esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing
## 2 Related Works
### Absolute Matrix Block Truncation Coding  
The Block Truncation Coding (BTC) algorithm is a widely used image compression technology. Two appropriate gray levels can be selected to approximately represent the original gray level of the image block, and then it is necessary to indicate which gray level each pixel in the image block belongs to. The BTC compression algorithm has low complexity and a fast encoding and decoding speed, and can be applied to devices with relatively low computing power, such as mobile phones and drones, that require real-time transmission to obtain reconstructed images with better visual performance.  
The Absolute Moment Block Truncation Coding (AMBTC) algorithm is an improved version of BTC. It uses absolute moments to calculate and reconstruct high and low gray values, while BTC uses variance to calculate high and low brightness values, so this method is complicated to calculate. The degree of compression is lower, and the compression error is smaller under the same compression ratio; that is, the obtained image quality is better.  
In AMBTC, the image is first divided into non-overlapping image blocks, where the value of k can be set to 4, 8, 16, etc. The average value of each image block is calculated by:  
\[\overline{x}=\frac{1}{k\times k}\sum\limits_{i=1}^{k^{2}}x_{i} \tag{2}\]  
where \(x_{i}\) represents the gray value of the \(i\)-th pixel. For each image block, the bitmap BM represented by \(\{0,1\}\) is obtained by comparison with the average value:  
\[b_{i}=\begin{cases}1,\ if\ x_{i}\geq\overline{x}\\ 0,\ if\ x_{i}<\overline{x}\end{cases} \tag{3}\]  
The high and low volumes are calculated by:  
\[\begin{cases}\ H=\frac{1}{t}\sum\limits_{x_{i}\geq\overline{x}}x_{i}\\ L=\frac{1}{k\times k-t}\sum\limits_{x_{i}<\overline{x}}x_{i}\end{cases} \tag{4}\]  
where \(t\) represents the number of pixels represented by \"1\" in the bitmap. After the high amount H, the low amount L and the bitmap BM are calculated, they can be combined into the basic unit triplet {H, L, BM}, and then the image is compressed by AMBTC. For \(k=4\), \(8+8+16=32\) bits can be used to represent a \(4\ \times\ 4\) block of an original image, so the compression ratio is \((16\ \times\ 8)/32=4\). That is, for an image, 2 M bits can be compressed into 0.5 M bits.  
The Shamir threshold scheme can be used to segment the original secret message and store the subsecret messages in different servers, thereby it can improve the confidentiality and fault tolerance of the original secret message. However, the shadow images generated by these methods are often meaningless and easily attract attackers' attention. These shadow images cannot be effectively compressed. In this paper, the subsecret messages were embedded into multiple meaningful images that are compressed by AMBTC, which can effectively avoid attackers' attention.
## 1 Introduction
Based on the model, a new function involving the round-trip file delay and the energy consumption is proposed to reflect the number of files that can be delivered under time and energy constraints, in order to comprehensively evaluate the impact of these two metrics on the transmission efficiency. By maximizing the function, the optimal network configuration parameters can be obtained, achieving the best compromise between delay and energy consumption, thus improving the transmission efficiency and reliability. The main contributions of this paper are threefold:  
1. We propose a reliable transport scheme for real-life human ONs, which optimizes the delay of the source file and the energy consumption of relays. The scheme makes use of acknowledgements and coding at the source to enable successful file delivery. A cache management rule referred as the \"full-duplex\" strategy is formulated and a mathematical model is set up to analyze the proposed scheme;
2. A new function based on round-trip file delay and the corresponding energy consumption is proposed to reflect the number of files that can be delivered under time and energy constraints. Through optimization procedure, the configurations that can optimize these two metrics are obtained;
3. The accuracy of the analytical model is verified by numerical simulations, and the performance of the proposed reliable transmission mechanism is evaluated. Specifically, the performance metrics we consider are the energy consumption and the mean round-trip delay of the file (until the final acknowledgement reaches the source). The remainder of this paper is organized as follows: We discuss related work in Section 2. In Section 3, the network setting is described. In Section 4, a detailed account of the proposed transport scheme is presented. In Section 5, an analytical model is developed to analyze the proposed scheme, and a new function based on round-trip file delay and the corresponding energy consumption is proposed and optimized to make a good trade-off between the two metrics. In Section 6, we demonstrate the accuracy of the analytical model, optimization procedure, and the performance of the proposed scheme. Finally, Section 7 concludes this work and provides future directions of research.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 2 Related Works
### Hash Tree  
A hash tree is a hierarchical data structure, where each leaf node represents a data block and is labeled with its corresponding cryptographic hash [18]. In a blockchain, a hash tree enables efficient data integrity verification without requiring the download of the entire blockchain. Each block contains a root hash, a cryptographic hash of all its transactions. The hash tree provides a tamper-proof way to summarize and verify the transactions within a block [19]. This allows for the efficient verification of whether a specific transaction is included in a block without downloading the entire block. For instance, a Merkle tree is a tree data structure where each leaf node is labeled with the cryptographic hash of a data block, and each non-leaf node is labeled with the cryptographic hash of its children's labels. The hash tree used for integrity verification is created and used as a Merkle tree with a height of \(O(log_{2}n)\) and \(2n-1\) nodes, for the given n verification target data. Merkle tree is also used in blockchain as a technique to ensure that block data have not been changed or damaged since they were recorded [20]. This allows the efficient and secure verification of the contents of large data structures. Figure 2 describes the structure of a hash tree with leaves of five hash values, \(k_{0}\) to \(k_{4}\). \(hash_{0}\) is the result of hashing the concatenation of \(hash_{0-0}\) and \(hash_{0-1}\), where \(||\) denotes concatenation. \(hash_{root}\) is the root of a hash tree given its leaves:  
\[hash_{0}=hash(hash_{0-0}||hash_{0-1})\]  
\[hash_{root}=hash(hash_{0}||hash_{1})\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **SHA-256** & **SHA-3** \\ \hline Standard & Part of SHA-2 family & NIST standard (Keccak algorithm) \\ Use case & Digital signatures & General-purpose hashing \\ Security & Considered secure & Considered secure \\ Processing time & Moderate & Slower than SHA-256 \\ Usability & Widely adopted & Relatively newer than SHA-256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison hash functions: SHA-256 and SHA-3.  
To verify the data integrity in the proposed method, we apply the hash tree to find whether the data frame has been tampered with or not. In the case of receiving an image that has been tampered with, the part of the image with tampering is detected by the verification method using the hash tree.
# Electro-Thermal Properties of Carbon-Fiber-Reinforced Mortar Prisms
## 3 Some Relationships Governing the Mortar Prism Heating with the Electric Power  
When a voltage _V_is applied to two electrodes attached to a material with resistance \(R\), then current \(I\), flowing through the channel between the electrodes, and power \(P\), deposited on the material, are expressed as [20]:  
\[I=V/R \tag{1}\]  
\[P=VI=RI^{2}=V^{2}/R \tag{2}\]  
Equations (1) and (2) indicate that the power deposited on the material is inversely proportional to the resistance. This means that the power applied to the material with higher resistance will be smaller for a given voltage. Power \(P\) has the unit of \(W(\mathrm{Watt})\), while \(V\) and \(I\) values have the units of volt (V) and ampere (A), respectively. Resistance \(R\) has the unit of \(\Omega\) (Ohm) and is related with resistivity \(p\) as,_R = \(\rho A/l\)_, where \(A\) (mm\({}^{2}\)) is the average of the two electrode areas and _l_(mm)is the thickness of the material. Energy \(E\) deposited on the material by the given power \(P\) is described as _E = Pt J_(Joule), where \(t\) is the time of applying power to the material in seconds.  
Due to this energy, the temperature of the material will increase. The temperature increments \(\Delta T\), due to the deposited electric power, _Pt_, is expressed as [21]:  
\[\Delta T=E/mS \tag{3}\]  
where \(m\) denotes mass of the minimum volume, as shown in Figure 2, in the unit of Kg, and \(S\) is specific heat of the material in the unit of J/(Kg\(\cdot\)K), where K is the absolute temperature. For the specific heat of the mortar prism, 980 J/(Kg\(\cdot\)K) is used [22]. Since the temperature increment is inversely proportional to the prism weight as shown in Equation (3), the smaller weight prisms will gain higher temperatures. This should be normalized by considering their weights to correctly compare the thermal properties of the prisms with different fiber percentages.  
The normalized numbers calculated previously, which are specified with an asterisk (*) in Table 1, work as the compensation factor for each percentage prisms. When the factor is multiplied by its corresponding temperature increment, the increment should be the same as that of the 1.0% prisms. However, since the presence of fibers with different volume percentages transforms the prisms with different fiber amounts to the materials with different conductivities, the actual temperature increment will be different for different fiber percentages. The compensation factor should be multiplied by the temperature increments from the heating.
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### Overview of Evaluation Metrics  
Table 6 provides a comprehensive overview of the most commonly used evaluation metrics for GANs and segmentation models, particularly in the context of histopathological image analysis. What immediately stands out is that these metrics are not applied generically; they are directly aligned with the distinct goals of each model type. For GANs, the focus lies in the visual fidelity of generated images, aiming to make them as realistic as possible. In contrast, segmentation models are evaluated based on their precision in identifying and delineating specific structures, such as cell nuclei, which are essential for accurate pathological assessment.  
Among the metrics used for evaluating GANs, the Frechet Inception Distance (FID) emerges as one of the most prevalent, cited in numerous studies [56; 58; 62; 66; 68; 74; 82; 86; 94]. Another widely adopted metric is the Structural Similarity Index (SSIM), found in works such as [73; 76; 81], which assesses structural fidelity, an important factor for ensuring that fine-grained histological details are preserved.  
Other metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE), continue to be used due to their simplicity, though they are less effective in capturing perceptual quality. More specialized metrics, including Between-image Color Constancy (BiCC) and Within-set Color Constancy (WsCC), appear in studies like [76; 90] and address color consistency, a crucial consideration in histological images, where subtle color differences can significantly impact interpretation.  
On the segmentation side, the Dice Similarity Coefficient (DSC) stands out as the leading metric. It is referenced in over twenty papers [58; 65; 67; 81; 87], underscoring its importance for quantifying overlap between predicted and annotated segmentations. This metric is particularly vital in nucleus segmentation tasks, where even minor pixel-level inaccuracies can affect diagnostic outcomes. Complementary metrics such as the F1-score, precision, and recall/sensitivity are also common [82; 90; 97], offering a deeper understanding of a model's ability to handle false positives and false negatives, critical aspects in clinical applications.  
The Intersection over Union (IoU), used in works like [57; 68; 81], provides a strong measure of segmentation accuracy by comparing the predicted and ground-truth masks. Metrics such as Pixel Accuracy (PA) and Jaccard Index (JI) are also employed to offer broader performance insights. Additionally, spatial metrics like the Hausdorff Distance (HD), which calculates the largest distance between corresponding boundary points, are important when the shape and positioning of segmented nuclei have clinical implications.
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
## 3 Grey-Total-Influence-Based Tolerance Rough Sets
### Determining Grey Total Influence
#### 3.3.3 Generating a Grey Total Influence Matrix  
All diagonal elements of \(\mathbf{Z}\) should first be set to zero [24]. \(\mathbf{Z}\) is in turn normalized to produce a normalized direct influence matrix \(\mathbf{X}\):  
\[\mathbf{X}=\lambda\mathbf{Z} \tag{24}\]  
where  
\[\lambda=\frac{1}{\underset{i,j}{\max}\{\underset{i}{\max}\underset{j=1\ldots m }{\sum}z_{ij}.\underset{i=1\ldots m}{\sum}z_{ij}.\underset{i=1\ldots m}{\sum}z_{ ij}\}} \tag{25}\]  
Finally, the grey total influence matrix \(\mathbf{T}=[t_{ij}]_{m\times m}\) can be further generated by \(\mathbf{X}(\mathbf{I}-\mathbf{X})^{-1}\). Unlike \(\mathbf{Z}\), \(\mathbf{T}\) considers not only direct, but also indirect relationships for each pair of patterns. As \(\mathbf{Z}\) and \(\mathbf{T}\) express different prospects regarding the impact of \(\mathbf{x}_{i}\) on \(\mathbf{x}_{j}\), considering \(z_{ij}\) and \(t_{ij}\), it is reasonable to aggregate \(\mathbf{Z}\) and \(\mathbf{T}\) into a new hybrid matrix \(\mathbf{G}=[g_{ij}]_{m\times m}\). By balancing \(\mathbf{Z}\) and \(\mathbf{T}\), \(\mathbf{G}\) is defined as follows:  
\[\mathbf{G}=\alpha\mathbf{Z}+(1-\alpha)\mathbf{T} \tag{26}\]  
This means that  
\[g_{ij}=\alpha z_{ij}+(1-\alpha)t_{ij} \tag{27}\]where \(0\leq\alpha\leq 1\), and the relative importance of the two items is measured by \(\alpha\). \(g_{ij}\) tends to affect direct influence when \(\alpha\) > 0.5 and total influence when \(\alpha\) < 0.5. The higher the value of \(g_{ij}\), the greater the degree to which \(\mathbf{x}_{i}\) influences \(\mathbf{x}_{j}\).
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 5 Parametric Study
### Influence of Flange Size  
Figure 15 demonstrates the significant impact of flange thickness on the pull-out performance of notched T-perfobond connectors. Increases in flange thickness from 2 mm to 4 mm, 6 mm, 8 mm, and 10 mm resulted in corresponding pull-out resistance changes of \(-4.88\%\), 2.97%, 13.73%, and 13.90%, respectively. The findings indicate that enhancing flange thickness augments the pull-out resistance of these connectors. The primary rationale is the pronounced anchorage effect of the notched perfobond rib's bottom flange. Between 2 mm and 8 mm flange thickness, the flange is the weaker constraint, and the pull-out resistance increases linearly with flange thickness. However, when the flange thickness exceeds 8 mm, the notched perfobond rib or the concrete becomes the limiting constraint and further increases in flange thickness yield minimal changes in pull-out resistance.  
Figure 16 illustrates the substantial effect of flange width on the pull-out performance of notched T-perfobond connectors. Increasing the flange width from 6 mm, which matches the connector's thickness, to 12 mm, 18 mm, 26 mm, and 34 mm, resulted in increases in pull-out resistance of 71.35%, 68.19%, 59.66%, and 49.05%, respectively. The data indicate that broadening the flange width from 6 mm to 12 mm significantly enhances the pull-out resistance due to the robust anchorage provided by the bottom flange of the connectors. However, as the flange width extends beyond 12 mm, there is a gradual decline in the ultimate pull-out resistance. This decrease may be attributed to the excessive length of the flange, which leads to a more pronounced upward force on the concrete at the junction of the notched perfobond rib and the flange. The resultant moment at the flange's outer edge due to this force is larger and may cause stress concentration, leading to premature concrete failure at the flange's outer edge and a consequent reduction in pull-out resistance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Model** & \(\mathbf{t_{f}}\) & \(\mathbf{b_{f}}\) & \(\mathbf{c_{w}}\) & \(\mathbf{d_
## 5 Conclusions  
DEM simulations were carried out to investigate the factors influencing the \(K_{0}\) value of granular materials, including the initial fabric anisotropy, initial void ratio, inter-particle friction coefficient, and particle shape. The main findings of this study can be summarized as follows:  
(a) When the initial anisotropy degree is high, the initial anisotropy does have a certain effect on the value of \(K_{0}\), but the effect is limited. We consider that the effect of initial anisotropy on \(K_{0}\) can be neglected under laboratory or engineering conditions as the initial anisotropy degree is smaller in these conditions.  
(b) The evolution of microstructure (\(CN\), \(a_{r}\)) in specimens with different initial anisotropy shows different trends during the \(K_{0}\) loading. The larger the angle between the principal direction of the initial anisotropy and the principal stress is, the larger the fabric redistribution will occur in the initial stage of \(K_{0}\) loading.  
(c) The shape of particles, the initial void ratio, and the coefficient of inter-particle friction all have significant effects on \(K_{0}\) values.  
(d) It is more appropriate to adopt the peak friction angle in [PERSON]'s simplified formula for calculating \(K_{0}\) values. However, this equation still cannot provide accurate predictions. Based on the simulation results in this paper, we establish a prediction formula of \(K_{0}\) values which relate to initial relative density, shape parameter, and critical state friction angle. The new proposed formula gives better results of \(K_{0}\).  
Conceptualization, J.Q. and X.G.; Data curation, W.L. and Y.Q.; Formal analysis, Y.Q.; Investigation, L.X.; Resources, X.G.; Software, X.G.; Visualization, C.Z. and L.X.; Writing--original draft, C.Z. and W.L.; Writing--review & editing, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 52178345.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 16: Comparison between the predicted \(K_{0}\) values and the measured ones; (**a**) Aspect ratio = 1; (**b**) Aspect ratio = 1.3; (**c**) Aspect ratio = 1.7; (**d**) Comparison for all points.
## 5 Conclusions  
The mechanical properties and microstructural characteristics of geopolymer concrete were reviewed. From the above literature, the following are the final thoughts, along with suggestions for more research:  
* Construction with geopolymer concrete is more durable and stronger than with PC concrete;
* Many factors, including curing conditions, the ratio of alkaline to the binder, and the type of activator, have an important impact on the mechanical properties of geopolymer concrete. Consequently, a proper mix design is required to achieve the target strength;
* Geopolymer concrete possesses all the potential characteristics for future applications in civil engineering because it is a green material and requires strength and durability properties for all types of projects in the construction industry;
* Even though it is known that GPC could be used as a replacement material and is a cleaner and more sustainable form of concrete, it is still not widely used in construction;
* Geopolymer concrete needs a standard code to be used more often in building structures;
* In terms of mechanical and microstructural performance, geopolymer concrete was better than PC concrete, especially after exposure to high temperatures;
* The effect of using geopolymer as a partial replacement for PC on the microstructure can be easily noticed; the microstructure has become significantly denser and more homogenous compared with the control specimen, while the number of voids has decreased;
* C-S-H gel and geopolymer gel enhance the mechanical and microstructural properties of precursors that are either high in Ca or contain a combination of Ca components.  
**Author Contributions:** Conceptualisation, A.S., A.H., H.M.N., S.Q., M.M.S.S. and N.S.M.; methodology, A.S., A.H., H.M.N., M.M.S.S. and N.S.M.; software, A.S. and A.H.; validation, H.M.N., S.Q., M.M.S.S. and N.S.M.; formal analysis, A.S. and A.H.; investigation, A.S., A.H., H.M.N., S.Q., M.M.S.S. and N.S.M.; resources, A.S., A.H., H.M.N., M.M.S.S., N.S.M. and K.A.; data curation, A.S., A.H., H.M.N., M.M.S.S., S.Q., K.A. and N.S.M.; writing--original draft preparation, A.S. and A.H.; writing--review and editing, A.H., H.M.N., M.M.S.S. and N.S.M.; visualisation, A.S. and A.H.; supervision, A.S., A.H. and H.M.N.; project administration, M.M.S.S.; funding acquisition, M.M.S.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program \"Priority 2030\" (Agreement 075-15-2021-1333 dated 30 September 2021).