esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion  
### Solving for the Optimal Strategy  
The sequential decision making process elements defined in the above process, along with the initial strategy, were sequentially incorporated into the dynamic programming algorithm for iterative computation. In this study, the iterative calculation was implemented in the form of MATLAB code, as shown in the pseudo-code in Algorithm 1. The maximum number of iterations was set to 1000, and the policy iteration method was used for iterative computation to obtain the optimal strategy. The optimal strategy obtained from the iterative computation was as follows:  
\[\pi(a\mid s)=P[A_{t}=a\mid S_{t}=s]=\begin{bmatrix}1&0&0&0\\ 1&0&0&0\\ 0&1&0&0\\ 0&1&1&0\\ 0&1&1&1\end{bmatrix} \tag{27}\]  
The optimal strategy output above represents the optimal maintenance plans for elements in different states in the next three years. Specifically, the following observations were made: (1) For elements in States \(1\) and \(2\), the recommended maintenance plan is no maintenance. (2) For elements in State \(3\), the recommended maintenance plan is to repair all the concrete cracks in the elements that are in this state. (3) For elements in State \(4\), the recommended maintenance plan is to repair all concrete cracks in the elements that are in this state and to repair all exposed reinforcement corrosion in the elements in this state. (4) For elements in State \(4\), the recommended maintenance plan is to repair all of the concrete cracks in the elements that are in this state, as well as to repair all the exposed reinforcement corrosion and all the visible defects in the elements in this state.  
According to Table 5 corresponding maintenance measures were applied to repair the elements. For example, if R-8-1 is in State \(2\) and there is a visible defect but there is an insufficient maintenance budget, then this element will not be repaired. If R-1-1 is in State \(4\), then it will be repaired using measure a1 to repair the concrete cracks and measure b2 to repair the exposed reinforcement corrosion. If R-11-1 is in State \(5\), it will be repairedusing measure a1 and a2 to repair the concrete cracks, measure b2 to repair the exposed reinforcement corrosion, and measure c2 to repair the visible defect.
## 2 Materials and Methods  
### Vineyard, Treatments and Grape Samples  
Tempranillo grape (_Vitis vinifera_ L.) variety plants grown in Finca La Grajera (Spain) were used for trial development. The vine was planted in 1995 (2.80 m \(\times\) 1.20 m) and the experiment was performed during the 2020 season. Foliar sprays included Ca nitrate (CaNO\({}_{3}\) 4H\({}_{2}\)O \(\geq\) 99.0%, Sigma-Aldrich-Merck, Darmstadt, Germany) or/and 120 mM silicic acid potassium solution (Tradecorp, Madrid, Spain), which were applied separately or in combination. The commercial products were used to prepare solutions with concentrations of 120 mM Ca, 120 mM Si and 120 mM Ca + 120 mM Si. Foliar sprays contained 0.1% (\(v/v\)) Genapol X-80 surfactant (Sigma-Aldrich-Merck). Control plants were foliar treated with 0.1% Genapol solutions. All treatments were applied twice, at veraison and one week later, and approximately delivered 200 mL of foliar spray solution per plant. Foliar applications were carried out in triplicate and were performed following a completely randomized block design, with 3 vines for each treatment and repetition.  
For harvesting, grapes from all treatments were picked at the optimal technological maturity, i.e., when the weight of 100 berries remained constant and the probable alcohol reached 13 (% \(v/v\)). A random set of 150 grape berries was picked, per treatment and replicate, and frozen at \(-20\)\({}^{\circ}\)C until the determination of volatile (50 berries), phenolic (50 berries) and nitrogen (50 berries) composition. Another set of 100 berries was used to obtain the average berry weight. Then, the berries were crushed, and the physico-chemical parameters were determined in the must.
# Innovative Application of Blockchain Technology for Digital Recipe Copyright Protection
###### Abstract  
With the advent of the digital age, traditional lifestyle activities, such as reading books, referencing recipes, and enjoying music, have progressively transitioned from offline to online. However, numerous issues plague the conventional approach to digital copyright protection. This is especially true in the realm of recipe protection, where the rights and interests of original creators are inadequately safeguarded due to the widespread dissemination of a large number of recipes on the Internet. This primarily stems from the high costs of gathering evidence, incomplete coverage of evidence collection, and the inability to identify and halt infringement activities in a timely manner during the process of traditional digital copyright protection. Therefore, this study designs and implements a blockchain-based digital recipe copyright protection scheme to address the issues of insufficient legal evidence and cumbersome processes in traditional digital copyright protection. First, we enhance standard short text similarity calculation method SimHash, boosting the accuracy of text similarity detection. We then utilize the decentralization, immutability, time-stamping, traceability, and smart contract features of blockchain technology for data privacy protection. We employ the Interplanetary File System (IPFS) to store raw data, thereby ensuring user privacy and security. Lastly, we improve the proxy voting node selection in the existing delegated proof of stake (DPOS) consensus mechanism. According thorough evaluation and empirical analysis, the scheme effectively improves the accuracy of text similarity detection. Simultaneously, the enhanced DPOS mechanism effectively rewards nodes with excellent performance and penalizes nodes exhibiting malicious behavior. In this study, we successfully designed and implemented an innovative digital recipe copyright protection scheme. This scheme effectively enhances the accuracy of text similarity detection; ensures the privacy and security of user data; and, through an enhanced DPOS mechanism, rewards well-performing nodes while penalizing those exhibiting malicious behavior.  
blockchain; text similarity; consensus protocol; digital recipes; SimHash +
Footnote †: journal: Applied
## 2 Problem Formulation  
The class of 2D singular continuous systems under consideration is represented by a Roesser model of the following form:  
\[E\begin{bmatrix}\frac{\partial x^{h}(t_{1},t_{2})}{\partial t_{1}}\\ \frac{\partial x^{v}(t_{1},t_{2})}{\partial t_{2}}\end{bmatrix} = \begin{bmatrix}A^{hh}&A^{hv}\\ A^{vh}&A^{vv}\end{bmatrix}\begin{bmatrix}x^{h}(t_{1},t_{2})\\ x^{v}(t_{1},t_{2})\end{bmatrix}+\begin{bmatrix}B^{h}\\ B^{v}\end{bmatrix}u(t_{1},t_{2}) \tag{1}\]  
where \(x^{h}(t_{1},t_{2})\in\mathbf{R}^{h_{h}}\) is the horizontal state, \(x^{v}(t_{1},t_{2})\in\mathbf{R}^{h_{v}}\) is the vertical state, \(u(t_{1},t_{2})\in\mathbf{R}^{h}\) is the control input, \(A^{ij}\), \(B^{i}\), \((i,j=h,v)\) are real constant matrices of appropriate dimensions, and \(E\) is eventually singular.  
The boundary conditions are given by  
\[x^{h}(0,t_{2}) = \phi^{h}(0,t_{2}),\forall t_{2}\in\mathbb{R}_{+} \tag{2}\] \[x^{v}(t_{1},0) = \phi^{v}(t_{1},0),\forall t_{1}\in\mathbb{R}_{+} \tag{3}\]  
where \(\phi^{h}\) and \(\phi^{v}\) are bounded and have compact support, that is, there exist non-negative \(L_{1}\), \(L_{2}\), \(T_{1}\), and \(T_{2}\) such that \[\left\{\begin{array}{ll}\left\|\phi^{h}(0,t)\right\|\leq L_{1}&\text{if} \quad 0\leq t\leq T_{2}\\ \left\|\phi^{h}(0,t)\right\|=0&\text{if}\quad t>T_{2}\\ \left\{\begin{array}{ll}\left\|\phi^{v}(t,0)\right\|\leq L_{2}&\text{if} \quad 0\leq t\leq T_{1}\\ \left\|\phi^{v}(t,0)\right\|=0&\text{if}\quad t>T_{1}\end{array}\right.\end{array}\right. \tag{5}\]  
**Definition 1** ([34]).:
1. _The system (_1_) is said to be regular if_ \(det(EI(z_{1},z_{2})-A)\) _is not identically zero, where_ \(I(z_{1},z_{2})=diag\left\{z_{1}I_{t_{h}},z_{2}I_{t_{v}}\right\}\)_._
2. _The system (_1_) is said to be impulse-free if the degree of_ \(det(EI(z_{1},z_{2})-A)\) _is equal to_ \(rank(E)\)_._
3. _The system (_1_) is said to be stable if_ \(\lambda(E,A)\subset D_{int}(0,1)\)_._
4. _The system (_1_) is said to be admissible if it is regular, impulse-free, and stable._  
**Definition 2** ([38]).: _A 2D singular Roesser model is said to be of the separated standard form if it is of the form (1) with \(E=\text{diag}\left\{E^{h},\ E^{v}\right\}\), where \(E^{h}\in\mathbb{R}^{n_{h}\times n_{h}}\) and \(E^{v}\in\mathbb{R}^{n_{v}\times n_{v}}\)._  
**Remark 1**.: _Note that asymptotic stability requires the horizontal and vertical components \(x^{h}(t_{1},t_{2})\) and \(x^{v}(t_{1},t_{2})\) to converge to the equilibrium state \(x^{h}\equiv 0\), \(x^{v}\equiv 0\) as \(t\rightarrow\infty\) with \(t=t_{1}+t_{2}\)._
## 3 Development of New Applications for DIC
#### 3.1.1 Synopsis  
Advanced thermoplastic composites are of special interest because of their superior properties, such as unlimited shelf life, high-fracture toughness, high-temperature resistance, high-fatigue performance, etc., in comparison to thermoset composites. They have a unique possibility to be manufactured in-situ using the automated fiber placement (AFP) process, which eliminates the need for a secondary process leading to significant savings in cost and energy. In AFP processing of thermoplastic composites, heat and pressure are applied simultaneously to a composite tow to achieve in-situ consolidation of the part. The composite tow consisting of fiber and a matrix is passed by a high-temperature heat source that melts the matrix and is placed by a pressure roller on the tool. High processing temperature and pressure combined with fast layup speed involved in AFP cause transient strain development, which in turn leads to residual stresses in the thermoplastic composites.  
Conventional strain gages cannot be used to measure transient strain during AFP of thermoplastic composites because they are processed at a high temperature and pressure. In this case study, 3D DIC was proposed to measure full-field transient strain and deformation of the thermoplastic composite tow during manufacturing [19].
# Changes in Oil Quality and Peroxidase Activity during Germination of Rape Seeds and Mustard Seeds
## 3 Results and Discussion
### Changes in Tocochromanols during Germination  
Results of the tocochromanols analysis were shown in Figures 5 and 6. In mustard, it is \(\gamma\)-tocopherol (day 0: 66.10 mg/100 g) that dominates, but this decreases during germination (day 3: 55.24 mg/100 g). On the other hand, \(\alpha\)-tocopherol increases during germination (day 0: 5.61 mg/100 g; day 3: 18.17 mg/100 g). The tocopherools thus increase during germination of mustard seed. In the study by [PERSON] et al. [43] on mustard seeds, a similar relationship was found: \(\alpha\)-tocopherol increased and \(\gamma\)-tocopherol decreased in sprouts left in the dark. \(\alpha\)-Tocopherol is predominant in photosynthetic tissues,  
Figure 4: Levels of individual phytosterols [mg/g oil] in mustard oils at different stages of germination (days 0, 1, 2, 3) (values are means \(\pm\) SD from three experiments. Values with different letters are significantly different (\(p\) < 0.05)).  
Figure 3: The level of individual phytosterols [mg/g oil] in rapeseed oils at different stages of germination (days 0, 1, 2, 3) (values are means \(\pm\) SD from three experiments. Values with different letters are significantly different (\(p\) < 0.05)).  
e.g., in leaves, and \(\gamma\)-tocopherol is found primarily in seeds, fruits and tubers [43]. Therefore, at the start of germination, the amount of \(\gamma\)-tocopherol begins to decrease and the amount of \(\alpha\)-tocopherol begins to increase. For soybeans, however, thetocopherol level had increased by the third day of germination, but began to decrease from day 5 [17]. In rapeseed, \(\alpha\)-tocopherol predominates (41.61 mg/100 g), increasing during germination (day 3: 54.02 mg/100 g) while \(\gamma\)-tocopherol decreases. The total tocopherols did not change over the three days of germination, but in the longer study of [PERSON] et al. [44] thetocpherols increased by a factor of over four. During the roasting of rapeseeds, the amount of tocopherols in the oil also increases. Changes in the amount of total tocopherols are greater. During 100 min of roasting, the amount of tocopherols can increase by 11% [39]. Consuming tocopherols in the diet protects against cardiovascular disease, neurodegenerative disease and cancer [45].  
Figure 5: Level of tocochromanols [mg/100 g oil] during mustard seed germination (days 0, 1, 2, 3) PC-8—plastochromanol-8 (values are means \(\pm\) SD from three experiments. Values with different letters are significantly different (\(p<0.05\))).  
Figure 6: Levels of tocochromanols [mg/100 g oil] during rapeseed germination (days 0, 1, 2, 3) PC-8—plastochromanol-8 (values are means \(\pm\) SD from three experiments. Values with different letters are significantly different (\(p<0.05\))).
## 4 Conclusions  
This study mainly analyzed the dependence of J-C parameters on the formation mechanism of serrated chips. The J-C parameters of Ti6 Al4V reported in previous studies differ significantly, which decreases the reliability of the cutting simulation. It also guides to improve the accuracy of cutting simulation models, as well as a reference for the selection of J-C constitutive parameters of simulation with Ti6 Al4V manufactured with different heat treatments and additive manufacturing. The following conclusions were drawn:  
1. Based on Abaqus/Explicit, a Ti6 Al4V micro-cutting simulation model was established, the formation process of Ti6 Al4V serrated chips was analyzed in detail, and the law of stress S\({}_{12}\) and the temperature field during the formation of Ti6 Al4V serrated chip was revealed.
2. Among material constitutive parameters (i.e., \(A\), \(B\), \(C\), \(m\), and \(n\)), initial yield stress \(A\) and thermal softening coefficient \(m\) exhibited the greatest effect on the sensitivity coefficient of the sawtooth. Serrated segmentation frequency and _C.S.I._ depend on material constitutive parameters (i.e., \(A\), \(C\), \(n\), and \(m\)), while the hardening modulus \(B\) can be ignored.
3. The temperature field and cutting force were positively related to the material constitutive parameters (i.e., \(A\), \(B\), \(C\), and \(m\)), and increase with increases in the constitutive parameters while the constitutive parameter \(n\) decreases. Simultaneously, it was concluded that the temperature of the first adiabatic shear zone was the highest.
4. The cutting force value during the micro-cutting process periodically fluctuated with the formation of the sawtooth.
5. The damage parameters affected serrated chip sensitivity, serrated segmentation frequency, _C.S.I._, temperature field in the shear zone, and shear band spacing. However, the exponent factor \(D_{2}\) exhibited the greatest effect.  
**Author Contributions:** Conceptualization, Z.Z. and C.N.; methodology, Z.Z. and C.N.; software, Y.Y.; validation, Y.Y.; formal analysis, Z.Z.; investigation, Z.Z.; resources, Z.Z.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, Y.B.; visualization, Z.Z.; supervision, X.J.; project administration, X.J.; funding acquisition, X.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Pre-research Project, grant number 41423020201.  
**Institutional Review Board Statement:** Not applicable.  
Figure 19: Comparison of average cutting force under different J-C damage parameters (\(D_{1}\), \(D_{2}\), \(D_{3}\), \(D_{4}\), and \(D_{5}\)). I, II, III, and IV correspond to variation of the original J-C damage parameters (\(-\)100%, \(-\)50%, +50%, and +100%, respectively).
# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
## 3 Proposed Approach for Emotion Recognition
### Classification Using CA-WGNN
The following equations explain the aggregation Equation (14) and combination processes Equation (15) at any given layer \(f\in[1,F]\) :  
Aggregation: \[P^{(f)}_{A}=\rho^{(f)}_{A}(s^{(f-1)}_{a},s^{(f-1)}_{w}:weM(a)),\] (14) Combination: \[S^{(f)}_{A}=\phi^{(f)}_{A}(p^{(f)}_{a}),\] (15)  
where for each layer \(f-1\), the aggregated edge \(\rho_{A}\) includes the edge's feature vector Equation (16) \(s_{a}\) and the terminal vertex feature vectors \(s_{w}\), where \(weM(a)\). The combination Equation (17) \(\phi_{A}\) uses this aggregation as input. Similarly, for nodes:  
Aggregation: \[e^{(f)}_{c}=\rho^{(f)}_{C}(z^{(f-1)}_{c},z^{(f-1)}_{c}:weM(c)),\] (16) Combination: \[z^{(f)}_{c}=\rho^{(f)}_{C}(e^{(f)}_{c}),\] (17)  
The final output feature vector \(\hat{x}\) is obtained by applying a readout function Equation (18):  
Readout: \[\hat{x}=\phi_{S}(\rho_{S}(z^{F}_{c},s^{F}_{a}:c,a\in S))\] (18)  
In the domain of speaker characteristic extraction, three distinct methodologies are employed: textual speaker embedding via LSTM networks, acoustic speaker embedding through DNN, and visual speaker embedding utilizing s CNN. The process of emotion recognition benefits substantially from the amalgamation of these diverse modalities, facilitated by a Cross-Attention Weighted Graph Neural Network CA-WGNN. The DNN effectively models acoustic patterns derived from audio inputs, whereas the LSTM network is adept at discerning temporal relationships in textual data. Concurrently, the CNN excels at extracting spatial features from video frames. Fusion of these modalities is achieved through the construction of a weighted graph, where the edges represent inter-modal correlations and weights are assigned based on the relative importance of each modality. The CA-WGNN leverages these weights to integrate data, enabling the detection of nuanced emotional cues. This integrative approach fosters a robust and versatile framework for emotion recognition across multiple modalities, thereby enhancing classification accuracy by harnessing complementary information from varied sources. Table 1 delineates the performance of Emotion Recognition Models on the CMU-MOSI andIIEMOCAP datasets, detailing their respective experimental configurations.
## 5 Results and Discussion  
### Satellite Observable Time  
This study samples 200 satellites from the existing Starlink constellation and computes their velocity and positional data using publicly accessible Two-Line Element (TLE) sets. Figure 6 illustrates the ground tracks of these satellites. An observation site was established in Xi'an, China, to monitor the selected 200 satellites. The visibility durations are depicted in Figure 7. Figure 7 reveals that the observation site maintains continuous visibility of at least one satellite at any given time, suggesting the practicality of SOPs positioning utilizing Starlink satellite. Following the doppler shift positioning principle, the user's location can be ascertained. We chose 12:00 p.m. as the observation time, and Figure 8 indicates that 18 satellites are visible at this time, with their visibility periods fulfilling the positioning criteria. Figure 7 merely displays the visibility times of the 200 satellites, yet the positioning approach outlined in this paper essentially satisfies the positioning needs at all times. It is conceivable that, once the full deployment of both Starlink and China Satellite Network's constellations is complete, a greater number of satellites will be available for positioning purposes.  
Figure 5: The flowchart of the satellite selection strategy.
## 3 Results and Discussion
### Microstructure and Phase Analysis after the Single and Two-Stage Aging Processes  
The microstructures and XRD comparisons for the as-cast, as-compressed (A.C.), and A.C. T6_SSAT ZM6 alloys are shown in Figure 4.  
Figure 4a demonstrates that the as-cast microstructure is composed of two parts: a grey \(\alpha\) matrix and a white island Mg\({}_{12}\)Nd phase that are distributed at the grain boundary. The \(\beta\) phase (Mg\({}_{12}\)Nd) exists at the grain boundary on its own, and the average grain size is about 40.5 \(\upmu\)m. In Figure 4b, the grain is remarkably refined and is uniformly distributed after hot compression. The grain boundary phase is broken, dispersed, and then refined, with an average grain size of 25.6 \(\upmu\)m, which is in contrast with the almost chrysanthemumb-shaped grains that are seen in the as-cast ZM6 alloy. The cotton-wadding shape inside the grain is considered to be ZnZr\({}_{3}\) or Zr-containing particles [16]. The small grains are mainly formed by slip and twinning, and the continuous dislocation movement along the slip surface promotes the nucleation of the aging phase. As seen in Figure 4c, the second phase disappeared at the grain boundary and was replaced by the dispersed cluster precipitates, most of which were distributed in the grain boundary. The eutectic phase was distributed on the grain boundaries in the form of islands, the shape of which depends on the growth of the primary \(\alpha\)-Mg phase [2; 10; 13]. The grain boundary is shown to be clear and regular, and the strip phase near the grain boundaries no longer exists. From the XRD comparison analysis of the ZM6 alloys in Figure 4d, only the \(\alpha\)-Mg matrix and Mg\({}_{12}\)Nd compounds can be detected, and the main diffraction peaks are all Mg diffraction peaks. It is evident from Figure 4d that the as-compressed specimen with the T6_SSAT process has more texture compared to the as-cast specimen, which has a marked effect on mechanical properties.  
Figure 4: OM and XRD analysis using different heat treatment methods: (**a**) As-cast ZM6 alloy; (**b**) as-compressed ZM6 alloy; (**c**) as-compressed ZM6 alloy with T6_SSAT process; (**d**) XRD comparison of ZM6 alloys with T6_SSAT process.
# Research on Equalization Strategy of Lithium Battery Pack Based on Multi-Layer Circuit
## 5 Simulation Results
### Comparison with Adjacent Equilibrium  
A simulation analysis of the generally used adjacent cell equalization methods was also carried out to prove the performance of the multi-layer equalization strategy presented in this study. Figure 12 shows the circuit arrangement utilized to balance adjacent cells. The SOC of the two neighboring cells determines the current direction of each equalizer, and the size is always 1 A. The current variation in battery 2 is shown in Figure 13, with a maximum current of 4.83 A. Some battery cells in the battery pack have a charge and discharge current that is more than the permitted current, resulting in negative consequences such as increased heat generation and reduced performance in some circumstances. Figure 14 shows each battery SOC change, and the equalization time is 350.65 s. According to the equalization results, the proposed multi-layer equalization strategy's equilibrium efficiency is raised by 23.55% compared with the adjacent battery equalization approach, demonstrating the proposed multi-layer equalization strategy's good performance.  
Figure 11: (**a**) the first layer equalization current, (**b**) the second layer equalizes the current, (**c**) the third layer equalizes the current, the blue dotted line represents the equalization current range.  
Figure 12: The adjacent cell balancing circuit.