esa-sceva/satcom-chunk-collection huggingface dataset:
Initiated in 1948 in Framingham, MA, USA, the study originally enrolled over 5000 participants and has since expanded to include their children and grandchildren, enabling intergenerational analysis of health data [28; 29]. Its primary objective is to identify common and modifiable risk factors for CVD by collecting longitudinal data on clinical parameters, lifestyle behaviors, and biological measurements [28; 30; 31]. Over the decades, FHS has generated pivotal findingsthat have shaped global cardiovascular prevention guidelines, including the development of the widely used Framingham Risk Score for estimating 10-year CVD risk [30, 31]. More recent extensions of the study have incorporated novel vascular measures [32], genetic data [29], machine-learning models for disease prediction [33], and social determinants of health such as educational mobility and aging trajectories [34]. Furthermore, FHS has played a crucial role in clarifying sex-specific CVD risks [35], establishing the relevance of composite health metrics like Life's Essential 8 [36], and highlighting cross-cultural comparisons with other landmark studies [37]. These contributions collectively affirm the Framingham dataset's continued relevance as a foundational tool in cardiovascular epidemiology and risk prediction research. Several studies have already utilized the Framingham dataset to evaluate models for predicting cardiovascular-related mortality. For example, prior research includes comparative studies of machine-learning algorithms, Bayesian analyses that account for time-varying treatments and heterogeneity, evaluations of methods for imputing missing data, and multimodel data mining approaches for predicting heart failure [38, 39, 40, 41]. Many of these studies have focused on improving the accuracy of risk prediction by addressing missing data using techniques such as multiple imputation and deep-learning-based approaches. While these studies have advanced cardiovascular risk stratification, there remains a need for further exploration of how key risk factors contribute to long-term outcomes in broader, non-clinical populations. Therefore, the objective of this study is to analyze the Framingham Heart Study dataset using various machine-learning approaches and identify cardiovascular risk factors with a strong predictive value for mortality. By leveraging comprehensive longitudinal data and applying modern analytical methods, this study aims to contribute to the development of more robust and interpretable predictive models. The contribution of this research lies in its practical application to personalized health management and preventive medicine.
## 3 Problem Definition
### Definition of Terms
#### 3.2.4 Correctness  
One often thinks of correctness in terms of accuracy, which is right in translation, but not entirely so in the context of the computing world, where correctness is the accuracy of the answers; if an answer is accurate, but slow to achieve, it is difficult to assume that it is correct.  
Flink supports four notions of time for data processing, namely, event time, storage time, ingestion time and processing time, as shown in Figure 7, which highlights where each of the four is recorded and used. Event time is as the name suggests, the time where all events on the device occurred, recorded before the stream enters the system, while storage time is the arrival time of the events into the message queue. Ingestion time is recorded when events enter the system, and each record gets the source's current time as ingestion timestamp at the source operator, while processing time refers to the current system time of the main machine executing the operations. For example, if an application run at hourly processing time window, and application begins at 8:25 am, the first hourly processing time  
Figure 5: Generating the sequence and transferring it between machines using 500 ms as buffer timeout.  
Figure 6: Subtask concept based on the condensed and parallelized view of the streaming dataflow.  
window will only include the events processed between 8:25 am and 9:00 am, and the next window will include the events processed between 9:00 am and 10:00 am, and so on.  
While modern streaming engines are capable of using event time windows to reflect the occurrence of events, system elements can be delayed at a moment's notice, making it difficult to pinpoint the entire series of events that occur in a specific timestamp; these elements are known as _late elements_. As a result, these modern distributed systems have two critical drawbacks, namely, the increased amount of data buffering and the aforementioned point about late elements. Flink handles these issues through its use of watermarks, which marks the progress of events throughout the flow of data using timestamps, giving us a more reliable, hands-on estimate of a window's completeness. Watermarks tend to use the available information such as partition ordering within partitions, among others, to produce an accurate progress estimate, but as seen in Figure 8, this can easily backfire, resulting in the appearance of late elements and an inaccurate estimation. As in this case, the stream is an out of order stream, and two late elements occur due to their arrival after the specified watermark accordingly. Late elements can be defined through a watermark as the elements with the timestamp less than or equal to the current watermark at the time of their arrival. Window correctness is vital towards meeting a user-defined SLA like end-to-end latency or throughput for the above reasons.
# Effects of Epigallocatechin-3-O-Gallate on Bone Health
## 3 Biochemical Effects of EGCG
### Attenuation of p44/p42 MAP Kinase or p44/p42 MAP Kinase and p38 MAP Kinase  
The main characteristic of enzymes named kinases involves the transference of the \(\gamma\)-phosphate from ATP to serine, threonine, or tyrosine amino acid residues of a downstream protein substrate, therefore ensuring a communication cascade that is fundamental to eukaryotic cells [135]. Generally, kinases are dually phosphorylated at serine and threonine residues and, consequently, named Ser/Thr kinases [133; 136]. MAPKs constitute a group of serine/threonine protein kinases consisting of three major subfamilies: (i) ERK p42/p44 ERK, also known as p42/p44 MAPK; (ii) _c_-Jun N-terminal kinases; and (iii) p38 MAPKs. The p42/p44 MAPKs are mainly involved in the regulation of cell proliferation and differentiation, while the JNKs and p38 MAPKs are involved in apoptosis, inflammation, and responses to environmental stress [137]. [PERSON] et al. [133] described numerous interconnections between the MAPK pathways, which can occasionally make it difficult to differentiate between them. For example, the JNK and p38 pathways are commonly linked to inflammation and cellular death and are both initiated by cytokines as well as additional cellular stressors [138; 139; 140]. At the same time, both enzymes may not always work the same way; they can sometimes induce anti-apoptotic processes. A recent study indicates that p38 has a detrimental effect on JNK in some cellular situations [141].  
The EGCG influence was studied on the cloned osteoblast-like MC3T3-E1 cells, derived from newborn mouse calvaria. It was found that the attenuation of the p44/p42 MAP kinase pathway led to a reduction in the ET-1-stimulated IL-6 synthesis [58]. Studies performedon the same cell line also showed that the inhibition of p44/p42 MAP kinase led to the suppression of PGD2-stimulated HSP27 induction through the inhibition of p44/p42 MAP kinase and thus the modulation of osteoblast cell function towards bone formation [27]. In a similar study, the attenuation of p44/p42 MAP kinase and p38 MAP kinase led to a reduction in FGF-2-stimulated IL-6 synthesis at least partly via the attenuation of p44/p42 MAP kinase and p38 MAP kinase [59]. The suppression of p44/p42 MAP kinase can also lead to the suppression of IGF-I-induced osteoblast migration [39].
###### Abstract  
With the advancements in wireless sensor networks and the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have attracted much attention, which has also been widely used in marine engineering exploration and disaster prevention. However, UASNs still face many challenges, including high propagation latency, limited bandwidth, high energy consumption, and unreliable transmission, influencing the good quality of service (QoS). In this paper, we propose a routing protocol based on the on-site architecture (SROA) for UASNs to improve network scalability and energy efficiency. The on-site architecture adopted by SROA is different from most architectures in that the data center is deployed underwater, which makes the sink nodes closer to the data source. A clustering method is introduced in SROA, which makes the network adapt to the changes in the network scale and avoid single-point failure. Moreover, the Q-learning algorithm is applied to seek optimal routing policies, in which the characteristics of underwater acoustic communication such as residual energy, end-to-end delay, and link quality are considered jointly when constructing the reward function. Furthermore, the reduction of packet retransmissions and collisions is advocated using a waiting mechanism developed from opportunistic routing (OR). The SROA realizes opportunistic routing to choose candidate nodes and coordinate packet forwarding among candidate nodes. The scalability of the proposed routing protocols is also analyzed by varying the network size and transmission range. According to the evaluation results, with the network scale ranging from 100 to 500, the SROA outperforms the existing routing protocols, extensively decreasing energy consumption and end-to-end delay.  
Keywords:routing protocol; underwater sensor networks; Q-Learning; clustering; Internet of Underwater Things (IoUT) +
Footnote †: journal: Applied
## 2 Data and Methods
### EPB Detection Based on a Local Variance Measure of the Electric Field
Concurrently, on the other hand, such a choice minimizes statistical fluctuations in the MWLVM, since 120 km corresponds to roughly 80.000 points of EFD ELF data. The intervals returned by the clustering algorithm are identified as crossing of an EPB by the spacecraft. As an example, the vertical red stripes in the three upper panels of Figure 4 denote the latitudinal extension of the detected EPB. The EFD algorithm has been run on the EFD-ELF dataset available from 2019 to 2024 and on the night side semiorbits (consistently with the LP dataset selection). The performance of the algorithm has been assessed by visual inspection of 515 sample semiorbits, randomly picked from the dataset. For this sample, the algorithm returned a correct answer for 454 semiorbits (corresponding to \(>\)88% of the sample), consisting of 26 positive and 428 negative detections. In addition, the algorithm returned 35 false positive detections  
Figure 4: From top to bottom: electron density as measured by LP, electric field amplitude \(E(t)\) from EFD ELF data, proxy \(V^{\mathrm{max}}(t)\) (see Equation (5)) together with the threshold of 5000 set for the clustering algorithm, and colored contour of \(\mathrm{V^{med}_{i}}(t,T_{i})\) as calculated using Equation (3), for the reference dataset. (out of the 61 wrong answers), corresponding to a rate of false positive detections of 57%, if one considers only positive detections. Both false positive and negative detections are mainly caused by transients in the ionospheric electric field, such as, e.g., whistler events. For instance, in Figure 5, we report an erroneous negative detection (left panel) returned by the EFD algorithm. A plasma depletion (clearly present at a latitude of 10\({}^{\circ}\)) causes an increase in the activity proxy, which, however, is below the threshold. This is due to the presence of transient induced electric activity between \(-30^{\circ}\) and \(-10^{\circ}\), which increases the median value of the electric field variance V\({}_{i}(t,T_{i})\) (see Equation (4)), thus decreasing the value of MWLVM and, consequently, the value of V\({}^{\text{max}}\). At the same time, strong transient events can be misidentified as EPBs. This is the case shown in the right panel of the same figure.
## 4 Discussion  
### Relationship between Respectfulness and Sentiment  
The computed correlation based on the respectfulness and sentiment labels was 0.594, thus necessitating subsequent interpretation and consideration. The respectfulness-sentiment correlations computed per annotator ranged from 0.49 to 0.62; therefore, for various people, the differentiation between respectfulness and sentiment varies. When considering the final unified across-annotators respectfulness labels, we found that 2934 of 4964 data instances were assigned the same respectfulness and sentiment classes. However, inspection of the per-class details of the created data set revealed that disrespectful tweets were considered negative 73.73% of the time, respectful tweets were considered neutral in 51.9% of instances, and very respectful tweets were positive in 96.62% of cases. Therefore, we conclude that, for our annotators, very respectful tweets were almost always found to be positive, and disrespectful tweets were likely to be found negative.  
When responding specifically to the previously defined research question of whether positive tweets are always respectful, we observed that, according to our annotators, in most cases, such tweets were considered either respectful or very respectful. In our data set, there were 1875 positive tweets, only 28 of which (1.49%) were simultaneously considered to be disrespectful. An example of this small group can be represented by the tweet: \"[PERSON], are you going to New Orleans to help with Katrina survivors next?\" This tweet, although labeled as very positive, also received a disrespectful annotation. In the given case, one could question why this tweet was labeled disrespectful. When labeling for respect, the annotators agreed that this was a sarcastic tweet that in fact suggests that \"you shouldn't be going there,\" whereas presumably earlier in the sentiment annotation process, the annotators perceived this tweet to be straightforward and non-sarcastic. This example demonstrates how tricky tweet-level classification can be if sarcastic language is introduced without the corresponding broader context.  
To answer the second research question of whether a negative tweet is always disrespectful, we begin with the observation that there were 947 negative tweets in the data set. From this group, the majority (626 or 66.1%) were considered disrespectful; however, the remainder were considered respectful. Therefore, negative tweets are not always disrespectful. Some example tweets that can provide background for this conclusion are \"The link isn't working\" (obviously negative, but not disrespectful) or \"@USNavy I do not accept any culpability, blame or responsibility.\" The latter was labeled as negative and yet is obviously not disrespectful.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 3 Antenna Gain Estimation  
The directivity \(D\) of a parabolic reflector antenna for a specific frequency is given by \((\frac{\pi d}{\lambda})^{2}\), with \(d\) being the reflector diameter, and \(\lambda\) the corresponding wavelength, resulting in this case in 33.63 dBic. The gain is then calculated by \(G=\eta D\), where \(\eta\) is the overall antenna efficiency given by [15], which accounts several factors, such as impedance mismatch, antenna aperture, and reflector surface roughness, among others. These contributions must be estimated for calculating the antenna effective gain. Apart from peak loss, antenna sidelobes must be carefully controlled, as they can increase range ambiguities, degenerating the final image. For the CP-SAR, sidelobe levels must be below \(-20\) dBic.
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
## 5 Conclusions  
This paper focuses on the measurement set partition problem for extended target tracking in a clutter environment. Based on the idea of spatio-temporal correlation, the ET-GM-PHD filter based on the directed graph SNN is proposed, and the corresponding implementation scheme is given. Simulation results show that the method is effective. In addition, in the process of target tracking, the tracking of the extended target is converted to the tracking of the point target, which effectively reduces the amount of calculation. In the future, we will verify the proposed algorithm in practical scenarios. In addition, to ensure the security of tracking results and prevent information interception, it is urgent to study how to use the estimated target results to identify the observation platform (estimation problem of stealth estimator) [24].  
Figure 14: (**a**) Comparison of measurement set partition number in Scenario 4. (**b**) Comparison of running times of each algorithms for Scenario 4. (**c**) Comparison of target number estimation in Scenario 4.  
Figure 13: (**a**) Real target trajectory for Scenario 4. (**b**) Comparison of OSPA errors in Scenario 4.  
**Author Contributions:** Conceptualization, W.Z. and L.G.; methodology, W.Z. and C.L.; validation, W.Z., C.L. and T.L.; formal analysis, W.Z., C.L. and T.L.; investigation, L.G. and W.Z.; writing--original draft preparation, W.Z. and C.L.; writing--review and editing, T.L. and L.G.; supervision, L.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
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.
## 3 Results and Discussion
### Main Chemical Components of Garlic Essential Oil
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**RT a, min**} & \multicolumn{2}{c}{**Relative Amount (\%)**} & **Identification** \\ \cline{3-5}  & & **HS-SPME** & **SD b ** & **Method d ** \\ \hline Disulfide, dimethyl & 3.43 & 0.05 & Tr c  & MS \\ Diacetone alcohol & 5.53 & 0.25 & 7 & MS, RI \\ Diallyl sulfide (DAS) & 5.9 & 5.69 & 1.78 & MS, RI \\ Disulfide, methyl 2-propenyl & 7.48 & 1.39 & tr & MS, RI \\ Disulfide, methyl (1E) -1-propenyl & 8.13 & 0.15 & 0.08 & MS, RI \\
1,2-Dithioie & 8.64 & 0.08 & 0.05 & MS, RI \\ Dimethyl trisulfide & 9.04 & 0.08 & tr & MS, RI \\ \(\alpha\)-Limonene & 10.92 & 0.73 & 0.19 & MS, RI \\ Diallyl disulfide (DADS) & 12.62 & 60.12 & 19.09 & MS, RI \\
1-Allyl-2-isopropyldisulfane & 12.92 & 1.43 & 0.43 & MS, RI \\ (E)-1-Allyl-2-(prop-1-en-1-yl) disulfane & 13.1 & 1.07 & 0.32 & MS, RI \\ Disulfide, dipropyl & 13.38 & 9.13 & 2.95 & MS, RI \\ Trisulfide, methyl 2-propenyl & 14.25 & 2.24 & 0.86 & MS, RI \\
4-Methyl-1,2,3-trithiolane & 14.77 & 0.17 & 0.22 & MS, RI \\
3-Vinyl-3,6-dihydro-1,2-diithineine & 15.75 & 0.35 & 0.15 & MS, RI \\
2-Vinyl-4H-1,3-dithiune & 16.5 & 0.66 & 0.36 & MS, RI \\ Carvone & 17.31 & 0.12 & 0.11 & MS, RI \\ Anethol & 18.53 & 0.42 & 0.57 & MS, RI \\ Trisulfide, di-2-propenyl (DATS) & 19.02 & 14.18 & 17.98 & MS, RI \\
1-Allyl-3-propyltrisulfane & 19.29 & 0.14 & 0.17 & MS, RI \\
5-Methyl-1,2,3,4-tetrathiane & 20.83 & 0.14 & 0.33 & MS, RI \\ Tetrasulfide, di-2-propenyl & 25.12 & 0.45 & 3.85 & MS, RI \\
1-Allyl-3-(2-(allylthio) propyl) trisulfane & 31.23 & tr & 1.31 & MS, RI \\ n-Hexadecanoic acid & 34.37 & tr & 4.26 & MS, RI \\
1-Allyl-3-(2-(allyldisulfanyl) propyl) trisulfane & 36.41 & tr & 0.28 & MS, RI \\
9,12-Octadecadienoic acid (\(Z\),\(Z\))- & 37.92 & tr & 16.72 & MS, RI \\
9-Octadecenoic acid, (\(E\))- & 38.01 & tr & 7.74 & MS, RI \\ Octadecanoic acid & 38.2 & tr & 0.42 & MS, RI \\ Isopropyl Inloate & 39.28 & tr & 0.64 & MS, RI \\ \(\beta\)-Monolinolein & 43.7 & tr & 0.61 & MS, RI \\ Total identified (\%) & & 99.03 & 88.47 & \\ \hline \hline \end{tabular}  
* RT, retention time, b SD, solvent diluted, c tr Trace (\(<\)0.05%), d Identification method, MS: Comparison of mass spectra with mass spectral libraries, RI: Comparison to retention indices in libraries or literature.  
\end{table} 
Table 1: Chemical composition of garlic essential oil (GC and GC-MS analysis).
# Crasworthiness Analysis and Multi-Objective Optimization for Concave I-Shaped Honeycomb Structure
## 4 Multi-Objective Optimization of Concave I-Shaped Honeycomb Structure
### Presentation of NSGA-II  
To solve the multi-objective optimization problem as formulated in Equation (28), the non-dominated sorting genetic algorithm II (NSGA-II) is used herein. It is an algorithm inspired by biological evolution and is a method for finding the optimal solution among all solutions. The non-dominated sorting genetic algorithm NSGA is an improvement of the genetic algorithm based on practical needs, and it has good results in solving multi-objective planning problems. The optimized second-generation non-dominated sorting genetic algorithm NSGA-II, compared with the previous generation algorithm, speeds up the computation in the optimal retention strategy and ensures population diversity in the genetic process [39]. The algorithm flow graph is shown in Figure 21. In this paper, the NSGA-II is used to solve the set of Pareto frontier solutions required by the multi-objective optimization design as a way to determine the optimal solution. The algorithm setup for NSGA-II is shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Response**} & \multirow{2}{*}{**Order**} & \multicolumn{4}{c}{**Accuracy Evaluation Coefficient**} \\ \cline{3-5}  & & \(R^{2}\) & **RMSE** & **MAE** & **AARE** \\ \hline \multirow{4}{*}{M} & 1 & 0.99127 & 0.02589 & 0.08527 & 0.0187 \\  & 2 & 0.99987 & 0.00318 & 0.00691 & 0.00243 \\  & 3 & 0.99975 & 0.00438 & 0.00979 & 0.00346 \\  & 4 & 0.99945 & 0.0065 & 0.01361 & 0.00522 \\ \hline \multirow{4}{*}{SEA} & 1 & 0.87318 & 0.10032 & 0.20534 & 0.08042 \\  & 2 & 0.88697 & 0.09471 & 0.25124 & 0.07478 \\ \cline{1-1}  & 3 & 0.9013 & 0.0885 & 0.21597 & 0.10662 \\ \cline{1-1}  & 4 & 0.5665 & 0.18548 & 0.60132 & 0.1316 \\ \hline \multirow{4}{*}{PCF} & 1 & 0.77801 & 0.12509 & 0.27177 & 0.10061 \\  & 2 & 0.87245 & 0.09482 & 0.21535 & 0.08153 \\ \cline{1-1}  & 3 & 0.74157 & 0.13497 & 0.35768 & 0.06777 \\ \cline{1-1}  & 4 & 0 & 0.40264 & 1.86361 & 0.22084 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy assessment of the surrogate model of response surface models 1–4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Option** & **Value** \\ \hline Population Size & 20 \\ Number of Generations & 20 \\ Crossover Probability & 0.9 \\ Crossover Distribution Index & 10 \\ Mutation Distribution Index & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The general information on NSGA-II.