esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 4 Load Forecasting Methodology
### Fully Connected Layer  
The PCC and MIC between the load values and the influencing factors of the used case were calculated, as shown in Table 4. The influencing factors with strong correlation were selected. The maximum temperature, minimum temperature and average temperature of the forecasting day were normalized as feature values. The month and time of day to be predicted have been processed by one-hot coding, which is used as feature value fusion to construct a feature vector. The feature values obtained from PCC and MIC selection, the feature values obtained from ACF analysis, and the component load forecasting values obtained in the first stage are connected to obtain the feature vector \(X\), which is fed into the fully connected layer to output the final load forecasting values. In this paper, \(X=\left(I\hat{M}F_{1},I\hat{M}F_{2},I\hat{M}F_{3},I\hat{M}F_{4},I\hat{M}F_{5}, \hat{g}_{t-25},\hat{g}_{t-48},\hat{g}_{t-72},\hat{g}_{t-168},T_{\text{max}},T_ {\text{min}},\overline{T},month,time\right)^{T}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types of Influencing Factors** & **Influencing Factors** & **PCC** & **MIC** \\ \hline \multirow{4}{*}{Meteorological factors} & Maximum temperature (\({}^{\circ}\)C) & 0.4182 & 0.2572 \\  & Minimum temperature (\({}^{\circ}\)C) & 0.4494 & 0.2771 \\  & Average temperature (\({}^{\circ}\)C) & 0.4465 & 0.2856 \\  & Average relative humidity (RH\%) & 0.1257 & 0.0729 \\  & Rainfall (mm) & 0.0460 & 0.0710 \\ \hline \multirow{4}{*}{Time factor} & Year & 0.0778 & 0.0721 \\  & Month & 0.2807 & 0.1942 \\ \cline{1-1}  & Day & 0.1019 & 0.0602 \\ \cline{1-1}  & Always & 0.4252 & 0.4036 \\ \cline{1-1}  & Week date & \(-\)0.1063 & 0.0745 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PCC and MIC between influencing factors and loads in Place 1.  
Figure 5: Power load of Place 1 ACF plot.
# Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience
## 2 Market Analysis and Regulations
### Market Analysis: Significant Growth Expected
This opens the door for the satellite industry to participate in the mainstream production and distribution ecosystem [18]. By adopting the 5G unified core network framework, the satellite system will become seamlessly integrable with other communication service provider systems, e.g., terrestrial mobile communication systems or other satellite communication systems. Moreover, with the adoption of the 5G core network, satellite systems can leverage technologies developed in the 5G ecosystem to increase the system's flexibility and optimize its operation. The application areas where MLSS satellite systems are seen as especially useful are ones requiring ultra-high availability and resilience, including industrial systems, military users, and mission-critical communications in remote areas. The multi-layer architecture can provide decent connectivity even when the LEO layer is targeted by malicious users by means of jamming or anti-satellite weapons.
## 2 Materials and Methods  
### Wind Tunnel Testing  
A simple test in a wind tunnel laboratory was conducted to determine the tip speed ratio \(\lambda\) of the vertical-axis carousel wind rotor with three dual-coherent blades. A turbine model (Figure 2) with a manual wind direction guidance system was constructed in accordance with the patent [18]. The main elements of the model, such as the blades and rotor housing, were made of Plexiglas. The use of transparent material was intended to visualize the principle of operation by showing the rotor structure with visible bearings and gear transmission design. Based on the technical documentation of the entire turbine model, every detail of the model's construction was cut out of Plexiglas using a numerical machine tool. The gears responsible for the transmission of power and rotation of the blades relative to the rotor axis were made of polystyrene and bronze, while the remaining elements were made of steel and brass. The main overall dimensions of the model were as follows: blade height of 494 mm, blade width of 200 mm, and distance between the blade axis and the rotor axis of 140 mm.  
The turbine model (Figure 2a) consisted of three identical dual-coherent blades (7), and a rotor connected to a drive shaft (5) that rotated relative to a stationary housing (2). After assembly, each rotor blade was balanced so that the resistance on the bearings was as low as possible. A bearing sleeve (4) was fixed on the housing, which was kinematically  
Figure 2: Model of a VAWT with a carousel rotor: (**a**) construction diagram: a main rotor (1); a stationary housing (2); a worm gear (3); a bearing sleeve (4); a drive shaft (5); the blade axles (6); three dual-coherent blades (7); the planetary gearbox (8); a rotor mast (9); vertical pins of the blades (10); (**b**) model during wind tunnel testing.  
connected to the axis of rotation of the blade, and a gearbox (8) with a reduction ratio of 1.2. In turn, the wind direction guidance mechanism was coupled to the sleeve. There was a self-locking gear between the rotor shaft and worm gear (3), which provided a fixed position for a specific wind direction. The rotor model included a manual adjustment system for wind direction. The angular position of the blades was adjusted so that the starting torque of the rotor was as high as possible. At the moment when air pressure exerted the greatest pressure on the surface of one of the blades that forced the rotor into a rotary motion, at the same time, via a planetary gear, the blades began to rotate around their axis. The other blades forced the air resistance by aligning the chord more parallel to the wind direction.  
The rotor's principle of operation was recorded using a digital camera in the wind tunnel of the Wind Engineering Laboratory of the Cracow University of Technology [31]. The wind tunnel tests were conducted to validate the design and also to determine the tip speed ratio, which for wind speed \(W\) = 5 m/s was \(\lambda\) = 0.27.
## 3 Results and Discussion
### Performance Based on Geometry Optimization Using Objective Functions
#### 3.3.2 Performance Normalized to the Reference Shape  
The average values in each region were calculated and non-dimensionalized using the baseline values for comparison to evaluate the performance improvements achieved through optimization. The values were determined based on the operating conditions, such as airflow rate, to verify whether the optimal shape for the design airflow rate also exhibited satisfactory performance under partial load conditions.  
Figure 11a compares the pumping power for the three shapes. For the shape optimized for \(P\), compared to the reference shape, the reduction trend remained constant within a range of 2% regardless of changes in the Reynolds number. However, for the Gv shape, as the airspeed decreased and the Reynolds number decreased, \(P\) increased by 7-11% compared to the reference shape. This was attributed to the definition of Gv: It is proportional to 1/3 rd of the f-factor compared to the j-factor.  
Figure 11: Performance variation with airflow rate for optimized shapes: pumping power and volume goodness factor. (**a**) Pumping power. (**b**) Volume goodness factor.  
Figure 11b compares the volume goodness factor for the three shapes. For the shape optimized for \(P\), Gv remained constant at a value of 1.5% less than the reference shape across the changes in Reynolds number. The Gv shape showed a maximum improvement of 2% in Gv under the lowest airspeed conditions and a performance improvement of 1.8% under the maximum airspeed conditions.
## 3 Materials and Methods
### Algorithm to Calculate B-W Parameters  
The determination of the parameters of the B-W model was carried out using the command lsqnonlin of Matlab Software [50]. This provides a nonlinear least squares fit, and various algorithms can be used. In this case, the L-M algorithm was employed. To briefly explain this algorithm, consider a nonlinear system, as in (26), where vector \(q\) gathers the variables of the problem.  
\[\Psi_{\rm d}=Q(q) \tag{26}\]  
\(\Psi_{\rm d}\) indicates the desirable configuration and \(J(q)\) indicates the Jacobian of the vectorial function \(Q(q)\) obtained as \(J(q)=\frac{\partial Q}{\partial q}\). The problem can be solved with an iterative procedure in which a linear approximation from the step \(i\) to \(i+1\) is assumed as in Equation (27).  
\[Q(q_{\rm i}+\zeta)=Q(q_{\rm i})+J\zeta \tag{27}\]The value of \(\zeta\) at each step was determined by using the L-M iterative technique, which consists of a linear system solution, as shown in Equation (28).  
\[\left(J^{\mathrm{t}}J+\lambda I\right)\zeta=J^{\mathrm{t}}(\Psi_{\mathrm{d}}-Q(q_ {\mathrm{i}})) \tag{28}\]  
In the linear system, (28) \(\lambda\) is a powerful leverage, which allows for one to obtain different method behaviours: for low values of \(\lambda\), the algorithm tends toward the Newton-Raphson method, whereas for high values of \(\lambda\), it tends toward the gradient method. The possibility of varying the value of \(\lambda\) during the iteration allows for one to prefer the gradient method next to the singularities, while Newton-Raphson method lacks accuracy, while far from singularities, the Newton-Raphson method is preferred because it converges to the solution more quickly than the gradient method. In our case, B-W constants were determined to minimize the difference between the computed output and the experimental output.
# Interpretable Optimization: Why and How We Should Explain Optimization Models
## 3 Problem Reduction and Surrogate Model Building
### Presolve Techniques
Commercial solvers such as CPLEX, Gurobi, and Xpress have strong presolve capabilities that automatically perform variable-fixing, eliminate dominated and redundant constraints, aggregate constraints to reduce the problem size, and tighten variable bounds, potentially fixing their value [38; 39; 40]. Other open access solvers--such as SCIP, CBC, HiGHS, and GLPK--also include presolve functionalities, but they may lag behind the above-mentioned commercial solvers. While presolve techniques may enhance the interpretability of optimization models by improving simplicity, they also introduce several challenges. The primary issue is the loss of the original formulation, as removing redundant constraints and fixing variables can obscure the initial problem structure, making it harder to explain how the simplified model relates to the original decision problem. Several studies have shown the importance of presolve techniques. [PERSON] et al. [34] performed a presolve analysis of MIP problems with Gurobi and showed that around 500 models out of a set of approximately 3000 were unsolvable when the presolve was disabled. For the remaining models, the presolve reduced the computation time needed by a factor of approximately nine. [PERSON] and [PERSON] [41] performed a presolve analysis of MIP problems with CPLEX, reaching similar conclusions. To the best of our knowledge, the literature does not include a detailed benchmark of presolve capabilities among different solvers, but there are performance comparisons in terms of computation time when solving problems, and commercial solvers tend to outperform the open access ones [42].
# Cooperation-Assisted Spectrum Handover Mechanism in Vehicular Ad Hoc Networks
## 2 Related Work
### Existing Spectrum Handover Schemes  
Previous studies [7; 8; 9; 10] proposed spectrum management schemes in railway networks. These schemes provide significant improvement to network performance due to their knowledge of the the trajectory and speed of the train. However, the trajectory and speed of each vehicle are changeable. This will cause difficulty in spectrum management. Many spectrum handover methods [11; 12; 13; 14; 15; 16] assume that the SU can accurately detect the conditions of all spectrums in order to find a suitable spectrum for handover. However, this assumption is too strong and is unlikely to happen in reality. Moreover, these schemes do not take into account the movement of users. In fact, the SU in the vehicle network will need to spend more time scanning the spectrum to avoid affecting the performance of the PU. Figure 2 shows the operations of the traditional spectrum handover. We can see that the SU needs to spend more time scanning the spectrum when the number of BSs increases.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 5 Electrospinning of Chitosan
### Molecular Weight  
In some cases, the lower molecular weight (9.5-10.5%) of the chitosan solution leads to the formation of large beads in nanofibers. For chitosan solutions with higher molecular weights (2.5-3%), coarser and finer nanofibers with fewer pearl defects are formed [20].  
It can be seen from observations that very high molecular weights create significant entanglement interactions between long polymer chains and repulsive forces between ionic groups [105; 106]. This has a negative effect on electrospinningning and the production of continuous fibers is inhibited [107; 108]. For the optimal production of nanofibers with homogeneous nanofibers without beads, it is recommended to use a chitosan solution with a medium molecular weight.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Data Sources and Processing  
1. The vector data of the administrative boundaries of Changing County, Longyan City, and Fujian Province were provided by Fujian Provincial Geographical Information Center located in Fuzhou City, Fujian Province, China.
2. Landsat images from 1995-2019 were downloaded from the \"Geospatial Data Cloud Website \"[[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/))\" (accessed on 5 July 2023)\" (Table 1) to generate the evaluation indicators.
3. The study area was divided into a 0.3 \(\times\) 0.3 km\({}^{2}\) grid to generate 35,091 sample points which were used to analyze the spatial autocorrelation characteristics combined with the _RSEI_.
4. As shown in Figure 2, the evaluation indicators, the _RSEI_, and the sample points were generated using ArcGIS Pro 3.2 and ENVI 5.3 software, which were used to further analyze the spatial-temporal distribution characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Acquired Date** & **Sensor Type** & **Path/Row** & **Cloud Cover** \\ \hline
1995 & 7 December 1995 & TM5 & 121/42 & 0.00\% \\
2000 & 27 January 2000 & ETM7 & 121/42 & 0.00\% \\
2010 & 14 January 2010 & TM5 & 121/42 & 0.00\% \\
2019 & 23 January 2019 & OLI/TIRS & 121/42 & 2.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Landsat images used in this study.  
Figure 1: Geographical location of the study area.
# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### Cost-Effective Dimension
#### 3.3.2 Energy Efficiency

Table 7: Cost overview. As technology and implementation methods evolve, solar-powered irrigation is likely to become an increasingly viable and attractive option for farmers and agricultural enterprises.
# Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs
## 4 Simulation and Results  
Simulations were conducted to evaluate the performance and accuracy of the proposed algorithm. After analyzing the simulation results, the properties of the proposed approach were compared with that of the previous works discussed in related work. Comparisons were performed in terms of integrity of data acquisition, real time performance, length of flying time and energy consumption. The simulation parameters, setup and results are given in this section.
## 3 Results
### Unilateral or Bilateral Inclusion  
Linear and angular measurements were also compared depending on unilateral or bilateral inclusion: statistically significant values were found for angulation with respect to the midline in orthopontomography and for maxillary width, both of which were higher in unilateral impactions (Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Outcome**} & \multirow{2}{*}{
\begin{tabular}{c} **Mean** \\ **Difference** \\ \end{tabular} } & \multicolumn{2}{c}{**95\% Confidence Interval**} & \multirow{2}{*}{\(p\)**Value**} \\ \cline{3-3} \cline{5-5}  & & **Lower** & & **Upper** \\ \hline Dist\_Cusp C\_Ocl Plane & \(-2.867\) & \(-4.446\) & \(-1.287\) & \(0.001\)** \\ \hline Ang\_C\_Ocl Plane & \(15.092\) & \(5.216\) & \(24.968\) & \(0.005\)** \\ \hline Ang\_C\_Middle Line & \(0.338\) & \(-11.537\) & \(12.212\) & \(0.955\) \\ \hline Ang\_C\_palatal suture & \(14.198\) & \(3.053\) & \(25.344\) & \(0.014\)* \\ \hline Ang\_C\_Middle Line\_2D & \(-1.120\) & \(-12.884\) & \(10.645\) & \(0.846\) \\ \hline Transv\_\_\_4 & \(2.041\) & \(-0.095\) & \(4.178\) & \(0.061\) \\ \hline Transv\_\_6\_6 & \(0.462\) & \(-1.095\) & \(2.019\) & \(0.553\) \\ \hline \end{tabular} Dist\_Cusp C\_Ocl Plane, distance from the canine cusp to the occlusal plane; Ang\_C\_Ocl Plane, angle of the canine with respect to the occlusal plane; Ang\_C\_Middle Line, angle of the canine in relation to the midline; Ang\_C\_palatal suture, angle of the canine with respect to the middle palalatine suture; Ang\_C\_Middle Line\_2D, canine angle in relation to the midline measured in OPG; Transv\_4\_4 indicates the maxillary width of the palatal cusp of the right first premolar to the left first premolar; Transv\_6\_6, maxillary width of mesopalatine cusp from right first molar to left first molar; *: statistically significant difference, \(p\leq 0.05\); **: statistically significant difference; \(p\leq 0.01\).  
\end{table}
Table 1: Comparison between canine buccal and palatal position.
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
## 10 Effect of Ohmic Heating on Inactivation of Enzymes in Food  
Enzymes are proteins that spontaneously accelerate the chemical reactions and are considered a vital catalyst [98]. The presence of enzymes significantly reduces the shelf life of fruits and vegetable products [99]. Therefore, the enzymatic activity in foods must be controlled through the treatment stages in order to get rid of their harmful effects on the quality characteristics such as the production of unwanted flavors, changes in rheological properties, and color [34]. Therefore, electrical techniques were of great importance in treating and preserving food from structural and sensory changes, maintaining the properties of enzymes and the ability to control the treatment conditions accurately [100]. Several studies have examined the effect of electrical techniques on the inhibition of enzymes in food, to understand the mechanism. [PERSON] et al. [101] found there was a strong link between enzymatic activities and their 3-D structures. The functional moieties in the proteins, which linked to the charged separation would be dissociated by using electro-technological treatments. However, there is more to discover about the mechanism of electric technologies, regarding inactivation of enzymes [102]. A number of researchers mentioned that the electric and thermal energy applied in treating food individually or mutually, affects the enzymatic structure and changes its functions [101; 103; 104]. [PERSON] et al. [105] examined the effect of ohmic heating on the polyphenol oxidase in sugar cane juice at three levels of electric field (24, 32, and 48 V/cm) and four temperatures of (60-90 \({}^{\circ}\)C) with a time period 5-20 min. There was a 97.8% decrease in the enzyme activity at a temperature of 60-90 \({}^{\circ}\)C with an increase in the electric field. Ref. [106] studied the possibility of ohmic heating to prevent brown enzymatic discoloration on the quality of the water chestnut juice using different electric fields and frequencies. Ohmic heating can increase the effectiveness of the peroxidase enzyme and then the activity begins to decrease quickly. The higher the electrical conductivity, the greater the inhibition of the enzyme was observed.
## 4 Experiment
### Effect of Classifier  
In this section, we study the effect of different classifiers on the performance of our method, including LinearSVM with parameters \(c=10\) and \(s=1\), ridge regression classifier (RRC) with parameter \(\lambda\) equal to 0.005, nearest neighbour classifier with cosine distance  
Figure 16: Top 10 retrieval images for five leaf images from Leafsnap dataset.  
Figure 14: Retrieval MAP performance comparison between our method and the state-of-the-art method IMTD+relu5_2.  
Figure 15: Top 10 retrieval images for five leaf images from Flavia dataset.  
and ensemble classifier Bagging (fitensemble (data, label, 'Bag', 100, 'tree', 'type', 'classification')). For comparison fairness, each experiment is repeated for 20 rounds here. It should be emphasised that we do not optimize the parameters of the four classifiers; the usual parameter values are applied. We believe that the performance of our method could be further promoted if the parameters are fine-tuned. Without loss of generality, the two complicated datasets Leafsnap and Turkey Plant are used. From the box plot Figure 17, we can see LinearSVM yields the second best results. Surprisingly, the RRC obtains slightly better performance than LinearSVM. Even the results of nearest neighbour classifier decrease by 3-5%, but it is still promising compared with the results in Tables 9 and 10. Bagging classifier obtains unsatisfying results; the reason is probably that the ensemble method and tree number should be chosen carefully; however, we just use the default setting.