esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
A series of simulations was conducted to examine how the backup typology and heat pump size affect the seasonal energy efficiency of the entire system. In all scenarios, the cut-off temperature for the HHP systems was set to the outdoor design temperature. In hybrid setups, both heaters operated simultaneously throughout the heating season, with the heat pump given priority as the primary heating source.  
Figure 5 presents a comparative analysis of the thermal energy production for the various hybrid heat pump-boiler configurations. It illustrates the energy contribution from heat pumps and boilers across different system designs and heat pump capacities. It is seen that all configurations can cover the heating demand of the building for the whole year. In the hybrid configurations, the heat pumps are sized to cover only a portion of the design load (33%, 50%, and 66%) and a biomass boiler is used as a backup to meet the remaining heating demand. This co-working results in better efficiency for the hybrid system, as highlighted in previous research [6, 11]. Moreover, in all configurations, as the heat pump capacity increases from 4 kW to 8 kW, the heat pump's contribution generally increases while the boiler's decreases.
## 5 Determination of the Interference of the Links of the Mechanism
In this case, the condition for the absence of interference of the links will take the form:  
\[\sqrt{u_{1}^{2}+u_{2}^{2}}>\tau_{link1}+\tau_{link2}, \tag{25}\]  
where \(u_{1}\) is the distance between the axis of a link that does not belong to the plane and the auxiliary plane, \(u_{2}\) is the distance between the nearest points of the segments connecting the centers of the joints of each of the links when projecting a segment that does not belong to the auxiliary plane onto this plane, and \(\tau_{link1}\), \(\tau_{link2}\) are the radiuses of the links. It is worth noting the special case when the links are parallel to each other and the construction of an auxiliary plane is not required. The algorithm for determining the interference of links is as follows:  
Determine the coordinates of the centers of the joints of each of the links. Draw segments 1 and 2 between the centers of the joints at each of the links, respectively. Figure 11: Interference of links with a fixed platform of the mechanism. * If the line segments are parallel to each other, then go to step 5. Otherwise, go to step 9. * Rotate the line segments relative to the vertex of one of the line segments so that they become parallel to the OX axis. * Determine the distance \(u_{1}\) between the straight lines passing through the segments. * Let us project the line segments onto the OX axis. * Determine the distance \(u_{2}\) between the nearest points of the line segments. * If condition (25) is satisfied, then there is no interference. Completion of the algorithm. * Construct an auxiliary plane in which segment 1 will lie, and to which segment 2 will be parallel. * Determine the distance \(u_{1}\) between line segment 2 and the construction plane. * Project line segment 2 onto a construction plane. * Determine the distance \(u_{2}\) between the nearest points of segment 1 and the projection of segment 2. * If condition (25) is satisfied, then there is no interference. Completion of the algorithm. A more detailed approach to the definition of the third group of interference is presented in [35]. The proposed approach can be similarly applied to determine the interference between the links of the Uni and DeLi robots. In this case, it is possible to discretely separate the trajectories of the robots in the process of collaborative work of two robots. Then, it is required to determine the positions of the joints for each of the positions of the robots. Using the coordinates of the joints, one must check the intersection of all combinations of links using condition (25) and the above algorithm.
# An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach
## 2 AC/DC Distribution Network Model  
The AC/DC distribution system generally consists of three main components: an AC distribution network, a DC distribution network, and a voltage source converter (VSC).  
The main function of the VSC is to realize the bi-directional flow of active power on the AC side and the DC side, and at the same time to regulate the reactive power. The control mode of the converter mainly includes master-slave control and droop control [15; 16; 17]. This paper mainly studies the distribution network DG planning method when the converter adopts master-slave control mode.  
To study the AC/DC distribution network trend, we must first establish a VSC model. When the VSC model is obtained, the power flow additional equation of the DC system should be given to consider the different control modes of the converter, so that the power flow of the AC/DC power distribution system can be solved. In the AC/DC distribution network described in this paper, the quasi-steady state model is used to equivalently process the DC part. The model not only effectively reflects the power characteristics of the DC converter, but also accurately and fully meets the actual engineering needs. The specific modeling process is as follow [18].
## Numerical Simulation and Parametric Analysis
### Discussions
#### 4.4.5 Effect of Spacing of Binding Bars  
The binding bar spacing for SDC-REF, SDC-S100, and SDC-S150 are 75, 100, and 150 mm, respectively, which leads to different plate slenderness ratios (s/t\({}_{\text{s}}\)) of 25, 33, and 50. SDC-S150 is the specimen with partial composite design in which local buckling of steel plates occurs showing a brittle failure. Figure 10b illustrates the failure modes of SDC-S150, according to the LE contours, the buckling phenomenon is observed between two adjacent binding bars and no splitting failure occurs in the concrete core. This indicates insufficient connector design results in premature buckling of the steel plate that causes inferior use of the cross-sectional resistance of concrete and lower compressive resistance and ductility of the wall.  
Figure 18 shows the influence of binding bar spacing on the ultimate compressive resistance and DI ratios of the SDC wall. It can be seen that the ultimate compressive resistance and ductility of the SDC wall decrease as the spacing of binding bar increases. Compared with SDC-REF, SDC-S1 and SDC-S2 exhibit a 5.8% and 22.5% decrease in the ultimate compressive resistance, respectively. Besides, the DI ratio is reduced by 9.7% and 14.5% from 1.44 to 1.31 and 1.26, respectively. These decrements in  
Figure 17: Influence of thickness of steel plates on compressive resistance of SDC wall.  
both ultimate compressive resistance and ductility are caused by lessened lateral constraint due to the insufficient shear connectors with wider spacing.  
Figure 19 shows the relation between plate slenderness ratio (s/t\({}_{\text{s}}\)) and normalized compression resistance including the FE data of specimens SDC-S1 and SDC-S2. As can be seen, the increase in plate slenderness ratio could decrease the normalized compressive resistance. However, the normalized compressive resistance difference is within 10% with (s/t\({}_{\text{s}}\)) \(\leq\) 15. The normalized maximum compressive load resistances are reduced by about 30% and 40% when the plate slenderness ratio increases from 12.5 to 25 (s = 75 mm) and 50 (s = 150 mm), respectively.
## Appendix E Calculus of the Point Vector with Swarm Data  
_Swarm_ satellite was equipped with an instrument, the Thermal Ion Imager (TII), that was able to measure the ion drift speed along the track of the satellite perpendicular and almost vertical directions [100]. The instrument is composed of two perpendicular detectors--one vertical and the second horizontal--which produce a distribution of the low-Energy ions in terms of angle and energy recorded by a Charged Coupled Device (CCD) [100]. ESA from the ion velocity and magnetic field measurement provided an estimation of the electric field along the track (_Ex_), perpendicular to the satellite direction speed (_Ey_) and almost vertical (_Ez_) in the advanced data products of Swarm, plasma Section 2 Hz TII. The speed vector (\(S_{N}\), \(S_{E}\), \(S_{C}\)) for each sample in _NEC_ (North-East-Centre) frame is also provided and has been used in this work to rotate the electric field in the NEC frame, i.e., the same as the magnetic field:  
\[\begin{vmatrix}E_{N}\\ E_{E}\\ E_{C}\end{vmatrix}=\begin{vmatrix}\cos(\varphi)&0&\sin(\varphi)\\ 0&1&0\\ -\sin(\varphi)&0&\cos(\varphi)\end{vmatrix}.\begin{vmatrix}\cos(\theta)&\sin( \theta)&0\\ -\sin(\theta)&\cos(\theta)&0\\ 0&0&1\end{vmatrix}.\begin{vmatrix}E_{N}\\ E_{y}\\ E_{z}\end{vmatrix}\]where \(\theta\) is the horizontal rotation angle, therefore \(\cos(\theta)=\frac{S_{N}}{I}\), \(l=\sqrt{{S_{N}}^{2}+{S_{E}}^{2}}\) and \(\varphi\) is the vertical one, therefore \(\cos(\varphi)=\frac{l}{\varphi}\) where \(v\) is the speed intensity (, \(v=\sqrt{{S_{N}}^{2}+{S_{E}}^{2}+{S_{C}}^{2}}\)). Substituting the above quantities, the two rotation matrixes are simplified as:  
After the rotation of the electric field, the pointing vector \"\(S\)\" was calculated using the residual value of the magnetic field (\(B_{N}\), \(B_{E}\), \(B_{C}\); shown for each track in Supplementary pdf file) by its definition with the vectorial product of electrical and magnetic vector fields:  
\[S=\frac{\xrightarrow{\mathcal{\overline{E}}\times\xrightarrow{\mathcal{ \overline{B}}}}}{\mu_{0}}=\frac{1}{\mu_{0}}.\left|\begin{matrix}{E_{E}}\cdot{B _{C}}-{E_{C}}\cdot{B_{E}}\\ {E_{C}}\cdot{B_{N}}-{E_{N}}\cdot{B_{C}}\\ {E_{N}}\cdot{B_{E}}-{E_{E}}\cdot{B_{N}}\end{matrix}\right|\]  
Before applying the above multiplication, the magnetic field was interpolated with PCHIP [75] technique at the same point of the electric field by the comparison of their latitude.
# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 2 Methods and Materials  
### Reed-Solomon Codes (RS Algorithm)  
#### 2.1.1 Experience and Reinvention of the Algorithm  
Our approach originated from studies of LU/LC changes using final products of the European research project CORINE (Coordination of Information on the Environment) Land Cover (CLC), maps and data sets, as well as attempts to use cellular automata applications and further research projects [71; 72; 73; 74; 75; 76]. Applications, aimed at predicting LU/LC changes, as well as the valorization of landscapes, did not used remote-sensing images as the input datasets at all. The final formula of the algorithm was expressed in mathematical form in 2012 [74]. This concept arose from the empirical, heuristic combination of geographic information systems (GIS) map algebra and two-dimensional cellular automata. Later, awareness of the robust form of algorithm (described below) was the reason for looking for its application in different fields. It turned out that the mathematical formula was identical to the known formula of a wide range of used Reed-Solomon codes (RS algorithm).
## 3 Design of CP-ABE
### Preliminaries Works
#### 3.1.2. Bilinear Pairing  
In our CP-ABE scheme, bilinear pairing is used to create a public key. The Attribute Authority generates this public key based on a composite order bilinear group with a distinct prime order \(N\), which we adopted from [23]. The algorithm takes an input \(1^{\lambda}\) where \(\lambda\) is a security parameter and produces a tuple (\(\mathbb{G}\), \(\mathbb{G}\)\({}_{T}\), \(e\), \(p1,p2,p3,p4\)) where \(p1,p2,p3,p4\) are distinct primes. The order of cyclic group \(\mathbb{G}\) and \(\mathbb{G}\)\({}_{T}\) is \(N\)= \(p1p2p3p4\), and map \(e\): \(\mathbb{G}\times\mathbb{G}\rightarrow\mathbb{G}\)\({}_{T}\), with properties:  
1. Bilinearity: for all \(g\), \(y\in\mathbb{G}\) and \(d,w\)\(\in\mathbb{Z}_{N}\), where \(e(g^{d},y^{w})=e(g,y)^{dw}\)
2. Non-degenerate: there exists \(g\in\mathbb{G}\) such that \(e(g,g)\) has order \(N\) in \(\mathbb{G}\)
3. Computable: \(e\) can be computed efficiently.
## 4 Simulation Results
### IEEE 30 Bus Test System
#### 4.1.1 Simple Objective OPF
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Fuel Cost (\$/h)** & **Emission (t/h)** & \(P_{\text{loss}}\) (MW)** & **VD (p.u.)** & \(L\) **Index** \\ \hline MSA & 646.84 & 0.28 & 6.80 & 0.84 & 0.14 \\ MPSO & 646.73 & 0.28 & 6.80 & 0.77 & 0.14 \\ MDE & 650.28 & 0.28 & 6.98 & 0.58 & 0.14 \\ MFO & 649.27 & 0.28 & 7.29 & 0.47 & 0.14 \\ FPA & 651.38 & 0.28 & 7.24 & 0.31 & 0.15 \\ LTLBO & 647.43 & 0.28 & 6.93 & 0.89 & \\ TLBO & 647.92 & & 7.11 & 1.42 & 0.12 \\ HIC-GWA & 645.85 & 0.28 & 6.59 & 1.25 & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of HIC-GWA with the literature for case study 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Fuel Cost (\$/h)** & **Emission (t/h)** & \(P_{\text{loss}}\) (MW)** & **VD (p.u.)** & \(L\) **Index** \\ \hline MSA & 930.74 & 0.43 & 13.14 & 0.45 & 0.16 \\ MPSO & 952.30 & 0.30 & 7.30 & 0.72 & 0.14 \\ MDE & 930.94 & 0.43 & 12.73 & 0.45 & 0.16 \\ MFO & 930.72 & 0.44 & 13.18 & 0.47 & 0.16 \\ FPA & 931.75 & 0.43 & 12.11 & 0.47 & 0.15 \\ HIC-GWA & 902.25 & 0.45 & 11.18 & 0.96 & 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of HIC-GWA with the literature for case study 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Fuel Cost (\$/h)** & **Emission (t/h)** & \(P_{\text{loss}}\) (MW)** & **VD (p.u.)** & \(L\) **Index** \\ \hline MSA & 944.50 & 0.2048 & 3.24 & 0.87 & 0.14 \\ MPSO & 879.95 & 0.2325 & 7.05 & 0.57 & 0.14 \\ MDE & 927.81 & 0.2093 & 4.85 & 0.40 & 0.15 \\ MFO & 945.46 & 0.2049 & 3.43 & 0.71 & 0.14 \\ FPA & 948.95 & 0.2052 & 4.49 & 0.43 & 0.14 \\ ARCBO & 945.16 & 0.2048 & 3.26 & 0.86 & 0.14 \\ MSBICA & 942.84 & 0.2048 & & & \\ GBICA & 944.65 & 0.2049 & & & \\ ABC & 944.44 & 0.2048 & 3.25 & 0.85 & 0.14 \\ DSA & 944.41 & 0.2583 & 3.24 & & 0.13 \\ HMPSO-SFLA & & 0.2052 & & & \\ HIC-GWA & 959.54 & 0.2009 & 2.67 & 1.68 & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of HIC-GWA with the literature for case study 4.
## 2 Materials and Methods
### Methods and Algorithms
#### 2.3.3 Inverse Kinematics  
Inverse kinematics is a subdomain of kinematics, and it calculates the required positions of joints to achieve a desired position and orientation [50]. Inverse kinematics is a complex and unpredictable problem relative to direct kinematics:  
* There may be multiple, or even infinite, solutions;
* There may be no solution (the generalized position is outside the workspace);
* They are non-linear equations, so an analytical solution is not always possible.  
In general, inverse kinematics requires large calculations, using analytical or iterative numerical methods. The analytical method is applied when the problem is simple and allows all the solutions to be obtained; otherwise, iterative number methods are used (several iterations to try to converge to the solution with a given error). The application of inverse kinematics, the values of the joint parameters that will place the tool structure in the desired position and orientation (within the working area), and a homogeneous matrix H with the desired positions and orientations are provided by Equation (6) [51].  
\[\text{H}=\begin{bmatrix}\text{R}&\text{O}\\ 0&1\end{bmatrix}, \tag{6}\]  
for a given robotic manipulator with n degrees of freedom, applying the DH convention, Equation (7) is given by [51]:  
\[\text{T}^{0}_{n}(\text{q}_{1},\ldots\text{q}_{n})=\text{A}_{1}(\text{q}_{1}) \cdot\text{A}_{2}(\text{q}_{2})\cdot\text{A}_{3}(\text{q}_{3})\ldots\,\text{ A}_{n}(\text{q}_{n}), \tag{7}\]  
finding the solution (angle of each joint) to satisfy Equation (8) is given by [50]:  
\[\text{T}^{0}_{n}(\text{q}_{1},\ldots\text{q}_{n})=\text{H}\,, \tag{8}\]
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 7 Experimental Procedure
### Datasets
#### 7.1.3 Bank Marketing Dataset  
The Bank Marketing Dataset [35] originates from direct marketing campaigns (telephone contacts) conducted by a Portuguese banking institution between May 2008 and November 2010. It comprises 41,188 instances, each corresponding to a client interaction, and includes 16 input features--such as age, job, marital status, education, housing loan status, contact type, and outcomes of previous campaigns--along with an output variable indicating whether the client subscribed to a term deposit (yes/no). The class distribution is as follows: 'No Subscription' (0) with 36,548 instances and 'Subscription' (1) with 4640 instances. The sensitive attribute, age, is categorized as 'Below Median' (0) with 19,768 instances and 'Above Median' (1) with 21,420 instances.
## 4 Overview: Signal Processing Methods
### Filters Applied
#### 4.2.3 High-Pass Filter with a Cutoff of 0.1 Hz for DC Removal  
A high-pass filter is used to remove DC components from the signal. During the capturing of the EMG signals, these DC components may arise due to factors such as electrode offset or baseline drift. These DC components can interfere with the analysis of the EMG signal when focusing on changes or variations in muscle activity over time. By applying a high-pass filter with a cutoff frequency of 0.1 Hz, the low frequency DC components (typically known to be 0 Hz) can be attenuated or removed from the signal while preserving the higher frequency components that represent the muscle activity. This aids in eliminating any DC offset or baseline variations that could distort information about muscle contractions, thus ensuring an accurate reading.
# Forced Convection Heat Transfer for Stratospheric Airship Involved Flight State
## 3 Results and Discussions
### Effects of Different Parameters on Heat Transfer  
Figure 4 shows the relation between the average Nusselt number (\(Nu\)) and \(Re\), \(e\), \(\alpha\). \(Nu\) can be obtained by \(Nu=(\int_{S}Nu\varrho dS)/S\) where \(S\) is the area of the airship, \(\theta\) is the angular position.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**Values**} \\ \cline{2-6}  & **1** & **2** & **3** & **4** & **5** \\ \hline \(Re\) (\(\times 10^{8}\)) & 0.1 & 0.575 & 1.05 & 1.53 & 2.0 \\ \(e\) & 2 & 2.5 & 3 & 4 & 5 \\ \(\alpha\)/(\({}^{\circ}\)) & 0 & 5 & 10 & 15 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of different parameters.  
Figure 4a shows that \(Nu\) clearly increases with the increase of \(Re\). According to the definition of the Reynolds number, that is, \(Re=Ul/\
u\), where \(U\) is the velocity of the fluid, \(l\) is the characteristic length of the airship, and \(\
u\) is the kinematic viscosity of the fluid. When \(L\) and \(\
u\) are constant, \(UcRe\). The larger the value of \(Re\), the larger the value of \(U\) around an airship, which results in the removal of more heat and the increase of \(Nu\).  
Figure 4b demonstrates that \(Nu\) decreases with the increase of \(e\). According to the definition of \(e\) above, the shape of an airship is close to a sphere when \(e\) is small. However, the shape of an airship is close to a flat plate as \(e\) increases. References [35; 36; 37] indicate that a vortex occurs in the tail region of a sphere while a uniform flow passes over a sphere, thereby resulting in enhanced convection heat transfer.  
Figure 4c indicates that \(Nu\) increases with the increase of \(\alpha\). The presence of the angle of attack (\(\alpha\)) can cause changes in the flow structure around an airship, thereby resulting in a windward side, which eventually leads to a higher convective heat transfer.  
Figure 5 shows the velocity contours and local velocity vectors around the airship with angle of attack of \(0^{\circ}\) and \(20^{\circ}\). The airflow flows from left to right. It is obvious that, at the front region, the velocity of the airflow decreases significantly, and then increases along the airship hull. It reaches a maximum value at the middle region of the airship hull and then gradually decreases. At the tail region, the velocity of the airflow is almost zero due to the separation of the airflow from the surface of the airship. Comparing Figure 5b with Figure 5a, it can be seen that the flow field structure around the airship is changed due to the existing of angle of attack. On the windward side, the position where the velocity of the airflow reaches its maximum moves backward, and in the leeward side, it shows an opposite trend. The change of flow field structure around the airship is the root reason for the change of forced convection heat transfer.  
Figure 5: Velocity contours and local velocity vectors at \(\alpha=0^{\circ}\) and \(\alpha=20^{\circ}\).
## 4 FEM of Composite Laminates with a Bolt-Filled Hole  
A filled-hole composite laminate was selected to analyze the interlaminar stress distributions within the laminate near the bolt. The geometric model is designed according to the geometry of the filled-hole specimens in References [16; 39]. Henceforth in this paper, \"filled-hole laminate\" will be used to refer to the laminate with tightened bolts, and the specimens with untightened bolts will be special notes.  
A 3D FE model was developed in Abaqus to predict the interlaminar stress distributions near the hole edge of the filled-hole composite laminate. This 3D FE model consists of the bolt, laminate, and the interface based on the linear elastic traction-separation behavior. The bolt and nut simplified as a whole in the FE model is shown in Figure 5. The stacking sequence of the laminate is \([0_{2}/45/90/-45/0/45/0/-45/0]\)s. One linear solid element per ply in the thickness direction was used to model the laminate, and the interface element was introduced between plies. The bolt was defined as isotropy material, and linear solid element was also employed. As high interlaminar stress concentrations were expected near the hole, a high mesh refinement was used in this area, as seen in Figure 5.  
As shown in Figure 5, the laminate dimensions are \(L=32\) mm by \(W=32\) mm, the hole of the laminate has a diameter of \(D=6.35\) mm, and the thickness of the lamina is \(t=0.25\) mm. The bolt has a shank diameter \(D_{\text{bolt}}=6.35\) mm, and head diameter \(d=11.3\) mm. Elastic, isotropic material properties are used for the bolt section, with \(E=109\) GPa and \(\
u=0.3\). The laminate is made of material plies that are idealized as homogeneous, elastic and orthorhombic. The following material properties are used: \(E_{11}=181\) GPa, \(E_{22}=E_{33}=10.3\) GPa, \(G_{12}=G_{13}=7.17\) GPa, \(G_{23}=4.13\) GPa, and \(\
u_{12}=\
u_{13}=\
u_{23}=0.25\). Subscripts 1, 2, and 3 denote the fiber, transverse, and thickness directions, respectively.  
The Coulomb friction model was adopted for the contact between the laminate and the bolt. Based on previous investigations [40], the friction coefficients between bolt shank and laminate is \(\mu=0.1\), and between bolt head and laminate is \(\mu=0.43\). The boundary and loading conditions are also  
Figure 4: Interlaminar shear stress distributions at the \([90/0]\) interface of a \([0/90/0]\) laminate (\(a=3\) mm, \(S=4\)), obtained from a linear elastic traction-separation description interface: (**a**) \(\tau_{xzz}\) (**b**) \(\tau_{yz}\).  
Figure 5: Finite element mesh and modeling strategy for a filled hole compression (FHC) specimen, and showing boundary conditions.  
shown in Figure 5. The in-plane surface traction load \(P\) = 100 N/mm\({}^{2}\) are applied to the both ends of the model. The bolt load is defined in terms of a concentrated force, and different pre-load \(F_{0}\) are applied on the bolt.