esa-sceva/satcom-chunk-collection huggingface dataset:
# DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain
## 2 Related Work
### Cp-Abe  
ABE is the most promising cryptographic primitive supporting fine-grained access. ABE was first proposed by [PERSON] and [PERSON] in Fuzzy Identity Based Encryption (FIBE). [15]. [PERSON] presented the first CP-ABE method based on ABE [17]. This scheme allows the data owner to define an access policy. Only users who satisfy the policy can decrypt the data. Afterwards, many scholars put forward their own schemes based on this [33; 34; 35]. A scheme to implement access control over system attributes was put up by [PERSON] [32] utilizing a linear secret sharing scheme (LSSS) matrix. [PERSON] et al. [34] implement an effective and innovative scheme that can be outsourced. A multi-authority attributes-based encryption scheme was put forth by [PERSON] et al. [35].  
The security of existing CP-ABE solutions is still inadequate. Most schemes cannot track down and deal with malicious users who leak their keys. Numerous academics have suggested original solutions to this issue. [PERSON] et al. used a dynamic traceable CP-ABE method with revocation [36]. This scheme dynamically tracks the decryptor during the outsourcing decryption process and helps to identify and revoke the malicious user who leaked the key. [PERSON] et al. [37] suggested a practical encryption scheme with multi-authority and efficient revocation of ciphertext policy attributes. This scheme achieves user revocation by algorithmically updating the key for unrevoked users. However, this approach is only suitable for some scenarios with many users. Based on the traceable revocable ciphertext policy attribute, [PERSON] et al. [38] suggested an equal-length ciphertext key encryption scheme. Based on achieving user revocation, the transmission efficiency is improved by constant-length ciphertext and key. These schemes effectively address the revocation of malicious users but cannot perform flexible revocation of user attributes when a user changes. A multi-permission ciphertext policy attribute and revocable permissions-based encryption scheme were presented by [PERSON] et al. [39]. The scheme allows multiple permission authorities to participate in key distribution and enables at-tribute revocation when the user's access rights change. A blockchain-based revocable CP-ABE method was suggested by [PERSON] et al. [40]. The scheme supports an expressive access control policy and allows attribute permissions to revoke some user attributes.
# Hydrogeochemistry of Fault-Related Hot Springs in the Qaidam Basin, China
## 4 Discussion
### Hydrochemical Characteristics of the Hot Springs
#### 4.1.4 Strontium Isotope Analysis  
The isotopic composition in hot spring water reflects the lithological characteristics of the strata through which the water flows. The Sr (strontium) concentration in the study region varied from 0.699 to 11.62 mg/L, with the highest content observed in the AK springs. The Sr\({}^{2+}\) content in atmospheric precipitation is extremely low. Consequently, the water-rock interaction in the aquifer media during groundwater transport determines the Sr\({}^{2+}\) content in the hot spring water [53]. The concentrations of Ca\({}^{2+}\) and Sr\({}^{2+}\) in the water samples from hot springs exhibited a good and positive correlation (Tables 1 and 2), mainly because calcium and strontium belong to the same group in the periodic table of elements and, therefore, have the same properties, such as isomorphism (the replacement of Ca with Sr). Therefore, in minerals with high calcium content, the content of strontium is also relatively high [54]. Ca substitution with Sr has also been found in hot water studies in northeastern Algeria [55].  
Sulfate, silicate, and carbonate minerals are important sources of Sr in groundwater. The \({}^{87}\)Sr/\({}^{86}\)Sr ratio of the hot spring is often similar to the \({}^{87}\)Sr/\({}^{86}\)Sr ratio of the rock minerals it contacts [56; 57]. It is generally accepted that the \({}^{87}\)Sr/\({}^{86}\)Sr ratio for the mantle magma source is 0.70400, the \({}^{87}\)Sr/\({}^{86}\)Sr ratio for the carbonate and sulfate weathering sources is approximately 0.70800, the \({}^{87}\)Sr/\({}^{86}\)Sr ratio for the aluminosilicate weathering sources ranges between 0.71600 and 0.72000, the \({}^{87}\)Sr/\({}^{86}\)Sr ratio for the sandstone and shale weathering sources is 0.72417, and the \({}^{87}\)Sr/\({}^{86}\)Sr ratio for the granite weathering sources is 0.73719 [58]. The relationship between the \({}^{87}\)Sr/\({}^{86}\)Sr ratio and the Sr content of the hot springs in the study region was analyzed. It was revealed that the main weathering sources of the water in these hot springs were carbonate and sulfate (Figure 6). The strontium isotope ratios of the hot springs varied less and remained almost at the same level, while the Sr content varied more. This could be due to the similarity of the surrounding rocks on the flow path and the different accumulation times [59].  
Figure 6: Strontium isotopic composition of the water samples.
# Haptic Texture Rendering of 2D Image Based on Adaptive Fractional Differential Method
## 1 Introduction
Therefore, the features of complex texture in the image can be extracted by fractional differentiation and applied to the haptic texture reconstruction. This paper introduces the Grunvald-Letnikov (G-L) definition of fractional differential in Euclidean space [24; 25]. Based on the G-L definition, the isotropic m \(\times\) n fractional differential mask is deduced. Then we propose a novel method to adaptively select the order of a fractional differential operator by using the composite sub-band gradient vector (CSGV) relate with the wavelet decomposition [26] and human visual characteristics [27; 28]. Thirdly, we apply the approach in haptic texture rendering and give a quantitative analysis by using the image information entropy and multi-scale structural similarity (MS-SSIM). We apply these extraction results to the haptic display system to reconstruct the three-dimensional texture force filed to render the texture surface of 2D images. Finally, some experiments were carried out on different types of texture images. Experimental results show that the proposed haptic texture rendering method based on the adaptive fractional difference can extract texture features well and obtain an excellent texture force field of 2D images.
# Towards Explainable Pedestrian Behavior Prediction:
## 2 Proposed Method
### Phase 1: Meta-Dataset Generation
#### 3.1.4 Pedestrian Proximity to the Road  
To determine the pedestrian's proximity to the road, we utilized the YOLOPv2 pre-trained network [35]. YOLOPv2 is a multi-task learning network capable of performing traffic object detection, drivable road area segmentation, and lane detection. For extracting pedestrian proximity to the road, we followed these steps:  
* **Step 1--Input Processing:** Images or video frames are provided as input.
* **Step 2--Segmentation and Detection:** YOLOPv2 extracts the drivable road area segmentation and lane detection.
* **Step 3--Distance Estimation:** The pedestrian's proximity is determined based on a minimum pixel distance threshold, which varies depending on the video resolution.
* **Step 4--Proximity Classification:** The pedestrian's position is analyzed to determine if, when displaced by the estimated pixel threshold, they intersect the drivable road area or a lane, using the segmentation mask provided by YOLOPv2.
## 3 Data and Application
### Method 1: Classical Statistical Techniques (e.g., Mean and Standard Deviation)  
This method involves calculating the mean and standard deviation of the signal and then comparing each data point to a threshold derived from these statistics. Points where the absolute deviation from the mean exceeds the threshold are identified as changes in the signal (see Figure 4).  
While Method 1 provides a straightforward approach to change detection based on statistical properties of the signal, its effectiveness can be limited by the presence of outliers, non-Gaussian distribution, suboptimal threshold selection, and lack of consideration for temporal dynamics. As a result, alternative methods that address these limitations may be more suitable for detecting changes in certain types of signals.  
We have displayed the result of the second example in Figure 5.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## Materials and Methods
### Survey Flights  
Flights were carried out with a fixed wing SenseFly eBee-RTK equipped with a compact camera Sony Cyber-Shot DSC-WX220 (18.2 Mpixel; focal length 4.5 mm). The camera was triggered by the drone's autopilot and set exposure parameters automatically. The differential corrections were sent from the master station through the SenseFly Emotion2 flight control software and the ground radio modem. Geotags were stored in the image metadata as well as in the flight log.  
Flight planning was designed with 85% forward overlap and 80% sidelap at ground level. Each block consisted of 12 strips (about 150 images), flown at about 90 m above ground level, with a GSD of about 2.3 cm. Flight lines were oriented along the main block side (roughly east-west), and images were acquired both ways along the passes.  
Figure 2: Location of the targets (ground control points (GCPs): red; checkpoints: green) and of the Digital Surface Model (DSM) checkpoints (yellow) on the orthophoto mosaic generated from one of the eight flights.  
The flight plan was repeated eight times in late summer 2016, between 12 a.m. and 6:15 p.m. In order to possibly enjoy similar satellite constellations and lighting conditions, RTK positioning through the local master and through the Network were alternated in consecutive flights. Image acquisition took on average 11 minutes per flight. The satellite constellation was always good, with PDOP (position dilution of precision) values below three in the first flight, and below two in all of the others. In the following, the flights are labelled as RTK1 to RTK4, and as NRTK1 to NRTK4.
# MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications
## 3 Proposed Method
### Network Architecture  
This paper introduces MS3D, a multi-scale feature fusion 3D object detection method for autonomous driving, as illustrated in Figure 1.  
First, a spatial segmentation strategy divided the point cloud into uniform 3D cells, termed Pillars. Each Pillar represents a 3D cell, with each containing M points. Each point featured nine dimensions: 3D coordinates \((\mathrm{X},\mathrm{Y},\mathrm{Z})\), reflection intensity \(\mathrm{r}\), offset from the Pillar center \((\mathrm{X_{C}},\mathrm{Y_{C}},\mathrm{Z_{C}})\), and offset from the grid center \((\mathrm{X_{P}},\mathrm{Y_{P}})\).  
Points within each Pillar were either randomly sampled or padded with zero-value points to form a uniform data structure \((\mathrm{D},\mathrm{N},\mathrm{P})\), where \(\mathrm{D}=9\) is the nine-dimensional features, \(\mathrm{N}\) is the point count threshold, and \(\mathrm{P}\) is the total number of Pillars. PointNet processed these Pillar features, converting them into a C channel feature representation \((\mathrm{C},\mathrm{N},\mathrm{P})\) that was then transformed into a pseudo-image with a size of \(\mathrm{H}\times\mathrm{W}\times\mathrm{C}\) through max pooling, where \(\mathrm{H}\) and \(\mathrm{W}\) are the height and width of the grid and \(\mathrm{C}\) is the number of channels.  
A 2D Convolutional Neural Network backbone extracted multi-scale features from the pseudo-image. The network consisted of two sub-networks: one for top-down feature extraction at different resolutions, and another that performed feature sampling via deconvolution and concatenates features from three different-sized feature maps.  
Following the backbone, a SecondFPN was integrated to refine feature extraction further and enhance multi-scale detection capabilities.  
For 3D object detection, an SSD (Single Shot MultiBox Detector) was used. The SSD head defined \(\mathrm{K}\) anchor boxes in the feature maps, each with varying sizes and aspect ratios to accommodate multi-scale detection needs. Regression predicted each anchor box's \(\mathrm{Z}\)-axis coordinate \(\mathrm{Z_{g}}\) and height \(\mathrm{h}\), constructing precise 3D bounding boxes: \(\left\{(\mathrm{X_{g}},\mathrm{Y_{g}},\mathrm{Z_{g}}),(\mathrm{l},\mathrm{w}, \mathrm{h}),\mathrm{\theta},\mathrm{score},\mathrm{c}\right\}\). Here, \((\mathrm{X_{g}},\mathrm{Y_{g}},\mathrm{Z_{g}})\) is the bounding box center, \(\mathrm{l}\) is the length, \(\mathrm{w}\) is the width, \(\mathrm{h}\) is the height, \(\mathrm{\theta}\) is the rotation angle relative to the xy-plane, score is the confidence of the bounding box containing the object, and \(\mathrm{c}\) is the object class (e.g., vehicle, pedestrian).  
The model training was optimized using the Adam optimizer [36], which adjusted learning rates adaptively based on parameter gradients, enhancing training efficiency and model performance.
# EvoSplit: An Evolutionary Approach to Split a Multi-Label Data Set into Disjoint Subsets
## 3 First Approach: Single-Objective Evolutionary Algorithm  
This work proposes an evolutionary algorithm (EA), EvoSplit, to obtain the distribution of a data set into disjoint subsets, considering their desired size as a hard constraint. The structure of the evolutionary algorithm follows the process presented in Algorithm 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Card\({}^{2}\)** & **Dens\({}^{2}\)** & **Max Frequency\({}^{2}\)** & **Div\({}^{2}\)** & **PDiv\({}^{2}\)** \\ \hline
**emotions** & 1.04 & 0.173 & 0.18 & 14 & 0.02 \\ \hline
**scene** & 0.07 & 0.012 & 0.03 & 8 & 0.00 \\ \hline
**genbase** & 0.40 & 0.015 & 0.04 & 36 & 0.05 \\ \hline
**medical** & 0.26 & 0.006 & 0.09 & 63 & 0.06 \\ \hline
**yeast** & 8.09 & 0.578 & 0.74 & 89 & 0.04 \\ \hline
**enron** & 5.19 & 0.098 & 0.34 & 675 & 0.40 \\ \hline
**rcv1 subset4** & 2.98 & 0.029 & 0.09 & 1452 & 0.24 \\ \hline
**rcv1 subset3** & 3.27 & 0.032 & 0.09 & 1665 & 0.28 \\ \hline
**rcv1 subset5** & 3.38 & 0.033 & 0.09 & 1680 & 0.28 \\ \hline
**rcv1 subset2** & 3.34 & 0.033 & 0.09 & 1660 & 0.28 \\ \hline
**rcv1 subset1** & 3.84 & 0.038 & 0.08 & 1802 & 0.30 \\ \hline
**tmc2007\_500** & 1.92 & 0.087 & 0.16 & 217 & 0.01 \\ \hline
**bibtex** & 3.11 & 0.020 & 0.02 & 4173 & 0.56 \\ \hline
**Corel5k** & 4.66 & 0.012 & 0.05 & 4342 & 0.87 \\ \hline
**COCO** & 26.69 & 0.201 & 0.22 & 8130 & 0.07 \\ \hline
**Imagenet** & 36.52 & 0.003 & 0.88 & 80,587 & 0.01 \\ \hline
**OpenImages** & 52.79 & 0.039 & 0.45 & 266,470 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measures of label pair imbalance of different multi-label data sets: label pair cardinality (Card\({}^{2}\)), label pair density (Dens\({}^{2}\)), maximum frequency of a label pair (Max Frequency\({}^{2}\)), label pair diversity (Div\({}^{2}\)) and normalized label pair diversity (PDiv\({}^{2}\)).  
```
Input: Data set \(D\), labels \(L\), number of disjoint \(k\) subsets, desired number \(c\) of examples, at each subset Output: Disjoint subsets \(S\) Initialize the population with \(n\) individuals generated randomly Rank the population by fitness repeat  Generate offspring  Create\(c\) new individuals by crossover Create\(m\) new individuals by mutation  Evaluate the offspring  Calculate the fitness of the \(c+m\) new individuals  Generate next generation's population  Rank the \(n+c+m\) individuals in the population by fitness Select the \(n\) best individuals to generate a new population until\(generations\_without\_changes>gen_{max}\)
```  
**Algorithm 1** EvoSplit
## 2 Signal Model  
In order to simplify the analysis without loss of generality, under the far-field condition, it is assumed that a single navigation signal and \(Q\) interference signal occur on the uniform line array (ULA) simultaneously. The ULA has \(N\) antenna elements. The array configuration of the ULA is shown in Figure 1.  
The coupled signal received by the GNSS receiver can be expressed as  
\[\mathbf{x}(t)=\mathbf{a}(\theta_{0})s(t-\tau_{0})+\sum_{q=1}^{Q}\mathbf{a}(\theta _{q})j_{q}(t)+\mathbf{n}(t) \tag{1}\]  
where \(\mathbf{x}(t)=[x_{1}(t),x_{2}(t),\cdots,x_{N}(t)]^{T}\) stands for array data vector. \(s(t)\) represents the baseband GNSS signal, \(\tau_{0}\) and \(\theta_{0}\) are the time delay and DOA parameter of GNSS signal, respectively. \(j_{q}\) denotes the \(q\)th interference signal component with DOA parameter \(\theta_{q}\). \(\mathbf{n}(t)\) represents the additive white Gaussian noise vector, and \(\mathbf{a}(\theta)\) is the array guide vector, which can be expressed as  
\[\mathbf{a}(\theta)=\left[1,e^{-j2\pi d\sin(\theta)/\lambda},\cdots,e^{-j2\pi d (N-1)\sin(\theta)/\lambda}\right]^{T} \tag{2}\]  
where \(\theta\) is the signal incident angle, \(\lambda\) is the wavelength, and \(d=\lambda/2\) is the array spacing.  
The baseband GNSS signal \(s(t-\tau_{0})\) can be expressed as  
\[s(t-\tau_{0})=A\cdot D(t-\tau_{0})c(t-\tau_{0})e^{i(2\pi f_{d}t+\varphi)} \tag{3}\]  
where \(D(\cdot)\) indicates the message bit. In this paper, the correlation accumulation time is all within a bit range, so the navigation message is assumed to be constant, i.e., 1. \(c(\cdot)\) represents a pseudo-random noise (PRN) code. \(A\), \(f_{d}\), and \(\varphi\) represent navigation signal amplitude, Doppler frequency, and carrier phase, respectively.  
By constructing an array beamformer, the array can be weighted to achieve an effective suppression of interference. The output after array weighting is  
\[\begin{array}{ll}y(t)&=\mathbf{w}^{H}\mathbf{x}\\ &=\mathbf{w}^{H}\mathbf{a}(\theta_{0})s(t-\tau_{0})+\sum_{q=1}^{Q}\mathbf{w}^{ H}\mathbf{a}(\theta_{q})j_{q}(t)+\mathbf{w}^{H}\mathbf{n}(t)\\ &\approx\mathbf{w}^{H}\mathbf{a}(\theta_{0})s(t-\tau_{0})+\mathbf{w}^{H} \mathbf{n}(t)\end{array} \tag{4}\]  
where \(\mathbf{w}\) stands for array weight vector.
## 2 System Model of RSST-SR-DCSK
### Receiver Structure of RSST-SR-DCSK
The \(k\)th decoded symbol for the \(l\)th user, denoted with \(\hat{b}_{k+2}\), is determined according to the following rule:  
\[\hat{b}_{k+2}= \begin{cases}+1&\quad y_{l}>0\\ -1&\quad y_{l}\leq 0\end{cases}  \tag{10}\]  
The demodulation method can be summarized in the following steps: Firstly, the receiver picks up the signal \(r_{i,k}\) transmitted through a Gaussian white-noise channel, which is a combination of the transmitted signal and noise. Next, the demodulator receives the signal \(r_{i-R,k}\) and outputs the delayed signal after a delay of \(R\), obtaining the signal \(r_{i-R,k}\) after descending. Subsequently, a correlation operation is performed by multiplying the received signal \(r_{i,k}\) with its delayed, descrambled counterpart \(r_{i-R,k}\), and the results are accumulated. Following that, threshold decision rules are applied to the output signal to obtain the recovered \(N\)-bit and \(M\)-bit information bits. Finally, the recovered \(N\)-bit information bits are compared with the originally set \(N\)-bit information bits, the recovered \(M\)-bit information bits are compared with the originally set \(M\)-bit information bits, and the bit error rate is calculated. This concludes the demodulation process. The signal transmission process flowchart of the RSST-SR-DCSK communication system is shown in Figure 7, including structures such as signal generation, the modulator, channel, and demodulator. The following sections provide detailed explanations of each component. Figure 7: The framework diagram of the RSST-SR-DCSK communication system.
# Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
## 3 Thermomechanical Characterization of Amorphous Metals
### Challenges of the Advanced Thermomechanical Methods for the Case of Amorphous Metals  
Thermomechanical analysis has emerged as a crucial technique for processing and studying amorphous metals and metallic glasses, thanks to its capability to analyze materials across all solid configurations, including thin films. Since the sample size for these methods is inherently sufficient to represent the bulk, they do not encounter significant challenges in terms of measurement limitations. However, an intriguing approach to temperature control has emerged through modulated temperature techniques [74], which have also been applied to this method. This allows for distinguishing between reversible and irreversible changes [75], potentially enhancing the understanding of the crucial properties of metallic glasses, such as relaxation kinetics and rejuvenation mechanisms.
## 2 Materials and Methods
### Reverse Transcriptase Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR)  
Total RNA was isolated from either the mouse cerebellum or cell pellets. RNA extraction was performed using TRI reagent (Sigma-Aldrich, St. Louis, MO, USA) following the manufacturer's protocol. To generate cDNA from RNA samples, the SuperScript IV Kit (Invitrogen, Carlsbad, CA, USA) was used. A total amount of 1 \(\upmu\)g RNA was first digested with ezDNase enzyme (Invitrogen, Carlsbad, CA, USA) for purification and finally reverse transcribed following the manufacturer's instructions. For gene expression analysis, RT-qPCR was performed using TaqMan Gene Expression AssaysTM (Thermo Fisher Scientific, Waltham, MA, USA). For this purpose, cDNA from 10 ng total RNA was used with 2\(\times\) FastStart Universal Probe Master ROX (Roche, Basel, Switzerland) and the corresponding TaqMan Assay. The reaction was performed in a StepOnePlus Real-Time PCR Cycler (Applied Biosystems, Waltham, MA, USA). Data were analysed using the 2\({}^{-\Delta\Delta\text{Ct}}\) method [63].  
The following TaqMan Assays were used for murine transcripts:  
_Atm_-Mm01177457_m1; _Atmin_-Mm01251229_m1; _Cecl_-Mm00469610_m1;  
_Faxo_3-Mm01185722_m1; _Grid2_-Mm00515053_m1; _Grin2b_-Mm00433820_m1;  
_Grin2c_-Mm00439180_m1; _Grm4_-Mm01306128_m1; _Itpr1_-Mm00439907_m1;  
_Mme_-Mm00485040_m1; _Nr4a1_-Mm01300401_m1; _Nr4a2_-Mm01278507_g1;  
_Nr4a3_-Mm00450074_m1; _Opm1_ (Exon 2-3)-Mm01188089_m1;  
_Opm1_ (Exon 5-6)-Mm01188387_m1; _Per_-Mm00501813_m1;  
_Rora_-Mm01173766_m1; _Slc1746_-Mm00499876_m1; _Slc32a1_-Mm00494138_m1;  
_Sst_-Mm00436671_m1; _Tac1_-Mm00436892_m1;  
_Tbp_-Mm00446973_m1; _Usp2_-Mm00497452_m1.  
The following TaqMan Assays were used for human transcripts:  
_ATM_-Hs01112311_m1; _ATMIN_-Hs00739820_m1; _CAMKZA_-Hs00947041_m1;  
_CAMK4_-Hs00174318_m1; _ECEL_1-Hs00191400_m1; _FOXO_3-Hs00818121_m1;  
_GRID2_-Hs00910017_m1; _ITPR1_-Hs00976045_m1; _MME_-Hs01115452_m1;  
_NR4A1_-Hs00374226_m1; _OPRM1_-Hs01053957_m1; _OPRM1_ (Exon 1-2)-Hs01053956_m1; _OPRM1_ (Exon 3-4)-Hs00168570_m1; _PER1_-Hs00242988_m1; _RORA_-Hs00536545_m1;  
_RRAGD_-Hs00222001_m1; _SGK1_-Hs00178612_m1; _TBP_-Hs9999910_m1;  
_Usp2_-Hs00275859_m1.