esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries
## 4 Case Study: Multisource High-Power Microwave Heating Process
### Settings of the Algorithms
\[\min_{\Delta u_{\text{P}j}(k)}J^{\text{CMPC}}(k)=\left\|\mathbf{y}_{ \text{t}}(k)\otimes\mathbf{I}_{\text{y}}-\left(\mathbf{y}_{\text{Pol}}(k)+\sum_{i=1}^{N_{ \text{a}}}\mathbf{R}_{i}\Delta u_{\text{P}j}(k)\right)\right\|_{\mathbf{Q}^{\text{CMPC1 }}}^{2}+\sum_{i=1}^{N_{\text{a}}}\|\Delta u_{\text{P}j}(k)\|_{\mathbf{Q}^{\text{CMPC2 }}}^{2} \tag{31}\] \[\text{s.t. }\left\{ \begin{array}{l}u_{\text{min},j}\otimes\mathbf{I}_ {\text{u}}\leq\mathbf{u}_{\text{P}j}(k)=u_{i}(k-1)\otimes\mathbf{I}_{\text{u}}+\mathbf{M} _{\text{u}j}\Delta u_{\text{P}j}(k)\leq u_{\text{max},j}\otimes\mathbf{I}_{\text{u}} \\ \Delta u_{\text{min},j}\otimes\mathbf{I}_{\text{u}}\leq\Delta u_{\text{P}j}(k)\leq \Delta u_{\text{max},j}\otimes\mathbf{I}_{\text{u}}\\ \mathbf{y}_{\text{min}}\otimes\mathbf{I}_{\text{y}}\leq\mathbf{y}_{\text{Pol}}(k)+\sum_{i =1}^{N_{\text{a}}}\mathbf{R}_{i}\Delta u_{\text{P}j}(k)\leq\mathbf{y}_{\text{max}} \otimes\mathbf{I}_{\text{y}}\end{array} \right.\]  
where the weight matrices \(\mathbf{Q}^{\text{CMPC1}}=\mathbf{Q}^{\text{DPC1}}\) and \(\mathbf{Q}^{\text{CMPC2}}_{i}=\mathbf{Q}^{\text{DPC2}}_{i}\) and the other settings used are the same as in scenarios 1-7. The most obvious difference between the CMPC and the proposed HCPC is that the CMPC strategy utilizes a single centralized controller to compute the optimal input electric power increments for all microwave sources, while the proposed HCPC strategy decentralizes the computational task to local controller associated with each microwave source in order to handle the widely existing integrated \"distributed-decentralized\" characteristics of large-scale process industries. This difference indicates that the CMPC controller knows the control inputs of all microwave sources, so the CMPC's open-loop prediction \(\mathbf{y}_{\text{Pol}}(k)\) as shown in the optimization problem (31) contains global information instead of using only local information as in Equation (6), even though they are structurally similar to each other.
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 2 Methodology
### Orbit Determination Strategy
[22], and it has been routinely applied to the GNSS products solution for CODE AC [23; 24], as well as the SLR-only orbit determination for Bury, Sosnica and Zajdel [15]. Additionally, the equal weight scheme is adopted for all available SLR observations in both our SLR validation and SLR-only orbit determination strategies. \begin{table}
\begin{tabular}{c c} \hline \hline
**Dynamic Models** & **Description for BDS SLR** \\ \hline Earth Gravity Field & EGM2008 \\ N-body Perturbation & JPL DE405 \\ Solar Radiation Pressure & ECOM \\ Tides and Relativistic & IERS2010 \\ \hline
**Measurement Models** & \\ \hline Basic Observations & SLR NPs \\ A priori Orbital Parameters & Precise orbit of WUM \\ A priori Station Coordinates & SLRF2014 \\ A Priori Troposphere Model & Mendes–Pavlis model \\ Centre-of-Mass Correction of LRAs & ILRS, CAST and SECM \\ \hline
**Estimated Parameters** & \\ \hline Station Coordinates & Strong constrained \\ Orbital Parameters & 6 Orbital Element and 9 ECOM SRP \\ Pseudo-Random Pulse & None \\ ERP & Fixed ERP of WUM \\ Geocentre Coordinates & None \\ Range Bias & All \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The configuration of parameters of Satellite Laser Ranging (SLR)-only orbit determination. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **C20/C21 (m)** & **C29/C30 (m)** \\ \hline
**X** & 0.6120 & 0.6646 \\
**Y** & \(-\)0.072 & 0.4249 \\
**Z** & 1.2230 & 0.6427 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Centre-of-Mass Correction of LRAs for BDS-3 satellites. Figure 1: The multi-day arcs solution strategy.
# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Review of ML on PD
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Objective** & **Metrics/AI Model** & **Strengths (1–5)** & **Weaknesses (1–5)** & **Reference** \\ \hline Early PD detection via & Improved AUC by \(\geq\)1\% & & & \\ human voices & compared to state-of-the-art & 4 & 3 & [160] \\ \hline Dynamic brain activity & SVM: 98\% correct identification & & & \\ in PD & (\(p<0.001\)) & 5 & 3 & [161] \\ \hline \multirow{4}{*}{Feature selection for  model improvement} & XGBoost: +10\% & & & \\ \cline{2-5}  & DNN: +38.18\% & & & \\ \cline{2-5}  & SVM: +0.91\% & 5 & 3 & [162] \\ \cline{2-5}  & DT: +7.27\% & & & \\ \hline PD classification using  BO-SVM & SVM: 92.3\% & 4 & 3 & [163] \\ \hline Early Detection of  Balance Disorders in  PD & FKNN: Accuracy: 95.6\% & & & \\ \cline{2-5}  & FKNN: Recall: 99\% & 5 & 3 & [164] \\ \cline{2-5}  & FKNN: Precision: 95.2\% & & & \\ \hline \multirow{4}{*}{Cognitive  degeneration prediction} & PCA-SVM: Accuracy: 92.3\% & & & \\ \cline{2-5}  & PCA-SVM: AUC: 0.929; & & & \\ \cline{2-5}  & With 13 features: Accuracy: & 5 & 3 & [166] \\ \cline{2-5}  & 100\% & & & \\ \cline{2-5}  & With 13 features: AUC: 1.0 & & & \\ \hline PD identification via & CNN-1D: F\({}_{1}\)-Score: 92.7\% & & & \\ \cline{2-5}  & voice disorders & LR: F\({}_{1}\)-Score: 92.2\% & 4 & 3 & [167] \\ \hline \multirow{4}{*}{Postural stability  analysis in PD} & SVM: 92\% & & & \\ \cline{2-5}  & Dempster-Shafer: 96.51\% & 5 & 3 & [129] \\ \hline \multirow{4}{*}{PD classification} & Accuracy: 95.4\%, & & & \\ \cline{2-5}  & Sensitivity: 94.9\% & & & \\ \cline{1-1} \cline{2-5}  & Specificity: 93\% & 5 & 3 & [169] \\ \cline{1-1} \cline{2-5}  & Precision: 95.2\% & & & \\ \cline{1-1} \cline{2-5}  & F\({}_{1}\)-Score: 95.5\% & & & \\ \hline \multirow{2}{*}{Early PD detection} & SVM: Accuracy: 98.2\% & & & \\ \cline{2-5}  & KNN: Specificity: 99\% & 5 & 3 & [170] \\ \hline \multirow{2}{*}{PD screening via voice } & Sensitivity: 67.43\% & & & \\ \cline{2-5}  & Specificity: 67.25\% & 3 & 4 & [171] \\ \hline \multirow{2}{*}{Tremor type  discrimination} & SVM: 100\% (single  measurement) & 5 & 3 & [172] \\ \cline{2-5}  & General: 82.62\% & & & \\ \hline \multirow{2}{*}{PD vs. Parkinsonism  differentiation} & ANN: Accuracy: 86\% & & & \\ \cline{2-5}  & ANN: Sensitivity: 81.8\% & 4 & 3 & [173] \\ \cline{2-5}  & ANN: Specificity: 88.6\% & & & \\ \hline \multirow{2}{*}{Medication state  monitoring in PD} & RF: Accuracy: 96.72\% & & & \\ \cline{2-5}  & RF: Recall: 97.35\% & 5 & 3 & [174] \\ \cline{2-5}  & RF: Precision: 96.92\% & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._
# Adaptation in Gait to Body-Weight Unloading
## 3 Results
### Emg
#### 3.4.2 Biecps Femoris Activity  
There were no changes in BF activity after the adaptation session during any of the gait phases in the control condition. However, the BF activity changed in the unloading condition during the terminal stance and mid-swing phases as a function of time. During the terminal stance phase, EMG activity remained unchanged during and immediately after unloaded walking at T\({}_{1}\) and T\({}_{2}\). However, it was reduced relative to T\({}_{0}\) (0.58 \(\pm\) 0.07) starting from T\({}_{3}\) (T\({}_{3}\): 0.43 \(\pm\) 0.07; T\({}_{4}\): 0.43 \(\pm\) 0.07) through T\({}_{5}\) (0.41 \(\pm\) 0.05). BF activity during the mid-swing phase was reduced during unloaded walking (0.83 \(\pm\) 0.13) relative to T\({}_{0}\) (1.37 \(\pm\) 0.17) and was not different from that of T\({}_{0}\) from T\({}_{1}\) through T\({}_{5}\) (Figure 8).  
Figure 7: Group means (\(\pm\)SEM) of EMG activity of the rectus femoris (RF) muscle across different epochs during the (**A**) loading, (**B**) mid-stance, (**C**) pre-swing, (**D**) mid-swing, and (**E**) terminal swing phases for control and unloading conditions. * Significant difference (\(p<0.05\)) relative to T\({}_{0}\) of the corresponding condition.
# FEM Analysis of Piezoelectric Resonator Polarization Process
## 2 Materials and Methods
### Task Assignment
#### 2.4.4 Calculation of the Polarization Process  
The values of the magnitude of the coercive electric field and the dielectric strength, Table 2, are the decisive parameters for the simulation of the polarization process. According to the procedure described in Section 2.3, the electric field will be applied to the transducer through the electrodes in the interval \(\left\langle 0,3\right\rangle\) kV/mm  
* in steps of 0.2 kV/mm in the interval \(\left\langle 0;\,0,8\right\rangle\) kV/mm,
* in steps of 0.05 kV/mm in the interval \(\left\langle 0,8;\,1,3\right\rangle\) kV/mm,
* in steps of 0.2 kV/mm in the interval \(\left\langle 1,3;\,3\right\rangle\) kV/mm.  
Figure 4: The finite element mesh created in the GMSH software, (**a**) in the shape of circular segment, (**b**) in the shape of an intersection of circles.  
Figure 3: The geometry of transducers created in the GMSH software, (**a**) in the shape of circular segment, (**b**) in the shape of an intersection of circles.  
The electric field will be entered through the Dirichlet boundary condition on the surfaces representing the electrodes, as described in the Section 2.3. The Dirichlet condition implies the input of a known electric potential. The relationship between the electric field strength and the electric potential is given by the Equation (6). If we consider the wrapped-around electrode to be grounded, the electric potential at the opposite electrode can be expressed as  
\[\varphi_{D}=E_{app}t, \tag{9}\]  
where \(E_{app}\) is the electric field we want to apply to the transducer and \(t\) is the thickness of the piezoelectric transducer.  
The boundary condition in the plane of symmetry will have the value  
\[\mathrm{D_{k}}n_{k}=\mathrm{D_{N}}=0, \tag{10}\]  
where D is the electric induction vector, \(n_{j}\) is the j-th component of the unit vector of the outer normal to the plane of symmetry. The induction flux across the surface in the plane of symmetry is therefore zero.  
Before starting the calculation, material properties will be assigned to each element according to the procedure given in Section 2.1. In each step, it will be evaluated in which elements the coercive electric field has been exceeded. According to the procedure from Section 2.3, the material properties will be modified in these elements according to the direction of the calculated field.
## 2 Results and Discussion
### Study of Curdlan Production Kinetics  
Curdlan production by free and immobilized cells was evaluated considering the previously optimized conditions, and the results are shown in Figure 3. Curdlan production by immobilized cells started 34 h earlier than that observed in free cells. Glucose exhaustion and maximum curdlan production occurred at approximately 10 days in the assay. The total glucose consumption was 94.74% by immobilized cells and 90.82% by free cells, and the maximum curdlan production was 21.35 and 14.20 g/L, respectively. In Figure 3A, it is possible to note the use of ammonium chloride until 30 h of incubation time, which means that the immobilized cells grew in the loofa sponge.  
As previously described, _Alcaligenes faecalis_ WX-C12 consumed glucose for cell maintenance and metabolic energy supply for curdlan biosynthesis [33]. Therefore, the maximum curdlan production can be considered as a function of the specific rate of glucose consumption and significantly varies with the conditions of cultivation.  
A proper fit to the experimental data was achieved in relation to the data predicted using the kinetic model, with an R\({}^{2}\)\({}_{\text{AJD}}\) of 0.97 for immobilized cells and 0.99 for free cells. A hypothesis test (_t_-test) for the parameters 1/k\({}_{0}\) and 1/k\({}_{1}\) was applied, and a statistically-significant difference was found at 1% for both parameters regarding the use of free or immobilized cells. The speed constants were higher for immobilized cells (k\({}_{0}\) = 1.17 \(\times\) 10\({}^{-1}\) g/L/h and k\({}_{1}\) = 2.08 \(\times\) 10\({}^{-2}\) h\({}^{-1}\)) compared to free cells (k\({}_{0}\) = 1.04  
Figure 3: Curdlan production (closed squares), biomass represented by intracellular protein (open triangles), glucose consumption (closed diamonds) and NH\({}_{4}\)Cl (open circles) by _Agrobacteriumium_ sp. IFO 13140 immobilized on loofa sponge (**A**) and free cells (**B**) at 30 \({}^{\circ}\)C and 150 rpm for 14 days of incubation. The immobilization conditions used were 72 h of incubation, 50 mg of cells and a shaking rate of 200 rpm. The error bars represent standard deviations of triplicates.  
\(10^{-1}\) g/L/h and k\({}_{1}\) = \(1.60\times 10^{-2}\) h\({}^{-1}\)). Therefore, the immobilization process resulted in a higher maximum production speed, and this speed was reached in a shorter time (Figure 4). This setting has confirmed the superiority of the immobilization system of _Agrobacteriumium_ sp. IFO 13140 in relation to free cells, under the immobilization conditions and production used.
## 4 Results
### Results of EMG Signal Processing and t-test  
#### 4.3.1 EMG Signal Processing Results  
Figure 10 shows the amputee patient and the extracted signal graph.  
The \(x\)-axis is time(sec) and the \(y\)-axis is amplitude(uV). The number of parameters of the model is 108,422, and the inference time is 0.98 ms. Real-time predictions are possible up to about 65,000 Hz, and the inference accuracy is 92.43% was shown.  
The second is a model for detecting whether an EMG signal is dynamic or static. Figure 11 is a graph of the loss and accuracy of the proposed Bidirectional LSTM.  
Figure 11a shows the learning accuracy, Figure 11b shows the learning loss rate. Table 6 shows the accuracy and loss of the deep learning model.  
\begin{table}
\begin{tabular}{c c} \hline Accuracy & 92.43\% \\ Loss & 0.405\% \\ Inference Time & 0.98 ms \\ \hline \end{tabular}
\end{table}
Table 6: Accuracy & Loss of Deep Learning Model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Items** \\ \hline
1.1 & It has an extroverted shape considering the amputation patient \\
1.2 & It was an interface for patients with amputated lower limbs \\ \hline
2.1 & The physical controls were convenient to reach \\
2.2 & The operation method was simple \\
2.3 & There was a help function for users. \\
2.4 & It was easy to switch to another menu when needed. \\
2.5 & It responded to the user’s different usage environments (home, hospital, etc.) \\
2.6 & It was easy to access functions frequently. \\ \hline
3.1 & Rehabilitation time was shortened compared to conventional methods. \\
3.2 & The rehabilitation training process was more effective than the existing methods. \\
3.3 & Compared with the existing method, the virtual prosthesis helped to accurately perform rehabilitation training. \\
3.4 & Rehabilitation training was more efficient than existing methods. \\
3.5 & It was easy to adapt to the Hololens2 device. \\
3.6 & It was easy to adapt to the virtual prosthesis rehabilitation training. \\
3.7 & Convenience compared with the existing rehabilitation methods. \\
3.8 & There was a motivating factor compared with the existing rehabilitation method. \\
3.9 & The virtual prosthesis was more friendly and comfortable than the robotic prosthesis. \\
3.10 & Overall satisfied with this program \\
3.11 & The rehabilitation content of this program was more helpful than the existing methods. \\
3.12 & Whether this program can be reused. \\
3.13 & I want to recommend this program to other amputated patients. \\ \hline \end{tabular}
\end{table}
Table 5: Items of Usability Test.  
The result when using epoch 100 and batch size 200. The accuracy of motion presence detection was 92.43%. The inference time is 0.98 m, and real-time prediction is possible up to about 65,000 Hz.
## 2 Related Work
### Polystore System--Datawnt0 (Existing Work)  
Datawnt0 is a top-down approach which uses a relational query language to query data from its headers database and subsequently access different resources. Datawnt0 supports a workflow-based query management system which uses a Polystore-based approach. It is a query language interface developed for skilled users. This system supports multi-object search to ease the work of the users belonging to the astronomical domain and also help these users by generating complicated programs [31]. This system uses a workflow method to make the queries understandable to the end user and also make the process of query execution faster. The multi-object search support also helps to perform a wider range of query execution. In Datawnt0, the user can relate multiple objects in the query as per the user's requirement. Every object selected during the query process generates a query component. Thus, multiple queries are added by a related objects operation and a complex query is formed, which can be easily executed by the Datawnt0 interface.  
Palomar Transient factory (PTF) presents a rare example of a data store that is a shared resource for all astronomers [14]. The data are huge in size and variety. The data are routinely updated and new data (with a different new type) may be added. The primary objective of PTF project is to identify and classify astronomical objects, such as variable stars and supernovae, in real time. This project also aims to classify and query a catalog which contains other celestial objects for deeper analysis. Currently, there are three sets of data: Level-0, Level-1, and Level-2. Level-1 data include epochal (single-exposure) image and photometric catalogs. A single-exposure (sci) image provides CCD-quadrant (readout-amplifier)-based files. Level 2 covers all of the northern sky, including all the g- and r-band data, which are known as reference images. Reference images are co-adds of individual epochal images on a CCD-quadrant/filter basis that have been selected to satisfy the quality criteria. Reference image provides a static representation of the sky with good-quality of images for a given field, CCD quadrant, and filter [21; 32]. Some drawbacks and limitations are addressed as follows:  
* A limitation of Datawnt0 is that users can access only single-exposure (sci) images form PTF data archive;
* Another main drawback of Datawnt0 is that recursive queries do not work in this system as there is a loss of data. If a user needs to search entire fields and galaxies or search by position (galactic coordinates), the cDatawnt0 interface does not support this option at this time.  
The limitations introduced by Datawnt0 can be addressed with proposed systems by providing users with Query by Example (QBE)-style language and a Set-Theoretic query language (relational algebra).
## 2 Material and Method  
### Experimental Site  
The field experiment was conducted in Rzecin peatland (52\({}^{\circ}\)45\({}^{\prime}\)41\({}^{\prime\prime}\)N, 16\({}^{\circ}\)18\({}^{\prime}\)35\({}^{\prime\prime}\)E). The vegetation found in this site is very heterogenous with a clear gradient of rich-poor fen vegetations, where rich fen vegetations dominated by vascular plants are found near the edge, and the poor fen vegetations, rich in bryophytes, are present in the middle of the ecosystem [26]. There are two climate manipulation sites developed in poor fen peatland areas, which are 50 m away from each other, but significantly different in vegetation due to their varying water table depth dynamics [18]. The CL site is the site dominated by _Carex lasiocarpa_, whereas the CR site is dominated by _Carex rostrata_ among vascular plants. The sites are further divided into plots with different microclimates, where the microclimate is being manipulated to raise temperatures by the use of open-top chambers, infrared heaters, and rain curtains. The plots are randomly distributed within the sites with three repetitions, where the plots without manipulation are control (C) plots, with open-top chambers and heaters are warming (W) plots, whereas the plots with night-time reduction in the rain, open-top chambers, and heaters are warming and reduced precipitation (WP) plots (Figure 1). The manipulation sites were developed in 2017 and described in [PERSON] et al. [18]. [PERSON] et al. [18] also describe all the infrastructure and routine measurements that are being performed at the manipulation site at regular intervals, such as water pH, water conductivity, nutrient content of water, vegetation chlorophyll, its abundance, morphological characteristic etc. The 100 W heaters were added in March 2021 to increase the night-time temperature of the manipulated plots. The measurements used in this study were collected during the growing season of 2021.  
Figure 1: Shows the location of Rzecin peatland where CL and CR represent the climate manipulation sites (Adopted from [PERSON] et al. [18], CC BY-NC-ND license).
# Unsteady Aerodynamic Design of a Flapping Wing Combined with a Bionic Wavy Leading Edge
## 4 Numerical Results
### Effects of Different Wavelengths on Aerodynamic Performance of Flapping Wing
The change of pressure is mainly reflected in the position above 60% of the wingspan, and the change of the pressure difference between the upper and lower surfaces will cause the change of lift. Figure 24 compares the pressure coefficients of the straight wing and the wavy leading edge wing with different wavelengths at 80% wingspan section. When t/T = 0 and 0.2, the pressure of the upper surface of the wavy leading edge flapping wing decreases compared with the straight wing, and the pressure of the lower surface increases. The pressure difference between the upper and lower surfaces increases, and the lift increases. Although Figure 24a is similar to Figure 24c, and Figure 24b is similar to Figure 24d, it can be seen from the pressure contours that the pressure on the upper surface of the flapping wing is greater than that on the lower surface. Therefore, after the wavy leading edge causes the pressure difference to increase, the downward force on the flapping wing is further increased. That is, the lift is reduced. Figure 25 shows the pressure coefficient contours and streamlines of the 50% spanwise section of the flapping wing when t/T = 0. As the wavelength decreases, the vortex generated by the wavy leading edge decreases the pressure coefficient on the upper surface of the wing section and increases the pressure coefficient on the lower surface, resulting in an increase in the lift coefficient. In addition, the pressure coefficient on the trailing edge decreases with the decrease of the wavelength, which leads to a decrease in the thrust coefficient of the flapping wing. Figure 23: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\overline{W}\) & **0.1** & **0.05** & **0.02** & **0.01** \\ \hline Time averaged lift coefficient \(\overline{C_{L}}\) & 16.01\%\(\uparrow\) & 28.89\%\(\uparrow\) & 34.32\%\(\uparrow\) & 41.04\%\(\uparrow\) \\ \hline Time averaged trust coefficient \(\overline{C_{T}}\) & 11.66\%\(\downarrow\) & 14.04\%\(\downarrow\) & 20.71\%\(\downarrow\) & 25.97\%\(\downarrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average change rates of flapping wings at different wavelengths.