esa-sceva/satcom-chunk-collection huggingface dataset:
Anomalies such as a bicuspid aortic valve, congenital variations like altered aortosepal angles (AoSAs), and disease conditions including aortic stenosis and hypertrophic cardiomyopathy can all contribute to helical flow in the LVOT [23; 24; 25]. Additionally, irregularities in the heart rate and rhythm or obstructions like ventricular thrombosis could also induce such flow patterns [26; 27]. Observations have shown that the interaction between helical LVOT flow and the aortic valve leaflets can result in considerable flow disruptions, uneven stress distribution across the leaflets, and altered leaflet dynamics. Expanding on the initial findings reported in our earlier conference paper [28], this research conducts a more detailed examination of localized calcifications in the aortic valve when subjected to a helical flow pattern, markedly broadening and deepening the range and insights of our previous work. The objective of this research is to investigate the impact of several grades of localized calcium deposits on aortic valve leaflets under both uniform and helical LVOT flow conditions. To model the aortic valve with various calcification grades, we employed the immersed boundary-finite element (IBFE) method [29] using Newtonian blood flow under realistic boundary conditions. These conditions encompass helical inflow velocity profiles and aortic outflow pressure, which is modeled using a three-element Windkessel framework. We conducted simulations using 3D geometries on leaflet surfaces to model different grades of deposition accumulation, categorized as Grades 1-6. This study focuses on a single aortic flow condition with the aortic pressure fluctuating between 120 and 80 mmHg, representing a healthy case. As calcification severity increases, the inflow pressure increases in graded cases, although the outflow pressure remains the same. In order to quantify the critical parameters of blood flow, numerous indices based on transvalvular hemodynamics and the WSS are calculated and analyzed. Further, the vortex structures of a flow field are investigated in relation to deposition patterns and inflow conditions. In the characterization of hemodynamic responses, we used a combination of transvalvular hemodynamic indices, such as energy loss, aortic jet velocity, kinetic energy, and the average magnitude of the vorticity, to investigate the effect of calcification severity on the aortic valve functioning. According to the authors' best knowledge, this is the first attempt to analyze the comprehensive hemodynamic characteristics of localized aortic valve calcification using a range of calcification grades under uniform and helical flows. Finally, this work introduces a novel investigation into the effects of right-handed helical inflow originating from the LVOT on aortic valve leaflets, employing a comprehensive set of metrics such as helicity-based bulk flow descriptors.
## 3 Data Clustering Algorithm
### Clustering Performance Evaluation  
Four criteria values are calculated to find the best number of clusters and to evaluate clustering method. These are: eigenvalues, silhouette coefficients, Calinski-Harabasz criterion and Davies-Bouldin criterion. To obtain well-separated clusters, eigenvalues should ideally be zero or small. To determine the proper number of data clusters, the number of clusters is increased gradually till a gap is observed in the eigenvalues [31]. If it is not possible to reach this gap, the silhouette analysis is used to measure how well the data is clustered. This analysis results in a coefficient in the range of [\(-\)1, 1], where silhouette coefficients close to +1 indicate that the sample is far away from the neighboring clusters. On the other hand, a value of 0 indicates that the sample is on or very close to the decision boundary between two neighboring clusters. In contrast, negative values indicate that those samples are assigned to the wrong cluster. The silhouette index (SI) is the average of these coefficients, the closer to +1 the better the separation between clusters [32,33]. Calinski-Harabasz index (CHI) is the ratio between the variance of the sums of squares of the distances of individual data points to their cluster center and the sum of squares of the distance between the cluster centers. The higher the value the better the performance of the clustering model [34]. The Davies-Bouldin analysis calculates two values (within cluster variance and the distance between the centroids of different clusters). Then, the nearest neighboring cluster is identified for each cluster and the sum of within cluster variances is divided by distance difference between clusters centroids. The Davies-Bouldin index (DBI) is the average of these values and it ranges from zero to infinity and the smaller the value the better the separation between clusters [35]. The last three criteria are suitable to evaluate clusters as they give more accurate results for convex data [36]. The optimal number of clusters occurs at the highest Calinski-Harabasz and silhouette coefficients while it is the lowest for the Davies-Bouldin value [37].
## 3 Results and Analysis
### Law of Slope Runoff
#### 3.1.2 Influence Law of Substrate Thickness on Slope Runoff  
From Figure 8, it can be seen that the slope runoff volume increases rapidly within 30 min after the production of flow, increases slowly from 30 min to 160 min, and stabilizes after 160 min. The main reason for this change in runoff volume in the rainfall process is that, within a short period of time after the start of rainfall, the infiltration capacity of the soil is greater than the rainfall intensity and all the rainfall infiltrates into the soil, with no runoff produced on the surface. As rainfall progresses, the water content of the surface soil increases, the infiltration capacity of the soil rapidly decreases to less than the rainfall intensity due to the effect of raindrop strikes and the filling of soil pores by fine particles, and the slope surface starts to produce runoff. The variability in runoff volume  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Content of Cement [\%]** & 0 & 1 & 3 & 5 \\ \hline
**Time of generation of slope runoff [min]** & 17.7 & 10.5 & 9.7 & 8.7 \\ \hline
**Cumulative slope runoff [L]** & 304.56 & 527.13 & 550.19 & 567.84 \\ \hline
**Average slope runoff [mL\(\cdot\)min\({}^{-1}\)]** & 1269 & 2196 & 2292 & 2366 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of each parameter without cement contents.  
Figure 7: Variation process of runoff and accumulated runoff under different cement content.  
with different substrate thicknesses is because the increase in substrate thickness leads to a decrease in rainwater volume through the substrate per unit time. As shown in Table 5, when the cement content in the substrate is 3%, the runoff generation time decreases by 0.53 min for each 1 cm increase in substrate thickness, the steady-state runoff volume increases by 158.5 mL\(\cdot\)min\({}^{-1}\), the average runoff volume increases by 123 mL\(\cdot\)min\({}^{-1}\) and the cumulative runoff rate increases by 22.17 L.  
The variability of flow production time on slopes with different substrate thicknesses is due to the variability of rainwater permeation time through the substrate. Due to the different substrate thicknesses, the amount of water per unit time that rainwater permeates through the substrate and enters the subsoil is different, and the time required for the substrate infiltration rate to decrease to \(i_{a}\leq\) rainfall intensity \(p\) is also different. Obviously, in this test, as the substrate thickness increases, the shorter the time that is required for the substrate infiltration rate to decrease to \(i_{a}\leq\) rainfall intensity \(p\), the shorter the time that is required for slope runoff generation.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 1 Introduction
### Contributions of this Work  
The Smart Farm Surveillance System (SFSS) is unique in that it employs advanced algorithms, cloud integration, real-time danger detection, efficient data processing, adaptation to varied sectors, proactive speaker activation, and drones. These qualities together make it a game-changing solution that meets current surveillance and security requirements.  
* Advanced Object Identification Algorithms: YOLOv8, a state-of-the-art object detection algorithm, is utilized to enhance the accuracy and speed of identifying both animals and humans in real time. Its advanced identification mechanism is attributed to its ability to process high-resolution images with minimal latency, even in challenging conditions such as low light or occlusion. The model's multi-scale detection capability ensures reliable performance across varying sizes and distances of detected objects.
* Cloud-Based Real-Time Monitoring: The cloud-based architecture of this system supports real-time monitoring by processing data from multiple IoT sensors (cameras) simultaneously. The system's advanced scalability ensures that, as additional sensors are integrated, the cloud infrastructure automatically adjusts to accommodate increased data loads without compromising performance. Real-time alerts are generated and delivered through cloud-based AI models that assess the likelihood of intrusion, continuously learning from new data inputs.
* Efficient Data Processing: The system's connectivity with the cloud allows for seamless data processing, allowing farmers to receive timely notifications and enhance crop management. This feature guarantees that any problems are handled as soon as possible.
* Adaptability Beyond Farms: The system's adaptability extends its utility to a wide range of industries, including railway track monitoring, border security, wildlife protection, forest fire detection, disaster response, industrial safety, environmental monitoring, pest control in agriculture, and oil and gas pipeline inspection. This versatility will transform approaches to monitoring in a variety of businesses.
* Speaker Activation for Intruder Deterrence: Once an incursion is detected, the system activates a speaker system to dissuade possible threats. This one-of-a-kind feature offers an extra degree of security.
# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 2 Related Work
### Scheduling and Control Methods  
Scheduling and control is one of the primary goals of HEMS. In [29], the authors conceived HEMS as a way to build consumption schedules based on several factors such as energy costs, environmental concerns, load profiles, and consumer comfort. In addition, the authors claim that these systems allow to save energy and efficiently manage the distributed energy resources and storage. They reviewed the evolution of HEMS from its emergence in 1979.  
There are various methods for scheduling and control in the context of HEMS which have been discussed in literature throughout the years. Most of these methods have been presented as an optimization problem, in which the main objective is to minimize the peak load and electricity cost of a the smart home. The authors of [62] proposed a self-scheduling model based on behavioral modeling and prospect theory. On the other hand, the authors of [60] based their scheduling solution in optimizing thermal and visual comfort. In Table 1, we present a summary of some of the most recent publications in this regard. From Table 1, it is possible to notice that there are two main strategies to solve the problem: rule-based algorithms (Heuristics Methods, Markov Chains) or deep reinforcement learning (Deep RL) based approaches.  
As a consequence of appliance scheduling, comfort level of users may be affected. The authors of [65,70] argue that the user inconvenience may be caused due to forcing consumers to change the use of their home appliances, even when the economic factor does not compensate for the discomfort. Therefore, this is a significant fact to consider when deploying a HEMS. In [65], the authors proposed a multi-object optimization method for both the electricity bill and Discomfort Index.
## 6 Computational Results
### Data Characteristics  
Three datasets were provided for the EURO/ROADEF Challenge 2010. These datasets are now non-confidential and available online [36]. The data characteristics are provided in Table 5. Dataset A is composed of five small instances given for the qualification phase. Production time steps are discretized daily for the instances of dataset A, in a horizon of five years, with 10 to 30 T2 units having six production cycles, and 10 to 30 stochastic scenarios. Instances B and X are more representative of real-world size instances. Production time steps are discretized with 8\(h\) time steps to analyze the impact of daily variability of power demands, in a horizon of five years, with 20 to 30 T1 units, around 50 T2 units, and 50 to 120 stochastic scenarios. Dataset X was secret for the challenge, it had to be similar to dataset B. This is not the case looking to the number of binaries, i.e., the cumulated amplitude of TW, given in in the column nbVar0 of Table 5. Instances B8 and B9 induce more binary variables, with very few TW constraints for cycles \(k\geqslant 3\). Actually, instances B8 and B9 are the most realistic for the real life application [24].  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Data** & **I** & **J** & **K** & **S** & **T** & **W** & **nbVar0** & **nbVar1** & **Gap** & **nbVar2** & **nbVar3** & **Gap** \\ \hline A1 & 10 & 11 & 6 & 10 & 1750 & 250 & 3892 & 463 & 88.10\% & 483 & 424 & 12.22\% \\ A2 & 18 & 21 & 6 & 20 & 1750 & 250 & 7889 & 961 & 87.82\% & 892 & 761 &  
To compare different lower bounds on an instance \(i\), we use for each instance \(i\) the cost of the best primal solution known (BKS), denoted \(BKS(i)\). The BKS are mainly the one reported by [15] allowing 10 h computation time per instance, except for A1, A2, A3 given by [32] and for instance B6 where the BKS obtained for the Challenge had not been improved since, and is available at [28]. We compare lower bounds using the gap indicator: \(gap_{i}=\frac{\mid v(i)-BKS(i)\mid}{BKS(i)}\), where \(v(i)\) denotes the considered lower bound for instance \(i\).
# Effective Heart Disease Prediction Using Machine Learning Techniques
## 3 Methodology
### Clustering  
Clustering is a machine learning technique where a group of instances is grouped based on similarity measures. One common algorithm used for clustering is the k-means algorithm, but it is not effective when working with categorical data. To overcome this limitation, the k-modes algorithm was developed. The k-modes algorithm, introduced by [PERSON] [29] in 1997, is similar to the k-means algorithm but utilizes dissimilarity measures for categorical data and replaces the means of the clusters with modes. This allows the algorithm to work effectively with categorical data.  
Since our data have been converted to categorical data, we will use k-modes analysis. To find the optimal number of clusters, we will first use the elbow curve with Huang initialization. An elbow curve creates a k-modes model with that number of clusters, fits the model to the data, and then calculates the cost (distance between the attribute modes of each cluster and the data points assigned to the cluster). The costs are then plotted on a graph using the \"elbow method\" to determine the optimal number of clusters. The elbow method looks for a \"knee\" or inflection point in the plot of costs, which is often interpreted as the point where the addition of more clusters is not significantly improving the fit of the model.  
Splitting the dataset on the basis of gender can be advantageous for prediction due to the existence of significant biological disparities between men and women that can impact the manifestation and progression of diseases. For instance, men tend to develop heart disease at an earlier age than women, and their symptoms and risk factors may differ. Studies have shown that men have a higher risk of coronary artery disease (CAD) compared with women, and that the CAD risk factors and presentations may differ between the sexes [30]. By analyzing the data separately for men and women, it is possible to identify unique risk factors and patterns of disease progression that may not be discernible when the data are consolidated. Additionally, heart disease has a varying prevalence rate among men and women.  
Subsequently, we utilized the elbow curve method to determine the optimal number of clusters for both the male and female datasets. As depicted in Figures 3 and 4, the knee point was located at 2.0 in both cases, indicating that 2 was the optimal number of clusters for both the male and female datasets.
## 3 Factor Graph Integrated Navigation Framework
### GNSS Factor Nodes  
GNSS factors are extremely valuable for providing absolute position information and long-term data, especially in open sky environments. However, in environments with obstructions such as urban high-rise buildings and tunnels, GNSS signal reception may be interrupted. Therefore, it is necessary to integrate GNSS data with other sensor data to improve navigation accuracy and robustness. The GNSS sensor measurement equation is given as [31]:  
\[z_{i}^{gnss}=h^{gnss}(x_{i})+n^{gnss} \tag{25}\]  
where \(n^{gnss}\) represents the measurement noise and \(h^{gnss}(x_{i})\) is the GNSS measurement function. GNSS positioning provides the coordinates of the antenna phase center, while INS mechanical arrangement provides the navigation results from the IMU measurement center. These two centers do not physically coincide, necessitating lever-arm effect correction during integrated navigation computation. The GNSS factor residual can be expressed as:  
\[r_{gnss}(z_{i}^{gnss},X)=z_{i}^{gnss}-z_{i}^{gnss}+C_{b}^{n}{}_{gnss} \tag{26}\]  
where \(z_{i}^{gnss}\) is the GNSS estimated value, and \(l_{gnss}^{b}\) is the GNSS antenna lever arm.
## 3 Results and Discussion
### Evaluation of SFE-CO\({}_{2}\) of Hops at 24-45 MPa Pressure
#### 3.2.1 Central Composite Design and Model Analysis
\begin{array}{ll}TEAC_{ORAC}&=344.65+15.61\times P-3.48\times T+33.49\times \tau+0.98\times(PT)-10.63\times(P\tau)-3.29\times(T\tau)\\ &\quad-33.70\times P^{2}+12.50\times T^{2}-9.15\times\tau^{2}\end{array}  \tag{6}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **SS** & **df** & **MS** & **F-Value** & _p-Value_ \\ \hline \multicolumn{5}{c}{**RFII: Extract yield, g/100 g HP**} \\ Model & 210.66 & 9 & 23.41 & 51.17 & \textless{}0.0001 * \\ P (pressure, MPa) & 51.71 & 1 & 51.71 & 113.06 & \textless{}0.0001 * \\ T (temperature, \({}^{\circ}\)C) & 8.85 & 1 & 8.85 & 19.36 & 0.0013 * \\ \(\tau\) (time, min) & 118.34 & 1 & 118.34 & 258.72 & \textless{}0.0001 * \\ PT & 0.4802 & 1 & 0.4802 & 1.05 & 0.3297 ** \\ P\(\tau\) & 2.98 & 1 & 2.98 & 6.51 & 0.0288 * \\ T\(\tau\) & 0.3200 & 1 & 0.3200 & 0.6996 & 0.4224 ** \\ P\({}^{2}\) & 8.93 & 1 & 8.93 & 19.52 & 0.0013 * \\ T\({}^{2}\) & 0.0030 & 1 & 0.0030 & 0.0066 & 0.9368 ** \\ \(\tau^{2}\) & 1.86 & 1 & 1.86 & 4.06 & 0.0716 ** \\ Residual & 4.57 & 10 & 0.4574 & & \\ Lack of Fit & 3.03 & 5 & 0.6051 & 1.95 & 0.2399 ** \\ Pure Error & 1.55 & 5 & 0.3097 & & \\ Cor Total & 215.23 & 19 & & & \\ \hline \multicolumn{5}{c}{**RFII TEACORAC, mg TE/g HP**} \\ Model & 20,284.94 & 9 & 2253.88 & 35.20 & \textless{}0.0001 * \\ P (pressure, MPa) & 2435.47 & 1 & 2435.47 & 38.03 & 0.0001 * \\ T (temperature, \({}^{\circ}\)C) & 121.10 & 1 & 121.10 & 1.89 & 0.1991 ** \\ \(\tau\) (time, min) & 11,215.80 & 1 & 11,215.80 & 175.15 & \textless{}0.0001 * \\ PT & 7.70 & 1 & 7.70 & 0.1203 & 0.7359 ** \\ P\(\tau\) & 903.34 & 1 & 903.34 & 14.11 & 0.0037 * \\ T\(\tau\) & 86.53 & 1 & 86.53 & 1.35 & 0.2721 ** \\ P\({}^{2}\) & 3123.23 & 1 & 3123.23 & 48.77 & \textless{}0.0001 * \\ T\({}^{2}\) & 429.66 & 1 & 429.66 & 6.71 & 0.0269 * \\ \(\tau^{2}\) & 230.26 & 1 & 230.26 & 3.60 & 0.0872 ** \\ Residual & 640.35 & 10 & 64.03 & & \\ Lack of Fit & 432.78 & 5 & 86.56 & 2.08 & 0.2196 ** \\ Pure Error & 207.57 & 5 & 41.51 & & \\ Cor Total & 20,925.29 & 19 & & & \\ \hline \hline \end{tabular}
*: significant; **: not significant; HP: hop pellets; SS: sum of square; df: degree of freedom; MS: mean square; F: Fisher value. SFE-CO\({}_{\text{P}}\): supercritical carbon dioxide extraction; ORAC: oxygen radical scavenging capacity; P: extraction pressure; RF: response factor; \(\tau\): extraction time; T: extraction temperature; TEAC: Trolox equivalent antioxidant capacity.  
\end{table} 
Table 3: Analysis of variance of the regression parameters for the response surface quadratic models of _Ella_ hop SFE-CO\({}_{2}\) extract yield (RFI) and TEACORAC (RFII).
## 4 Discussion
### Comparison with Functional NIRS and Multimodal Perspectives  
To contextualize the complementary roles of optical neuroimaging in multiple sclerosis, it is important to contrast functional near-infrared spectroscopy (fNIRS) with broadband NIR reflectance spectroscopy. The primary distinction lies in the nature of the signals measured. fNIRS in MS assesses task-evoked changes in oxygenated and deoxygenated hemoglobin within cortical regions, reflecting neurovascular and hemodynamic responses during functional or cognitive tasks [47,48]. Such studies reveal altered cortical activation, reduced neurovascular coupling, and impaired compensatory mechanisms, linking abnormal prefrontal activation to cognitive fatigue [15]. In contrast, broadband NIR reflectance spectroscopy captures a richer spectral fingerprint across visible and shortwave infrared bands, probing biochemical and microstructural properties at cranial and extracranial sites [3]. By analyzing absorption features linked to myelin integrity, oxidative stress, water, and protein/lipid composition, it identifies molecular alterations undetectable by hemodynamic changes alone. Unlike fNIRS, it also assesses extracranial muscle, informing systemic MS manifestations. Thus, fNIRS excels in functional monitoring, while broadband NIR spectroscopy reveals static biochemical and structural abnormalities, even at rest, detecting subclinical injury. Machine learning applied to multisite spectra shows high accuracy in differentiating MS from controls, supporting diagnostic and monitoring potential. Recent studies advocate combining functional and biochemical optical biomarkers for enhanced MS phenotyping and personalized care [49,50].