esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic-Based Heuristics for Regenerator Placement in Translucent Dynamic Optical Networks
## 4 Proposed Regenerator Placement Heuristics
### Regenerator Placement Algorithms  
The regenerator placement proposed in this paper is shown in Algorithm 1. The algorithm requires as input parameters the total amount of regenerators \(R\) to be deployed in the network, the forecast traffic matrices \(\{\omega_{ij}\}\) and \(\{\gamma_{ij}\}\) and the network topology. In the algorithm, \(\Gamma_{n}\) stands for either \(\alpha_{n}\) or \(\beta_{n}\) and \(\Delta(n)=\Delta BP_{N}(n)\). First, the algorithm identifies and places one regenerator in each essential node. After that, it places one regenerator per iteration. The estimates of node offered load are re-evaluated in each iteration. The node that returns the highest BP reduction (i.e., the highest value for \(\Delta(n)\)) receives one regenerator. Ties are broken, firstly by placing the regenerator in the node with the largest \(\Gamma_{n}\) and then randomly. The process is repeated until all regenerators are placed. Regarding the selection of \(\Gamma_{n}\), two heuristics are proposed: Fixed Load Distribution (FLD), which uses \(\Gamma_{n}=\beta_{n}\) and Dynamic Load Distribution (DLD), which uses \(\Gamma_{n}=\alpha_{n}\). Note that, in the particular case in which \(\Delta(n)=\Delta pdf_{3R}(n)\) and under uniform traffic, the FLD is equal to the Routing-&-Reach algorithm [2].  
Our algorithm can be classified as traffic-based because the traffic matrices \(w_{ij}\) and \(\gamma_{ij}\) directly affect the decision of placing a regenerator in a given node. Please note in Algorithm 1 that the decision of placing a regenerator in a node depends on \(\Delta(n)\), which, in the proposed algorithms, is evaluated as \(\Delta BP_{N}(n)\) by using Equations (6)-(8). Note that, in such equations, the estimated offered load \(\Gamma_{n}\) to a node and the current number of installed regenerators \(r(n)\) in a node are taken into account. Finally, \(w_{ij}\) directly appears in Equations (6) and (7), and the evaluation of the estimated offered load to a node, \(\Gamma_{n}\), takes into account \(\gamma_{ij}\) by using either Equations (3) or (4).
## Materials and Methods
### Batch Absorption Experiments  
The batch experiments were conducted to investigate the adsorption behavior of Cu(II) in lime sand bricks. The experiments were conducted at 25 \({}^{\circ}\)C. The adsorption kinetics was studied and adsorption isotherms were measured. All experiments were performed in duplicate. In the adsorption kinetics experiments, 500 mL solutions were prepared with a Cu(II) concentration of 5 mg/L. Brick powders of 2-5 mm were then added into the solution with a dosage of 8 g/L. Solution samples were taken at different time intervals and filtered with 0.45 \(\upmu\)m pore diameter nylon micropore membranes. The concentration of Cu(II) in the filtrate was then analyzed by ICP-MS (Elan 5000, Perkin Elmer, Waltham, MA, USA). To measure the adsorption isotherms, a series of 40 mL solutions with a Cu(II) concentration of 0-30 mg/L were prepared. Adsorbents having the same size distribution were then added into the solutions with a dosage of 8 g/L. After reaching equilibrium, the solutions were filtered and the concentration of Cu(II) was analyzed. The pH was controlled using NaOH and HCl to ensure that the pH was around 5.  
The data on the adsorption kinetics of Cu(II) were fitted using the pseudo-first-order (Equation (1)) and pseudo-second-order models (Equation (2)).  
\[q_{\mathrm{t}}=q_{\mathrm{e}}(1-e^{-k_{\mathrm{t}}t}) \tag{1}\]\[q_{\rm t}=\frac{q_{\rm e}^{2}\cdot k_{2}\cdot t}{1+q_{\rm e}\cdot k_{2}\cdot t} \tag{2}\]  
where \(k_{1}\) (h\({}^{-1}\)), \(k_{2}\) (g/mg\(\cdot\)h) are the rate constants of adsorption, \(q_{\rm e}\) (mg/g) is the adsorption capacity at equilibrium and \(q_{\rm t}\) is the amount of metal adsorbed at time \(t\) (h).  
Different models were used to correlate and compare experimental adsorption data to provide an accurate fit of the adsorption isotherms. In this study, the Freundlich and Langmuir isotherm models were used to fit the adsorption isotherms. The Freundlich and Langmuir models are shown in Equations (3) and (4).  
\[q_{\rm e}=K_{\rm F}\cdot C_{\rm e}{}^{1/n} \tag{3}\]  
\[q_{\rm e}=\frac{q_{\rm m}\cdot K_{\rm L}\cdot C_{\rm e}}{1+K_{\rm L}\cdot C_{ \rm e}} \tag{4}\]  
where \(q_{\rm m}\) (mg/g) is the maximum adsorption capacity of the materials; \(K_{\rm F}\)((mg/g) (L/mg)\({}^{n}\)) and \(K_{\rm L}\) (L/mg) are the constants for the Freundlich and Langmuir models, respectively; \(C_{\rm e}\) (mg/L) is the metal concentration in solution at equilibrium; \(n\) is a dimensionless empirical parameter, which gives information on the strength of the adsorption.  
To investigate the effect of ionic strength on adsorption, 40 mL of a Cu(II) solution with an initial concentration of 5 mg/L was added to each centrifuge tube. The pH was controlled at 5 using NaOH and HCl. The lime sand bricks with a particle size of 0.5-1 mm were added to the solution, and the solid-liquid ratio was 8 g/L. Sodium chloride was used to adjust different levels of ionic strength (i.e., 0, 0.2, 1, 5, 10, 20, 50, 100 and 200 mg/L). All samples were shaken at 135 rpm.
## 1 Introduction
### Contributions  
In this paper, we focus on the selection of the discriminating features from the SE-MF dataset utilizing the genetic algorithm (GA) to tackle the curse of dimensionality [8]. The optimal features selected from the SE-MF dataset are then used as input by the two proposed Euclidean distance (ED)-based AD schemes for the detection of a CCD assault. Contrary to the FE-based approach, the proposed FS-based method does not alter the original representation of the data. The main contributions of this paper can be summarized as follows:  
* We study intelligently-crafted CCD assaults on the SE-MF dataset, and we investigate how such an assault goes undetected in legacy systems that use bad-data detectors.
* To tackle the increasing computational complexity with the growing sizes of power systems, we use GA for the selection of independent and discriminating features from the SE-MF dataset. The selection of discriminative features leads to lower computational costs, a shorter time delay and improved accuracy.
* First, we propose an ED-based AD scheme to detect the presence of outliers in the unlabeled SE-MF dataset. Next, we extend the first scheme to propose a detection mechanism for the labeled SE-MF dataset. In both schemes, the optimal features selected through the GA are employed as input.
* We use the IEEE standard 14-bus, 39-bus, 57-bus and 118-bus test systems to evaluate the efficiency of the proposed schemes. The performance evaluation shows that the proposed schemes provide better accuracy, in comparison to existing AD-based schemes.
## 4 Experiments, Results, and Discussion
### Dataset, Data Pre-Processing, and Feature Selection  
Datasets are necessary in IIoT networks for both training and testing IDSs, so the selection of the appropriate dataset is crucial. There is now a new cybersecurity dataset called Edge-IIoTset [7] that was created for IIoT and IoT applications. Numerous IoT devices, such as heart rate sensors, flame sensors, temperature and humidity sensors, etc., generate the data. The testbed was subjected to 14 different types of attacks, including injection, malware, DDoS attacks, and man-in-the-middle (MITM) attacks. For FL-based tasks, the data distribution needs to be non-independently identically distributed (Non-IID), imbalanced, and reflect the elements of the real-world scenario. For the experimental purpose, we have divided our dataset (Edge-IIoTset) into several local datasets to train them as per requirement for FL. This was necessary because FL-specific datasets were unavailable.  
We begin by grouping the data and removing duplicates and missing elements such as \"NAN\" (Not a Number) and \"INF\" (Infinite Value). Following that, we eliminate additional flow characteristics such as tcp.payload, tcp.options, tcp.len, tcp.flags.ack, tcp.flags, tcp.connection.rst, tcp.connection.fin, tcp.checksum, tcp.ack_raw, tcp.ack, arp.dst.proto_ipv4, ip.dst_host, ip.src_host, and frame.time. In the second step, we performed the data encoding step by using dummy encoding categorical data and by normalizing numeric data using the Z-score normalization defined by \((\mathsf{x}-\mathsf{u})/\sigma\), where the feature value is \(\mathsf{x}\), the mean is \(\mathsf{u}\), and the standard deviation is \(\sigma\). Then, the oversampling was performed using the SMOTE.  
We have applied the feature selection strategy to enhance the proposed model's performance and reduce its training and classification times. The feature selection approach looks for the most pertinent features and eliminates the irrelevant ones. Recursive Feature Elimination (RFE) has been applied in the suggested model. It is a wrapper method that repeatedly assesses how well a certain model performs with various feature combinations. In order to enhance accuracy, RFE recursively removes features from each feature set. The features are then ranked according to the order in which they were eliminated.  
Table 2 displays the randomly selected data for ML models after data pre-processing (cleaning and splitting) and feature selection, as well as the train and test data sets that are generated.  
We performed a series of tests to evaluate the efficacy of FL with varying client numbers (from 3 to 15) contributing to model training. We trained our model for a total of 50 epochs before reaching optimal output. We investigated the system's efficiency for various client numbers while developing the federated model. Each client received a piece of training data from the deployment dataset that was chosen at random. To examine this potential loss in accuracy, we generated three federated models by sharing the entire training dataset among 3, 9, and 15 clients, and we compared them to a centralized model.
# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## 2 Background
### MITRE Corporation  
The MITRE Corporation, an acronym for \"Massachusetts Institute of Technology Research and Engineering\", is a not-for-profit organization that operates research and development centers sponsored by the United States federal government. It was established in 1958 to address complex problems in defense and national security, and it has since expanded its scope to encompass various domains, including healthcare, transportation, and cybersecurity.  
In 1999, MITRE launched the Common Vulnerabilities and Exposures (CVE) program as a collaborative effort to standardize the names and identifiers used for tracking and reporting vulnerabilities. Additionally, MITRE, in collaboration with the security industry, developed the Common Vulnerability Scoring System (CVSS) as a standardized method for assessing and scoring the severity of security vulnerabilities. Today, CVEs and the CVSS are widely adopted by security professionals and organizations worldwide to aid in the identification, prioritization, and management of vulnerabilities.  
As part of these initiatives, MITRE created the official website cve.org (accessed on 10 March 2024), which serves as a platform for security experts to track and report vulnerabilities in the United States. The data presented in Figure 1 are sourced from CVEs that adhere to MITRE's specifications, while the severity levels depicted in Figure 2 are inferred from the CVSS scores of the same CVEs, specifically version 3.1. All the data used in the figures were obtained from the US National Vulnerability Database (US NVD).
# Validation of Omega Subunit of RNA Polymerase as a Functional Entity
###### Abstract  
The bacterial RNA polymerase (RNAP) is a multi-subunit protein complex (\(\alpha 2\beta\beta^{\prime}\omega\)\(\omega\)) containing the smallest subunit, \(\omega\). Although identified early in RNAP research, its function remained ambiguous and shrouded with controversy for a considerable period. It was shown before that the protein has a structural role in maintaining the conformation of the largest subunit, \(\beta^{\prime}\), and its recruitment in the enzyme assembly. Despite evolutionary conservation of \(\omega\) and its role in the assembly of RNAP, _E. coli_ mutants lacking _rpoZ_ (codes for \(\omega\)) are viable due to the association of the global chaperone protein GroEL with RNAP. To get a better insight into the structure and functional role of \(\omega\) during transcription, several dominant lethal mutants of \(\omega\) were isolated. The mutants showed higher binding affinity compared to that of native \(\omega\) to the \(\alpha 2\beta\beta^{\prime}\) subassembly. We observed that the interaction between \(\alpha 2\beta\beta^{\prime}\) and these lethal mutants is driven by mostly favorable enthalpy and a small but unfavorable negative entropy term. However, during the isolation of these mutants we isolated a silent mutant serendipitously, which showed a lethal phenotype. Silent mutant of a given protein is defined as a protein having the same sequence of amino acids as that of wild type but having mutation in the gene with alteration in base sequence from more frequent code to less frequent one due to codon degeneracy. Eventually, many silent mutants were generated to understand the role of rare codons at various positions in _rpoZ_. We observed that the dominant lethal mutants of \(\omega\) having either point mutation or silent in nature are more structured in comparison to the native \(\omega\). However, the silent code's position in the reading frame of _rpoZ_ plays a role in the structural alteration of the translated protein. This structural alteration in \(\omega\) makes it more rigid, which affects the plasticity of the interacting domain formed by \(\omega\) and \(\alpha 2\beta\beta^{\prime}\). Here, we attempted to describe how the conformational flexibility of the \(\omega\) helps in maintaining the plasticity of the active site of RNA polymerase. The dominant lethal mutant of \(\omega\) has a suppressor mapped near the catalytic center of the \(\beta^{\prime}\) subunit, and it is the same for both types of mutants.  
\(\omega\)-subunit; RNA polymerase; structure; silent mutants; plasticity +
Footnote †: journal: biomolecules
# Applied Research of the UAV Illumination Measurement System in Sports Stadiums
## 4 Discussion  
In this paper, we have built a stadium illuminance measurement system, which takes advantage of the convenience of the UAV, combined with a widely used optical-flow positioning function, an illumination sensor, a distance measurement module, a Wi-Fi data transmission, and a cloud platform. Based on the real-time feedback data from the mobile-phone app, the UAV position information is corrected, and the location of the illumination acquisition point can be located in combination with the real-time image return from the drone. Faced with the problem of inaccurate measurements caused by the unstable hovering of UAVs during the measurement process, we used the LOF algorithm to detect outliers in the acquired data. In this way, the stability of the illuminance measurement system can be checked, and, at the same time, the acquired data can also be detected in time for large fluctuations. Then, the data collected by the system can be displayed in real-time via the cloud platform, and the system acquisition process can also be controlled via the cloud platform. On the one hand, it is possible to judge the data collection of the UAV illumination collection acquisition system in time. On the other hand, it is convenient to control the system until the system is accurately located.  
In the next step of research, the measurement system will be further improved to measure vertical illuminance under the different environmental requirements of the sports stadium and to explore the detailed light-flow distribution and three-dimensional representation of illuminance in sports stadiums.
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 2 Theoretical Performance
### The Effect of System Errors
#### 2.3.1 System Error of Clock Synchronization  
If there is only clock synchronization error, define \(E(\mathbf{n}_{t})=\delta_{t}\); hence, \(E(\mathbf{n})=\left[\gamma_{t}^{T},0\right]^{T}\), \(G(\theta_{1})=0\). Then, the localization accuracy is obtained from (48) as  
\[\begin{split}\mathrm{MSE}(\theta_{1})=4\mathbf{A}_{11}\mathbf{J}_{1}^{ T}C^{-1}\mathbf{J}_{1}\mathbf{A}_{11}^{\mathrm{T}}+4\mathbf{A}_{11}\mathbf{J}_{1}^{T}C^{-1} \begin{bmatrix}\gamma_{t}\gamma_{t}^{T}&0\\ 0&0\end{bmatrix}C^{-1}\mathbf{J}_{1}\mathbf{A}_{11}^{\mathrm{T}}\\ =4\mathbf{A}_{11}\mathbf{J}_{1}^{T}C^{-1}\mathbf{J}_{1}\mathbf{A}_{11}^{\mathrm{T}}+4\mathbf{A}_{ 11}\mathbf{B}_{1}\mathbf{A}_{11}^{\mathrm{T}}\end{split} \tag{50}\]  
where \(\mathbf{B}_{1}\) is defined as  
\[\mathbf{B}_{1}=\begin{bmatrix}\mathbf{J}_{t}^{T}C_{t}^{-1}\gamma_{t}\gamma_{t}^{T}C_{t }^{-1}\mathbf{J}_{t}&\mathbf{J}_{t}^{T}C_{t}^{-1}\mathbf{\gamma}_{t}\gamma_{t}^{T}C_{t }^{-1}\mathbf{J}_{t,nav}\\ \mathbf{J}_{t,nav}^{T}C_{t}^{-1}\mathbf{\gamma}_{t}\gamma_{t}^{T}C_{t}^{-1}\mathbf{J}_{u}&\mathbf{J}_{t,nav}^{T}C_{t }^{-1}\mathbf{\gamma}_{t}\gamma_{t}^{T}C_{t}^{-1}\mathbf{J}_{t,nav}\end{bmatrix} \tag{51}\]  
\(4\mathbf{A}_{11}\mathbf{B}_{1}\mathbf{A}_{11}^{\mathrm{T}}\) is the amount of degradation for localization accuracy introduced by clock synchronization system error.
## 5 Interactive Function Development and Plug-In Application Interface Design
### Platform Usage Evaluation
#### 5.3.1 Questionnaire Design  
The questionnaire in this study was divided into four parts. Part I is the basic survey data to collect the identity, background, and use experience of digital auxiliary tools, and Parts II and III are the accessibility evaluations of the platform interface and plug-in application interface, respectively. The accessibility evaluation adopted in this study is adapted from the System Usability Scale (SUS), as proposed by [PERSON] [25], and is composed of 10 statements, where half of the problems are expressed in positive terms, while the other half are expressed in negative terms. The scores calculated in the SUS represent the user's comprehensive evaluation of the system's accessibility. The last part is related to the background of platform administrators and experts.  
In terms of the usability evaluation of the platform interface, the interactive experience provided by the Unity integration platform and the simplicity of the operation interface are evaluated to understand (1) whether the integration platform can effectively interact with information models, (2) whether the integration platform can provide correct model restoration information, (3) whether the interface is easy and intuitive to use, (4) whether the interface switch and menu design are logical, (5) whether the threshold of operating the BIM software can be effectively reduced, and (6) whether it can effectively assist the design process of historic building restoration.  
In terms of the accessibility evaluation of the plug-in application interface, understand the functions provided by the iData+ application interface (1) whether it can rapidly export and update the information to improve the accuracy of information collection; (2) whether the connection between the iData+ application interface and Unity integration platform can realize instant information transmission; (3) whether the iData+ application program provides added value to information; (4) whether the iData+ application program provides a simple and logical operation process.  
In the last part, interviews were conducted with the platform administrator, experts, or architects of historic building restoreers to understand from the perspective of the platform administrator: (1) whether the platform can easily import historic information models and integrate the information and (2) whether the platform is free and feasible in development. From the perspective of experts, it was evaluated (1) whether the restoration information content and functions provided by the platform can effectively assist the needs of the historic restoration design process and (2) whether it is conducive to on-site communication and application.
# A Biologically-Inspired Power Control Algorithm for Energy-Efficient Cellular Networks
## 6 Results and Discussion
As the number of BSs increases, the energy efficiency of the proposed TPC increases linearly, and it significantly outperforms the other schemes, because it maintains a lower outage probability and reduces the transmission power while maximizing the minimum rate of the MSs. On the other hand, the scheme using maximum equal power without TPC shows very low energy efficiency, and the SINR-based TPC shows a lower energy efficiency as the interference level increases; additionally, its performance is still closely affected by the target SINR value. Figure 8: Outage probability power _vs._ number of BSs when \(\gamma_{req}\)= 0 dB. Figure 7: Average transmission power _vs._ number of BSs.
## 3 Results and Analysis
### Ultrasonic Pulse Velocity  
The effect of adding the TPSW to the concrete was evaluated using the ultrasonic pulse velocity (UPV), as shown in Figure 11. With a fixed water-cement ratio of 0.4, the ultrasonic velocity of the cement mortar ranged from 3788 m/s to 4630 m/s for the substitution amounts between 0% and 15%, measured over 3 to 28 days. At a water-cement ratio of 0.5, the velocity ranged from 3937 m/s to 4505 m/s, and at 0.6, it ranged from 3788 m/s to 4032 m/s. The results show that as the water-cement ratio decreased and the curing age increased, the UPV gradually rose. However, as the substitution amount of the TPSW increased, the UPV decreased, which reflected the impact on the mortar's density.  
Despite the decrease, within the studied substitution range, the UPV at 28 days reached 3650 m/s, indicating that the specimens retained good density and strength. As the TPSW content increased, more internal pores formed, which led to the reduction in UPV. In summary, lower water-cement ratios and longer curing periods resulted in higher UPV values, while an increased TPSW content tended to lower the UPV due to the reduced material density.  
Figure 10: Flexural strengths of cement mortars with different W/C ratios and TPSW amounts.