises to allay the fear of processing times, computational complexity, and classical bits challenges when QML is deployed for IIoTsec. However, possible limitations and challenges surrounding the deployment of QML are presented in Section 4.
## 3 Results and Discussion  
### EO and Resins Content (Yield)  
The hydrodistillation of frozen infloreses and frozen leaves gave EO yields of 0.447% and 0.0357% (in the Kompolti variety) and 0.601% and 0.0118% (in the Tisza variety).  
The solid-liquid extraction of infloreseses gave a good yield in resin, particularly in the Tisza cultivar (19.1%) (see Table 1).  
The statistical analysis carried out on the yield of essential oil and resin in replicates of extraction gave us interesting information, as reported in Table 2. The Honest Significant Differences (HSD) provided by [PERSON]'s test application indicated that there are not significant differences in the yield of essential oil derived from the leaves of both Kompolti and Tisza, and also, there are not significative differences in the yield of essential oil derived from infloreses coming from Kompolti and Tisza; on the contrary, the differences between the yield in essential oil from the infloresesences and leaves are significant. Also, the difference in the yield of resin between the two cultivars analyzed is really significant, and the Tisza cultivar is richer in resin than the Kompolti cultivar.