esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Automated Cyber Attack Intelligence to Context-Based Impact on System-Level Goals
## 5 Mapping Probabilities to System-Level Failure  
As we now have a methodology for mapping attacks on devices at a component level to a probability that these devices will be operational, we can now utilise this information to understand the impact at a system-level view. [PERSON] et al. [PERSON] et al. (2019) present Dependency Modelling (DM) as a method of determining the risk to an enterprise through system-level analysis. A top-down model of the system is built from an overall goal to its first-level dependencies, and then to next-level dependencies, and so on. Dependencies are assigned a number of possible states (failure or success being the simplest) and a conditional probability of being in those states. Repeating this for sub-processes produces a tree or graph-based model. This model can be interpreted both as a Bayesian network and as a failure-mode model (see the paper for more detail). The probabilities are used by a Bayesian analysis engine to determine a range of sensitivities or vulnerabilities, illustrating which elements in the model are most pivotal to its success, while a mode-analyser intuitively illustrates the most likely cause of failure in a model, given that a hypothetical (for simulation purposes) or actual (for response purposes) failure has occurred. We propose this modelling method can be enhanced by incorporating real-time _contextually relevant_ information produced through the methodology proposed in this paper. For instance, Table 6 captures the metrics from the examples in the previous section where IoT devices were under cyber attack. As confidence in the attack grows, the probability that the device is operational decreases. If these devices were underpinning a goal in a dependency model, we could use this information to translate the outcome of an automated attack detection method (such as machine learning methods for attack detection in IoT networks) into useful input for the dependency model.  
As an example, Figures 11 and 12 present two dependency models, representing hypothetical system-level dependencies in a water treatment plant. The plant contains a system-level goal (monitoring pH of water) that depends on a specific IoT device (a sensor). If the device fails, the goal fails. Using the methodology proposed in [PERSON] et al. (2019) for top-down Dependency Modeling, we show how intelligence mapped to probabilities of device success or failure changes the likely success of this system-level goal. In this scenario, an automated attack detection tool has identified a remote control attack with 80% confidence as an early warning alert. We push this information in the distribution, as suggested in  
Figure 10: Distribution of physical damage.
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
## 2 THM Coupling Model
### Energy Conservation Equation  
Given any unit body, the total intrinsic energy change rate of the phase is equal to the heat flux rate through the unit and heat source in unit time.  
Assuming that there are no physical or chemical reactions in the system that cause changes in the system heat, the amount of change in system heat caused by the deformation of the system due to external forces is zero. The sum of the total heat transferred by the interaction between various phases within the system is zero. The three-phase hybrid energy conservation equation can be written as [43; 44]  
\[\begin{array}{l}\
abla\cdot\Big{[}\frac{k_{pr}k}{k_{lr}}T\Big{(}\rho_{g}c_{ g}+K_{g}\beta_{g}\frac{1}{n(1-S)}\Big{)}\
abla p_{g}+\frac{k_{r}k}{k_{r}}T \Big{(}\rho_{l}c_{l}+K_{l}\beta_{l}\frac{1}{18}\big{)}(\
abla p_{l}-\rho_{l}g) \Big{]}\\ +\
abla\Big{\{}\big{[}\lambda+k_{gT}\Big{(}\rho_{g}c_{g}T_{g}+K_{g}\beta_{g}T _{g}\frac{1}{n(1-S)}\Big{)}\Big{]}\
abla T\big{\}}-\big{[}\rho_{s}(1-n)c_{s}+ \rho_{l}nS\rho_{l}+\rho_{g}n(1-S)c_{g}\big{]}\frac{\partial T}{\partial t}\\ =\big{\{}T\big{[}\rho_{s}(1-n)c_{s}+\rho_{l}nS\rho_{l}+\rho_{g}n(1-S)c_{g}\big{]} +T(K_{s}\beta_{s}+K_{l}\beta_{l}+K_{g}\beta_{g})\big{\}}\frac{\partial c_{sp}} {\partial t}+\rho_{l}\omega(p_{sp}-P_{v})\end{array} \tag{5}\]  
The variables \(K_{s}\), \(K_{l}\) and \(K_{g}\) are the volume strain modulus of solid, liquid and gas phases, respectively; the variables \(\beta_{s}\), \(\beta_{l}\) and \(\beta_{g}\) are the volume thermal expansion coefficients of solid, liquid and gas phases, respectively; the variables \(c_{s}\), \(c_{l}\) and \(c_{g}\) are the specific heat capacities of solid, liquid and gas phases, respectively; and \(\lambda\) is the thermal conductivity coefficient.
# Learning Airfoil Flow Field Representation via Geometric Attention Neural Field
## 2 Methodology  
### Problem Formulation  
In this study, we address the problem of solving steady two-dimensional airfoil flow fields, our goal is to analyze the characteristics of the flow around the airfoil to optimize its shape.  
Figure 4 illustrates the process of airfoil optimization and the role of GeoANF within it. The CFD process begins with generating a mesh around the airfoil, followed by numerical simulations to solve for the flow field. Once the flow field is obtained, an aerodynamic performance analysis is conducted. The results of this analysis are then used to further optimize the airfoil shape until aerodynamic performance meets the requirements. In our approach, the airfoil is represented as a point cloud input to the GeoANF, which produces an implicit neural field. The flow field is then acquired by querying points within this neural field. The integration of neural networks (NN) facilitates the acceleration of CFD simulations.  
Figure 4: Airfoil optimization and GeoANF.  
Figure 3: Airfoil flow field visualization.  
We describe the methodology using the mathematical notation defined in the Table 1. In this work, the neural network is required to learn the mapping of the solution space of airfoil flow field corresponding to different condition in a data-driven manners, such as geometric shapes, incoming flow velocities, and angles of attack. Assuming the problem domain \(D\in\Omega\) is bounded, our methodology learns a mapping \(\hat{\mathcal{G}}\) between two infinite dimension spaces from observed input-output pairs, which is the flow field of airfoil mapping. This problem encompasses varying inflow velocities, angles of attack, and a range of geometric shapes. The input space \(\mathcal{A}=(\mathcal{A};\mathbb{R}^{d_{a}})\) could contain multiple types, including the variables mentioned above as well as the vector parameters \(\theta\) of the system, the solution space \(\mathcal{U}=\mathcal{U}(D;\mathbb{R}^{d_{u}})\). We aim to build an approximation of \(\mathcal{G}^{\dagger}:\mathcal{A}\rightarrow\mathcal{U}\) by constructing a parametric map.  
\[\mathcal{G}_{\theta}:\mathcal{A}\rightarrow\mathcal{U} \tag{1}\]  
In practice, we have a dataset \(\mathcal{S}=\{(a_{k},u_{k}\}_{1\leq k\leq S}\), such that \(u_{k}=\mathcal{G}^{\dagger}(a_{k})\), this could define a loss function \(\mathcal{L}:\mathcal{U}\times\mathcal{U}\rightarrow\mathbb{R}\) and seek a minimizer of the problem.  
\[\min_{\theta\in\Theta}\mathbb{E}_{a\simeq u}[\mathcal{L}(\mathcal{G}(a,\theta )),\mathcal{G}^{\dagger}(a)] \tag{2}\]  
For our problem, the generalization capability to different geometric shapes is a key focus. During the airfoil shape optimization process, we require minor changes to the geometry, and the neural network must be able to perceive these changes in the geometric representation \(g\in\mathbb{R}^{d}_{H}\).
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Index Structures
#### 3.1.3 Learned Bloom Filter  
A Bloom filter is a structure that determines whether the data is a member of the set or not. By design, a Bloom filter is space efficient because it checks whether the key exists at the position that K hash-function returned to the bit-array, but Bloom filter still occupies a large amount of memory. Also, it has a false-positive result because of hash function conflict. The goal of this learned existence index [7] is to minimize the space and false positive. One way to construct a Bloom filter is to think of it as a binary classification model. Therefore, a predictive model has been proposed to determine whether an element is a key or not. However, since this model has a false negative, unlike the Bloom filter, the authors add an overflow Bloom filter to keep the false-negative rate (FNR) at zero. In Figure 4, if a key is given as input to the trained model and the predicted value by the model exceeds the threshold, queried key is determined that the key exists in the set. However, if it does not exceed the threshold, the existence of the key is checked through the actual bloom filter. In an experiment to track blacklisted phishing URLs, Bloom Filter of 1% FPR needed 2.04 MB of memory, while the learned Bloom filter reduced the size of the required memory with 1.31 MB of memory.  
Figure 3: Learned Hash-Map [7]: The top-level model received the key as an input and it predicts the final position in the leaf model.
# On the Optimal Calculation of the Rice Coding Parameter
## 4 Performance on Different Datasets Using Single-Parameter Rice Coding
### Compression Factor of Analysed Codes  
As a starting point, the effect of the preprocessing method used before encoding is analysed for Rice coding. Here, we consider only the optimal Rice coding solution.  
In Figure 3 is shown the average length needed for encoding a single measurement for each dataset and considering the three preprocessing functions mentioned above. The usage of the second preprocessing function provides shorter codes in all cases, with the exception of the interval rain dataset.  
Figure 3: Estimation of the minimum bit-length after Rice coding.
# Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays
## 3 Methods
### Calibration Software  
As outlined in Section 3.4, the calibration workflow was broken down into four main steps, each of which was associated with a different software routine:  
* Estimation of the intrinsic camera parameters of both the viewpoint camera and the tracking camera (intrinsic camera calibrations).
* Estimation of the pose of the tracking camera with respect to the viewpoint camera (heterogeneous stereo calibration).
* Estimation of the pose of the rendering camera with respect to the viewpoint camera (parallax contribution estimation).
* Estimation of the final eye-display projection matrix (eye-display matrix calculation).  
The calibration workflow is depicted in Figure 7. The details of the calibration steps are presented in the next subsections.  
Figure 6: 3D CAD of the mounting template used for placing the viewpoint camera in eight different positions within the eye-motion-box of the see-through display. (**a**) The eight fixing holes for camera positioning. (**b**) The mounting template.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Healthcare
#### 6.1.3 Example 3: Informed Clinical Decisions for Sepsis Prediction  
[PERSON] et al. [74] leveraged XAI to provide explanations for sepsis predictions, thereby enhancing clinical decision support. Sepsis is a life-threatening condition, and having transparent explanations for AI-driven predictions can be a critical factor in the timely intervention and treatment of patients. In this case, XAI acts as a vital aid for healthcare professionals, enabling them to make more confident and effective decisions in emergency situations.  
XAI has significantly reshaped healthcare by providing unprecedented insights into AI algorithms' decision-making processes. Its integration not only bolsters medical professionals' confidence in diagnostic and treatment recommendations but also ushers in a new era of informed medical interventions.
## 2 Materials and Methods
### Remote Sensing Data and its Processing
#### 2.3.2 Processing of Remote Sensing Data  
Prior to conducting biomass estimates using the MOD17A2 eight-day PSNnet data, preprocessing of the data was necessary. First, a mosaic image was obtained, and geometric projection transformation was performed. The administrative boundary vector data for the Xilingol grassland were then used to extract the data for the study area. In addition, there was noise in certain areas of the PSNnet data where pixels were filled with the invalid value 32,767. In this study, the noise mostly appeared in desert grasslands, which essentially have no vegetation cover. Therefore, to ensure the accuracy of the final estimates during processing, all these invalid values were set to 0. Because there were many ground sampling sites selected for this study and they were evenly distributed, setting the values in a few areas to 0 does not have a significant effect on the statistical results. Further, to obtain a more accurate start time for the growing season and to ensure the accuracy of the accumulated productivity data, a statistical analysis of the productivity time series of the Xilingol grassland for the years 2005-2012 was conducted. The start time of PSNnet value every eight-day great than 0 was defined the start of growing season here, and the analytical results indicated a start time for the growing season of early to mid-March. Furthermore, the accumulation of PSNnet data every eight days from the beginning of the growing season to the peak of the growing season was obtained. Specifically, we can extract the mean PSNnet within a 1 km range for each sampling sites from the accumulated PSNnet data and establish a database of PSNnet and fresh grass weight data for the corresponding time. Then, based on the database, a statistical model was built for inversion of the aboveground biomass.  
Figure 2: Flowchart of the PSNnet estimation process.
## 3 Queuing Model and Dynamic Spectrum Algorithm
### Impact of PU  
In a cognitive radio communication system, SUs must vacate channels when a PU uses the channel. This section discusses SUs that are insensitive to delay, which means that SUs choose to wait when encountering the PU's intervention and continue to transmit after the PU is finished.  
Since SUs must give up the channel when a PU is attempting to use it, the service time originally obeying a certain distribution will change due to the intervention of the PU.  
The status of the channel occupied by the PU can be divided into busy and idle [17; 18; 40; 41]; thus, the state transition diagram of the PU can be drawn--1 means busy, 0 means idle--as Figure 4 shows.  
Considering that the sum of all steady state probabilities is 1, a PU's busy probability \(P_{B}\) and PU's idle probability \(P_{I}\) are given as  
\[P_{B} =\frac{\lambda_{p}}{\lambda_{p}+\mu_{p}} \tag{2}\] \[P_{I} =\frac{\mu_{p}}{\lambda_{p}+\mu_{p}}. \tag{3}\]  
Let C be the service time of the SU under the PU's intervention and let \(T\) be the service time of the SU without the PU's intervention. \(N_{d}\) represents the number of PUs appear during the service time of the SU. \(D_{r}\) represents the service time remaining when the SU is interrupted by the PU. The letter s is the complex variable in the Laplace transform. We then obtain  
\[C=T+\sum_{r=1}^{N_{d}}D_{r} \tag{4}\]  
\[E[e^{-sC}|T,N_{d}] =e^{-sT}E[\exp(-s\sum_{r=1}^{N_{d}}D_{r})] \tag{5}\] \[=e^{-sT}\big{(}\frac{\mu_{p}}{s+\mu_{p}}\big{)}^{N_{d}}\]  
Through the total probability theorem, we obtain  
\[E[C]=-\frac{E[e^{-sC}]}{ds}|_{s=0}. \tag{6}\]  
In [19], the author discussed the case when the PU was involved. According to the Laplace transform of the service time distribution of the SU when the SU is under the PU's interference, the author obtained the average service time via statistical theory, and proved that an equivalent service time distribution is consistent with the negative exponential distribution when they have equal expectation. Let \(\mu_{q}\) be the average service rate of SU:  
\[\mu_{q}=\frac{1}{E[C]}. \tag{7}\]  
Figure 4: State transition diagram of the primary user (PU).  
Therefore, considering the two kinds of SUs with different priorities and the existence of a PU, let \(\mu_{q1}\)' and \(\mu_{q2}\)' be the average service rates of \(SU_{q1}\) and \(SU_{q2}\). We thus obtain  
\[\mu_{q1}{}^{\prime} = \mu_{q1}P_{I} \tag{8}\] \[\mu_{q2}{}^{\prime} = \mu_{q2}P_{I}. \tag{9}\]
# A Wideband Circularly Polarized Antenna with a Multiple-Circular-Sector Dielectric Resonator
## 4 Conclusions  
The design of a microstrip-fed wideband CP antenna using a multiple-circular-sector DR was proposed, fabricated, and measured. The DR was composed of twelve circular-sector DRs, all with identical central angles of 30\({}^{\circ}\), but with different radii. The radii were optimized using a GA to realize wideband CP operation. The experimental results proved that the proposed antenna has a wide \(-10\) dB reflection bandwidth of 31.39% (1.88-2.58 GHz), a wide 3 dB AR bandwidth of 19.30% (2.06-2.50 GHz), a peak gain of 7.65 dBic, and a high level of LHCP gain compared to the RHCP gain in the broadside direction. The CP operation frequency range of the proposed antenna covers the operating bands of several systems, such as UMTS-2100 (2.145 GHz), WiMAX (2.3 GHz), and Wi-Fi (2.445 GHz). Therefore, the proposed antenna is feasible for use as a wideband CP antenna element for wireless communication applications and satellite communication systems.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2014R1A5A1011478).  
The presented work was carried out in collaboration of all authors. [PERSON] performed the simulations. [PERSON], [PERSON], and [PERSON] participated to the conception, fabrication and experiment. [PERSON] wrote the paper which was edited by all co-authors.  
The authors declare no conflict of interest.
# Clustering of Heart Failure Phenotypes in Johannesburg Using Unsupervised Machine Learning
## 4 Discussion
Clustering algorithms have the potential to unravel distinct phenotypes of heart failure that may assist clinicians in triaging or risk profiling patients, therefore positively influencing the management of heart failure patients. Our study is unique since it offers insights into the role of machine learning in better understanding heart failure phenotypes in our region. The systolic blood pressure had a 2.6 times higher impact on differentiating between clusters, followed by the age at the time of admission with a mean SHAP value of 0.16, and the LVEF had a lower SHAP value of 0.14. These findings suggest that blood pressure and age may play a role in the diagnosis, risk stratification, and management of heart failure patients in centres without access to echocardiography. Despite removing the LVEF from the dataset, both systolic and diastolic blood pressure still had a significant impact on model output. Each institution should attempt to cluster its heart failure patients and discover new clinical patterns. For example, our study's findings could motivate for the design and implementation of protocols that guide the management of heart failure patients, specifically with hyperuricaemia, hypotension, and severe symptoms of fluid overload in our institution. However, we are mindful of the fact that clusters formed will differ across regions, partly due to the variation in the causes of heart failure and the availability of clinical parameters used to create clusters. Newer unsupervised machine learning techniques should take data analysis to the next level by creating a code that allows algorithms to interrogate the data and predict the risk of complications. Future studies should use larger, prospective cohorts of patients and subsequently train and test the performance of clustering algorithms using new data that were not used to create original clusters of patients. Most importantly, the prognostic value of the unsupervised machine learning algorithms should be verified by measuring clinical outcomes and comparing them with outcomes obtained from patients classified solely with the LVEF. A larger dataset would have provided more perspective into the characteristics of patients in each cluster. The study is also limited by its retrospective nature and was a single-centre study. As such, the findings from this study may not be generalisable to all heart failures treated in LMICs. In addition, pertinent features such as the cause of mortality were not available in the dataset. Most clinical history findings were documented in free text format and were not analysed. Furthermore, a significant number of patients diagnosed with heart failure with missing echocardiogram findings were not included in the analysis. Moreover, discharge oral medications were not captured for all patients, and were not included in the final analysis. Overall, the lack of electronic health records and dedicated data capturers are major hindrances in LMICs, such that patients may only partially benefit from the capabilities of artificial intelligence.
###### Abstract
SPECT V/Q is also used as a reference in the evaluation of other diagnostic techniques [10; 11; 12; 13; 14; 15; 16]. The quantitative analysis of lung perfusion on SPECT images by volumetric data has previously been proposed [17]. In the study, lung perfusion volume on SPECT images was segmented by thresholding and compared to anatomical lung volume, as well as clinical parameters, showing the feasibility of quantitative analysis of SPECT perfusion scans. Based on the guidelines and criteria defined for the diagnosis of CTEPH in SPECT V/Q images, as well as the qualitative analysis of those images presented in the cited studies, it can be assumed that functional volumetric data could aid in the diagnostic process. Given the presence of reduced perfusion compared to ventilation, the difference between both volumes is evaluated as a quantitative parameter for the diagnosis of CTEPH. A new quantitative metric based on volumetric data from SPECT V/Q images is proposed in this study. A quantitative scale based on reference values was designed and validated to automatically classify patients into CTEPH or presenting a respiratory pathology. The defined algorithm also takes into account the guidelines for visual inspection of SPECT V/Q images defined by the European Association of Nuclear Medicine (EANM) [3] and the European Society of Cardiology (ESC)/European Respiratory Society (ERS) [6]. Lastly, the utility of volumetric measurements from SPECT perfusion images for the evaluation of treatment response in CTEPH was assessed.