esa-sceva/satcom-chunk-collection huggingface dataset:
# Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods
## 1 Introduction
The inter-class occlusion refers to the object which is occluded by an object of another category such as the vehicle in pedestrian detection. Consequently, computer vision techniques might the troublesome for detection because the object to be detected may not appear similar to the objects in the training data set. To deal with the occlusion [20], it can be the generated de-occluded image using the generative adversarial network (GAN) to reconstruct the occluded object in the image. However, this method requires of a large various of object categories and labeling a dataset with feasible occlusions of every category. In term of occluded human image, if the computer vision techniques [10; 11; 12] can detect some parts of the body of human such as hand or face, these body parts may be possible to determine the position of the lower-body from the locations of the detected body parts by referencing anthropometric data [21]. Anthropometrics is the study of measurements of the human body taken from diverse populations such as age, gender or nationality. Applications of using the anthropometric consist of a suitable design of clothing [22] and workstation [23] from this human body reference. Anthropometric data involves human body measurements such as weight, height, and length. For human body detection, measurements of physical limb and body proportions can be used to investigate various associations with the height and width of the lower-body in an image [24; 25]. Anthropometric data include age, gender, nationality and human body measurements such as weight, height, and girth. These data are useful for applications such as the suitable design of clothing, machines, and workspaces based on human body references. Moreover, These data can be beneficial to examine differences in the anthropometric characteristics and physical capacity in padel players concerning their competing level [26]. This paper presents novel anthropometric ratios that can be used in combination with both deep learning and traditional methods for lower-body detection. The proposed ratios can be applied to human images to detect either certain parts of the body or the full human body. The detected lower-body will then be indicated on the output image, as shown in Figure 1.  
The rest of this paper is organized as follows: Section 2 briefly describes the related work in the literature. The proposed method is introduced in Section 3. Experimental results are reported in Section 4 and discussed in Section 5. Finally, the conclusion is presented in Section 6.
## 2 Experiments
### Experimental Results
#### 2.6.3 Fractography  
To understand the fracture mechanism, scanning electric microscope (SEM) observations were performed. Figure 12 shows the fracture surfaces near the crack tip region and along the crack flank resulting from RP2, WT3, and RF1. Heavily deformed ridges and valleys were rubbed out in the sliding direction and also several oxidized wear particles were found on the surface. No striation pattern was observed near the crack tip region in case of WT3 and RF1, but RP2 exhibited obscure striations that seemed to be scattered. Hence, the fractographic observation permitted the determination of the crack growth caused by non-proportional mixed mode I/II loading, which was clearly distinguishable from that generated by pure mode I loading.  
In summary, these were the crack growth characteristics observed in rail and wheel steel specimens subjected to non-proportional mixed mode I/II loading:  
1. When \(\Delta K_{\rm II}\)/\(\Delta K_{\rm I}\) increased, the crack tended to branch;
2. As \(\delta\) increased, the crack easily branched;
3. The coplanar crack growth rates in RF were lower than those in WT and RP;
4. The branch crack growth rate varied considerably depending on \(\Delta K_{\rm II}\)/\(\Delta K_{\rm I}\) even in the same material, unlike the coplanar growth rate, could not be correlated by a single line;
5. No clear striation patterns were found near the crack tip region.  
Figure 10: Coplanar crack growth rates of mixed mode I/II against \(\Delta K_{\rm v}\) for RP, RF and WT (solid line; RP, one dot chain line; RF, broken line; WT).
## 3 A Proposed Approach
### Flow Chart of the Approach  
The flow chart of the discussed method and its algorithmic presentation is shown in Figure 1. There are five main steps in the proposed method as follows:Step 1 is to set the FM method parameters, which is the basis of planning space representation.  
Step 2 is to plan a preliminary path. In this step, the route is scheduled based on an initial representation of the planning space and used to predict the own ship's (OS's) trajectory.  
Step 3 is to forecast the minimum relative distance and the corresponding time. In this procedure, the accuracy of the trajectory prediction of the own ship (OS) and TSs directly affects the efficiency of path planning results.  
Step 4 is to determine the encounter situation, which is the foundation for path optimization.  
Step 5 is to re-plan the path for collision avoidance.  
After that, if the planned path cannot be further optimized and is safe enough, it will be applied for collision avoidance. Otherwise, it is necessary to go back to Step 3 or Step 1 for the path planning updating process.  
Figure 1: The flow chart of the proposed path planning approach.
# Method for Detecting Underwater Microbubbles Using Dual-Mode Fusion of Laser Polarization
## 1 Introduction
[15] further developed an underwater image restoration algorithm based on polarization imaging under non-uniform light field. However, the above method relies heavily on the accuracy of extraction of the background region without any detection target, which is difficult to adapt to the complex Marine environment where the turbulent field induced by the bubble causes the sediment to float, and it is difficult to detect and extract the time-varying bubble characteristics of the polarization state. In order to address the underwater strong scattering complex field, non-static bubble recognition, and feature detection, this paper selects and merges laser polarization detection and active imaging detection, which have similar application modes, complementary detection modes, and a strong correlation of data sources, and proposes a detection mechanism based on laser polarization dual-mode fusion. This process employs the linear polarization degree of the image as the weight to fuse the collected polarized and unpolarized images. It achieves this by combining bubble polarization simulation, polarization, and unbiased image fusion into the laser active illumination imaging system. The image's region enhances the polarization features of bubbles. In order to detect bubble speckle and extract bubble size, number density, and other properties from the fusion image, a time-varying model of bubble polarization based on simulation and image processing techniques was employed. Lastly, a probabilistic fusion method based on laser polarization and bimodal image data is proposed. This method combines the different characteristics of the image, such as its size, number, and polarization degree, and establishes a probabilistic model for the posterior distribution of discriminative features like these. It also realizes the fitting of the small-sample and multi-source data of bubbles. The relationship function characterizing the fluctuation of image grayscale value by the dynamic occupancy ratio of the confidence value is also combined. This mechanism allows us to quickly and effectively detect the distribution of the underwater bubble field. In addition to actively predicting the influence of the bubble environment on optical imaging, we can also invert the bubble morphology, number, and other features based on grayscale changes.
## 2 Design of the Proposed DDL-TDC  
### Method of TDL-TDC  
TDL is the most popular structure used in TDC implementation (Figure 1); however, a lack of uniformity in delay cells tends to undermine time linearity, particularly in FPGA-based TDC devices. Figure 1 shows the architecture of the TDL-TDC, and the histogram illustrates the time distribution of the delay cell in the delay chain. It is easy to observe that the delay cell cannot reach an equal delay time in the chain. This impairs the linearity of the TDC; we want to correct this nonlinearity effect in this work. A calibration method is presented in [29], which provides a simple bin calibration scheme for FPGA-based TDC designs. Thus, the uniformity delay cell in an FPGA-based TDC can be calibrated, and the linearity can be improved. Unfortunately, the method addressed in [29] calibrated the TDC output by using a software program after the TDL-TDC output had been measured. Thus, that scheme cannot calibrate the uniform delay cell immediately in a run-time conversion. Unlike off-line methods, such as that proposed in [29], we adopted a statistical approach to the calibration of an FPGA-based TDC [30] to address the topic of time linearity in real time.  
Figure 1: Architecture of TDL-TDC.
## 5 Analysis and Discussion  
### Correlation Analysis Between CVR Fractures and Parameter Analysis  
This study identified parameters affecting CVR weld fractures caused by temperature variations around tunnel ventilation shafts and analyzed correlations between these  
Figure 14: Analysis of rail temperature variations caused by axial load.  
Figure 13: Displacement analysis of rail fracture under axial load.  
variables. The analysis of tunnel temperature variations during CWR fractures indicated that the temperature difference (\(\triangle\)t) in underground sections was underestimated. Further, increased localized temperature fluctuations near tunnel ventilation shafts led to a considerably higher frequency of CWR fractures. Temperature variations during CWR fractures predominantly occurred around 35 \({}^{\circ}\)C, reaching a maximum value of 50 \({}^{\circ}\)C. The average temperature variation (\(\Delta\)t) was calculated using a Gaussian probability density function and applied as a load condition for analysis and numerical simulations [34].  
The analysis of CWR fracture occurrences showed that more than 40% (20 cases) occurred within 0-20 m (40 m) of the ventilation shaft center. In addition, fractures were concentrated within \(\pm\)10 m (20 m) of the ventilation shaft, with the frequency increasing with a decrease in the distance from the ventilation shaft. Field investigations and machine learning analysis indicated a strong correlation between temperature variations and the distance from ventilation shafts.  
This study used machine learning to analyze the correlation and priority ranking of parameters affecting CWR fractures near ventilation shafts. A machine learning analysis using the final set of parameters yielded results consistent with the actual CWR fracture data, particularly regarding temperature variations and the distance from ventilation shafts to the CWR fracture when CWR fractures occurred. The most influential independent variables were temperature variations (minimum, maximum, \(\Delta\)t), the distance from the ventilation shaft in the longitudinal direction (x-direction, train length direction), and track geometry conditions (curve radius, inclination).
# Relation \"Greater than or Equal to\" between Ordered Fuzzy Numbers
## 3 Relation \"Greater than or Equal to\" for Fuzzy Numbers
\begin{array}{l}\
u_{|[GE]}(\mathcal{L}(a,b,c,d,L_{K},R_{K}),\ \mathcal{L}(e,f,g,h,L_{M},R_{M}))=\
u_{|[GE]}(\mathcal{L}(a-h,b-g,c-f,d-e,L_{W },R_{W}),\llbracket 0\rrbracket)=\\ =R_{W}(0).\text{QED}\end{array}  \tag{74}\]  
**Example 2**. _Let us take into account the FNs \(\mathcal{U}=\mathcal{L}(12,14,18,20,L_{U},R_{U})\) and \(\mathcal{V}=\mathcal{L}(5,6,11,13,L_{V},R_{V})\) respectively determined by identities (62) and (64). We compare these FNs with using of fuzzy preorder \([GE]\in\mathcal{F}\Big{(}\mathbb{F}^{2}\Big{)}\). We have here_  
\[-1=12-13\leq 0\leq 14-11\ =\ 3.\]  
_Therefore, we should establish the variability of the function \(R_{W}\in[0,1]^{[-1,3]}\) determined by the identity (28). First, by using identities (14) and (15), we assign functions_  
\[L_{U}^{\star}(\alpha)=\min\{x\in[12,14]:\ L_{U}(x)\geq\alpha\}=L_{U}^{-1}( \alpha)=\frac{10\alpha-24}{\alpha-2}, \tag{75}\]  
\[R_{V}^{\star}(\alpha)=\max\{x\in[11,15]:\ R_{V}(x)\geq\alpha\}=R_{V}^{-1}( \alpha)=\frac{15\alpha-26}{\alpha-2}. \tag{76}\]  
_In the next step, applying (25) and (27), we obtain_  
\[r_{W}(\alpha)=R_{V}^{\star}(\alpha)-L_{U}^{\star}(\alpha)=\frac{15\alpha-26} {\alpha-2}-\frac{10\alpha-24}{\alpha-2}=\frac{5\alpha-2}{\alpha-2}, \tag{77}\]  
\[R_{W}(x)=r_{W}^{\star}(x)=\min\{\alpha\in[0;1]:l_{W}(\alpha)=x\}=l_{W}^{-1}(x )=\frac{2\cdot(x-1)}{x-5}. \tag{78}\]_Finally, using identity (71), we get_  
\[\
u_{[GE]}(\mathcal{V},\mathcal{U})=R_{W}(0)=\frac{2}{5}. \tag{79}\]  
The above example together with Theorem 3 shows that fuzzy preorder \([\mathrm{GE}]\in\mathcal{F}\big{(}\mathbb{F}^{2}\big{)}\) depends only on the interaction between the right reference function of the first compared FN and the left reference function of the second compared FN. Moreover, Theorem 3 immediately implies that for any TrFNs we have:  
**Theorem 4**. : _For any TrFNs \(Tr(a,b,c,d),\ Tr(e,f,g,h)\in\mathbb{F}_{Tr}\) we have_  
\[\
u_{[\mathrm{GE}]}(Tr(a,b,c,d),\ Tr(e,f,g,h))=\left\{ \begin{array}{cl}0,&0<d -e,\\ \frac{d-e}{d+f-e-e^{\epsilon}},&d-e\leq 0<c-f,\\ 1,&0\leq c-f.\end{array} \right. \tag{80}\]
## 4 Comparison with a Design Example and Experimental Results
### Numerical Comparison of Losses  
Finally, the losses of the three converters were calculated for comparison purposes. Table 1 shows the parameters used for this purpose.  
Figure 13: Photograph of the experimental setup.  
Table 2 shows the losses in the LVC with the proposed operation, and no significant losses were observed in the LVC converter with the former and the proposed operation, and then the comparison was made with the traditional boost vs. the LVC converter in the proposed operation.  
From the comparison, we can see that although the proposed converter has more switches, they split the current among them, and the conduction losses are proportional to the square of the current. Furthermore, their reduced voltage helps reduce the power losses in each transistor. Something similar happened with the inductor. The current split among \(L_{1}\) and \(L_{2}\) helped reduce the losses compared to having a single inductor. However, the difference in power losses is relatively low; the main advantage of the LVC converter in the proposed operation is the significant reduction in the voltage ripple at the output port without increasing the stored energy in the capacitors or any other parameter.  
Table 3 shows the losses in the boost converter in the described operating point.
## 2 Results and Discussion  
### AMP Combination  
[PERSON] et al. investigated novel beta-Ala-modified analogues of anoplin (from solitary spider wasps) [11]. Ano-1\(\beta\) as well as Ano-8\(\beta\) showed synergistic effects against _Pseudomonas aeruginosa_ ATCC 27853 in combination with the antibiotics polymyxin B and rifampicin, as well as an additive effect with gentamicin. AMP cecropin A (CecA) was obtained from the greater wax moth (_Galleria mellonella_) and eradicated uropathogenic _Escherichia coli_ (UPEC) biofilms, destroying both sessile and planktonic UPEC cells. When combined with the antibiotic nalidixic acid, synergistic activity was observed against UPEC strain CFT073 [12].  
Periodontitis and caries are major diseases of the mouth and teeth that require the attention of dentists. [PERSON] et al. used chlorin-e6 (an extract from chlorophyll obtained from _Spirulina maxima_) conjugated to LL-37 in a nanoemulsion nanocarrier to inhibit biofilms that comprised multiple species during photodynamic therapy [13]. A combination of AMP GH12 with fluoride (GH12/NaF) was found to inhibit the development of caries, regulate the microbiota, and suppress both acid and exopolysaccharide production by biofilms [14]. With respect to the food industry, [PERSON] et al. report using a combination of starch/poly (butylene adipate-co-terephthalate) (PBAT) together with AMPs \(\varepsilon\)-PL and nisin in an antimicrobial film. This film effectively inhibited _Staphylococcus aureus_ and _E. coli_ growth and prolonged the shelf life of fresh peaches. This type of approach may be preferable to consumers compared to the use of chemical preservatives in foodstuffs themselves [15]. In hospitals, _Klebsiella pneumonia_ is a bacterium associated with difficult to treat multidrug-resistant (MDR) and extensively drug-resistant (XDR) infections. However, AMP K11 generated synergistic effects against this bacterium in combination with ceftaglime, chloramphenicol, meropenem, and rifampicin but not colistin antibiotics. K11 also exhibited good stability in physiological salts and serum, as well as high pH and high thermal stability [16]. Another AMP (Pt5-1c) showed synergistic activity in combination with traditional antibiotics against MDR bacteria including _K. pneumoniae_, _S. aureus_, and _E. coli_[17], while a combination of acidic sophorolipid nanoparticles and LL-37 peptides severely damaged the cell membrane of _E. coli_ as shown by atomic force microscopy (AFM) and effectively killed _E. coli_, _S. aureus_, _Staphylococcus epidermidis_ and _Pseudomonas aeruginosa_[18]. Finally, combinations of the frog skin AMP temporin L (TL) with different anionic cyclodext (types of polysaccharides) have been shown to be potent and safe agents against sessile bacteria [19].  
New combinations of AMPs with new or existing antimicrobials or materials could potentially generate killing and anti-biofilm synergies applicable in dental, food, and hospital environments.
# MPEG and DA-AD Resilient DCT-Based Video Watermarking Using Adaptive Frame Selection
## 3 Proposed Method  
### Methodology  
In our proposed scheme, we adopted DCT domain spread-spectrum watermarking [26] to work in a video frame watermarking and digital/analog-analog/digital (DA-AD) environment. A pseudo-random sequence of length \(N\) with zero-mean and unit variance was adopted as the watermark pattern. The general process of the watermarking method is summarized in Figure 1.  
The frames were adaptively selected for embedding the secret message. A 1-s interval was selected for watermarking for every 7500 ms of the video content. Before embedding into the selected frames, a total of 512 pseudo-random patterns were generated to encode 16 bits of information, i.e., 256 pseudo-random patterns for encoding the front 8 bits of information, and another 256 pseudo-random patterns for the back 8 bits. The pseudo-random patterns are stored as a watermark pattern and will be used for the extraction step. Therefore, two watermark patterns are inserted into the selected video frames. Because the variations in the blue channel are less perceptible for the human visual system than those in the red and green channels [27], the pseudo-random patterns are inserted into the blue channel. In the watermark extraction stage, the primitive vectors that include the watermark pattern were extracted from the complete frames. If two watermark patterns are accurately detected, we decode 16 bits of information from these patterns using cross-correlation values with the 512 generated pseudo-random patterns.
## 4 Experimentation and Results Analysis
### Experimental Results  
The C-MBR was analyzed in comparison with the remaining five baseline models to show its results in the dataset. Detailed information is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Indicators** & **Clarification** & **HR@10** & **NDCG@10** \\ \hline \multirow{3}{*}{EHCF} & Predicting each behavior by migration and & & \\  & unfolding multi-behavior recommendations & 0.1520 & 0.0796 \\  & using free-sampling learning & & \\  & Deep models for multi-behavioral & & \\  & recommendations & 0.1283 & 0.0654 \\  & Combining multi-behavioral relationships & & \\  & with graph neural networks & & \\  & Graph-based cross-behavioral comparative & & \\  & learning added to NGCF-M & & \\  & Self-supervised graph neural networks for & & \\  & multi-behavioral recommendations & & \\  & Cross-structural multi-behavioral model & 0.1687 & 0.0902 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of different models on the dataset (N = 10).  
Table 1 reveals that C-MBR exhibits superior performance compared to the remaining five baseline models. Across both rating indicators, C-MBR demonstrates an improvement of 4.5% to 6.9% compared to the optimal baseline model, indicating its superior performance and effectiveness in constructing local and global structures to capture users' dynamic preferences. The fusion of sequence-based and graph-based recommendation models enhances information complementarity, allowing for more comprehensive learning of user preferences. Through analyzing sequence- and graph-based user behavior representations and comparing the two, commonalities are identified, enhancing modeling quality. NGCF lacks consideration for the comparative learning task, while NGCF-CL, despite its addition of graph-based cross-behavioral comparative learning, still falls short. However, NGCF-CL outperforms NGCF-M, underscoring the effectiveness of cross-behavioral comparative learning in capturing behavior differences and commonalities. EHCF, NMR, and NGCF-F rely on supervised model construction, making it challenging to ensure accuracy in sparse data scenarios. In contrast, the C-MBR model effectively addresses this issue by analyzing multiple behaviors, learning user dynamic behavioral preferences across structures, and incorporating a comparative learning task simultaneously.