esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Works  
In this paper, we propose a distributed federated learning mechanism based on additive \(t\)-out-of-\(n\) secret sharing that ensures the availability and protection of gradients. Because of the properties of additive secret sharing, neither the server nor the adversary can obtain the complete gradient for each customer. This ensures that the customer's training data cannot be reconstructed. Additionally, our mechanism guarantees a certain probability of maintaining overall operation even in the event of server damage during federated  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Random** & **Accuracy (\%)** & **Maximum** & **Minimum** & **Average** & **Percentage Drop** \\ \hline Original & 95.09 & 94.09 & 94.62 & – \\ \hline (1, 10) & 94.85 & 94.04 & 94.54 & 0.08 \\ \hline (10, 20) & 94.77 & 94.07 & 94.44 & 0.18 \\ \hline (20, 30) & 94.26 & 89.32 & 92.62 & 2.11 \\ \hline (30, 40) & 94.77 & 81.45 & 91.64 & 3.15 \\ \hline (40, 50) & 94.35 & 62.52 & 89.29 & 5.63 \\ \hline (50, 60) & 94.34 & 51.11 & 83.97 & 11.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of models with gradients of different magnifications (50 times).  
Figure 13: Box plot of gradient model accuracy at different magnifications (50 times).  
learning operations. We also conducted experiments to compare the performance of our method with that of FedAvg [2] and FedShare [8], and the experimental results demonstrate that our method does not impact the model accuracy. In addition, we proposed using a membership level system that allows customers to choose membership levels according to their own needs and budgets, and customers are provided with different levels of gradient adjustments based on their membership level.  
In future work, we hope to develop new methods such that when customers perform secret sharing, the number of groups generated by shares will not change with the number of servers. This would allow reducing the cost and execution time, as only one group of shares is generated.  
Conceptualization, Y-T.T. and P.-W.C.; methodology, Y-T.T. and P.-W.C.; software, Y.-T.T.; validation, Y.-T.T. and P.-W.C.; formal analysis, Y.-T.T.; investigation, Y.-T.T. and P.-W.C.; resources, Y.-T.T. and P.-W.C.; data curation, Y.-T.T.; writing--original draft preparation, Y.-T.T.; writing--review and editing, Y.-T.T., P-W.C. and C.-T.K.; visualization, Y.-T.T.; supervision, P.-W.C. and C.-T.K.; project administration, P.-W.C.; funding acquisition, P.-W.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Science and Technology Council, Taiwan, under the Grant NSTC 113-2634-F-004-001-MBK.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
SA Selective Availability  
DLG Deep Leakage from Gradients  
SMC Secure Multiparty Computation  
GPS Global Positioning System
###### Abstract  
GPS radio occultation (RO) technology can fully describe the subtle structure of the ionosphere. This paper discusses the dynamic abnormity observed by the RO data from the Constellation Observing System for Meteorology Ionosphere and Climate (FORMOSAT-3/COSMIC) before the great earthquake case in Concepcion, Chile (27 February 2010, Mw 8.8). Traditional ground-based GPS monitoring was considered as the external conditions and references to the excitation response. Using kriging interpolation, the global Nmf2 map (GNM) was first constructed to study the ionosphere deviation from the normal state. Successively, the ionosphere abnormality in the F2 region (Nmf2), vertical structure (RO profiles), and multiple heights (electron density) of traveling are unfolded. The Nmf2 disturbances in the possibility of seismic influences were excluded from nonseismic noise factors, including the external input (e.g., space weather activity, 15 February) and meteorological events (e.g., lower atmospheric forcing in quiet periods). However, the results show that there were apparent local Nmf2 perturbations for up to 5 h in the epicenter area on 21 and 25 February. The disturbances of the RO profiles and the interaction of other layers of the ionosphere implied the fluctuation signals of prominent long-wavelength fluctuations \(>\)50 km in the F layer. The ionospheric fluctuates wildly, and these wave signals considered as the trace of gravity wave propagating upward are mainly distributed at the elevation of 200-300 km. The simultaneous reaction of GNSS TEC further evidenced the potential possibility of acoustic gravity by the COSMIC RO profiles, reflecting the compounding couplings of seismo-ionosphere effects. In terms of the presentation of VLF radiation noise and the aerosol ion clusters, the electromagnetic and chemical channels have been previously completed by DEMETER and Terra/Aqua satellites. These findings implied the great potential of the FORMOSAT-7/COSMIC-2 system (now in the testing phase), with \(\sim\)5000 soundings to investigate the subtle atmospheric stratification.  
FORMOSAT-3/COSMIC; GPS TEC; Concepcion earthquake; ionosphere multidimensions disturbance; multichannel coupling
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
## 3 Results and Discussion
### Analysis and Discussion
#### 3.2.3 Effect of Initial Deviation on Stabilization Distance and Maximum Deviation  
From Figure 12 and Table 3, it can also be obtained that the stability distance of the prototype vehicle reaching the steady state under four different initial states varies widely due to the inconsistency of the initial states. By Equation (3), we can see that when the initial deviation is more significant (Figure 12a,b), the time required to adjust for convergence is also more significant, the trajectory has a longer stability distance, and the maximum deviation is also the largest, and vice versa.
## 4 Results
### Signalling Data Traffic Characterisation
#### 4.1.1 Bit Rate Characterisation  
Figure 4 shows the bit rate transmitted when the signalling equipment performs the following process:  
* Communication establishment: process to establish the communication at the application layer marked between the green vertical lines.
* Signalling tasks marked between the yellow vertical lines:
* Point supervision: periodic task to supervise the state of the point machine;
* Trailing: detection of a point between trailers when the train passes.
* Communication failure: communication establishment and progress not performed successfully at the application level while having connected the communication system correctly.  
The results show that the bit rate varies according to the performed task. They include measurements with signal levels ranging between \(-75\) and \(-117\) dBm on LTE at 1800 MHz. The overall traffic is around 3.8 kbps, as summarised in Table 2, where the specific amount of data is described according to the signalling equipment involved. There are peaks of 150 kbps when communication is established. Moreover, communication disconnections cause the duplication of the throughput when radio conditions are degraded.  
Figure 4: Signalling bit rate for a set of tasks.
## 4 Path Ambiguity Solution
### Doppler Aiding Decision  
Another method to select the correct path is based on the Doppler effect, due to the relative motion between the satellite and the receiver. The Doppler effect is generally expressed in terms of frequency shift, on the basis of the quantities shown in Figure 9. If \(\vec{v_{r}}\) is the true velocity of the receiver, then the frequency shift can be written as:  
\[\Delta f=\frac{\|\vec{v_{s}}-\vec{v_{r}}\|\cos\theta}{\lambda} \tag{19}\]  
Figure 8: Illustration for pseudorange comparison.  
where \(\|\vec{v_{s}}-\vec{v_{r}}\|\) is the magnitude of the relative velocity of the receiver with respect to the satellite, \(\theta\) is the projection angle of the satellite-receiver relative velocity vector to the Line of Sight (LOS) vector and \(\lambda\) is the nominal wave length of the GNSS signal.  
During the path ambiguity phase, the receiver can compute a vector containing the coordinates of position and velocity for each path: one of them is correct, and the others are fake. For each possible vector, the receiver can then calculate the corresponding Doppler effect expressed as:  
\[\Delta f_{r}=\frac{\|\vec{v_{s}}-\vec{v_{r}}\|\cos\theta_{r}}{\lambda} \tag{20}\]  
where \(\vec{v}_{r\in\{1,2\}}\) is the possible velocity for the two positions and \(\theta_{r\in\{1,2\}}\) is the projection angle of the satellite-receiver relative velocity vector to the LOS vector for the two possible positions, which can be obtained as:  
\[\theta_{r}=\arccos\frac{(\vec{v_{s}}-\vec{v_{r}})\cdot(\vec{P_{s}}-\vec{P_{r}} )}{\|\vec{v_{s}}-\vec{v_{r}}\|\cdot\|\vec{P_{s}}-\vec{P_{r}}\|} \tag{21}\]  
It is known that a GNSS receiver is able to estimate a Doppler shift, \(\Delta f_{\rm GNSS}\), from the received signal. Therefore, by comparing the values of \(\Delta f_{r}\) with \(\Delta f_{\rm GNSS}\), the true trajectory can be selected. In this method, we do not need to consider the DVB-T receiver clock offset to calculate \(\Delta f_{r}\), since it is directly related to the estimated position.
## 4 Results and Discussion
### Descriptive Analysis (EDA)
#### 4.1.1 Waiting Time Distribution  
Most users wait between 0 and 10 min, although a smaller percentage exceed 30 min. The histogram (Figure 1a), shown with a logarithmic scale, exhibits a peak around 5 min and a \"long tail\" extending to 60. The box plot (Figure 1b) confirms a median of 5 min and indicates that, while public transport is relatively efficient for most users, there are groups experiencing very long waits, in some cases up to 60 min. These instances require further investigation into the conditions causing prolonged delays at certain times or on specific routes.
## 4 Experiment Verification  
### Experiment Apparatus  
To verify the feasibility of VCM-DHPV structure and the validity of the mathematical model, the experimental investigation was carried out. Experimental pneumatic circuit is shown in Figure 9, which is composed of a gas supply, stop valve, pressure reducing valve, flow meter, pressure sensor, pressure gauge, the on/off valve, current amplifier, position sensor, data acquisition (DAQ) and the controller. The control scheme of the on/off valve is realized by a programmable multi-axis controller (PMAC). The VCM and the position sensor were linked to a personal computer with Pewin32 Pro2 software through a DAQ, which was used to acquire position signal and control the input voltage of the VCM [26]. The rated current and rated force of the VCM used in this experiment are 8.4 A and 376 N, respectively. Therefore, a current amplifier could be adopted to amplify the current and supply to VCM. The voltage command is supplied from the computer through the DAQ, and the output voltages from the position sensor are fed back to the computer by the DAQ. Then, the spool of VCM-DHPV is actuated toward two directions. The test software is developed on the LabVIEW platform (version 11.0, National Instruments, Austin, TX, USA, 2011).  
A linear variable differential transformer (LVDT) is used to detect the displacement of spool, which is transferred by a push rod connected with the spool. When the spool is promoted, it will push the rod detected by the position sensor. The developed VCM-DHPV prototype and the test bench are shown in Figure 10, and the characteristic parameters of the prototype are listed in Table 2. The experiment processes are as follows:  
1. Open the stop valve and regulate the pressure reducing valve to make the compressed air as a certain pressure.
2. Use the computer to collect the pressure signal, flow signal and displacement signal when opening the on/off valve.
3. Set a new pressure and repeat Steps (1) and (2).  
Figure 8: Dynamic response curves under different half cone angle (\(P_{s}\) = 8 MPa, \(u\) = 240 V).
# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 5 Open Challenges  
In this section, we discuss the limitations of the literature review and suggest directions for future research.  
_Technical limitations:_ The limitations of studies using machine learning are that they may produce incorrect results in untrained situations, that is, they may be vulnerable to unexpected situations during the operation of the vehicle. Additionally, most machine learning models are black boxes, which makes them difficult to interpret. In order to improve the accuracy of the model, it is sometimes necessary to modify the model, but most machine learning algorithms are generated as a result of learning, and it is difficult for humans to understand the process whereby results are derived, which limits the improvement of the model. Therefore, it is valuable to develop a model that allows people to easily understand why the AI model made a certain decision and how it works, such as XAI (explainable AI).  
In addition, in the case of big data, the amount of data accumulates at a rapid pace in hyperconnected systems. As a result, query response times are sometimes delayed by unnecessary exploration when making decisions. Therefore, it is very important to study how to effectively determine and explore only the necessary data.  
_Legal limitations:_ As the concept of smart mobility has not been around for a long time, the system has not yet been firmly established. The active cooperation and participation of governments, companies, users, and related communities are required for commercialization. Therefore, it seems necessary to establish technical standards and legislation that specify punishments and responsibility for accidents or illegal acts caused by errors in the artificial intelligence embedded in smart mobility devices. For example, if an accident occurs due to a central line violation in a self-driving car, it is necessary to establish standards for determining whether the responsible party is the manufacturer, the driver, or another party and how to divide the fault.  
_Ethical limitations:_ There are ethical dilemmas in both smart mobility and artificial intelligence research. The dilemma of \"Which one should artificial intelligence choose between probabilistic choice and ethical choice?\" is an age-old issue. For example, in a situation in which an elderly person and a young man are standing on either side of the road and there is no way to avoid or stop them, opinions on what choice a self-driving car with artificial intelligence should make vary greatly among people. The young man is more likely to survive, even if an accident occurs. However, in our society, protecting the more productive young man is also an option. Lastly, there is a way for the driver to be injured with neither the young man nor the elderly person being injured by colliding with a wall or guardrail. There is no correct answer to these questions. However, artificial intelligence developers feel burdened when prioritizing certain choices over ethical choices and survival rates.
## 2 Problem Definition
### Mathematical Model
Thus, the mathematical model of the problem can be expressed as follows:  
\(min(teachers\text{'teaching hours' distribution cost}+lessons\text{'hours' distribution cost}+ teachers'gaps\text{ }cost)\), \(\forall\ t\in T,c\in C,l\in L,d\in D,h\in H,e\in E\)  
under the following constraints:  
* \(\sum_{t=1}^{T}\chi\left(\left(x_{tc_{i}dh}=1\right)\wedge\left(x_{tcjdh}=1 \right)\right)\leq 0,\forall\ c_{i},c_{j}\in C\ (i\
eq j),d\in D,h\in H\); (Teachers' clash)
* \(\sum_{c=1}^{c}\chi\left(\left(x_{l_{i}cdh}=1\right)\wedge\left(x_{l_{j}cdh}=1 \right)\right)\leq 0,\forall\ l_{i},l_{j}\in L\ (i\
eq j),d\in D,h\in H\); (Classes' clash)* \(\sum_{c=1}^{C}\sum_{d=1}^{D}\sum_{h=1}^{H}x_{tcdh}=Teacher\_Total\_Hours_{t},\forall\ t \in T\) ; (Teachers' teaching hours)
* \(\sum_{t=1}^{T}\sum_{d=1}^{D}\sum_{h=1}^{H}x_{tcdh}=Class\_Total\_Hours_{c},\forall\ c \in C\) ; (Classes' teaching hours)
* \(\sum_{t=1}^{T}\chi\big{(}(x_{tcdh}=1)\wedge h\in H_{t}^{not\ available}\big{)} \leq 0,\forall\ c\in C\), \(d\in D,h\in H\); (Teachers' availability)
* \(\sum_{c=1}^{C}\chi\big{(}(y_{tcdh}=0)\wedge h\in H_{last}\big{)}\leq 0,\forall\ l \in L\), \(d\in D\), \(h\in H\); (Classes' idle time slots)
* \(\sum_{c=1}^{c}\chi\big{(}(x_{t\in cdh}=1)\wedge\big{(}x_{t\in cdh}=1\big{)} \wedge teachers\ t_{i}\) and \(t_{j}\)are not involved in \(co-teaching\big{)}\leq 0\), \(\forall\ t_{i},t_{j}\in T\) (\(i\
eq j\)), \(d\in D\), \(h\in H\); (co-teaching/sub-classes)
# Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets
## 3 Results and Discussion  
### Analysis of Spectral Data Preprocessing  
Figure 4 shows that the spectral information of the seven rice varieties (245 samples) was intertwined in a disorderly manner, making it difficult to distinguish. Therefore, it was essential to preprocess the spectral data.  
Figure 5 shows that the filtered curve by the signal.filtfilt function was smoother and clearer than the original curve. The normalization algorithm was used to eliminate the dimensional influence [49] and optimize the spectral data [49], and the Wn (critical frequency or frequencies) parameter of this experiment was set to 0.002. This parameter was able to filter more details and invalid information as much as possible, and reserve more original information for the difference method to extract wave peaks.  
As shown in Figure 6, this experiment used the difference method to obtain 10 obvious peaks, which were extracted at 480 cm\({}^{-1}\), 865 cm\({}^{-1}\), 941 cm\({}^{-1}\), 1129 cm\({}^{-1}\), 1339 cm\({}^{-1}\), 1461 cm\({}^{-1}\), 1780 cm\({}^{-1}\), 2140 cm\({}^{-1}\), 2330 cm\({}^{-1}\), and 2910 cm\({}^{-1}\). At the same time as the peaks were obtained, small spectral clutters appeared. Therefore, the signal.filtfilt function was used to filter the peaks again.  
Figure 7 shows the curve of the peak information processed by the difference method. The scipy.signal.filtfilt method was smoother and clearer than that processed by the difference method. When filtering wave peaks, the Wn parameter 0.03 was selected, which filtered out invalid information again and retained more original effective information, thus improving the resolution of the identification model.  
Figure 4: The 245 raw spectral curves for 7 varieties. Raman shift is the reciprocal of wavelength, and its range is 200–3400 cm\({}^{-1}\). Intensity is the intensity of Raman scattering.
## 3 Experimental Verification
### Monitoring Performance Analysis
#### 3.2.2 Monitoring Performance During the Climbing Scaffold Ascending Phase
\begin{table}
\begin{tabular}{c c c c} \hline
**RMSE** & **E** & **N** & **U** \\ \hline Preprocessed Data & 0.0107 & 0.0044 & 0.0137 \\ Filtered Data & 0.0052 & 0.0021 & 0.0069 \\ \hline \end{tabular}
\end{table} 
Table 1: Static phase monitoring accuracy analysis table. variations but also highly sensitive to subtle displacement changes, ensuring accurate motion detection in complex construction environments. The results indicate that the BeiDou/GNSS-based monitoring system can reliably capture large-scale scaffold displacements (as observed in the 30 July dataset) while also \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline E & \(-0.0041\) & \(-0.0023\) & \(-0.0068\) & \(-0.0006\) & \(0.0005\) & \(0.0071\) & \(0.0101\) & \(0.0109\) & \(0.0144\) & \(0.0127\) \\ N & \(0.0044\) & \(0.0010\) & \(0.0047\) & \(0.0017\) & \(0.0029\) & \(-0.0020\) & \(-0.0075\) & \(-0.0042\) & \(0.0003\) & \(0.0030\) \\ \hline \end{tabular}
\end{table} 
Table 2: Hourly mean displacement values in the E and N directions. Figure 8: Comparison of monitoring data before and after processing on 30 June: (**a**) E direction; (**b**) N direction; and (**c**) U direction. Figure 9: Comparison of monitoring data before and after processing on 30 July: (**a**) E direction; (**b**) N direction; and (**c**) U direction. precisely detecting subtle horizontal perturbations and vibrations (as demonstrated by the 30 June dataset). The consistency of data across these two scenarios validates the system's high sensitivity and robustness in dynamic monitoring, providing a crucial technical safeguard for the safety of super-high-rise construction.
## 2 Principle and Model of TAN  
### Principle of TAN  
TAN is a critical technology for the intelligent development of underwater vehicles. Its positioning error does not increase with time and thus can be combined with the INS to improve AUV positioning accuracy. According to the principle of TAN, after measuring a series of depth values on the carrier's real-time trajectory, the depth values are input into the digital reference map for matching search. The sequence with the smallest difference from the measured terrain sequence is selected to obtain the optimal matching position of the carrier. Its real position is obtained to correct the cumulative drift error of the INS [15; 16]. Figure 1 depicts the principle of TAN.  
From Figure 1, it can be seen that the matching phase is the key technology of TAN. The search matching is to effectively fuse information between the real-time data of the carrier and the existing digital reference map, which directly affects the reliability of the matching results and the amount of matching calculation, significantly influencing the matching accuracy. The matching algorithm is the core of this technology, but the traditional terrain matching algorithm employs the traversal search method, which adds a significant amount of computation and severely impacts the matching time of the TAN algorithm.
## 4 Computational Experiments and Analyses  
### Data and Parameter Setting  
This paper used part of the data in the [PERSON] [37] standard sample data set for experiments. These experiments were implemented using Python3.8 programming. After repeated tests, the setting of the relevant parameters of the algorithm was related to the size of the data set used in the experiment, as follows: \(p_{c1}=0.7\), \(p_{c2}=0.5\), \(p_{m1}=0.01\), \(p_{m2}=0.008\), maximum iteration number \(maxit=100\!\sim\!\!300\), population \(popsize=100\sim 200\), maximum field search times \(S_{t}=15\!\sim\!\!30\). The weights of the nearest neighbor insertion method were set as follows: \(\sigma_{1}=0.4\), \(\sigma_{2}=0.4\), \(\sigma_{3}=0.2\). In order to get close to the real traffic situation, relevant parameters of the time-varying road network were set as follows: the time of 0 in the distribution center is 7:00 a.m., the traffic jam period is 7:30-9:00 and 17:30-19:00, and the speed is 20 km/h. The setting of vehicle speed refers to the research of [PERSON] et al. [22], with settings as follows: for the time period \(h\), according to the remainder function \(\eta=h\ mod\ 3\), when \(\eta\) is set to (1,2,0), it corresponds to (54,72,42) km/h, respectively, with three time-varying velocities. In the CW saving algorithm, the load factor \(\gamma=1\). Time window-related parameters [35] are: \(\theta=0.5\), \(p_{1}=1\), \(p_{2}=0.5\), \(p_{3}=1.5\), \(p_{4}=2\), \(\varphi=8\), \(\delta=60\). Referring to [PERSON] et al. [22], the correlation coefficient of carbon emission model is shown below: \(a_{0}=110\), \(a_{1}=0\), \(a_{2}=0\), \(a_{3}=0.000375\), \(a_{4}=8702\), \(a_{5}=0\), \(a_{6}=0\), \(b_{0}=1.27\), \(b_{1}=0.0614\), \(b_{2}=0\), \(b_{3}=-0.0011\), \(b_{4}=-0.00235\), \(b_{5}=0\), \(b_{6}=0\), \(b_{7}=-1.33\). Fresh products price \(p=5\) yuan /kg, shelf life \(T=36\) h, regulatory factor \(r=0.3\). The unit price of carbon emission is set at \(\mu=0.0528\) yuan /kg according to the trading price of Beijing carbon emission market on 30 April 2021, and all the experiments were repeated ten times to get the best result.