esa-sceva/satcom-chunk-collection huggingface dataset:
### Radiation Detection, Localisation, and Mapping  
The scientific and engineering concepts and principles underlying radiation detection and the subsequent use of such technologies for mapping variations in both gamma and neutron field(s) are long-established [5], with series of technical papers having been published detailing the processes and best practices [6; 7; 8]. Although technologies have seen continued advancement over recent decades, following the March 2011 accident at Japan's Fukushima Daiichi Nuclear Power Plant (FDNPP)notable advancements in the unmanned aerial vehicle (UAV) [9; 10; 11], unmanned ground vehicle (UGV) [12; 13], and static/mobile distributed detection systems [14] were realized. Even years after this driver, progress continues across radiation detection, localization, and mapping, whether in underpinning detector materials research (e.g., novel plastics, high-dose semiconductors, dual gamma-neutron scintillators) [15; 16; 17; 18]; innovative, autonomous, and miniaturized deployment mechanisms [19; 20; 21]; or sensor-fusion/data visualization methodologies, progressing from 2D to 3D scenarios [22; 23; 24; 25; 26; 27]. This research is not just occurring at a small number of institutions and laboratories, but it is a promising and increasingly cross-disciplinary area of active research around the world, applicable for nuclear plant decommissioning, nuclear security, and safeguards applications, whether or not in accident response [25].
## 4 Optimal Design Problem Formulation
### Reliability Criteria: Loss of Power Supply Probability (LPSP)  
The second objective considers the loss of power supply probability as the system reliability criteria. \(LPSP\) reliability index measures and ascertains the quality and reliability performance of the power system design under the different scenarios considered in this study. \(LPSP\) is defined as the ratio of the sum of all energy deficits (\(LPS\)) to the total power demand. Thus, \(LPSP\) can be evaluated by using the following expression:  
\[LPSP=\frac{\sum_{t=1}^{T}LPS(t)}{\sum_{t=1}^{T}L_{D}(t)} \tag{15}\]  
where  
\[LPS(t)=L_{D}(t)-\left[WT_{p}(t)+PV_{p}(t)+(SOC(t-1)-SOC_{min})\times\beta_{c}\right] \tag{16}\]  
\(LPSP\) value ranges between zero and one; a value of 0 for \(LPSP\) implies that the load demand will always be met or satisfied, and this is the most desired and preferred performance. The following system \(DRP\) constraints are considered during the optimization procedure, alongside the other system component constraints that are mentioned at each design stage.  
\[\begin{array}{ll}PV_{p}(t)+WT_{p}(t)+ESS_{p}^{ds}(t)-ESS_{p}^{ch}(t)&=L_{D} (t);&\text{without DRP}\\ PV_{p}(t)+WT_{p}(t)+ESS_{p}^{ds}(t)-ESS_{p}^{ch}(t)&=L_{D}^{CPP}(t);&\text{with CPP DRP}\\ PV_{p}(t)+WT_{p}(t)+ESS_{p}^{ds}(t)-ESS_{p}^{ch}(t)&=L_{D}^{TADP}(t);&\text{with TADP DRP}\end{array} \tag{17}\]
# A Novel Evolutionary Algorithm for Designing Robust Analog Filters
## 3 Methods: A Robust Analog Filter Design Using Evolutionary Algorithms
### Analog Filter Design using Bond Graphs and GP
#### 3.1.1 Bond Graphs  
The bond graph is a multi-domain modeling tool for the analysis and design of dynamic systems, especially hybrid multi-domain systems including mechanical, electrical, pneumatic, and hydraulic components [44]. One advantage of using bond graphs for open-ended design exploration is that the complex loops typical in electric circuit schematics can be transformed into tree-like structures by the bond graph's 1-junction (serial connection) and 0-junction (parallel connection) concepts, which tend to be easier to evolve in general. Another advantage is that the multi-domain nature of bond graph modeling facilitates the evolution of mechatronic systems. Many researchers have explored the bond graph as a tool for system design [47]. Details of notation and methods of system analysis related to the bond graph representation can be found in [44]. Figure 1 illustrates a small bond graph that represents the accompanying electrical system. Figure 2 shows the complex bond graph model of a lowpass filter. A typical simple bond graph model is composed of inductors (I), resistors (R), capacitors (C), transformers (TF), gyrators (GY), 0-Junctions (J0), 1-junctions (J1), sources of effort (SE), and sources of flow (SF). In this paper, we are only concerned with linear dynamic systems, or more specifically, analog filters as a case study, represented as bond graphs, which are composed of I/R/C/SE(as input signals)/SF(as output signal access points). Due to the limitation of our bond graph simulation, we did not include transistors into our model so the system will have difficulty evolving active analog filters. They can be evolved using SPICE simulation and modeling software, which is beyond the scope of this paper.
## 4 Results and Discussion
### Effect of Temperature on Aggregate-Bitumen Interface Shear Strength  
Asphalt is a typical temperature sensitive material, and temperature is the most important factor affecting the aggregate-bitumen shear strength. The aggregate-bitumen interface shear strengths of modified and unmodified asphalt are given in Figure 8. As seen in Figure 8, for modified and unmodified asphalt, the aggregate-bitumen interface shear strength decreased with the increase of temperature, this indicated that, with the increase of temperature, the asphalt became soft and its resistance to interface shear failure declined. Moreover, a higher slope of the fit curve means that a higher temperature sensitivity of the asphalt is observed. As can be witnessed, the temperature sensitivity of nano hydrophobic silane silica modified asphalt was lower than that of base asphalt, which is consistent with the conclusions of the conventional physical properties test in the published article.  
In the test temperature range of \(-20\)-\(20\)\({}^{\circ}\)C, the change trend of aggregate-bitumen interface shear strength of NHSSMA with temperature is generally consistent with that of BA. Moreover, the aggregate-bitumen interface shear strength of nano hydrophobic silane silica modified asphalt was always greater than base asphalt when the temperature was above \(-10\)\({}^{\circ}\)C, which is because incorporation of nano hydrophobic silane silica can improve the viscosity and resistance to interface  
Figure 8: Aggregate-bitumen interface shear strength of BA and NHSSMA at different temperatures.  
Figure 7: Tensile strength and shear strength of aggregate-bitumen interface for base asphalt (BA) and NHSS modified asphalt (NHSSMA) at different temperature.  
shear failure of asphalt. Asphalt tends to be in a glassy state at \(-20\)\({}^{\circ}\)C and exhibits more brittleness than viscosity. At the test temperature of \(-20\)\({}^{\circ}\)C, the incorporation of nano hydrophobic silane silica not only fails to improve the viscosity of the asphalt but also destroys the integrity and uniformity of asphalt, thereby causing the aggregate-bitumen interface shear strength of NHSSMA to be lower than that of BA at \(-20\)\({}^{\circ}\)C.
# Enhancing Data Security Through VLSM Subnetting and TCP/IP Model in an ENT
## 2 Literature Review
### Routing Protocols
#### 2.3.4 Border Gateway Protocol (BGP)  
BGP determines the best network routes for transmitting data on the internet--a network of networks made up of multiple smaller networks called autonomous systems (ASs). Border Gateway Protocol serves as part of an External Gate Protocol (EGP) which relates to dynamic protocols. BGP sluggishly locates another path in the network due to reduced (slow) connectivity [12]. BGP is the principal interdomain routing protocol, whereas OSPF operates amongst autonomous systems, with RIPv2 and EIGRP being interior gateway protocols.
# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 3 Materials and Methods
### Model Parameters and Training  
In order to perform training of the FixMatch semi-supervised algorithm, a Pytorch [62] implementation of the algorithm [63] was used, with appropriate modifications being made to apply it to the chest X-ray dataset, as well as performing the imbalancing step. The hyperparameters used for the training were kept similar to the parameters reported to be used by the authors of FixMatch [11] for their experimentation with the ImageNet dataset [64]. Table 5 summarizes the hyperparameters used in the training process.  
Figure 1: Sample x-ray images of (**a**) normal, (**b**) Bacterial Pneumonia, (**c**) COVID-19, (**d**) Lung Opacity and (**e**) Viral Pneumonia.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Batch Size & 64 \\ Epochs & 300 \\ Unlabeled Batch Size Coefficient & 5 \\ Unlabeled Loss Coefficient & 10 \\ Pseudo-label Threshold & 0.7 \\ Model & ResNet-18 \\ Weight Decay & 0.0003 \\ Initial Learning Rate & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Training parameters.
## 8 Implementation Challenges and Solutions
### Human-Machine Interaction  
Despite growing autonomy, human oversight remains indispensable in maritime operations, especially in emergencies or ethical dilemmas. Effective human-AI collaboration requires transparency, trust, and clear intervention protocols (Figure 6).  
The current issues are as follows:  
1. Non-intuitive or overly technical interfaces impede operator understanding;
2. Limited operator training in interpreting AI outputs or overriding decisions;
3. Risk of \"automation bias\" where humans over-trust or under-trust AI systems [159,160].  
Figure 6: Human-machine interaction in autonomous maritime systems (own elaboration).  
The following best practices are recommended:  
* Develop intuitive dashboards that visualize agent behavior, risk levels, and alternative decisions [161];
* Establ clear escalation paths and human override conditions, especially for collision avoidance, distress handling, or cybersecurity breaches;
* Launch AI-specific training programs for seafarers and fleet managers, emphasizing practical decision support and system diagnostics;
* Promote a cooperative culture, where AI agents act as assistants and not replacements, ensuring shared situational awareness and coordinated responses.  
The future of autonomous maritime transport lies not in removing humans but in elevating their strategic role through intelligent interfaces and explainable decision flows [159; 160].  
Addressing implementation challenges is critical to unlocking the full potential of AI agents in maritime autonomy. By combining technical innovation, ethical foresight, interoperable frameworks, and human-centered design, the industry can move toward a new era of safe, transparent, and scalable autonomous navigation.
## 4 Results and Discussion
### Localization Results Using a Combined Dictionary
Figure 16: Continuous states anomalies over time. Figure 15: Transition matrix considering different states. Figures 18 and 19 provide boxplots summarizing the distributions of continuous and discrete anomalies across three distinct scenarios: No Observation (NoObs), Single Track Observation (SingleObs), and Multiple Track Observations (MultiObs). In Figure 18, the NoObs scenario shows a higher median and a larger spread in continuous anomaly values, indicative of increased uncertainty and drift when relying solely on predictive models without sensor updates. Conversely, scenarios involving SingleObs and MultiObs demonstrate tighter distributions around lower anomaly values, clearly illustrating the stabilizing effect of receiving even sparse landmark observations. Similarly, the discrete-level anomalies depicted in Figure 19 present a parallel trend: periods without observations (NoObs) have greater variance and multiple outliers due to the inability to accurately assign discrete states without regular sensor feedback. Conversely, the SingleObs and MultiObs scenarios show reduced dispersion and lower median anomaly values, emphasizing the effectiveness of consistent or even intermittent landmark observations in maintaining accurate discrete state estimations. Overall, these findings underscore the value of our combined dictionary-based MJPF, which ensures robust localization even under challenging conditions by effectively integrating continuous and discrete state predictions. The anomaly analyses clearly demonstrate the MJPF's adaptability in dynamic environments, significantly contributing to reliable ego-vehicle navigation and localization performance.
# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 4 Experiments and Results
### Experimental Results
#### 4.3.2 Experimental Results of Dual-Emotion Features in Rumor Detector  
In the previous section, the validity of dual-emotion features in rumor detection is verified, but because the MLP is a simple multi-layer perceptron without any text features, the representativeness of the experimental results is limited. Next, this paper used different rumor detectors to test the dual-emotion features and analyzed whether dual affective characteristics enhance the effects on rumor detection under the influence of other confounding factors. Then, the two baseline features were introduced into the detector, and the optimization degree of the dual-emotion features was analyzed through data comparison.  
In this experiment, the Emoratio and EmoCred baseline features and dual-emotion features were embedded into the BiGRU model and CNN model. Four experimental indicators were obtained: the macroscopic F1 value and accuracy rate of each model, as well as the F1 values of \"rumor\" and \"non-rumor\". The data results in Tables 4 and 5 show that the substitution of dual-emotion features greatly improves the evaluation indexes of the two models. After a detailed analysis, we can see the following:  
1. In the semantic feature detectors GRU and CNN, the dual-emotion features are added to distinguish between Weibo-16 data categories, and the results show that the accuracy rate of detecting \"rumors\" and \"non-rumors\" is increased by 2.7% and 0.7%, respectively.
2. In the GRU model, the macro F1 score of dual-emotion features is increased by at least 1.5%, and the addition of Emoratio and EmoCred not only does not improve the performance of the detector but also decreases the index of the macro F1 score. This is because the two baseline features only focus on the rumor text and ignore the emotional information of the comments.
3. The addition of bi-affective features improves the performance of the GRU model and CNN model; moreover, comparing the F1 scores of the two detectors combined with those of the Emoratio and EmoCred baseline features reveals that the compatibility and generalization ability of dual-emotion features are more prominent.  
Figure 8: F1 score after subtracting different characteristics from the dual affective characteristics.
###### Abstract
In this paper, we consider the importance problem that arises in the MVD format, and the combined strategy harnesses the strengths of both techniques, aiming to deliver high-quality 3D video content with efficient bandwidth utilization and robust error resilience. In this manuscript, we explore the theoretical foundations of our proposed scheme, evaluate its performance under various conditions, and demonstrate its effectiveness through simulations. Our approach addresses the unique challenges of MVD content exchange by optimizing error protection and modulation strategies, ultimately enhancing the robustness and quality of transmitted 3D video. This research contributes to the advancement of efficient and reliable 3D video communication. The rest of this paper is organized as follows: Section 2 details the proposed system model. Section 3 describes the simulation setup and results, highlighting the performance improvements achieved by our approach. Finally, Section 4 concludes with a summary of our findings and suggestions for future research directions.
## 4 Early Warning of Rockburst Based on Microseismic Indices
### Commonly Used Microseismic Indices
#### 4.1.3 Energy Index  
The \(EI\) is a practical analysis tool in seismology used to compare the radiation energy between microseismic events with similar seismic moments. It is defined as the ratio of the radiation energy \(E\) of a given microseismic event to the average radiation energy \(\overline{E}(M)\) corresponding to the seismic moment \(M\) of the microseismic event:  
\[EI=\frac{E}{\overline{E}(\rm M)} \tag{5}\]  
The average radiation energy of a microseismic event is positively correlated with its seismic moment, which can be obtained from the following relationship:  
\[\log\overline{E}(M)=c_{1}\log M+c_{2} \tag{6}\]  
where \(c_{1}\) and \(c_{2}\) are constants, which can be obtained by fitting. Therefore, we have:  
\[EI=10^{-c_{2}}\frac{E}{M^{c_{1}}} \tag{7}\]  
The \(EI\) is similar to the formula of the apparent stress. In particular, when \(c_{1}=1.0\), the \(EI\) is proportional to the apparent stress. Therefore, it can be considered that the mechanical behavior in the rock mass reflected by the \(EI\) is equivalent to the apparent stress. \(c_{1}\) and \(c_{2}\) should be fitted on the basis of the source parameters of all the microseismic events in the region when solving the \(EI\). Typically, the average \(EI\) over a time period, or a certain number of microseismic events, is determined, and their evolution trend is observed. [PERSON] et al. [45] analyzed the rockburst formation process of a TBM excavation tunnel and found that \(EI\) decreased before a rockburst, which is related to the local stress release in the rock mass before the rockburst. However, when [PERSON] et al. [17] analyzed the characteristics of the microseismic evolution in the rockburst process, they found that the \(EI\) increased rapidly to a high level before the rockburst. In the process of rockburst formation under different geological conditions, the release and accumulation stages of the local stress in the rock mass are different, leading to different evolution modes of the \(EI\).
# In Vitro Antibacterialential Activity of Marine Microalgae Extract against _Vibrio harveyi_
## 5 Conclusions  
Out of four species of microalgae, only _N. oceanica_, _Isochryssi_ sp. and _T. weissflogii_ demonstrated a significant ((\(p<0.05\)) inhibitory effect against _V. harveyi_ in an in vitro co-culture assay. This indicated a positive effect of the addition of these microalgae in the rearing of fish larvae and implicated the production of antibacterial compounds. Ethanolic extract from _N. oceanica_, _Isochryssi_ sp. and _T. weissflogii_ demonstrated promising antibacterial activity against _V. harveyi_. Meanwhile, _C. gracilis_ inhibited the development of _V. harveyi_ with minimal inhibition but no significant difference (\(p>0.05\)) in the co-culture assay and disc diffusion method, indicating that it had less potential to limit the growth of _V. harveyi_. Therefore, in the aquaculture industry, _N. oceanica_, _Isochryssi_ sp. and _T. weissflogii_ act as potential inhibitory bacteria and probiotics in controlling the disease. More comprehensive studies are recommended to optimize its cultivation conditions, the extraction process, and the purification of its antibacterial compounds to unleash their potential.  
**Author Contributions:** Conceptualization, R.O., S.R.M.S. and M.R.J.; methodology, R.O. and M.R.J.; validation, S.R.M.S. and R.O.; formal analysis, M.R.J.; investigation, M.R.J.; resources, F.F.C. and S.R.M.S.; data curation, M.R.J. and R.O.; writing--original draft preparation, M.R.J., R.O. and S.N.H.O.; writing--review and editing, R.O., S.N.H.O. and S.S.; visualization, M.R.J. and F.F.C.; supervision, R.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project was funded by the Minister of Higher Education Malaysia under the Fundamental Research Grant Scheme FRGS0469-2017.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
**Acknowledgments:** This project was funded by the Minister of Higher Education Malaysia under the Fundamental Research Grant Scheme FRGS0469-2017. The publication fee was provided by the Research Management Centre, Universiti Malaysia Sabah.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Mathematical Model  
The mathematical model was implemented in the Python programming language. The research objectives entailed testing two models: a simple model via multiple linear regression (MLR), and a complex model through random forest regression (RFR). In addition, MLR was specifically applied to model linearity, while RFR was used to navigate nonlinearity. In both implemented models (MLR and RFR) the following libraries were utilised: pandas for data manipulation and analysis, NumPy for numerical computations, scikit-learn for machine learning tasks including data splitting, normalisation, regression modelling, and metric evaluation, and finally matplotlib for data visualisation. The workflow diagram below (Figure 7) outlines the different processes performed for the machine learning algorithm implementation. Further details of these processes are discussed in the following subsections.  
Figure 5: Determining particle shape through sphericity and roundness, with diagonal dotted lines indicating consistent particle regularity \(\rho_{t}=\frac{(\text{R}+\text{S})}{2}\)[2; 49].
## 4 Experimental Research
### Model Learning and Training  
#### 4.2.1 Optimization Algorithm Design  
The Adaptive Moment Estimation (Adam) algorithm was selected to improve training accuracy without increasing computational cost. By incorporating both stochastic gradient descent principles and adaptive learning rate strategies, Adam effectively performs mini-batch updates to improve convergence stability and avoid local optimum traps. In addition to maintaining the benefits of SGD, Adam computes both the first-order moment (the mean) and the second-order moment (the uncentered variance) of the gradients to dynamically adjust the step size for each parameter update. To mitigate the oscillation commonly encountered in SGD during updates, Adam introduces a learning rate decay mechanism, allowing the convergence process to proceed more smoothly and autonomously, reducing the need for manual learning rate tuning and facilitating more intelligent convergence toward a global optimum. The overall procedure of the proposed methodology is summarized in Algorithm 1.  
Figure 9: Dataset labels. (**a**) the original image; (**b**) the label image.  
```
0: Learning rate \(\eta\) (e.g., \(10^{-4}\)), numerical stability constant \(\varepsilon\) (e.g., \(10^{-8}\))
0: Exponential decay coefficients \(\beta_{1}\), \(\beta_{2}\) (e.g., 0.9, 0.999)
0: Model parameter set \(\theta\), iteration step \(t\gets 0\)
0: Moment vectors \(m_{0}\gets 0\), \(v_{0}\gets 0\) while termination condition is not satisfied do  Draw mini-batch \(\{x^{(1)},\ldots,x^{(m)}\}\) with labels \(\{y^{(1)},\ldots,y^{(m)}\}\)  Compute gradient: \(g_{t}\leftarrow\
abla_{\theta}\Big{(}\frac{1}{n}\sum_{t=1}^{m}L(f(x^{(i)}),y^{ (i)})\Big{)}\) \(t\gets t+1\)  Update biased first moment: \(m_{t}\leftarrow\beta_{1}m_{t-1}+(1-\beta_{1})g_{t}\)  Update biased second moment: \(v_{t}\leftarrow\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2}\)  Bias correction: \(\hat{m}_{t}\gets m_{t}/(1-\beta_{1}^{2})\)  Bias correction: \(\hat{v}_{t}\gets v_{t}/(1-\beta_{2}^{2})\)  Compute update step: \(\Delta\theta\leftarrow-\eta\cdot\hat{m}_{t}/(\sqrt{\hat{v}_{t}}+\varepsilon)\)  Apply parameter update: \(\theta\leftarrow\theta+\Delta\theta\) endwhile
```  
**Algorithm 1** Training routine based on adaptive moment estimation