esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 3 Doppler Effect Modelled in Its Scaling Relation
### Doppler as an Instantaneous Relation  
It is important to mention that the Doppler scaling is actually based on the instantaneous effective velocity \(v_{\mathrm{eff}}(t_{0})=0.5\,\mathrm{d}\,R_{\mathrm{bi}}/\mathrm{d}t\,|_{t=t_ {0}}\) that is a differential relation [1] and may  
Figure 7: Constantly moving point scatterer extended uniformly in range-Doppler (\(B_{\mathrm{eff}}\approx B_{\mathrm{occ}}\)).  
not be static. This can be derived from signal phase [13] caused by the bistatic range \(R_{\rm bi}\) change due to a constant velocity to  
\[f_{\rm D}(v_{\rm eff},f_{\rm c},R_{\rm bi},t_{0})=\frac{1}{2\pi}\,\frac{\partial \,\Phi(f_{\rm c},t)}{\partial\,t}\,\bigg{|}_{t=t_{0}}=-\frac{1}{\lambda(f_{\rm c })}\,\frac{\mathrm{d}\,R_{\rm bi}(t)}{\mathrm{d}t}\,\bigg{|}_{t=t_{0}}=\frac{2 \,v_{\rm eff}(t_{0})}{c}\,f_{\rm c}. \tag{16}\]  
It can be seen that the wavelength \(\lambda(f)\) already depends on the frequency \(f\). Interestingly, both relations (14) and (16) show this particular frequency dependency over a bandwidth from \(f_{\rm l}\) to \(f_{\rm u}\) even in the case of a constant effective velocity that is here defined as positive for an approaching target.
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 3 Proposed System Architecture
### Evaluation Metrics  
Three main evaluation metrics are used in this paper to analyze and compare models: F1-score, average inference time per image, and performance-to-size ratio. F1-score, shown in Equation (1), measures how well a model can predict both positive and negative classes as a function of both precision and recall. It is usually preferred as it provides a much better indicator of model performance than other numerical metrics.  
\[\text{F1-Score}=2\times\frac{\text{Precision}+\text{Recall}}{\text{Precision} \times\text{Recall}} \tag{1}\]  
Performance-to-size ratio, shown in Equation (2), is a value calculated to better estimate the trade-off between F1-score and model size, similar to the metric used in [25]. The better the performance and the smaller the size, the higher the ratio. We will be using this value to help in deciding the optimal model for deployment on edge devices.  
\[\text{Performance-to-size ratio}=\frac{\text{F1-Score}}{\text{Size}} \tag{2}\]  
In addition, various visuals are provided to better compare and evaluate the presented models, including bar graphs and confusion matrices.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 6 Implementations and Use Cases
### 5G Implementations  
Following advances by the United States, Europe, and South Korea, China pledged to roll out 130,000 new 5G base stations and relay stations by the end of 2019, spread over 50 major cities [106]. These new base stations can support massive data collection to benefit network science and efficient management to provide cost-effective and efficient systems for a growing number of users. China's efforts to become a leading center for 5G were enabled by the collaboration between the country's three largest telecommunication companies (China Mobile, China Unicom, and China Telecom). Some benefits of increasing 5G small-cell base station availability are improving overall network spectral efficiency [107] and real-time insights into network capacity and performance for increasingly automated and centralized network management. However, how these new local stations and potential data processing centers will be incorporated into the 5G mobile edge computing system has not been determined. Without a doubt, ML will allow the utility of these stations and their supporting technologies to scale and empower new industries and verticals for 5G.  
In 2019, Dell Technologies and the telecom Orange began working together \"to jointly explore developing key technology areas for distributed cloud architectures to deliver the real-time edge use cases and new services opportunities 5G will create\" [108]. Within months, Microsoft and NVIDIA announced a collaboration to advance mobile edge computing AI computing capabilities for enterprises [109] and MobiledgeX and World Wide Technology became partners to accelerate the commercialization of scalable mobile edge computing deployments [110]. Many telecommunication companies are teaming up with leaders in ML and AI to focus on intelligent mobile edge computing because of the many new applications that will benefit.
## 2 Materials and Methods
### Applications of Class Data and Dimension Reduction within Classes  
For images where no supervised information is available, the only information needed for the proposed approach is rough category information. Thus, only a quick classification or coarse clustering [15] is needed before dimensionality reduction. Afterwards, the classification results can be used as the displayed category information in the visualization strategy. For HSI with available supervised information or where the supervised information can be provided by other means, precise category space distribution information or fuzzy geographic space information can be used as category information.  
According to the determined category information, in order to maintain the distance characteristics and nonlinearity within the class of original hyperspectral data, pixels belonging to different classes are respectively extracted, and a manifold approach is then used to reduce data dimensionality of each class. When the separation factor is used, the original N-band hyperspectral data are reduced into two-band (saturability and lightness) plus one-band (hue) data. Otherwise, the HSI is directly reduced into two-band data. In an ideal situation, the single dimension that has the most class information of the three dimensions should be used to determine the hue. The other two dimensions are used as saturation and brightness to distinguish the internal changes within the same class. Here, the first two dimensions are set to display saturation and lightness, and the third dimension is set to determine hues with a separation factor.  
Because of their characteristics and advantages in nonlinear analysis of high-dimensional data, manifold learning algorithms have been preliminarily applied in hyperspectral remote sensing image analysis and data processing [4]. The purpose of manifold learning is to find mapping from the feature space to a low-dimensional space, and this mapping is required to maintain certain kinds of geometrical characteristics. In this way, the structure of the original sample distribution can be observed in a low-dimensional visual space. However, for large-scale hyperspectral data, regardless of whether they use Euclidean or geodesic distances, large memory space is required to store the distance matrix for calculations [14]. Because of memory limits, the manifold coordinates will not unify blocks when processing large-scale hyperspectral images. In this study, five kinds of commonly used manifold learning methods were used for testing and comparison with the proposed method: locally linear embedding (LLE) [16], local tangent space alignment (LTSA) [17], isometric feature mapping (ISOMAP) [18], t-distributed stochastic neighbor embedding (T-SNE) [19], and Laplacian eigenmaps (LE) [20].
## 4 In-Situ Technique Combinations
The combination of neutron scattering with light scattering or birefringence should be more straightforward to achieve and would extend the length scales over which information can be obtained. However, this also seems to be an area waiting to be developed since few publications can be found. A combination of SANS with diffuse wave spectroscopy (DWS) was reported. DWS can be used in high concentration colloid solutions and can be used to obtain information about the ensemble dynamics of particles, while SANS provides the structural information [156]. An example where an industrially relevant testing method, Rapid Visco Analysis (RVA), was combined with SANS experiments was used to determine the nature of the structural changes in starch during pasting. This combined mechanical and thermal treatment of materials plays an important role in process- and quality-control in the food industries. By combining the neutron scattering data with the viscosity, as measured with an adapted RVA system, the abrupt changes in viscosity could be correlated with the changing morphology of the starch and the starch derivative network [157].
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 2 Related Works
(2020) designed a new anchor point optimization algorithm, GA-K-Means, based on the K-means algorithm, and proposed the ACD-YOLO model. By using a Genetic Algorithm (GA) to adjust the anchor point boundaries, they reduced dependencies. Although the model achieved 72 FPS in experiments, its detection speed still lagged behind YOLOv7 and YOLOv8, requiring further optimization. To address the problem of aluminum surface defect detection, [PERSON] et al. (2020) introduced the WIoU loss function based on YOLOv8, focusing on anchor boxes of normal quality to reduce harmful gradients generated by extreme samples. By using a convolutional residual module to enhance feature extraction capabilities, the model's mAP improved by 3.4% compared to the original YOLOv8, reaching 74.9%, although the computational load increased by about 20%. [PERSON] (2020) proposed the ECC-YOLO algorithm, which is based on YOLOv7 and incorporates the MPCE module, improving detection accuracy for small steel surface defects. [PERSON]'s C2f-DD algorithm (2020) expanded the receptive field, effectively enhancing the network's feature extraction ability. [PERSON] (2020) proposed an adaptive weighted downsampling method based on YOLOv8, which combined different downsampled feature maps to improve the model's focus on defect information. The above examples provide valuable insights for subsequent research, but they generally suffer from insufficient detection accuracy. In response to these shortcomings and the requirements of defect detection, this paper selects YOLOv8n, the model with the smallest weights among the five versions of YOLOv8, as the base model and proposes an improved YOLOv8-TLC (YOLOv8-Triple Space Pyramid module and Large Selective Kernel attention and C2f-DS module) algorithm, which effectively enhances the accuracy of steel surface defect detection. YOLOv8n Algorithm  
YOLOv8n consists of three main components: a feature extraction network, a feature fusion network, and a detection head, as shown in Figure 1. This model is an optimized version of YOLOv5, not only improving detection accuracy but also achieving greater lightweight efficiency. YOLOv8n replaces the C3 module in YOLOv5 with a more lightweight C2f module. Additionally, the detection hea YOLOv8n adopts an Anchor-Free structure and utilizes a decoupled head design that separates classification and regression tasks, enhancing both the model's accuracy and convergence speed [33].
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 2 Literature Review
### Finite Field  
Also called the Galois field, it is based on the Galois finite field theorem [11]; if it a positive prime number and a positive integer, then there is a finite field containing elements, and the structure of the finite fields with elements is absolutely isomorphic.  
Finite fields are widely used in modern coding, theoretical computer science, combinatorics, and cryptography. The AES algorithm proposed in this paper uses the Galois field for calculation, and the finite field calculation is divided into two types.  
The first type is finite field addition, where adding a finite field is a simplification of a polynomial modulo, and its eigenvalues ensure that the result of the computation stays in the finite field. AES uses the Galois field, and its eigenvalue is 2, so the addition needs to be modulo 2, which can be simplified to the XOR equivalent to the logical operation. The second type is finite field multiplication, and like finite field addition, when the result of multiplying two polynomials may be larger than nth power, the value will exceed the range of finite field, so the finite field multiplication also needs to go through the modulo operations. The modulus used by finite field multiplication must be an integrable polynomial, while the AES algorithm uses AND, which is equivalent to the logical operation.
## 2 HT Distribution and Simulation Results
### \(M\times N\) Planar Array  
Regarding the N HT excitations whose dp is \(-60\) dB as a horizontal \(\overset{\rightarrow}{a}\) and the M HT excitations whose dp is \(-60\) dB as a vertical \(\overset{\rightarrow}{b}\), their vector sum is \(\overset{\rightarrow}{c}\), as shown in Figure 12; subsequently, we can obtain HT excitations of the planar array. Feeding the planar array seen in Figure 13a requires using the modulus of \(\overset{\rightarrow}{c}\) as the amplitude and the \(\theta\) of it as the phase of the source. Figure 13b illustrates that the far-field gain pattern is ONF following  
Figure 10: The HT network.  
Figure 9: Beam navigation of the HT array.  
HT formation when M \(\times\) N is 4 \(\times\) 12. Figure 13c shows that the null filling is successful even when M \(\times\) N is 8 \(\times\) 8. The HT method can still improve gain at most null positions by 10-20 dB when applied on a 12 \(\times\) 12 planar array, as shown in Figure 13d; the filled curve can be processed more smoothly by varying the amount of dp, and the maximum gain is almost identical to the uniform one.  
Figure 11: Simulated results of the HT network.  
Figure 12: Vector addition method.  
Figure 13: (a) M \(\times\) N planar array diagram. Simulated gain results of M \(\times\) N planar array: (b) 4 \(\times\) 12; (c) 8 \(\times\) 8; (d) 12 \(\times\) 12.
## 1 Introduction  
Travel time reliability is defined as the consistency or dependability in travel times. Due to heavily congested traffic in metropolises like Beijing, the operation of transit systems always becomes unreliable. Although bus services have timetables, the unpredictable and frequent road congestion makes commuters' travel time fluctuate greatly. As a result, low-level travel reliability has significantly influenced passengers to choose the public transportation system. It has become the core issue for transit operation managers and researchers to improve reliability conditions of bus networks and attract more passengers to use the bus system. Recently, the exclusive bus lane (EBL) has been implemented as an effective solution to the above problem in a lot of cities. An EBL is a lane restricted to buses. Compared to the existing transit priority strategies, such as the transit signal priority for buses to pass through intersections first [1; 2; 3], the queue-jumper lanes that allow buses to easily enter traffic flow in a priority position [4], and the intermittent bus lanes that give buses temporary running priority [5], an EBL can provide an isolated running environment and prevent various disturbances on the road, which is attractive to travel time and reliability, and supports a shift of demand from private cars to public transportation [6].  
This paper proposes a practical method for EBL implementation to enhance reliability for transit-commuting passengers. A bi-level optimization model is established, incorporating dynamic demand considerations in the lower-level model. In addition, travel time reliability indexes are defined for bus passengers by considering their in-vehicle and waiting process during the whole trip. Statistical regression analysis is used for evaluating the key parameters when estimating the passengers travel time reliability. Finally, a real-world case study is conducted to assess the applicability of the proposed model. The research will provide theoretical support for improving the reliability of bus systems through scientific EBL planning.  
The work is organized as follows: in Section 2, the literatures are reviewed; in Section 3, the method for optimizing EBL location and a bi-level optimization model are established; in Section 4, a case study with actual data is discussed; Sections 5 and 6 give the results, discussions, and conclusions.
###### Abstract  
Today, power electronic-based converters are at the core of many modern systems, such as smart grids and electric vehicles. In this context, the Dual Two-Level Inverter (DTLI) supplying an open-end winding machine offers an innovative and promising solution for marine propulsion, aeronautics, and electric vehicles. This configuration provides several advantages, including a reduced DC bus voltage, enhanced fault tolerance, and improved overall system performance. However, ensuring optimal energy efficiency and high-power quality remains a major challenge given the increasing demands for performance and sustainability. This paper presents a state-of-the-art review of the main DTLI configurations and their impact on system performance. Three architectures are analyzed, highlighting their benefits and limitations. This study aims to demonstrate the influence of the DC bus voltage ratio and pulse width modulation strategies on power quality and energy efficiency. The objective is to enhance the understanding of the DTLI's potential and to guide its integration into other electrical systems.  
Keywords:dual two-level inverter; open-end winding; power quality; efficiency; common-mode voltage; overcharging DC bus; zero sequence voltage +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Mortality and Leaf Area Index Determination  
The mortality rates were determined from the 13 circular 7-m radius vegetation monitoring plots established within the flux tower footprint. Within each plot, tree species were identified and individual trees with diameter at breast height (i.e., 1.4 m aboveground level) greater than 2.5 cm were tagged. Diameter at breast height were measured each winter season. Dead trees were also recorded. The leaf area index (LAI) from 2009 to 2019 was generated using the Global Land Surface Satellite (GLASS) LAI datasets [65] from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data at an 8-day and 1-km resolution.
## 3 Optimization
### Algorithms  
This research optimizes the balancing of a six-bar Watt linkage, comparing the performance of sixteen optimization algorithms: six single-objective algorithms and ten multi-objective algorithms.  
The single-objective algorithms evaluated in this study include Differential Evolution (DE), Evolution Strategy (ES), the Genetic Algorithm (GA), Improved Stochastic Ranking Evolution Strategy (ISRES), Stochastic Ranking Evolution Strategy (SRES), and Pattern Search (PS). The investigated multi-objective optimization algorithms are the Non-dominated Sorting Genetic Algorithm II (NSGA-II), NSGA-III, R-NSGA-III, R-NSGA-III, UNSGA-III, the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), the Adaptive Geometric-based Multi-objective Evolutionary Algorithm (AGE-MOEA), the Convergence and Trade-off-based Multi-objective Evolutionary Algorithm (C-TAEA), the S-Metric Selection Evolutionary Multi-objective Optimization Algorithm (SMS-EMOA), and the Reference Vector Guided Evolutionary Algorithm (RVEA).  
Section 3.3.1 concisely describes each single-objective optimization algorithm employed in this research. Conversely, Section 3.3.2 details the multi-objective optimization algorithms.
# Research on Wind Speed Inversion Method for X-Band Networked SAR Satellite
## 2 Materials
### Simulation of X-Band SAR Observations  
In order to theoretically analyze the effectiveness of the method, this paper takes different wind speed, relative wind direction, and incidence angle as input, and uses XMOD2 geophysical model function to simulate the backscatter coefficient of networked spaceborne SAR.  
The networked SAR system consists of three spaceborne SARs. The three SARs were in different orbits at the same altitude. The data from the three SARs observing the same sea surface at different angles of incidence are simulated through the coordinated observation of networked satellites. A schematic diagram of the three SAR observations of the same sea area is shown in Figure 1.  
When simulating the backscatter coefficient of the spaceborne SAR of the network, the input angle of incidence is 20-50\({}^{\circ}\), and the input wind speed is 1-30 m/s. The specific information of the experimental data is shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set Number** & **Relative Wind Direction** & **Incidence** & **Wind Speed** \\ \hline
1 & 240\({}^{\circ}\) & 23\({}^{\circ}\); 33\({}^{\circ}\); 43\({}^{\circ}\) & 1–30 m/s \\ \hline
2 & 240\({}^{\circ}\) & 26\({}^{\circ}\); 36\({}^{\circ}\); 46\({}^{\circ}\) & 1–30 m/s \\ \hline
3 & 240\({}^{\circ}\) & 29\({}^{\circ}\); 39\({}^{\circ}\); 49\({}^{\circ}\) & 1–30 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental data set (constant relative wind direction).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set Number** & **Relative Wind Direction** & **Incidence** & **Wind Speed** \\ \hline
4 & 45\({}^{\circ}\) & 25\({}^{\circ}\); 35\({}^{\circ}\); 45\({}^{\circ}\) & 1–30 m/s \\ \hline
5 & 90\({}^{\circ}\) & 25\({}^{\circ}\); 35\({}^{\circ}\); 45\({}^{\circ}\) & 1–30 m/s \\ \hline
6 & 180\({}^{\circ}\) & 25\({}^{\circ}\); 35\({}^{\circ}\); 45\({}^{\circ}\) & 1–30 m/s \\ \hline
7 & 240\({}^{\circ}\) & 25\({}^{\circ}\); 35\({}^{\circ}\); 45\({}^{\circ}\) & 1–30 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental data set (constant incident angle).  
Figure 1: Simulation of networked synthetic aperture radar (SAR) satellite observations of the same sea area.
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Conductive Polymers  
Electroconductive polymers are organic polymers that retain conductivity over a wide range of magnitudes based on the doping mechanism and bond-conjugated system in their polymeric structures. These polymers combine the beneficial characteristics of typical polymers and metals [9]. The most used conductive polymers are Polypyrrole (PPy), Polyaniline (PANI), and Poly(3,4-ethylenedioxythiophene) (PEDOT), and their chemical structures are given in Figure 1 with descriptions afterward.
# Short-Run Contexts and Imperfect Testing for Continuous Sampling Plans
## 2 Background
### Operating Procedures for CSP-1  
The CSP-1 procedure is depicted in Figure 1. The process begins in 100% Inspection, in which every single item is sampled. The process breaks out of 100% Inspection when a specified number of consecutive non-defective items is reached, denoted as \(n\). When the consecutive number of non-defective items is reached, the sampling procedure enters reduced inspection where the sampling process skips a specified number of items, _skip_. For example, if \(skip=4\) then you inspect every 5-th item. Sampling remains under reduced inspection until an item sampled is found to be defective. At that point, the process returns to 100% Inspection. Every time a sampled item is found to be defective, it is replaced with a non-defective item.  
Figure 1: Operating Procedures for the initial continuous sampling plan (CSP-1).  
The analytical formulas developed for CSP-1 assume infinite batch sizes and assume that the test is perfect. That is, defective items do not test as non-defective and non-defective items do not test as defective. The simulation algorithm we describe in the next section, and have implemented in the R code, relaxes both of these assumptions in order to extend the applicability of CSP-1 plans to short-run contexts that have imperfect testing. Navy applications often fall into this category, particularly since the units under examination can be very sophisticated electronic equipment that are difficult to exhaustively test.