esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Materials  
Steel vessels with Teflon liners from the company Buchi AG (capacity: 100 mL) were used. According to the experiments of [PERSON] et al. [23] and [PERSON] et al. [6], the carbonation experiments were performed at 2 MPa. The temperature was chosen at 333 K. The composition of the used CO\({}_{2}\) (doped with -70 ppm SO\({}_{2}\) and -70 ppm NO\({}_{2}\)) and synthetic brine were identical to the studies of the authors [6; 7; 8; 12]. The natural wollastonite, i.e., CaSiO\({}_{3}\) was supplied by Alfa Aeser (calcium silicate, meta, reagent grade, <20 \(\upmu\)m powder). As described by [PERSON] et al. (2006), the chosen grain size (20 \(\upmu\)m) of the wollastonite raw material benefits the carbonation reaction. The chemical composition of the used raw material was determined by X-ray fluorescence spectroscopy (XRF) [6]. The main elements in the used natural CaSiO\({}_{3}\) were SiO (50.55 wt.%) and CaO (45.95 wt.%), while the impurities (Fe\({}_{2}\)O\({}_{3}\), Al\({}_{2}\)O\({}_{3}\), MgO, MnO, TiO\({}_{2}\), K\({}_{2}\)O, SrO) were <1.5%. The loss of ignition was determined at 1323 K and accounted for 2.09 wt.%.  
The composition of the used raw material is given in Table 1 and Figure 1. Besides wollastonite (90.7 wt.%), the calcium carbonates, calcite (1.9 wt.%) and aragonite (1.0 wt.%), could be determined as well. For the determination of the X-ray amorphous content rutile (TiO\({}_{2}\)) as internal standard was added. The amorphous part amounts to 6.4 wt.%.  
\begin{table}
\begin{tabular}{c c} \hline
**Phases** & **Content [wt.\%]** \\ \hline Wollastonite & 90.7 \(\pm\) 0.8 \\ Calcite & 1.9 \(\pm\) 0.2 \\ Aragonite & 1.0 \(\pm\) 0.5 \\ Amorphous & 6.4 \(\pm\) 0.5 \\ \hline \end{tabular}
\end{table}
Table 1: Phase composition of the wollastonite raw material (CaSiO\({}_{3}\)), qualitatively and quantitatively analyzed, using powder X-ray diffraction (PXRD) and the Rietveld method [24].  
Figure 1: Diffractogram with Rietveld plot [24] of natural wollastonite used for the experiments. Wollastonite (-91 wt.%), calcite (-2 wt.%) aragonite (-1 wt.%), and an X-ray amorphous content (-6 wt.%) could be observed and were determined by adding an internal TiO\({}_{2}\) standard (10 wt.%) of known weight.  
An ISCO-pump (Teledyne ISCO, Model 500D Syringe pump) was used for supplying the gases and adjusting the pressure. CO\({}_{2}\) (purity 4.5; 99.995%), SO\({}_{2}\) (purity 3.0; 99.9%), and NO\({}_{2}\) (purity 1.8; 98%) were supplied by Westfalen AG. The chloride-rich brine (H\({}_{2}\)O with 230 g/L NaCl, 15 g/L CaCl\({}_{2}\), 5 g/L MgCl\({}_{2}\)) was produced using NaCl (Carl Roth GmbH + Co. KG), CaCl\({}_{2}\) (AppliChem GmbH; Panreac Quimica SLU; ITW Companies), and MgCl\({}_{2}\) (Carl Roth GmbH + Co. KG).
# A Signaling-Free Underwater Code Division Multiple Access Scheme
## 4 An Example of Traffic Estimation  
Figure 2 illustrates a topological scenario as an example for which the proposed scheme is applied. The reference node \(X\) indicates who is trying to estimate the number of transmitting nodes and is located at \((2,1.5\pi)\) in polar coordinates, and the other five nodes are randomly located. Let us say that \(S(f)\cdot B\) is 81 dB re 1 \(\upmu\)pa (i.e., the power associated with the reference sound pressure level of 1 \(\upmu\)pa). We note that this value is chosen based on the range of the power spectral density of ambient underwater noise, which is from 60 to 100 dB re 1 \(\upmu\)pa/Hz [4]. Then, the expectation \(E[P_{X\gets Y}^{PCV}]\) is calculated to be 67 dB re 1 \(\upmu\)pa using Equation (7). Meanwhile, the measured receiving power at node \(X\), \(P_{X}^{rcv}\), which depends on the node distribution, is \(\approx\)74 dB re 1 \(\upmu\)pa. Then, the reference node \(X\) finds the number of nodes, \(n\), such that the difference between \(n\cdot E[P_{X\gets Y}^{rcv}]\) and \(P_{X}^{rcv}\) is minimized, as described in Equation (8). The procedure to find \(N_{e}\) is summarized in Table 1, where \(N_{e}\) is found to be 5, which is equal to the number of transmitting nodes in the example of Figure 2, and thus the estimation is correct.  
Figure 1: Nodes’ locations.  
Figure 2: A topological scenario as an example for traffic estimation.
## 2 System Model and Problem Formulation
### Problem Formulation  
SSL-SEI aims at achieving high SEI performance with limited labeled samples. The real received space-specific emitter signal \(x(t)\) can be represented as  
\[x(t)=[s(t)*h(t)+n(t)]\cdot e^{-\beta\pi f_{ml}t} \tag{1}\]  
where \(s(t)\) denotes the signal containing useful information, \(h(t)\) denotes the pulse response of the channel, and \(n(t)\) denotes the noise and interference during transmission. Additionally, \(f_{m}\) denotes the frequency generated by the mixer to convert a radiofrequency signal into an intermediate frequency signal.  
Assume that the sample \(\mathbf{x}\) is from an unlabeled dataset \(\mathcal{D}_{ul}=\{\mathbf{x}_{i}\}_{i=1}^{N}\), where \(\mathbf{x}_{i}\in\mathbb{R}^{L}\) denotes a time-domain signal of length \(L\). In this paper, positive and negative sample pairs are generated through data augmentation. Positive samples are defined as two augmented views of the same signal, while negative samples are augmented views of all remaining samples in the same batch.  
Using an encoder network \(f_{\theta}(\cdot)\) and a neural projection head \(g_{\phi}(\cdot)\), we obtain low-dimensional embedding features:  
\[\mathbf{z}_{i}=g_{\phi}(f_{\theta}(\mathbf{\bar{x}}_{i}))\in\mathbb{R}^{p} \tag{2}\]  
where \(\theta\) and \(\phi\) denote the parameters of the encoder and projection networks, respectively.  
To perform SEI with limited labeled data, a small labeled dataset is introduced to fine-tune the model, defined as \(\mathcal{D}_{l}=\{(\mathbf{x}_{i},y_{j})\}_{l=1}^{M}\), where \(\mathbf{x}_{j}\in\mathbb{R}^{L}\) is a time-domain signal  
Figure 1: The structure of the SEI system: data collection, feature extraction, and classification.  
and \(y_{j}\in\{1,2,...,C\}\) denotes its corresponding emitter class label, with \(C\) being the number of classes and \(M\ll N\).  
The goal of the fine-tuning phase is to optimize the feature enhancement module and classifier using the labeled dataset \(\mathcal{D}_{l}\), while keeping the pretrained encoder \(f_{\theta^{*}}\) fixed. The objective is defined as:  
\[\min_{\Psi^{*},\omega}\ \mathbb{E}_{(\mathbf{x}_{j},y_{j})\sim\mathcal{D}_{l}} \ \mathcal{L}_{\text{sup}}\big{(}c_{\omega}\big{(}q_{\Psi}(f_{\theta^{*}}( \mathbf{x}_{j}))\big{)},y_{j}\big{)} \tag{3}\]  
where \((\mathbf{x}_{j},y_{j})\sim\mathcal{D}_{l}\) indicates that the sample-label pair \((\mathbf{x}_{j},y_{j})\) is drawn from the labeled dataset \(\mathcal{D}_{l}\), \(\mathbf{x}_{j}\) denotes the signal sample, and \(y_{j}\) is its corresponding class label, \(q_{\Psi}(\cdot)\) denotes the feature enhancement module and \(c_{\omega}(\cdot)\) is the classifier. The function \(\mathcal{L}_{\text{sup}}(\cdot)\) typically denotes the supervised classification loss used to match predictions to ground-truth labels.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 6 Analysis
### Performance Analysis  
In our proposed scheme, the general authentication procedure is based only on an XOR operation and a hash function; thus, the computation cost is low. To demonstrate the performance of the proposed scheme, we compare the the critical secure communication procedure with the existing two-party secure communication schemes with session key agreement [6; 11; 12; 15; 16]. Next, we implement our scheme based on cryptographic libraries and present a concrete comparison of execution times. Then, we compare the security features of these schemes. Some notations are defined as follows for convenience:  
* The execution time of a hash function operation.
* The execution time of a bilinear pairing operation.
* The execution time of an ECC-based scalar point multiplication operation.
* The execution time of an ECC-based scalar point addition operation.  
The detailed comparison is presented in Table 4, where the middle and right columns list the complexity and total execution time, respectively, of each scheme. The transmission time is not considered in the comparison since it depends on the actual characteristics of the network, not the scheme. All operations listed in Table 4 were implemented using the OpenSSL library and the JPBC library, and the experiments were conducted on a Windows 7 PC (Samsung Electronics, Hwaseong, Korea) equipped with an Intel(R) Core(TM) i7-6500U CPU (Santa Clara, CA, USA).  
As seen in Table 5 and Figure 6, the execution time of our scheme is less than those of some other schemes [11; 12]. Although the execution times of [PERSON]'s scheme and [PERSON]'s scheme are less than that of our scheme, their schemes fail to resist internal attack because the participants'aliases depend only on a random number that is hidden by \(PSK\) as shown in Table 6. Therefore, a trusted vehicle can reveal a participant's real identity because it holds \(PSK\). Meanwhile, because [PERSON]'s scheme uses certificates for authentication, the unlinkability of messages cannot be preserved, and a user's anonymity can be violated. Therefore, our proposed scheme is a preferable solution for secure communication in vehicle sensor networks compared with the existing similar schemes presented in [6; 11; 12; 15; 16].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operation** & \(T_{h}\) & \(T_{mul}\) & \(T_{bp}\) & \(T_{add}\) \\ \hline Execution time (ms) & 0.004 & 0.326 & 6.28 & 0.038 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The execution time of basic operation.  
Figure 6: Execution time(ms) of different authentication protocols.
## 3 Results and Discussion
### Plant Physiological Experiments
#### 3.3.1 Malondialdehydedehyde  
The MDA index fluctuated sharply in the different plant species and treatments, as shown in Figure 7. _S. argenteostriatus_ cultivated in SC containers exhibited a high MDA content (approximately 32.15 umol/mg on average) because its cytoplasmic membranes had been damaged more than those of the LC and NC plants; the SC value was significantly (\(p<0.05\)) higher than the SC values of the LC and NC plants. This phenomenon means that plants suffered more severe stress damage than other samples. The mean values of the LC and NC plants were 16.99 and 19.42 umol/mg, respectively, and their outcomes were more stable than those in SC, showing that the cytoplasmic membranes of _C. dactylion_ and _A. stolonifera_ suffered less damage and had better drought resistance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & **Compressive** & **Alkalinity** & **Void** & **MDA** & **Pro** & **REC** & **SP** & **Chl** \\  & & **Strength** & & & & & & & & \\ \hline Compressive & Pearson Test & \(1\) & & & & & & & & \\ Strength & Sig. & & & & & & & & \\  & Pearson Test & \(-\)0.061 & \(1\) & & & & & & & \\  & Sig. & 0.868 & & & & & & & & \\  & Pearson Test & \(-\)0.093 & \(-\)0.404 & \(1\) & & & & & & \\  & Sig. & 0.798 & 0.247 & & & & & & & \\  & Pearson Test & \(-\)0.132 & 0.317 & \(-\)0.666 & \(1\) & & & & & \\  & Sig. & 0.734 & 0.406 & 0.050 & & & & & & \\  & Pearson Test & \(-\)0.028 & \(-\)0.006 & 0.285 & \(-\)0.129 & \(1\) & & & & \\ Pro & Sig. & 0.943 & 0.987 & 0.457 & 0.741 & & & & & \\  & Pearson Test & \(-\)0.201 & 0.290 & \(-\)0.614 & 0.838 ** & \(-\)0.016 & \(1\) & & & \\  & Sig. & 0.604 & 0.449 & 0.078 & 0.005 & 0.966 & & & & \\  & Pearson Test & \(-\)0.194 & 0.262 & \(-\)0.586 & 0.952 ** & \(-\)0.004 & 0.927 ** & \(1\) & & \\  & Sig. & 0.617 & 0.495 & 0.097 & 0.000 & 0.992 & 0.000 & & & \\  & Pearson Test & \(-\)0.102 & 0.238 & \(-\)0.283 & 0.720 * & 0.279 & 0.824 ** & 0.880 ** & \(1\) \\ Chl & Sig. & 0.793 & 0.538 & 0.460 & 0.029 & 0.468 & 0.006 & 0.002 & \\ \hline  & & & & & & & & \\ \end{tabular} \({}^{*}\) Indicates a significant correlation at the 0.05 level (bilateral), ** Indicates a significant correlation at the 0.01 level (bilateral).  
\end{table}
Table 6: Correlation coefficients between physical indexes and plant physiological indexes, \(n=9\) (Group LC).
# Reliability Model Based Dynamic Multi-Level Trust Analysis
## 3 Multi-Level Trust Model and Problem Statement
### Multi-Level Trust Evaluation
: _Multi-level trust propagation operator \(\oplus\) in Equation (2) is defined as:_  
\[P_{A}^{B}(A\to B\to C)=\mathit{dir}(A,B)\oplus\mathit{dir}(B,C) \tag{2}\]  
1. _If_ \(A\) _trusts_ \(B\) _on level_ \(i\) _(_\(0\leq i\leq k-1\)_,_ \(k\) _is the number of levels) and_ \(B\) _trusts_ \(C\) _on level_ \(j\) _(_\(i\leq j\leq k-1\)_), then the trust level of_ \(A\) _to_ \(C\) _is_ \(i\)_; if_ \(B\) _trusts_ \(C\) _on level_ \(i\) _(_\(0\leq i\leq k-1\)_,_ \(k\) _is the number of levels) and_ \(A\) _trusts_ \(B\) _on level_ \(j\) _(_\(i\leq j\leq k-1\)_), then the trust level of_ \(A\) _to_ \(C\) _is_ \(i\)_. The probability of level_ \(i\) _(_\(0<=i<=k\) _- 1_) on this path is shown as Equation (3):_ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Situation** & **A\(\rightarrow\)B** & **B\(\rightarrow\)C** & **A\(\rightarrow\)C** \\ \hline
1 & T & T & T \\
2 & T & D & D \\
3 & T & U & U \\
4 & D & T & D \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Trust propagation rules between three parties. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **A\(\rightarrow\)B** & **B\(\rightarrow\)C** & **A\(\rightarrow\)C** \\ \hline
1.1 & \(i\) (\(0<i<k-1\)) & \(j\) (\(i<=j\)) & \(i\) \\
1.2 & \(i\) (\(0<i<=k-1\)) & \(j\) (\(j<i\)) \(i>=j\) & \(j\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Trust propagation rules of T in detail. \[P_{(A,C),\ i}=t_{<A,B>,i}*\sum_{j=i+1}^{k-1}t_{<B,C>,j}+t_{<A,B>,j+1}*t_{<B,C>,j+ 1}+t_{<B,C>,i}*\sum_{j=i+1}^{k-1}t_{<A,B>,j} \tag{3}\]
2. _If_ \(A\) _distrusts B and B distrusts C, then the trust level of_ \(A\) _to C is uncertainty. If the trust level of_ \(A\) _to C is uncertainty._ _If the trust level of_ \(A\) _to C is uncertainty. Then the probability of level uncertainty on this path is shown as Equation (4):_ \[U_{A,B,C}=t_{<A,B>,0}*t_{<B,C>,0}+u_{<A,B>}*(1-u_{<B,C>})+u_{<B,C>}*(1-u_{<A,B>})\] (4)  
**Definition 2**. : _The trust level and probability on a path:_  
1. _The trust level of path_ \(i\) _is j, if and only if at least one link has trust level j, and the remaining links have a trust level higher than j._
2. _The trust level of path_ \(i\) _is uncertainty, if and only if at least two links on the path has trust level 0 or at least one link has trust level of uncertainty._  
Suppose the \(i\)th path from \(A\) to \(B\) with nodes sequence \(A,\ q_{1},q_{2}\ldots,\ q_{n},\ B\). Then the trust calculation on this path can be calculated as Equation (5). \[P_{A}^{B}(i)=dir(p,q_{1,})\oplus dir(q_{1,}q_{2})\oplus\ldots\oplus dir(q_{n},r) \tag{5}\]
# Multi-Tag Fusion Localization Method Based on Geometric Constraints
## 4 Fusion of Multiple Observations
### Geometric Constraint Effectiveness
#### 5.2.1 Single-Frame Validation  
For a demonstrative purpose, we extracted a specific frame from the simulation dataset, capturing both the true and observed positioning values. Within this frame, eight tags were discerned, with the identifiers [1120, 1121, 1136, 1137, 1408, 1409, 1410, 1425]. Implementing geometric constraints, we assigned a consistent weight coefficient of 0.5 to all observations, as per Equation (15), to ascertain the outlier factor for each. The results of these calculations are presented in Table 2.  
After sorting the observations by their outlier factors in ascending order, we designated the top 4 observations with the lowest outlier factors as reliable. These reliable observations were identified by the IDs [1121, 1410, 1409, 1137], while the remaining observations were considered outliers: [1425, 1408, 1120, 1136].  
Figure 8 presents a scatter plot of the observed tag coordinates within the world coordinate system, where each distinct point corresponds to a single observation identified by its unique tag ID. The positioning of these points in proximity to the true values illustrates the precision of our method, demonstrating the successful application of our filtering process in discerning reliable observations from outliers.  
Furthermore, Table 3 illustrates the variance of the observations before and after the removal of outliers. The variance in the X, Y, and yaw dimensions of the reliable observations is consistently lower than that of all observations combined. This reduction in variance demonstrates that the data become more concentrated and stable after the application of geometric constraints, confirming the reliability of the filtered observations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Outlier Factor** & **Whether It Is a Reliable Observation** \\ \hline
1121 & 0 & yes \\
1410 & 0.04049 & yes \\
1409 & 0.08078 & yes \\
1137 & 0.25164 & yes \\
1425 & 0.67949 & no \\
1408 & 0.80974 & no \\
1120 & 0.83909 & no \\
1136 & 1 & no \\ \hline \hline \end{tabular}
\end{table}
Table 2: Outlier factor.  
Figure 8: Schematic representation of the observed values.
# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 2 Data and Methodology
### Methodology  
The methodology comprises four main parts: pre-processing, super-resolution model training, object detection, and performance evaluation. In the first stage, a tiling and masking process is applied to the Sentinel-2A and PlanetScope images. In the second stage, model training is conducted by adjusting hyperparameter settings within the super-resolution model. During the third stage, the model is trained using the Sentinel-2A images and the enhanced Sentinel-2A images. In the last stage, an evaluation is performed based on performance metrics related to super-resolution and object detection. A detailed overview of the system architecture is provided in Figure 2.
## 1 Introduction
However, in the complex landscapes (i.e., structurally heterogeneous vegetation or fragmented land cover), there could be many pixels with similar spectral properties to the wilted pine trees in the image, such as seasonal discoloration of deciduous trees, brown herbaceous, and sparsely vegetated regions [26; 27]. For instance, the incidences of PWD in southern China are highly spatially dispersed, with a complex distribution [28], and the seasonal discoloration of the deciduous trees in the mixed forest is very similar to the wilting process of the pine trees. On the other hand, high resolution satellite imagery provides spatial detail at the tree-scale but mostly only four (visible and near infrared) bands on which the wilted pine trees and the easily confused objects show indistinguishable spectral properties. Moreover, high-resolution imagery often yields more variable reflectance values within one cover type [29; 30; 21]. These issues significantly confuse the recognition process and specifically causes a lot of commission errors into the result, which is more problematic than omission errors from an operational perspective [17]. In summary, the main reason for the limitations in using high-resolution images to monitor PWD is that the approaches have relied excessively on the spectral information while overlooking the spatiotemporal patterns of the discoloration process. By addressing these problems, we insist that a change detection method based on fusing spatial and temporal characteristics is a reasonable strategy to improve the accuracy (especially user accuracy) in PWD detection. The objective of this study is to incorporate spatial and temporal information extraction and to explore its improvements in tree-scale PWD detection in a complex landscape. Specifically, a spectral index is calculated to represent the canopy greenness in a single-date satellite image; the temporal behavior of the wilting process is measured using bi-temporal change detection; the spatial feature of wilted pine trees is enhanced by a spatial convolution operation using a proposed kernel. The PWD-induced wilted trees are subsequently determined by the extracted features.
# Effect of Thick-Hard Main Roof Fracturing on the Spatiotemporal Evolution of Overburden Fractures
## 3 Advanced Abtment Pressure Response Characteristics of the Main Roof Fracturing Weakening
### The Weakening Effect of Fracturing on the Advanced Abutment Pressure  
According to the numerical simulation schemes mentioned above, measuring lines were arranged in the coal seam in front of the working face and the coal seam floor of the goaf to study the advanced abutment stress of the working face and the recovery of the stress in the goaf. To avoid accidental errors, three measuring lines were arranged in the middle of the coal seam, 1.5 m above the middle of the coal seam and 1.5 m below the middle of the coal seam. The average stress of the three lines was used to characterize the advanced abutment stress of the working face. Moreover, one measuring line was arranged at the junction of the coal seam and the floor 1 m downward to measure the stress recovery of the goaf.  
When the main roof was not treated, or fractured in three different schemes, the corresponding peak values of the advanced abutment stress as the working face advanced by 80 m were 17.97 MPa, 17.76 MPa, 17.58 MPa, and 17.43 MPa, respectively. When the working face advanced by 100 m, the corresponding peak values were 16.21 MPa, 15.50 MPa, 15.96 MPa, and 16.08 MPa, respectively, as shown in Figure 4. When the working face advanced by 100 m, the corresponding goaf stress peaks were 0.61 MPa, 3.74 MPa, 2.06 MPa, and 1.29 MPa, respectively, as shown in Figure 5. Compared with the main roof non-fracturing scheme, the fracturing weakening of the main roof alleviated the concentration of the advanced stress concentration of the working face, and the recovery-bearing stress of the goaf increased significantly, among which the advanced abutment stress of the vertical fracturing scheme decreased the most, by 12.73%.  
Figure 3: Strata migration diagrams of the 4 schemes when the working face advanced by 80 m and 100 m: (**a**) Scheme 1; (**b**) Scheme 2; (**c**) Scheme 3; (**d**) Scheme 4.  
The Influence of Different Fracturing Schemes on the Distribution Characteristics of Overburden Fractures