esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Reversible Data Hiding Scheme for AMBTC-Compressed Images
## 4 Experimental Results

Table 1: Performance of the proposed scheme under difference values of \(m\). Figure 8: Six test grayscale images sized \(512\times 512\). To show the efficient performance of the proposed scheme over some previous schemes, we compared our proposed scheme with seven previous schemes, i.e., [PERSON] and [PERSON] (2014), [PERSON] et al. (2015), [PERSON] et al. (2016), [PERSON] et al. (2017), [PERSON] et al. (2018). [PERSON] et al. (2019), and [PERSON] et al. (2020). Table 2 shows the performance comparison of the proposed scheme and seven previous schemes ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018), with the same block size 4 \(\times\) 4. As can be observed in Table 2, our proposed scheme offers the highest embedding capacity, followed by the schemes of [PERSON] et al. (2020) and [PERSON] et al. (2019). As for the image quality of the stego images, the proposed scheme and five previous schemes ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018) offer precisely similar PNSRs as the results of the traditional AMBTC technique. The main reason is that these five schemes did not modify the AMBTC-compressed images during the data embedding process. However, Table 2 also shows that [PERSON] and [PERSON]'s scheme ([PERSON] et al., 2018) offered the worse PSNR among the seven schemes because this scheme was not reversible. It is clear that the proposed scheme obtained the highest embedding capacity among the five schemes. The proposed scheme is based on the high correlation of neighboring values in the two mean tables to embed three bits into each selected mean value, whereas only two bits were embedded in [PERSON] et al. 's scheme, and one bit was embedded in [PERSON] et al. 's scheme. [PERSON] and [PERSON]'s scheme used each smooth block of BTC-compressed image to embed only one secret bit. Three previous schemes ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018) only embedded one bit into each 4 \(\times\) 4 block of the BTC-compressed image. Obviously, the performance of [PERSON] et al. 's scheme ([PERSON] et al., 2018) was slightly better than the two other schemes ([PERSON] et al., 2018; [PERSON] et al., 2018). However, if more blocks with the same high mean value and the low mean value were determined in the image, the scheme of [PERSON] et al. provided better embedding capacity than that of the scheme of [PERSON] et al.
## 3 Results and Discussion
### Adsorption Studies
#### 3.2.5 Effect of Initial Concentration  
The influence of the initial copper ion concentration on adsorption amounts was evaluated using initial metal ion concentrations ranging from 50 to 500 mg L\({}^{-1}\) at a pH of 4.0 (Figure 9). Results indicated that capacity increased within this concentration range. To better understand the adsorption mechanism, various isotherm models were applied to interpret the equilibrium experimental data. Specifically, three linear isotherm models--Langmuir; Freundlich; and Dubinin-Radushkevich--were employed to analyze the experimental results. The specified models, whose equations are formulated based on different principles, are described in [PERSON] et al. [40]. Additionally, the chi-square (x\({}^{2}\)) test values for each model were calculated. Figure 8: Effect of sorbent amount on the percent adsorption of copper ions at the surface of L, M, MO, and LA. Figure 9: Influence of initial concentration on the adsorption of copper ions at the surface of MO, M, LA, and L.  
The Langmuir isotherm assumes a monolayer adsorption on a surface with a finite number of identical sites. It is best suited for processes where adsorption occurs uniformly on the adsorbent surface with no interaction between adsorbed molecules. The Freundlich isotherm is an empirical model that describes adsorption on heterogeneous surfaces with a non-uniform distribution of adsorption heat. The Dubinin-Radushkevich isotherm is used to describe adsorption on porous materials and accounts for the potential energy in the pores. It is particularly useful for distinguishing between physical and chemical adsorption processes, as it provides information about the adsorption mechanism. To evaluate the agreement between the experimental data and the predictions from the applied models, the chi-square (\(\chi^{2}\)) test was used as a statistical criterion to determine the suitability of each model for the adsorption process under study. A lower \(\chi^{2}\) value indicates a better fit of the experimental data to the corresponding model. The \(\chi^{2}\) value is calculated using the formula  
\[\chi^{2}=\Sigma((\mathrm{Q_{e,cal}}-\mathrm{Q_{e}})^{2}/\mathrm{Q_{e,cal}}),\]  
where \(\mathrm{Q_{e,cal}}\) represents the adsorbed amount (mg g\({}^{-1}\)) predicted by the model, and \(\mathrm{Q_{e}}\) is the adsorbed amount (mg g\({}^{-1}\)) obtained experimentally [41]. The isotherm constants, correlation coefficients, and \(\chi^{2}\) values for the three models are presented in Table 4. Based on the analysis, the Langmuir model was identified as the most suitable for describing the adsorption process of biomaterials MO, M, and L, indicating homogeneous surfaces, no interactions among adsorbed metal ions, and monolayer copper ion uptake. In contrast, for material LA, the Dubinin-Radushkevich model provided the best fit, suggesting adsorption occurs on heterogeneous surfaces, likely involving pore filling and varying energy sites (see Figure 10).
# A Review of Medication Adherence Monitoring Technologies +
## 4 A Review of Medication Adherence Monitoring Systems
\begin{table}
\begin{tabular}{c l l l l} \hline \hline  & **Main Application Differences** & **Strengths** & **Limitations** \\ \hline \multirow{4}{*}{**Sensor Systems**} & **Smart Pill Container** & Detects cap opening and bottle pick up & Possibility to allow mobility & System’s life is constrained by the battery \\  & **Smart Pill Container** & Detects cap opening and bottle pick up & Non-invasive & Detect medication taking activity with low accuracy \\  & & & Possibility to detect medication intake & User’s comfort and social acceptance due to their \\  & **Wearable Sensors** & & & activity with high accuracy \\  & & & and pill swallowing & Relatively easy to use & possible invasiveness \\  & & & Allow mobility & Require frequent battery charging or replacement \\ \cline{2-5}  & **Ingestible Sensors** & Detect pill ingestion & Possibility to detect concurrent pills ingestion & User’s comfort and social acceptance \\  & & & Allow mobility & System’s lifetime is constrained by the battery \\  & & & Allow mobility & Security issues due to their limited resources \\ \hline \multirow{2}{*}{**Proximity-Based Systems**} & Detects medication presence or absence & Non-invasive & Need to be coupled with other monitoring \\  & & within the proximity of reader’s antenna & or sensing techniques for verification \\ \cline{2-5}  & **Vision-Based Systems** & Detects medication presence or absence & Non-invasive & Need to be coupled with tech or sensing \\  & & within the scope of the camera & techniques for verification \\ \hline \multirow{2}{*}{**Fusion-Based Systems**} & Try to verify the operation of monitoring & Higher accuracy as compared to & Resource consuming \\  & & the medication taking activity & standalone technology & Do not usually support mobility \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of main applications, strengths, and limitations of the current technologies used in medication adherence.
# Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
## 2 Materials and Methods
### k-Nearest Neighbor  
This is a classification technique that exploits the neighborhood principle to define different classes in the data [34]. Each new sample is classified according to the class of the surrounding samples in the search space, also called the nearest neighbors samples. The classification rule labels a new sample according to the class that most of its k nearest neighbors belong to. Therefore, a metric for evaluating the distance between two data points has to be set--the Euclidean distance being the most common one, and the one used in this work. Additionally, the number of neighbors checked is defined by a parameter k, that can also be tuned.
## 5 Analysis of Trade-Off between Traffic Congestion and Packing Density
### Message Traffic Density  
The main idea underlying the parallel computer systems is to distribute the computation work to many nodes. After that, the outcome of the individual node's results are merged together to have the final result of the problem or task to be solved. Initially the traffic is a bit less, however, it will be congested as more and more packets are injected in the network. The phenomena resembles traffic congestion on city road network.  
Usually the traffic density of packets in a network absolutely depends upon the computation problem being executed in an MPC system or the traffic patterns created for that computation problem. The message latency and network throughput are dependent on the traffic situation or traffic congestion in the network and the deploying routing algorithm. The evaluation of these two parameters by computer simulations or prototype design is quite challenging, tiring, and also quite expensive; therefore, before going to the evaluation of latency and throughput, it is necessary to statically justify whether or not the network will result low latency and high throughput. This statistical assessment can be carried out by evaluating the message traffic density (MTD), or the packet distribution in a network.  
The number of links in each node denotes the average number of pathways to transfer a message from one node to another. The average distance is calculated as the mean of the distances between each distinct pair of nodes using the shortest path algorithm. The average distance as well as the total number of nodes and links in an MPC system should be known in order to calculate the MTD. The MTD is calculated by multiplying the average distance by the ratio of total nodes to total connections, which produces the efficiency of traffic distribution in a network, as shown in Equation (4).  
\[MTD=\textit{Average Distance}\times\frac{\#of\;Nodes}{\#of\; Links} \tag{4}\]The message traffic density of the studied interconnection networks have been calculated according to the derived Equation (4) and then enlisted in Table 2. The results portray that TFBN is much less congested compared to all the other studied networks because the MTD of the TFBN is much lower than all 2D networks such as torus, mesh, TESH, and TTN networks and even remarkably lower than 3D-Torus and 3D-Mesh networks for both 256 and 4096 nodes.
## 2 Results and Discussion
### Method Validation and Quantification of Phenolic Compounds  
The linearity, LOD, LOQ, and other method validation parameters are shown in Table 5. Based on the respective equation for each substance, the subcritical water and maceration extracts were quantified and the results are shown in Table 6. The results showed that pyrocatechol and epicatechin were the phenolics with the highest concentration determined after SWE of both parts of the plant. The roots of the plant contained the highest concentration of pyrocatechol (1062 ug/g) and epicatechin (1109 ug/g), whereas the aboveground part  
Figure 3: PCA of the main classes of compounds identified. M-AG: maceration aboveground part, SWE-R: subcritical water extraction roots, M-R: maceration roots, SWE-AG subcritical water extraction aboveground part.  
contained approximately half these concentrations, 544 and 513 ug/g, respectively. This result contrasts with studies that have reported that _O. mutabilis_ species do not contain epicatechin, pyrocatech, and catechin [52; 53]. The concentration of the remaining phenolics obtained from SWE was comparable for both parts of the plant with the exception of quercetin, the concentration of which in the aboveground part was 5-fold that in the roots. Compared to maceration, SWE showed a superior performance for the selected phenolics with the exception of caffe and ferulic acids where the extracted concentration was slightly reduced. With respect to pyrocatech and epicatechin, SWE of the roots yielded a much higher concentration compared to conventional maceration, 1062 as compared to 10.2 and 1109 as compared to 23.4 ug/g, respectively.
# Optimization and Diversification of Cryptocurrency Portfolios: A Composite Copula-Based Approach
## 2 Methodology
### Machine Learning: K-means Clustering  
**Definition 1**.: _K-means is an unsupervised algorithm for non-hierarchical clustering. It allows the observations of the data set to be grouped into distinct clusters. Thus, similar data will be found in the same cluster. In addition, an observation can only be found in one cluster at a time (membership exclusivity). The same observation cannot therefore belong to two different clusters._  
K-means is an iterative algorithm that minimizes the sum of the distances between each observation and the centroid. The initial choice of number \(K\) of the centroids determines the final result. Admitting a cloud of a set of points (data or observations), K-means changes the points of each cluster until the sum can no longer decrease. The result is a set of compact and clearly separated clusters, provided the researcher chooses the right \(K\) value for the number of clusters.  
The convergence of the K-means Algorithm 1 can be one of the following conditions:  
* A pre-set number of iterations, in this case K-means will perform the iterations and stop regardless of the shape of compound clusters.
* Stabilization of cluster centers (centroids no longer move or change during iterations).  
```
Input:
* \(K\) the number of clusters to be formed
* \(X\) the Training Set (\(m\times n\) data matrix) Output:
1. Randomly choose \(K<m\) points (\(K\) rows of the data matrix). These points are the centers of the clusters (called centroid).
2. Assign a cluster to each point (or observation), randomly.
3. Calculate the centroid of each cluster (i.e. the vector of the means of the different variables)
4. For each point calculate its Euclidean distance with the centroids of each of the clusters
5. Assign the closest cluster to the object
6. Calculate the sum of the intra-cluster variability
7. Repeat steps 3 to 5, until an equilibrium is reached, that is convergence: no more change in clusters, or stabilization of the sum of the intra-cluster variability.
```  
**Algorithm 1**K-means Algorithm
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 2 Related Work
### Propagation-Based Recommendation Methods
#### 2.3.3 Both User and Item Representation-Refinement Approaches  
Different from the aforementioned two types of propagation-based recommendation methods, the both user and item representation-refinement approaches construct user-item knowledge graphs [1; 8], a.k.a. collaborative knowledge graphs (CKGs), which contain users, items, item attribute entities, and the relations between them, and use graph neural networks to refine user embeddings and item embeddings. For example, [PERSON] et al. [8] proposed the knowledge graph attention network (KGAT) method, which first uses TransR [31] to obtain the initial representations of the entities in a CKG, and then uses the graph attention network to refine user embeddings and item embeddings. [PERSON] et al. [32] proposed an extensible recommendation framework IntentGC, which uses graph convolutional networks to capture user preferences and heterogeneous relations in the knowledge graph, and then learns user representations and item representations for recommendations. [PERSON] et al. [33] proposed the entity-aware collaborative relation network (ECRN) method, which uses a knowledge graph to explore the entity-granularity relation semantics behind user-item interactions, thus refining user representations and item representations. The X-2 ch model proposed by [PERSON] et al. [34] employs a four-channel mechanism to propagate information in the knowledge graph to obtain representative user representations and item representations. The knowledge graph-based intent network (KGIN) method, proposed by [PERSON] et al. [6], uses auxiliary item knowledge to explore the users' intention behind the user-item interactions, and uses an information aggregation mechanism to refine the information related to the users' intention, and finally encodes this information in the user representations and the item representations to improve these representations. However, the CKGs that these methods rely on do not distinguish between user nodes and other entity nodes. Therefore, it is impossible to make full use of the collaborative signals in the user-item interactions to refine user representations and item representations, and it is difficult to deal with new users [4]. Our CKGAT method belongs to the both user and item representations refinement approaches. Different from the existing work, CKGAT can make full use of both the topological proximity structures of entities and the collaborative signals in user-item interactions to refine user representations and item representations, thus achieving better recommendation results.
# Microstructure of Mortar with Ballast Waste as a Cement Replacement
## 1 Introduction
In addition to these advantages, blended materials exhibit improved micro- and macrostructural properties [31]. When pozzolanic materials are incorporated into cement-based systems, they react with portlandite formed during cement hydration [32], modifying both fresh and hardened-state properties. Changes in particle size distribution influence setting times [33] and viscosity. At early hydration stages, pozzolanic materials act primarily as fillers, increasing the effective water-to-binder ratio and reducing hydration heat [34; 35]. At later stages, they contribute pozzolanically, enhancing cement properties. These structural modifications improve macro-properties, such as expansion control [36] and durability [37]. The aim of this study is to analyze the behavior of novel silico-aluminous mineral additions with pozzolanic activity and added value in cement manufacturing and similar materials. Specifically, the study investigates the use of railway infrastructure waste--track ballast--as a replacement material in pozzolanic cement for applications in slab tracks and concrete sleepers. The research aims to evaluate the pore structure (porosity and other morphological parameters) of a cement matrix composite using \(\upmu\)-CT on an intact sample. Initially, the pozzolanic characteristics of the materials and their workability in their raw state will be examined.
# Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD
## 6 Conclusions  
The method for decomposing multicomponent micro-Doppler signals based on AMD-HHT is proposed in this paper. This method solves the problem that Hilbert-Huang transform cannot identify and separate the signal with close-frequency components. The process is as follows. Firstly, the signal is decomposed by EMD, and Hilbert-Huang spectrum and marginal spectrum are obtained by Hilbert transform. If there is frequency aliasing in the decomposed signal, the close-frequency components are separated by AMD. A set of simulations and the experimental testing on radar echo are used to assist in the interpretation of the method analytically. By comparing with the conventional HHT, the proposed method is capable of yielding correct decomposition of the effective data. The work presented here extends the prior work in this area. The main limitation of the method is the optimal selection problem of the parameters, which will be further studied and reported in a separate paper in the near future.  
All authors discussed the contents of the manuscript. [PERSON] conceived the experiments, conducted the theoretical analyses, and wrote the paper; [PERSON] and [PERSON] provided the experimental equipment and analysis tools; [PERSON] and [PERSON] revised the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No.61701508) and Hunan Provincial Natural Science Foundation of China under Grant 2018J[3613.  
Figure 12: Decomposition results of IMF2 signals: (**a**) TFD of AMD1; (**b**) TFD of AMD2.  
Figure 13: Decomposition results of IMF3 signals: (**a**) TFD of AMD1; (**b**) TFD of AMD2; (**c**) TFD of AMD3.  
**Acknowledgments:** The funds for covering the costs to publish in open access are provided by the State Key Laboratory of Complex Electromagnetic Environment Effects on Electronic and Information System, National University of Defense Technology.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methods
### Structural Equation Model  
Figure 1 illustrates the research model based on the established hypotheses. This study uses an SEM to analyze the causal relationships between work environment, work-family conflict, musculoskeletal pain, sleep-related problems, and burnout among food manufacturing production workers. That is, this study explores the effects of the work environment (H1) and work-family conflict (H2) on musculoskeletal pain in food manufacturing production workers. Furthermore, it investigates the influence of work environment (H3), work-family conflict (H4), and musculoskeletal pain (H5) on sleep-related problems. Lastly, the study aims to determine the relationship between musculoskeletal pain (H6) and sleep-related problems (H7) and their impact on burnout.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Measurement Variable**} & \multicolumn{6}{c}{**Component**} \\ \cline{3-6}  & & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{4}{*}{Work–family conflict} & Concentration & 0.900 & \(-\)0.021 & 0.087 & \(-\)0.012 & 0.049 \\  & Responsibility & 0.889 & \(-\)0.034 & 0.085 & \(-\)0.031 & 0.065 \\  & Family & 0.762 & 0.066 & 0.089 & 0.123 & 0.142 \\  & Tiredness & 0.693 & 0.149 & 0.165 & 0.172 & 0.282 \\ \hline \multirow{4}{*}{Work environment} & Noise & \(-\)0.028 & 0.821 & \(-\)0.026 & \(-\)0.025 & 0.137 \\  & Vibration & \(-\)0.062 & 0.783 & 0.047 & \(-\)0.053 & 0.099 \\  & Low temperature & \(-\)0.046 & 0.645 & 0.073 & 0.148 & 0.013 \\  & High temperature & 0.096 & 0.613 & 0.080 & 0.030 & \(-\)0.101 \\  & Handling heavy loads & 0.183 & 0.590 & 0.054 & 0.197 & 0.024 \\ \hline \multirow{4}{*}{Sleep-related problems} & Waking up repeatedly & 0.104 & 0.067 & 0.896 & 0.142 & 0.097 \\  & Difficulty falling asleep & 0.108 & 0.036 & 0.885 & 0.083 & 0.059 \\  & Exhaustion/fatigue & 0.161 & 0.132 & 0.817 & 0.176 & 0.165 \\ \hline \multirow{4}{*}{Musculoskeletal pain} & Upper limb pain & 0.012 & 0.062 & 0.122 & 0.819 & 0.115 \\  & Back pain & 0.101 & 0.058 & 0.106 & 0.793 & 0.127 \\  & Lower limb pain & 0.057 & 0.117 & 0.124 & 0.788 & \(-\)0.056 \\ \hline \multirow{2}{*}{Burnout} & Exhaustion & 0.201 & 0.055 & 0.133 & 0.097 & 0.907 \\  & Drained & 0.204 & 0.057 & 0.153 & 0.081 & 0.892 \\ \hline \multirow{4}{*}{Burnout} & \% of Variance & 26.478 & 14.381 & 11.361 & 8.847 & 7.805 \\  & Cumulative (\%) & & & 68.872 & & \\ \cline{1-1}  & Kaiser–Meyer–Olkin test & & & 0.765 & & \\ \cline{1-1}  & [PERSON]’s test & & & \(p<0.001\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Final results of reliability and factor analysis.
## 4 Discussion
#### Limitations of the Study  
Although the size of the cohort (144 cases) is consistent, a total of eight cases diagnosed with lymph nodes metastases is small for a correlation study, which is a limitation. A single surgical team, thus the uniformity of the surgical technique and unbiased results, performed the operations. Another limitation is the relatively short follow-up period of 2 years, which, if it had been extended, may have slightly raised the number of cases with LNs metastases.