esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Effect of Angle Distribution on Air Spring Constants
### Discussion of Air Bag Modeling  
The torque at the fixing point of the lower plate is shown in Figure 15, according to the analysis steps. Step 1 is the pressurization and reference position, step 2 is compression, step 3 is the reference position, step 4 is tension, step 5 is the reference position, step 6 is lateral deflection, and step 7 is the reference position. Up to step 5, the load step is generally used to obtain the vertical stiffness, and usually axisymmetric or half-symmetry is used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic Data** & **Constant Ply Angle** & **Variable Ply Angle** & **Difference Ratio** \\ \hline Vertical Force (kN) & 128.6 & 130.6 & 1.5\% \\ Vertical Stiffness (N/mm) & 850.6 & 880.2 & 3.5\% \\ Lateral Stiffness (N/mm) & 99.1 & 75.0 & \(-\)24.3\% \\ Torsional Stiffness (Nm/Deg.) & 450.2 & 314.1 & \(-\)30.2\% \\ Outer Diameter (mm) & 676 & 678 & 0.3\% \\ Height (mm) & 178.7 & 176.8 & \(-\)1.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of characteristic data with respect to the ply angle variation for the air spring analysis at inflating pressure of 6 bars.  
Figure 14: Variation of characteristic data for the air spring simulation: (**a**) Vertical force vs. internal pressure; (**b**) Vertical force vs. compressive deflection; (**c**) Lateral force vs. lateral deflection; (**d**) Torque vs. rotation.  
In Figure 15, even step 5 produces rotational torque, which means that it is not an axisymmetric or half-symmetric problem. The reason the torque appears is that the plies are attached symmetrically and assume that they are symmetrical, but in actuality, the plies are positioned differently in the radial direction. Therefore, even if the angles are the same, different results are displayed when a load is applied. Therefore, for a more accurate analysis, a full model analysis should be performed for airbag characteristic analysis.
## 5 Conclusions
### Practical Suggestions  
First, the psychological risk of ISFR is a negative factor in regard to forming AT. Consumers can feel anxiety towards food-tech products, such as genetically modified foods, cultured meat, and ISFR menus. ISFR is a much safer food option because it cultivates natural plant seeds, which is unlike genetically modified foods and cultured meat. Marketers should highlight this advantage when they plan advertisements in regard to promoting ISFR. Nonetheless, consumers can feel some psychological apprehension about new technology, so the marketers should reduce this apprehension for novelty items.  
Emphasizing ISFR as a new type of restaurant in the fourth industrial revolution era as well as the research and development of seasonally new menus are suggested.  
Second, the quality risk of ISFR also negatively affects AT. The food ingredients that are used in ISFR are organic and fresher than commercial agriculture because smart farms produce vegetables in the store without pesticides. Managers should actively promote the high quality of the ingredients on the menus and the promotional materials in their stores. They can also collaborate with celebrity chefs to develop new menu items in order to reduce the perceived quality risks that are associated with ISFR.  
Third, the SN of using ISFR can positively influence attitudes and behavioral intentions. This means that subjective norms play a crucial role in regard to forming technology acceptance behavior. Marketers can plan promotional content with social influencers, such as cooking/foodie YouTubers in order to enhance SN.  
Furthermore, marketers can consider social media as a marketing tool in order to conduct these types of suggestions. Social media marketing plays a crucial role in regard to forming consumers' trust in brand products [92]. Social media can be a suitable channel for advertisements to reduce psychological risks and collaborate marketing with celebrity chefs in order to reduce the quality risk. In addition, social media influencer marketing can also aid in regard to enhancing consumers' SN.  
Lastly, PBC also has a positive effect on behavioral intentions. The commercialization of ISFR and the establishment of a market environment are necessary in order to enhance this non-volitional behavior. For instance, ISFR restaurateurs can consider the various types of ISFR by including a low-cost restaurant chain that offers various pricing options from more locations.
# Perpendicular Electrical Conductivity in the Topside Ionosphere Derived from Swarm Measurements
## 3 Results
### Electrical Conductivity Variation Perpendicular to the Geomagnetic Field Due to Solar Activity
In the Northern Hemisphere,  
Figure 11: Maps in QD-MLT coordinates of Hall electrical conductivity, \(\sigma_{H}\), during the first (**top panel**) and third (**bottom panel**) biennia as selected in Figure 9. Values within each bin are saturated above 10\({}^{4}\) s\({}^{-1}\) and below 10\({}^{-2}\) s\({}^{-1}\). \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Minimum** & **Maximum** & **Minimum** & **Maximum** & **Maximum Conditioned** \\  & **Value (s\({}^{-1}\))** & **Value (s\({}^{-1}\))** & **Uncertainty (\%)** & **Uncertainty (\%)** & **Uncertainty (\%)** \\ \hline First Biennium & 0.0 & \(1.1\times 10^{5}\) & 1.1 & 99.7 & 4.9 \\ Third Biennium & 0.0 & \(5.3\times 10^{3}\) & 0.8 & 109.4 & 5.2 \\ \hline \end{tabular}
\end{table} 
Table 5: Values of \(\sigma_{H}\) for the first and third biennia, respectively, representing medium–high and low solar activity. during the first biennium, \(\sigma_{P}\) ranges from 0.6 \(\times\) 10\({}^{3}\) s\({}^{-1}\) to 7.2 \(\times\) 10\({}^{3}\) s\({}^{-1}\) with a mean value of 3.0 \(\times\) 10\({}^{3}\) s\({}^{-1}\), while \(\sigma_{H}\) ranges from 0.3 s\({}^{-1}\) to 11.0 s\({}^{-1}\) with a mean value of 2.8 s\({}^{-1}\). During the third biennium, \(\sigma_{P}\) ranges from 11.0 s\({}^{-1}\) to 1.0 \(\times\) 10\({}^{3}\) s\({}^{-1}\) with a mean value of 0.4 \(\times\) 10\({}^{3}\) s\({}^{-1}\), while \(\sigma_{H}\) ranges from 0.0 s\({}^{-1}\) to 0.5 s\({}^{-1}\) with a mean value of 0.1 s\({}^{-1}\). Thus, in the Northern Hemisphere during the first biennium, the mean values of \(\sigma_{P}\) and \(\sigma_{H}\) are about one order of magnitude higher than that retrieved during the third biennium. In the Southern Hemisphere, during the first biennium, \(\sigma_{P}\) ranges from 10.7 s\({}^{-1}\) to 16.0 \(\times\) 10\({}^{3}\) s\({}^{-1}\) with a mean value of 2.1 \(\times\) 10\({}^{3}\) s\({}^{-1}\), while \(\sigma_{H}\) ranges from 0.0 s\({}^{-1}\) to 38.2 s\({}^{-1}\) with a mean value of 1.8 s\({}^{-1}\). During the third biennium, \(\sigma_{P}\) ranges from 0.6 s\({}^{-1}\) to 2.7 \(\times\) 10\({}^{3}\) s\({}^{-1}\) with a mean value of 0.3 \(\times\) 10\({}^{3}\) s\({}^{-1}\), while \(\sigma_{H}\) ranges from 0.0 s\({}^{-1}\) to 3.7 s\({}^{-1}\) with a mean value of 0.1 s\({}^{-1}\). Thus, in both hemispheres, during the first biennium, the mean values of \(\sigma_{P}\) and \(\sigma_{H}\) are about one order of magnitude higher than those retrieved during the third biennium. Figure 12: Maps in QD-MLT coordinates and stereographic projection of Pedersen electrical conductivity, \(\sigma_{P}\), in the Northern (top row) and Southern (bottom row) Hemispheres during the first (left column) and third (right column) biennia, respectively.
## 2 Model of Ship-Radiated Noise and Sample Generation  
Due to the cost of underwater target data acquisition, this study developed, trained, and evaluated a Siamese network using simulated data. We achieved high recognition accuracy for a small number of real samples, but due to the small number and variety of real samples, we did not count and show them. A model of underwater ship-radiated noise was built, and then the radiated noise was processed to obtain its DEMON spectra. DEMON spectra were used as the samples to generate the datasets for evaluating the Siamese network.
To facilitate the analysis, \(S(t)\) was assumed to have one component, which can be expressed as  
\[S^{\prime}(t)=A(1+b\sin\Omega t)\cos\omega t \tag{3}\]  
where \(A\) is the amplitude of the signal, \(b\) is the modulation factor, and \(0<b<1\)\(\Omega\) is the frequency of the modulated signal.  
To obtain the frequency of modulation signal \(\sin\Omega t\), signal \(S^{\prime}(t)\) was processed by square value demodulation, and \([S^{\prime}(t)]^{2}\) can be written as  
\[\begin{array}{rl}\left[S^{\prime}(t)\right]^{2}&=A^{2}(1+b\sin\Omega t)^{2} \cdot\cos^{2}\omega t\\ &=A^{2}\cos^{2}\omega t+2 bA^{2}\cos^{2}\omega t\sin\Omega t+b^{2}A^{2}\cos^{2 }\omega t\sin^{2}\Omega t\\ &=\frac{A^{2}}{2}+\frac{A^{2}}{2}\cos 2\omega t+bA^{2}\sin\Omega t+\frac{bA^{2}}{ 2}\sin(\Omega+2\omega)t+\frac{bA^{2}}{2}\sin(\Omega-2\omega)t+\frac{bA^{2}}{ 4}\\ &-\frac{b^{2}A^{2}}{4}\cos 2\Omega t-\frac{b^{2}A^{2}}{8}\cos(2\Omega+\omega)t+ \frac{bA^{2}A^{2}}{4}\cos 2\omega t-\frac{b^{2}A^{2}}{4}\cos(2\Omega-2 \omega)t\end{array} \tag{4}\]  
The cutoff frequency \(F_{lpf}\) of the low-pass filter satisfied Equation (5). Equation (5) can be written as  
\[2\Omega<F_{lpf}<2\omega-\Omega \tag{5}\]  
After low-pass filtering, the signal \(X(t)\) contained the low-frequency modulated signal, which can be written as  
\[X(t)=\frac{A^{2}}{2}+\frac{b^{2}A^{2}}{4}+bA^{2}\sin\Omega t-\frac{b^{2}A^{2}} {4}\cos 2\Omega t \tag{6}\]  
The discrete form of signal \(X(t)\) is expressed as \(x(n)\). The discrete Fourier transform of \(x(n)\) can be written as  
\[F(k)=\sum_{n=0}^{N-1}x(n)e^{-\frac{2\pi i}{N}kn}\ k=0,\ldots,N-1 \tag{7}\]  
\(F(k)\) is the DEMON spectrum of ship-radiated noise, and it is also the sample for the neural network.
## 5 Result  
### Experimental Result  
The ground calibration experiment was carried out on the Yellow River Beach in China in April 2022. The experimental environment is far away from towns and the magnetic field is stable. The aircraft platform was a UAV designed and assembled independently in China. The data sampling rate was 10 Hz. Then, the flight experiment was conducted to obtain the real flight data. The flight experiment consists of three parts: climb, cruise and landing. The cruise maintained a flight altitude of 2000 m. The ground calibration experiment data were used as the calibration dataset to calculate the current model compensation coefficients using the least squares algorithm, as shown in Table 4. The real flight data were used as the verification flight dataset to verify the effect of the current compensation model. The magnetic interference curves before and after compensation of the ground calibration experiment are shown in Figure 20. Figures 21 and 22 more specifically show the magnetic interference curves before and after compensation when the UAV was placed in the north-south direction and the east-west direction.  
The flight altitude variation curve is shown in Figure 23. The magnetic interference curves before and after compensation in the flight experiment are shown in Figures 24-26, showing the magnetic interference curves before and after compensation during climb, cruise and landing, respectively. (a) Represents unfiltered data and (b) represents bandpass-filtered data.  
Figure 21: Magnetic interference curves before and after compensation when the UAV is placed in the north–south direction.  
Figure 23: Flight altitude variation curve.  
Figure 22: Magnetic interference curves before and after compensation when the UAV is placed in east–west direction.  
Figure 24: Magnetic interference curves before and after compensation in climb: (**a**) Raw data; (**b**) Filtered data.  
Figure 25: Magnetic interference curves before and after compensation in cruise: (**a**) Raw data; (**b**) Filtered data.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 3 Methodology
### System Architecture and Dataset Design for IoT-Supported Blockchain Integration
#### 3.1.3 Weight Measurement  
At checkpoints such as cooperative centers or export hubs, IoT-enabled weighing systems can be used to verify the weight of coffee bags for integrity verification and loss prevention. Arlyn Scales Ultra Precision IoT-connected weighing systems can be deployed to capture real-time weight measurements [42]. These values can be automatically compared to the original harvest weight recorded during RFID registration. If weight loss exceeds acceptable tolerance thresholds, smart contracts can prevent further transactions or require manual interventions, ensuring that product integrity is upheld throughout the supply chain.
## 2 Prototype System and Methods
### Motion Models  
This study uses a model-based object tracking approach to address the issue of small number of captured points. Three types of motion models, namely, constant velocity (CV), constant acceleration (CA), and polynomial motion model, are used to describe the dynamics of taxiing or landing airplanes. The models for one coordinate axis are shown in Table 1; details of these models can be found in Appendix B. For all three models, the following two assumptions are made. First, the tracked plane follows a rigid body motion, and second, the heading of the aircraft body points towards the motion direction. The second condition is approximate for landing aircraft, as side wind may introduce an error in the heading and aircraft attitude. These assumptions mean that all points from the aircraft have the same motion parameters. The faster the aircraft moves the better the assumptions hold. We also assumes 2D motion for taxiing and 3D motion for landing. Note that, in the simplest case, the trajectory of the CV model is a straight line, and thus, the limitation of this model is that it cannot be used for tracking turns, accelerations or decelerations. However, this motion is still valid within a short time window. The CA model is able to describe more complex motion, namely curvilinear motion, but it is still inadequate to model sharp turns. The general or polynomial model allows for describing arbitrary trajectories besides the assumptions presented above.
# Wideband E\({}_{00}\)-E\({}_{10}\) Silicon Mode Converter Based on 180 nm CMOS Technology
## 2 Device Design
The normalized transmission spectra and conversion efficiency of E\({}_{00}\) mode and E\({}_{10}\) mode investigated by FD-BPM calculations under 25 \({}^{\circ}\)C room temperature is presented in Figures 4 and 5, respectively. The minimum mode crosstalk of \(-\)41.7 dB occurs at 1566 nm. Moreover, the crosstalk is less than \(-\)29.2 dB within the wavelength range from 1540 nm to 1600 nm, which implies the conversion efficiency of the E\({}_{00}\) to E\({}_{10}\) mode is larger than 87.9% within the interested wavelength range. The maximum mode conversion is around 99.6% happened at 1575 nm. The normalized transmission (T) as dB can be represented by Equation (4), where \(a\) is the transmittance of the corresponding mode. \[\text{T}=10\text{lg}(a) \tag{4}\]  
To confirm the temperature characteristic of the proposed mode converter, the E\({}_{00}\) to E\({}_{10}\) mode conversion at different temperatures was investigated. When the thermophotic coefficient of silicon is 1.88 \(\times\) 10\({}^{-4}\)/K [28], the simulated conversion efficiency as a function of wavelength is as shown in the Figure 6. It can be seen that no obvious bandwidth change happens. However, when the operation temperature varies from 25 \({}^{\circ}\)C to 40 \({}^{\circ}\)C, the maximum conversion efficiency happens at 1575 nm, 1573 nm, 1572 nm, and 1571 nm, respectively. The blueshift of maximum conversion efficiency with the increment of temperature can be observed. Fortunately, the 3 dB bandwidths are all larger than 60 nm  
Figure 3: (**a**) The effective indices of E\({}_{00}\) and E\({}_{10}\) modes as a function of core layer waveguide width with 220 nm silicon core thickness, (**b**) mode field distribution of mode converter at 1550 nm. Figure 2: Schematic diagram of the (**a**) E\({}_{00}\)-E\({}_{10}\) silicon mode converter, and (**b**) cross-section of silicon waveguide. at temperatures of 25 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C, which proves the favorable temperature stability of the proposed device. This phenomenon can be explained by the unremarkable size change of the silicon waveguide and the limited variation in the effective refractive index, which is favorable to stable mode conversion. Figure 4: Normalized transmission of the proposed E\({}_{00}\)-E\({}_{10}\) mode converter when the wavelength varies from 1540 nm to 1600 nm. Figure 5: Conversion efficiency as a function of wavelength from 1540 nm to 1600 nm. \[L=\frac{M}{4N}(3L_{\pi}). \tag{2}\]0 Simulated mode conversion as a function of wavelength at different temperatures.
## 3 Results
### A Proof-of-Concept Implementation
Figure 12 shows the average of the optimal total times (i.e., reservations not taken into account), which is the same for both approaches; the average of the best total times found by the EAs in the WRA (i.e., taking into account the reservations); and the average of the total travel times for both approaches. The average times are given per one MRSA, taking into account all ten pre-defined routes. There was no point in measuring the best total time found by the EAs under the NRA, as the EAs were deployed several times during the trip. The EAs under the WRA were deployed only once, at the beginning of the trip. Each MRSA eventually reached its destination. Figure 13 shows similar results, but with the average measured distances. We can conclude the same: the WRA outperforms the NRA as the number of MRSAs increases. In the case of the WRA, the average of the total distances traveled was identical to the average of the distances found by the EAs. Figure 12: Comparison of the average times. Figure 13: Comparison of the average distances. Another perspective that we can examine is a comparison of two states: the average amount of waiting and the average amount of moving per one MRS, considering all ten pre-defined routes. This perspective can be expressed by the duration of the transport orders. Figures 14 and 15 show the duration of those states for the NRA and the WRA, respectively. The more MRSAs there are in the environment, the longer it takes to find and reserve a new route. However, similarly to the previous cases, as the number of MRSAs increases, the movement and the total durations are shorter; thus, the WRA outperforms the NRA. The usage of reservations increases the throughput of the GSN. Figure 14: Durations of the states in the NRA. Figure 15: Durations of the states in the WRA.
# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 1 Introduction
The main objective of this work is energy optimization by efficient CH selection and evaluation process of all the nodes. Three main sets of nodes are determined by the BS, i.e., (a) suitable, (b) not suitable, and (c) prohibited nodes for being CHs. The nodes' performance is continuously assessed by the BS to determine these sets. The selfish nodes are marked prohibited in the beginning. The CH selection is then further made dependent on some key parameters considered by the BS. The BS predicts these parameters' values by itself to reduce the communication overhead on the nodes. A new concept of nodes' importance based on the closed neighbors (CNs) is also introduced in this work. Once the parameters are evaluated, the BS gives benefits to the most appropriate nodes or gives penalties to the non-suitable nodes. An evolutionary game is applied to all of these nodes combined in suitable and non-suitable sets, along with their profits and penalties values. The rest of the paper is organized as follows. Section 2 comprises of related work; many related mechanisms used for energy optimization have been discussed. Section 3 explains the detail of our proposed mechanism. Many subsections are used to clearly explain the entire proposed mechanism. Section 4 consists of the experimental results where two clusters-based and two evolutionary clusters-based approaches were taken and compared with our proposed mechanism. Lastly, conclusion and future works are given in Section 5.
## 2 A Voltage Ride-Through (VRT) Control Model for Energy Storage Systems
### Hybrid Optimization Algorithm Design
#### 3.3.3 Local Optimization Phase  
The objective function is decomposed into differentiable (feature error) and non-differentiable (THD) components:  
\[J(\theta)=\underbrace{\sum_{i=1}^{5}\alpha_{i}\cdot\left(f_{i}^{sim}-f_{i}^{ \text{meas}}\ \right)^{2}+\alpha_{P,Q}\,\text{MSE}_{P,Q}}_{J_{\text{smooth}}\ (\theta)}+\underbrace{\beta\,\text{THD}}_{J_{\text{non- smooth}}\ (\theta)} \tag{22}\]  
The Levenberg-Marquardt (LM) algorithm optimizes the differentiable part, and the THD term is indirectly optimized via weight constraints \(\beta\) THD.  
The residual vector is defined as follows:  
\(\mathbf{r}(\theta)=\left[\sqrt{\alpha_{1}}\big{(}f_{1}^{sim}-f_{1}^{meas} \big{)},\dots,\sqrt{\alpha_{5}}\big{(}f_{5}^{sim}-f_{5}^{meas}\big{)},\sqrt{ \alpha_{P,Q}MSE_{P,Q}}\big{)}^{T}\right.\) with the Jacobian matrix:  
\[\mathbf{J}_{r}(\theta)=\begin{bmatrix}\frac{\partial\tau_{1}}{\partial\theta_ {1}}&\dots&\frac{\partial\tau_{1}}{\partial\theta_{j}}\\ \vdots&\ddots&\vdots\\ \frac{\partial\tau_{P,Q}}{\partial\theta_{1}}&\dots&\frac{\partial\tau_{P,Q}} {\partial\theta_{j}}\end{bmatrix} \tag{23}\]  
In the equation, the partial derivative \(\frac{\partial\tau_{i}}{\partial\theta_{j}}=\sqrt{\alpha_{i}}\cdot\frac{ \partial f_{i}^{sim}}{\partial\theta_{j}}\) is computed via the finite difference method or adjoint equations.  
The Levenberg-Marquardt (LM) algorithm combines gradient descent and the Gauss-Newton method, with its iteration formula expressed as follows:  
\[\theta^{(k+1)}=\theta^{(k)}-\left(\mathbf{J}_{r}^{\text{T}}\mathbf{J}_{r}+ \lambda\mathbf{I}\right)^{-1}\mathbf{J}_{r}^{\text{T}}\mathbf{r} \tag{24}\]  
where \(\lambda\) is a dynamically adjusted damping factor:  
\[\lambda^{(k+1)}=\begin{cases}\lambda^{(k)}/10,&\text{if }J_{\text{smooth}}( \theta^{(k+1)})<J_{\text{smooth}}(\theta^{(k)})\\ \lambda^{(k)}\times 10,&\text{otherwise}\end{cases} \tag{25}\]
## 3 Results and Discussion
### Validation of Decomposition Degree Predictive Models and Comparison of Contributing Factors
Predictions of the experimental  
Figure 2: The results of predictive modeling of the degree of decomposition using the weight average molecular weight (Mw), analytical values, experimental conditions, and principal component analysis (PCA) scores of cross-polarization and magic angle spinning (\({}^{13}\)C-CP/MAS spectra) using the random forest (RF) machine learning algorithm. (**a**) A bivariate plot of the observed and predicted Mw of validation data. \(R^{2}\) is 0.78 and RMSE is 10.78%. (**b**) Bar chart of importance in the decomposition degree predictive model. Details of PC1 and PC2 scores used for machine learning are shown in Figure S2. conditions, such as moisture content in the compost environment, were also possible, but the predicted values of the degree of decomposition did not exceed the actual measured values. This was thought to be due to the influence of the range on the objective variables of machine learning. However, the results may be improved by implementing other machine learning methods to perform the verification calculations [28; 29; 30], which should be tested in future studies.