esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Guide Vane Scheduling Method Based on the Kinematic Model and Dual Schedule Curves
## 2 Mechanical Structure of the VGVS
### DOFs and Hysteresis of the VGV Link Mechanisms  
Figure 2 shows schematics of link mechanisms and joints representing bell cranks, vertical links, and unison rings of VGVs. There are four links below the bell crank in the mechanisms, including the ground part (\(L_{a},\ L_{b},\ L_{c},\ L_{d}\)) which are represented in red lines. The DOF of the mechanisms was calculated using the Grubler's Equation with joints as follows [16].  
\[DOF = 6(N_{link}-1)-5N_{J1}-4N_{J2}-3N_{J3}-2N_{J4}-N_{J5} \tag{1}\]  
Here, \(N_{link}\) represents the total number of links, \(N_{Jk}\) represents the number of joints having k DOF. The numbers of joints and links in the link mechanism in Figure 2 are listed in Table 1.  
Figure 2: Simplified diagram of VGV links and joints  
The number of DOF's calculated using the Grubler's equation was six. However, two rotational motions of vertical links about their longitudinal axes do not affect the motion of the unison ring motion; thus, the resultant DOF's number is four (as kinematic redundancy). The kinematic redundancy causes eccentricity of rotation at the center of the unison ring, and inevitably causes the VGV link mechanisms to have hysteresis from the actuator to the unison ring.  
For this reason, research about reducing DOF redundancy by modifying the joint type or array have been conducted, and additional springs and bushings to apply a pre-load have been widely used. However, elimination of redundancy and addition of pre-load parts also induces unnecessary frictional forces and increases system complexity.  
The proposed methodology in this paper can be applied to the VGVs system to obtain vane angles according to the VGV schedule with minimization of VIGV hysteresis based on the control algorithm with dual schedule curves, as presented in the following sections.
# Mathematical Apparatus of Optimal Decision-Making Based on Vector Optimization
## 6 Statement and Optimal Decision Making with Experimental Data in Problems with Two Parameters
### Problem Definition of Decision Making of the Second Type with Two Parameters  
The problem setting is performed by the system designer based on experimental data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(x\) & \(\lambda_{1}\)**-max** & \(\lambda_{2}\)**-max** & \(\lambda_{3}\)**–min** & \(\lambda_{4}\)**–max** \\ \hline
630 & -0.0276 & 0.933 & 0.451 & 1.0298 \\
1580 & 0.0471 & 1.0214 & 0.4971 & 0.9463 \\
2662 & 0.1653 & 1.0155 & 0.6781 & 0.8351 \\
3704 & 0.2545 & 0.9919 & 0.8383 & 0.7795 \\
4800 & 0.3334 & 0.933 & 0.9289 & 0.7239 \\
5929 & 0.4093 & 0.8153 & 1.0402 & 0.6127 \\
7284 & 0.4748 & 0.7034 & 0.9741 & 0.507 \\
9353 & 0.5864 & 0.5798 & 0.9289 & 0.5014 \\
14,505 & 0.7627 & 0.1029 & 0.6057 & 0.2234 \\
18,810 & 1.0415 & 0.0205 & 0.0346 & \(-0.0269\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized criteria.  
It is given that we have an engineering system functioning according to a vector of controlled variables \(X=(x_{1},x_{2})\) with two parameters that take values: \(x_{1}\), \(x_{2}\in\)[0 2.5 5. 7.5 10].  
Decision-making criteria are represented by five functions \(f_{1}(X)\),...,\(f_{5}(X)\). For the first two, it is desirable to obtain values as high as possible (maximum) while for the other three it is desirable to obtain values as small as possible (minimum). Experimental data are given in Table 5.  
The requirement is to make the best (optimal) decision given the experimental data available.
## Methods
### Purpose and Scope of TribAln  
Following the methodology of [PERSON] and [PERSON] [25], the development of an ontology is motivated by initial scenarios. For the field of tribology, we defined scenarios concerning reuse, search, and retrieval, as well as the comparison of experimental models and their results. The scenarios were supplemented with competency questions (CQs), which specify the requirements for the ontology informally, and are used for evaluation by transformation into formal queries. The following general use cases and exemplary competency questions define the purpose and scope of tribAln:  
1. _Influencing factors on the behavior of a specific tribological system:_ Many experiments in tribology aim to investigate the influence of a specific parameter or testing condition on the behavior of a specific tribological system. Containing all relevant constraints, like material pairing, ambient medium, as well as the properties of the bodies being in contact, the tribological system functions as a controllable reference model for real-world entities. _Example CQs: What is the influence of a parameter on experimental results (e.g., frictional/wear behavior) within a certain tribological system? What is the influence of a coating/lubricant on experimental results within a tribological system?_
2. _Experimental coverage:_ Avoiding re-testing, the ontology model should be able to answer queries about which kind of experiments are already conducted and documented in the knowledge base. _Example CQs: Which variables were tested regarding their influence on the behavior of a material pairing (e.g., a steel/steel-contact)? Which tribological systems were investigated under dry-running conditions using a solid lubricant?
3. _Querying, analyzing, and comparing sensor data:_ Tribological experiments produce lots of sensor data in the form of measurement series. These are assessed and compared to interpret the behavior of a tribological system. One major challenge in comparing this data is the different terminology used within the domain. _Example CQs: Which values has the coefficient of friction (COF) of sample XY? How does the value series of the COF for sample XY differ from the series of sample AB?_
4. _Annotating experimental results_: The practice of presenting results from tribological experiments in the natural language leads to the need for formal annotations; thus, the context and interpretation of measured sensor data can be provided. _Example CQs: What was the research hypothesis behind an experimental result? What was the interpretation of a higher wear volume of one sample compared with another?_  
The former introduced CQs should be interpreted as examples for the general use and scope of the ontology. For the evaluation of more specific use cases, these questions are substantiated for the specific data of a use case (see Section 3.1). As tribAIn should capture the terminology for describing tribological experiments, we formulated CQs following research questions in the domain of tribology.
## 4 Measurement
### Measuring the Comparison of Input Powers  
Another characteristic that was looked into was the input power at a constant speed. These tests were carried out for three different motor speeds. It should be noted that the measurements were carried out on the same mechanical torque values for all four states, as evidenced by the waveform.  
Figure 12 depicts the torque dependence of the input powers and the mechanical power of the motor for a constant speed of 2500 rpm. The waveforms show that in the case of a single-phase failure, the torque must be reduced from 3.5 Nm to 2.7 Nm in order to achieve the rated input power and avoid overloading the power supply system and the motor. This means that the rated motor torque must be reduced by 22.86%. However, if the edited control scheme is used, this decrease is significantly reduced. The nominal torque is reduced by 8.58% when using unbalanced currents and 12.86% when using symmetrical currents.  
Figure 11: Motor efficiency, constant torque 1 Nm.  
Figure 10: Motor efficiency, constant torque 2 Nm.  
Figure 12 also shows how the slope of input power increases due to the fault, whereas the slope of mechanical power remains constant. The fault-tolerant control reduces the amount of power drawn from the power supply while increasing machine efficiency.  
Figures 13 and 14 show the same measurement, but at speeds of 1500 rpm and 500 rpm, respectively. With decreasing mechanical torque, the difference between fault-tolerant control for unbalanced currents and symmetrical currents becomes negligible.
## 4 Results
### Comparison and Evaluation Results of Different Models
In conclusion, the AnimalEnvNet model achieves a single-step trajectory prediction MAE of 28.4 m. By incorporating environmental information feature modules, AnimalEnvNet demonstrates a significant improvement in single-step prediction accuracy. To further validate the generalisation capability of the models, we conducted experiments on the African elephant dataset. The results of the training and validation processes are shown in Figures 9 and 10. Similar to the giant panda dataset, the AnimalEnvNet model also exhibited the best predictive performance on the African elephant dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** & **Training Batch** & **Model Loss** & **MAE** & **Mean** & **Model Size** \\ \hline LSTM-basic & 100 & 0.0021 & 102.9 & 109.2 & 2 M \\ GANS & 100 & 0.0007 & 55.5 & 79.5 & 17 MB \\ AnimalEnvNet & 100 & 0.0002 & 28.4 & 64.2 & 18 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance comparison in single-step prediction. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Architecture** & **Learning Parameters** \\ \hline LSTM-basic & 2 LSTM units, Attention, Fully Connected & Loss: MSELoss, LR: 0.001 \\ GANS & Generator and Discriminator & Loss: MSELoss, LR: 0.0002 \\ AnimalEnvNet & Deep reinforcement learning LSTM, Attention, Fully Connected & Loss: MSELoss, LR: 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detailed information of the different model parameters. Despite the use of different datasets, the AnimalEnvNet model was still able to quickly converge during the training process and maintain the lowest prediction error. In contrast, the GAN model and LSTM-basic model had similar error performances, but the GAN model slightly outperformed the LSTM-basic model. By conducting experiments on two different datasets, it can be seen that the AnimalEnvNet model has strong generalisation capabilities across different trajectory datasets. This indicates that the AnimalEnvNet model has high robustness in capturing temporal and spatial features, performing excellently not only on the giant panda dataset but also maintaining low prediction errors on the African elephant dataset. Figure 8: Comparison of model MSELoss results. Figure 7: Comparison of model MAE results.
## 2 Research Background and Related Works
In the literature [29], a DRL-based distributed resource allocation algorithm has been proposed for shop floor communication, supporting both unicast and broadcast scenarios. Each vehicle is treated as an intelligent body that makes autonomous decisions based on the local information of the channel. In the literature [30], a DRL-based resource allocation algorithm has been proposed for the joint optimization problem of transmission mode selection and resource allocation in C-V2X to maximize the total throughput of V2I links while guaranteeing the delay and reliability of the V2V link. Frequent network switching during the fast movement of high-speed trains is the problem that has received the most attention in the [31] literature. This study applied deep reinforcement learning to the HSR network and proposed the Hd-TCP algorithm, thus addressing the situation of poor experiences due to this problem. Recent experiments in the literature [32] proposed a multi-channel intelligent access method based on deep reinforcement learning. Another study [33] proposed an adaptive online decision-making method by using SDN and deep reinforcement learning algorithms to compute the size of the congestion window and learn the optimal policy in a stable and fast manner. For disaster 5G mm Wave networks with highly mobile UAVs, a DL-TCP algorithm based on deep learning has been proposed in the literature [34], which learns the movement information and signal strength of nodes and adjusts the TCP congestion window by predicting the network disconnection and reconnection times. For Named Data Networks (NDNs), a study [35] proposed a deep reinforcement learning congestion control protocol (DRL-CCP) based on deep reinforcement learning, where the algorithm automatically learns the optimal congestion control strategy from historical congestion control experience. Another study [36] proposed an intelligent congestion control algorithm, TCP-Drinc, based on deep reinforcement learning, which is a model-free algorithm. Moreover, TCP-Drinc was able to adapt to complex and dynamic network environments by learning from past experiences and finally deciding how to adjust the size of the congestion window based on a certain set of measured feature values. Obviously, great progress has been made in congestion control, however, for its application in V2X, no clear advancement has been seen so far. Therefore, in the heterogeneous V2X network architecture of \"vehicle-human-road-cloud\", how to design an intelligent congestion control strategy will be the key to achieving high-capacity and high-efficiency communication for intelligent, dense and complex heterogeneous communication scenarios, and differentiated and high-quality communication service requirements. In view of this, the paper aims to introduce DRL into the congestion control of vehicle communications to meet the diversified and differentiated needs of V2X services and effectively ensure real-time and reliable information interaction in V2X.
## 3 Proposed Method
### Step 3. PCA  
Once the MFCC matrix has been obtained, a reduction in dimensions is required. This process is done through PCA (principal component analysis), which decreases the amount of data through statistical analysis of input information. The process begins with the application of an orthogonal transformation to obtain a group of new uncorrelated variables, where the first component is characterized by having the greatest variance [35].  
The application of PCA within the proposed method is carried out in order to reduce the number of observations in the \(M\) matrix, thus obtaining at the output a single vector \(V\) of 12 elements (corresponding to the number of coefficients of the MFCC block). In other words, PCA is being applied in time frames. Although the dependence of the samples of the original audio signal is strong, the application of PCA is proposed based on the following two reasons:  
1. In time series data, the closer the vectors are (in time), the greater the dependency, and vice versa [36]. Since the columns in matrix \(M\) correspond to 250 ms window frames (FD-FS), the time separation of these frames is greater than the corresponding separation of the samples in the original signal (\(1/sampling\ frequency=1/8000=125\)\(\upmu\)s).
2. The main purpose of the proposed hash function is descriptive, not inferential, i.e., to summarize the data. Therefore, non-independence does not seriously affect the application of the PCA [36].  
Finally, each element of vector \(V\) is represented in integer format with \(b\)-bit precision. Its value will be masked using the values supplied by the binarization block.
## 2 Materials and Methods
### Antioxidant Activity  
The antioxidant activity of the extracts was assessed through the free radical scavenging activity measured by 2,2-Diphenyl-1-picryhydrazyl radical (DPPH) based on the procedure described in [PERSON] et al. (2018). This activity is obtained through a system that generates a stable free radical as it is useful to determine the antioxidant potential at a preliminary stage. Briefly, in a 96-well microplate protected from light, 2 \(\upmu\)L of each sample was added to 198 \(\upmu\)L DPPH in ethanol (0.1 mM) (Sigma, Steinheim, Germany). The reaction was determined after 0.5 h at room temperature in the dark. The absorbance was measured spectrophotometrically (Evolution 201, Thermo Scientific, Madison, WI, USA) at 517 nm. Control was obtained with 2 \(\upmu\)L of absolute ethanol/distilled water and 198 \(\upmu\)L of DPPH solution, and a blank measurement was made with 2 \(\upmu\)L of extract and 198 \(\upmu\)L of absolute ethanol. The results were expressed in percentage of inhibition of DPPH using Equation (1).  
\[\text{DPPH }inhibition\left(\%\right)=\left[\left[1-\left(As/Ab\right) \right]\div Ac\right]\times 100 \tag{1}\]  
where \(As\) is the absorbance of the algae extract, \(Ab\) is the absorbance of blank samples, and \(Ac\) is the absorbance of the control.  
The quantification of TPC was made through the Folin-Ciocalteu method ([PERSON], 1979) and adapted to the microscale. In a 96-well microplate, protected from light, it was added to each well 158 \(\upmu\)L of ultrapure water, 2 \(\upmu\)L of the sample (or gallic acid (Merck) to calibration curve or 2 \(\upmu\)L of ultrapure water using as control), and 10 \(\upmu\)L of the Folin-Ciocalteu reagent (VWR Chemicals). After 2 min, 30 \(\upmu\)L Na\({}_{2}\)CO\({}_{3}\) (20%) (Chem-Lab) were added. After incubation in the dark at RT for 1 h, the samples were measured spectrophotometrically (Evolution 201, Thermo Scientific, Madison, WI, USA) at 755 nm. The phenolic content results are expressed as Gallic acid equivalents (mg GAE g extract \({}^{-1}\)).  
All measurements for the four independent extraction procedures were performed in triplicates (\(n=12\)).
# Strength Analysis of Real-Life Passwords Using Markov Models
## 2 Related Work
### Entropy  
One of the earliest method for password scoring is entropy. In information theory, entropy is the expected value of the information contained in a message. Authors in [18] attempt to determine the effectiveness of using information entropy (more specifically, Shannon entropy) as a measurement of the security of various password policies. They accomplish this by analysing the success rate of standard password cracking techniques against multiple sets of real-life passwords. Their experiments show that password entropy does not provide a valid metric for measuring the security provided by password creation policies. Furthermore, they found that the most common password creation policies remain vulnerable to online attacks, since users are using easy-to-guess passwords that still comply with the requested policy (e.g., \"Password1!\").
## 6 Example Analysis
### Comparison and Analysis of Prediction Results
#### 6.4.1 Iterations of the NRBO Algorithm  
The iteration curve of the NRBO algorithm is presented in Figure 9. As shown in the figure, the algorithm approaches the convergence value after the second iteration and reaches convergence at the 13 th iteration, with the fitness value stabilizing at 0.09819.  
Figure 8: Waveforms of each sequence after VMD.  
Figure 9: The iteration curve of the NRBO algorithm.
## 5 Conclusions  
In this study, the XGBoost algorithm was used to build a high-precision SIF prediction model by combining physiological and light-related variables, and to generate downscaled HRSIF products. The main conclusions of the study include (1) XGBoost shows superior performance in this task compared to several other algorithms. The model effectively improves the GOME-2 SIF coarse resolution data and performs well in different ecosystem types. (2) The reliability of the HRSIF product and the improvement in spatial details are highlighted by the dual validation of the ground-based sites and satellite products. (3) The HRSIF considering land use types showed a strong linear correlation with flux tower GPP observations, especially in mixed forest ecosystems (R\({}^{2}\) = 0.85). In addition, we investigated the importance of individual input features to the model, highlighting the impact of land use type on model performance. These findings confirm the feasibility of applying machine learning to improve the spatial and temporal resolution of satellite SIF products and expand their utility in ecosystem productivity assessment.  
C.H.: Writing--original draft, Investigation, Methodology, Software, Data curation. P.X.: Conceptualization, Resources, Project administration, Funding acquisition, Validation, Writing--review and editing. Z.H.: Methodology, Funding acquisition, Writing--review and editing. A.L.: Investigation, Validation, Supervision, Writing--review and editing. H.F.: Investigation, Visualization. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant numbers: 42030609 and 42105133), National Key Research and Development Program of China (grantnumbers: 2022 YFC3700300, 2022 YFC3703502, and 2023 YFC3705601), and Industrialization Project of Wanjiang Emerging Industry Technology Development Center (grant number: WJ21 CYHXM03).  
The code for generating the HRSIF and relevant data are available at [[https://github.com/zzmxv/HRSIF.git](https://github.com/zzmxv/HRSIF.git)]([https://github.com/zzmxv/HRSIF.git](https://github.com/zzmxv/HRSIF.git)) (accessed on 3 June 2025).  
We extend our gratitude to the scientists involved in the GOME-2 mission, as well as those responsible for the MODIS and CERES data products, for providing access to these valuable resources for the research community. Our appreciation also goes to the principal investigators and research staff at FLUXNET sites for sharing their flux data. This research utilized eddy covariance data collected and distributed by the FLUXNET community. We are grateful for the data provided by the researchers at the ChinaSpec sites, which mainly include Jurong and Huaiali. We would also like to express our gratitude to [PERSON] and colleagues for generously sharing the PhotoSpec site observation data and to [PERSON] and colleagues for providing the downscaled GOME-2 data. Additionally, we are thankful to the anonymous reviewers for their insightful feedback on our manuscript.  
The authors declare no conflicts of interest.
## 2 Inductive-Based Digital Isolation System
### Tx Design  
Figure 2 shows the circuit diagram, including the operation waveforms of the Tx side of the digital isolator system supporting single channel one direction (half duplex) data communication. The MLU input digital signal is processed through the Tx subcircuit stages and transmitted to the Rx side through inductive coupling. The Tx circuit consists of two differential parts: a negative pulse generator and a positive pulse generator, for shaping the negative pulses and positive impulses, respectively. Each of these branches consists of a pulse detector, cascaded inverters, a current limiter, and driver transistors.  
The pulse detector is the first circuit of the Tx chain, and it is responsible for detecting the positive and negative edges of the input digital signal \"IN.\" The sizing of the transistors is designed so that it generates a 2 ns pulse width signal, negative edge (NE) or positive edge (PE), to accommodate the desired system bandwidth. The current limiting inverter controls the current passing through the large-sized driving transistors (M\({}_{5}\), M\({}_{6}\), M\({}_{7}\), and M\({}_{8}\)) by shaping the generated pulse from the edge detector to have a sharp rise and a lower falling slope. This is achieved by increasing the current of M\({}_{1}\)/M\({}_{3}\) and decreasing the current of M\({}_{2}\)/M\({}_{4}\), i.e., the high change of the current with respect to time, the sharper the edge of the signal and vice versa. During the high-to-low transition of IN, the G\({}_{6}\) signal is determined and activates the driver transistor M\({}_{6}\). Then, the cascade-connected inverters output (G\({}_{5}\)) at the upper circuit branch triggers the driving transistor M\({}_{5}\). This operation is repeated for the transmission of IN from low to high, which corresponds to the lower part of the Tx.