esa-sceva/satcom-chunk-collection huggingface dataset:
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 3 Results and Discussion
### Clustering Results  
Clustering analysis results vary according to the number of clusters which is a challenging task to determine. Some algorithms have the advantage of running without identification of the \(k\) value such as HAC. Internal evaluation criteria are a powerful tool to assess the clusters such as Davies-Bouldin, Silhouette, or GAP as used in this paper. Unfortunately, these indexes take the similarities of the objects into account, not the tectonics or the velocity gradients. Moreover, algorithms such as \(k\)-means and/or mini batch \(k\)-means can not detect non-convex clusters. On the other hand, HAC, BIRCH, and spectral clustering are suitable for data set with arbitrary shape but they suffer from high time complexity [58]. As a solution, one can use the \(k\)-means algorithm and then apply HAC to take better results [15]. In our paper, the coincides borders with NAF and EAF, and the compatible Aegean cluster with the literature show that GAP gives a fine solution.  
BIRCH, HAC, \(k\)-means, mini batch \(k\)-means, and spectral clustering algorithms were used to cluster GPS-derived velocity field into 5 different classes. The results are illustrated in Figure 4(a)-e, respectively. All clustering results identify NAF and EAF (seen in Figure 1) as borders of different clusters. In all figures (Figure 4(a)-e), red stars and blue diamonds show the Eurasian and the Arabian tectonic plates, respectively. The Aegean coast cluster, which is illustrated as pink squares, is changing depending on the clustering method. Moreover, the Anatolian part is divided in two but with different borders according to all methods. In the tectonic sense, Anatolia is accepted as a rigid body with no clusters inside as mentioned previous section. This situation is clearly explained in [7] which has been recently published. The reader is referred to [7] for a background on the clustering analysis of Turkey from published GPS velocities.  
As seen in Figure 5, clustering borders can vary and clusters of some GPS sites can change with different algorithms. Let us consider the two sites in Figure 4(a the sites in the navy-blue circle) and the one in Figure 4(b) (the site in the navy-blue triangle). As compared with Figure 1, these sites have an anomaly compared to the velocity field; therefore, this is natural to be in another cluster. However, site number 2 is in the right cluster in the HAC and mini batch \(k\)-means methods. Furthermore, in Figure 4(b) the marked site is different only in the HAC method. Moreover, in the spectral clustering method, the four sites in Cyprus are in one of the Anatolia clusters (Figure 5e), while in the other methods they are all in the Eurasian cluster. These subjects will be discussed later in the comparison of clustering results with the ensemble clustering part.
# Hadoop vs. Spark: Impact on Performance of the Hammer Query Engine for Open Data Corpora
## 6 Experimental Campaign
### Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**1000 Data Sets** & **Step 1** & **Step 2** & **Step 3** & **Step 4** & **Step 5** & **Step 6** & **Total** \\ \hline \(Conf_{1}\) & 69,699 & 150 & 5277 & 1,410,492 & 158,891 & 610,013 & 2,254,522 \\ \(Conf_{2}\) & 83,689 & 102 & 1844 & 1,001,012 & 171,160 & 556,510 & 1,814,317 \\ \(Conf_{3}\) & 137,742 & 143 & 1440 & 895,619 & 152,791 & 27,897 & 1,215,632 \\ \(Conf_{4}\) & 128,504 & 163 & 1082 & 5,075,391 & 79,785 & 554,400 & 5,839,325 \\ \(Conf_{5}\) & 159,436 & 109 & 1884 & 6,796,491 & 836,515 & 583,513 & 8,377,948 \\ \(Conf_{6}\) & 133,152 & 125 & 987 & 5,821,900 & 73,433 & 49,266 & 6,078,863 \\ \(Conf_{7}\) & 164,107 & 166 & 1206 & 29,952,490 & 55,401 & 463,736 & 30,637,106 \\ \(Conf_{8}\) & 173,785 & 127 & 1649 & 24,480,779 & 694,722 & 651,068 & 26,002,130 \\ \(Conf_{9}\) & 133,349 & 127 & 1273 & 23,848,456 & 84,014 & 46,749 & 24,113,968 \\ \hline
**10,000 Data Sets** & **Step 1** & **Step 2** & **Step 3** & **Step 4** & **Step 5** & **Step 6** & **Total** \\ \hline \(Conf_{1}\) & 125,162 & 133 & 8289 & 6,247,375 & 1,774,413 & 494,387 & 8,649,759 \\ \(Conf_{2}\) & 145,730 & 142 & 1442 & 8,940,382 & 1,793,918 & 563,101 & 11,444,715 \\ \(Conf_{3}\) & 257,793 & 75 & 995 & 11,585,182 & 559,855 & 111,507 & 12,515,407 \\ \(Conf_{4}\) & 265,262 & 190 & 1277 & 44,882,444 & 1,374,816 & 464,295 & 46,988,284 \\ \(Conf_{5}\) & 127,091 & 125 & 1615 & 54,313,736 & 11,374,301 & 521,144 & 66,338,012 \\ \(Conf_{6}\) & 94,438 & 75 & 847 & 84,234,220 & 349,861 & 197,854 & 84,877,295 \\ \(Conf_{7}\) & 87,629 & 64 & 1238 & 36,259,183 & 1,250,844 & 349,380 & 37,948,338 \\ \(Conf_{8}\) & 157,812 & 117 & 2051 & 66,990,740 & 12,107,156 & 746,812 & 80,004,688 \\ \(Conf_{9}\) & 204,534 & 129 & 1386 & 104,930,839 & 505,326 & 163,578 & 105,805,792 \\ \hline
**15,000 Data Sets** & **Step 1** & **Step 2** & **Step 3** & **Step 4** & **Step 5** & **Step 6** & **Total** \\ \hline \(Conf_{1}\) & 109,365 & 50 & 6263 & 6,367,932 & 805,445 & 683,549 & 7,972,604 \\ \(Conf_{2}\) & 130,563 & 385 & 979 & 13,668,545 & 875,351 & 690,821 & 15,366,644 \\ \(Conf_{3}\) & 224,922 & 139 & 1026 & 16,693,411 & 1,097,360 & 122,420 & 18,139,278 \\ \(Conf_{4}\) & 294,392 & 153 & 1091 & 51,177,294 & 2,852,516 & 523,599 & 54,849,045 \\ \(Conf_{5}\) & 170,759 & 193 & 1750 & 72,817,934 & 20,378,100 & 673,203 & 94,041,939 \\ \(Conf_{6}\) & 103,225 & 72 & 661 & 128,535,201 & 689,922 & 213,775 & 129,542,856 \\ \(Conf_{7}\) & 71,493 & 93 & 1719 & 67,185,486 & 3,118,435 & 457,630 & 70,834,856 \\ \(Conf_{8}\) & 190,613 & 139 & 2486 & 90,892,440 & 23,580,870 & 771,655 & 115,438,203 \\ \(Conf_{9}\) & 161,401 & 198 & 1787 & 128,640,058 & 1,163,036 & 242,773 & 130,209,253 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Execution times for the corpora of 1000–10,000–15,000 data sets for version without Map-Reduce of the algorithm.
## 3 Results and Discussion
### Tortuosity Distribution
Equation (13), fitting a Gamma distribution, yields a mean of 3.48 (std = 0.48), low skewness (0.07), and negative kurtosis (\(-\)0.51), reflecting a peaked distribution with limited asymmetry, whereas Equation (17) following a log-gamma distribution has a lower mean of 2.27 (std = 0.41), mild skewness (0.28), and slightly negative kurtosis (\(-\)0.28). The results underscore the greater pore-scale complexity of carbonate rocks compared to sandstones, particularly in the Dijkstra and streamline tortuosity statistics. The pronounced skewness and kurtosis in carbonates indicate irregular, anisotropic flow paths that are not well captured by empirical equations alone. Among the evaluated methods, the LBM-derived flow tortuosity appears most representative of true flow behavior due to its sensitivity to microstructural heterogeneity, making it the preferred approach for accurate modeling of transport properties in porous media.
# Seareed Exhibits Therapeutic Properties against Chronic Diseases: An Overview
## 6 Pharmacological Properties of Seaweed to Overcome Chronic Disease
### Obesity  
Recently, the anti-obesity activity of seaweed has received considerable attention (Table 5). Obesity is generally considered a risk factor for a number of chronic metabolic diseases, dyslipidemia, hypertension, and hyperglyemia [177]. Obesity can also lead to musculoskeletal problems and an increased risk of cancers, such as colorectal, breast, and endometrial cancers [178]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algal Source** & **Constituents** & **Study Type** & **Biological Effects** & **Ref.** \\ \hline \multicolumn{5}{c}{Brown seaweed (Phaeophyta)} \\ \hline \multicolumn{5}{c}{Reduced lipid accumulation and} \\ _Sargassum miyabei_ & Crude extract & In vitro &
\begin{tabular}{c} Identification \\ Inhibited adipogenic and lipogenic gene \\ expression \\ \end{tabular} & [179] \\ _Saccorrhiza polyschides_ & Polysaccharide & In vivo & Regulated intestinal and systemic glucose metabolism & \\ \multicolumn{5}{c}{Inhibition of \(\alpha\)-amylase activity} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The effects of seaweed-derived compounds on obesity. The mechanism of obesity is shown in Figure 4e. Obesity upregulates proinflammatorycytokines such as tumor necrosis factor (TNF)- \(\alpha\), interleukin (IL)-1\(\beta\), IL-6, and leptin; TNF-\(\alpha\) and IL-1\(\beta\) subsequently mediate the regulation of matrix metalloproteinases (MMPs) such as MMP-1, MMP-3, MMP-10, MMP-12, and MMP-1310-12 by stimulating the nuclear factor-kappa B (NF-\(\kappa\)B) pathway. MMP levels are also influenced by mitogen-activated protein kinases (MAPKs), such as ERK1/2, c-Jun NH2-terminal kinase, and p38 subfamilies [190]. A high-fat diet increases the levels of gut inflammatory cytokines such as TNF-\(\alpha\), IL-1\(\beta\), and IL-12, which are linked to weight gain, adiposity, and enhanced plasma insulin and glucose levels [191]. Obesity-related diseases such as metabolic syndrome may be reduced with an intervention that decreases gut and systemic inflammation [192]. Modulating the gut microbiota seems to be an option for reducing obesity [30]. Moreover, siphonaxanthin from _Codium cylindricum_ showed anti-obesity activity since it accumulates in the stomach and small intestine, while its metabolites are absorbed and accumulated in the white adipose tissue (WAT) [109]. In addition, elevated aP2, ACC, and PPAR\(\gamma\) gene expression levels cause excessive fat accumulation [181]. _Sargassum miyabei_ and _Saccorhiza polyschides_ exhibited anti-obesity effects by downregulating adipogenic and lipogenic gene expression, and intestinal and systemic glucose metabolism, thus inhibiting \(\alpha\)-amylase activity [179; 180]. Furthermore, _Plocamium telfairiae_[188] and _Gelidium amansii_[187] also inhibited adipogenic and lipogenic gene expression in 3T3-L1 preadipocytes. In vivo, seaweed reduced body weight, triglyc
# Simulation Model and Method for Active Torsional Vibration Control of an HEV
## 2 Materials and Methods
### Structure and Working Principles of a Series-Parallel Hybrid System
#### 2.1.2 Energy Management Strategy  
This paper employs a rule-based logic threshold energy management strategy, as shown in Figure 2. T\({}_{req}\), T\({}_{brake\_tm\_mass}\), and T\({}_{ice\_opt}\) represent vehicle real-time demand torque, maximum regenerative braking torque, and engine real-time optimal torque; V\({}_{veh}\) and V\({}_{c}\) are the real-time vehicle  
\begin{table}
\begin{tabular}{c c c c} \hline
**Part Name** & **Type** & **Parameter** & **Value** \\ \hline \multirow{2}{*}{ICE} & \multirow{2}{*}{Natural gas engine} & Power (kW) & 155 \\ \cline{3-4}  & & Peak torque (Nm) & 710 \\ \hline \multirow{2}{*}{ISG motor} & \multirow{2}{*}{PMSM (Pole pairs: 8)} & Power (kW) & 100 \\ \cline{3-4}  & & Peak torque (Nm) & 850 \\ \hline \multirow{2}{*}{TM motor} & \multirow{2}{*}{PMSM (Pole pairs: 8)} & Power (kW) & 135 \\ \cline{3-4}  & & Peak torque (Nm) & 2100 \\ \hline \multirow{2}{*}{Clutch} & \multirow{2}{*}{Two-stage torsion spring} & Stiffness (Nm/\({}^{\circ}\)) & \(\leq\)\(\pm\)1.5\({}^{\circ}\): 66.7 \\ \cline{3-4}  & & \(>\)\(\pm\)1.5\({}^{\circ}\): 457 \\ \hline \multirow{2}{*}{Torsional damper} & \multirow{2}{*}{Three-stage torsion spring} & \multirow{2}{*}{Stiffness (Nm/\({}^{\circ}\))} & \(\leq\)\(\pm\)3\({}^{\circ}\): 60 \\ \cline{3-4}  & & \(\pm\)3\({}^{\circ}\)\(\pm\)8\({}^{\circ}\): 190 \\ \cline{3-4}  & & \(>\)\(\pm\)8\({}^{\circ}\): 650 \\ \hline \end{tabular}
\end{table}
Table 1: Main components and parameters of the hybrid system [31].  
Figure 1: Schematic diagram of the hybrid powertrain system.  
speed and mode switching speed threshold; and state of charge (SOC), SOC\({}_{low}\), and SOC\({}_{opt}\) are the real-time state of the battery, the low and high state of charge thresholds, respectively. The strategy is based on the torque demand, vehicle speed, and battery SOC real-time values as the logical judgment value of mode switching. The vehicle's operation is roughly divided into seven modes: regenerative braking, common braking, series power generation, pure electric, parallel power generation, combined drive, and parallel engine drive. At the combined drive mode, the output torque is provided by the engine and the TM. However, during the parallel engine drive, the output torque is provided only by the engine.
## 5 Case Study: Practical Implementation of DT for HEMS
### Digital Twin for HEMS
#### 5.3.2 Indoor Monitoring in DT  
The DT environment is not only designed for monitoring the operational status of sensors but also for taking actionable steps to interact with their physical counterparts. By integrating real-time sensor data into the 3D house model, the DT enables a more comprehensive understanding of indoor conditions such as temperature and humidity as depicted in Figure 11. In the DT, the virtual sensors corresponding to real ones are placed in their physical locations within the house layout. These sensors actively track key parameters (e.g., temperature, humidity). The DT retrieves real-time data from IoT sensors through API calls, ensuring continuous synchronization with the physical environment. For instance, in Figure 11, a  
Figure 10: DT visualization of HEMS with virtual sensors: green indicates functional sensors, and red indicates malfunctions.  
green indicator signifies that the corresponding sensor is functioning properly and provides data on the current indoor conditions, such as 28 \({}^{\circ}\)C temperature and 75% humidity.  
Beyond monitoring, the DT enables users to take direct actions based on the displayed sensor data. For example, if indoor conditions deviate from optimal comfort levels, users can trigger changes, such as adjusting the thermostat or activating ventilation systems, directly through the DT interface. These actions are communicated back to the physical devices via the IoT network, ensuring that the desired changes are implemented seamlessly. This bidirectional communication and interaction make the DT a powerful tool for proactive indoor environment management. By combining real-time monitoring, intuitive visualization, and actionable controls, the DT enhances user engagement and ensures that indoor conditions are maintained effectively. The figure illustrates this functionality, showing how real-time data and user-triggered actions are visually and operationally integrated into the system.
## 2 Materials and Experimental Methods
### Microstructure Analysis  
For an analysis of the material's microstructure, both the unprinted prepreg filament and 3D printed untested specimens were examined. First, the proportion of the different constituents of the filament was determined. A thermogravimetric analysis (TGA) of two unprinted CF/PA6 filament samples was conducted to determine the fiber weight fraction \(f_{w}\) of carbon within the prepreg filament. The fiber volume fraction \(f\) was calculated from this result, applying the rule of mixtures for composite materials [58]:  
\[f=\frac{V_{f}}{V_{f}+V_{m}}=\frac{f_{w}}{f_{w}+(1-f_{w})\frac{\rho_{f}}{\rho_{m }}}, \tag{2}\]  
where \(V_{f}\) and \(V_{m}\) are the volume and \(\rho_{f}\) and \(\rho_{m}\) the densities of carbon fibers and the matrix, respectively. The TGA measurement was performed in a temperature range of [20-600 \({}^{\circ}\)C] in a nitrogen atmosphere (gas flow rate of 25 mL/min), with a linear heating rate of 10 \({}^{\circ}\)C/min. During this process, the PA6 matrix material of the CF/PA6 filament degraded completely. The residue corresponds to the fraction of carbon fibers in the filament and is measured by weighting.  
For a further analysis of the CF/PA6 filament, cross-sections of the unprinted filament were prepared for optical examination with a microscope to gain information about the distribution of the continuous fibers within the filament. Additionally, micrographs of the 3D printed bending test specimens were prepared for microscopic analysis to characterize fiber, matrix and void distribution in the 3D printed material. The binocular microscope Axio Lab.A1 (Zeiss) [59] was used for the microscopic examination of the samples. Images were acquired with a 2.0 MP pixel-for digital camera (Dietermann & Heuser Solution) [60] and processed with the manufacturer's image measuring software. For the micrographs, the test specimens were cut in the middle of the longitudinal axis after cleaning and embedding in VersoCit-2 (Struers) [61], a two-component cold mounting for materialographic specimens. The specimens' surface was then finished with a multi-stage grit process, and further polished using the LaboPol polisher (Struers).  
Figure 4: Set up of the three-point bending test according to ISO 14125:1998. In (**a**) the set-up of the three-point bending test is shown including the loading and support pins and the bending test specimen. (**b**) shows a close up of the inserted CF/PA6 specimen before testing.
## 3 Proposed Method
### Image Fusion
#### 3.1.4. A Domain Adaptive Algorithm Combining MK-MMD and CDAN+E  
Based on the backbone model KAResNet, this paper proposes a domain adaptation algorithm that combines MK-MMD and CDAN+E. The integrated model architecture is shown in Figure 5. By merging kernel-based and adversarial-based domain adaptation methods, this approach addresses the limitations of using a single domain adaptation method. MK-MMD minimizes the distribution discrepancy between the source and target domains by measuring the difference in the mean embeddings of their feature distributions in the RKHS. It enhances the testing capability by utilizing different kernel functions to find the optimal kernel and minimize distribution differences. CDAN+E overcomes the limitations of MK-MMD by accounting for the joint distribution differences of features and labels. CDAN+E leverages multilinear conditioning and adversarial learning to consider the joint distribution of features and labels, addressing the limitations of MK-MMD, which cannot account for the differences in the joint distribution of features and labels. Meanwhile, MK-MMD effectively aligns the distributions of the source and target domains using the multicore method, providing a solid foundation for the adversarial learning of CDAN+E and enhancing the generalization capability of the CDAN+E classifier. The adaptive algorithm, through fusion, enables KAResNet to better extract domain-invariant features and achieve better domain-adaptive effects and fault classification effects.  
The specific process of domain-adaptive fusion is as follows: First, features are extracted from the input images using KAResNet. Simultaneously, MK-MMD is utilized to reduce the distribution differences between the source and target domains through kernel mapping. CDAN+E weights the entropy predicted by the classifiers through entropy conditioning, allowing the domain discriminator to prioritize the higher-weighted parts. CDAN+E simultaneously considers the joint distributions of features and labels using multilinear conditioning. Additionally, the distributions of the source and target domains are further aligned through adversarial learning between the feature extractors and domain classifiers. After completing feature extraction and adversarial learning, faults are identified based on the category classifiers.  
The MK-MMD loss, CDAN+E loss, and classification loss are combined to form the total loss function of the MCR-KAResNet-TLDAF method. The total loss function is given as follows:  
\[L=L_{C}+\lambda_{MK-MMD}L_{MK-MMD}(D_{s},D_{s})-\lambda_{CDAN+E}L_{CDAN+E}( \theta_{f},\theta_{d}) \tag{20}\]  
\[\lambda_{MK-MMD}=\lambda_{CDAN+E}=\frac{2}{1+e^{-10\times\left(\frac{E_{target }}{E_{max}}\right)}}-1 \tag{21}\]  
where \(E_{current}\) represents the current number of iterations and \(E_{max}\) represents the maximum number of iterations. By adjusting \(\lambda_{MK-MMD}\) and \(\lambda_{CDAN+E}\), the model achieves a better balance between classification and domain adaptation.
# A Symmetric Matrix-Aided MIMO to Improve Reliability for Maritime Visible Light Communications
## 3 Symmetric Matrix-Aided MIMO-VLC System
### Theoretical SER Analysis  
In this part, we analyze the theoretical symbol error rate (SER) of the proposed scheme. First, from the ML detection in the receiver, we can define the conditional pairwise error probability (PEP) as the detector mistaking \(\mathbf{x}_{i}\) into \(\mathbf{x}_{j}\). Then we can receive the conditional PEP as [18]:  
\[PEP(\mathbf{x}_{i}\longrightarrow\mathbf{x}_{j}|\mathbf{\Lambda})=Q(\sqrt{ \frac{\Gamma\|\mathbf{\Lambda}(\mathbf{x}_{i}-\mathbf{x}_{j})\|_{F}^{2}}{4}}), \tag{20}\]  
where the \(\Gamma\) means the signal-to-noise ratio (SNR).  
Then we apply the minimum Euclidean distance to Formula (20), and average over all the conditional PEP [25], we can have:  
\[PEP(e|\mathbf{\Lambda})=\frac{1}{M^{p}}\sum_{i=1}^{M^{p}}Q(\sqrt{\frac{\Gamma \cdot\min_{i\
eq j}\|\mathbf{\Lambda}\mathbf{S}(\mathbf{u}_{i}-\mathbf{u}_{j })\|_{F}^{2}}{4}}). \tag{21}\]  
Finally, we numerically approximate the integral sum of Formula (21) to obtain a lower bound of the SER for the proposed scheme:  
\[P_{e,l}\approx\frac{1}{W}\sum_{w=1}^{W}PEP(e|\mathbf{\Lambda}), \tag{22}\]  
where \(W\) is the number of the samples and \(w\) is the index of the sample [25].  
Figure 3: The receiving structure of the structure.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 4 LoRaWAN v1.1 Security Structure Analysis
### Pre-Operational Stage
The network session keys have different roles: (1) The Network Session Encryption Key (NwkSEncKey) is responsible for the MAC payload encryption between the end-device and the network server; (2) The Forwarding Network Session Integrity Key (FNwkSIntKey) is in charge of the message integrity check for uplink; (3) The Serving Network Session Integrity Key (SNwkSIntKey) is the message integrity check for downlink; The Application Session Key (AppSKey) is taking care of the application payload encryption/decryption between end-device and application server. The four network session keys can be derived as follows [16]. \[NwkSEncKey=aes_{128 encrypt}(NwkKey,0\times 04|JoinNonce|JoinEUI|DevNonce|pad_{16}) \tag{3}\]  
where aes_128 encrypt in (3) represents 128 bits AES encryption function using Electronic Codebook (ECB) mode. \[FNwkSIntKey=aes_{128 encrypt}(NwkKey,0\times 01|JoinNonce|JoinEUI|DevNonce|pad_{16}) \tag{4}\]  
\[SNwkSIntKey=aes_{128 encrypt}(NwkKey,0\times 03|JoinNonce|JoinEUI|DevNonce|pad_{16}) \tag{5}\]  
\[AppSKey=aes_{128 encrypt}(AppKey,0\times 02|JoinNonce|JoinEUI|DevNonce|pad_{16}) \tag{6}\]  
Equations (3)-(6) indicate that there are three parameters JoinNonce, JoinEUI, and DevNonce that are essential to the key derivation. Also, JoinEUI and DevNonce are not encrypted during the join procedure, meaning that these parameters are known to the public.