esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The primary objective of this investigation was to assess the potential applicability of hemp (_Cannabis sativa_ L. var. _sativa_) lateral infloresces extract in mitigating the growth of fungi, including phytopathogens, on agricultural plants. The extract, comprising a blend of biologically active compounds, holds promise for integration into contemporary plant protection methodologies. The research involved a comprehensive analysis of the extract's chemical composition, encompassing the determination of total polyphenol and flavonoid content (utilizing spectrophotometric methods), antioxidant activity (evaluated through the DPPH method employing synthetic 2,2-diphenyl-1-picrylhydrazyl radical), and cannabinoid content (analyzed using HPLC techniques). Additionally, this study employed the poisoned substrate method to gauge the impact of 5, 10, and 20% extract concentrations on the growth of various microfungi, including _Alternaria alternata, Botrytis cinerea, Colletotrichum cccccodes, Fusarium avenue, F. culmorum, F. graminearum, F. oxysporum, F. sportorichioides_, and _Trichoderma koningii_. The hemp extract demonstrated a substantial presence of total polyphenic compounds, with polyphenol and flavonoid concentrations measuring 149.65 mg/mL and 1.55 mg/mL, respectively. Furthermore, the extract contained cannabinoids at a concentration of 0.12%. The most pronounced antifungal activity was observed with the 20% extract, particularly against _T. koningii_ (62.22-84.79%), _C. oxocodes_ (61.65-81.82%), and _B. cinerea_ (45.00-75.42%). However, the efficacy of hemp extracts exhibited large differences against _Fusarium_ spp. (3.10-72.95%), dependent on the specific extract and fungus strain. Introduction of hemp extracts to the substrate induced a reduction in substrate pigment and a discernible color alteration in the mycelium to a lighter shade compared to the control. These findings mark the initial phase in the exploration of practical applications for plant extracts, setting the groundwork for subsequent field trials to ascertain the extract's impact on phytotoxicity and the health status of agricultural plants.  
**Keywords:**_Cannabis sativa_; _Alternaria alternata; Botrytis cinerea_; _Celletotrichum cccodes_; _Fusarium_ spp._; _Trichoderma_ sp._; antifungal activity; biologically active compounds
###### Abstract  
Wind turbines can participate in frequency regulation by controlling active power output, but the indeterminacy and volatility of wind power result in low reliability of frequency support. Therefore, as a kind of energy storage system, an electric vehicle is adopted to coordinate with wind turbines to regulate system frequency considering its large-scale development. First, based on the reasonable division of wind speed regions and operation point selection of pitch angle, the de-loading strategy of doubly-fed induction generator for reserve capacity under continuously varying wind speed is proposed. Then, through the combination of rotor speed and pitch angle control, frequency regulation model of a doubly-fed induction generator in whole wind speed range is established. Finally, taking into account the driving demand of electric vehicle owners, through the real-time allocation of system frequency regulation task based on frequency regulation capacity, the coordinated control strategy of doubly-fed induction generator and electric vehicle cluster for secondary frequency regulation is put forward. The simulation results show that the coordinated frequency regulation strategy based on real-time allocation can suppress frequency deviation effectively, and the regulation effect is better than the situations of wind turbine coordinating with the conventional unit or coordinating with electric vehicle cluster based on fixed allocation ratio.  
f  
requency regulation; doubly-fed induction generator; de-loading operation; rotor speed control; pitch angle control; electric vehicle
# Heat Sink Equivalent Thermal Test Method and Its Application in Low-Orbit Satellites
###### Abstract  
In order to shorten the length of satellite thermal testing and reduce the cost of satellite development, a new method of satellite thermal testing using a heat sink to simulate space heating flow has been proposed. First, based on the characteristics of low-orbit satellites and the current research of thermal tests, the necessity of studying high-efficiency thermal test methods for satellites is expounded, and the advantages of the heat sink equivalent thermal tests compared to conventional tests are explained. Then, the principle of the heat sink equivalent thermal tests is described, the formula to calculate the heat sink temperature is given, and an error analysis of the formula is conducted. It is found that when the emissivity of the heat sink surface is greater than 0.9 and the ratio of the heat sink's surface area to the satellite's is greater than 10, the error of the heat sink equivalent tests should be within 1 \({}^{\circ}\)C. Next, the application of the heat sink equivalent thermal test is described using the Jilin\(-\)1 GR02F satellite as an example. Finally, the test results and the flight temperature of the GR02F satellite are acquired and analyzed. The results show that the error of the heat sink equivalent thermal test is 0.9 \({}^{\circ}\)C, the test time is shortened by one-third compared to traditional thermal tests, and the cost of the thermal test is reduced by more than 70%.  
satellite development; thermal test method; heat sink equivalent; high-efficiency test; on-orbit application
# Calculation of the Center of Mass Position of Each Link of Multibody Biped Robots
## 3 Second Theoretical Formulation
### Procedure for \(n=2\) Degrees of Freedom  
Figure 4 shows a sketch of a robot in a plane \(X_{P}\), \(Z_{P}\) with two degrees of freedom (\(n=2\)) and three links (\(L_{a}\), \(L_{b}\), \(L_{c}\)). From (21) \(k=3\) is obtained. This result means that two configurations must be used to calculate the coefficients \(\alpha_{i},\ \beta_{i},\ \gamma_{i},\ \delta_{i}\), and one configuration must be used to calculate the coefficients \(\alpha_{j},\ \beta_{j},\ \gamma_{j},\ \delta_{j}\) using respectively (10)-(13) in order to find the CoM position of the links of the robot. In particular, the following iterative procedure should be used:  
* The robot is placed on a walking surface and the platform should be kept in a first balance configuration \(j\) allowing a measurement of the forces (\(\stackrel{{\rightarrow}}{{F}}_{0},\stackrel{{ \rightarrow}}{{F}}_{1}\)) and the torques (\(\stackrel{{\rightarrow}}{{M}}_{0},\stackrel{{ \rightarrow}}{{M}}_{1}\)) by means of the force-torque sensors on the feet, and of the armature currents (\(\sum M_{j\_roll}\), \(\sum M_{j\_pitch}\), \(\sum M_{j\_yaw}\)). These values are used to calculate the coefficients \(\alpha_{j},\ \beta_{j},\ \gamma_{j},\ \delta_{j}\), of the (22) using respectively (10)-(13);
* The robot is placed in a second and third balance configuration \(i\), and in the same way, forces, torques, and motor currents associated with each balance configuration (second and third) are measured. These values are used to calculate the coefficients \(\alpha_{i},\ \beta_{i},\ \gamma_{i},\ \delta_{i}\) using respectively (10)-(13).
* For each balance configuration \(i\), the total CoM position is calculated using (22);
* Each total CoM position allows to determine \(r_{wi}\), \(q_{wi}\), \(l_{wi}\), using (23) and the CoM position of each link of the robot using (27). For each configuration \(i\), an equation using (26) is created. \(n\) linearly independent equations are used to find \(n\) vector positions.  
Figure 4: Sketch of a robot in a plane with 2 degrees of freedom. Three Configurations (a)–(c). \(m_{2}\) is the total mass of the system; \(m_{a}\),..., \(m_{c}\) are the masses of the links and \(r_{a}\),..., \(r_{c}\) and \(l_{a}\),..., \(l_{c}\) are respectively the x and z components of the center of mass of the links respect to the relative revolute joint; \(L_{a}\),..., \(L_{c}\) are the lengths of the links.
# Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification
## 3 Materials and Methods
### Logistic Regression (LR)  
To provide a benchmark for linear and probabilistic modeling, we implemented logistic regression in both frequentist and Bayesian frameworks. All LR models were trained on the original 11-dimensional input space without dimensionality reduction to assess the impact of transformation on classification performance.
## 3 Results and Discussion  
### Selection of Extraction Solvent  
Selection of extraction solvent is a crucial aspect of plant analysis. Optimal solvent must ensure quantitative extraction of target analytes but, at the same time, it should be sufficiently selective to avoid excessive extraction of other groups of compounds that might interfere with the analytical process. It is especially important in UHPLC, where columns with relatively low capacities are used. In the case of Ericaceae plant materials the interference might come from two different sources. The lipoidal substances (waxes, fatty acids, etc.), easily extractable with lipophilic solvents, like _n_-hexane and chloroform, are not easily washed from the column even with solvents of high eluotropic strength and may cause plugging problems, increasing the pressure, and shortening the column life. On the other hand, high concentrations of polyphenols present in polar extracts are likely to result in column overload causing peak broadening, decreasing the resolution and efficiency.  
Basing on some previous reports considering the solubility of OA and UA and their mixtures [35; 36], a couple of easily accessible solvents suitable for reflux extraction were selected (_n_-hexane, chloroform, methanol, _n_-butanol), and tested in terms of extraction efficiency. To this end, the leaves of _A. uva__ursi_ were extracted with every solvent thrice and the obtained extracts were combined. The content of both target analytes as well as total polyphenic content (TPC) was determined (as described previously [37]) for all extracts. Hexane proved to be least efficient for OA and UA extraction, which is consistent with the studies carried out by [PERSON] et al. [36], where the mixture of OA and UA was only scarcely soluble in this solvent. No statistical differences were found between chloroform, methanol, and butanol in terms of OA extraction, but the content of UA was slightly lower in chloroform extract in comparison to the other two. However, high TPC levels reaching over 17% GAE (gallic acid equivalent) per leaf dry weight (dw) were found in both polar extracts. To limit the phenolic recovery, mixtures of chloroform and methanol in different volume combinations were tested. Finally, an optimal extraction procedure was established comprising two successive extraction periods with chloroform followed by one with chloroform-methanol 1:1 (_v_/_v_). This scheme, allowed for quantitative extraction of both OA and UA as well as reduced the TPC level in the extract by over 40% (Figure 2).
## 2 Materials and Methods
The measured transmission of ITO film-based glass at 300-800 nm wavelength is illustrated in Figure 4, and it is noticeable that the transmittance of the ITO film can reach ~90% at 600 nm. CSP is generally a kind of conductive ink, and it is mainly composed of conductive silver powder, binder, solvent and trace additives for performance improvement. The major component of conductive silver powder is nano-silver particles with a content 60-70% [23]. In case 4, the radiation structure and ground plane of the proposed glass antenna are made of CSP. For the fabrication of the CSP-based antenna, a CSP layer was printed directly on both sides of the glass substrate with the help of printing technology. To be specific, it is an electric field driven jet-deposition micro/nano forming 3D printing technology, which is based on electrostatic field induction and electro-hydrodynamic theory. Unlike conventional electro-hydrodynamic jet 3D printing, this method only requires a conductive nozzle to be connected to a high-voltage pulsed power supply without a grounded electrode, and it could make the required electric field excited by electrostatic induction. Compared with the described laser etching technique, the printing technology is more convenient, cost effective and mature for antenna design and glass metallization. Figure 2: The photo of the fabricated glass antennas presented in Table 2. (**a**) Case 1. (**b**) Case 2. (**c**) Case 3. (**d**) Case 4. However, due to the fluidity of CSP, it might result in lower machining accuracy, and advanced processing technology and equipment are required. In the above three metallization schemes, CCF is a kind of metallic copper layer with extremely high conductivity of \(5.8\times 10^{7}\) S/m [24]. In contrast, CSP contains only 60-70% nano-silver, resulting in slightly lower electrical conductivity than that of the CCF. Compared with CCF and CSP, ITO films own the lowest electrical conductivity. Due to the nanoscale thickness of ITO films, the light transmission of the ITO films is much higher than that of CCF and CSP. Moreover, due to the ultra-thin property of ITO films, sheet resistance is very high. In order to analyze the performance of the three metallization schemes, a comparative experiment was conducted for these four types of antennas, as presented in the next session. Figure 4: The measured transmission of the ITO film-based glass. Figure 3: The ITO film deposited on glass. The image on the left is the top view; the image on the right is the bottom view.
## 4 RACAA: Reliability-Aware Cooperative Routing with Adaptive Amplification
#### Cooperative Routing with Adaptive Amplification
It shows that deployment of nodes is followed by hello packets exchange to share the vital information with one another. The vital information includes the number of neighbors, depth, ID and the physical distance by which a node is far from the surface sink. A source node at the verge of transmitting data first checks the existence of its neighbor nodes. If it does not find any neighbor, it checks again until the specified time for neighbors checking expires and, consequently, information is dropped. However, if neighbors are found, the source node checks that it has a connected path towards water surface. This is computed by looking at the number of neighbor nodes of all the nodes (from the neighborhood of the source node to sea surface) using the vital information of the sensor nodes obtained using hello packets exchange. If a connected path is found, the the source node advances packets. This process carries on unless the packets reach water surface or the timer is expired, which leads to dropping of packets. As shown in Figure 3, the RACAA scheme adds one more step to the routing process of RAR. As soon as information packets are received by destination, it checks whether the error probability in data is below a certain threshold. If it is so, destination further advances the data. Otherwise, it sends  
Figure 3: Flow chart of RAR and RACAA. a request to the relay node to send the data to it. The source then combines all the copies of data, from source and relay, and extracts the desired information. This process continues unless information packets reach to surface sink.
## 4 The Audio-Based Emotion Detection Component: GEVA
### Classifier
* Databases with a tagged voice corpus:
* Emotional Prosody Speech Database (LDC): with 15 types of emotions. * Berlin Emotional Speech Database (EmoDB): with seven types of emotions. See [64]. * Danish Emotional Speech Corpus (HUMANAINE): with five types of emotions. See [65]. * FAU Aibo Emotion Corpus: 8.9 h of spontaneous voice recordings from 51 children, with 11 types of emotions. See [66, 67]. The automatic learning program, Weka, provides many automatic learning techniques, which allow us to classify from the training patterns. Using this linguistic corpus and our voice features extraction module, a file with training patterns for Weka [68] has been built. This file has about 500 emotion-tagged locutions (these samples are in _arff_ format, which is valid for Weka). During the training phase, the different techniques provided by Weka have obtained the following success rates:  
1. **Bayesian**: Bayesian network: 68.95%; Naive Bayes: 65.52%. 2. **Fuzzy logic**. IBK (K-nearest neighbours classifier): 85.65%; IB1: 82.22%; LWL(Locally weighted learning): 62.74%. 3. **Rules**: JRIP (Routing Information Protocol): 81.15%; ConjuntiveRule: 61.88%; DecisionTable: 70.02%; ZeroR (determines the most common class): 57.17%; PART (a partial C4.5 decision tree): 77.94%. 4. **Decision tree learning**: J48 (a pruned or unpruned C4.5 decision tree): 80.51%; BFTree (a best-first decision tree classifier): 79.01%; LADTree (a multi-class alternating decision tree using the LogitBoost strategy): 68.95%; LMT (classification trees with logistic regression functions at the leaves): 79.44%. The final selection of the classifiers integrated in GEVA is based on the best results obtained using the \"cross-validation\" method over the training set. Besides, the simplicity of their implementation using Chuck has also been considered. The selected algorithms are the following:  
* Decision tree learning J48: This is an implementation of the tree decision C4.5 made by Weka, used in data mining or automatic learning. * Decision rule JRIP. The Routing Information Protocol is a vector-distance algorithm used in data mining or automatic learning. Using these classifiers, GEVA gives the emotion and gender outputs and their confidence values. Moreover, a novel _voice activity detection_ mechanism is implemented in GEVA. This mechanism is able to determine the start and the end of the voice locutions. In general, the majority of the systems that work with voice signals detect these moments based on a volume threshold. Nevertheless, this approach (implemented in the first version of GEVA) is not robust, since it is not able to differentiate between the human voice and other kinds of sounds or noises. The new developed mechanism for human voice detection is more complete, since it considers a bigger number of statistics.
## 7 Conclusions  
Traffic flow data contain multi-mode characteristics and provide a foundation for researching intelligent transportation systems. In this paper, we use a 3-order tensor to represent the traffic flow data to make full use of \"temporal-spatial-periodic\" multi-mode characteristics. To deal with the inevitable missing data in transportation systems, we propose an Algorithm called the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD), which combines linear regression, univariate spline, and CP decomposition. This approach can extract critical different granularity features and preserve potential multi-mode characteristics. Experimental results show that recovering the missing data is helpful in improving the prediction accuracy. To predict the future traffic flow data, we propose an Algorithm called Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS), which performs CP decomposition on the united tensor. Experimental results show that the prediction accuracy of the proposed Algorithm is better than gray model and traditional tensor CP decomposition method.  
The advantages of the method based on tensor decomposition include preserving correlation of the original data, representing the multi-mode characteristics, and solving the problems of dimension disasters. However, the proposed Algorithms also require improvement. For our future work, we plan to integrate multiple context factors, such as weather parameters and regions partition.  
Conceptualization, J.Y. and H.L.; methodology, J.Y. and H.L.; validation, J.Y., H.L. and Y.B.; formal analysis, Y.B.; investigation, Y.L.; resources, H.L.; data curation, Y.L.; writing--original draft preparation, J.Y.; writing--review and editing, J.Y.; supervision, H.L.; All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National key R&D Program of China under Grant 2019 YFB2102500.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Indicator** \\ \hline Gray Model & 12.01486 \\ Traditional CP-WOPT & 11.63461 \\ Uncompleted-Missing-Data-Based & 13.055 \\ Proposed Algorithm & 3.6672 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of prediction accuracy.  
Figure 8: Prediction results. (**a**) Morning peak prediction; (**b**) Noon peak prediction; (**c**) Evening peak prediction.  
**Acknowledgments:** This work was supported by the National key R&D Program of China under Grant 2019 YFB2102500.
## 4 Simulation Validation
### Simulation Results  
Figure 10 shows the process of the Lamb wave traversing the breathing crack. It can be clearly seen that the stress induced by Lamb wave propagation is continuously transmitted through the breathing crack when it is closed (Figure 10a), while on the contrary, the transmission process is interrupted when the crack is open (Figure 10b). This phenomenon is defined as so-called contact acoustic nonlinearity (CAN) [9].  
From the results of the stress transmission, the breathing behavior of the crack was observed; meanwhile, higher harmonics were generated. As a typical result in the intact and the current states, Figure 11a shows the excitation signal and the Lamb wave signal received from the sensing path AB with a distance of 80 mm. In addition to the received signal at fundamental frequency, the received signal at double frequency obtained by means of a band pass filters with cutoff frequencies of 450 and 550 kHz is also shown in Figure 11b. It was found that there was no noticeable difference between the two states at the fundamental frequency, which allowed no acquisition of the crack-induced signal. However, compared to the intact state, a wave packet at the double frequency was distinct in the current state, which represented nonlinear response generated by interacting with the breathing crack.  
Figure 10: Snapshots of Lamb waves traversing a breathing crack in modeling: (**a**) stress traversing when the crack was closed (**b**) stress being blocked when the crack was open.