esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles and Model Establishment
### Normalized Radar Cross Section Analysis  
Assume that the average surface backscattering cross section of ground is \(\sigma_{0}\). When the radar beam enters the area with hydrometeors which are rain and snow in this paper, the surface scattering echoes will be attenuated:  
\[\sigma_{sf}(x)=\sigma_{0}\cdot L^{2}(x), \tag{8}\]  
where \(L\) is the one-way atmospheric loss factor, relevant to the path of the radar beam. Then, Equation (8) can be rewritten as:  
\[\sigma_{sf}(x)=\sigma_{0}\cdot exp(-2/\sin\theta\int_{x_{s_{1}}}^{x_{s_{2}}}k (\delta)d\delta), \tag{9}\]  
where \(x_{s_{1}}\) and \(x_{s_{2}}\) are the \(x\)-coordinates of the left and right limits of the attenuation path of the scattering echo along \(l\)-direction caused by ground.  
Similarly, when the radar beam enters the area with hydrometeors, the precipitation scattering echoes will be attenuated:  
\[\sigma_{vol}(x)=\tan\theta\int_{x_{s_{1}}}^{x_{s_{2}}}L^{2}(x)\cdot\eta(X)dX, \tag{10}\]  
where \(x_{v_{1}}\) and \(x_{v_{2}}\) are the \(x\)-coordinates of the left and right limits of the attenuation path of reflecting along \(t\)-direction caused by meteorological particles. Furthermore, it can also be rewritten as:  
\[\sigma_{vol}(x)=\tan\theta\int_{x_{v_{2}}}^{x_{v_{2}}}exp\left(-2/\sin\theta \int_{x_{v_{3}}}^{x_{v_{4}}}k(\delta)d\xi\right)\eta(X)dX, \tag{11}\]where \(x_{v_{3}}\) and \(x_{v_{4}}\) are the \(x\)-coordinates of the left and right limits of the attenuation path of scattering echo along \(l\)-direction caused by meteorological particles. \(\eta\) is the radar reflectivity, defined as the summation of radar cross sections of all precipitation particles per unit volume, relevant to the distribution and size of the meteorological particles and the frequency of the microwaves. A widely accepted way to express \(\eta\)[13; 14] is that:  
\[\eta(x)=\frac{\pi^{5}|K_{0}|^{2}}{\lambda^{4}}i_{1}[R(x)]^{b_{1}}=a_{1}[R(x)]^{ b_{1}}, \tag{12}\]  
where \(|K_{0}|^{2}\) is an expression of negative refractive index, which is approximately 0.19 for snow and 0.93 for rain when neglecting the influence of the temperature. Furthermore, \(\lambda\) is the wavelength of electromagnetic waves, which is about 3.1 cm for X-band SAR. \(i_{1}\) and \(b_{1}\) are empirical values. Lots of studies have been carried out on the values of \(i_{1}\) and \(b_{1}\). In this paper, the values calculated by [PERSON] [15] (pp. 318-328) are adopted. For rain, \(i_{1}\) is 300, \(b_{1}\) is 1.1; for snow, \(i_{1}\) is 182, \(b_{1}\) is 1.4.
# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 3 Two-Stage Model  
The method proposed in this paper aims to identify the location and degree of icing in the icing images of stay cables. An icing recognition model using semantic segmentation and image classification is proposed. The model transforms icing detection into a two-stage detection problem. Firstly, the semantic segmentation method is used to locate the icing position, and then the image classification method is used to rate the icing degree.
## 3 Proposed Improved Hybrid SSA with an EDO (ISSAEDO) Algorithm
### Sparrow Search Algorithm (SSA)
```
0:\(G\)--the maximum iterations \(PD\)--the number of producers \(SD\)--the number of scroungers \(R_{2}\)--the alarm value \(n\)--the number of sparrows Output:\(X_{best}\)--the comprehensive best position discovered while searching \(f(X_{best})\)--the comprehensive best fitness discovered, which should be decreased
1: Start
2: Initializes a population of \(n\) sparrows and define their relevant parameters;
3: Rank the individuals in an ascending order based on their fitness function values \(f(X)\);
4:while\((t<G)\)do
5: Rank the fitness values and find the current best individual and the current worst individual;
6:\(R_{2}\leftarrow\) a random value in [0; 1];
7:for producer \(i=1:PD\)do
8: upgrade the current producer's position through Equation (15) to obtain \(X_{i}^{t+1}\) ;
9:for sparrow \(i=(PD+1):n\)do
10: upgrade the current sparrow's position through Equation (16) to obtain \(X_{i}^{t+1}\) ;
11:for scrounger \(i=1:SD\)do
12: upgrade the current scrounger's position through Equation (17) to obtain \(X_{i}^{t+1}\) ;
13:\(f(X_{i}^{t+1})\leftarrow\) Assess the value of fitness function for \(X_{i}^{t+1}\);
14:if\(f(X_{i}^{t+1})<f(X_{i}^{t})\)then\(\triangleright\) Update the previous position if the present novel position is better than it
15:\(X_{i}^{t}\gets X_{i}^{t+1}\); \(f(X_{i}^{t})\gets f(X_{i}^{t+1})\);
16: Re-arrange the positions ascendingly based on their fitness function values \(f(X)\);
17: Locate the best position \(X_{best}^{t+1}\) and its best fitness value \(f(X_{best}^{t+1})\) at completion of the present iteration \(t+1\);
18:\(X_{best}\gets X_{best}^{t+1}\); \(f(X_{best})\gets f(X_{best}^{t+1})\);
19:\(t\gets t+1\);
20:End
```  
**Algorithm 1** The original SSA algorithm
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 2 Related Work
### Security Mechanisms for WBAN
#### 2.1.2 Biometric-Based Authentication Mechanisms  
Biometric-based authentication mechanisms aim to find a unique feature from the human body and then use these traits as an authentication identity. Said features are derived from behavioral or physiological characteristics exhibited by a human. Common primitives used by biometrics systems are fingerprint, face, hand geometry, iris, and voice. These systems overcome the problem of distributing pre-shared keys among sensors. In [30; 31], researchers have exploited physiological parameters such as electrocardiogram (ECG), photoplethysmogram (PPG), heartbeats, and fingerprints. The efficiency of these authentication mechanisms lies in the correlation coefficient of physiological parameters calculated at the sender and receiver. The main reason for dissimilar physiological signals is due to the position of sensors at different parts of the human body. Biometric-based systems require specialized sensing hardware, which is an overhead for the miniature on-body sensors.
## 4 Experiments and Analysis
### Experimental Results and Analysis  
#### 4.5.1 Comparative Experimental Results and Analysis  
Table 5 presents the experimental results categorized by question type and answer type, with Hits@1 and Hits@10 as evaluation metrics. Table 6 shows the experimental results at different time granularities using Hits@1 as the evaluation metric. In the table, the bolded data represents the best performance, while the underlined data indicates the second-best performance. Based on the experimental results of the proposed model and six comparison models, the following conclusions can be drawn:  
(1) Among the knowledge graph question-answering models, ALBERT performs lower than MTQADC in both the Hits@1 and Hits@10 evaluation metrics. In terms of Hits@1, its accuracy is 0.099 lower than MTQADC for the Multiple question type, 0.290 lower for the Single question type, 0.291 lower for the Entity answer type, and 0.096 lower for the Time answer type. Regarding time granularity, these three models also perform worse than other models that use embedding operations. For example, in the Equal Multi type of daily granularity questions, ALBERT is approximately 0.057 lower than MTQADC. This is because ALBERT does not use embedding operations, ignoring the representation of the knowledge graph, and without an embedding-based scoring mechanism, the model's answer retrieval is less accurate. (2) Among the models that combine language models and embedding operations, EmbedKGQA performs worse than CronKGQA, MultiQA, and MTQADC. Overall, EmbedKGQA's Hits@1 is about 0.136 lower than that of MTQADC, and its Hits@10 is about 0.174 lower than that of MTQADC. For Equal-type questions, it is lower by 0.419, 0.040, and 0.096 in daily, monthly, and yearly granularities, respectively. This is because EmbedKGQA uses the ComplEx embedding method, which is an algorithm that does not include time em \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Equal**} & \multicolumn{4}{c}{**Before/After**} & \multicolumn{4}{c}{**Equal Multi**} \\ \cline{2-11}  & **Day** & **Month** & **Year** & **Day** & **Month** & **Year** & **Day** & **Month** & **Year** \\ \hline ALBERT & 0.069 & 0.082 & 0.132 & 0.221 & 0.277 & 0.308 & 0.103 & 0.144 & 0.144 \\ EmbedKGQA & 0.200 & 0.336 & 0.218 & 0.392 & 0.518 & 0.511 & 0.145 & 0.321 & 0.263 \\ T5 & 0.418 & 0.365 & 0.239 & 0.611 & 0.637 & 0.637 & 0.201 & 0.319 & 0.284 \\ GPT-2 & 0.49 & 0.33 & 0.247 & 0.556 & 0.572 & 0.599 & 0.173 & 0.289 & 0.28 \\ CronKGQA & 0.425 & 0.389 & 0.331 & 0.375 & 0.474 & 0.450 & 0.295 & **0.333** & 0.251 \\ CTRN & 0.522 & 0.337 & 0.288 & 0.592 & 0.611 & 0.604 & 0.192 & 0.293 & 0.296 \\ MultiQA & 0.445 & **0.393** & **0.350** & 0.379 & 0.548 & 0.525 & **0.308** & 0.321 & 0.283 \\ \hline MTQADC & **0.619** & 0.376 & 0.314 & **0.640** & **0.707** & **0.723** & 0.160 & 0.308 & **0.313** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of multi-granularity time under the Hits@1 metric.
# Development of Multi-Cation-Doped M-Type Hexaferrite Permanent Magnets
## 2 Materials and Methods  
M-type hexaferrites with nominal compositions of SrFe\({}_{12-2x}\)Si\({}_{x}\)Mg\({}_{x}\)O\({}_{19}\) (\(x\) = 0, 0.05, 0.1, 0.2), Sr\({}_{0.4}\)Ca\({}_{0.3}\)La\({}_{0.3}\)Fe\({}_{11.7-y}\)Co\({}_{0.2}\)Si\({}_{0.05}\)Mg\({}_{0.05}\)O\({}_{19}\) (\(y\) = 0, 1, 1.5, 2), and Sr\({}_{0.4}\)Ca\({}_{0.3}\)La\({}_{0.3}\)Fe\({}_{10.2}\) Co\({}_{0.2-z}\)Mn\({}_{z}\)Si\({}_{0.05}\)Mg\({}_{0.05}\)O\({}_{19}\) (\(z\) = 0, 0.05, 0.1, 0.2) were prepared using solid-state reaction routes. Precursor powders of SrCO\({}_{3}\), CaCO\({}_{3}\), MgO, and Co\({}_{3}\)O\({}_{4}\) (all 99.9% purity, Kojundo Chemical Lab. Co., Ltd.), Fe\({}_{2}\)O\({}_{3}\) (\(\sim\)99.5%, industrial use), and SiO\({}_{2}\) (99.0%, industrial use) were weighed according to their chemical formulas. Each powder mixture was placed in a polypropylene bottle with yttria-stabilized ZrO\({}_{2}\) balls and deionized (DI) water and balls milled at a rotation speed of 100 rpm for 24 h. Then, the mixed powder was dried in an oven, placed in an alumina crucible, and calcined at a temperature of 1100 \({}^{\circ}\)C in air for 4 h. The calcined powders were ground, sieved, ball-milled again for 20 h, and dried. For selected samples, additional powder milling was conducted for 2-6 h with a high-speed (200 rpm) milling machine (PM100, Retsch, Haan, Germany). The ball-milled powders were pressed in a disk-shaped mold with a diameter of 15.0 mm at a pressure of approximately 15 MPa to produce green compact pellets. The pelletized samples were sintered in air in the temperature range of 1200-1230 \({}^{\circ}\)C for 2 h. For the selected cation composition, anisotropic sintered magnets with diameters of approximately 35 mm were fabricated at a commercial ferrite magnet manufacturing site (Union Materials Corporation, Seoul, Korea). Here, all process conditions except for the magnetic-field molding process were conducted under the same conditions as those mentioned above. During the molding process for the anisotropic magnet, a magnetic field of 10 kOe was applied to align the hexaferrite particles.  
The sample density was calculated based on the weight and geometric dimensions of the disk-shaped samples. X-ray diffraction (XRD; D2 Phaser, Bruker, Mannnheim, Germany) was conducted for phase identification using a Cu-K\(\alpha\) radiation source (\(\lambda\) = 0.15406 nm). Field-emission scanning electron microscopy (FE-SEM; JSM-7610F, JEOL, Tokyo, Japan) was used for phase identification. The magnetic hysteresis curves were measured using a vibrating-sample magnetometer (VSM; Lakeshore 7410, Westerville, OH, USA) and a B-H loop tracer (BH-5501, Denshijiki Industry, Ukima, Japan) at room temperature. For the VSM and B-H loop tracer measurements, fragments of sintered samples weighing approximately 0.1 g and disk-shaped sintered pellets with diameters of approximately 10.5 and 35 mm, respectively, were used.
# Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation
## 2 Literature Review
### Image Enhancement  
Deep learning has recently seen considerable success in the fields of low-level image processing, modeling, and interpretation of nocturnal scenes. The authors in [7] have presented an elaborate overview and pragmatic guide of deep learning applications in dark image enhancement for beginners to advanced level researchers. The authors represented a performance analysis of available deep learning algorithms and established methodologies based on standard datasets. The Deep Retinex-Net [11] enhanced images using deep decomposition and trained a model from end to end, including Enhance-Net for illumination adjustment and Decom-Net for decomposition. According to the Retinex theorem [7], there are two types of image classification, illumination, and reflection:  
\[I(x,y)=r(x,y)\times S(x,y) \tag{1}\]  
where \(I\) and \(r\) represent the captured input image and the intended recovery of the output image, respectively. In natural vision science, the single-scale Retinex is based on the center/surround (SSR) [5]. The mathematical form of this is given in Equation (2).  
\[R_{i}(x,y)=\log I_{i}(x,y)-\log\left[F(x,y)\times I_{i}(x,y)\right] \tag{2}\]  
RED-Net architecture was introduced [12], where the image was first enhanced by increasing contrast using an Image Contrast Enhancement Network (ICE-Net) and then reduced noise using a Re-Enhancement and Denoising Network (RED-Net). However, this model is not compatible with sequential or bracketing images. Furthermore, the researchers concentrated on image enhancement by improving parameter-decomposition, denoising, and contrast enhancement. From this, R2 RNet [9] was developed. This method consists of Decom-Net, Denoise-Net, and Relight-Net. Many of these learning techniques rely on conventional denoising techniques when they do not expressly include these. Another cutting-age method for image processing is using Generative Adversarial Network [13], where the fine details of low quality images are restored using the combination of multi-scale Retinex color reproduction and adaptive histogram equalization. Our method, considering the impacts of noise, uses dual attention plots with SKFF [14] to direct the boost and denoising processes. Therefore, our approach is compatible with current learning-based approaches.
# Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks
## 2 Related Work
D2D queueing stability was one of the constraint conditions of the proposed distributed flow control and power allocation strategies. In [18], the notion of delay outage was proposed to evaluate the effect of different scheduling policies in multi-point-to-multi-point communications. However, in some literature works about D2D communications with retransmission schemes [22, 23], they did not construct the spatiotemporal arrival of the traffic model. For example, the work of [22] addressed the impact of retransmission protocols on the total energy efficiency of the D2D system. The goal was to minimize the average total transmission power, but it did not consider the impact of unsaturated traffic to some substantive performance parameters. Furthermore, the authors of [23] considered the end-to-end queuing model for relay-assisted D2D communications, in which each user maintained a fixed-size buffer for storing the arrived packets. A relay selection scheme was proposed. As for the works in [34, 35], the authors built a queuing model to describe the influence of retransmission schemes on D2D users. They discussed the influence of channel time-variant characteristic on the probability of an erroneous transmission, but they assumed an error-prone channel model without interference. The assumption was quite unpractical in an actual communication network. We try to further take into account the impact of interference caused by other active users sharing the same frequency spectrum.
# A Decomposition Algorithm for Dynamic Car Sequencing Problems with Buffers
## 4 DCSPwB Decompositional Algorithm
### Greedy Algorithm for Postponed Car Release  
The greedy algorithm (GDA) is an algorithm that takes the best or optimal choice in the current state at each step of selection, and thus, hopes that the result is the best or optimal. GDA is not considered in terms of overall optimality, and the solution obtained each time is a local optimal solution based on some strategy [35; 36]. GDA has been studied in solving static car sequencing problems [37], but its performance on dynamic car sequencing problems still needs to be further explored. Here, the GDA-based outbound method requires releasing the car in the active sequence of the buffer that causes the minimum sum of the number of new violations and the sorting time cost of the outbound car sequence. The number of new violations of the outgoing car sequence caused by the outgoing car is the sum of the number of violations for each option. For option \(o\), if the outgoing car requires option \(o\) and the number of cars requiring option \(o\) in the outgoing car sequence \(s_{o}\) is equal to \(r_{o}\), then the outgoing car causes one violation of option \(o\). The number of additional violations caused by the outgoing car \(i\) is calculated according to Equation (14), where \(x\) denotes the position of the outgoing car in the sequence of already outgoing cars. The time cost of the outgoing car increase is calculated according to \(t_{fl}\).  
\[\begin{split}&\text{{V}io}(i)=\sum_{o\in\text{O}}w_{o}\cdot \min\bigl{\{}a_{io},z_{eq}\bigr{\}}\\ & q=\max\{0,x-s_{o}+1\}\end{split} \tag{14}\]  
Here, we improve the GDA and propose a greedy algorithm (PGDA) for delaying car exiting by setting the parameter \(M\). Only when the number of cars in the buffer is larger than \((L\times V-M)\) are the cars allowed to exit the buffer, and the optimal exit of cars is solved by the greedy algorithm. The specific setting of parameter \(M\) needs to be obtained by simulation experiments.
# Rehabilitative Ultrasound Imaging Evaluation in Physiotherapy: Piloting a Systematic Review
## 4 Discussion
### Validity and Reliability Studies  
We appreciate that the validity studies are of great importance and usefulness, as in some cases they have been able to offer security and confidence for clinical applications in addition to assessing the security in the monitoring processes or for the use in future research projects.  
The intraclass correlation coefficient (ICC) is one of the principles in many areas, and the lumbar region has had several situations with different strategies such as the use of proprioceptive neuromuscular facilitation in order to assess muscular contractions at rest and contractions in patients with lumbar pain [54], finding an ICC > 0.93-0.99. These types of correlations in all studies reach 0.90, and even exceed it in some cases. The comparisons in ultrasound measurements between healthy and pathological subjects and comparisons on the same day and between days during contractions and at rest [55] give an ICC intra-rater (0.78-0.95 and 0.74-0.94) for older people and an ICC inter-rater (0.87-0.97 and 0.80-0.95) for younger people.  
Comparisons were also carried out to the point of assessing the confidence of experts against novices. This study suggests that it is one of the only ones which compares two measurements in two assessors over several days, reaching a conclusion that an inter-rater ICC of 0.99-1.00 in healthy subjects. At the same time, they achieved an intra-rater ICC of 0.99-1.00 in healthy subjects. The sampling method and size of the sample may possibly have yielded some of the highest data.  
Along this line, in another study, the healthy population data was compared to the pathological population with respect to ICC within the same day and between days [24]. However, on this occasion, it involved comparing ultrasound measurements of a static image with measurements from a video clip. In the first place, no significant differences were found in this regard, and in the second place, the ICCs on the same day were 0.99 and between days were 0.93-0.98. This work could provide great validity for measurements of post-acquisition muscle activations as measurements based on ultrasound in certain patients' delayed sampling.  
The ICC calculation was also useful for specific tasks [56] such as a maximum isometric contraction in healthy patients compared with those with lumbar pain. However, it is true that the population was very small. The ICC range was between 0.84 and 1.00.  
Authors should discuss the results and the way in which they can be interpreted with respect to the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.
## 1 Introduction
Therefore, in addition to the framework with the dynamic situation function mentioned earlier, the proper environment temperature has to be integrated into the framework of any cyclic fatigue resistance testing apparatus to correctly evaluate the properties of the endodontic instrument in correlation with the phase transformation temperatures of the material of the instrument in the study [16; 18]. The ProTaper Universal (PTU) and the ProTaper Next (PTN) (Dentsply Sirona, Mailfer, Ballaigues, Switzerland) are two brands from the same manufacturer that have been familiar with clinicians for a long time [20]. These two instrument systems have different materials and designations representing different generations, symbols, and technologies. The conventional NiTi material (PTU), made of the austenitic phase of NiTi alloy, can be transformed into the martensitic phase by stress or external force and can return to the original shape of the instrument after being distorted at the body temperature [21]. Because of the reversible thermoelastic martensitic transformation, the flexibility of the NiTi instrument is much better than the stainless steel instruments. Along with the special convex triangle cross-sectional design, the PTU has a negative rake angle and considerable core diameter that enhance its longevity and safety in clinical treatment. However, the multiple files design of the PTU system prevents it from becoming a popular instrument in endodontic therapy, although this concept is an appropriate approach for students in preclinical education. The material of PTN, M-wire, is the product of the heat treatment process involving conventional NiTi alloy, which changes the phase transformation temperature of the NiTi alloy, enhancing its cyclic and torsional fatigue resistance. The rectangular cross-sectional design with the off-center feature and the reduced files of the system are special characteristics that the manufacturer claimed for the enhanced capabilities of the instrument in higher cyclic fatigue resistance and effectiveness in the curved canal [21]. Although there are many studies on cyclic fatigue resistance at body temperature under both static and dynamic conditions, there is no investigation of the double-curved canal at body temperature under both static and dynamic conditions. The present study aims to measure the number of cycles until fracture (NCF) of instruments in static and dynamic cyclic fatigue situations, in a body temperature environment, in the stainless steel double-curved canals.