esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Sequence Diagram and Process Flow  
The interaction between the CSCC, EVs, and CSs follows a clear operational flow, which can be visualized in the UML diagrams. The sequence begins with a charging request (CR) sent from the user to the CSCC, followed by data retrieval, optimization, and the transmission of control signals to the charging stations. This process ensures that all EVs receive updated charging instructions tailored to current grid conditions and user preferences (see Figure 5). The following steps outline the sequence of operations that occur after a charging request is initiated at the charging station:  
* The user at the charging station sends a charging request to the system (1).
* The system stores the request in the database and launches the optimization calling the method provided by the planner (2, 3, 4).
* The planner must fill the model with the updated data stored in the database (5 and 6).
* The planner starts the optimization process (7).
* When completed, the planner stores the result in the database and notifies the system (8, 9, and 10).
* The system sends the updated control data to all the active charging stations, including the cost for the one that makes the request (11).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{OptimizationResult} & \multirow{2}{*}{\(U\)} & Control variable related to the charging operations & \multirow{2}{*}{double} & \multirow{2}{*}{(calculated at runtime)} \\  & & on the EV associated with the request & & \\ \hline \(c\) & Estimated cost associated with the request & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithm output data description.  
Figure 4: UML diagram.  
A similar process occurs during DSM events, where the system adapts to grid demands by altering the power allocated to charging stations. These operational flows highlight the flexibility and responsiveness of the control system in handling diverse real-world scenarios (see Figure 6).  
Figure 5: UML sequence diagram when a CR occurs.  
Figure 6: UML sequence diagram when a DSM occurs.  
The sequence of operations is almost identical to the charging request case. The only difference is that, in this case, the event that starts the process is triggered internally by the system, and the output of the algorithm does not include any cost notification for the users. Regarding database modeling, as previously mentioned, the model is built based on updated information stored in the database. Each element required by the algorithm is constructed from the class definitions and then populated with the stored values.
# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
## 4 Construction of the Unified Battlefield Situation Information Space
### Aggregation of External Description Information about Entities  
As for the aggregation of external information about entities, it can be initially realized by the merge of semantic relationship matrices constructed from different combat information systems separately, which is denoted as \(SRM_{u}\)\(=\cup_{i}^{n}SRM_{i}\).  
The process of realizing the merger of semantic relationship matrices is as shown in Figure 9, and the main steps are as follows:  
* Get \(SRM_{i}\) from the \(i\)th combat information system \(i\in(0,n]\), and a collection of SRMs can be obtained, which is denoted as \(C_{SRM}\).
* Get the corresponding \(SRM_{i}^{+}\) of \(SRM_{i}\) by the general description of \(SRM_{i}\) based on the entity relationship concept-logic tree, and then, \(C_{SRM}^{+}\) can be obtained.
* Get the combined semantic relationship matrix \(SRM_{u}^{+}\) by merging the matrices contained in \(C_{SRM}^{+}\).
* Based on the reasoning rules in \(Rl\), realize the reasoning on semantic relationships between entities in \(SRM_{u}^{+}\), and an extended semantic relation matrix of \(SRM_{u}^{+}\) can be obtained, which is denoted as \(SRM_{u}^{\prime+}\).  
\(SRM_{u}^{\prime+}\) is the final result of realizing the merge of SRMs, and it describes the complete set of entity relationships between the entities in UBSIS. After that, by the evolution of \(SRM_{u}^{\prime+}\), the newly obtained external information of \(e_{i}\) can be integrated with that contained in \(SRM_{u}^{\prime+}\), which reflects the operations such as add node, delete node, add edge, or delete edge related to \(e_{i}\) in SLN.
## 2 Materials and Methods
### Data Resources
#### 2.2.3 Gross Primary Productivity (GPP) Data  
There are two GPP products used in this study: \(\rm GPP_{LUE}\) and \(\rm GPP_{GLASS}\). \(\rm GPP_{LUE}\) is estimated using a light use efficiency (LUE) model, and LUE is improved by introducing a clearness index (CI) and an evaporation ratio [20], with a resolution of 8 d and a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\). \(\rm GPP_{GLASS}\) is a collection of eight currently international models with a Bayesian multi-algorithm integration approach [21], with a temporal resolution of 8 d and a spatial resolution of 1 km. Both products are derived from the National Earth System Science Data Center ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)) accessed on 22 May 2021). The GPP product data from 2003 to 2018 are selected for this paper, and the spatial resolution is resampled to 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) using a bilinear interpolation method. The purpose of using these two GPP products is for comparison with the SIF-based GPP.  
This study also used eight flux tower GPP data based on the vorticity correlation method (Dangxiong, Dinghushan, Haibei, Xilingole, Qianyanzhou, Xishuangbanna, Yucheng and Changbaishan sites) in China from 2003 to 2010 with SIF data to establish linear fitting equations of GPP-SIF for different vegetation types and calculate fitting coefficients for different vegetation types (in Section 2.3.1, we denote these coefficients by \(Si\)). The vegetation types included alpine meadows, typical grasslands, scrub, woodlands, and farmlands. In addition, we also used data from the Changling flux site that were not involved in the linear fit to verify the accuracy of the simulated GPP (due to data limitations, we currently only have access to flux data for the Changling site from 2007-2010, so the data period for accuracy verification in Section 3.2 is 2007-2010).  
The eight flux site data used for the linear fit were obtained from the China Terrestrial Ecosystem Flux Observation Research Network (China FLUX, [[http://www.chinaflux.org/accessed](http://www.chinaflux.org/accessed)]([http://www.chinaflux.org/accessed](http://www.chinaflux.org/accessed)) on 23 May 2021). The data for the Changling site was obtained from FLUXNET ([[https://fluxnet.org/data/download-data/](https://fluxnet.org/data/download-data/)]([https://fluxnet.org/data/download-data/](https://fluxnet.org/data/download-data/)) accessed on 3 March 2022).
## 3 Results and Discussion
### Effects of Different Sterilization Treatments on the Sensory Quality  
#### 3.6.1 Color Analysis  
The color properties are also important indicators used to evaluate the juice quality. A pleasant color can also improve consumer acceptance. Compared with CK, the L* value of grape juice after TS and TSN treatment decreased significantly (\(p<0.05\)) (Table 1), which was similar with US-treated orange juice sample [10]. In addition, the a* and b* values decreased significantly after all different sterilization treatments, which indicated that sterilization treatment led to significant green/blue changes in juice color, among which CTS had the most significant impact while TSN had the least. Currently, there is still controversy about the effects of different sterilization treatments on the a* and b* values of juice. [PERSON] et al. [10] reported that a* and b* values in US-treated orange juice clearly decrease. Conversely, [PERSON] et al. [11] and [PERSON] et al. [19] found a* and b* values in TSN-treated apple juice and TS-treated cactus pear juice tended to increase. Therefore, further research is still needed.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Processing Method**} & \multicolumn{4}{c}{**Color**} \\ \cline{2-5}  & \(\mathbf{L^{*}}\) & \(\mathbf{a^{*}}\) & \(\mathbf{b^{*}}\) & \(\mathbf{\Delta E}\) \\ \hline Control group & \(56.67\pm 2.32\)\({}^{\text{a}}\) & \(15.05\pm 0.30\)\({}^{\text{a}}\) & \(26.31\pm 0.23\)\({}^{\text{a}}\) & \(0.00\pm 0.00\)\({}^{\text{d}}\) \\ CTS & \(56.13\pm 0.70\)\({}^{\text{a}}\) & \(7.50\pm 0.52\)\({}^{\text{c}}\) & \(5.12\pm 0.85\)\({}^{\text{d}}\) & \(22.51\pm 0.99\)\({}^{\text{a}}\) \\ TS & \(48.30\pm 0.62\)\({}^{\text{c}}\) & \(10.60\pm 0.18\)\({}^{\text{b}}\) & \(15.32\pm 0.59\)\({}^{\text{c}}\) & \(14.53\pm 0.06\)\({}^{\text{b}}\) \\ TSN & \(51.42\pm 0.43\)\({}^{\text{b}}\) & \(11.09\pm 0.11\)\({}^{\text{b}}\) & \(17.19\pm 0.97\)\({}^{\text{b}}\) & \(11.24\pm 0.95\)\({}^{\text{c}}\) \\ \hline \hline \end{tabular} Note: values with different letters in the same column are significantly different (\(p<0.05\)) from each other.  
\end{table}
Table 1: Color parameters changes of grape juice after different sterilization treatments.  
Figure 6: Correlation between the color attributes, antioxidants, and antioxidant capacity of grape juice.  
In addition, US treatment can also cause cell disruption, resulting in the release of intracellular compounds, which might affect the final color of grape juice [38]. A noticeable difference could be observed between two colors when the \(\Delta\)E value is greater than 2. CTS-treated showed a most obvious color changes compared to CK (22.51), which significantly affects the sensory performance, while TS (14.53) and TSN (11.24) showed much lower color changes.
# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 3 Methods and Materials
### Classification  
Two different classifiers were used in this study: the Multilayer Perceptron (MLP) and Support Vector Machine (SVM), to classify and differentiate between healthy subjects and patients. A ten-fold cross-validation method was used to evaluate the performance of the classifiers. The EEG signals were classified into ten parts with equal numbers of signals, except for two groups. One part was used for testing and the other nine parts were used for training the classifier. This process was repeated ten times for each different test part, and the average performance for accuracy, sensitivity, and specificity was calculated and reported. Each of the ten processes was repeated fifteen times to improve the accuracy.
## 4 The Proposed Algorithm-TCEB
### Non-Cooperative-Game-Based Cluster-Head Selection
* **Step 1** A triad game model, namely, \(G=\{N,S,U\}\) is firstly built for the network. * **Step 2** According to current node distribution, the optimal number of clusters (i.e., \(k_{opt}\)) is calculated through \(N\)th-order nearest-neighbor analysis theory [26]. * **Step 3** Each node broadcasts the _HELLO_ message with its maximum transmission power and set a _timeout_ for waiting for the reply message. Meanwhile, it collects the neighbor node's message under the specified _timeout_ and establishes its neighbor list. * **Step 4** Each node calculates its own payoff (i.e., \(u_{i}\)) according to Equation (12), and then broadcasts its calculation result of \(u_{i}\) within the _timeout_. * **Step 5** A node receives the payoff (\(u_{i}\)) of its neighbor node and stores \(u_{i}\) to the corresponding node in the neighbor list. After getting all payoffs of nodes in the neighbor list, each node sorts the payoffs (includes itself) out in terms of descending order. * **Step 6** According to the optimal cluster number, the former \(k_{opt}\) nodes with greater \(u_{i}\) are selected as the final cluster-head by the game from the network. Then, the newly elected cluster-heads broadcast the message that they have been selected as the cluster-head of the \(k_{opt}\) cluster. That means all \(k_{opt}\) nodes make a decision to be cluster-head in this round during the game, and the process of cluster-head selection is ended. ```
0: A triad game model \(G=(N,S,U)\)
1: Initialize the network where \(n[i]\leftarrow\) 'N'
2: Calculate \(k_{opt}\) using \(N\)th-order nearest-neighbor analysis method from [26]
3:\(n_{i}\) broadcasts the HELLO and set a timeout
4:while\(t<timeout\)do
5:\(n_{i}\) collects its neighbor node's HELLO
6:endwhile
7:\(n_{i}\) establishes its neighbor list
8:\(n_{i}\) calculates its \(u_{i}\) according to Equation (12)
9:\(n_{i}\) broadcasts its \(u_{i}\) and set a timeout
10:while\(t<timeout\)do
11:\(n_{i}\) receives payoff (\(u_{i}\)) of its neighbor nodes and stores in the neighbor list
12:endwhile
13:\(n_{i}\) sorts the payoffs (\(u_{i}\)) out by descending order
14:\(chs\leftarrow\) Chooses the former \(k_{opt}\) nodes with greater \(u_{i}\) as the cluster-head
15:for\(k\gets 1\) to \(k_{opt}\)do
16:\(n[chs[k]]\leftarrow\) \"C\"
17:\(n_{chs[k]}\) broadcasts its message of newly elected to be cluster-head
18:endfor
```  
**Algorithm 1** Cluster-Head Selection  
Through the process of cluster-head selection, we can find that it is precisely because of the introduction of the game model and node's payoff function. The \(k_{opt}\) cluster-heads are correctly elected under the premise of the comprehensive balance of energy consumption and path loss. Then, the intra-cluster and inter-cluster topology are beginning to construct one after the other.
###### Abstract  
This paper presents a comprehensive review of the design and implementation methods of low-power piezoelectric energy harvesting circuits, which in the last few years have gained an extremely large range of applications like the power sources of wearable electronic devices, such as biometrical sensors. Before examining the electronic circuitries of the self-supplied power devices, an overview of the structure, equivalent electrical circuits, and basic parameters of the piezoelectric generators and MEMSs as energy harvesting elements is presented. The structure of energy storage elements (parallel-plate capacitors and thin-film supercapacitors), suitable for this type of application, is also presented. The description of these components from an electrical point of view allows them to be easily workable when connected to the various power conversion electronic circuits. Based on an overview of the structure and the principles of operation, as well as some analytical expressions for energy efficiency evaluation, a comprehensive comparative analysis is presented. Depending on the advantages and disadvantages of the known circuit configurations, the basic electrical and design parameters are systematized in tabular form. Practical realizations of piezoelectric power conversion circuits are also presented in graphic form, ensuring the optimal value of energy efficiency and compactness in the construction of the devices.  
energy harvesting; piezoelectric element; rectifier; SSH; voltage multiplier; DC-DC converter; buck regulator; power management circuit; MEMS; parallel-plate capacitor; supercapacitor +
Footnote †: journal: Applied Sciences
## 3 Results
### Mortar Proprieties
#### 3.1.5 Thermal Properties  
The addition of 5% SS-PCM in the mortar led to a significant drop (26%) in terms of thermal conductivity. However, there was a slight variation in thermal conductivity among  
Figure 9: Compressive strength according to density [2; 29; 30; 31].  
the SS-PCM composite mortars (Figure 10a). The Figure 10b shows the rise of thermal conductivity with the porosity of mortars. Indeed, the thermal conductivity of cementitious materials depends on their air content. In addition, it should be noted that the low thermal conductivity of the added SS-PCM (0.231 W.m\({}^{-1}\).K\({}^{-1}\)) promotes the high insulation capacity of mortar composites. The SS-PCM conductivity was about 7 times lower than that of the reference mortar. Unlike the mechanical strength decreased, the thermal conductivity drop is an advantage for building applications. The SS-PCM composite mortars have better thermal insulating capability compared to reference mortar, and seem suitable for low-energy buildings.  
Unlike the compressive strength, the thermal conductivity of PCM composites was slightly lower than that of lightweight materials for a given density (Figure 11). This is due to the low thermal conductivity of the added PCM in the matrix. Our results (red squares) remain in the range of thermal conductivity for PCM materials. SS-PCM and lightweight materials are similar in terms of insulating capacity (similar thermal conductivity), but not in terms of thermal inertia. The advantage of SS-PCM over lightweight materials is its latent enthalpy, i.e., its high solar energy storage capacity providing high thermal inertia of building (unlike, the thermal inertia of lightweight material is low). In addition, the SS-PCM provides better durability and rheological properties. However, it should be noted that the lightweight materials are cheaper than PCM composites.  
Unlike thermal conductivity, specific heat was higher when SS-PCM was added to mortar (Figure 12). In fact, the specific heat only depends on solid and phases of the mortar (i.e., cement paste, sand and SS-PCM), while the thermal conductivity takes into account all phases of the material (i.e., including the gas phase (porosity)). However, the specific heat of SS-PCM mortars was not a linear function of the SS-PCM amount.  
The mechanical and thermal performances of SS-PCM composite mortars remain sufficient for most building applications. Moreover, its high thermal insulating capability and heat storage [23] are also relevant benefits for low-energy consumption buildings. This allows to improve the thermal inertia of the building, thus avoiding sudden changes in the indoor temperature, all while maintaining the supporting structure capacity. However, its higher porosity could lead to durability problems.  
Figure 10: Thermal conductivity of mortars related to (**a**) SS-PCM content and to (**b**) porosity of mortars.
## 5 Results
### Smoothing Algorithm
#### 5.1.1 Smoothing Algorithm Applied with Pulsed Sinusoidal and Chirp RFI
When the window size increases, RFI power peaks are convolved over the spectrogram leading to an increase of the number of RFI-contaminated pixels. Degradation also increases with the RFI power as high powers are more likely to be detected by the algorithm even when they are smoothed, and contaminate a larger area of the time-frequency image. Therefore, as the filter size increases, the number of eliminated pixels increases, degrading the radiometric resolution. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline
**Filter window** & **35 \(\times\) 35 Hann** & **25 \(\times\) 25 Hann** & **15 \(\times\) 15 Hann** & **5 \(\times\) 5 Hann** & **Without Smoothing** \\ \hline \(P_{\mathrm{fh}}\) & \(3.52\times 10^{-3}\) & \(2.09\times 10^{-3}\) & \(7.24\times 10^{-4}\) & \(7.06\times 10^{-5}\) & \(2.35\times 10^{-3}\) \\ \hline Threshold value & \(1.24\ \sigma^{2}_{n}\) & \(1.37\ \sigma^{2}_{n}\) & \(1.72\ \sigma^{2}_{n}\) & \(4.04\ \sigma^{2}_{n}\) & \(6.08\ \sigma^{2}_{n}\) \\ \hline Max \(\varepsilon_{T_{A}}\) & \(2.05\) K & \(2.09\) K & \(2.33\) K & \(3.71\) K & \(5.89\) K \\ \hline Max \(\varepsilon_{T_{A}}\) & \(1.16\) K & \(1.41\) K & \(1.84\) K & \(3.71\) K & \(5.89\) K \\ for RFI = 0 dB & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Maximum retrieved \(T_{\mathrm{A}}\) error for the best threshold \(P_{\mathrm{fh}}\) for four different cases of Smoothing Algorithm filtering, including the case of not applying the algorithm. Maximum retrieved \(T_{\mathrm{A}}\) error in absence of RFI for the most suitable threshold is also shown. A Hann window between 15 \(\times\) 15 and 25 \(\times\) 25 is recommended, as for higher values, retrieved \(T_{\text{A}}\) error shows little improvement, while radiometric resolution may decrease excessively leading to a worsening of the retrieved geophysical parameters.
# Influence of Interference Fit and Temperature on High-Speed Permanent Magnet Motor
## 3 Static Strength Check of Interference Fit
### Finite Element Modeling  
Finite element analysis (FEA) is a kind of engineering analysis technology based on numerical analysis. It is an effective method to solve various engineering problems effectively by using mathematics and computer technology. This method can effectively estimate the performance and reliability of the structure, determine the existence of factors in the production process, and optimize the design scheme.  
The basic principle of FEA is to discretize the continuous solution domain into a combination of elements. Within each element, an assumed approximate function is used to piecewise represent the unknown field function to be solved on the solution domain. Typically, the approximate function is represented by the numerical interpolation functions of the unknown field function and its derivatives at the nodes of the element. Thus, a continuous infinite degree of freedom problem becomes a discrete finite degree of freedom problem.  
In this paper, the interference fit of the rotor system originates from the high-speed motor module in the centrifugal compressor. The sheath and end cover are made of stainless steel 3 Cr13, and the magnet is made of Sm\({}_{2}\)Co\({}_{17}\). Using SolidWorks2021 modeling software (Dassault Systemes SolidWorks Corporation, Waltham, MA, USA, [[https://www.solidworks.com/contact-us](https://www.solidworks.com/contact-us)]([https://www.solidworks.com/contact-us](https://www.solidworks.com/contact-us))), the drawn model is imported into the static analysis module of ANSYS2021 Workbench (Ansys, Canonsburg, PA 15317 USA, [[https://www.ansys.com/contact-us](https://www.ansys.com/contact-us)]([https://www.ansys.com/contact-us](https://www.ansys.com/contact-us))), and the corresponding material properties and working temperature are set. After that, the contact conditions and mesh division are created for the structure. When the FEA simulation is only involved in the inherent elements of the rotor (i.e., the sheath, the end covers, and the magnet), which rotate at the same angular velocity to maintain their relative positions, the static mesh editor is used. Once the FEA simulation is not involved in the parts from the stator and gap, there is no need to use dynamic mesh. As shown in Figure 3, because the structure of the impellers is not axisymmetric, a 3D mesh model is used for the interference simulation. The results of the mesh sensitivity analysis are shown in Figure 4. When the number of elements reaches 120,000, the maximum stress value tends to be stable. It is obvious that the grid being divided into 120,739 can shorten the simulation time and ensure the accuracy of the results. Then, the parameters are set and solved.  
Figure 3: Meshing diagram of the rotor system.