esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Cyber Security Risk Management Approach for a Cyber-Physical System
## 4 An Integrated Risk Management Approach
### Modeling Concepts
They also describe the vital components and actions taken to protect the assets. The overall goal of risk assessment will be partly defeated if relevant controls are not applied. * Compliance Programs: These are sets of requirements designed to secure the CPS to operate without any form of disturbance. Critical infrastructures are increasingly using compliance programs as a mechanism for demonstrating cybersecurity for CPS protection. The North American Electric Reliability Corporation Critical Infrastructure Protection (NERC CIP) is a compliance program designed to secure the assets necessary for operating a bulk electric system. In this case, the SCADA system of the CPS is an asset. Therefore, a significant sum of their budget and time is necessary to ensure security compliance with standards such as NERC CIP, NIST, NIPP, and other relevant standards. * Cyber-attack scenario: A cyber-attack scenario is an event that leads to a negative impact on the organization's assets when it occurs. There are some certain components that determine a cyber-attack on a CPS. They include threat types, actor's skill, capability and location, assets, events, and time. With certain scenarios, the organization tends to think broadly by developing a range of possible outcomes to increase their readiness for a range of possibilities in the future. * Policy: Policies are the principles of action adopted or proposed by an organization. There are a number of security policies, such as access control and backup that are necessary to formulate and implement the CPS security program. * Threats and vulnerabilities: Vulnerability is the weakness in an organization security program that is exploited by a threat to gain unauthorized access to an asset. It has three properties. i.e., impact, type, and weight score. The Metamodel illustrated in Figure 2 above shows the relationship between the concepts. The actor is represented as having an interest in SCADA system services offered by the CPS. The actor introduces security goals such as confidentiality, integrity, and availability, and organizational goalssuch as business continuity and reputation of the organization and the key performance indicators such as authenticity, consistency, resilience, etc., and the attainment of one or more is always their focus. As concerns are raised in regards to risk which may impede the fulfilment of the goals, controls regarding security and the organization are introduced to help mitigate the risks. The actor has full control over its assets and needs to keep the assets secure for the continuity of the business, but these assets are prone to weaknesses in their systems, known as vulnerabilities. These vulnerabilities, when not addressed on time, can lead to a threat which will introduce risk, and this risk is likely to lead to the exploitation of the assets. Once the risk factors have been identified, risk assessment is carried out to mitigate them.
# Resting-State Functional Connectivity in Mathematical Expertise
## 1 Introduction
It should be noted that various factors such as types of methods (parametric or non-parametric), types of variables (discrete or continuous), or the sample size yield varying statistical results, even with identical fMRI data [33; 34; 35]. Therefore, the significance of the result, as well as its interpretation and generalizability to the population at large, should be examined carefully. With respect to this issue, previous studies suggested to use another type of analysis, that is, the machine learning approach, which can provide supporting information for the data interpretation and generalizability of fMRI-based results by classifying and predicting significant neuroimaging features [36; 37; 38]. For example, the difference between musical experts and non-experts was investigated using the machine learning approach, and the results showed a classification accuracy of 77%, implying that the significant features from fMRI data not only provide psychophysiological interpretation, but also grasp functional or structural differences between groups [38]. In the present study, we investigated how mathematicians and non-mathematicians differ in the correlation of their spontaneous BOLD fluctuations across brain regions during resting state, with the aim of delineating the functional network structures modulated by varying levels of mathematical expertise. To this end, we recruited two groups of participants, that is, mathematicians with a high-level of mathematical expertise and non-mathematicians as a control group, and we performed a group comparison using resting-state fMRI data. Most of the previous studies postulated specific regions of interests(ROIs), examined with which areas or networks the ROIs are correlated, and interpreted the results with respect to cognitive processes in which the ROIs are known to be involved. Therefore, interpreting these results is usually dependent on the functions of the predetermined ROIs. However, in the present study, we explored the resting-state functional connectivity in the whole brain network instead of selective ROIs, and thus we did not limit our investigation to subjectively selected areas. We hypothesized that the functional connectivity of mathematicians and non-mathematicians would be characterized by a set of brain regions reflecting the different levels of expertise in each group. In particular, mathematicians' resting-state fMRI networks would be similar to task-based fMRI networks, which pertains to experts' preconfigured connectivity and a neural efficiency. Finally, we expected that the functional networks would identify the mathematicians and non-mathematicians by the classification performances based on machine learning algorithms, which provides a vital piece of information about the different functional connectivity between the groups.
## 2 Unified Modeling of LEO Satellite Constellation  
This section proposes a unified model architecture for satellite multidomain simulation, constellation system simulation and communication link simulation. It also describes in detail the principles and implementation of the important components of the LEO satellite constellation system model developed by MWorks/Modelica.
## 5 Future Scope and Conclusions  
### Future Scope  
Although ZnO-based nanomaterials have been applied in organic pollutants removal from water and conduct antibacterial reactions in water, there is still plenty of space for improvement. The following points are the aspects that can be improved and strengthened in the application process of ZnO-based nanomaterials in the future:  
1. Exploring strategies for changing the weak toxicity of ZnO-based nanomaterials so that they can be better used in drinking water treatment, clinical medicine, virus killing, and other fields closely related to human beings.
2. Enhancing the ability of ZnO-based nanomaterials to respond to visible light enables them to have a wider range of applications. Visible light is one of the most abundant light sources, and a better response under visible light can maximize the use of existing energy and reduce investment.
3. Exploring the use of ZnO-based nanomaterials for photocatalytic removal of resistant bacteria, cancer cells, and other difficult-to-remove microorganisms and pathogen cells to improve the availability of the material.
4. Exploring stronger ZnO-based nanomaterial structures and carriers to improve recyclability and improve existing problems such as high solubility and difficulty in recycling.
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
## 3 Preliminaries and Notation  
All the algorithms studied in this paper work in planar, eight-connected grid maps with regions of obstacles consisting of blocked cells. A wavefront launched by a cell state change can propagate from one cell to its neighbor in any cardinal or diagonal directions, and the length of the cardinal and diagonal moves are \(1\) and \(\sqrt{2}\), respectively. We follow the definition of octile distance to compute the heuristic distance between any two cells in a grid map, i.e., the distance between cells \(s\) and \(s^{\prime}\) is computed by following Equation (2):  
\[dist(s,s^{\prime})=\sqrt{2}\times min(dx,dy)+\left|dx-dy\right| \tag{2}\]  
In Equation (2), \(dx\) and \(dy\) denote the differences of the 2D coordinates of cell \(s\) and \(s^{\prime}\). For each cell \(s\), \(\mathit{obst}_{s}\) maintains coordinates of the obstacle cell \(s_{0}\) to which \(s\) is currently closest, and \(\mathit{dist}_{s}\) maintains the distance between \(s\) and \(s_{0}\). The notation \(\mathit{dir}_{s}\) maintains the direction along with which s would propagate the wavefront. The options of \(dir_{s}\) include four cardinal directions (denoted as left = 1, up = 3, right = 5, and down = 7), four diagonal directions (denoted as up-left = 2, up-right = 4, down-right = 6, and down-left = 8), and full directions (denoted as full-dir = 0, declaring that \(s\) is the source of the wavefront; thus, all the eight directions need to be considered in the next round propagation). The notation \(raise_{s}\) shows if the wavefront on \(s\) is a raise wavefront or a lower one (the differences between raise and lower wavefronts are explained in Section 4.1.1). Given a cell \(s\), a direction \(d\), and an integer \(k\), the notation \(s^{\prime}=s+kd\) denotes a cell \(s^{\prime}\) that is reached from \(s\) by moving \(k\) steps along \(d\). For two perpendicular directions \(c_{1}\) and \(c_{2}\), we have \(d\)=\(c_{1}\)+\(c_{2}\) to denote that the sum of \(c_{1}\) and \(c_{2}\) results in their corresponding diagonal direction \(d\).
###### Abstract  
The storage enclosures are vital for stabilizing the micro-environment within, facilitating preventive conservation efforts, and enabling energy savings by reducing the need for extensive macro-environmental control within the room. However, real-time conformity monitoring of the micro-environment to ensure compliance with preventive conservation specifications poses a practical challenge due to a limitation in implementing physical sensors for each enclosure. This study aims to address this challenge by using an ANN (Artificial Neural Network)-based prediction for temperature and RH (Relative Humidity) changes in response to macro-environmental fluctuations. A numerical model was developed to simulate transient heat and mass transfer between macro- and micro-environments and then employed to determine an acceptable macro-environmental range for sustainable preventive conservation and to generate a dataset to train a sequence-to-sequence ANN model. This model was specially designed for 24 h real-time prediction of heat and mass transfer and to simulate the micro-environmental conditions under varying levels of control accuracy over the macro-environment. The effectiveness of the prediction model was tested through a real trial application in the laboratory, which revealed a robust prediction of micro-environments inside different enclosures under various macro-environmental conditions. This modeling approach offers a promising solution for monitoring the micro-environmental conformity and further implementing the relaxing control strategy in the macro-environment without compromising the integrity of the collections stored inside the enclosures.  
heat and mass transfer; artificial neural network (ANN); prediction; hygrothermal condition; buffering effect; storage enclosure; preventive conservation +
Footnote †: journal: Applied Sciences
# Uplink NOMA-MIMO Systems with Rayleigh Power Distribution
## 4 Simulation of MU-SISO and MU-MIMO Uplink System in Overload Mode  
For a demonstration of the potential possibilities of using the overload mode, we consider the case of receiving, on one receiving antenna at the base station, signals from several users, i.e., consider the MU-SISO uplink system \(N_{rx}=1,\ N_{tx}=1\).  
Figure 4 shows the dependencies of BER and FER for this case with the number of users--\(1,2,3\) and \(4\)--which corresponds to the system load of \(100\%,200\%,300\%\) and \(400\%\). The follow simulation parameters were used: QPSK modulation and turbo encoder (speed of \(\frac{1}{2}\)) and Rayleigh fading channel. The optimal maximum likelihood detector was used at the base station receiver side.  
Figure 4: Dependencies of (**a**) BER and (**b**) FER for MU-SISO uplink systems with FEC from SNR for different number of users (different degree of loading).  
Figure 3: The dependence of MU-MIMO uplink total channel rate for discrete–continuous channel on the number of users of different values of the SNR ratio.  
The results of Figure 4 show that the MU-SISO uplink system is functional, even under a 400% load. To ensure the level of FER = 0.01 in conditions of load increasing up to 200% and 400%, increasing the SNR ratio up to 2.2 dB and 7 dB respectively is necessary. This is to ensure that the level of FER = 0.001 is necessary to increase SNR ratio up to 2 dB and 5 dB respectively.  
Figure 5 shows the dependencies of FER from SNR for the MU-MIMO-Uplink system. It is well known that to maximize the efficiency of overloaded systems, it is obligatory to use optimal, or close to optimal, MIMO detectors [12]. For overload systems, the question of computational complexity is the first one. Computational complexity of optimal detector increases proportionally to \(2^{UlN_{tx}k_{b}}\). Linear detectors (MMSE, ZF) and receivers which use linear detectors (V-BLAST, SIC) [8] in overload conditions become ineffective.  
The use of detectors based on QR decomposition (Sphere detector [9] and K-best detector [18]) is not possible, since the decomposition of the channel matrix does not lead to a triangular matrix. However, it exists in a solution based on a K-best algorithm using factorization of MMSE solution [5; 18; 19; 20]. This algorithm was also simulated for the considered MU-MIMO uplink system.  
Figure 5 result curves for the optimal MIMO detector are plotted with solid lines, and results from the MMSE-based K-best detector with the parameter \(K_{best}=16\) are represented with dotted lines.  
From the results shown, it follows that for the considered variations the characteristics of a simpler MMSE-based K-best detector almost match with the characteristics of the optimal ML detector. Increasing the number of users by 1.5 and 2 times (150% and 200% overload) requires an increase in the SNR ration by 1.1 dB and 2.5 dB at the FER = 0.001 level respectively. This confirms the possibility of using such a mode in MU-MIMO uplink systems.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Analysis and Insights from Performance Metrics
#### 5.8.2 Precision vs. Recall  
Precision is generally higher than recall across the epochs, suggesting that when the model predicts a shot as accurate, it is more likely to be correct. However, the lower recall values indicate that the model may occasionally miss identifying some accurate shots.
# Comparing Link Budget Requirements for Future Space-Based Interferometers
## 4 Conclusions  
Future inter-satellite interferometric missions may use new configurations that improve on cost, redundancy, and scientific instrument sensitivity. However, alternative system architectures, such as the retro-reflector configuration, have not been pursued as they struggle to meet the received optical power requirements for robust phase tracking set for the transponder schemes currently used. Simulations and analytical modeling demonstrated that the transponder scheme crossed the threshold for linear phase tracking at a carrier-to-noise density ratio of 58.6 dB-Hz--140 femtowatts in shot-noise-limited detection. The retro-reflector and dual-stabilized configurations benefited from suppressed laser frequency fluctuations and stably operated down to optical power levels three orders of magnitude below the transponder scheme, down to a carrier-to-noise density ratio for the retro-reflector of 29 dB-Hz--a signal strength of 150 attowatts in shot-noise-limited detection, and for the dual-stabilized configuration, 30 dB-Hz--a signal strength of 200 attowatts in shot-noise-limited detection. This transponder power requirement is four orders of magnitude below the current GRACE-FO requirement and below the expected received power of retro-reflector schemes in the satellite separation range of 100-300 km. This work supports further exploration of retro-reflector configurations for future missions either as a cheaper alternative to transceiver architectures or as a secondary sensing configuration for increased robustness. Further work is needed on the lock acquisition of weak optical fields with low-bandwidth phasemeters.  
Conceptualization, methodology, validation, writing, review and editing, C.S.S., J.T.V. and K.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav, CE230100016), and the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors would like to thank [PERSON] for his valuable insights around the minimum phasemeter bandwidth requirements to acquire a lock on a moving signal.  
The authors declare no conflicts of interest.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario
### Risk-Based Access Control Implementation
#### 4.3.2 Contextual Risk  
In dynamic Risk-Based Access Control models, contextual and real-time attributes play a critical role in evaluating access decisions. Among these attributes, time and location were leveraged to assess contextual risk at the time of the access request. The time attribute corresponds to the predefined duty schedule of hospital personnel, such as doctors and nurses, where access requests made during an individual's assigned shift are associated with a lower contextual risk. Conversely, requests made outside of scheduled hours are flagged as higher risk. Similarly, the location attribute evaluates whether the access request originates from within the hospital premises. Requests from inside the hospital are considered low-risk, whereas those from external locations trigger a higher risk score. This contextual evaluation allows the system to dynamically adapt access decisions based on situational awareness and potential threat indicators. The values of the same scenario proposed in [12] were utilized, as shown in Table 4.
# Forest Fire Detection Using New Routing Protocol
## 8 Simulation Results and Discussion
This is because nodes in the PCE-OLSR have a table of routing information in order to establish the routes, and these nodes update their routing information periodically, which leads to an increase in the overhead in the overall network, while the proposed LARRR protocol is considered as a type of reactive routing protocol, wherein the nodes in this protocol create routes when they are needed only. For instance, when a node needs to transmit a packet, a process called the Route Discovery Process (RDP) will start to search and find a route to the \(D\) node. This RDP is finished once a route is discovered or all potential routes to the \(D\) node have been examined. In addition, Table 2 shows the comparison between the proposed LARRR protocol and the PCE-OLSR protocol in terms of the methodology of each protocol in the detection of forest fires.
###### Abstract  
Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) forecasting of these batteries. Using advanced machine learning models, this research uses past usage data and essential performance characteristics to forecast the RUL of NMC-LCO 18650 batteries. The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. This research also emphasizes the importance of algorithm design that can provide reliable RUL predictions even in cases when cycle count data is lacking by properly using alternative features. On further investigation, our findings highlighted that the introduction of cycle count as a feature is critical for significantly reducing the mean squared error (MSE) in all four models. When the cycle count is included as a feature, the MSE for LSTM decreases from 12,291.69 to 824.15, the MSE for LR decreases from 3363.20 to 51.86, the MSE for ANN decreases from 2456.65 to 1858.31, and finally, the RF with ETR decreases from 384.27 to 10.23, which makes it the best performing model considering these two crucial performance metrics. Apart from forecasting the remaining useful life of these lithium-ion batteries, the web application gives options for selecting a model amongst these models for prediction and further classifies battery condition and advises best use practices. Conventional approaches for battery life prediction, such as physical disassembly or electrochemical modeling, are resource-intensive, ecologically destructive, and unfeasible for general use. On the other hand, machine learning-based methods use extensive real-world data to generate scalable, accurate, and efficient forecasts.  
litigium-ion (Li-ion) batteries; remaining useful life (RUL); machine learning (ML); artificial intelligence (AI); linear regression; neural networks; optimization strategies; battery management; sustainability; web application
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 3 Design of Current Controllers
### PI (Proportional-Integral) Controller Design  
Based on the parameters in Table 1 and using (2), the transfer function of the plant can derived as:  
\[G_{plant}(s)=\frac{100}{s+10} \tag{8}\]  
Figure 7a shows the frequency response of the plant, the designed PI Type-2 controller, and the loop transfer function of the overall designed system. The PI controller has a high gain at lower frequency regions, which ensures that the controller achieves a zero steady-state error. The controller was designed for a cut-off frequency of 1 kHz, which is a decade below the switching frequency (10 kHz). The controller bandwidth can theoretically be increased by designing the controller for a higher cut-off frequency; however, this will make the system susceptible to switching noises. The slope of the controller eventually decreases around the cut-off frequency region, ensuring the cut-off frequency specification is met. The controller rolls off at a higher slope at high frequency ranges to attenuate the switching noises. The phase margin of the loop transfer function (of the plant and the current controller combined) was selected to be 45\({}^{\circ}\) at the cross-over frequency of 1 kHz, which ensures an acceptable overshoot in the system response. The constants \(k_{PI}\), \(\tau\), and \(T_{p}\) of (3) were thus calculated to be 62.93, 383.24 us, and 66.09 us, respectively, which corresponds to the following transfer function for the PI Type-2 controller:  
\[C_{PI}(s)=\frac{62.96s+1.642e^{5}}{6.609e^{-5}s^{2}+s} \tag{9}\]