esa-sceva/satcom-chunk-collection huggingface dataset:
# Application and Evaluation of Surrogate Models for Radiation Source Search
## 2 Physical Model
### Model Geometry  
To apply and test this numerical model for a realistic scenario, we select the domain to be a \(250\ \text{m}\times 180\ \text{m}\) block in downtown Washington, D.C. [2; 5]. We construct a 2D representation of the domain using data from the OpenStreetMaps database. We treat the buildings as disjoint polygons of uniform density and composition, which define their individual macroscopic cross-sections. A satellite photo with the cross-sections overlaid is provided in Figure 1.  
We semi-randomly assign each building an optical thickness between one and five mean free paths, with the larger and more dense buildings having larger optical thicknesses. This is motivated by an approximation that the wood and concrete buildings in this domain would have an average optical thickness of three mean free paths. We generate the detector locations, plotted as diamond marks in Figure 1, by sampling from a uniform distribution across the domain while excluding the walls of the buildings. We assume the detectors have facial areas \(A_{i}=0.0058\) m\({}^{2}\) associated with three-inch diameters and three-inch lengths, intrinsic efficiencies of \(\epsilon_{i}=62\%\), and dwell times of \(\Delta t_{i}=5\) s for \(i=1,...,10\). These efficiencies are typical for a standard NaI scintillator measuring 662 keV gamma particles. We use a background rate of \(B=300\) counts per second, which is typical for this type of detector in an urban environment. In prior work [2], we showed that we are able to locate a source within the geometry despite these simplifications. Additionally, we are able to localize a source when building cross section uncertainties are increased as high as 50% of their nominal values [16].
## 3 Results
### Added Noise  
To determine which cells, and by extension which days were most important to the prediction, a defined level of noise (1%) was added to all inputs in a particular cell. Complementing the ablation results, it is further evidence that almost all the predictive power lies in the last three (90-92) cells, as seen in Figure 6. Furthermore, the decay of importance between models is more clearly visible, where the LSTM shows the most significant decay in importance whilst GRU shows the slowest change. However, it is interesting to note that the same banding pattern present in the RNN ablation results in Figure 4 is also present in Figure 6. Notably, the LSTM shows a banding pattern, but to a lesser extent, between the 87-91 th cells. Interestingly, the GRU is the only three to demonstrate a recovery in the magnitude of the error at cells 0-3.  
Figure 5: The absolute log attributions for (**A**) RNN, (**B**) LSTM, and (**C**) GRU neural networks. Bordered areas represent negative attributions.  
Figure 6: The log error introduced after adding 1% of noise at each day.
# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 4 Experimental Setup
### Evaluation Criteria  
Two evaluation procedures were used. Model comparison relied on five-fold cross-validation, while hyperparameter tuning, and the resilience analysis employed a fixed 80/20 train/test split. In cross-validation, the dataset was partitioned into five subsets, with each subset used once as a test set and the remainder for training, yielding mean and standard deviation metrics.  
Performance was assessed using accuracy, balanced accuracy, and macro-averaged F1 score (\(F1_{macro}\)). Balanced accuracy was the primary metric due to class imbalance, as it averages recall and specificity and mitigates bias toward dominant classes. In contrast to standard accuracy--which is defined as the proportion of correctly predicted samples, and may be misleading when one class dominates--balanced accuracy ensures equal consideration for both classes by computing the average recall for each. It is formally defined as  
\[\text{BAcc}=\frac{1}{2}\bigg{(}\frac{TP}{TP+FN}+\frac{TN}{TN+FP}\bigg{)},\]  
where \(TP\) (true positives), \(TN\) (true negatives), \(FP\) (false positives), and \(FN\) (false negatives) are elements of the confusion matrix. This makes balanced accuracy particularly suitable for our imbalanced dataset, in which UXO samples constitute approximately 28% of the total.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 7 Conclusions  
An aircraft electric control box was employed as the research object, and the modal and random vibration response analyses of the electric control box were performed using the finite element software. In addition, the fatigue life of the electric control box was estimated using the frequency-domain analysis theory of fatigue damage and the Gaussian-distribution Steinberg method. Moreover, the finite element analysis results were verified by performing the random vibration test on the electric control box, and the following conclusions were obtained:  
1. A comparison of the results of finite element calculation and mode analysis revealed that the mechanical model of the electrical control box could be established using the finite element software. The random vibration response analysis of the electrical control box in the X, Y, and Z directions was performed using this model. Furthermore, the fatigue life estimation of the electrical control box under random excitation was performed.
2. A comparison of the fatigue damage under random vibration excitation in the X, Y, and Z directions revealed that random excitation in the Y direction has the greatest influence on the fatigue life of the electrical control box, followed by random excitation in the Z and X directions.
3. The durability random vibration test on the electrical control box in the X, Y, and Z directions revealed that the dimensions, weight, and internal components are not abnormal, further verifying the correctness of the finite element numerical analysis results.  
Conceptualization, D.Z.; Supervision, D.Z.; Writing--original draft, Y.C.; Writing--review & editing, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"Fatigue life test and verification of airborne equipment (20210101)\".  
Not applicable.  
The data presented in this study cannot be shared at this time as the data also forms part of an ongoing study.  
The authors declare that they have no conflict of interest in this work. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Analysis
#### 6.2.2 Energy-Efficiency Analysis  
Now, consider three paths represented by \(p_{1}\), \(p_{2}\), and \(p_{3}\) having different \(FTR\)\(f_{1}\), \(f_{2}\), \(f_{3}\) and key ratios \(t_{1}\), \(t_{2}\), \(t_{3}\) as illustrated in Figure 9b.  
In order to select the forwarding node, a compromise is required between the \(k_{w}\) keys' existing en-route nodes and the energy. By the number of \(k_{w}\) on en-route nodes, verification nodes as nodes with keys consequently assume verification responsibility. In the case of higher attacks, a path with more verification nodes is selected to save energy by dropping fabricated reports at minimum hops in the proposed scheme. Whereas, in the lower attacks case, a path with a proportionally lower number of verification nodes would be selected--since, in this case, legitimate nodes have to concur with a lower number of verification, hence saving energy.  
Example: In the case of a higher number of attacks (e.g., \(f_{3}\)), a path is chosen (i.e., \(p_{3}\)) having a proportionally higher number of verification nodes, which results in dropping more fabricated reports earlier, hence saving energy. Similarly, for a lower number of attacks (e.g., \(f_{1}\)), a path is chosen (i.e., \(p_{2}\)) having a lower number of verification nodes; as a result, legitimate reports would need less verifications that save energy. Therefore, dynamically selecting based on attack information, energy could be saved in both cases.  
Figure 9: Network Performance analysis: (**a**) lifetime and (**b**) energy-efficiency.
###### Abstract
Therefore, an approach based on various thematic layers is required, so as to have an overview of ES loss that supports the resource management decision-making process. Furthermore, monitoring land use and land cover spatial distribution, especially in urban areas, is essential for providing accurate and timely information to be used as input data that allows soil-based ES provision changes to be assessed. Within this context, major advances in satellite remote sensing have improved data collection and analysis methodologies used for the purpose of detecting artificial land covers [38; 39; 40; 41]. These include the Sentinel satellite constellations of the EU Copernicus Programme [42] which provide high-resolution (spatial, spectral, and temporal) multispectral and SAR images suitable for measuring land consumption in a detailed manner [6]. In Italy, where land degradation processes due to land consumption are increasing, especially in agricultural and peri-urban areas [43], the Italian Institute for Environmental Protection and Research (ISPRA) is in charge of a monitoring program at the national level regarding: (i) land consumption mapping, based on a Sentinel satellite images analysis that is integrated with the photointerpretation of national orthophotos; (ii) land cover mapping, based on the integration of the Copernicus Land Monitoring Services (CLMS) datasets (Corine Land Cover, High Resolution Layers, Urban Atlas, Riparian Zones, and Natura 2000) and the national land consumption map; (iii) estimate of land degradation and ES changes (biophysical and economic) caused by annual land consumption. This study is the first work that is focused on Italy and that, in trying to collect and harmonize existing data, analyzes nationwide soil-based ES loss with a detailed spatial resolution. The paper aims to describe the multilayered approach that has been adopted in assessing biophysical loss of ES flows caused by land consumption increase in the period 2012-2020, as recorded by the aforementioned national monitoring program. After having introduced the topic of ES and ES losses due to urban expansion, we describe the procedure adopted for the purpose of assessing changes in six ES in the period under examination. We describe the input data used and the methodologies applied for the purpose of analyzing the reference data and the additional thematic layers. The section in which the results are set out shows what losses were recorded for each ES. We then discuss the results of the analysis and considering the procedure's achievements and shortcomings. Finally, we summarize the conclusions derived therefrom.
## 2 Materials and Methods
### Cell Culture and Testing  
Human skin keratinocytes (HaCaT cell line--Cell Lines Service, Eppelheim, Germany) were cultured in 25 cm\({}^{2}\) flasks in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% (\(v/v\)) fetal bovine serum, 1% fungizone, 1% glutamine, and 1% penicillin-streptomycin, incubated at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). The cell culture was observed daily under a microscope to check morphology, confluence, and possible contamination.  
The cytotoxicity of the treated and untreated solutions was assessed based on cell viability determined by the 3-(4,5-dimethyl-2-thiazol)-2,5-diphenyl tetrazolium bromide (MTT) colorimetric assay. The assay is based on the reduction of MTT, by mitochondrial dehydrogenase, to formazan crystals. Previously cultured cells were seeded in 96-well plates with supplemented DMEM at a density of 60,000 cells/mL, 40,000 cells/mL, or 20,000 cells/mL, for assays lasting 24, 48, and 72 h, respectively. Then, the plates were incubated for 20-24 h at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) for cell adhesion. Afterwards, the medium was replaced with supplemented DMEM (negative control) or supplemented DMEM with each reconstituted sample (100%) and its dilutions (12.5; 25; 50%) and test plates were incubated at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) for the desired exposure time. Three independent assays were run for each treated and untreated PPCP solution, each including six technical replicates of each test treatment. After 24, 48, or 72 h of exposure, the MTT reagent in PBS (1 mg/mL) was added to each well and left to incubate for four hours at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) in the dark. The culture medium with the MTT reagent was then removed and dimethyl sulfoxide (DMSO) was added to each well for crystal solubilization. The plates were then shaken on an orbital shaker for two hours. The optical density of the reduced MTT was measured at 570 nm in a microtiter plate reader (Synergy HT, BioTeK Instruments Inc., Winooski, VT, USA). The values obtained allowed the determination of the percentage of viable cells, according to Equation (1).  
\[\text{Cell viability}\;(\%)=\frac{\text{Optical density(test)}-\text{Optical density(blank)}}{\text{Optical density(control)}-\text{Optical density(blank)}}*100 \tag{1}\]  
The schematic outline of the experimental workflow from sample preparation to cytotoxicity testing is presented in Figure 1.
# Efficient Multi-Objective Simulation Metamodeling for Researchers
## 1 Introduction  
A simulation model of a physical and/or social system of interest can unlock useful experimental capabilities [1]. Not only are the costs of simulation experimentation in general much lower than an equivalent series of real-world experiments, but there are also scenarios where the latter are simply unfeasible--for example, if the system does not yet exist. However, there always exists a frontier where computational costs limit viable use cases [2].  
An Agent-Based Simulation (ABS) usually features decision-making entities who interact with their environment and, directly or indirectly, with one another. When the decisions of many so-called \"agents\" are critical to system dynamics, utilizing an ABS approach for extending standard models would be a good option, but also a computationally expensive one.  
Complicating problem-solving further, many real-world problems, from designing ships to chemical engineering or saving energy, cannot be neatly simplified into a single objective to be maximized or minimized [3]. If one option performs worse in all objectives compared to another option, the first one is \"dominated\" and would never be worth picking even when both are feasible. At the same time, it is possible to have two options such that neither dominates the other; in this scenario, the decision between them is external to the optimization that produced the options. A collection of all the non-dominated options is most commonly known as a Pareto front but also as Pareto frontier, and Pareto set [4], and asin the two-option case above, does not allow improvements in any objective(s) without having to suffer a worsening of other objective(s). This presents distinctive challenges [5], in particular, the need to thoroughly explore a greater proportion of the input space to generate a Pareto front [6] compared to the single objective problem.  
Fortunately, for many optimization problems, a \"model of a model\", commonly referred to as a metamodel, can enable experimentation and thus optimization that is less costly than directly using a simulation model. Metamodels are \"a mathematical approximation to the implicit input-output function of a simulation model\" [7]. They have been adopted in various domains, and depending on the context are referred to as surrogate models, response surface models, or proxy models. Some domains have a rich literature on the use of metamodels within their context [8], but such findings do not necessarily apply to other domains in terms of the most effective use of a metamodel and optimizer. A more general way to make comparisons is by using benchmark problems [9] which may involve a mathematical function [10], domain-specific models [11] or data sets [12]. However, the issue still remains when moving outside domains or problems that are \"similar\" to these, which led to some siloing and duplication of effort in terms of optimization research in general [13], although there have been efforts to compile this work in a more practitioner-salient form [14].
# Antiplatelet Activity of Phenolic Compounds-Fortified Merlot Wine and Pure Phenolic Compounds
## 2 Materials and Methods
### Antiplatelet Activity of Wine Extracts  
Platelet aggregation studies were performed by turbidimetry in an aggregometer (AggRAM Module) following the methodology previously described by [PERSON] and [PERSON] [13; 21]. The wine samples were dissolved in PBS for analysis. First, the adjusted PRP (200-300 \(\times\) 10\({}^{9}\) platelets/L) was incubated with PBS (negative control, maximum platelet aggregation). The study on platelet aggregation was carried out by incubating the PRP for 4 min with the extracts of wine at a concentration of 1 mg/mL. Platelet aggregation was induced by adding ADP (4 \(\upmu\)M), TRAP-6 (10 \(\upmu\)M), or collagen (1 \(\upmu\)g/mL) for 6 min at 37 \({}^{\circ}\)C. Once the extracts were evaluated at 1 mg/mL, the samples with the highest antiplatelet potential, % platelet inhibition above 50%, were selected to continue platelet aggregation studies at lower concentrations. The concentration necessary to reduce platelet aggregation by 50% (Ic\({}_{50}\)) was obtained from the concentration curves of the extracts (concentrations 0.1, 0.25, 0.50, 0.75, and 1 mg/mL) [22].  
The antiplatelet activity of the phenolic compounds quercetin, epicatechin, catechin, gallic acid, and caffe acid was also evaluated. Compounds were dissolved in DMSO. For these studies, the PRP was incubated with the compounds (0.1, 0.25, 0.50, 0.75, and 1 mg/mL) or vehicle (DMSO, 0.2%) and, subsequently, the platelet agonists (ADP, TRAP-6, or collagen) were added.  
Platelet aggregation was measured as the increase in light transmission over 6 min. Results were obtained as the mean \(\pm\) SEM of 6 volunteers provided by AGGRO/LINK software (Chrono-Log, Haverttown, PA, USA). The inhibition of platelet aggregation was calculated as follows: % Inhibition of platelet aggregation = 100 \(-\) ((% Platelet aggregation of the extracts or compounds * 100)/% Platelet aggregation of the negative control) [13].
## 3 System Model
### Virtual Networking
#### 4.2.2 Route Connection
The unreachable and reachable nodes have to do the following operations respectively. * Unreachable node: After joining the network, the unreachable node needs to keep alive the long connection with N nodes, in addition to executing lines 12-20 of Algorithm 2 cyclically to ensure that it is always connected to the N nodes closest to its address. When a closer node is found, the long connection to the more distant node is disconnected while contact is made with the closer node. * Reachable node: After the reachable nodes join the network, they need to respond to the query operations of other nodes with the connection operations of unreachable nodes. In this process, the two k-buckets are split and merged in a timely manner according to the k-bucket update method described in the previous section. The long connection with the unreachable node is periodically tested for normalness, and if it is adjudged to be a deactivated node, this node is removed from the k-bucket.
# A Survey of Game Theoretic Approaches to Modelling Decision-Making in Information Warfare Scenarios
## 4 Games for Representing Information Warfare Operations
### A Taxonomy of Information Warfare Games
#### 4.2.5 Economic Information Warfare Games
They propose a game theoretical model to find the optimal information system security investment and investigate the economic looses caused by terrorism and common hackers. Results of their simulations indicate that organizations should increase investment to protect strategic information systems from cyber terrorists. Other work [69] investigates the impact of cyber-threats on information systems investment using game theory approach. Specifically, they propose a non-state non-cooperative static 2 \(\times\) 2 general-sum game to capture the interaction between an insider and a target. Results of the simulation indicate that the magnitude of optimal investment needed to protect the infrastructure from insiders is higher compared to protecting the system from cyber-threats against external hackers. [PERSON] et al. [70] study the economic aspects of botnet activity and suggest defensive strategies to deal with botnet herders. In their study, botnets are defined as a number of connected computers infected with malicious software that permit botnet herders to remotely control the infected systems without the knowledge of the owner. Botnet activities are often motivated by economic incentives, such as profit that the herders obtain from their illegal activity. To address this problem, [PERSON] et al. [70] develop a game theory approach that can be used to choose the optimal strategy to deal with botnet herders. In this study, the amount of computers that are infected evolves based on a modified susceptible-infectious-susceptible (SIS) epidemic model. Game theory allows the defender to analyse botnet business equilibrium given available defense strategies. A summary of economic information warfare games can be found in Table 7.
## 4 System Architecture
### Architecture Description
#### 4.2.5 Deployment View  
The deployment view represents the preproduction environment of the system components located in the cloud infrastructure. In this paper, we use Google Cloud Platform services to define a basic K8s cluster with minimal technical characteristics. The characteristics of the K8s cluster and cluster nodes are shown in Appendix C. The e-health system component deployments were carried out with the main K8s elements: pod resources and service resources. The pods are part of an internal communications network of the cluster. The K8s service resources provide the mechanisms for the communication of the pods inside or outside of the cluster. The advantage of placing components that work closely with each other in the same K8s cluster is because the network and subnets are defined at the cluster level. Although it is possible to use additional K8s clusters, the placement of a component of a part of the system in another independent cluster implies the introduction of unnecessary latencies in the system.  
We created two groups of nodes within the cluster to organize the e-health system in two parts. Each group of nodes was dedicated to providing sufficient exclusive resources for each of the parts of the system. Additionally, this organization facilitated the independent analysis of scalability and performance. Figure 8 shows an abstraction of the deployment view. The e-Health-System-K8s-cluster nodepool-1 includes the microservices of the subapplications and the different components for its control of continuous integration and continuous deployment. The e-Health-System-K8s-cluster nodepool-2 includes the software used by the system as third-party software, which was downloaded from the Docker Hub. The e-Health-System-K8s-cluster nodepool-2 mainly contains the messaging subsystem.  
Figure 7: Development view.  
Some K8s organization and configuration patterns were used for the deployment of software components, such as multiple availability zone design, single containers, automated placement, stateful service, service discovery, environment variable configuration, etc. For a detailed analysis on the use of the most relevant patterns used, see Appendix D. For additional details of the deployment view, see Appendix E.