esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Satellite-to-Ground QKD Simulator
### Atmospheric Link Modeling
#### 3.2.3 Atmospheric Transmission  
Atmospheric transmission involves the interaction of photons with atmospheric constituents, thus leading to signal attenuation, scattering, and degradation. The transmittance varies with the satellite's elevation angle, and it can be calculated under clear sky conditions using the following formula [55]:  
\[L_{a}=L_{zen}(\frac{1}{\alpha\varpi(\zeta)}), \tag{5}\]  
where \(L_{zen}\) is the transmittance for a vertical link at a specific wavelength and \(\zeta\) is the zenith angle of the link. It is evident that, for low elevation angles, atmospheric attenuation is higher since the light must travel a longer path through the atmosphere to reach the receiver.
# The Two-Parameter Bifurcation and Evolution of Hunting Motion for a Bogie System
## 2 Dynamic Model
### Wheel-Rail Interaction Force
#### 2.1.2 The Creep Forces  
The relative sliding of the wheelset and the rail between the contact surfaces when the vehicle runs at speed \(v\) is called creep. The longitudinal creep \(\xi_{x}\), lateral creepage \(\xi_{y}\), and rotational creep \(\xi_{sp}\) are given by [49]:  
\[\left\{\begin{array}{l}\xi_{(L,R)x}=\frac{v+\psi_{w}\dot{\psi}_{w}+\tau_{(L,R) }(\psi_{w}\dot{\phi}_{w}-\Omega)\pm a_{0}\dot{\psi}_{w}}{v}\\ \xi_{(L,R)y}=\frac{-v\psi_{w}+\dot{\psi}_{w}+\tau_{(L,R)}\dot{\phi}_{w}}{v\cos \delta_{(L,R)}}\\ \xi_{(L,R)sp}=\frac{\pm(\Omega-\dot{\psi}_{w}\dot{\phi}_{w})\sin\delta_{(L,R) }+\dot{\psi}_{w}\delta_{(L,R)}}{v}\end{array}\right., \tag{1}\]  
where \(\Omega=v/r_{0}\) is the nominal angular velocity. The symbols \(\pm\) and \(\mp\) are assigned as upper signs for the left wheel (\(L\)) and lower signs for the right wheel (\(R\)). The expressions for creep forces calculated with the [PERSON] linear creep theory [50] are given by  
\[\left\{\begin{array}{l}F_{x}=-f_{11}\xi_{x}\\ F_{y}=-f_{22}\xi_{y}-f_{23}\xi_{sp}\\ M_{z}=f_{23}\xi_{y}-f_{33}\xi_{sp}\end{array}\right., \tag{2}\]  
where \(f_{11},f_{22},f_{23},f_{33}\) represent the longitudinal, lateral, lateral/spin, and spin creep coefficients and can be given by  
\[\left\{\begin{array}{l}f_{11}=\mathrm{G}(ab)C_{11}\\ f_{22}=\mathrm{G}(ab)C_{22}\\ f_{23}=\mathrm{G}(ab)^{3/2}C_{23}\\ f_{33}=\mathrm{G}(ab)^{2}C_{33}\end{array}\right., \tag{3}\]  
To accommodate large creep, the linear creep force in Equation (2) needs to be revised using the Shen-Hedrick-Elkins method [51]. The revision coefficient \(\varepsilon\) can be given by  
\[\varepsilon=\left\{\begin{array}{ll}(\beta-\beta^{2}/3+\beta^{3}/27)/\beta& \beta\leq 3\\ 1/\beta&\beta>3\end{array}\right., \tag{4}\]  
where \(N\) stands for normal contact force, and \(\beta\) is expressed as follows  
\[\beta=\sqrt{F_{x}^{2}+F_{y}^{2}}/(\mu N), \tag{5}\]  
Then, the revised creep forces/torque are  
\[F_{x}^{\prime}=\varepsilon F_{x},F_{y}^{\prime}=\varepsilon F_{y},M_{z}^{ \prime}=\varepsilon M_{z}. \tag{6}\]  
The revised \(F_{x}^{\prime}\)\(F_{y}^{\prime}\), \(M_{z}^{\prime}\) can be converted from the wheel-rail contact patch coordinate system to the track coordinate system. The expression of the coordinate transformation is  
\[\left\{\begin{array}{l}F_{(L,R)x}\\ F_{(L,R)y}\\ M_{(L,R)z}\end{array}\right\}=\left[\begin{array}{ccc}\cos\psi_{w}&-\cos( \delta_{(L,R)}\pm\phi_{w})\sin\psi_{w}&0\\ \sin\psi_{w}&\cos(\delta_{(L,R)}\pm\phi_{w})\cos\psi_{w}&0\\ 0&0&\cos(\delta_{(L,R)}\pm\phi_{w})\end{array}\right]\left\{\begin{array}{l}F ^{\prime}_{(L,R)x}\\ F^{\prime}_{(L,R)y}\\ M^{\prime}_{(L,R)z}\end{array}\right\}. \tag{7}\]
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 2 Channel Sounding Using IEEE 802.11n Wireless LANs
### Developed System
#### 2.4.1 System Configuration  
The system specifications are listed in Table 1. Two Linux PCs equipped with an Atheros AR9380 WLAN card were used for the transmitter and receiver, as shown in Figure 5. To extend the bandwidth, fifty packets are transmitted and received continuously at every channel while sweeping the channel from 1 to 11 at two-channel intervals. Separately averaging the amplitude and phase of the CSI value at each subcarrier filters out abrupt fluctuations and improves the SNR. Here, the beacon interval was set to a minimum value of 15 ms for fast channel switching.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Values** \\ \hline Hardware & Linux (Kernel 4.1 Modified version) PC \(\times\) 2 (equipped with Raportw AR9380 WiFi card) \\ \hline Software & AP (transmitter): hostapd, hostapd\_cli \\  & STA (receiver): WPA\_supplicant \\ \hline WiFi channels & 1 ch (2412 MHz), 3 ch (2422 MHz), \\  & 5 ch (2432 MHz), 7 ch (2442 MHz), \\  & 9 ch (2452 MHz), 11 ch (2462 MHz) \\ \hline Sub-carriers & 217 \\ \hline Extend bandwidth & 67.8125 MHz (bonded by 6 channels) \\ \hline Beacon interval & 15 ms (usually, 100 ms) \\ \hline Packet transmission method & Send 50 packets in a row, and wait for 50 ms \\  & Repeat while switching channels \\ \hline \hline \end{tabular}
\end{table}
Table 1: System setup.  
Figure 4: Bandwidth extension by six-channel bonding.
## 2 System and Channel Models
### Statistical Models
#### 2.2.1 Deterministic Attenuations  
Wireless propagation at THz frequencies also suffers relatively high attenuation due to interactions between gas molecules and THz waves. The attenuation of THz wave due to molecular absorption has been widely studied. We choose the ITU-R model, which can perform the molecular absorption effect well, especially in the low frequency band of THz frequency (0.1 to 1 THz). The 0.1-1 THz band offers a balance between bandwidth  
Figure 1: Block diagram of a coherent THz link.  
availability and manageable atmospheric attenuation, making it highly suitable for short- to medium-range communication scenarios.  
Even if there is no additional attenuation along the wave propagating path, the received power at the receiver will decrease quadratically with the increase in distance. This inevitable loss, named free space path loss (FSPL), always exists and can be calculated using the Friis formula [21] as  
\[L_{\text{FS}}=32.4+20\log(f)+20\log(d)-G_{\text{TX}}-G_{\text{RX}} \tag{2}\]  
where \(f\) is the frequency in MHz, \(d\) represents the distance between the transmitter and receiver in kilometers (km), and \(G_{TX}\) and \(G_{RX}\) denote the antenna gains for the transmitter and receiver, respectively. We use the Beer-Lambert law [22] to describe the loss over the propagation path due to atmospheric attenuation.  
\[h_{l}(z)=\frac{P(z)}{P(0)}=\exp(-\sigma z) \tag{3}\]  
where \(P(0)\) is the transmission power, \(P(z)\) is the power at a distance of \(z\) (km), and \(\sigma\) (1/km) is the attenuation coefficient. This parameter can also be measured physically in the atmosphere.
## 5 Impact Assessments on Estimated Parameters at Collocated Stations  
The impact of various aspects of the new strategy on all the tropospheric parameters can be optimally assessed using closely collocated GNSS stations, e.g., within few meters. Although different  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution** & **Software** & **Strategy Description** & **Latency** & **Mean** & **Mean** \\ \hline
**RT PPP** (HR:59) & G-Nut/Tefnut & Kalman filter in simulated real-time solution & \textless{}5 min & 2.4 mm & 5.7 mm \\
**NRT PPP** (HR:00) & G-Nut/Tefnut & Hourly backward smoothing in real time & \textless{}60 min & 2.5 mm & 4.6 mm \\
**PP PPP** (HR:59) & G-Nut/Tefnut & Kalman filter in offline processing, IGS final & \textless{}5 min & 0.1 mm & 4.7 mm \\
**PPP** (HR:00) & G-Nut/Tefnut & Hourly backward smoothing with IGS final & \textless{}60 min & \textless{}0.2 mm & 3.6 mm \\
**NRT DD** (HR:59) & Bernese V52 & Last ZTD of hourly PW linear LSQ & \textless{}90 min & 0.4 mm & 4.9 mm \\
**NRT DD** (HR:00) & Bernese V52 & First ZTD of hourly PW linear LSQ & \textless{}30 min & 0.2 mm & 3.7 mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary statistics of three processing strategies and six ZTD solutions compared to EUREF combined tropospheric product.  
Figure 12: ZTD standard deviations and biases for 13 EUREF stations and six processing strategies compared to the reference EUREF product.  
instrumentation-specific effects, such as phase center modeling and the quality of a receiver tracking, can affect analyses at both stations, the station should principally observe the same tropospheric delays. For the purposes of our evaluation, we selected two IGS station pairs, ZIM2-ZIMJ (Zimmerwald, Switzerland) and MAT1-MATE (Matera, Italy), all collecting data from GPS, GLONASS and Galileo systems within 10 m and 2.5 m in horizontal and vertical distances, respectively. In the following sections, we evaluate impacts of multi-constellation analyses and various input products for PPP during September 2017. We used the GFZ MGEX (GBM) product [49] and CODE MGEX (COM) product [50] as multi-GNSS final products and, additionally, we compared them with the utilization the IGS RTS (IGS03) product for the operation in real time.
## 2 Problem Formulation
Unfortunately, the movement of the SV is almost always disturbed by wind, sea currents, and wave action. Under their influence, the SV hull tilts uncontrollably, changing course and speed; consequently, this leads to high variability in measurement accuracy. In particular, this problem can be attributed to the data-processing specificity of camera measurements taken against land and floating beacons. At this point, therefore, two questions of scientific interest can be asked regarding the choice of method for determining SV positions:  
1. Must the accuracy of EKF position determination always be greater than the accuracy of GLSA or GRA position determination? 2. If the answer to the first question is no, in which situations can GLSA or GRA be indicated as a better method for determining positions. In which cases will an extended Kalman filter be better? The article's authors will attempt to answer the questions thusly posed in a later section. The results of simulation studies will be helpful for this purpose. These will consist of generating measurement results with errors (including gross errors), and then, estimating position coordinates on their basis using the extended Kalman filter method (according to the algorithm presented in Figure 1) and geodetic adjustment: classical and robust to gross errors (according to the algorithms presented in Figure 2 and Figure 3).
# Rotating Lorentz Force Magnetic Bearings' Dynamics Modeling and Adaptive Controller Design
###### Abstract  
To address the issues of our agile satellites' poor attitude maneuverability, low pointing stability, and pointing inaccuracy, this paper proposes a new type of stabilized platform based on seven-degree-of-freedom Lorentz force magnetic levitation. Furthermore, in this study, we designed an adaptive controller based on the RBF neural network for the rotating magnetic bearing, which can improve the pointing accuracy of satellite loads. To begin, the advanced features of the new platform are described in comparison with the traditional electromechanical platform, and the structural characteristics and working principle of the platform are clarified. The significance of rotating magnetic bearings in improving load pointing accuracy is also clarified, and its rotor dynamics model is established to provide the input and output equations. The adaptive controller based on the RBF neural network is designed for the needs of high accuracy of the load pointing, high stability, and strong robustness of the system, and the current feedback inner loop is added to improve the system stiffness and rapidity. The final simulation results show that, when compared to the PID controller and robust sliding mode controller, the controller's pointing accuracy and anti-interference ability are greatly improved, and the system robustness is strong, which can effectively improve the pointing accuracy and pointing stability of the satellite/payload, as well as provide a powerful means of solving related problems in the fields of laser communication, high score detection, and so on.  
rotating Lorentz force magnetic bearings; adaptive control; multiple closed-loop control; point to stability 2023238543[[https://doi.org/10.3390/s23208543](https://doi.org/10.3390/s23208543)]([https://doi.org/10.3390/s23208543](https://doi.org/10.3390/s23208543))
## Materials and Methods
### Experimental Setup and Performance Measure  
Parameter values were automatically adjusted using WEKA. The classification performance of the SVM, C4.5, CART, and LGR models was compared to select the optimal model. Ten-fold cross-validation was applied to all experimental evaluations. Specifically, each dataset is partitioned into ten complementary subsets; any nine were used for model training, and the remaining subset was used for model testing. The validation was repeated 10 times, and the average results were reported in our study.  
To evaluate the efficacy of these prediction models, the accuracy, sensitivity, and specificity were evaluated using a confusion matrix (Table 2). These metrics were determined using the following formulas: sensitivity = \(TP/(TP+FN)\), specificity = \(TN/(TN+FP)\), and accuracy = \(TP+TN/(TP+FP+FN+TN)\). In addition, the area under the receiver operating characteristic curve (AUC) was included as an indicator of the model performance, with larger AUCs indicating higher accuracy.
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
###### Abstract  
Food packaging encompasses the topical role of preserving food, hence, extending the shelf-life, while ensuring the highest quality and safety along the production chain as well as during storage. Intelligent food packaging further develops the functions of traditional packages by introducing the capability of continuously monitoring food quality during the whole chain to assess and reduce the insurgence of food-borne disease and food waste. To this purpose, several sensing systems based on different food quality indicators have been proposed in recent years, but commercial applications remain a challenge. This review provides a critical summary of responsive systems employed in the real-time monitoring of food quality and preservation state. First, food quality indicators are briefly presented, and subsequently, their exploitation to fabricate intelligent packaging based on responsive materials is discussed. Finally, current challenges and future trends are reviewed to highlight the importance of concentrating efforts on developing new functional solutions.  
food; intelligent packaging; food quality indicators; real-time monitoring; sensors +
Footnote †: journal: Applied Sciences
## 3 Solution Methodology
### Master Stage  
The master stage can be considered the brain of the solution methodology, since it is entrusted with guiding the exploration of the solution space by choosing the best set of nodes to install D-STATCOMs. To conduct this task, this study proposes to apply the CBGA with an integer codification, as proposed in [24], to solve the problem of the optimal placement of distributed generators in electrical distribution grids. The codification proposed in implementing the CBGA is presented in Equation (19):  
\[w_{i}^{t}=[n,\ 2,\ \cdots,\ k], \tag{19}\]  
where \(w_{i}^{t}\) represents the \(i^{th}\) component of the population in the iteration \(t\), \(n\) is the number of nodes of the system, and \(k\) is a value between 2 and \(n\). Note that the slack bus is not considered in the codification since this position is not suitable for the connection of a D-STATCOM.  
The CBGA implemented in this research works with a population of about 20 individuals for up to 100 iterations, where a tournament is conducted with 4 individuals (randomly selected) to select the 2 individuals with the best objective function, i.e., m \(A_{\text{cost}}\). Then, these individuals are crossed using one recombination point to produce two new offspring. For these two individuals, the recombination strategy is applied with a probability of 75% using one bit for mutating. In the case of mutation, it is selected for one of these individuals, and an arbitrary position of the vector is changed for a node between 2 and \(n\). Finally, these offspring are evaluated with the SOCP model presented in Equation (18) to determine the values of their objective functions, which implies that the best individual (minimum \(A_{\text{cost}}\) value) is selected to replace the worst individual in the current population [25]. To make this replacement, the diversity criteria are applied to avoid the current population's information. The procedure mentioned above is repeated until it reaches the maximum number of iterations.  
Figure 2 summarizes the master-slave optimization algorithm's application to the problem of the optimal placement and sizing of D-STATCOMs in distribution networks considering different load profiles to minimize the annual operative costs of the grid.  
Figure 2: Flowchart of the Chu and Beasley genetic algorithm (CBGA)-second-order cone programming (SOCP) approach for the optimal sizing and placement of D-STATCOMs in electrical distribution grids.
## 2 System Model
### Interference Scenario
#### 2.3.3 Secrecy Rate for Interference Scenario  
The secrecy rate of the system for the interference scenario is defined as follows:  
\[R_{\rm{sec}}^{3}=\Bigl{(}R_{\rm{s}_{3}}-R_{\rm{ED}}^{3}\Bigr{)}^{+}. \tag{18}\]
# Trajectory-Tracking Control of Unmanned Vehicles Based on Adaptive Variable Parameter MPC
## 2 Unmanned Vehicle Trajectory-Tracking Model
### Tire Cornering Stiffness Correction  
Repeat the above steps to obtain the estimated values of tire lateral force \(\hat{F}_{yf}\) and \(\hat{F}_{yr}\). Due to the influence of estimation residuals, the ratio of tire lateral force and lateral deflection angle is directly used to solve the tire cornering stiffness [27; 28; 29], which is prone to produce singular values. This study adopts the normalization parameter to correct the tire cornering stiffness.  
\[\varepsilon_{i}=\frac{\hat{F}_{yi}-F_{yi}}{|\hat{F}_{yi}|}\ i=f,r \tag{23}\]  
In order to avoid singular values of the correction factor with a denominator of 0 and to prevent the correction factor from varying too much [30], the following constraints are placed on \(\varepsilon_{i}\):  
\[\left\{\begin{array}{l}\varepsilon_{i}\in(-1,1)\\ \varepsilon_{i}=0\ \alpha\leq 0.1^{\circ}\end{array}\right.\ i=f,r \tag{24}\]  
In the formula, when the tire side deflection angle is less than \(1^{\circ}\), the correction coefficient is set to 0, and the corrected tire cornering stiffness can be expressed as  
\[\hat{C}_{ci}=(1+\varepsilon_{i})C_{ci} \tag{25}\]  
Thus the modified system equation of state can be obtained as  
\[\left\{\begin{array}{l}\dot{x}=\hat{A}x+\hat{B}u+w\\ y=Cx\end{array}\right. \tag{26}\]  
where \(\hat{A}\) and \(\hat{B}\) are the corrected system state matrix and system control matrix. So far, the tire stiffness is corrected in real time according to the low-cost sensor information, and the vehicle model based on SRCKF tire cornering stiffness correction is obtained, which can reduce the influence of parameter uncertainty on the system and then be embedded in the MPC to improve the accuracy and stability of trajectory tracking, which is used in this study for the design of the subsequent controller.
## 3 Results
### Optimal Parameters and Model Validation  
The regression model was optimized using DOE experimental design software and the optimal simulation parameters were obtained; the normal stiffness per unit area was 7.392 \(\times\) 10\({}^{10}\) N/m\({}^{3}\), the critical normal stress was 5.293 \(\times\) 10\({}^{6}\) Pa, the critical tangential stress was 5.001 \(\times\) 10\({}^{6}\) Pa, and the predicted critical load of wheat crushing was 122.677 N. According to the optimal simulation parameters, the critical load for wheat crushing was 122.392 N, with a relative error of 3% compared to the physical test. The test results were consistent with the predicted value, which further confirmed the accuracy and reliability of the model. In addition, the wheat was compressed to obtain the actual crack of the wheat grain. In the discrete-element simulation software, the constructed discrete-element model and the calibrated bonding parameters for the extrusion simulation experiment, as shown in Figures 7 and 8, were used. The actual cracks in wheat grains were compared with the cracks in the discrete-element simulation model we constructed, as shown in Figures 9 and 10. It can be clearly observed that the location of the crack is approximately the same, proving the veracity of our model and parameters. These results provide an important basis for the subsequent study of wheat-grain crushing.  
Through the scientific experimental design method, the discrete-element simulation bonding parameters of wheat grains were calibrated, with an error of only 3%. The actual crack location of wheat-grain cracks was compared with the crack location of the discrete-element simulation model, and they are roughly similar. This provides a basis for a subsequent study of wheat-grain shattered traits. However, it has the limitation of a single variety. This article is only based on FengdeCunmai No.5 as the research object. The discrete-element modeling parameters need to be recalibrated once the experimental subject changes.  
Figure 7: Discrete-element simulation particle compression diagram.  
Figure 8: Discrete-element simulation bond compression diagram.  
Figure 10: The 3D model crack contrast. (The **left** is the actual broken 3D model, and the **right** is the discrete-element simulation fragmentation model).  
Figure 9: YZ face crack contrast. (The **left** is the actual broken 2D slice, and the **right** is the discrete-element simulation fragmentation model).
# Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process
## 3 Validation
### Weld Pool Experiment: Validation of Multiphysics Modeling
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Property** & **Symbol** & **Unit** & **Solid** & **Liquid** \\ \hline Dynamic viscosity & \(\mu\) & Pa\(\cdot\)s & 1.0 & 0.01 \\ Heat conductivity & \(k\) & W/(m\(\cdot\)K) & 20.93 & 209.3 \\ Specific heat capacity & \(c_{P}\) & J/(kg\(\cdot\)K) & 711.2 & 937.4 \\ \hline Melting temperature & \(T_{m}\) & K & 1732 \\ Evaporation temperature & \(T_{c}\) & K & 3100 \\ Melting bandwidth & \(\delta T\) & K & 100 \\ Absorption coefficient & \(a_{L}\) & – & 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material properties used for the weld pool simulation, according to [28]. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dimension** & **Present SPH** & **[PERSON] et al. SPH [28]** & **[PERSON] et al. Experiment [55]** \\ \hline Radius in mm (\% error) & 0.43 (\(-\)10\%) & 0.46 (\(-\)4\%) & 0.48 \\ Depth in mm (\% error) & 0.27 (\(+\)4\%) & 0.25 (\(-\)4\%) & 0.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the weld pool dimensions: SPH vs. experiment. Another check on the model is performed by reporting the maximum temperature in the last time step. Figure 8 presents two snapshots for this purpose, showing the temperature distribution of the solidified SPH particles alongside a top view of the weld pool. It is observed that the maximum temperature in this simulation is 3152 K found at the center of the weld spot. This value is close to \(T_{max}=3200\) K from the high-resolution SPH simulation of [PERSON] and [PERSON] [57] and the experimental measurement of [PERSON] et al. [55].