esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Execution of MAMC-MAC Protocol  
The proposed MAMC-MAC is divided into three stages. During the primary stage, cluster formation occurs; here, all of the nodes are grouped as distinct clusters on the basis of their speed. During the second stage, TDMA time slot allocation occurs. The main motivation behind TDMA schema employment is to guarantee successful transfer of safety messages. In VANET, the safety applications always aim to achieve less delay, successful transmission, and time slots allocated to every node by transferring safety messages without interrupting other nodes, which are challenging. TDMA stands out best in assigning different time periods to each cluster inside the network. During the third stage, progressive hopsector safety message distribution takes place. Progressive hopsector is defined as an aggressive message routing schema in which messages progress ahead to hops that are arranged superior to handover them to upcoming hops. In the vehicular scenario, the purpose of multi-hop routing is to elevate the broadcasted messages transmission range. However, for the effective multi-hop progressing, heavy traffic scenarios are considered since there are better positioned nodes enclosed by the communication range [27]. In the next subsections, the proposed algorithm phases are explained. The layout of the MAMC-MAC protocol is shown in Algorithm 1.  
Figure 1: Vehicle–vehicle communication in multi-hop scheme.  
```
1 Signaling of HELLO message
2 Splitting of bandwidth into frames
3 Formation of mobility-based cluster
4 Depending on preferences, frame allotment to the clusters is done by following the decreasing mobility order.
5 At node i, a safety message is generated.
6 Message is transferred to (i+\(h_{c}\)) node distance clusters. (firstly, \(h_{c}\) value is considered as one)
7 Examine \(h_{c}\)
8 Message is transmitted
9 Augment \(h_{c}\)
10 if\(h_{c}\) less than or equal to 4 (\(h_{c}\leq\)4), direct the message to(i+\(h_{c}\)) node distance clusters
11 then
12 Go back to 7 th step and continue the loop until it reaches to \(h_{c}\)=4
13 else
14 Message gets rejected
```  
**Algorithm 1**MAMC-MAC layout.
# Detection and Localization of Multiple Damages through Entropy in Information Theory
## 4 The Finite Element Models
### Input Definition and Simulation Settings
Then, since (i) the Fourier Transform is a linear operator, and thus:  
\[\Big{|}H_{(r)}\Big{|}^{2}=k^{2}\Big{|}H_{(u)}\Big{|}^{2} \tag{2}\]  
where \(\Big{|}H_{(r)}\Big{|}^{2}\) and \(\Big{|}H_{(u)}\Big{|}^{2}\) are generic power spectra of the components of \(\mathbf{r}\) and \(\mathbf{u}\) (with \(\mathbf{r}\) and \(\mathbf{u}\) two generic proportional response displacement fields) and since (ii) the WE is scale-invariant, i.e.,:  
\[S_{w,(u)}=B\frac{\sqrt[R]{\prod_{i=1}^{B}\Big{|}H_{(r)}\Big{|}_{i}^{2}}}{\sum_ {i=1}^{B}\Big{|}H_{(r)}\Big{|}_{i}^{2}} \tag{3a}\] \[S_{w,(r)}=B\frac{\sqrt[R]{\prod_{i=1}^{B}\Big{|}H_{(r)}\Big{|}_{i}^{2}}}{\sum_ {i=1}^{B}\Big{|}H_{(r)}\Big{|}_{i}^{2}}=\frac{\sqrt[R]{\left(k^{2}\right)^{B}} }{k^{2}}B\frac{\sqrt[R]{\prod_{i=1}^{B}\Big{|}H_{(u)}\Big{|}_{i}^{2}}}{\sum_ {i=1}^{B}\Big{|}H_{(u)}\Big{|}_{i}^{2}}=S_{w,(u)} \tag{3b}\]  
Thus, for any value of the chosen variance or standard deviation \(\sigma_{Y}=k\) of the external force vector \(\mathbf{x}\), the WE will remain unchanged. Therefore, a transient dynamic analysis was performed on the baseline (undamaged) model and for all the investigated damage scenarios (see Section 4.6) for a total duration of \(20+20\) s (to obtain a frequency resolution of \(\Delta\mathrm{f}=0.05\) Hz). The readings at the 800 output channels were sampled at \(f_{\mathrm{s}}=1000\) Hz. The resulting time series of axial strains have been then artificially corrupted by adding Gaussian noise with zero mean and standard deviation proportional to the standard deviation of the uncorrupted output signals. This is intended to simulate measurement noise during acquisition. Specifically, the robustness to noise has been addressed by considering three levels of noise to signal ratio (NSR) (in terms of noise and signal standard deviation): 0% (ideally noise-free), 5%, and 10%.
## 1 Introduction
The RDG and RFP extraction are also carried out based on IPS, and with the help of inversed equalized hybrid-domain IFA. The image resolution analysis is carried out based on the pixel-based, correlation-based, local entropy, and local range measurements. Specifically, this paper not only focuses on the behavioral analysis of the multiplicative speckle noise with the help of speckle formulation and spectral modeling of their presence within the images, but also evaluates the performance efficiency of a total of seven different spatial filters, categorizing them while the system resolution and image resolution are being analyzed. The results not only verify that the Frost, Gamma, Kuan, Lee, Lee sigma, Local adaptive median, and Local sigma de-speckling filters have different patterns of noise suppression, but also that they have different evaluation indexes, which are measured quantitatively and presented qualitatively for further investigation. This paper is organized as follows. Section 2 describes the Maxwell formulation of 2-D received backscattered SAR raw data, as well as the speckle behavioral formulation and noise spectral modeling in the presence of different spatial de-speckling filter implementations. This section also presents the SAR system resolution extraction based on RDG and RFP, with the help of hybrid-domain IFA. In Section 3, the SAR spatial resolution analysis, including RDG and RFP as well as de-speckled image resolution analysis based on objective quality metric assessment, are presented. The objective quality assessment metrics include the pixel-based, correlation-based, SAR local entropy, and local range measurements. All the simulations are carried out on the reference SAR image and its seven de-speckled counterpart images. Finally, the discussion and conclusion are given in Sections 4 and 5.
# A New Method for Fatigue Evaluation of Titanium Alloy Welded Structures
## 2 The Principle of Notch Equivalent Stress and the Proposal of a New Method for Life Prediction
### Selection of Test Specimens and Determination of Fatigue Life  
According to the classification criteria for steel welded joints in the BS7608 standard, the cross joints, T-joints, and lap joints shown in Figure 5 are classified into one joint class. That is, these types of steel welded joints use an S-N curve in the BS7608 standard to predict fatigue life. Using this analogy, it can be considered that an S-N curve can be used for fatigue life prediction of cross joints, T joints, and lap joints of titanium alloys.  
Literature [25; 26; 27] used 45 titanium alloy welded joints to make fatigue test specimens and applied 15 different loads. In order to reduce the error, three fatigue specimens were used for the test under each load. In order to make the obtained S-N curve more convincing, the fatigue samples were divided into 15 groups, including 10 groups of cross joints, 4 groups of lap joints, and 1 group of T-joints. The geometrical dimensions of the four types of joints are shown in Figure 6.  
Figure 4: Ideological roadmap of this study.  
Figure 5: Four joints of the same grade in BS7608 standard [24].  
In order to make the titanium alloy S-N curve fitted by the test data more representative of this grade, different loading frequencies (f) and stress ratios (R) are used for different joint types, and the specific loading methods are consistent with the literature [25; 26; 27], as shown in Table 3, and the loading position and loading direction are shown in Figure 6.  
Through the fatigue test, the fatigue life of the sample under different stress ranges at the weld position is obtained, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Test Groups** & **Type of Welded Joint** & **Logarithmic Stress Range** & **Logarithmic Fatigue Life** \\  & & **S/MPa** & **lg/NCycle** \\ \hline
1 & & 2.2648 & 6.4645,6.8202,6.7662 \\
2 & & 2.2788 & 6.4206, 6.1412,6.1562 \\
3 & Cross joint with transverse vertical plate & 2.3160 & 5.9492, 5.8316, 5.7289 \\
4 & & 2.3464 & 5.1135, 5.3650, 5.7816 \\
5 & & 2.3617 & 5.0233, 5.3374, 5.3045 \\ \hline
6 & & 2.3711 & 5.4236, 6.0530, 5.801 \\
7 & & 2.3874 & 6.0829, 5.7923, 5.9747 \\
8 & Cross joint with longitudinal vertical plate & 2.4065 & 5.3891, 5.6703, 5.2765 \\
9 & & 2.4330 & 5.4665, 3.1845, 5.284 \\
10 & & 2.4594 & 4.6801, 4.8922, 5.0905 \\ \hline
11 & & 2.5549 & 4.6423, 4.6435, 4.6544 \\
12 & & 2.5682 & 4.1500, 4.1700, 4.3000 \\
13 & & 2.5911 & 4.0414, 4.1067, 4.1289 \\
14 & & 2.6232 & 3.6149, 3.9306, 3.8156 \\ \hline
15 & T-joint & 2.6021 & 4.7361, 4.7534, 4.5563 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fatigue test results of titanium alloy welded structures.  
Figure 6: Geometric dimensions of welded joints [25; 26; 27]. (a) Cross joint with transverse vertical plate. (b) Cross joint with longitudinal vertical plate. (c) Lap joint. (d) T-joint.
# Rootstock Effect on Volatile Composition of Albarino Wines
## 3 Results and Discussion
### Chemical Composition of Wines  
The chemical composition of the wines resulted from _V. vinifera_ cv Albarifio grafted on nine different rootstocks (110R, SO4, 196-17C, Riparia G, 161-49C, 420A, Gravesac, 3309C and 41B) was also evaluated, during two consecutive years (Table 3).  
In this study, chemical parameters were not affected by rootstock or season; however, some tendencies were observed. Malic acid was similar between years and rootstocks; however, a slight tendency to increase for SO4 in both vintages was observed. In general, after the fermentation process, the malic acid stayed almost the same, or increased slightly, with a maximum of 0.3 g/L. Tartaric acid diminished after the fermentation for the wines from vintage 2009. This may be due to a crystallization of the tartaric acid in the stored samples. However, the tartaric acid before and after the fermentation for grapes vintage 2010 was very similar for all samples. Malic and tartaric acids are normally found in large amounts in grapes and musts, and they do not undergo large changes during fermentation [25]. As expected from the initial concentration of sugars of the grapes, measured as \({}^{\circ}\)_Brix_, the alcoholic strength by volume of the wines from the 2009 vintage was higher compared to the wines from the 2010. A tendency to increase the alcohol values for 2009 wines from rootstocks 41B, 110R and 196-17C (14.48%, 14.35% and 14.16%, respectively) was observed. These results are in concordance with the results obtained for \({}^{\circ}\)_Brix_ as the same rootstocks were the ones with the highest values. The same behavior was found for the wines from the 2010 vintage. The rootstocks that proportioned the highest \({}^{\circ}\)_Brix_ were the ones with the highest alcohol values, i.e., 3309C, 41B and 161-49C, with values of 13.55%, 13.44% and 13.44%, respectively. Between the two vintages, rootstock 41B was the one that kept the tendency to proportion highest accumulation of sugars in the grafted vine and consequently higher alcohol values in the resulting wines. With respect to total acidity, wines from vine grafted on the rootstock SO4 proportioned the highest values (9.40 g/L and 10.33 g/L for the 2009 and 2010 vintages, respectively). The pH values of the resulting wines from grafted vines were similar to the pH values of the grapes for both vintages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Rootstock** & **pH** & \begin{tabular}{c} **Titratable** \\ **Acidity (g/L)** \\ \end{tabular} & \begin{tabular}{c} **Tartaric Acid** \\ **(g/L)** \\ \end{tabular} & \
\begin{tabular}{c} **Malic Acid** \\ **(g/L)** \\ \end{tabular} &
\begin{tabular}{c} **Alcoholic** \\ **Strength by Volume** \\ \end{tabular} \\ \hline \multicolumn{6}{c}{**2009 vintage**} \\ \hline
110R & 3.42 \(\pm\) 0.02 & 8.63 \(\pm\) 1.05 & 3.31 \(\pm\) 0.33 & 4.01 \(\pm\) 0.56 & 14.35 \(\pm\) 0.20 \\ SO4 & 3.39 \(\pm\) 0.02 & 9
## 5 Implications for Nucleation of Continental Earthquakes
Furthermore, increasing cumulative slip allows more time for grain-scale deformation processes such as pressure solutions and ductile creep to operate, promoting a rheological transition from brittle to plastic behavior. Figure 7: **(a,b)** Depth profile of \((a-b)\) values for granite gauges at different \(V\) and \(P_{t}\), respectively. **(c)** Shear strength versus depth profile. Geothermal gradient is 25 \({}^{\circ}\)C/km as representative average value for continental crust [36; 37; 100]. Figure reproduced from [9] with permission. These findings underscore the complex interplay among \(T,P_{t},V\), and \(S\) in controlling the micromechanical processes governing fault strength and stability (Figure 5, C). Elevated pore fluid pressure is widely recognized as a critical factor in fault weakening and the initiation of unstable slip, thereby serving as a potential trigger for earthquake nucleation. In continental settings, numerous studies have demonstrated a strong correlation between high fluid pressure zones and seismogenic fault segments, particularly in regions such as the Tibetan Plateau and the North China Craton, where deep crustal fluids are actively involved in fault zone dynamics. Advances in microstructural analysis, including transmission electron microscopy (TEM) and synchrotron-based X-ray imaging, have revealed fluid-assisted grain boundary weakening, pressure solution features, and enhanced mineral reaction kinetics at asperity contacts under high fluid pressure conditions. These microscale observations provide compelling evidence that elevated pore fluid pressure facilitates the activation of ductile deformation mechanisms and reduces effective normal stress, thus promoting the transition from stable to unstable sliding. As such, incorporating fluid-rock interaction processes into fault mechanics models is essential for a more realistic understanding of earthquake nucleation in continental lithosphere. Future work should aim to bridge the gap between laboratory-derived frictional behavior and field-scale observations, with a particular focus on the spatiotemporal evolution of pore pressure and its role in modulating fault slip behavior across the seismic cycle.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 7 Challenges and Limitations of AI Chatbots in Infectious Disease Outbreaks
### Technical Limitations in Communication  
AI chatbots and LLMs face significant technical challenges in effectively communicating with users during health crises, often struggling with ambiguous queries, slang, and emotional cues. The misinterpretation of symptoms or concerns can result in inappropriate or unsafe advice, delaying necessary medical intervention [152]. This problem is exacerbated by limitations in NLP, as chatbots often fail to comprehend nuanced language or emotional states [153].  
While transformer-based models like BERT and GPT-3 have improved language understanding, they still struggle with context-specific slang and emotion recognition [154]. Emotion recognition, a key area for improvement in healthcare chatbots, remains under-explored, and integrating it into real-world applications is challenging [155]. Expanding chatbot training datasets to include slang, colloquial language, and regional dialects is an important step for improving communication with diverse populations [156].  
To address these challenges, adopting advanced NLP techniques and emotion recognition technologies is essential. Context-aware algorithms and multimodal systems that combine text analysis with speech and emotion recognition offer promising solutions for improving chatbot communication [157]. In addition, human-AI collaboration is also of paramount importance--systems should allow human experts to intervene when a chatbot detects ambiguity or emotional distress, ensuring appropriate responses [158; 159].  
User education is also important; informing users about chatbot limitations and encouraging clear communication can reduce misunderstandings [160]. Feedback mechanisms that enable users to report errors can lead to continuous improvements in chatbot performance [161].  
Research is needed to explore how AI chatbots can better interpret ambiguous queries and context-specific language, especially in healthcare. Developing domain-specific language models and integrating emotion recognition technologies are critical steps toward improving chatbot effectiveness in high-stakes health scenarios [48]. Interdisciplinary approaches from linguistics, psychology, and human-computer interaction will be vital for enhancing chatbot communication [162].
## 2 Materials and Methods
### Transmission Availability Data System (TADS)
#### 2.2.2 Analysis Dataset and Definitions  
For this analysis, TADS automatic (momentary and sustained) outages of TADS elements of 200 kV and above for years 2013-2019 were grouped by Transmission Owner (TO). These outages were sorted in chronological order, then examined to select groups of outages inside a TO with starting times of two consecutive outages separated by at most 2 min. This process resulted in 4246 groups that contained 10,501 outages (or 32.6% of all TADS automatic outages over the 7 years). Next, these groups were examined to detect outages that do not overlap in time with at least one other outage in the group. (Overlapping outages are defined here as outages that overlap in time, for any period of time. Namely, if two outages start at the same time, they overlap; if one of the outages starts earlier, the second outage should start before the first one ends for them to overlap.) These outages were removed from the study, and groups were redefined to contain only outages that overlap with one or more outages in the group. The resulted sets of outages are called clusters. Namely, a cluster is a set of automatic outages of transmission elements in the same company that satisfies the following conditions: (a) when sorted by their start time, a difference between start time of any two consecutive outages does not exceed 2 min; (b) each outage in a cluster overlaps in time with at least one other outage in a cluster. Condition (b) implies that outages in each cluster are \"continuous,\" i.e., at any moment from the earliest start of all outages in the cluster to the latest end of all outages at least one outage continues. The size of a cluster is defined as the number of outages it contains. For any cluster of size 2 and greater, the operator has at least one N-2 contingency, but depending on the cluster size may have multiple N-2, N-3, N-4... contingencies.  
The final data set processed for this study consists of 2918 clusters comprised of 6942 automatic outages (or 21.6% of all 32,198 automatic outages of TADS elements 200 kV and above from 2013 to 2019). Table 1 illustrates a breakdown of the outages in clusters by transmission element type and by voltage class as reported in TADS. For transformers, the voltage class is the high-side voltage. Voltages are operating voltages.
## 4 Framework
### HPA-C Framework
The results are presented in Table 7.

These findings demonstrate that HPA-C, combined with the custom CNN, achieves remarkable improvements in feature selection, even in scenarios where existing techniques have already reached extremely high performance levels (beyond 99% accuracy). In smart agricultural datasets such as _Plant Leaf Diseases, Weed Detection, Fruits-360, and Fresh n Rotten_, methods like GWO and GA already perform at near-optimal levels, yet HPA-C still manages to push the boundaries further, achieving up to a 0.5% to 1.5% increase in accuracy and a 1% to 3% boost in macro F1 score. While these numerical gains may seem small, at such high-performance levels, even a fraction of a percent can represent a significant improvement in real-world applications, where misclassification can lead to costly agricultural losses. The Fresh n Rotten dataset stands out, where HPA-C reaches 99.8% accuracy, surpassing GWO by 0.5% and significantly outperforming ReliefF by 12.8%, demonstrating its superior feature selection capability. Similarly, in the Plant Leaf Diseases dataset, while GWO achieves 98.3% accuracy, HPA-C improves it to 98.6%, further enhancing class-wise performance as evidenced by a 1.5% boost in macro F1 score. These refinements highlight the ability of HPA-C to extract highly discriminative features, even when existing techniques seem to have saturated performance levels. The accuracy plot for the different datasets and feature selection techniques is also shown in Figure 4. \begin{table}
\begin{tabular}{c c} \hline
**Feature Type** & **Selected Features** \\ \hline Texture & Contrast, entropy, and homogeneity \\ Color & Mean RGB, mean HSV, and standard deviation of L\({}^{\star}\) (Lab) \\ Shape & Aspect Ratio, compactness, and convexity \\ \hline \end{tabular} Note: L\({}^{\star}\) represents the lightness component in the CIE Lab color space. The asterisk (\({}^{\star}\)) denotes that value is perceptually uniform.

\end{table} 
Table 6: Most Frequently Selected Features Across Agricultural DatasetsBeyond smart agriculture, HPA-C proves its adaptability to remote sensing datasets, where class diversity and imbalance present additional challenges. Although benchmark techniques like GWO perform well, HPA-C consistently delivers improvements of up to 6.5% in macro F1 score and 5% in accuracy in complex datasets such as NWPU-RESISC45 and AID. Even in scenarios where GA and PSO struggle with minority-class recognition, HPA-C ensures a 30% to 35% improvement in macro recall, making it more robust for diverse class distributions. The key takeaway is that, while traditional feature selection techniques appear to have reached their peak, HPA-C successfully extracts more meaningful feature subsets, leading to noticeable refinements, even at the highest performance levels. This reinforces its reliability for real-world agricultural and remote sensing applications, where even the smallest increase in classification accuracy can have substantial economic and operational benefits.