esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Output Power Modelling for the Switching Process of the DFVSPS Unit
### Generating Mode  
#### 4.1.1 Start-Up in Generating Mode  
During start-up of generating mode, since the unit has not been connected to the grid, the output power of the unit to the grid is 0. The specific model is equivalent to the relationship shown in Formula (15), and the duration is the time when the unit speed is loaded to the rated speed:  
\[P_{S1}=0,\;0\leq t<\tau_{1}, \tag{15}\]  
where \(P_{s1}\) is the output power of the DFVSPS unit to the grid during the start-up, and \(\tau_{1}\) is the duration of the start-up. The calculation of \(\tau_{1}\) is as follows.  
The control diagram of the whole unit in this stage is shown in Figure 15.  
Figure 14: Simulation waveforms of unit in pump mode: (**a**) active power in load ramping stage; (**b**) active power in stable operation stage; (**c**) active power in load rejection stage; (**d**) speed in stable operation stage; (**e**) guide vane opening in stable operation stage; (**f**) actual dynamic responses for a DFVSPS unit in stable operation.  
The guide vane opening of pump turbine is set at 6% in the start-up stage and maintains the entire start-up process. Therefore, the dynamic process of the pump turbine is neglected, and \(P_{m}=6\%\) (This value is expressed as per unit value, which needs to be converted to an actual value when connected with DFIM), and the start-up process can be simplified to the Equation (16):  
\[\left\{\begin{array}{l}J\frac{d\omega_{m}}{dt}=\frac{P_{m}}{\omega_{m}}-B_{m }\omega_{m}\\ P_{m}=6\%\cdot P_{base}\end{array}\right.. \tag{16}\]  
It can be seen from the above equation that \(\tau_{1}\) depends on the mechanical parameter, friction factor of DFIM, and the no-load guide vane opening. According to the values of the relevant parameters in this paper, \(\tau_{1}=94\) s.
## 4 Main Result
### Numerical Study  
#### 4.4.1 Satellite Model Parameters  
For the numerical investigations, two satellites are represented by 3U-cubesates in the form of rectangular parallelepipeds with dimensions 10 cm \(\times\)10 cm \(\times\)30 cm. The aerodynamic drag force acting to \(i\)-th satellite in the acceleration units is given by the relation \(f_{i}=-\dfrac{\rho V^{2}}{2}C_{\alpha}(\Delta S\sin\alpha_{i}-S_{0})m^{-1}\) mswhere \(V\) denotes the satellite running speed with respect to the Earth's atmosphere; \(\rho=\rho(h)\) is the air density on the height \(h\) of the satellite orbit; \(\alpha_{i}\in[0,\pi/2]\) stands for the angle of attack; \(C_{\alpha}\) is the drag derivative coefficient with respect to attack angle; \(\Delta S\) denotes the difference between the satellite cross-sectional areas for the cases of \(\alpha=0\) and \(\alpha=\pi/2\) rad; \(S_{0}\) is the cross-sectional area as \(\alpha=0\); and \(m\) is the mass of the satellite. Therefore the differential drag can be found as \(f_{ij}=-\dfrac{\rho V^{2}}{2}C_{\alpha}(\Delta S(\sin\alpha_{j}-\sin\alpha_{i})\), which divides the following maximal control action \(u_{\max}=\dfrac{\rho V^{2}}{2}C_{\alpha}\Delta Sm^{-1}\) ms\({}^{-2}\). The satellite running speed \(V\) is related to the orbital angular velocity \(\omega\) as follows: \(V=\omega r_{0}=\sqrt{\dfrac{\mu}{r_{0}}}\), where \(\mu=M_{\rm earth}G\), \(G=6.6743\times 10^{-11}\) m\({}^{3}\)kg\({}^{-1}\)s\({}^{-2}\), \(M_{\rm earth}=5.972\times 10^{24}\) kg, so that \(\mu=3.986\times 10^{14}\) m\({}^{3}\)s\({}^{-2}\) and \(r_{0}=R_{\rm earth}+h\), \(R_{\rm earth}=6.371\times 10^{6}\) m.  
The following values are taken for the numerical study: \(\Delta S=0.02\) m\({}^{2}\), \(C_{\alpha}=2\), \(\rho=6.06\times 10^{-11}\) kg/m\({}^{3}\), \(h=250\times 10^{3}\) m, \(m=2\) kg. These parameters lead to \(V=7.76\times 10^{3}\) m/s, \(\omega=0.001172\) rad/s. \(u_{\max}=2.4\times 10^{-5}\) m/s\({}^{2}\).  
For controller design, parameter \(\Omega\) in (15) is taken as \(\Omega=10^{-3}\) rad/s. The pole-placement technique [36] leads to \(K=[-2.43\times 10^{-7}\)\(2.61\times 10^{-3}\)\(5.71\times 10^{-6}\)\(9.74\times 10^{-4}\)\(]\) in (13) (in the corresponding SI units).
# Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM
## 1 Introduction
As the brightness constrains optical flow problems, the hand shapes are robust features because they not only focus on the local movements of the hand components, but also track the displacement of every element in the data such as the depth value in the depth data or the hand joint in the skeletal data between two consecutive times. With our hypothesis that robust hand shape features impact the learning of local movements directly and global movements indirectly, our work explores the hand shape approach with the derivatives from the depth data and skeletal data. For the hand depth feature approach, we exploited hand component features from the hand segmentation model as shown in Row 5 in Figure 1. This can capture the shape changes of a gesture based on the hand component labels referring to the local movement in a hand gesture. For the hand skeleton features, we could exploit the point-cloud data from the depth data by 3D hand reconstruction; however, due to the time constraints in real-time hand applications, we focused on the 3D point-cloud under the hand joint coordinates in the 3D world space from the skeletal data. Our model will be able to learn 3D geometric transformation features. Simultaneously, we also use the displacement and rotation of the hand joints with respect to their neighbors for the hand skeletal shape features. Therefore, our model of dynamic hand gesture recognition addresses the diversity problem in hand features from the two modalities of the hand depth and hand skeletal data. To benefit from the various features, we propose multi-level feature LSTM using Conv1D, the Conv2D pyramid, and the LSTM block to exploit the diversity of the hand features from handcrafted data to automatically generate data from the deep learning model for the time series data and depth data. Our proposed method achieves the best accuracy on Dynamic Hand Gesture Recognition (DHG) [12] on 14 and 28 classes, respectively, with the skeletal data. It is good for real-time applications requiring low processing costs. Our paper's contribution consists of three parts. Firstly, we identify various hand features from two modalities with depth and skeletal data. We then propose the best features for exploiting skeletal data and depth data to achieve the best results. Secondly, we build the multi-level feature LSTM with Conv1D, the Conv2D pyramid, and the LSTM block to use the effective hand features. Finally, we experimented on DHG 14 and 28 and obtained the best results. The rest of this paper is composed of the following sections: Related Work, Proposed Idea, Hand Posture Feature Extraction, Network Architecture, Experiment, and Discussion and Conclusion. In Related Work, we discuss the datasets and approaches of 3D hand gestures. In the next two parts, our proposed method is described in detail. We then analyze the strengths of our method and make comparisons with the state-of-the-art in the experiments and discussion. The conclusions are given in the final part.
## 2 Materials and Methods
### Field Dataset  
#### 2.2.1 Field-Measured Dataset  
The in situ spectral data were collected from four field experiments carried out at three locations in 2016. The data were employed to assess the performance of the improved f\({}_{\rm esc}\) estimation model in canopies with varying structures. The first and second spectral measurements of winter wheat were taken at the Xiaotangshan Farm (XTS) in Beijing on 8-9 April, 18 April, and 8 December, when the winter wheat was at the stages of jointing, booting, and tillering, respectively. The third experiment was conducted on 18 December at the Nanbin Farm (NBF) in Sanya and included vegetables and crops such as sweet potato, cotton, pumpkin, and maize. The fourth spectral measurement was taken on 18 December at the Sanya Remote Sensing Satellite Data Receiving Station (SYS) and included gold coin grass. The details of these four ground measurements are listed in Table 3. According to the visual judgment of canopies in Figure 3, the LIDF type in XTS was spherical and, in NBF and SYS, it was planophile. All the spectral measurements were performed using a custom-made Ocean Optics QE Pro spectrometer (Ocean Optic, Inc., Dunedin, FL, USA).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Definition** & **Values** & **Unit** \\ \hline Canopy height & Canopy height & 1.5 & m \\ Soil spectra & Soil reflectance & loam\_gravelly\_brown\_dark & – \\ SZA & Solar zenith angle & 30.9303 & Degree \\ SAA & Solar azimuth angle & 249.1069 & Degree \\ VZA & Viewing zenith angle & 0–90 & Degree \\ VAA & Viewing azimuth angle & 0–360 & Degree \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 2: Multi-angle canopy-level SIF results of maize canopies in the near-infrared band (760 nm) using the DART model. The angular coordinate and diameter coordinate represent the viewing azimuth angles (VAAs, 0–360\({}^{\circ}\)) and the viewing zenith angles (VZAs, 0–90\({}^{\circ}\)), respectively. The red pentagram indicates the position of the sun (SZA = 30.9303\({}^{\circ}\), SAA = 249.1069\({}^{\circ}\)).
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 4 Applications of Blockchain in Agricultural and Animal Husbandry IoT
### Ensuring System Security and Privacy
Disadvantages:  
Implementing blockchain in IoT systems for agriculture faces several challenges, particularly resource and cost constraints, as significant computing power and storage demands can strain small-scale or low-power IoT devices [121; 122; 123]. Scalability and performance bottlenecks also pose issues, with high data throughput and frequent transactions potentially overwhelming current blockchain frameworks hindering real-time agricultural applications. Additionally, the deployment and integration of smart contracts, cloud services, and IoT sensors ([126; 127]) add considerable complexity, making it difficult to maintain interoperability and security across multiple platforms. Best Results:  
Recent studies demonstrate significant advancements in integrating blockchain with IoT to address security and trust challenges in agriculture. Robust security frameworks, ranging from UAV-focused solutions [125] to end-to-end monitoring platforms [130], show how combining blockchain, deep learning, and cloud computing can substantially enhance IoT security. Additionally, research [124; 128; 129] highlights blockchain's effectiveness in mitigating DDoS attacks and preventing data tampering, fostering greater trust in shared agricultural IoT data. Practical proof-of-concept implementations [130; 131; 120] further validate these technologies, showcasing prototypes that improve surveillance, privacy, and reliability--key enablers for scaling these systems across broader agricultural applications. Overall, these papers underscore blockchain's potential to secure and optimize IoT-based agricultural applications while acknowledging that issues of scalability, resource constraints, and complex integration still need further exploration.
## 2 Literature Review on the Personalized Recommendation in the MOOC Context
### Recommendation Algorithms Based on Mixed Models  
In practical applications, it is usually rare to directly use one algorithm alone for a recommendation because each of the aforementioned recommendation algorithms has certain shortcomings. Therefore, many personalized recommendation systems use a combination of different algorithms to obtain a more accurate recommendation performance.  
Given the characteristics of MOOC platforms, the resource recommendation model is constructed by integrating a deep belief network model. As a result, the contrast divergence algorithm has higher accuracy [18]. The matrix decomposition model of a multi-social network framework based on a joint probability distribution helps improve the runtime efficiency [19]. Furthermore, a collaborative filtering algorithm based on a dichotomous graph context can effectively solve the cold-start problem in collaborative filtering [20]. In addition, both the hybrid recommendation model integrating the collaborative filtering algorithm and deep-learning algorithm [21], and the personalized recommendation method based on deep-learning and multi-view fusion [22] have better recommendation results.  
Consequently, recommendation algorithms based on hybrid models, which combine the advantages of multiple algorithms, have gradually become popular in the MOOC context.
# Roller-Integrated Acoustic Wave Detection Technique for Rockfill Materials
## 3 Case Study  
### Testing Site and Materials  
A reservoir project is located in Henan Province, China. The main structures include the main dam, an auxiliary dam, a spillway, a water tunnel, and a power station. The main dam is a rockfill dam with a maximum height of 90.3 m. This experimental research focused on the NGM at the upstream cofferdam as a part of the main dam. During the early stage of the reservoir project, IWHR (China Institute of Water Resources and Hydropower Research) carried out a gravel material rolling test. The grading curve of the gravel material taken from VI zone material field is shown in Figure 11. In the five sets of tests, only one group of test materials was graded in the representative grading envelope, and the others were graded beyond the original grading. Taking into account the great change of the gravel material in each material field, the NGM had a maximum particle size of 200 mm based on a conservative estimate. The test results carried out by IWHR indicated that the proportion of particles less than 5 mm was 8-26%, and the proportion of particles more than 200 mm was 1.3-7.1%. The maximum size of the natural gravel material was up to 400 mm, and the content of particles less than 5 mm was controlled within 15%. The designed standard of the dry density of the NGM was greater than 2.144 g/cm\({}^{3}\), and compactness needed to be greater than 75% according to the design criteria. Due to a wide range of particle size distribution for NGM and above analysis, we know that CMV is not suitable for this material. Therefore, it is necessary to propose a new technique to detect the compactness of NGM quickly and accurately.
## 2 Background
### Relevance Estimation and Value Calibration  
Relevance estimation and value calibration (REVAC) is a method developed to calibrate the parameters of evolutionary algorithms (EAs), but could be applied to meta-heuristics in general. REVAC is itself an evolutionary algorithm and uses these techniques to explore the search space of parameter configurations. The concepts of REVAC, described in detail by [PERSON] and [PERSON] [10], are presented in this section.  
REVAC estimates the relevance of a parameter using information theory. Unlike SPO, REVAC does not estimate the performance of a heuristic on a specific parameter configuration, but instead estimates the performance if the parameter configuration was chosen from some probability density function. REVAC starts off with a uniform distribution over the parameter space. It then iteratively adds higher probabilities to the parameter vector that performs best. Additionally, this joint distribution is continuously smoothed to remove noise generated by the stochastic nature of the underlying heuristic.  
From the initial uniform distribution, REVAC samples \(m\) parameter vectors (configurations). The response of each vector is then determined by evaluating the performance of the heuristic on the given set of parameters. This is carried out in terms of some performance objective. Of these \(m\) vectors, the \(n<m\) best-performing candidates are selected, and are used to alter the joint distribution. These elite vectors are also chosen as parents to generate a new parameter vector via recombination and mutation. This new offspring vector replaces the oldest vector in the current population. Only one new vector is generated at each iteration.  
The details of the mutation process are rather complex and beyond the scope of this text. Suffice to say, since REVAC is an evolutionary algorithm, it requires a control parameter \(h\), which affects the mutation process. While [PERSON] and [PERSON] [10] suggested a value for \(h\approx\frac{n}{10}\), it is rather undesirable to have a control parameter for an algorithm designed to find an optimal parameter configuration.
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 4 Results
### Validation of Radiometric Cross-Calibration  
#### 4.2.1 Consistency Analysis of Calibration Results Using Different Test Sites  
As no ground data are available on the Golmud test site, the ground reflectance of the Dunhuang test site is used as alternative data source. Therefore, the consistency of the calibration results obtained by using Dunhuang and Golmud sites is analyzed, and the relative errors (1-Dunhuang cross-calibration coefficient/Golmud cross-calibration coefficient) of the calibration results of the two sites are calculated, which are shown in Table 6. It can be seen from the table that the variation of relative error using OLI as reference sensor ranges from 3.22% to 6.10%, and \(-\)0.89% to \(-\)7.70% using MODIS as a reference sensor. The absolute value of the maximum relative error is 7.70% over the green band by using MODIS as reference sensor. This indicates that the cross-calibration results of the two test sites have good consistency. It is proved that the Golmud site can be used for radiometric calibration of remote sensing satellites even without ground-based measured data.
## 4 Conclusions and Perspectives  
In this review, we focused on tRNA core modifications, starting from their impact on the structure and dynamics of tRNAs and, subsequently, exploring their reported functions in translation and fitness and their implications in stress adaptation mechanisms. Although the phenotypes associated with particular modifications, or a lack thereof, must ultimately be caused by changes in the structural and/or dynamic properties of the tRNA, thereby altering its ability to interact with cellular partners, what emerges from this review is that the mechanisms by which tRNA core modifications affect fitness, translation and response to stress are not yet fully understood. Although it was not the topic of this review, it is worth mentioning that tRNAs are also implicated in several other stress sensing and response mechanisms, which are not related to post-transcriptional modifications, e.g., the stringent response in bacteria or the nuclear accumulation of tRNAs upon amino acid starvation in yeast [170; 171]. In addition, mismethonylation of non-methionine tRNAs confers a protective role against oxidative stress [172; 173]. We have also not covered the aspects related to tRNA cleavage upon stress, which is known to be affected by modifications in the ACL region (reviewed in [174]). An important role in stress response mechanisms is therefore occupied by tRNAs, and post-transcriptional modifications are closely involved. This is not surprising given that tRNA modifications are at a crossroads connecting translation with metabolism through their biosynthetic pathways [29]. For instance, the cofactors of tRNA methyltransferases, i.e., S-adenosyl-L-methionine (SAM) and methylenetetrahydrofolate (5,10-methylene-THF), occupy key positions in the one-carbon metabolism, and these metabolites thus affect many aspects of translation, in part through their connections with tRNAs [175]. In recent years, major technological advancements have been made in the fields of mass spectrometry, RNA sequencing, and kinetic studies of translation. These methods have provided, and will continue to provide, important insights into the mechanisms by which tRNA modifications shape gene expression and stress response. In these future studies, although it may be tempting to follow the threads involving anticodon modifications on the pretext that the underlying mechanisms are now better understood, we believe it would be wise not to set aside the threads involving tRNA core modifications.  
Conceptualization, M.-J.Y., A.M. and P.B.; Writing--Original Draft Preparation, M.-J.Y., A.M. and P.B.; Writing--Review and Editing, M.-J.Y., A.M. and P.B.; Visualization, M.-J.Y., A.M. and P.B.; Supervision, P.B.; Funding Acquisition, P.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the ANR JCJC CiMoDyMo (ANR-19-CE44-0013) and the Labex DYNAO (ANR-11-LABX-0011). M.-J.Y. was supported by a Q-Life PhD fellowship (Qlife ANR-17-CONV-0005).  
The authors declare no conflict of interest.
## 6 Discussion
### Reduced Predictor Set  
We are using 12 predictors for each subsystem, which is more than most other studies. It is desirable to determine which predictors are the most influential to the study of coupling and coordination degrees. This is typically performed via a sensitivity analysis. Because the weight for each predictor is calculated as the first step in our study, we choose to use weights as the indication for the most influential predictors. Because the reference dataset that we use employs seven predictors to model each subsystem, we decide to use the seven predictors that have the largest weights to calculate the comprehensive evaluation index for each subsystem, the degree of coupling, the degree of the coordination, and subsequently to make projection. The results are in general rather similar to those produced using the 12-predictor set for each subsystem. In Figure 39, we show the training performance and the testing performance for the coordination degrees using our dataset \(O\) using the reduced seven-predictor set for each subsystem. Compared with the result obtained using the full 12-predictor set, 12 regions have positive \(R^{2}\) with the 7-predictor set while there are 18 regions have positive \(R^{2}\). Nevertheless, the result is significantly better than that using the reference dataset \(R\), where only two regions have positive \(R^{2}\).
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Traveling Salesman Graph  
The _traveling salesman graph_ is a complete undirected graph with weighted edges where nodes represent cities and edges represent paths between cities such that weights of edge dictate distance [120]. Weights may also be assigned to nodes depending on the problem. A circuit that reaches each node once (and only once) is known as a _tour_ and the length of a tour is the summation of weight of traversed edges. The objective of the  
Figure 11: An agreement graph, \(G=(A\cup B,E)\), that is also a bipartite graph in which agreeing jobs are connected by edges.  
problem associated with the traveling salesman graph is to find the optimal tour with the shortest length [120].  
Traveling salesman graph is used to model the cyclic shop scheduling problem [121] which is a less common scheduling problem. Cyclic shop scheduling problem focuses on batches of products being produced over a fixed interval of time which is known as cycle time [77; 121]. Cyclic scheduling problem looks to minimize cycle time [122].  
Within a manufacturing context, the cyclic shop scheduling problem may have n jobs and \(m\) machines. A corresponding travel salesman graph can be defined for a \(k\)th machine in the cyclic problem by \(H_{k}\)(_V,E,p,s_) in which (_V_) represent jobs with weights corresponding to Operation Time (_OP_) and edges (_E_) represent the sequence of jobs with weights corresponding to Setup Times (_ST_). Then, the problem is formally defined, and the optimal schedule is equivalent to the optimal tour within a traveling salesman graph [121]. Figure 12 illustrates a sample of traveling salesman graph \(H_{k}\).