esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Newtonian Behaviors of Crumb Rubber-Modified Bituminous Binders
## 3 Results and Discussion  
### Influence of Temperature and Shear Rate on Complex Viscosity  
Figure 3 plotted the complex viscosity of different bituminous binders as a function of shear rate (angular frequency) at 10 \({}^{\circ}\)C and 50 \({}^{\circ}\)C respectively. Viscosities at other temperatures showed similar trends between these two cases therefore omitted here. In Figure 3a, the behaviour of binders is non-Newtonian and dependent on frequency and temperature. Figure 3b shows that at 50 \({}^{\circ}\)C, base bitumen 70/100 and chemical additive modified bitumen 70/100-C both behave like Newtonian fluids, whose viscosities are almost independent of shear rates. In contrast, 70/100-W and all CRMB based binders exhibit non-Newtonian behaviors as the viscosities increase significantly with the shear rate decreases, which is categorized as the shear-thinning behavior. However, the above statements are of no validity when the testing temperature shifted to 10 \({}^{\circ}\)C while all binders exhibit obvious non-Newtonian behaviors as shown in Figure 3a. In addition, it is notable from Figure 3a that although the viscosity of CRMB is higher than that of base bitumen at lower shear rates, it becomes lower when the shear rate reaches 100 rad/s. This is due to the more evident shear-thinning behavior of CRMB, whose mechanism will be explored in the following subsections.  
Figure 2: Laboratory equipment used to prepare crumb rubber-modified bitumen (CRMB).  
In terms of the warm additives, the addition of chemical additive slightly increases the viscosity of both base and CRMB binders at a certain shear rate. However, organic additive dramatically increases the viscosities of base and CRMB binders, which seems contradictory to the reported viscosity-reduction effect of wax-based additive at high construction temperatures [27]. It should be noted that additive W is a synthetic wax with high molecular hydrocarbon chains (C\({}_{40}\)\(\sim\)C\({}_{120}\)), and its melting point is around 100 \({}^{\circ}\)C [28]. Therefore, when tested at 50 \({}^{\circ}\)C, additive W exists in bituminous binders as the form of microcrystalline and hence make the binders stiffer. Beyond its melting point, the wax liquefies and can be fully miscible in the binder, which significantly reduces the binder viscosity [29]. Rheological tests at temperatures around 100 \({}^{\circ}\)C are recommended to be carried out in the future to verify this conclusion.
# Cross-Entropy Method in Application to the SIRC Model
## 1 Introduction
### Cross-Entropy Method  
This part will contain information on the methods used in the paper. The cross-entropy method developed in [PERSON] (1985) is one of the versions of the Monte Carlo method developed for problems requiring the estimation of events with low probabilities. It is an alternative approach to combinatorial and continuous, multi-step, optimization tasks. In the field of rare event simulation, this method is used in conjunction with weighted sampling, a known technique of variance reduction, in which the system is simulated as part of another set of parameters, called reference parameters, to increase the likelihood of a rare event. The advantage of the relative entropy method is that it provides a simple and fast procedure for estimating optimal reference parameters in IS.  
Cross-entropy is the term from the information theory (v. [PERSON] and [PERSON] (1985); [PERSON] and [PERSON] (1986) (Chapter 2)). It is a consequence of measuring the distance of the random variables based on the information provided by their observation. The relative entropy, a close idea to the cross-entropy, was first defined by [PERSON] [PERSON] [PERSON] and [PERSON] (1986) as a measure of the distance \(\mathcal{D}(p||q)=\mathbf{E}_{p}\log\frac{p(X)}{q(X)}\) (v. [PERSON] and [PERSON] (1986) ((1.6) on p. 9; Definition on Section 2.3 p. 19), [PERSON] and [PERSON] (1986)) for two distributions \(p\), \(q\) and is known also as Kullback-Leibler distance. This idea was studied in detail by [PERSON] (1999) and [PERSON] (1999). The application of this distance measure in the Monte Carlo refinements is related to a realization of the importance sampling technique.  
The CE algorithm can be seen as a self-learning code covering the following two iterative phases:  
1. Generating random data samples (trajectories, vectors, etc.) according to a specific random mechanism.
2. Updating parameters of the random mechanism based on data to obtain a \"better\" sample in the next iteration.  
Now the main general algorithm behind the cross-entropy method will be presented on the examples.
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 4 Experiments Results
### Application  
To provide further evidence that our method could be useful for computer vision applications, we use Google Vision API ([[https://cloud.google.com/vision/](https://cloud.google.com/vision/)]([https://cloud.google.com/vision/](https://cloud.google.com/vision/))) to test whether using our outputs can improve the recognition performance. The results are shown in Figures 11 and 12.  
As can be seen, using our enhanced image, the recognition of API is better than original low-light image. For example, in Figure 11, the API does not recognize trees in the original image, but trees are  
Figure 11: Results of Google Cloud Vision API for “27” from DICM dataset. (**a**) Recognizing result of low-light image; (**b**) Recognizing result of our enhanced image.  
Figure 12: Results of Google Cloud Vision API for “Room” from LIME dataset. (**a**) Recognizing result of low-light image; (**b**) Recognizing result of our enhanced image.  
Figure 10: The convergence performance for SSIM and PSNR of HSI and RGB methods under BN and without BN. (**a**) The average SSIM within 50 Epochs, (**b**) The average PSNR within 50 Epochs.  
identified with our enhancement method. Another example is shown in Figure 12, where the original image is marked as \"lighting\" and \"darkness\" by the API, and after enhancement, the plant in the image is marked.
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 3 Reconstruction Algorithm
### Greedy Algorithm  
The greedy iterative-reconstruction algorithm aims at combinatorial optimization problems, which indirectly solves the problem of sparse signal reconstruction by sparse approximation. Its basic principle is to find the support set of the sparse vector in an iterative manner, and to reconstruct the signal by using the constrained least-squares estimation method. In other words, sparse signal reconstruction constructs the sparsest signal on the basis of linear measurements \(y\), which is expressed as follows:  
\[\min\{\mid I\mid_{y}=\sum_{i\in I}\phi_{i}x_{i}\}\,, \tag{28}\]  
where \(I\subseteq\{1,\cdots,N\}\) represents an index set, and \(\phi_{i}\) is the \(i\)-th column of matrix \(\Phi\).  
Common greedy reconstruction algorithms include matching pursuit (MP) [46], orthogonal matching pursuit (OMP) [47], stagewise orthogonal matching pursuit (StOMP) [48], regularized orthogonal matching pursuit (ROMP) [49], compressive sampling matching pursuit (CoSaMP) [50], and iterative hard thresholding (IHT) [51]. The key feature of these algorithms is the introduction of special operations in the identification step to select multiple promising indices.  
OMP is a representative greedy algorithm and it is widely used because of its simplicity and superior effects. Noise affects the accurate reconstruction of sparse signals. For this reason, [PERSON] et al. studied the sufficient conditions for accurate OMP support in the presence of noise [52]. Their analysis showed that, for any k-sparse signal, the OMP algorithm could accurately recover the signal on the premise that the sensing matrix should satisfy the RIP criterion.  
In contrast to algorithms performing a deliberate refinement of the identification step, a recently proposed extension of OMP, referred to as generalized OMP (gOMP) [53] (also known as OSGA or OMMP [54; 55]), simply chooses columns that are most correlated with the residual.  
New analysis of the improved gOMP algorithm was presented by using the restricted isometry property (RIP) [56]. It showed that the gOMP algorithm could perform high-quality signal reconstruction from noisy measurements under the RIP.
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 2 Model Description and Problem Formulation  
The target TEC states are estimated using a non-linear filter along with multiple dynamic models to capture the motion characteristics of smart targets. Then, the beam scheduling problem is formulated as an RMABP, of which each bandit process is associated with a target.
## 5 Experimental Results and Analysis  
In this section, simulation experiments are conducted on both secure and credibility scenarios, and the relevant parameters of the experimental process and analysis of the experimental results are listed. In the analysis, it is demonstrated that adopting adaptive encryption can balance security and efficiency, which should be particularly advantageous in industrial scenarios that require the transmission of large amounts of data.
## 2 Related Work
### ARMv8 Architecture  
ARMv8 is a high-performance embedded 64-bit architecture that supports both 64-bit (i.e., AArch64) and 32-bit (i.e., AArch32) architectures. ARMv8 provides 31 general-purpose registers; x0-x30 can be used in 64-bit units, and w0-w30 can be used in 32-bit units. In addition, ARMv8 provides 32 128-bit vector registers (v0-v31). The ARMv8 processor has shown great influence in the smartphone and it is also widely used in various laptops and smartphones. In Table 1, the instruction set for ARM processors used in the parallel implementation of ARIA block cipher are given.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**asm** & **Operands** & **Description** & **Operation** \\ \hline EOR & Vd, Vn, Vm & Bitwise exclusive OR & \(\begin{array}{c}\text{Vd}\leftarrow\text{Vn}\oplus\\ \text{Vm}\end{array}\) \\ \hline SUB & Vd, Vn, Vm & Subtract & \(\begin{array}{c}\text{Vd}\leftarrow\text{Vn}-\\ \text{Vm}\end{array}\) \\ \hline LD1R & Vt, (Xn) & Load single-element and replicate to all lanes & \(\begin{array}{c}\text{Vt}\leftarrow\text{(Xn)}\end{array}\) \\ \hline LD4 & Vd1–4, (Xn) & Load multiple single-element structures & Vd1–4 (Xn) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Instruction set for optimized parallel implementation ARIA block cipher. Xd: destination scalar register, Xn: source scalar register, Vd: destination vector register, Vt: transferred vector register, Vn, Vm: source vector register [17].  
Figure 1: ARIA block cipher algorithm and two types of substitution layers.
## 4 Results and Analysis  
We conduct experiments in two steps to evaluate the performance of the proposed system. First, we evaluate the performance of the sensor node, mainly related to sound acquisition and recording. Second, we evaluate the performance of the proposed system based on WASN that consists of 50 sensor nodes, mainly related to the recognition and monitoring platform.
## 3 Results and Discussion
### Chemical and Physical Storage Stability  
After four weeks at 4 \({}^{\circ}\)C, SLM-NLCs suspensions were chemically and physically stable. No changes in size and homogeneity occurred: dimensions were 200.3 \(\pm\) 2.6 nm with PDI of 0.10 \(\pm\) 0.03. The \(\zeta\)-potential was monitored up to 30 days of storage, and no significant evolution was found, with final value of \(-\)24.3 \(\pm\) 0.7 mV. Furthermore, EE% and recovery% did not decrease during storage (EE% 86.7 \(\pm\) 2.4; recovery% 89.3 \(\pm\) 1.2). Thus, developed formulations are able to prevent the degradation of incorporated compound. This represents an important result, considering that, in many cases, chemical and physical instabilities were observed when NLCs were stored as aqueous suspension due to lipid crystallization, polymorphic transformations, aggregation phenomena and hydrolysis processes.  
Analogous study was performed on freeze-dried products. Both products maintain the same physical characteristics as before freeze-drying (size 216.7 \(\pm\) 4.7 nm, PDI 0.34 \(\pm\) 0.01 and \(-\)35.2 \(\pm\) 1.3 mV, in the case of glucose; and size 300.1 \(\pm\) 2.1 nm, PDI 0.28 \(\pm\) 0.01 and \(-\)33.0 \(\pm\) 0.1 mV, in the case of mannitol) but there is a small decrease in EE% (79.8 \(\pm\) 2.5 for glucose and 73.5 \(\pm\) 1.2 for mannitol). Recovery for both formulations is more than 80%.
## 4 Experimental Results and Analysis
### Performance in Watermark Embedding  
To test the differences between SVD and edge detection, the proposed method and the method with only SVD [7] are compared along four aspects: the number of smooth blocks \(N_{\text{SB}}\), the number of texture blocks \(N_{\text{TB}}\), the value of PSNR in dB and the value of SSIM. The results are listed in Table 1.  
The proposed method marks more texture blocks with the implementation of edge detection, which also proves that SVD cannot extract all the edge information. As the number of texture blocks increases, more information can be stored in the watermarks; therefore, the PSNR values generally show a downward trend. However, all the values exceed 35 dB, which means that the information is invisible. The SSIM value indicates the similarity between the watermarked image and the original image. Similar to the PSNR, the higher the SSIM value is, the better the objective quality of the results is.
## 7 Discussion  
The results of the extensive experiments show GeneDiff can detect similarity with high accuracy across different compiler optimization options and different targeted architectures. In this section, we discuss why GeneDiff performs well, as well as its limitations.  
Unlike existing clone detection methods for cross-ISAs [4; 13; 14; 15; 16; 24], GeneDiff converts assembly functions from different architectures into the same intermediate representation and generates semantic vectors for the functions via a representation model inspired by NLP. To address the unique instructions of different architectures, GeneDiff drops their unimportant instructions and unifies the necessary instructions (e.g., registers), which reduces the number of differences caused by different architectures and improves its clone detection accuracy.  
In addition, GeneDiff treats each VEX instruction as a letter instead of a word. An assembly instruction is converted into multiple VEX instructions. Generating a semantic representation for each VEX instruction is similar to interpreting the semantics of each letter in the word \"apple\". Therefore, in GeneDiff, each VEX instruction is treated as a letter, and the combinations of VEX instructions are regarded as words. This reduces the number of tokens and increases the diversity of the tokens, which enriches the semantics of the words.  
GeneDiff also suffers from certain limitations. GeneDiff uses VEX as an intermediate representation. However, VEX is not explicit. For example, VEX uses variables (cc_op, cc_dep1, cc_dep2, and cc_ndep) to store abstract information about the machine status instead of storing the status flags directly, which may introduce some deviations. Besides, GeneDiff is designed for 32-bit architectures (i.e., X86, ARM, and MIPS) and 64-bit architectures (i.e., AMD64,Arch64, and MIPS64). At this stage, it is not directly applicable for semantic clones for eight-bit or 16-bit IoT devices, even though its clone search engine is architecture-agnostic. In the future, we will optimize an intermediate representation to share tokens with low-power IoT devices.
# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 4 Experiments and Analysis
### Description of Data
It is observable that, at the onset of the fault, the actual speed sharply increased, while the predicted speed, although responsive, slightly lagged behind in capturing the exact magnitude of change and the specific timing of the fault. After the fault was resolved, the model quickly readjusted to the normal operational pattern, with the predicted curve closely following the actual data once again, demonstrating the model's recovery capabilities. However, there is still room for improvement in the model's response speed and accuracy at the precise moment when a fault occurs. Figure 8: Data schematic diagram after dimension reduction. Figure 10 illustrates the trends in training and validation losses for the PCA-LSTM model during elevator fault prediction. As depicted, both the training loss (the blue line) and the validation loss (the orange line) decreased rapidly as training progressed, and they stabilized early in the process, demonstrating the model's quick adaptation to the dataset. The swift reduction and early stabilization of loss values indicate that the PCA-LSTM model effectively learns the principal features and patterns within the elevator experimental data. The close proximity and near overlap of the training and validation loss lines suggest good consistency in the model's performance across both datasets, with no significant signs of overfitting or underfitting. This consistency confirms the effectiveness of the PCA-LSTM model in processing and predicting elevator fault data, enabling stable fault pattern recognition.
## 6 Conclusions  
1. When the inner and outer rotors rotate in the same direction, the fluid's dynamic pressure effect on the gas film gap is the most significant, and the gas film forces are maximised. When the inner and outer rotors are in counter-rotation, the dynamic pressure effect is the weakest, and the gas film force is minimised. When only one rotor rotates, the gas film force generated will be between the two aforementioned extremes. The inlet differential pressure weakens the adverse dynamic pressure effects at the counter-rotation interface and improves the gas film's bearing capacity to some extent. When the differential pressure is 80 kPa and under the calculation condition herein, the gas film force increases by about 68.1% more than without differential pressure when the inner and outer rotors are in counter-rotation. When the inner and outer rotors rotate in the same direction, the gas film force increases by about 16.37% more than without differential pressure. When rotating in the same direction, the gas film force is 34.87% greater, and the friction power consumption is 34.73% greater than when the two rotors are in counter-rotation. 2. For the seal between counter-rotating shafts, when the outer rotor speed is fixed but the inner rotor speed increases, the seal's leakage rate and friction power consumption decrease. Before the minimum film thickness reaches the critical film thickness, contact with the rough peak increases friction power consumption significantly. When the inner rotor speed is fixed but the outer rotor speed increases, the centrifugal expansion and the leakage rate of the sealing ring increase, but the friction power consumption decreases first and then increases. When the absolute difference in the speed of the inner and outer rotors is fixed, and the inner and outer rotors are synchronously accelerated, the leakage increases due to the increase in the centrifugal expansion of the sealing ring; however, the friction power consumption decreases. 3. For the seal between counter-rotating shafts, when the sealing differential pressure increases, the seal's leakage rate and friction power consumption increase significantly. Conceptualization, S.Z. and H.S. ; methodology, S.Z. ; software, S.Z. ; validation, S.Z. and H.S. ; formal analysis, S.Z. and H.S. ; investigation, S.Z. ; resources, S.Z. and H.S. ; data curation, S.Z. ; writing--original draft preparation, S.Z. and H.S. ; writing--review and editing, S.Z. and H.S. ; visualization, S.Z. and H.S. ; supervision, S.Z. and H.S. All authors have read and agreed to the published version of the manuscript. Figure 14: Effects of differential pressure on sealing performance. **Funding:** This research was funded by: National Natural Science Foundation of China 51575445. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
[62] showed that the spatial distribution of the Rs rate was significantly negatively correlated with SWC in subtropical evergreen broad-leaved forests in southern China. The results of the present study showed (Figure 3) that the variation of Rs at the local scale could be well explained by SWC (5 m \(\times\) 5 m: R\({}^{2}\) = 0.32; 10 m \(\times\) 10 m: R\({}^{2}\) = 0.34). In addition, compared with the sampling scale of 5 m \(\times\) 5 m, the large spatial variation of Rs at the scale of 10 m \(\times\) 10 m could be explained by SWC (Figure 4, \(p<0.05\)). This is because SWC, at levels that are too low or too high, limits Rs [85], especially in arid or semi-arid areas. When SWC becomes a stress factor, it may replace temperature and become the main control factor of the Rs rate [19; 86]. The effect of SWC on Rs is very complex and changeable. When the soil is dry, the soil metabolic activity increases with the increase of water content, and there is a positive correlation between them. When SWC exceeds a certain range (80% of saturated soil moisture), the Rs decreases with the increase of SWC, and there is a negative correlation between them [36; 87]. These results indicated that the synergistic changes of plant functional traits and soil nutrients with SWC caused the spatial variation of the Rs rate at the local scale. Therefore, to improve the uncertainty of existing models for predicting Rs and better elucidate the potential mechanism of the spatial pattern of Rs in arid areas, it is necessary to quantify the spatial variation of Rs and its relationship with related environmental factors (plant traits, soil nutrients, and soil microclimate) at a fine scale (10 m \(\times\) 10 m).
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Drone Revocation Phase
#### 7.1.4 Idealized Forms  
The idealized forms of the authentication messages are presented below:  
\[\begin{array}{ll}\mathit{Msg}_{1}:&MD_{i}\to GCS:(V_{M},r_{3})_{h(HID_{M}|K _{M})}\\ \mathit{Msg}_{2}:& GCS\to D_{j}:(V_{G},r_{3},r_{4},h(HID_{M}\parallel K_{M}))_{h( PID_{D}\parallel K_{D})}\\ \mathit{Msg}_{3}:& D_{j}\to GCS:(r_{5},R_{j},Aut_{D})_{h(HID_{M}|K_{M})\parallel h( PID_{D}\parallel K_{D})}\\ \mathit{Msg}_{4}:& GCS\to MD_{i}:(r_{4},r_{5},R_{j},h(PID_{D}\parallel K_{D}), Aut_{GCS})_{h(HID_{M}|K_{M})}\end{array}\]