esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Sensor Fusion Framework Model Using PDR and W-Fi Localization Systems  
The proposed sensor fusion framework model uses a Wi-Fi fusion algorithm results with PDR to enhance the position accuracy for indoor localization. The Wi-Fi fusion model in the proposed algorithm utilizes the effective features of two classical localization algorithms such as trilateration and fingerprint for user position estimation. The Wi-Fi fusion results combined with PDR results and the results from the proposed model are free from smartphone sensor errors and Wi-Fi RSSI signal strength problems. Figure 1 shows the proposed sensor fusion framework model.  
The position results from the PDR algorithm in the proposed model utilizes different smartphone sensors such as the accelerometer, gyroscope and magnetometer for position estimation. The data from accelerometer and gyroscope is used for pitch and roll estimation and the step detector uses the pitch values for step detection. The pitch and roll values together are used for heading estimation from the magnetometer. The data from gyroscope is also estimating the user heading and an LKF filter combines the gyroscope and magnetometer heading together for better performance. The step length is estimated from step detector and the position estimation algorithm uses the step length values and heading together for identifying the current user position. The results from PDR algorithm used in the sensor fusion framework for further position improvements. In the case of Wi-Fi fusion algorithm in the proposed model, it uses the results from trilateration and fingerprint algorithms together for user position estimation. The results of the Wi-Fi fusion algorithm are used in the sensor fusion framework for combining with PDR results. The linear Kalman filter explained in [33] is used for sensor fusion framework implementation.
## 4 Experiments
### Experimental Setup
#### 4.1.2 Baseline Methods  
We compared ARCS against several state-of-the-art methods:  
* **Traditional Rule-Based (TRB)**[19]: A conventional signature-based system using predefined rules and thresholds.
* **ML-Enhanced Response (MLER)**[23]: An ensemble of machine learning models (Random Forest, XGBoost, etc.) for threat classification and response.
* **Deep Q-Network (DQN)**[5]: A standard DQN implementation for security response automation.
* **A3C-Security**[14]: An asynchronous advantage actor-critic model adapted for cyber-security.
* **HRDL**[28]: A hierarchical reinforcement learning approach for cyber defense.
* **ACNN**[30]: A recent adaptive neural network framework that employs evolutionary optimization for attack detection and classification. This approach demonstrates strong capability in identifying zero-day attacks through continuous model adaptation.
* **DRL-Sim**[6]: A deep reinforcement learning framework designed for cyber attack simulation and response, which excels in understanding complex attack patterns through simulated environments.
## 4 Discussion
### Energy Estimation and CO\({}_{2}\) Reduction Estimation  
The energy efficiency and environmental impact was estimated based on the conversion rate of the biomethane into electricity. This was estimated based on the assumed 78% of the energy produced (Table 2), where 33% was electricity generated from the biogas and 45% was generated from the heat in a cogeneration process. In Expression (6), for the electricity generated \((El_{bio}\) ) from the biomethane per unit effluent volume (kWh/m\({}^{3}\)), the \(El_{bio}\) is the total biogas generated [6; 26].  
\[El_{bio}=0.33E_{bio} \tag{6}\]  
Herein, CO\({}_{2}\) emission reduction was therefore estimated as a function of the electricity produced to power the UV lamp of the photoreceptors with respect to the base line grid emission factor of 0.957 kg CO\({}_{2}\) e/kWh as given in Equation (7).  
\[CER=GEF\times E_{bio} \tag{7}\]  
Generally, the bioconversion of carbon dioxide to methane of anthropogenic emission from wastewater treatment plants and landfill gases can necessitate the reduction in global warming [15; 17; 30]. In this case, the maximum CO\({}_{2}\) emission reduction (kg CO\({}_{2}\)) was estimated to be 0.640 kg CO\({}_{2}\)/L for the BPM system with the rest depicted in Table 2.  
Therefore, the BPM system has shown great potential in significantly contributing to decreasing greenhouse gas emissions and mitigating unusual climate change. Additionally,the abatement of greenhouse effects in the wastewater settings can easily be managed with less effort as compared to petrochemical industries. Moreover, the wastewater from the sugar refinery used in this study constitute a high carbon content, which can be used by the BPM system to produce enough energy to offset the photoreceptor. Furthermore, the magnetic field incorporated in the BPM, as reported in previous studies [7], has the potential impact for recoverability of the magnetised photocatalyst for reuse. Hence, expanding this technology will also warrant reduction in operation costs such as the pressing cost of chemicals used in the wastewater settings.
## 4 Results and Analysis
### Limitations and Future Work
#### 4.3.1 Limitations  
**Overhead in Model Training**  
One challenge observed during the simulation phase is the computational overhead introduced by real-time model training and tuning. While Ray Tune offers efficient hyperparameter optimisation, training time could be a bottleneck, especially in networks with rapid changes in traffic patterns. This is particularly relevant for ultra-low-latency services, where any delay in resource allocation decisions can degrade performance.  
Limited Real-World Validation  
The current version of the ONSSO framework has been validated through simulated environments, using generated and modelled datasets. While these results are promising, real-world deployment and testing in a live 5G network environment are necessary to confirm its effectiveness and reliability. Simulations, while controlled and comprehensive, cannot account for all the uncertainties and complexities of real-world network conditions.  
Security Concerns  
As ONSSO introduces machine learning to autonomously control resource allocation, new security vulnerabilities might emerge, particularly concerning model integrity. Malicious attacks on the models (e.g., data poisoning) or adversarial attacks could compromise decision making, leading to suboptimal performance or network outages. Currently, these risks are not explicitly addressed in the framework.
### Enzymes Involved in Nucleotide Metabolism
#### 1.2.3 Enzymatic 5'-NTP Synthesis
The PPK2 family was further divided into three subclasses: class I, which involves 5\({}^{\prime}\)-NDP phosphorylation; class II, which is associated with 5\({}^{\prime}\)-NMP phosphorylation (also known as PAP (polyphosphate:AMPphosphotransferase)); and class III, where both 5\({}^{\prime}\)-NMP and 5\({}^{\prime}\)-NDP phosphorylation is observed. However, due to numerous exceptions, predicting an enzyme's substrate specificity based solely on its sequence has proven unreliable [5]. As a result, [PERSON]'s group suggested that this classification should be viewed primarily as reflecting the enzymes' preferences, as many enzymes have been shown to catalyze all types of reactions [73]. _Escherichia coli_ PPK is one of the most well-studied enzymes in the PPK1 family, first identified in the 1950s for its ability to regenerate ATP from ADP in a coupled hexokinase reaction [74]. Notably, research has shown that the 5\({}^{\prime}\)-NDP phosphorylating activity of _Escherichia coli_ PPK1 is reversible only with ADP, while phosphate transfer from polyphosphate to other 5\({}^{\prime}\)-NDPs occurs irreversibly [75; 76]. Initially, PPK1 enzymes were primarily used in ATP regeneration for coupled enzymatic reactions. However, recent focus has shifted toward PPK2 enzymes, especially the class III subtype [70]. Recent studies on novel PPK2 class III enzymes revealed promising candidates, which were successfully applied in a large-scale, coupled carboxylate reduction to aldehyde [71]. PPKs are also known for their broad substrate specificity. For instance, the PPK2 enzyme from _Cupriavidus reactor_ (formerly _Ralstonia eutropha_) exhibits one of the widest substrate spectra recorded, accepting ADP, GDP, CDP, UDP, and TDP as substrates [77]. _Escherichia coli_ PPK1 was shown to phosphorylate all natural NDPs and dNDPs, namely ADP, dADP, dGDP, GDP, TDP, UDP, CDP, and dCDP [76]. The PPK2 class III enzyme from _Meiothermus ruber_ was found to phosphorylate ADP, GDP, CDP, and UDP, in addition to their corresponding NMPs [72]. In general, purine nucleotides were preferred over pyrimidines (AMP > GMP > CMP > UMP > TMP) [78]. Due to their broad substrate range, PPKs have been utilized in enzymatic cascade reactions for the synthesis of both natural and modified 5\({}^{\prime}\)-NTPs [66; 79; 80].
## 7 Proposed Solution
### ANN-Based Detection System  
Once the network traffic is captured, an artificial neural network is processing to detect and identify malicious traffic. ANNs are a type of machine learning algorithm that can recognize patterns and identify anomalies in network traffic. The detection system is implemented on the SDN controllers to identify malicious traffic in real time.  
This system is built around five main steps as shown in Figure 5: (a) data preprocessing, (b) feature selection, (c) splitting the dataset, (d) training the model, and (e) test.  
Figure 5: ANN-based detection system flow.
# BeiDou Augmented Navigation from Low Earth Orbit Satellites
## 4 Results and Discussion
### Satellite Visibility  
The satellite visibility of BeiDou-3 only as well as BeiDou-3 and LEO constellation is computed. With dividing the global to the grids with \(5^{\circ}\) in latitude and \(5^{\circ}\) longitude, the number of satellites tracked by the location in the center of grid is calculated with 0 mask elevation and 30 s data sampling interval. Afterword, the average number for 1 day is used as the indicator of satellite visibility. Figure 3 demonstrates the global distribution of the satellite visibility for BeiDou-3 only (Figure 3a) and BeiDou-3 as well as LEO constellation (Figure 3b). For BeiDou-3, in general, the satellite visibility is distributed symmetrically in Northern hemisphere and Southern hemispheres, but unevenly in the Eastern hemisphere and Western hemisphere. More than 12 satellites can be tracked in Asia-Pacific region, where the 3 GEO and 3 IGSO satellites are located. In particular, for the low latitude region in the Asia-Pacific region, up to 15 satellites can be tracked. However, the number of tracked satellites are lower in the western hemisphere, particularly for the mid-latitude region of north and South American, there are only 8 satellite can be tracked in averaged. For the region with latitude beyond \(55^{\circ}\), about 10 satellites are in view, as the orbital inclination of BeiDou-3 MEO satellites are set as \(55^{\circ}\).  
Once the 120 LEO satellites have been incorporated, although the global distribution is still uneven in Western and Eastern hemisphere, the satellite visibility has been improved significantly, particularly in the mid-latitude region, as the low-orbit navigation satellites are designed to have an orbital inclination of \(55^{\circ}\). In China, at least 18 satellites can be tracked, particularly it can be reach to more than 20 in the north part of China. The great satellite visibility can improve the accuracy obviously, and explain the reason for selection of such kind of LEO constellation configurati  
Figure 3: Global distribution for satellite visibility of BeiDou-3-only (**a**) and BeiDou-3/LEO combined constellation (**b**).  
BeiDou-3 only, the satellite visibility in the mid-latitude region of north and South American has been improved, and more then 14 satellites can be tracked. However, the contribution of LEO to the satellite visibility is limited, as the limitation of orbital inclination as well as ground coverage of LEO satellites. Table 5 lists the statistical results for the two configurations. In general, once the LEO satellites have been incorporated, the maximum number of visible satellites has increased from 15.5 to 21.6. Meanwhile, the minimum number of visible satellites has increased from 7.6 to 10.1. Most significant improvement can be observed by the averaged visible satellite, which is increased from 10.7 to 16.3, indicating that the LEO has greatly improved the performance of navigation services worldwide.
## 4 Discussion
Finally, future research in the study area could be directed towards the application of the technologies and methodologies developed within the A-DInSAR techniques, such as: (i) application of the post-processing methodology proposed by [23; 70], consisting in the definition, delimitation, and analysis of Active Deformation Areas (ADA) at both regional and local scales; (ii) conversion of VLOS measurements to VELOPE values [14; 71], a procedure that consists in the projection of the VLOS along the VELOPE. It would allow the correction of the geometric distortions produced by the type of satellite orbit with respect to the terrain topography; (iii) new processing in ascending trajectories by means of both approaches used. This re-processing of the study area will help to explain and interpret the movements toward the sensors, which are difficult to interpret under the current research conditions.
## 3 Three-Port Electrical Converter Technologies
### Three-Port Converters
Reference [70] presented a high voltage gain topology with the advantages of continuous input current with a low ripple and low voltage stress on the switches, resulting in high efficiency. In reference [71], a non-isolated TPC was proposed without the use of multiple windings that reduce the costs and the volume. A power control strategy was proposed based on large- and small-signal models. In reference [72], a fully reconfigurable topology with a single inductor was proposed. Since it uses just one inductor, the density is improved, and the volume is reduced. In addition, single-stage power conversion between any two of three ports can be achieved. In [73], the topology has the advantages of high voltage gain without the employment of a transformer and low voltage stresses on switches. In [72], a higher voltage gain was achieved without the use of coupled inductors and voltage multiplier cells. Nevertheless, in [70; 71; 72; 73; 74], a number of components (including switches, capacitors, transformers, and inductors) were used, resulting in higher control complexity, lower efficiency, and higher costs. In [75], a soft-switching TPC was proposed with ZVS and leakage inductor energy recycling. However, the complexity of the control increases with the number of power switches and an active clamp circuit. A summary of the characteristics of the mentioned non-isolated TPCs is shown in Table 4.
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 3 Performance Analysis
### Asymptotic Outage Probability Analysis  
To get more insight into the outage behaviour of the TWR C-NOMA system with IQI and ipSIC, the formulas of asymptotic outage probabilities at the high SNR region are derived in this subsection.  
**Proposition 1**.: Based on (22), when \(\rho_{u}=\varsigma\rho_{r}\rightarrow\infty\) and \(\varsigma>0\), the asymptotic outage probability of \(D_{1}\) is derived as:  
\[\mathrm{P}_{out,D_{1}}^{\infty,IQ,ipSIC}=1-\sum\limits_{l_{3}=0}^{\infty}\sum \limits_{j=0}^{\infty}\sum\limits_{l=0}^{\infty}\sum\limits_{m=0}^{l}\frac{ \tilde{a}_{1}\tilde{a}_{2}^{2}K^{l+j+l_{3}}\tilde{b}_{1}^{m-1}\tilde{b}_{2}^{j- 1}\left(\beta^{\prime}_{f}\right)^{m}\left(\left|A\right|^{2}\right)^{m}a_{2}^ {m}\left(m+j\right)!}{\left(j!\right)^{2}!m!l!_{3}!\left(\left|A\right|^{2} \tilde{b}_{1}\beta^{\prime}_{f}a_{2}+\tilde{b}_{2}\right)^{\left(m+j+1\right)}}, \tag{24}\]  
_where \(\beta^{\prime}_{f}=\frac{\gamma_{thf}}{a_{1}\left(\left|\mu_{r}\right|^{2}- \gamma_{thf}\left|\
u_{r}\right|^{2}\right)}\), with \(\left|\mu_{r}\right|^{2}>\gamma_{thf}\left|\
u_{r}\right|^{2}\)._  
**Proposition 2**.: Based on (23), when \(\rho_{u}=\varsigma\rho_{r}\rightarrow\infty\) and \(\varsigma>0\), the asymptotic outage probability of \(D_{2}\) is given by:  
\[\mathrm{P}_{out,D_{2}}^{\infty,IQ,ipSIC} =1-\sum\limits_{l_{2}=0}^{\infty}\sum\limits_{m_{2}=0}^{l_{2}} \frac{\Phi_{1}\Phi_{2}\left(n+j\right)!\left(j_{1}+n_{1}\right)!}{\left(j_{1}! \right)^{2}!m_{2}!\left(\left|A\right|^{2}\tilde{b}_{1}\beta^{\prime}_{f}a_{2}+ \tilde{b}_{2}\right)^{\left(n+j+1\right)}}\] \[\times\frac{K^{l_{2}}\tilde{a}_{1}\left(\tilde{b}_{1}\right)^{m_{2 }-1}\left(\beta^{\prime}_{0}\right)^{m_{2}}e^{-\tilde{b}_{1}\beta^{\prime}_{0}} }{\left(\tilde{b}_{2}\beta^{\prime}_{m}a_{1}\left(\left|\
u_{r}\right|^{2}+ \varepsilon\left|\mu_{r}\right|^{2}\right)+\tilde{b}_{1}\right)^{\left(j_{1}+n _{1}+1\right)}}, \tag{25}\]  
_where \(\Phi_{2}=\sum\limits_{j_{1}=0}^{\infty}\sum\limits_{l_{1}=0}^{\infty}\sum \limits_{m_{1}=0}^{l_{1}}\frac{\tilde{a}_{1}\tilde{a}_{2}K^{l+j_{1}}\tilde{b}_{ 2}^{m_{1}-1}\tilde{b}_{1}^{j_{1}}}{\left(j_{1}!\right)^{2}!m_{1}!m_{1}!}\), \(\Phi_{3}=\left(\beta^{\prime}_{m}\right)^{m_{1}}a_{1}^{m_{1}}\left(\left|\
u_{r} \right|^{2}+\varepsilon\left|\mu_{r}\right|^{2}\right)^{m_{1}}\), \(\beta^{\prime}_{m}=\frac{\gamma_{thm}}{a_{2}\left(\left|\mu_{r}\right|^{2}- \gamma_{thm}\left|\
u_{r}\right|^{2}\right)}\) with \(\left|\mu_{r}\right|^{2}>\gamma_{thm}\left|\
u_{r}\right|^{2}\) and \(\Phi_{1}{=}\sum\limits_{j=0}^{\infty}\sum\limits_{l=0}^{\infty}\sum\limits_{m=0}^{l} \frac{a_{1}a_{2}K^{l+j}\tilde{b}_{1}^{m_{1}-1}\tilde{b}_{2}^{j}\left(\beta^{ \prime}_{f}\right)^{m}\left(\left|A\right|^{2}\right)^{m}a_{2}^{m}}{\left(j! \right)^{2}!m!m!}\)._
# Estimating the Memory Consumption of a Hardware IP Defragmentation Block
## 4 Results
### Simulation Results
#### 4.3.3 Flow Parallelism  
In Figure 9, the number of flows in a packet window was approximated by a linear function for each of the data series associated with ratio \(R\). The original metric measures how many unique flows can be found in a rolling window of a certain size.  
\[W(n)=\{N,w=\{p_{i},\ldots,p_{i+n}\},UniqueFlows(w)=N\} \tag{11}\]  
\[\widehat{W}(n)=T^{95}(W(n)) \tag{12}\]  
where \(p_{i}\) is a \(i\)th packet in a trace, \(w\) is a packet window of size \(n\), and \(UniqueFlows(w)\) is a flow counting function. This metric can be interpreted as a measure of \"flow parallelism\", i.e., how many flows are \"active\" in the same unit of time.  
For a single test, a single window of size \(n\) produces a population of measurements, i.e., \(W(n)\) is a sampled random variable. So, the final metric computesthe 95 th percentile of all measurements \(W(n)\) for n. For example, a window of 256 with a number of flows 100, that is, \(\widehat{W}(256)=100\), means that in 95% cases in 256 consecutive packets there are no more than 100 unique IP fragment flows. Since there are \(M\) tests, the estimate \(\widehat{W}(n)\) is itself a population sample, i.e., there are \(M\) samples of \(\widehat{W}(n)\) for each data series in Figure 9.  
The point measurements \((n,\widehat{W}(n))\) are approximated by \(f(n)=an\) using the least squares method (constant term, a.k.a. intercept, is fixed at 0). The slope coefficient \(a\) for each data set is slightly different, so Figure 9a displays the mean slope for each series of \(M\) tests along with the two biggest \([5\%,95\%]\) confidence regions. Each data series represents all tests created with the same ratio of tests \(R\). The detailed results of the linear fit can be found in Table 3. The minimal coefficient of determination \(r^{2}\) is defined as  
\[r^{2}=\frac{\widehat{v}(f(n))}{\widehat{v}(\widehat{W}(n))} \tag{13}\]  
where \((n,\widehat{W}(n))\) are samples, \(f(n)\approx\widehat{W}(n)\) is an approximating function and \(\widehat{v}\) is a variance estimator. \(r^{2}\) is more than 0.97 for all cases (shown in Table 3), which suggests a strong linear relationship (at least 97% of the variance is explained by the linear function \(f(x)=ax\)). Figure 9b shows the Gaussian kernel density estimate of the distribution of the slope parameter \(a\). The variance becomes smaller with the growing number \(R\). None of the probability mass functions can pass a t-test for identity with any other. The \(p\)-value is negligible for all pairs of distributions (less than \(10^{-36}\)).  
Measuring the flow parallelism provides additional evidence for flow locality. Highly local traffic should have lower \(\widehat{W}(n)\) for a given \(n\) than non-local traffic.  
Figure 9b shows the relation between mix ratio \(R\) and the distribution of the slope of \(\widehat{W}(n)\) in the samples. The differences between slope distributions are consistent with the previous metrics, e.g., intra-arrival. That is, the variance becomes smaller with higher \(R\).
## 3 Results and Discussion
### Finding a Potential Technological Application of the ZnO/Clay Composite
The fresh paint with ZnO/Clay composite incorporated shows a SID greater than control. By contrast, the dry paint practically does not have sporulation inhibition. The dry paint with ZnO/Clay composite added improves considerably the inhibition of fungi growth. Figure 4b collects the sporulation inhibition  
Figure 3: Antifungal activity test of ZnO/Clay composites against _Aspergillus niger_. Panels (**a**) and (**b**) show photographs of Bauer-Kirby disk diffusion assay for different composition of ZnO (wt.%) content in clay: (A) Control, (B) clay, (C) 2%, (D) 6%, (E) 10%, (F) 20%, (G) 40%, (H) 60%. Panel (**c**) shows sporulation inhibition diameter data mean values and standard errors of a 0.5 wt.% concentration of ZnO/Clay. The effectiveness zone is represented in green and one of less effectiveness is colored in red. Each data represents an average of triplicate measurements. diameter (SID) observed in the Bauer-Kirby disk diffusion assay. The fresh paint SID (I) may be related to the paint composition such as additives, fillers, pigments, solvents, and resins. Once the ZnO/Clay composite is incorporated into the paint (II), the action diameter against fungi growth increases. This increase in antifungal activity indicates synergy between fresh paint and ZnO/Clay composite. When the paint is dry, the SID difference between control (III) and modified paint (IV) increases significantly. Therefore, the antifungal response of waterborne paint without modification is due to the volatile compounds of its formulation. It should be highlighted that the ZnO/Clay composite keeps the good antifungal response both for fresh paint and for dry paint. In both cases, fresh and dry paint with ZnO/Clay composite improve the antifungal response. The antifungal activity observed in ZnO/Clay composite (Figure 3) is maintained for its application in paints (Figure 4). The excellent antifungal activity of fresh and dry paint points to the availability of ZnO in the matrix thanks to the dry dispersion method. The ZnO concentration added in the finished product for antifungal response is 0.05 wt.%, a value well below other antifungals such as zinc pyrithione, typical addition \(\sim\)2.0% [7]. At a very low concentration of ZnO/Clay (0.5 wt.%), the paint does not significantly increase the cost production and improves considerably the antifungal response.