esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Eddy Current Sensor in Tension Measurement at a Resonant Frequency
## 4 Experimental Results and Discussion
### The Influence of Sleeve Structure on Sensor Performance  
#### 4.3.1 Correlation Coefficient  
According to the repetitive experimental results, in order to obtain the relationship between the external tension and the impedance parameters, the data of impedance magnitude and phase at the resonant frequency were separately extracted and fitted into linear curves. To study the influence  
Figure 11: Impedance phase analysis of eddy current sensor in the (**a**) loading and (**b**) unloading processes.  
Figure 12: Fitted results between the impedance phase and the external tension in the (**a**) loading and (**b**) unloading processes.  
of sleeve structure on sensor performance, the correlation and relativity sensitivity coefficients of fitted curves with and without the sleeve structure were analyzed. Comparison results of correlation coefficient are shown in Table 3.  
It should be noted that all correlation coefficients exceed 0.974, indicating the existence of a linear relationship between the external tension and the impedance parameters of the sensor. Moreover, the sensor correlation coefficient with the sleeve structure was obviously larger than that with the non-sleeve structure. The reason for the larger coefficient is that the sleeve structure provides a higher permeability path to magnetic field lines than the non-sleeve structure, thus optimizing magnetic field distribution and improving sensor performances.
## 7 Conclusions and Limitations  
This paper aims to offer an overview of the current DDoS attack detection methods and a survey of research perspectives in this domain. DDoS attack detection methods are classified based on how they are used. Different research studies under each classification are presented. DDoS attack detection studies, which range from a conventional entropy-based approach to more sophisticated deep learning models, are extensively covered. These approaches have all been used, with varying levels of effectiveness and restrictions. Although information-entropy-based DDoS attack detection approaches use fewer resources, they all require selecting the right threshold to achieve the desired detection results. It can be challenging to choose the right detection threshold in various attack environments, since different networks have different traffic patterns. The features of network traffic can be understood by machine learning and deep learning algorithms, which can then be trained to create a very precise model for detecting these features. However, they have more computational overhead, which limits their applicability to resource-constrained IoT devices. The relevance of research advancements in this field is demonstrated by the sizeable amount of research now being carried out. It is clear from this review that the approaches currently in use can identify DDoS attacks with varying degrees of accuracy. However, every approach has one or more limitations to overcome. Thus, more research studies are still required in this domain. With the advent of 5G and beyond, which will enable the connectivity of more devices, more attacks with varying attack patterns are anticipated, making improved attack detection more important. This paper has identified six research gaps that could influence future research on improving attack detection performance. Even though this survey concentrates primarily on DDoS attack types and detection studies in the IoT, studies focusing on attack detection in SDN, RPL, NDN, and vehicular (the internet of flying things) are also researched and presented. However, detailed discussions on the attack types in RPL, NDN, and the internet of flying things are part of the limitations of this study. In addition, this study is limited to a few carefully chosen datasets, even though they are thought to be the most pertinent and related to the research topic of this study. However, other surveys may also include some additional datasets pertinent to this subject.  
[PERSON] conceived the original idea of the paper and was in charge of the manuscript draft, while [PERSON]M. and [PERSON] helped with some improvements to the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors acknowledged the resources provided by the Tshwane University of Technology, Pretoria, and CSIR, Pretoria, South Africa.  
The authors declare no conflict of interest.
## 3 Results & Discussion
### Comparison of MAE, SLE & SFE
On the other hand, SFE presented the lowest extraction yield and selectivity towards chlorophylls, yet a significantly improved extract was obtained in terms of carotenoid content, antioxidant activity and smell. Eventually, the addition of a cosolvent improved the dissolving ability of SC-CO\({}_{2}\), therefore, increasing the yield of SFE, the bioactive compound content, and the extract's antioxidant activity. Figure 8: Comparison between the conventional solid-liquid extraction (SLE), microwave-assisted extraction (MAE), supercritical fluid extraction (SFE) and supercritical fluid extraction with 10% \(w\)/\(w\) cosolvent addition (SFE + 10% Ethanol) of _Chlorella vulgaris_, regarding the (**a**) selected and (**b**) total carotenoid content expressed in amount of bioactive component per amount of biomass.
## 5 Conclusions  
The T-matrix is a complete scattering information set that only depends on the particle size, refractive index, and shape; therefore, once the T-matrix is obtained, the scattering properties of the particles with arbitrary orientations can be calculated analytically, which is a tremendous advantage over DDA, FDTD, and other scattering models. In this paper, a T-matrix scattering model was developed by combining the invariant imbedding iterative technique and the Lorenz-Mie theory. Compared with the T-Matrix model developed by [PERSON], it can be applied to the light scattering simulation of particles with arbitrary shape and inhomogeneous compositions. In this paper, the basic principle of the IIM T-Matrix method was firstly derived, and then, an iterative acceleration scheme was proposed based on the Lorenz-Mie theory and the invariant imbedding technique. To improve the computational efficiency, the model was further parallelized by the OpenMP technique. To validate the calculation accuracy of the model, the scattering phase matrix computed by the IIM T-Matrix method was compared with that of EBCM, DDASCAT, and MRTD. The results show that good agreements were achieved between the results of the IIM T-Matrix method and those well-tested scattering models, indicating that the IIM T-Matrix method can accurately simulate the scattering properties of non-spherical particles with different shapes and inhomogeneous compositions. After being improved by the iterative acceleration scheme based on the Lorenz-Mie theory, the computational efficiency of the IIM T-Matrix method was improved notably. Owning to its excellent performance, the IIM T-Matrix model might become a powerful tool for the light scattering simulation of non-spherical particles in the atmospheric radiation field.  
Conceptualization, S.H. and L.L.; methodology, S.H.; software, S.H.; validation, S.H., L.L. and T.G.; writing--original draft preparation, S.H.; writing--review and editing, Q.Z.; project administration, L.L.; funding acquisition, L.L. This research was funded by the National Science Foundation of China, grant number 41575025, 41575024. Thanks for the T-matrix code provided by [PERSON]. ([[http://www.giss.nasa.gov/staff/mmishchenko/t_matrix.html](http://www.giss.nasa.gov/staff/mmishchenko/t_matrix.html)]([http://www.giss.nasa.gov/staff/mmishchenko/t_matrix.html](http://www.giss.nasa.gov/staff/mmishchenko/t_matrix.html))). The authors declare no conflict of interest.
# Applications of Magnesium and Its Alloys: A Review
## 4 Magnesium and Its Alloys as a Biomaterial
### Corrosion
It must be biocompatible, not known to produce any biotoxins or avoidable inflammatory response, and optimized for its respective biomedical applications [33; 42; 59]. Aluminum (Al), calcium (Ca), zinc (Zn), zirconium (Zr), and strontium (Sr) are commonly used as alloying elements [56]. Rare earth elements, such as yttrium (Y), gadolinium (Gd), lanthanum (La), and dysprosium (Dy), are also considered even though their biocompatibility remains a question [4]. Presently, alloying, by itself, seems to be limited in clinical practice as its ability to improve corrosion resistance is insignificant [65]. However, modifying the surface with a coating layer effectively controls the initial degradation of magnesium and its alloys [66]. Compared to alloying, surface modification can retain the material's critical properties while achieving enhanced corrosion resistance and biocompatibility [67]. Taking a step further, current trends suggest combining both methods (coating on alloys) to be widely accepted and adopted. Similar to alloying, the applied coating should be optimized to its substrate and its intended biomedical purpose. Surface modification methods include micro-arc oxidation (MAO), chemical transformation, electrodeposition, and biomimetic deposition [65]. The main types of coating include polymeric coatings, calcium-phosphate coatings, and graphene coatings [46]. For an in-depth discussion on surface modifications for magnesium and its alloys, one can refer to publications by [PERSON] et al. [66] and [PERSON] et al. [62].
## 2 Proposed Optimization Problem
### Optimization Algorithm  
The optimization algorithm applied in this work was a hybrid simulated annealing (SA) algorithm. The algorithm was selected from the study by [PERSON] et al. [49] and modified using a mutation operator (SAMO2). This algorithm allows a combination of the advantages of the diversity of genetic algorithms with the good convergence of SA. The SA algorithm, proposed by [PERSON] et al. [50], simulates the formation of crystals to reach optimum solutions. The parameter that controls the probability of acceptance of higher cost or EE solutions is the temperature (\(T\)). This parameter starts with an initial value T0 and, as the algorithm generates solutions, the temperature decreases, allowing less acceptance of higher cost or EE solutions. The initial value of the temperature is determined by the procedure proposed by [PERSON] [51]. The probability of acceptance, Pa, depends on the temperature and the increment between the new and old solution, \(\Delta E\). This function (Equation (7)) proposed by [PERSON] [52] can reject better solutions. On the other hand, the promotion of diversity is introduced in this algorithm by the generation of new solutions through a mutation operator. [PERSON] [53] studied the search for good solutions by introducing the concepts of selection, crossover, and mutation in a mutation operator. [PERSON] and [PERSON] [54] combined the two algorithms effectively. Figure 4 illustrates a flowchart of the SAMO2 process.  
\[P_{a}\ =\ \frac{1}{1+e^{\frac{\Delta E}{T}}} \tag{7}\]  
The parameters used in this study in relation to cost optimization were the following: length of Markov chains of 25,000, cooling coefficient of 0.90, simultaneous variable changes per movement of 5%, and number of unimproved Markov chains of 1. The algorithm stopped when the temperature was lower than 5% of the initial temperature. For energy optimization, the parameters used were length of Markov chains of 25,000, cooling coefficient of 0.95, simultaneous variable changes per movement of 3%, and number of unimproved Markov chains of 1. To obtain the value of these parameters, a tuning was carried out. The parameters were obtained by a tuning process to obtain those which gave good results for all calculated wall heights, reducing deviation of the results in terms of energy consumption up to 2.26% for a 12 m wall height.  
The algorithm was programmed in MATLAB(r) and run using Intel(r) Core(tm) i7-3820 CPU with 3.60 GHz. The computing time needed to run the algorithm and to obtain one solution was five minutes. In this study, we calculated nine walls for each height and objective function considered [43].
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## Appendix A  
In order to reduce the computation payload, the colored image is transformed into a gray image. The method for rail track extraction is described as follows.  
Firstly, the longest line of rail edge is detected by Hough transform, as shown in Figure 11B, and the image is rotated by the angle \(\theta\) between the line and horizontal direction so that the rails are parallel to the vertical direction, as shown in Figure 11C. And then the followed Algorithm A1 is used to find the most left position of a rail track after the matrix \(Cg(n)\) is obtained by Equation (1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Defects Inspection Model** & **LWLC+ME** & **LWLC+GSTA** & **MLC+PEME** & **LWLC+GSME** \\ \hline Original images (A1) & 228 & 140 & 226 & 189 \\ Original images (A2) & 213 & 120 & 225 & 170 \\ Original images (A3) & 188 & 82 & 206 & 161 \\ \hline \hline \end{tabular} In Table 2, original images are correspond to the three images in Figure 10A. For example, A1 is the first line image in Figure 10A.  
\end{table}
Table 2: Examples of segmentation threshold values with four segmentation methods; LWLC: The proposed Local Weber-like Contrast algorithm; ME: The maximum entropy algorithm; GSTA: The gray stretch and threshold algorithm; MLC: The Michelson-like contrast algorithm; PEME: The proportion emphasized maximum entropy method; GSME: The gray stretch maximum entropy method.
## 3 Materials and Methods
### Other Datasets  
ShanghaiTech PartA Dataset [35]: Created specifically for the crowd counting task, it consists of images randomly obtained from the Internet. The dataset contains 300 training images and 182 test images, with a total of 241,677 people annotated. The minimum number of annotations per image is 33, the maximum number of annotations reaches 3139, and the average resolution is 589 \(\times\) 868. Since the images come from different viewpoints and cover a variety of scenes and different density levels, it is a popular dataset widely used in crowd counting research.  
URCAL Dataset [36]: Created specifically for rice counting and localization tasks, it consists of images taken at different growth stages (seedling stage and tillering stage). The dataset contains 235 training images and 130 testing images, with a total of 173,352 rice plants annotated. The minimum number of rice plants per image is 154, the maximum is 710, and the image resolution is 1600 \(\times\) 1600. Since the images were taken at different times and there are differences in lighting conditions and nutrient supply, resulting in diverse rice morphologies, this dataset is significantly challenging and is an important data resource for rice counting and localization research.  
Figure 3: (**a**) Histogram of the number of images and the number of migratory birds in the DTH training set. (**b**) Histogram of the number of images and the number of migratory birds in the DTH testing set.  
Figure 2: Image examples of various migratory birds in the DTH dataset.  
PUCPR+ Dataset [37]: This dataset was designed specifically for vehicle counting tasks, extended from the PUCPR subset of the PKLot dataset [38]. The dataset contains 100 training images and 25 test images with a total of 16,916 annotated cars, with the number of cars per image ranging from 0 to 331, and a resolution of 1280 \(\times\) 720. The PUCPR+ dataset covers a variety of weather conditions, adding challenge to the vehicle counting task. However, due to the fixed shooting angle and single image scene, the deep learning model may be biased. Nonetheless, the PUCPR+ dataset remains an important reference benchmark for vehicle counting research.
# Chiral Dielectric Metasurfaces for Highly Integrated, Broadband Circularly Polarized Antenna
## 5 Conclusions  
The introduction of a chiral metasurface on the design of low-profile CP DRAs was shown to yield a wide AR bandwidth, high-gain and low AR values. Of special relevance is the compactness of the CP antenna designed, suitable for mobile communication systems. The concepts and design were validated through numerical simulations using two commercial software packages, and no significant difference was noted in the results obtained with CST Studio and ANSYS HFSS. From these simulations one learns that the CP antennas can be tunable and integrated into front-end circuits or phase-arrayed systems (directly fed by a coax-wire). The design also included materials and conditions which are readily available for implementation, and therefore we may expect the fabrication of CP antennas for high throughput 5G and satellite communications in the _Ka_-band.  
Conceptualization, B.F.-G. and J.R.M.-S.; investigation B.F.-G.; supervision, [PERSON]-[PERSON]; Writing--original draft preparation, B.F.-G.; Writing--review and editing, O.N.O.J. and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the RNP, with resources from MCTIC, Grant No. 01245.010604/2020-14, under the 6G Mobile Communications Systems project of the Radiocommunication Reference Center (Centro de Referencia em Radiocomunicacoes--CRR) project of the National Institute of Telecommunications (Instituto Nacional de Telecomunicacoes--Intael), Brazil. The authors wish also acknowledge the financial support from the Brazilian agencies FAPESP (2018/22214-6), Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES)--Finance Code 001 and the National Council for Scientific and Technological Development-CNPq (429496/2018-4, 305958/2018-6).  
Not applicable.  
The authors declare no conflict of interest.
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 3 Deformable Attention-Guided Network (DAGN)
### Target Optimization
#### 3.2.2 Non-Maximum Suppression for Prediction  
Non-maximum suppression is used to suppress the redundant boxes and reject the false positive results. For NMS, DIoU-NMS [13] is adopted in our work. The DIoU-NMS penalizes the detection scores of neighbors with an adaptive threshold by the factor of the distance-IoU penalty term \(R_{DIoU}(B_{max},B_{i})\), yielding better suppression for the occlusion cases. \(R_{DIoU}\) considers the distance between central points of a box \(B_{i}\) and the box with the highest score \(B_{max}\). The DIoU-NMS is defined as follows:  
\[s_{i}=\left\{\begin{array}{cl}s_{i},&IoU-R_{DIoU}(B_{max},B_{i})<\varepsilon, \\ 0,&IoU-R_{DIoU}(B_{max},B_{i})\geq\varepsilon,\end{array}\right. \tag{8}\]  
where \(s_{i}\) is the classification score. The NMS threshold \(\varepsilon\) is experimentally set to 0.45.
## 3 Results and Discussion
### Optimization of the Production Conditions by PB Design  
Single factor does not consider the interaction between factors. In order to optimize fermentation conditions for YHM-G to produce 3-Met more scientifically, PB design was carried out according to the above single factor results to select the factors that have a greater impact on 3-Met production. A matrix of fifteen runs with eleven factors along with the corresponding responses for 3-Met production was designed. Changes in 3-Met production ranged from 1.00 g/L to 2.60 g/L with the test conditions (Table 6), indicating that these factors affected 3-Met production. A polynomial equation E1 that explained 3-Met production was fitted to the results:\[\begin{array}{ll}\mathrm{Y}=1.45+0.0567\;\mathrm{X}_{1}-0.1236\;\mathrm{X}_{2}+0. 2246\;\mathrm{X}_{3}+0.1285\;\mathrm{X}_{4}-0.0373\;\mathrm{X}_{5}+0.0186\; \mathrm{X}_{6}-0.2471\;\mathrm{X}_{7}-\\ 0.0202\;\mathrm{X}_{8}-0.1368\;\mathrm{X}_{9}+0.1270\;\mathrm{X}_{10}+0.0228\; \mathrm{X}_{11}\\ \end{array}\]  
where Y is 3-Met concentration and X\({}_{1}\), X\({}_{2}\), X\({}_{3}\), X\({}_{4}\), X\({}_{5}\), X\({}_{6}\), X\({}_{7}\), X\({}_{8}\), X\({}_{9}\), X\({}_{10}\) and X\({}_{11}\) represent shaking speed, temperature, glucose concentration, initial pH, loading volume, inoculum size, time point of L-Met addition, culture time, yeast extract concentration, L-Met concentration and Tween-80 concentration, respectively.  
According to the analysis of variance (ANOVA) using [PERSON]'s test, the model was significant as the \(p\) value of the model is significant (\(p=0.0011\)). The coefficient of determination (\(R^{2}\)) was 0.9998, suggesting the model could be used to guide further study. Based on the Pareto chart (Supplementary Figure S3), all eleven factors examined significantly influenced 3-Met production at the 5% level of significance. X\({}_{1}\), X\({}_{3}\), X\({}_{4}\), X\({}_{6}\), X\({}_{10}\) and X\({}_{11}\) had positive coefficients, whereas the other five factors showed negative coefficients (Table 2). Thus, all eleven factors merit further study. Additionally, six positive factors should be enhanced and the others should be reduced in subsequent experiments, which positively affect 3-Met production.
## 2 Data Acquisition and Preprocessing
### Prediction Model
#### 2.4.2 Deep Learning Models  
Convolutional neural networks (CNNs) are a type of deep learning model that imitate the working mode of the biological visual nervous system and use convolutional layers to extract local features from input data [24].  
Recurrent neural networks (RNNs) are a special type of neural network model mainly used for sequence data [25]. Long short-term memory networks (LSTMs) are an improvement on traditional RNNs, aiming to solve the vanishing gradient problem of standard RNNs when processing long sequences [26]. Gated recurrent units (GRUs) are an improved version of LSTMs that simplify the structure of the LSTMs to enhance computational efficiency [27]. Transformer is a deep learning model for processing sequence data first proposed by [PERSON] et al. in 2017 [28]. Unlike traditional recurrent neural networks, Transformer relies on self-attention mechanisms, enabling the model to process all elements in a sequence in parallel rather than step by step. This feature significantly improves training efficiency and enables the capture of long-range dependencies.