esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation Details
### Safety Parameter Setting  
As with other encryption schemes, the CKKS homomorphic encryption scheme requires parameters to be set to ensure that known attacks are computationally infeasible. We chose different configurations for the different models, and all configurations satisfy 128-bit security, which means that an adversary would need to perform at least \(2^{128}\) basic operations to crack the scheme with probability 1. The polynomial degree \(N\) of the first configuration is set to \(2^{15}\), with integer and fractional partial precision set to 10 and 50, respectively, and a multiplication depth of 14. The second configuration is set to \(N=2^{15}\), with integer and fractional partial precision set to 10 and 39, respectively, and a multiplication depth of 20. The third configuration is set to \(N=2^{16}\), with integer and fractional partial precision set to 12 and 48, respectively, and a multiplication depth of 9. In addition, in the third configuration, we use bootstrapping technology. The degree of the approximation polynomial of the modulus function is set to 14, and the maximum length of the modulus is 1332, which meets 128-bit security, while [PERSON] et al. [14] only meets 111.6 bits of security. The parameters for each configuration are listed in Table 2.
# Improving Performance of a Resonant String-Based Pulsation Attenuator in Hydraulic Systems
## 2 Modelling of the Resonant String-Based Hydraulic Pulsation Attenuator (RSHPA)
### Modelling
This method is accurate enough under the assumption that the fluid in the pipeline is laminar flow and the major pulsation frequency is within a certain range, which would not heavily affect the impedance of the hydraulic elements [12]. These parameters are calculated as [10]:  
\[R_{ECA}=\frac{128\mu_{0}l}{\pi D^{4}},\ Z_{\mathrm{C}_{ECA}}=-j\frac{4\rho_{0 }c_{0}^{2}}{\omega\pi lD^{2}},\ Z_{\mathrm{L}_{ECA}}=\frac{4j\omega\rho_{0}l}{ \pi D^{2}} \tag{5}\]  
\[Z_{string}=Z_{1}//Z_{2}//\cdots//Z_{i}//\cdots//Z_{g} \tag{6}\]  
where \(\mu_{0}\) denotes the kinetic viscosity of the fluid, \(l\) and \(D\) denote the length and diameter of the expansion chamber, \(Z_{i}\) denotes the impedance of the \(i\)-th string and \"\(//\)\" denotes the parallel operator for the impedance. The equivalent circuit diagram of the string array is illustrated as Figure 3. Each string is composed of infinite resonant LC (Inductance-Capacitance) units, which represent the response characteristics of different-order frequency of the string. The resonant frequency for each component is formulized as follows [13; 14]:  
\[f_{in}=1/2\ \pi\sqrt{L_{in}C_{in}} \tag{7}\]  
where \(L_{in}\) and \(C_{in}\) denote the equivalent inductance and capacitance of the \(n\)-th order response of the \(i\)-th string. Based on Figure 3, the equivalent impedance of \(i\)-th string is given by:  
\[Z_{i}=(\frac{1}{j\omega C_{i1}}+j\omega L_{i1})//(\frac{1}{j\omega C_{Z}}+j \omega L_{22})//\cdots//(\frac{1}{j\omega C_{in}}+j\omega L_{in}),\ n=1,2, \cdots. \tag{8}\]  
where [15],  
\[L_{in}=\frac{\rho_{0}l_{str}^{i}}{\pi(\Theta_{i}/2)^{2}}. \tag{9}\]  
In Equation (9), \(l_{str}^{i}\) and \(\Theta_{i}\) denote the length and diameter of the \(i\)-th string, respectively. The equivalent capacitance in Equation (8) can be calculated using Equation (7). With Equations (1)-(9), the transfer matrix of the RSHPA can be determined.
## 4 Experiments and Results
### Results
#### 4.3.1 Age Estimation  
We reported our age estimation results in MAE. We evaluated our system on the Morph dataset for estimating the real/biological age of a person. The Morph dataset has become one of the standard benchmarks for the real age estimation over the last few years.  
Figure 9: Distribution of age samples among the datasets experimented in our system.  
We compare our results with the classic and state-of-the-art age estimation methods, such as Deep Expectation (DEX) [24], Ordinal Hyperplanes Ranker (OHRank) [49], AGES [31], AGE group-n encoding (AGEn) [57], OR-CNN [58], and Compact yet efficient Cascade Context-based Age Estimation (C3 AE) [78] on the Morph dataset as shown in Table 5. As per the comparison table, the proposed method has a beneficial impact in estimating the age of a person over the same dataset and demonstrates better results than the traditional as well as deep learning-based age estimation models. The qualitative results of our model for the Morph dataset are presented in Figure 10.  
From the comparison presented in Table 2, it can be concluded that the resulting MAE of our method for the Morph dataset is 2.42, and the results were meaningfully improved in comparison with the other hand-crafted feature-based age estimation approaches such as OHRank, and also exceeded the deeply learned models, namely DEX, Ranking-CNN, and C3 AE.  
As stated in the calculation procedure of the cumulative score (CS), the CS values for the Morph dataset under distinct error thresholds are plotted in Figure 11. From the figure, a steady growth of the CS value is observed if the allowable error thresholds increase. We plot the cross-entropy training and validation loss curve during the age estimation task in Figure 12. We stopped our training when the validation loss was increasing constantly.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **MAE** \\ \hline [PERSON] et al., (2016) [22] & 3.45 \\ DEX [24] & 3.25 \\ DEX (IMDB-WIKI) & 2.68 \\ AGES [31] & 8.83 \\ CA-SVR [48] & 5.88 \\ OHRank [49] & 6.07 \\ [PERSON] and [PERSON] (2014) [50] & 3.92 * \\ [PERSON] et al., (2014) [51] & 3.63 * \\ [PERSON] et al. [54] & 2.68 \\ [PERSON] et al. [55] & 2.32 * \\ [PERSON] et al. [56] & 4.29 * \\ AGEn [57] & 2.93 \\ AGEn (IMDB-WIKI) & 2.52 \\ OR-CNN [58] & 3.27 \\ C3 AE [78] & 2.78 \\ Ours & **2.42** \\ \hline \hline \end{tabular}  
* indicates different split.  
\end{table}
Table 5: Result (MAE) comparison for real (biological) age estimation for the Morph dataset.  
Figure 10: A pictorial presentation of some of the test images with predicted age of the Morph dataset evaluated by our model.  
In Table 6, we present the insightful parameters that lead the whole real age estimation experiments. In this comparative analysis, two factors (e.g., balance dataset, batch normalization) basically make the differences in the overall performance of the model. From the table, it is evident that when we train the model with a balanced dataset along with introducing a batch normalization layer in the deployed network, a good performance is obtained compared to the other experiment setup.
## 2 Experiment Setup and Model Configuration
### Model Configuration
The three suction/jet holes are equidistantly-distributed on the leading and trailing edges of the lower surface. The suction/jet holes of the upper surface are set on the center of the leading/trailing edges of the lower surface, respectively. Combined with Figure 2b-f, the five proposed control schemes are listed in Table 1.  
In order to evaluate the effectiveness of the control for the five different control schemes, a non-dimensional suction/jet momentum coefficient \(f_{suc}\) was defined in the present study as follows:  
\[J_{suc}=\frac{n\bigg{(}H_{Ulsuc}L_{suc}+\sum\limits_{i=1}^{N}iH_{Lsuc}L_{suc} \bigg{)}}{HL} \tag{1}\]  
where \(N=1\)-3 is the suction/jet hole number on the leading or trailing edge of the lower surface in the cross-section; \(n=16\) is the suction/jet hole number along the span-wise direction; \(H_{Ulsuc}\) is the height of the suction/jet hole on the upper surface; \(H_{Lsuc}\) is the height of the suction/jet hole on the lower surface; \(L_{suc}\) is the length of the suction/jet hole; \(H\) is the height of the cross-section of the test model; and \(L\) is the span-wise length along the test model. The suction/jet momentum coefficients for the five control cases and Case 0 are listed in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Control** & **Suction/Jet** & **First Row of Suction/Jet** & **Second Row of** & **Third Row of** & \multirow{2}{*}{\(J_{suc}\)} \\
**Case** & **Holes on US** & **Holes on LS** & **Suction/Jet Holes on LS** & **Suction/Jet Holes on LS** & \\ \hline Case 0 & closed & closed & closed & closed & 0 \\ Case 1 & open & closed & closed & closed & 0.0667 \\ Case 2 & open & open & closed & closed & 0.1333 \\ Case 3 & open & open & open & closed & 0.2000 \\ Case 4 & open & open & open & open & 0.2667 \\ Case 5 & closed & open & open & open & 0.2000 \\ \hline \hline \end{tabular} Note: US = upper surface and LS = lower surface.  
\end{table} 
Table 1: Control schemes of the test models. Figure 2: Geometry information of the cross-section of the segment models and five control schemes (unit: mm). (**a**) Case 0; (**b**) Case 1; (**c**) Case 2; (**d**) Case 3; (**e**) Case 4; (**f**) Case 5. Figure 3 provides a view of the model from the downstream side. Two end plates were installed on both ends of the model to avoid the effects of having free ends and to improve the accuracy of the test results. The length of the suction/jet holes is 20 mm in the span-wise direction, the distance between two neighboring suction/jet holes is 10 mm along the span-wise direction, while the suction/jet holes at both ends are 5 mm from the edge of the model.
# Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications
## 5 SA Applied to Aircraft Design
### Aircraft Design Evaluation Process  
Applying SA to the optimization of aircraft design requires a method for evaluating prospective designs that can be applied at each iteration of the algorithm [53]. In this case, the design must be validated according to the competition rules and a given score. The competition outlines several constraints for the design, and a violation of any one of these constraints invalidates it. As such, only designs that meet all the requirements can be given a score. The constraints are checked in the order outlined in Figure 8 and are described as follows:  
* Dimensional constraint: The sum of the height, width, and length must be below the upper limit;
* Aerodynamic evaluation: This depends on the wing and tail geometry;
* Stability evaluation: The approximate weight of the aircraft is determined using the material density and the volume of the stringer;
* Motor placement: The motor must be more than 8 cm from the fuselage;
* Structural evaluation: All mechanical stresses are evaluated.  
Once the design is validated, it is given a score based on its maximum load, which is estimated from the geometry and the runway length as set by the regulations.  
The design must also be parameterized for the algorithm to be applicable. The para metrization scheme consists of 23 parameters (19 continuous variables and 4 discrete variables). Such parametrization is sufficient to calculate the aerodynamic coefficients in the aerodynamic evaluation as well as to estimate the score.  
\begin{table}
\begin{tabular}{l l l} \hline
**Arrangement** & **Cost** & **Total Iteration** \\ \hline Linear arrangement & 28.02 & 1,876,819 \\ Triangular arrangement & 19.71 & 1,930,430 \\ \hline \end{tabular}
\end{table}
Table 5: Results of tests for the two arrangements. The final cost and the total number of cost function evaluations.
## 5 Operational Descriptions of the Future Distribution Networks with the Sundom Smart Grid
### Energy Management Use Cases
#### 5.1.1 Energy Management Use Cases of the Self-Sufficient Phase  
At the self-sufficient phase, the LV distribution network customers can have a building automation system (BAS), including a HEMS. The BAS can control heating, ventilation, lighting and sockets, sauna stove, water boiler, and security. The home-away function is mostly applied for heating and ventilation, reduced while the residents are away. Customers have increasing micro-generation, which are PV units with or without BESS. The micro-generation unit's maximum output power is 50 kVA, according to the standard EN 50,438 [72]. In Finland, the network recommendations are set for connecting the micro generation units maximum of 100 kVA [73]. Notable is that if connecting units based on the three-phase overcurrent protection for the unit, 3 \(\times\) 16 A, the thermal limit is 11 kVA. The customers mainly aim at PV generation for their own use, but some might be interested in feeding in the utility grid. Three types or roles of customers appear in this evolution phase [70] as follows. Traditionally (in the traditional phase), the customers are just using electricity for their demand, and they are called consumers. In this evolution phase, most customers, called responsive consumers, become more active in the network operation participating in the DR programs with their controllable loads. Third, prosumers emerge increasingly, who own a generation unit and can produce, store, and sell electric energy in addition to the operations of the customer types above. There can be SS-BESS, and there exists a PS-BESS. A WT unit is located near the substation and connected with its own feeder to the MV bus (see Figure 3). The customers or the energy community obtain a common small-scale generation unit, in this case, a CHP unit for the regional energy generation, aiming for self-sufficiency in energy. The CHP unit is connected to the district heating system. A small-scale generation unit's maximum output power is allowed to be 2 MVA [74,75]. A central energy management system (CEMS) over both voltage levels monitors the CHP unit's energy flows. The BESS units' and the customers' demand can be monitored, and the utility network for cost and compensation fee calculation purposes. Smart energy meters measure the energy consumption in the customers' premises and the secondary substation areas. A measuring unit for the generation from the CHP unit is required. The HL-UCs for energy management over the LV and the MV distribution networks in the self-sufficient phase are described in Table 3.
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
## 2 Cross-Domain Fusion Constellation Design Method  
The CDFC design method based on MDCRO divides constellation design into two stages: basic constellation design and constellation reverse fusion optimization. Traditional constellation design is generally aimed at a single business, such as the design of dedicated navigation constellation and remote sensing constellation. In order to effectively adopt the traditional constellation design method, in the first stage, the multidisciplinary collaborative design optimization method is used for each domain to obtain the constellation that meets the requirements of each task. After entering the reverse design stage, the satellite is eliminated according to the requirements, and the optimized constellation scheme is solved. Section 2.1 first introduces the requirements and design methods, and Section 2.2 describes the process of the two stages in detail.
# Worldwide Coverage Mobile Systems for
###### Abstract  
Current terrestrial mobile communications networks can't provide worldwide coverage. Satellite communications are expensive, and terminals are large and heavy. Worldwide mobile coverage requires the use of satellites providing an appropriate QoS, including polar regions. The analysis of the potential satellite constellations demonstrates that LEO one is the best solution. A new generation of low cost, small size, lightweight and global mobile coverage LEO satellites is emerging. The main limitation of the terminals is the antenna size factor, and innovative antennas must be developed to meet this goal. This paper investigates the technologies and techniques for designing and developing antennas aimed at LEO satellite communications in Smart Cities and beyond, which are especially beneficial for mobile communications in areas without 4G/5G coverage. The paper focuses on the terrestrial segment and future mobile devices, remarking the design constraints. In this scenario, the paper reviews the most relevant technologies and techniques used to design suitable antennas. The investigation analyses the state-of-the-art and most recent advances in the design of antennas operating in the Ku-band. The main contribution of the authors is a novel antenna design approach based on SIW technology. The antenna features are compared with other approaches, highlighting the benefits, advantages and drawbacks. As a conclusion, the proposed antenna demonstrates to be a good solution to meet the design constraints for such an application: light, low cost, small size factor.  
s mart cities; worldwide coverage mobile systems; SIW; satellite antenna; LEO; radiation pattern; antenna gain; circular polarization +
Footnote †: journal: Smart Cities  
0  
0
## 4 Dynamic Response of the System
### The Influence of Train Speed on System Dynamic Response  
We compare the dynamic response of the system under no seismic excitation with that under seismic excitation, and analyze the influence of an earthquake on the dynamic response of the train-track-bridge system. The train passed over the bridge at speeds of 150, 200, 250, 300, and 350 km/h, respectively, and we recorded the peak dynamic response of the system, as shown in the following figures.  
From Figures 8 and 9, it can be seen that seismic excitation has a significant impact on the dynamic response of bridges, with a particularly prominent impact on the acceleration response. The lateral dynamic response of the bridge increases more than the vertical dynamic response. This is because when there is no earthquake, the lateral dynamic response of the train mainly comes from the lateral track's irregularity, and the vertical dynamic response mainly comes from the train's gravity. The seismic excitation is input according to the vertical seismic wave strength, which is 0.65 times the horizontal seismic wave strength. Therefore, the seismic excitation has a more intense and sensitive impact on the lateral dynamic response of the bridge than the vertical dynamic response, and under earthquake excitation, some responses exceed the safety index limit, seriously reducing the smoothness and safety of the train. Overall, the dynamic response of the bridge increases with the increase in vehicle speed.  
Figure 7: Wheel–rail contact diagram.  
From Figure 10, it can be seen that the train response generally increases with the increase in operating speed, and the lateral response is more affected by seismic excitation than the vertical response, for the same reason as the above bridge response influence law. From Figure 11, it can be seen that under seismic excitation, the wheel rail load reduction rate and derailment coefficient of trains generally increase with the increase in vehicle speed. When the operating speed is 350 km/h, the two safety indicators of trains approach the limit values [42]. When the seismic acceleration is greater, the vehicle speed is faster, and the bridge structure is more flexible, the risk of train derailment will greatly increase, and the safety and comfort of trains will be greatly reduced.  
Figure 8: Peak lateral response of bridge midspan.  
Figure 10: Peak train acceleration.  
Figure 9: Peak vertical response of bridge midspan.
## 2 PMSG Wind Power Systems
### Rotor Side Converter Model  
To describe RSC power production, PMSG electrical equations reflecting voltage and current are used; its electrical and mechanical torque can be calculated from the equations given below [16].  
\[\begin{split}& v_{dg}=R_{s}i_{dg}+L_{s}\frac{di_{dg}}{dt}-\omega_{s}L_{ s}i_{qg},\\ & v_{qg}=R_{s}i_{qg}+L_{s}\frac{di_{qg}}{dt}+\omega_{s}L_{s}i_{qg }+\omega_{s}\lambda_{f},\\ & T_{e}=\frac{3}{2}p\lambda_{f}i_{qg},\\ & T_{m}-T_{e}=\int\frac{d\omega_{m}}{dt},\end{split} \tag{2}\]where \(v_{dg}\) and \(v_{qg}\) denote the stator voltages of the PMSG while \(i_{dg}\) and \(i_{qg}\) indicate stator currents. \(L_{s}\) and \(R_{s}\) denote the stator inductance and resistance, respectively, and \(\omega_{s}\) denotes the rotor flux electrical speed. \(\omega_{m}\) denotes the speed of the PMSG mechanical rotor, \(\lambda_{f}\) indicates the rotor flux, and \(p\), which represents the ratio of electrical speed to mechanical speed, indicates the machine pole pairs. \(T_{e}\) denotes the electromagnetic torque and \(T_{m}\) indicates the mechanical torque. \(J\) represents rotor inertia. Therefore, rotor speed can be calculated using equations that describe the relationship between \(T_{e}\), \(T_{m}\), and \(J\). Hence, a surface-mounted PMSG with similar d- and q-axis inductances was used in this investigation. Thus, a reluctance torque that is induced by the difference between these inductances does not exist. Therefore, we can use the Equation (2) to calculate the electromagnetic torque.
## 3 Results
### The 3D Cephalometric Measurements of T0 Stage  
Table 2 presents the linear and angular 3D cephalometric measurements of T0 stage, and the comparisons with the Taiwanese norms in which data gender differences were not found. SNA, ANB, and A-N perpendicular were significantly smaller than norms (\(p<0.01\)). There were no significant differences between the patients with UCLP and normal population on SNB and Pog-N perpendicular (\(p>0.05\)). All five midline landmarks (ANS, A, Me, U1, and L1) presented a significant deviation in relation to MSP in T0 stage (\(p<0.01\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**T0 Stage**} & \multicolumn{2}{c}{**Norms**} & \multirow{2}{*}{\(p\)**Value**} \\ \cline{2-2} \cline{4-6}  & **Mean** & **SD** & **Mean** & & **SD** \\ \hline SNA & 75.90 & 4.32 & 83.22 & 2.95 & 0.000 ** \\ SNB & 80.44 & 4.70 & 80.02 & 3.24 & 0.768 \\ ANB & \(-4.72\) & 4.10 & 3.34 & 1.56 & 0.000 ** \\ A-N perpendicular & \(-4.98\) & 3.47 & 1.10 & 2.66 & 0.000 ** \\ Pog-N perpendicular & \(-0.11\) & 7.36 & \(-3.10\) & 6.50 & 0.462 \\ SN/MP & 35.30 & 7.42 & 33.63 & 5.11 & 0.639 \\
**The midline discrepancy** & & & & & \\ ANS & 2.87 & 2.09 & 0.73 & 0.50 & 0.000 ** \\ A & 1.57 & 1.99 & 0.67 & 0.43 & 0.000 ** \\ Me & 2.58 & 2.99 & 0.92 & 0.42 & 0.000 ** \\ U1 & 3.01 & 2.97 & 0.59 & 0.26 & 0.000 ** \\ L1 & 2.16 & 2.76 & 0.47 & 0.24 & 0.000 ** \\ \hline \end{tabular}  
* \(p<0.01\).  
\end{table}
Table 2: The 3D cephalometric measurements of the preoperative image (T0 stage) and the comparisons with normative data.  
Figure 2: The Bland–Altman plot showing adequate intra-observer reproducibility (\(n=540\)).
## 5 Conclusions  
A special aerial seeding system for rapeseed based on a miniature air-assisted centralized metering device was proposed. The system was designed and tested based on a commercial six-rotor plant protection UAV from Lockheed Co. Ltd. In the field tests, it was compared with two other rape seeding modes based on systems including a ground planter of Weiye 2 BFG-4 from Hunan Province and another aerial seeding system with XAG P20 UAV. The main research conclusions are presented as follows:  
1. The designed aerial seeding system could work stably, uniformly, and efficiently, well meeting the agronomic requirements of rapeseed planting. It resulted in an equivalent average yield and crop numbers per hectare compared with the other two seeding modes.
2. Results from the standard orthogonal experiment showed that the rotational speed of the gear disc was the main influencing factor, followed by the fan air pressure, and the seed layer thickness. The best combination of parameter levels for the seed filling effect was B\({}_{3}\)A\({}_{3}\)C\({}_{2}\) (i.e., the seed layer thickness was 45 mm, the rotation speed was 45 r/min, and the fan air pressure was 2450 Pa).
3. Both the stability variation coefficients of the total seeding rate and the consistency variation coefficient of the seeding rate between each port were very low, with the maximum value being 2.37% and 4.89%, respectively. The total seeding rate \(Y\) (g/min) increased with the rotational speed of the gear disc \(X\) (r/min) according to a polynomial fitting relationship \(Y=0.46014X^{2}-20.7614X+544.78086\) with R\({}^{2}=0.9934\).