esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation of ASC-RL
### Joint Gaussian Distributions in ASC-RL  
For measuring the actual energy flow in cognitive sensors, the joint Gaussian distributions \((JGDs)\) over energy, noise, and signal strengths were measured. These parameters are statistically correlated, and their behavior is visually mapped. JGD was calculated for real-time network scenarios, and here in ASC-RL, it is used for these three interdependent variables. In ASC-RL, the total cognitive sensors in these experiments consisted of four states: \(S_{Act},S_{Wt},SRot\), and \(S_{Slp}\). Different messages were sent, and the network was dynamically adjusted for optimal data flow with fully connected sensors. In Figure 4, the JDC for signal strengths and energy is visually mapped, with the x-axis showing the signal strength and the y-axis showing the energy with reward \(R_{t}\). In this figure, the award \((R_{t})\) values are reflected by colored points at different locations. More color points at a higher order mean good/better \(R_{t}\) values, and that ASC-RL performs better. These colored values indicate strong signals with optimized or minimum energy consumption.  
Based on the reward function, the \(JGD\) over-energy and noise are compared with the attributed values of the award \(R_{t}\). In Figure 5, the \(JGD\) values are mapped at different points. The colored points at different positions depict the values of the reward function. This figure suggests lower noise values, moderate energy consumption, and a good \(R_{t}\) value. On the other hand, when the usage of noise and energy is considered, \(R_{t}\) decreases.  
When comparing the values of the signal strength \(JGD\) with the noise and signal strength in Figure 6, the noise is inversely related to the signal strength, with low \(R_{t}\). This suggests that low signals ultimately lead to poor communication with a low average \(R_{t}\). In Table 4, a summary of the three graphs is shown with the appropriate attributes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Figure** & **Parameters** & **Key Observations** & **Optimal Condition** \\ \hline \multirow{3}{*}{Figure 4} & \multirow{3}{*}{Signal–Energy} & Good signal strength and energy & \multirow{3}{*}{Better signal strengths, moderate energy} \\  & & result in a good \(R_{t}\), while lowering or raising energy consumption may & \\ \cline{1-1}  & & result in a bad \(R_{t}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 4: ASC-RL using different parameters in JGD.  
Figure 4: JDC for energy and signal strengths.
## 7 Multi-Objective Optimization in Wireless Sensor Networks: Trends
### Focus of Research with Respect to Multi-Objective Optimization Algorithms  
Contrary to the single objective optimization, a solution to a multi-objective problem is a concept rather than a definition [3]. Therefore, there is usually no single global solution, and it is therefore necessary to find a set of solutions satisfying the optimality conditions. Pareto optimal solutions consist of solutions that are not dominated by any other solutions. A solution \(X\) is said to dominate \(Y\) if \(X\) is better or equal to \(Y\) in all attributes, and strictly better in at least one attribute [12]. Therefore, Pareto optimal solutions provide different trade-off scenarios where none is better than the other and the decision maker chooses one according to the preferences or specific requirements. Due to the its characteristics to achieve different trade-off solutions, Pareto optimal solution approaches are being preferred which is evident from Figure 6. In more than half of the articles in the literature, Pareto optimal approach has been used to solve the multi-objective optimization problems. Other commonly used technique is the weighted sum approach. It scalarizes a set of objectives into a single objective by assigning different weights to each objective. Conceptually this method is simplest and also widely used but it is affected by the selection of different weights. The selection of weights depends on the preference of each objective which is decided by the decision maker [11]. Therefore, the outcome of the approach is highly sensitive to the choice of the weights. There are few other less commonly used approaches namely, weighted average, Pareto archived evolution strategy, normal boundary intersection, weighted Chevyshev norm, weighted sum of square, lexicographic and epsilon constrained.
# A Novel Channel Calibration Method for Bistatic ISAR Imaging System
## 5 Conclusions  
An effective and feasible channel calibration method only using echo information for bistatic ISAR imaging systems with LFM waveform is proposed. The alignment method of multi-period echoes in the time domain according to the pulse compression result is provided. The SNR of the satellite echoes was improved through the coherent accumulation of multi-period echoes in the time domain, which provides the foundation for estimating accurate channel transfer function. The channel calibration coefficient is constructed based on the channel transfer function estimated  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Figure 9a** & **Figure 9b** & **Figure 9c** & **Figure 9d** \\ \hline Image contrast & 18.9 & 15.4 & 32.7 & 33.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The image contrasts of ISAR images in Figure 9.  
Figure 9: ISAR two-dimensional images achieved through different processing methods: (**a**) Adding channel non-ideal characteristics and without channel calibration; (**b**) adding channel non-ideal characteristics and the channel calibration using single period echo; (**c**) adding channel non-ideal characteristics and the channel calibration using signal accumulated 1000 periods; (**d**) without adding non-ideal channel characteristics in echoes.  
by the accumulated signal. The channel calibration process for the system is presented based on the calibration satellite echoes. Simulation based on real satellite echoes confirms that the proposed method can effectively compensate non-ideal channel characteristics and improve the image quality.  
Conceptualization, L.S. and B.G.; data curation, L.S., B.G. and J.M.; formal analysis, C.S. and H.Z.; funding acquisition, C.S.; investigation, J.M.; methodology, L.S. and B.G.; resources, H.Z.; software, J.M., validation, L.S.; writing--original draft, L.S.; writing--review and editing, C.S. and H.Z.  
This research was funded by National Natural Science Foundation of China under Grant 61601496.  
The authors would like to thank the editors and peer reviewers for their valuable comments and suggestions.  
The authors declare no conflicts of interest.
## 2 Antenna Design
### Two-Element MIMO Antenna Design
#### 2.2.1 Distance Optimization  
The two antennas are placed at distances of D = 2 mm, 4 mm, and 6 mm. The graphical depiction of the fluctuation in reflection coefficient and transmission coefficient can be observed in Figure 6a-c, while the summarized results are presented in Table 4. At a distance of 4 mm, the antenna exhibits resonance at frequencies of 9.7, 16.3, 18.8, and 21.4 GHz. These resonant frequencies are accompanied by increased bandwidths of 3100, 600, and 10,100 MHz, respectively, as compared to the bandwidth achieved at distances of 2 mm and 6 mm. The maximum level of isolation attained for a distance (D) of 4 mm is \(-\)40 dB. In comparison, for diameters of 2 mm and 6 mm, the maximum isolation levels are \(-\)38.9 dB and \(-\)38.6 dB, respectively. This shows that at D = 4 mm, the antenna provides better results.  
Figure 4: Simulated E and H plane radiation patterns at **(a)** 9.2 GHz; **(b)** 21.5 GHz; **(c)** 26.7 GHz.  
Figure 5: Two-port MIMO antenna.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Bandwidth (MHz)** & **f\({}_{\mathbf{r}}\) (GHz)** &
\begin{tabular}{c} **S12 (dB)** \\ **(Max. Isolation)** \\ \end{tabular} \\ \hline D = 2 mm & 3100, 400, 10,000 & 9.2, 16.3, 18.9, 21.3 & \(-38.9\) \\ \hline D = 4 mm & 3100, 600, 10,100 & 9.7, 16.3, 18.8, 21.4 & \(-40\) \\ \hline D = 6 mm & 2800, 600, 9900 & 9.1, 16.5, 18.9, 21 & \(-38.6\) \\ \hline \end{tabular}
\end{table}
Table 4: MIMO distance variation analysis.  
Figure 6: _Cont._
# Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection
## 2 Literature Review  
The rough set theory is a frame represented by [PERSON] in 1989, which can construct concept approximation with incomplete information. The available information contains a set of examples of concept and the relationship to each other, such as indiscernibility, set approximation, reducts, and dependency [3; 4]. Rough set as a method of soft computing receives more and more attention. Nowadays, rough set is still a research hotpot in the field of artificial intelligence. [PERSON] and [PERSON] proposed structured rough set approximation in complete and incomplete information systems to serve as a basis of three-way decisions with rough set [5]. To deal with an incomplete information system, a more generalized approach that considered potential candidates was presented [6].  
Rule induction and feature selection are two important applications of rough set. Every component of the model of rule induction is introduced in detail in [7]. In the literature [8; 9], rule induction is carried out for the absence of feature values in the information system. In the literature [10; 11], the researchers used the result of attribute reduction to classify datasets with neural networks; the testing result indicated that with less study time the misclassification does not increase significantly, and they declared that the attribute reduction of rough set has the possibility of practical application. Because the attribute reduction of rough set is an NP-hard problem, many pieces of research pay attention to the acceleration algorithm [12; 13; 14]. Recently, two quick feature selection algorithms based on the neighbor inconsistent pair were presented which can reduce the time consumed in finding a reduct [15].  
Fuzzy sets were introduced independently by [PERSON] and [PERSON] in 1965 as an extension of the classical notion of set [16]. Because both rough set and fuzzy set are used to deal with uncertain data, so many scholars compared the two methods and make great contributions [17; 18]. [PERSON] and [PERSON] first combined fuzzy set and rough set together [19], hereafter the research centering on fuzzy rough set appear one after another [20; 21; 22; 23; 24; 25], and in the meantime, the accelerating algorithm came out, such as feature selection based on ant colony optimization [26] and information entropy [27]. In recent years, the feature selection algorithm based on a new definition of fuzzy rough set approximations based on the divergence measured of fuzzy sets is proposed and its properties were explored [28]. Another interest is the accelerator of fuzzy rough feature selection, a method based on sample reduction and dimensionality reduction was proposed [29].
# Development of Additive Fibonacci Generators with Improved Characteristics for Cybersecurity Needs
###### Abstract  
Pseudorandom sequence generation is used in many industries, including cryptographic information security devices, measurement technology, and communication systems. The purpose of the present work is to research additive Fibonacci generators (AFG) and modified AFG (MAFG) with modules \(p\) prime numbers, designed primarily for their hardware implementation. The known AFG and MAFG, as with any cryptographic generators of pseudorandom sequences, are used in arguments with tremendous values. At the same time, there are specific difficulties in defining of their statistical characteristics. In this regard, the following research methodologies were used in work: for each variant of AFG and MAFG, two models were created--abstract, which is not directly related to the circuit solution, and hardware, which corresponds to the proposed structure; for relatively small values of arguments, the identity of models was proved; the research of statistical characteristics, with large values of arguments, was carried out using an abstract model and static tests NIST. Proven identity of hardware and abstract models suggest that the principles laid down in the organization of AFG and MAFG structures with modules of prime numbers ensure their effective hardware implementation in compliance with all requirements for their statistical characteristics and the possibility of application in cryptographic information security devices.  
pseudorandom sequences; additive Fibonacci generator; statistical characteristics; cybersecurity; information security +
Footnote †: journal: Applied sciences
## 3 Results
### Primary Part: Individual Patient-Level Data for Patients Initially Diagnosed with Stage I-III NSCLC
#### 3.1.3 Initial Treatments  
The majority of the overall early-stage NSCLC population (96.2%; 307/319) received (non)pharmacologic treatment as part of ITS (Table 2). \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Overall (N = 319)** & **Stage I (N = 115)** & **Stage II (N = 98)** & **Stage III (N = 106)** \\ \hline \hline
**Frequencies of treatment modalities, \% (n/N)** & & & & \\ \hline Any (non)pharmacologic treatment (SUR, ST, and/or RT) \({}^{\ddagger}\) & 96.2 (307/319) & 99.1 (114/115) & 96.9 (95/98) & 92.5 (98/106) \\ \hline SUR (\(\pm\)RT or ST) & & 67.4 (215/319) & 81.7 (94/115) & 84.7 (83/98) & 35.8 (38/106) \\ Receipt of (neo)adjuvant ST, among resected patients & & & & \\ Receipt of neoadjuvant ST & 8.8 (19/215) &. & 3.6 (3/83) & 42.1 (16/38) \\ Receipt of adjuvant ST & 29.3 (63/215) & 8.5 (8/94) & 44.6 (37/83) & 47.4 (18/38) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Treatment modalities received as part of initial therapeutic strategy for early-stage NSCLC, overall and per disease stage at initial diagnosis. Figure 1: Findings for biomarkers, among patients with early-stage NSCLC who underwent specific biomarker testings \({}^{\dagger}\), from start of diagnostic evaluation of lung cancer until EOP, overall and per NSCLC stage at initial diagnosis: (**A**) PD-L1 TPS via IHC, and (**B**) other biomarkers examined in \(\geq\)20.0% of patients in any subpopulation. \({}^{\dagger}\) Excluding patients tested using commercially available panels, since for those patients specific biomarkers that tested negative were not recorded, thus introducing information bias in the respective positivity rates. \({}^{\ddagger}\) Specific TPS score was missing for 1 patient with Stage I NSCLC. Abbreviations: ALK, Anaplastic Lymphoma Kinase; EGFR, Epidermal Growth Factor Receptor; EOP, End of Observation Period; HER2, Human Epidermal Growth Factor Receptor 2; IHC, Immunohistochemistryistochemistry; IQR, Interquartile Range; KRAS, Kirsten Rat Sarcoma Virus; N, number of patients with available data (i.e., tested for specific biomarker); NRTK, Neurotrophic Receptor Tyrosine Kinase; NSCLC, Non-Small Cell Lung Cancer; PD-L1, Programmed Death Ligand 1; SD, Standard Deviation; TPS, Tumour Proportion Score. Most Stage I-II patients (83.1%; 177/213) and 35.8% of Stage III patients underwent surgery (Table 2). ST rates increased with advancing disease stage from 7.8% in Stage I to 43.9% in Stage II and 82.1% in Stage III. ST rates mostly reflect adjuvant ST post-surgery in
# Microscope-Based Optical Frequency Comb Generator for 200 GHz Spaced WDM Data Transmission System
## 4 Discussion and Conclusions
The major drawback of the current CO architecture is that individual lasers' array is used to sustain WDM data channels; however, here we use an OFC generator, employing only one single laser. An OFC generator based on silica microspheres is a cost-effective solution, making the possibility to add more end-users to one PON. The latter is especially interesting for those countries where service providers still use copper infrastructure, considering that service providers will need to install fewer PONs to connect a more significant number of end-users. So, our solution can potentially lower expenses to upgrade existing fiber infrastructure and change infrastructure in favor of optical fiber systems. Conceptualization, E.A.A., A.V.A. and V.B. ; methodology, E.A.A., M.P.M. and S.S.; software, E.A.A., M.P.M., L.S. and I.L. ; validation, E.A.A., M.P.M. and T.S. ; formal analysis, R.M., I.V.O. and A.S.; investigation, E.A.A., M.P.M., A.V.A., R.A.A., R.M., M.D.T., S.S., G.L. and V.B. ; data curation, M.P.M., L.S. and I.L. ; writing--original draft preparation, E.A.A., R.M. and T.S. ; writing--review and editing M.P.M., A.V.A., R.M. and S.S.; visualization, E.A.A., M.P.M. and T.S. ; supervision, E.A.A. and V.B. ; project administration, E.A., A.V.A. and V.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the Mega-grant of the Ministry of Science and Higher Education of the Russian Federation, Contract No.14.W03.31.0032 (conceptualization and development of the optical frequency comb generator) and by the Russian Science Foundation, Grant No. 20-72-10188 (calculation of microresonator characteristics and numerical simulation of dissipative Kerr solitons) and has been supported by the European Regional Development Fund project No. 1.1.1.1/18/A/155 \"Development of optical frequency comb generator based on a whispering gallery mode microresonator and its applications in telecommunications\", the Doctoral Grant programme of Riga Technical University in Latvia. The authors declare no conflict of interest.
## 2 System Model  
In this section, we first introduce the fundamental principles underlying OTFS modulation. Subsequently, we expound upon the input-output correction of OTFS symbols within the DD domain [47; 48; 49; 50; 51; 52].
## 4 Multi-Source Data Fusion Technology and Algorithm
### Principles and Methods of Data Fusion Technology
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm Type** & **Main Features** & **Applicable Scenarios** \\ \hline Fusion algorithm based on probability statistics [27] & By calculating the probability distribution of data sources, weighted fusion is achieved, which is suitable for processing uncertain and random data. & Fusion of environmental data and citizen feedback in real estate management and urban optimization. \\ \hline An integrated fusion algorithm based on evidence theory [28] & By constructing a body of evidence, information from different data sources is synthesized to handle contradictory and conflicting data. & When faced with conflicting data from multiple sources, such as reducing the impact of erroneous information in decision making. \\ \hline Fusion algorithm based on fuzzy set theory [29] & Deal with uncertainty and fuzzy data to improve the interpretability and practicality of the results. & Process feedback data with a high degree of neutrality, such as citizen opinions. \\ \hline Neural network-based algorithms [30] & Automatically learn data features with adaptability and generalization capabilities, suitable for large-scale, high-dimensional data. & Real estate management combines sensors and GIS data to support urban optimization. \\ \hline Algorithms based on machine learning [31] & Data fusion is achieved through model training, which is adapted to specific goals and is powerful in processing complex data relationships. & Extract useful information and analyze complex relationships to improve real estate management and urban optimization. \\ \hline  & Including Randomized gossip algorithm (RG), Geographic gossip algorithm (GG), Broadcast gossip algorithm (BG), Push-Pull protocol (PP) and Push-Sum protocol (PS). Applicable to network size estimation in multi-agent systems. & Real-time traffic monitoring, energy management, environmental monitoring, public safety, real estate valuation, urban planning, and energy optimization of smart buildings to improve urban management efficiency and residents’ quality of life. \\ \hline  & The data are analyzed by analyzing the Number of Pixels per Change Rate (NPCR) and entropy information. Public key encryption is performed using the Edwards-curve Digital Signature Algorithm (EdDSA) and a Schnorr signature variant based on the Edwards curve. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Types and comparison of data fusion algorithms.
# Condenser Design for On-Board ORC Recovery System
## 3 Thermodynamic Model of the Condenser
### The Heat Exchanger Method  
HEEM offers an alternative to optimize the design. In this case, various heat exchangers can be compared to select the optimal configuration corresponding to the optimum efficiency. Efficiency is defined as the ratio of the actual heat transfer to the maximum possible heat transfer. The following formula can be used to calculate the effective heat transfer in a reverse flow configuration:  
\[\varepsilon=\frac{actual\ heat\ transfer}{maximum\ possible\ heat\ transfer} \tag{6}\]  
The maximum value of the heat transfer is expressed by the maximum heat received by the fluid. It is defined by the minimum value of the product of mass and specific heat.  
\[Q=\dot{m}\ cp\ \Delta T \tag{7}\]  
In addition, the NTU (number of transfer units) can be used to calculate effectiveness in a different way.  
\[NTU=\frac{U\ A}{\left(m\ cp\right)_{min}} \tag{8}\]  
In this case, we have two different effectiveness values depending on the evaluated phase. For the monophasic scenario, the effectiveness is expressed by the formula:  
\[\varepsilon=\frac{actual\ heat\ transfer}{maximum\ possible\ heat\ transfer} \tag{9}\]  
In the condensation phase:  
\[\varepsilon=1-e^{NTU} \tag{10}\]
# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
## 5 Practical Simulations, Analysis, and Discussion
### Deadline Violation Rate Analysis  
Finally, in the last analysis in simulations with artificial dataset, the deadline violation rate has been evaluated in order to validate the QoS of the proposed scheduling metaheuristics. Simulations were conducted with 200, 400, and 500 tasks and varying deadlines (from 100 to 3000) as in [64].  
Results of deadline violation comparative analysis with 200, 400, and 500 tasks with varying deadline constraints are shown in Figures 11-13, respectively. In all presented graphs, violation rate was plotted expressed in percents on the ordinate axis, while the deadlines were represented on abscissa axis.  
Figure 11: Comparative analysis—WOA-AEFS vs. others using artificial dataset (deadline violation with 200 tasks and varying deadlines).  
Figure 10: Comparative analysis—WOA-AEFS vs. others using artificial dataset (costs with 500 tasks and varying deadlines.  
The results shown in Figures 11-13 prove the superiority of our WOA-AEFS approach over other metaheuristics and heuristics. Results are similar as in the previous two simulations.  
On average, the WOA-AEFS is the best approach included in comparative analysis, followed by the CPSO that obtained the second best performance in deadline violation tests. The WOA again showed similar performance as PBACO, while all four metaheuristics included in comparative analysis established much better results than Min-Min and FCFS heuristics.