esa-sceva/satcom-chunk-collection huggingface dataset:
# A Finite State Machine-Based Improved Cryptographic Technique
## 3 Proposed Methodology
### Finite State Machine with Reverse Data Hiding  
For the \(t^{th}\) received bit, the decoder computes the algorithm of the Likelihood Ratio (LLR), \(L_{ext}^{1}\) (\(d_{i}\)) as  
\[L_{ext}^{1}(d_{i})=\left\{log\left[\frac{P_{r}\bigg{(}d_{i}=+1\mid R_{1} \bigg{)}}{P_{r}\bigg{(}d_{i}=-1\mid R_{1}\bigg{)}}\right]\right\} \tag{11}\]  
Equation (11) shows the data-hiding text-using Finite State Machine with the Likelihood Ratio and message. \[L_{ext}^{2}\left(d_{i}\right)=\left\{ \begin{array}{l}log\left[\frac{P_{r} \bigg{(}d_{i}=+1\mid R_{2}\bigg{)}}{P_{r}(d_{i}=-1\mid R_{2})}\right]\\ \end{array} \right\}\text{and }\Big{(} \begin{array}{l}L_{o}(d_{i})=L_{ext}^{1}(d_{i})+L_{ext}^{2}(d_{i })+L_{a}\big{(}A^{-}{}_{L}\big{)}\Big{)}\end{array} \right\} \tag{12}\]  
\[d_{t}= \begin{array}{l}1\text{ if }\quad\left(L_{o}(d_{i})>0\right)\\ d_{1}=0\text{ if }\left(L_{o}(d_{i})<0\right)\end{array}  \tag{13}\]  
If the receiver uses the incorrect key to the FSM, the decoders receive incorrect bit sequences \(R_{1}\) and \(R_{2}\), thereby recovering the incorrect information shown in Equation (12). \[Y(v)=X(u),\;v=\pi(u) \tag{14}\]  
where \((u)\) is the mapping function. A secret number is used in conjunction with a complicated procedure on a computer to encrypt and decode data. To prevent unauthorized parties from understanding sensitive information sent over public networks, it is necessary to encrypt the message before  
Figure 4: Cryptographic Turbo Code (CTC) decoder using MCTC de-FSM. sending it. Only the receiver to whom they are addressed should be able to decode them. A communication encrypted with a key should be decipherable only by the owner of the same key. In traditional TCs, the input data are interleaved with a well-known permutation pattern that is intended to increase the efficiency of the coding process. This makes it possible for a random FSM to be employed. However, to store the interleaving pattern, the random FSM needs an additional memory and, to send data to the receiver, it needs a wider bandwidth (Table 2). The development of a key-based cryptographic FSM that can create an extremely random output on the fly is a difficult task, which is one of the primary motivations for the work that is being suggested here. In this line of study, a proposal has been made for a cryptographic FSM that uses a secret key and is known as the Elliptic Curve Cryptographic FSM (MCTC). This is performed to protect the pattern from being accessed by those who
## 3 Basic Concepts
### Location Attacks  
In trajectory datasets, a \"location attack\" involves determining sensitive information about individuals by analyzing their movement patterns and the locations they visit.  
Various attacks have been proposed in the literature to exploit such vulnerabilities. For instance, the location sequence attack, as outlined in works such as [9; 23], involves adversaries possessing knowledge of a subset of the locations visited by an individual and the temporal sequence of these visits. Similarly, the visit attack, outlined in studies like [3; 24; 25], requires adversaries to be privy to information about a subset of the locations visited by an individual along with the specific times of these visits. For example, in a trajectory dataset containing GPS coordinates or timestamps of individuals' movements, a location attack could involve analyzing this data to identify specific places individuals frequently visit, such as their homes, workplaces, or other sensitive locations. By correlating these patterns with additional information, such as social media posts or public records, adversaries may be able to deduce private details about individuals, such as their daily routines, habits, or interests.
## 4 Results
### Risk Factor Extraction  
The trained and optimized TRBERT-BiLSTM-CRF model was applied to PA and SA reports to extract risk factors and risk types, respectively. Subsequently, a sensitivity analysis of the similarity threshold was conducted. Specifically, we manually labeled 600 pairs of risk factors to evaluate the F1 score at different similarity thresholds and determine the optimal threshold. Figure 4 shows the F1 score at different similarity thresholds. The results showed that a similarity threshold of 0.5 yielded a higher F1 score and accurately distinguished risk factors. Additionally, we found that, compared with higher thresholds, lower thresholds achieved higher F1 scores, which could be attributed to the presence of many entities in the dataset that had low similarity but were still correctly matched.  
On this basis, the similarity threshold was set to 0.5, and redundant risk factors were removed using the cosine similarity method. Figure 5 presents the entity deduplication results for the five risk types in PA and SA. The redundancy of the \"management\" risk type was the highest, possibly because the associated risk factor descriptions were relatively vague and frequently mentioned. Finally, 61 and 45 risk factors, along with five risk types, were extracted from PA and SA, respectively, as listed in Tables A1 and A2 in Appendix A.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Entity Category** & _precision_ & _recall_ & _f1 score_ & _support_ \\ \hline HUMAN & 0.8833 & 0.9298 & 0.9060 & 114 \\ VEHICLE & 0.8846 & 0.9388 & 0.9109 & 49 \\ ROAD & 0.9310 & 0.9643 & 0.9474 & 28 \\ WEATHER & 1.0000 & 1.0000 & 1.0000 & 6 \\ MANAGEMENT & 0.8511 & 0.8989 & 0.8743 & 89 \\ \hline _micro avg_ & 0.8803 & 0.9266 & 0.9029 & 286 \\ _macro avg_ & 0.9100 & 0.9464 & 0.9278 & 286 \\ _weighted avg_ & 0.8806 & 0.9266 & 0.9030 & 286 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the TRBERT-BiLSTM-CRF model.  
Figure 4: F1 scores at different similarity thresholds.
## 4 Analysis of Numerical Results
### Glycerin Resonator
For the trapezoidal prism-based geometries, Figure 9b illustrates \(|S_{11}|\). It can be seen that the LPA hollow inverted trapezoidal and trapezoidal prism geometries outperform the patch antenna without a resonator in terms of \(|S_{11}|\). The WPA inverted trapezoidal and trapezoidal prisms show potential for designing antennas with multiple resonant frequencies. Regarding BW, as listed in Table 5, the LPA trapezoidal prism achieves the highest value among these geometries, maintaining performance close to the patch antenna without a resonator. The radiation pattern plots, shown in Figure 11b and detailed in Table 5, reveals that the MLM values generally exceed the patch antenna without a resonator, except for the LPA trapezoidal prism. Furthermore, all the trapezoidal prism-based geometries exhibit reductions in terms of HPBW and improve the antenna directionality. In terms of SLL, most of the prism-based geometries show reductions, with the exceptions being the LPA trapezoidal prism and WPA hollow trapezoidal prism. Overall, the analyses of the chosen geometries shapes for glycerin resonators show that the LPA hollow inverted trapezoidal and trapezoidal prism geometries exhibit the highest \(|S_{11}|\) values. In terms of BW, the LPA trapezoidal prism achieves the widest BW, followed by the WPA hollow inverted half-cylinder and concave hemisphere. Moreover, several geometries show potential for antenna size reduction, with the cylindrical hemisphere performing best in this regard because it offers multiple resonant frequencies. In terms of MLM, the concave hemisphere and LPA inverted trapezoidal prism show significant improvements compared to the patch antenna without a resonator. Regarding the HPBW, the hemisphere exhibits the most substantial decrease, while the LPA half-cylinder shows an increase compared to the patch antenna without a resonator. Lastly, the rectangular prism offers the lowest SLL value.
## 3 Results and Discussion  
### Evaluation of Concrete Mass Loss during Cyclic Sulfate Attack  
Figure 7 shows the mass loss values of experimental samples depending on the number of sulfate attack cycles.  
Figure 6: General plan of experimental studies.  
Figure 7: Concrete mass loss values depending on the number of sulfate attack cycles (V—vibrated, C—centrifuged, VC—vibrocentrifuged).  
Figure 7 shows that with an increase in the number of sulfate attack cycles, the percentage of mass loss of concrete samples made using all three technologies also increases.  
The dependences of the mass loss on cycle number \(x\) shown in Figure 7 are well approximated by a polynomial of the second degree (5)-(7) with a high coefficient of determination \(R^{2}\).  
\[\Delta m_{V}=-0.0769+0.087x+0.000335x^{2},R^{2}=0.999 \tag{5}\]  
\[\Delta m_{C}=-0.0735+0.0596x+0.000389x^{2},R^{2}=0.998 \tag{6}\]  
\[\Delta m_{VC}=-0.1150+0.0421x+0.000474x^{2},R^{2}=0.996 \tag{7}\]  
Figure 7 and Equations (5)-(7) show that the smallest percentage of weight loss after 60 cycles was recorded for vibrocentrifuged concretes and amounted to 3.96%; for centrifuged concretes, it is 4.79%, and for vibrated concretes, it is 6.32%. Thus, after 60 cycles of sulfate attack, the mass drop of vibrocentrifuged concrete is 17% less than that of centrifuged concrete, and 37% less than that of vibrated concrete.
## 4 Experimental Evaluation of Performances
### Community Anomaly Detection in Synthesis Data
#### 4.3.1 First Experiment  
We evaluate the quality of our model with an increasing number of attributes by using the AUC with datasets of 1000 nodes. Figure 7 shows the variation of the AUC of the tested models when the number of attributes increases. We can conclude that our model has the best AUC compared with ConSub and CODA, which is between 0.87 and 0.97. The runtime evaluation is illustrated in Figure 8, which shows the runtimes with an increasing number of attributes. Both CAnom and ConSub show the best scalability with respect to the attributes' increase, while the CODA runtime roughly increases with the number of attributes. It is important to note that matrix operations are costly, and in CODA, these operations are performed for each attribute, which increases its runtime.  
Figure 8: Variations in runtime according to the different number of attributes for each of the tested models.  
Figure 7: Variations in AUC according to the different number of attributes for each of the tested models.
## 2 Methodology
### Separation Process Design TCED  
A TCED process with a preconcentration column (PC), an extractive distillation column (EDC), and a solvent recovery column (SRC) was simulated in a steady state using the AVEVA PRO/II v.2024 software for IPA dehydration with EG and DMSO as solvents. The flowsheet of the IPA-water separation TCED process is depicted in Figure 1.  
Figure 1: TCED process for IPA dehydration.  
The same flowsheet illustrated in Figure 1 was utilized for the IPA/water separation in our previous paper [18]. In summary, the primary column (PC) is a conventional distillation column that removes most of the water from the feed as the bottom product (B1) while separating the IPA/water minimum-boiling azeotrope mixture as the distillate product (D1). The D1 flow is directed to the lower section of the extractive distillation column (EDC), while ethylene glycol (EG) or dimethyl sulfoxide (DMSO) solvents are introduced at the top section of the column. The distillate product from the EDC (D2) is anhydrous IPA, and the bottom flow (B2) contains a mixture of solvent and water. In the solvent recovery column (SRC), the solvent is recovered as the bottom product stream (B3) and is then recycled to the EDC after cooling. The distillate flow from the SRC (D3) is water with minimal IPA loss. All columns operated at atmospheric pressure, and their feeds were in the liquid state at bubble-point temperatures.  
In these simulations, a Non-Random Two-Liquid (NRTL) model coupled with the UNIFAC fluid package was selected to simulate the thermodynamic IPA/water/EG and IPA/water/DMSO systems [19]. Table S1 in the Supporting Information file shows the binary interaction parameters of the above systems from AVEVA PRO/II v.2024. The INSIDE-OUT mathematical algorithm incorporated in the simulator was used to solve the mass and energy balance, and the outputs results were used for equipment design and then to evaluate the total capital cost and operating cost.
# A Linear Fitting Algorithm Based on Modified Random Sample Consensus
## 3 Modified Random Sample Consensus-Based Linear Fitting Algorithm
### Random Sample Consensus Based on Locality-Sensitive Hashing Algorithm
In order to ensure the identification of an appropriate line model, the maximum iteration count \(K\) is calculated as follows: \[K=\frac{\log(1-p)}{\log(1-w^{n})}\] (20) where \(p\) is the probability of finding a reliable model, and \(w^{n}\) is the probability that all \(n\) randomly selected points are inliers. 8. After completing all iterations, the line model with the highest number of inliers is output as the final fitting result. The flowchart of the LSH-RANSAC algorithm is shown in Figure 2.  
Figure 2: LSH-RANSAC algorithm workflow. As illustrated in Figure 3, given a set of observed data points, this dataset represents the center point coordinates of a line laser stripe image, with the x-axis data as pixel column coordinates and the y-axis data as pixel row coordinates. The objective is to fit an appropriate line from these observation data points that accurately reflects the trend of the valid data. This dataset contains a certain number of interfering noise points (i.e., outliers), which deviate from the true line and can reduce the fitting accuracy of the model. The LSH-RANSAC algorithm is employed to eliminate the outliers and fit the line model parameters from the processed dataset. Figure 4 displays the results of fitting the observed data point set using the LSH-based RANSAC algorithm, with the fitted line model's slope \(a\) being \(-0.009\) and the intercept \(b\) being \(2617.515\).
# 3D Tiled Code Generation for [PERSON]'s Algorithm
## 1 Introduction  
The goal of this article is to show a way to create 3D parallel tiled code from serial dynamic programming code executing [PERSON]'s RNA folding algorithm. There exist different serial implementations of that algorithm, but the main drawbacks of those programs are poor performance and cache efficiency for a large problem size. Parallelization and loop tiling transformations are used to enhance serial code performance. Loop tiling is often applied to optimize both serial and parallel programs. It increases parallel code granularity and data locality for multi-core architectures. Our goal is to generate code enumerating tiles whose dimension is maximal because the greater the tile dimension is, the more tiled code locality is reached. On the other hand, we aim to generate tiles of a similar size because such tiles allow us to better balance thread load, which improves parallel code performance. The maximal tile dimension of a loop nest is defined with the number of loops in that nest. [PERSON]'s algorithm is implemented with a nest including three loops, so for that nest, the maximal tile dimensions are 3D. There are known codes implementing 2D tiling for [PERSON]'s algorithm, for example [1, 2]. The articles [3, 4] introduce 3D tiling for [PERSON]'s algorithm, but generated tiles are of different sizes and some tiles are parametric, i.e., their size is unlimited. This makes it very difficult to balance thread load and as a consequence reaching the maximal performance of those parallel tiled codes is impossible. In this article, we present a method of creating the 3D parallel tiled program with tiles of a similar bounded size, implementing [PERSON]'s algorithm and showing the advantages of that code. The major contributions of the article are the following.  
* Proposition of a modification of [PERSON]'s algorithm to a form allowing us to produce 3D parallel tiled code with tiles of a similar bounded size.
* Presentation of a way to produce 3D parallel tiled code implementing [PERSON]'s algorithm.
* Demonstration of the generated 3D parallel tiled code performance on different platforms and the comparison of its performance with that of known implementations of [PERSON]'s algorithm.  
The rest of this article is organized as follows. Section 2 introduces background and notations. Section 3 shows a method of generation of 3D parallel tiled code. Section 4 presents related work. Section 5 discusses experimental results. Section 6 includes conclusions.
## 3 Results
### Accuracy of kNN Classification
The spectral similarity between urban soil and bare soil classes, which share spectral characteristics, may have contributed to the significant omission and commission errors in this category, as illustrated in Table 4 and discussed by [10]. For the \"Water\" class, the omission error remained at 0% from 2018 to 2023, indicating high accuracy in identifying this class in both years. However, the commission error increased slightly from 0% to 0.07%, suggesting a slight tendency to overestimate the area of water bodies in 2023. These results also corroborate previous studies [22], which highlight the high detection quality of water bodies, attributed to their high spectral absorption characteristics compared to general soil and vegetation classes. The water bodies identified in the study area, primarily small lakes and intermittent ponds, reflect the typical seasonal dynamics of the semi-arid region of Pernambuco, influenced by variations in precipitation and evapotranspiration patterns. This characteristic directly impacts local gold mining practices, which are not exclusively reliant on perennial water bodies such as streams. Instead, mining operations frequently occur in dry areas or near intermittent water bodies, often utilizing artificial systems for ore washing. This reality was incorporated into the revised maps, which now more accurately highlight the spatial distribution of these water resources in the context of mining activities, providing a more robust foundation for environmental impact analysis. The high OE in the Constructions class reveals that the model failed to correctly identify many true positive cases, while the high CE indicates that the model mistakenly classified some cases as belonging to the class when they did not. These results highlight the need for improvements or adjustments in the model to increase both sensitivity and specificity, reducing the rates of false negatives and false positives (Figure 8). Figure 8: Classification analysis with false positives in 2018 northwest of Cedro-PE city: (**a**) samples indicated in the classified image; (**b**) samples indicated in the PlanetScope image (RGB); (**c**) false positives in the bare soil area; (**d**) false positives in the bare soil area; (**e.f**) false positives in the water target. Figure 8 shows areas that, although devoid of constructions, were erroneously classified as such. These false positives generated in the classification can be attributed to the characteristics of the materials used in the construction roofs, which are predominantly clay. In Figure 9, a visualization of the coverage of a constructed area in the study region is presented, comparing PlanetScope images and the Google Earth platform. Due to the satellite's spatial resolution, the lot boundaries are not well-defined, and non-ceramic roofs are merged with areas of bare soil. Additionally, the area features frequent arborization between lots, which can also result in spectral mixing for the construction target.
## 2 Materials and Methods
### The Solution of the Lp_HOGS Model
\begin{array}{l}\mathbf{\Lambda}_{2}{}^{(k+1)}=\mathbf{\Lambda}_{2}{ }^{(k)}+\gamma\beta_{2}(\mathbf{K}_{v}*\mathbf{F}-\mathbf{Z}_{2}{}^{(k+1)})\\ \mathbf{\Lambda}_{3}{}^{(k+1)}=\mathbf{\Lambda}_{3}{}^{(k)}+\gamma\beta_{3}( \mathbf{K}_{h}*\mathbf{K}_{h}*\mathbf{F}-\mathbf{Z}_{3}{}^{(k+1)})\\ \mathbf{\Lambda}_{4}{}^{(k+1)}=\mathbf{\Lambda}_{4}{}^{(k)}+\gamma\beta_{4}( \mathbf{K}_{v}*\mathbf{K}_{v}*\mathbf{F}-\mathbf{Z}_{4}{}^{(k+1)})\\ \mathbf{\Lambda}_{5}{}^{(k+1)}=\mathbf{\Lambda}_{5}{}^{(k)}+\gamma\beta_{5}( \mathbf{K}_{v}*\mathbf{K}_{h}*\mathbf{F}-\mathbf{Z}_{5}{}^{(k+1)}).\end{array} \right. \tag{29}\]  
Hence, the subproblems are solved. Given the above descriptions, the specific description of the Lp_HOGS algorithm is shown in Algorithm 1. ```
Input Observed image \(\mathbf{G}\). Output Deblurred image \(\mathbf{F}\). Initialize: \(k=0,tol=10^{-4},err=1,p,\gamma,\mathbf{F}=0,\mathbf{Z}_{i}=0,\mathbf{\Lambda}_{i}=0, \mu_{j},\beta_{i}\) \((i=0,1,\cdots,5;j=1,2,\cdots,5)\). 1:If\(err>tol\)do
2:Use Equations (15)-(17) to update \(\mathbf{F}_{i}\);
3:Use Equation (20) to update \(\mathbf{Z}_{0}\);
4:Use Equations (22) and (24) to update \(\mathbf{Z}_{i}(i=1,2,\cdots,5)\);
5:Use Equation (26) to update \(\mathbf{\Lambda}_{0}\);
6:Use Equations (28) and (29) to update \(\mathbf{\Lambda}_{i}(i=1,2,\cdots,5)\);
7:\(k=k+1\);
8:\(err=\|\mathbf{F}^{(k+1)}-\mathbf{F}^{(k)}\|_{2}/\|\mathbf{F}^{(k)}\|_{2}\);
9:End if
10:Return\(\mathbf{F}^{(k)}\) as \(\mathbf{F}\). ```  
**Algorithm 1** Lp_HOGS  
where \(tol\) represents the threshold.
## 3 Optimization
### Single- and Multi-Objective Optimization  
Numerical optimization algorithms can be broadly classified as single- or multi-objective. Single-objective optimization algorithms aim to optimize (minimize or maximize) a single objective function. As previously defined, the problem of balancing the six-bar Watt linkage involves two objective functions: \(\beta_{ShM}(X)\) and \(\beta_{ShF}(X)\). Prior work [3; 22] employed a single-objective optimization approach by constructing a linear combination ofthe objectives, as shown in Equation (16), where \(\gamma\) is a hyperparameter ranging from 0 to 1, controlling the relative weight assigned to each objective function.  
\[f(X)=\gamma\beta_{SHM}(X)+(1-\gamma)\beta_{SHF}(X) \tag{16}\]  
However, this research compares the performance of single- and multi-objective algorithms.  
Multi-objective optimization algorithms address problems involving two or more conflicting objectives [30], as is the case with \(\beta_{SHF}(X)\) and \(\beta_{SHM}(X)\). These algorithms aim to identify a set of solutions that balance these competing objectives. A single, universally \"best\" solution rarely exists in multi-objective optimization. Instead, a given solution may excel for one objective but perform poorly with respect to another.  
To assess solution quality, solutions are categorized as either dominated or non-dominated. A solution (\(X_{d}\)) is considered to be dominated by another solution (\(X_{o}\)) if the following two conditions are met:  
* \(X_{o}\) is at least as good as \(X_{d}\) in all objectives. \[\beta_{SHM}(X_{o})\leq\beta_{SHM}(X_{d})\text{ and }\beta_{SHF}(X_{o})\leq \beta_{SHF}(X_{d})\] (17)
* \(X_{o}\) is strictly better than \(X_{d}\) in at least one objective. \[\beta_{SHM}(X_{o})<\beta_{SHM}(X_{d})\text{ or }\beta_{SHF}(X_{o})<\beta_{SHF}(X_{d})\] (18)  
A solution is considered non-dominated if no other solution exists that dominates it. Non-dominated solutions are also called Pareto-optimal solutions, representing trade-offs where improvement in one objective necessitates a degradation in the other.  
Hypervolume is a widely adopted indicator in multi-objective optimization for the evaluation of the quality of a solution set. The hypervolume indicator quantifies the \"size of the space covered\" [31]. Higher hypervolume values indicate a superior solution set, implying a larger region of dominance and typically better proximity to the Pareto-optimal front. This metric simultaneously assesses two key aspects: convergence, reflecting how close the solutions are to the Pareto-optimal front, and diversity, indicating how well the solutions are distributed across the front.