esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
### Crustal Contamination and Fractional Crystallization  
The majority of the Early-Middle Devonian samples displayed elevated Nb/Ta and Nb/Th ratios, surpassing those typically associated with crustal-derived magmas [71; 72]. These samples also exhibited low Rb/Sr ratios (0.0003-0.1064) and high Ti/Y ratios (223-431), further supporting the absence of significant crustal contamination [73]. Additionally, substantial crustal contamination would typically result in negative anomalies for Sr, P, and U in primitive mantle-normalized trace element diagrams [68]; however, the Early-Middle Devonian volcanic rocks in this study showed positive anomalies for these elements (Figure 8a). Collectively, these findings indicate that the Early-Middle Devonian volcanic rocks in the Fuyun-Qinghe area did not undergo extensive crustal contamination.  
Regarding fractional crystallization, there was an inapparent correlation between MgO and TiO\({}_{2}\) for both alkaline and subalkaline series rocks in the variation diagrams for major oxides and Cr, Ni, and MgO contents (Figure 9), and the correlations between MgO, TFe\({}_{2}\)O\({}_{3}\), CaO, and CaO/Al\({}_{2}\)O\({}_{3}\) of subalkaline samples (mostly CA rocks) were also not obvious. However, the alkaline series had obvious positive correlations. The aforementioned characteristics suggested that there had been no fractional crystallization in the CA series rocks; they might have undergone magmatic processes primarily characterized by varying degrees of partial melting or involved multiple magma sources. The relatively obvious correlations among some elements of the alkaline series rocks suggested a relatively consistent depth of magma sources, or they were subjected to fluid processes. Furthermore, a strong positive correlation was observed between Cr, Ni, and MgO, indicating the fractionation of the Cr-spinel (Figure 9).
## 2 The Mechanism of Action in Picosecond Laser Treatment
The tissue effects of fractional PS lasers depend on chromophore-assisted ionized plasma formation, known as laser-induced optical breakdown (LIOB) [2]. LIOB is initiated with the generation of accelerated seed electrons, which collide with the surrounding molecules and release more free electrons. This process, which is referred to as ionization or electron avalanche, leads to the formation of a collection of free electrons, i.e., plasma. Plasma absorbs the incoming electromagnetic radiation, causing optical failure or optical breakdown and the subsequent tissue ablation. Following LIOB, a mechanical response occurs in the skin due to the rapid expansion of plasma, leading to photodisruption. Photodisruptive effects may manifest as shockwave emission, formation of cavitation bubbles, or shockwave recoil following bubble collapse, which also may contribute to tissue ablation. The findings of histological studies demonstrated that melanin and hemoglobin serve as primary chromophores for LIOB after fractional PS laser irradiation [7]. The photomechanical stress generated by fractional PS lasers permeates into the dermis, and stimulates fibroblast proliferation, collagen regeneration, and skin remodeling [11].
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 3 Elastography Techniques
### B: Shear Wave Imaging
#### 3.2.2 Two-Dimensional Shear Wave Elastography (2D-SWE)  
Two-dimensional shear wave elastography (2D-SWE) is the newest shear wave imaging method developed, which uses dynamic stress by ARFI as the excitation method. In 2D-SWE, the shear waves measured are perpendicular to the ARFI application and use multiple focal zones that are interrogated quickly, rather than one single location. The evolution of this method has given us the ability to visualize a color quantitative elastogram superimposed on a B-mode image and can easily produce tissue stiffness information [46; 47; 48; 49; 50; 51].
# Design of Multi-Chain Traceability Model for Pepper Products Based on Traceability Code
## 4 Construction of Multi-Chain Traceability Model for Pepper Products
### Multi-Chain Model Performance Tests
#### 4.5.2 Throughput Performance Test  
TPS refers to the number of transactions that the system can process per second, which is one of the important indicators of blockchain system performance. The multi-chain traceability model is tested in comparison with the traditional single-chain blockchain structure to test the throughput of the two storage structures when sending 50, 100, 150, 200, 250, 300, 350, 400, 450, and 500 requests, respectively, under the same test environment, where the requests sent are data query requests. The test results are shown in Figure 5.  
The throughput of single-chain model data queries increases linearly when the number of blockchain transactions is between 50 and 150, and it levels off after reaching 125 TPS. The throughput of multi-chain model data queries increases linearly when the number of blockchain transactions is between 50 and 300, and it levels off after reaching 240 TPS. Single-chain query throughput is up to 130 TPS, and multi-chain query throughput is up to 246 TPS. The multi-chain model also works more efficiently than the single-chain model based on the number of transactions completed per second.
## 4 Discussion
### Limitations and Future Research Directions
Studies should specifically test model performance with new participants from different demographic backgrounds, educational environments, and geographical locations to significantly enhance our understanding of model generalizability. Finally, the integration of multiple sensor types and locations to potentially improve classification accuracy, as suggested by the varying success rates between hip and wrist placements, should also be investigated [19; 22]. The development of standardised protocols for data collection and processing would facilitate better comparison across studies and implementation in real-world settings.
# In-Situ Observation of Adhesion Behavior During Ultrasonic Al Ribbon Bonding
## 3 Results
### In-Situ Observation of Adhesion Behavior Captured at a Frame Rate of \(10^{3}\) fps  
Figure 9 shows the in-situ observation results captured at \(F_{rate}=10^{3}\) fps under bright illumination conditions. The bonding condition was \(F_{b}=7\) N and \(P_{u}=3\) W. Under this shooting condition (at the shutter speed of 1 ms), the Al ribbon was brightly captured, as shown in Figure 9a, that is, reflected light was obtained from aluminum surface roughness. The apparent contact width became greater than that of the bonding tool at \(t=1\) ms. Local adhesion occurred due to large sliding and is followed by the macro folding at bonding initialization. The island streaks appeared at \(t=5\)-10 ms; however, these were not clearly visible. Both of the uncontacted sides of the Al ribbon were warped (separated from the substrate). The central belt zone was observed at \(t=50\) ms and it became clear at \(t=100\) ms. The central portions of the island streaks appeared to be covered gradually with the belt zone at \(t>50\) ms.  
Figure 8: Explanation of Areas A, B, and C by illustrating the video capture at \(t=400\) ms under \(F_{b}=7\) N and \(P_{u}=4\) W: (**a**) Video capture at \(t=100\) ms; (**b**) Schematic illustration of the video capture.  
Figure 9: In-situ observation results (video captures) of the bonding interface between Al ribbon and silica substrate obtained at \(F_{rate}=10^{3}\) fps. The bonding condition was \(F_{b}=7\) N and \(P_{u}=3\) W. Simultaneous measurement result of ultrasonic vibration amplitude is shown in Figure 3 (a–2): (**a**) \(t=0\) ms without ultrasonic vibration (US); (**b**) \(t=1\) ms immediately after introducing US; (**c**) Island streaks began to appear at \(t=5\) ms; (**d**) \(t=10\) ms; (**e**) \(t=20\) ms; (**f**) Central belt zone began to be observed at \(t=50\) ms; (**g**) \(t=100\) ms; (**h**) \(t=400\) ms.  
Figure 10 shows the in-situ observation results captured under \(F_{b}\) = 7 N and \(P_{u}\) = 4 W. The apparent bonded width achieved three times the tool width at \(t\) = 5 ms, as shown in Figure 10c; however, the width decreased due to Al ribbon warping, as shown in Figure 10d. Several island streaks appeared at the central area at \(t\) = 5 ms; however, the stripes became unclear at \(t\) = 20 ms. The central belt zone was then observed, as shown in Figure 10f. Another belt zone appeared in the right-hand side of the central bond area, as shown in Figure 10g. A blackish gray area became visible around the central belt zone. This blackish area corresponded to be the peripheral part of the island streaks (Area A). Another gray area appeared outside of Area A, as shown in Figure 10f-h. This corresponded to be the peripheral bond area formed by the microslip mechanism [11; 12; 1] after the macro fold was complete. The touch and detach area (Area C) could not be detected at \(10^{3}\) fps. The peripheral bond area could contain the outermost touch and detach area.
# Reliability-Based Design of Driven Piles Considering Setup Effects
## 2 Basic Estimation Methods of File Setup  
Due to the pile setup, the total ultimate resistance of driven piles includes two parts, initial ultimate resistance and setup resistance, which can be expressed as the following equation:  
\[R=R_{0}+R_{\text{setup}} \tag{1}\]  
where \(R\) is the total ultimate resistance at time \(t\) after driving, \(R_{0}\) is the initial ultimate resistance at initial time \(t_{0}\), and \(R_{\text{setup}}\) is the setup resistance at time \(t\) after driving.  
The significant importance to accurately assess setup resistance for the reliability-based design of driven piles is indicated by Equation (1). Therefore, many researchers have paid attention to this issue and proposed empirical relationships for quantifying the setup effects of driven piles. An empirical formula for the setup was presented by [PERSON] and [PERSON] [24] based on a logarithmic augment in pile bearing capacity with time. [PERSON] et al. [25] proposed a formula predicting the rate at which the pile capacity augmented with the increase in time. The formula presented by [PERSON] et al. [26] was used to predict the setup based on load test data on five concrete piles. An alternative method of using a hyperbolic function to predict the setup was developed by [PERSON] and [PERSON] [27]. Among the existing formulas, the logarithmic empirical relationship by [PERSON] and [PERSON] [24] has been commonly used for the prediction of the pile setup, that is:  
\[R_{\text{setup}}=R_{0}A\log\frac{t}{t_{0}} \tag{2}\]  
where \(t\) is the elapsed time from the end of initial pile driving; \(t_{0}\) is the reference time after which the resistance increases become linear on this plot; \(A\) is a factor that is a function of soil type and is the rate of pile resistance (\(R/R_{0}\)) increase per log cycle of time (\(t/t_{0}\)).
## 1 Introduction
An efficient TDMA-based MAC protocol for IoT nodes to transfer their data in a contention-free manner to meet adaptive data requirements. 3. TMPAD allows varying lengths of communication sessions. If the number of data-requesting slots is less, the session is reduced. However, if the slot requests increase, the session size increases till a certain threshold. 4. TDMA slots are assigned to the nodes by applying TOPSIS and 0/1 knapsack algorithms. The organization of the manuscript is as follows:  
The previous studies and trust-based data management in different areas of communications applications are discussed in Section 2. Our proposed TDMA-based MAC protocol with different frame structures, a trust management system, and the slots allocation mechanism are described in detail in Section 3. Comparative performance analysis of the proposed scheme with other well-known algorithms, along with extensive simulation results, is discussed in Section 4, and Section 5 provides the conclusion of our manuscript.
## 2 Materials and Methods
### Kinematic and Dynamic Equation of Motion
#### 2.2.1 Kinematic Analysis for Wheel 1 and Caster Wheel 1  
Position of W\({}_{1}\) and CW\({}_{1}\)  
The positions of wheel 1 and caster wheel 1 are centroid coordinates of the driving wheel that can be achieved by Equations (1)-(4):  
\[\mathbf{Z_{W1}}=L_{O_{1}A}\cos\varnothing_{1} \tag{1}\]  
\[\mathbf{X_{W1}}=L_{O_{1}A}\text{sin}\;\varnothing_{1} \tag{2}\]  
\[\mathbf{Z_{CW1}}=\mathbf{Z_{W1}}+L_{AE}\cos\varnothing_{2}=L_{O_{1}A}\cos\varnothing_ {1}+L_{AE}\cos\varnothing_{2} \tag{3}\]  
\[\mathbf{X_{CW1}}=\mathbf{X_{W1}}+L_{AE}\sin\varnothing_{2}=L_{O_{1}A}\sin\varnothing_ {1}+L_{AE}\sin\varnothing_{2} \tag{4}\]  
Speed of W\({}_{1}\) and CW\({}_{1}\)  
By calculating the first-order derivative of Equations (1)-(4), the centroid speed of wheel 1 and caster wheel 1 can be expressed as follows:  
\[\dot{\mathbf{Z_{W1}}}=-\ddot{\varnothing_{1}}L_{O_{1}A}\sin\varnothing_{1} \tag{5}\]  
\[\dot{\mathbf{X_{W1}}}=\ddot{\varnothing_{1}}L_{O_{1}A}\cos\varnothing_{1} \tag{6}\]  
\[\dot{\mathbf{Z_{CW1}}}=\dot{\mathbf{Z_{W1}}}-\ddot{\varnothing_{2}}L_{AE}\sin\varnothing _{2}=-\ddot{\varnothing_{1}}L_{O_{1}A}\sin\varnothing_{1}-\dot{\varnothing_{2} }L_{AE}\sin\varnothing_{2} \tag{7}\]  
\[\dot{\mathbf{X_{CW1}}}=\dot{\mathbf{X_{W1}}}+\ddot{\varnothing_{2}}L_{AE}\cos\varnothing _{2}=\ddot{\varnothing_{1}}L_{O_{1}A}\cos\varnothing_{1}+\ddot{\varnothing_{2}}L _{AE}\cos\varnothing_{2} \tag{8}\]  
Acceleration of W\({}_{1}\) and CW\({}_{1}\)  
By calculating the second-order derivative of Equations (5)-(8), the centroid acceleration of wheel 1 and caster wheel 1 can be expressed as follows:  
\[\ddot{\mathbf{Z_{W1}}}=-\dot{\varnothing_{1}}^{2}L_{O_{1}A}\cos\varnothing_{1} \tag{9}\]  
\[\ddot{\mathbf{X_{W1}}}=-\ddot{\varnothing_{1}}^{2}L_{O_{1}A}\sin\varnothing_{1} \tag{10}\]  
\[\ddot{\mathbf{Z_{CW1}}}=\ddot{\mathbf{Z_{W1}}}-L_{AE}\Big{(}\;\ddot{\varnothing_{2}}^{2 }\cos\varnothing_{2}\;\Big{)}=-\dot{\varnothing_{1}}^{2}L_{O_{1}A}\cos\varnothing _{1}-L_{AE}\Big{(}\;\ddot{\varnothing_{2}}^{2}\cos\varnothing_{2}\;\Big{)} \tag{11}\]  
\[\ddot{\mathbf{X_{CW1}}}=\ddot{\mathbf{X_{W1}}}-L_{AE}\Big{(}\;\dot{\varnothing_{2}}^{2 }\sin\varnothing_{2}\;\Big{)}=-\dot{\varnothing_{1}}^{2}L_{O_{1}A}\sin\varnothing _{1}-L_{AE}\Big{(}\;\dot{\varnothing_{2}}^{2}\sin\varnothing_{2}\;\Big{)} \tag{12}\]  
Equations (9)-(12) constitute the kinematic equation for the robot driving wheel and caster wheel in climbing the wind turbine tower capability.  
Figure 3: Model of the tower-climbing robot’s wheel.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 3 Multi-Agent Planning and Control
### Sequential Decision-Making  
In multi-agent tracking, some agents may migrate to new areas in search of interest-points while others monitor the areas already explored. This requires coordination between agents, which adds another layer of complexity to the problem; certain outcomes depend on particular sequences of actions (\"UAV1 moves here then UAV2 moves there\"). Leaving aside the communication requirements, there are exponentially many permutations of these action sequences (with respect to their length).  
The complexity of sequential decision-making has been studied in the literature. One way of reducing complexity is by expanding the local state-space at each agent to include the actions of relevant (neighboring) agents, so that the system dynamics \"seen\" by an individual agent appear stationary [33]. The fixed-point techniques in Section 2 for optimizing systems with stationary dynamics, then reduce to this expanded local system (provided the agents are sufficiently homogeneous).  
Sequential decision-making has also been studied in the context of game-theory, where the objective is to choose action sequences that maximize some joint utility function. The Nash equilibrium of the system depends on the information \"profiles\" (i.e., observations of the global state) available to each agent and the timing of their availability [34]. In addition, distributed multi-agent optimization appears in clean-slate approaches to network security where some of the agents are adversarial [35].  
Option-spaces offer a possible solution to the complexity of sequential decision making in multi-agent tracking. Every agent action is determined by a fusion of policies over multiple hierarchical levels, where higher levels determine the activity of larger groups (clusters of clusters of clusters) of agents. The complexity of any action sequence reduces to the sum (over all levels) of the complexity within each cluster. If cluster sizes stay constant, the sequence complexity depends on the number of levels in the hierarchy, which grows logarithmically in the number of agents (instead of exponentially).
# Rainfall Standard of Disaster Prediction for Agricultural Droughs in S. Korea
###### Abstract  
With the climate change adding to the frequency and intensity of natural disasters, drought has devastated large areas of lands in South Korea. Still, the exact beginning and end of the drought is difficult to identify, and this impedes the development and implementation of disaster predictions. Although the drought phenomenon has been well-documented, predictions thereof are limited due to the non-linear and complex temporal fluctuations of the hydrologic factors. Hence, this study set up some reference points for disaster-prediction rainfall based on South Korea's agricultural drought damage data, to help in drought relief. To set up the proposed reference points for disaster-prediction rainfall, we analyzed rainfall in light of the disaster-prevention relevance to agricultural droughs and the disaster reduction. As an analysis method, rainfall of municipality was calculated through [PERSON]'s polygonal method, to apply rainfall weighting value for each rainfall observatory. In addition, the linear regression analysis was applied to suggest the calculation formula for setting the annual disaster reduction rainfall. The results of this study, standard of judgment point for disaster prevention of agricultural drought at the time of disaster management, were analyzed for rainfall for local governments and the whole country. Rather than using various drought indices that are currently developed, policy makers or public servant made suggestions based on rainfall that is most accessible and convenient for judging the timing of agricultural drought. As the disaster-prevention rainfall with agricultural droughs is expected to occur, we established the average annual rainfall of \(\leq\)1200 or 100 mm below the preceding year's average annual rainfall. Moreover, as the disaster-reduction rainfall for agricultural droughs to end, we determined the average monthly rainfall of \(\geq\)150 mm.  
agricultural drought; droughs-damage data; agricultural-disaster prediction; agricultural-disaster prevention; agricultural-disaster reduction
###### Abstract  
Highly dispersed Mn-Ce binary metal oxides supported on carbon nanofibers (MnO\({}_{\mathrm{x}}\)-CeO\({}_{2}\)/CNFs(OX)) were prepared for Hg\({}^{0}\) removal from coal-fired flue gas. The loading value of the well-dispersed MnO\({}_{\mathrm{x}}\)-CeO\({}_{2}\) was much higher than those of many other reported supports, indicating that more active sites were loaded on the carbon nanofibers. In the present study, 30 wt % metal oxides (15 wt % MnO\({}_{\mathrm{x}}\) and 15 wt % CeO\({}_{2}\)) were successfully deposited on the carbon nanofibers, and the sorbent yielded the highest Hg\({}^{0}\) removal efficiency (>90%) within 120-220 \({}^{\circ}\)C under a N\({}_{2}\)/O\({}_{2}\) atmosphere. An increase in the amount of highly dispersed metal oxides provided abundant active species for efficient Hg\({}^{0}\) removal, such as active oxygen species and Mn\({}^{4+}\) cations. Meanwhile, the carbon nanofiber framework provides the pathway for charge transfer during the heterogeneous Hg\({}^{0}\) capture reaction processes. Under a N\({}_{2}\)+6%O\({}_{2}\) atmosphere, a majority of Hg\({}^{0}\) was removed via chemisorption reactions. The effects of flue gas composition were also investigated. O\({}_{2}\) replenished the active oxygen species on the surface and thus greatly promoted the Hg\({}^{0}\) removal efficiency. SO\({}_{2}\) had an inhibitory effect on Hg\({}^{0}\) removal, but NO facilitated Hg\({}^{0}\) capture performance. Overall, carbon nanofibers seems to be a good candidate for the support and MnO\({}_{\mathrm{x}}\)-CeO\({}_{2}\)/CNFs(OX) may be promising for Hg\({}^{0}\) removal from coal-fired flue gas.  
Keywords:coal-fired flue gas; mercury; carbon nanofibers; metal oxides; adsorption +
Footnote †: journal: Applied Sciences
# MuIm: Analyzing Music-Image Correlations from an
## 3 Multi-Layered Music-Image Correlation
### Artistic Media: Emotional Complexity  
Referring to Equation (1), when applied to artistic content--particularly complex and subjective works--the challenge of capturing emotional complexity becomes evident. The emotional content in artistic media often cannot be accurately represented using basic categorical emotion states (CES) or valence-arousal (VA) dimensional emotion space (DES) models. This limitation arises because the emotions in such works are inherently subjective and can vary significantly across different regions of an artwork, as illustrated in Figure 1(a). Moreover, the traditional use of CES models (assigning only a single label to a piece of media) and DES models (applying VA values uniformly across an entire image) has struggled to capture the nuanced emotional interplay between visual and audio data, as demonstrated in Figure 1(b).  
To address this complexity, we adopt a hybrid approach informed by psychological studies [40; 41]. This approach incorporates 28 distinct emotions, combining both categorical and dimensional components to more precisely capture the emotional nu  
Figure 2: (a) Illustrative example of semantically similar but contradicting emotional media, where visual data and music data are represented. (b) Illustrative comparison of 2D (VA) vs. 28-category dimensional emotional representation model.  
Categorical Component: Utilizes 28 distinct emotions, aligning with CES by defining clear emotion categories.
* Dimensional Component: Adds a 9-point Likert scale to capture emotion intensity, similarly to VA-DES, for nuanced representation.
* Hybrid Nature: Supports multiple emotions per input, blending categorical and intensity-based approaches for complex overlapping emotional states.  
Figure 2b illustrates how the hybrid model facilitates clearer differentiation between pieces of artistic data, effectively overcoming the cluttered and ambiguous emotional labeling often observed with standard valence-arousal (VA) models. For additional clarity, a comparison of the three emotion labeling models is provided in Table 1.  
In real-world scenarios, multimedia data, such as music-image pairs, are often tagged with descriptors that reflect various embedded layers of information. Building on the correlation function defined in Equation (1), we identify key layers relevant to realistic media, emphasizing their roles in influencing semantic and emotional correlations:  
* **Semantic Layer**[10; 25; 42]: This layer pertains to identifiable objects, themes, or contexts within multimedia content, such as a forest image or classical music.
* **Affective/Emotional Layer**[18; 32; 43]: This layer addresses intangible emotional responses, such as joy, which are tagged within visual and auditory media to denote mood or atmosphere.
* **Contextual/Historical Layer**[44]: While providing insights into the origin, significance, or intention of the content, such as historical periods or social concepts, this layer was not included in this study to maintain simplicity.