esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Processing and Analysis for Smartphone Real-Time Kinematic (RTK) Positioning  
RTK positioning is a widely used high-precision technology in GNSS. As it can eliminate most of the external system errors, RTK positioning can enable high-precision applications in smartphones. In RTK positioning, we apply the double-difference method to both pseudo-range and carrier phase observations to eliminate observation errors. First, single-difference observations are obtained by the difference between observations from rover station \(r_{1}\) and reference station \(r_{2}\). The obtained single-difference observations are differenced by satellites \(i\) and \(j\) (reference) to calculate the double-difference observations. In short baselines, the double difference can eliminate satellite-orbit and clock errors, receiver-clock errors, ionospheric and tropospheric delays, and other errors, leaving mainly multipath and measurement noise. When the multipath is significant, the observations for low-cost receivers may be correlated. Therefore, in this study, we first minimize the multipath from the source of data collection and then use a C/N0 stochastic model to reduce the impact of multipath. Hence, it is assumed that the remaining error is random. Under this condition, the double-difference observation equation is given by [23]:  
\[P_{ij}^{r_{1}r_{2}}=\rho_{ij}^{r_{1}r_{2}}+\varepsilon_{ij}^{r_{1}r_{2}}, \tag{1}\]  
\[\phi_{ij}^{r_{1}r_{2}}=\rho_{ij}^{r_{1}r_{2}}+\lambda N_{ij}^{r_{1}r_{2}}+\eta _{ij}^{r_{1}r_{2}}, \tag{2}\]  
where the superscript and subscript represent the station difference and satellite difference, respectively, \(P\) represents the pseudo-range observations, \(\phi\) represents the carrier-phase  
Figure 5: Statistical results of cycle slips for four constellations. **(a)** Ratio of cycle slips to number of observations. **(b)** Ratio of cycle slips to number of cycle slips in various C/N0 ranges.  
observations, \(\lambda\) is the wavelength of the carrier phase, \(\rho\) is the geometric distance calculated by the satellite and receiver coordinates, \(N\) is the carrier-phase ambiguity, and \(\varepsilon\) and \(\eta\) are pseudo-range and carrier-phase measurement noise terms, respectively. A more compact expression is given by:  
\[y=Aa+Bb+\Delta, \tag{3}\]  
where \(y\) represents the observation vector, \(a\) represents the coordinate parameter vector, \(A\) is its coefficient matrix, \(b\) represents the ambiguity parameter vector, \(B\) is its coefficient matrix, and \(\Delta\) represents the noise vector. In RTK positioning, the Kalman filter [23] is typically used to estimate the unknown floating-point parameters. If high-quality observations \(P\), observation data \(\varphi\), and correctly resolved ambiguity \(N\) are obtained, high-precision RTK positioning is achieved.  
According to the data quality analysis above, we adopt improved data quality control and integer ambiguity resolution for smartphone RTK positioning. In addition, we analyse RTK positioning for different combinations of observations and its convergence time in different periods.
# MPC and PSO Based Control Methodology for Path Tracking of 4 WS4 WD Vehicles
## 3 Control Methodology
### MPC-SQP Method
#### 3.1.2 Model Predictive Control
According to the Lagrange multiplier method, the optimal control input vector \(\Delta\mathbf{U}^{*}\) can be found by solving the following equation system. The solution is obtained by taking the first partial derivatives of \(J\) with respect to the vectors \(\Delta\mathbf{U}\) and \(\lambda\), and then equating these derivatives to zero:  
\[ \begin{split}\frac{\partial J}{\partial\Delta\mathbf{U}}& =\Delta\mathbf{U}^{T}(\Phi^{T}\Phi+\bar{R})+\Phi^{T}Fx(k)+\Psi^{T} \xi=0,\\ \frac{\partial J}{\partial\xi}&=\Psi\Delta \mathbf{U}+CA^{N_{p}}x(k)=0.\end{split}  \tag{47}\]Then, its optimal solution can be obtained:  
\[\Delta\mathbf{U}^{*}=-\Xi[\Gamma-(\Psi\mathbf{\Xi})^{-1}(CA^{N_{p}}+\Psi\mathbf{\Xi}\Gamma)]x( k), \tag{48}\]  
where  
\[\Xi=(\Phi^{T}\Phi+\bar{R})^{-1},\] \[\Gamma=\ \Phi^{T}F.\]  
According to the receding horizon control principle, the first increment of \(\Delta\mathbf{U}^{*}\) is applied as the control inputs. Then, the control law in Equation (41) can be obtained. It can be seen that Corollary 1 gives an offline solution of the MPC algorithm, which significantly improves its computing efficiency. Based on Corollary 1, the desired accelerations \(\widetilde{A}=[\widetilde{a}_{r},\ \widetilde{a}_{l},\ \widetilde{\gamma}]^{T}\) can be obtained by integrating the optimal solution \(\Delta u(k)^{*}\).
# Gain-Scheduled Disturbance Observer-Based Saturated Controllers for Non-Linear First-Order System +
## 3 Illustrative Example
### Constrained Control  
In recent decades, much attention has been devoted to the research of the relationship between traditional and generalized PIDs and postmodern model-based controllers, which is important especially from the point of view of effective control saturation consideration. A collection of such attempts can be well illustrated in the ADRC area, where the problem usually is the higher phase lag due to the second-order ESO filter [42; 75; 76; 77; 78]. However, the context of the simpler DOBs design with reduced-order filters, their connection with historical ARCs, and interpretation within the framework of the control of non-linear processes have not yet been sufficiently clarified [46; 47; 79; 80]. Figure 13: Performance measures of the setpoint step responses of one-tank hydro system with a variable cross-section (63) from Figure 12 for \(T_{c}=0.1\) (left) and \(T_{c}=0.5\) (right), \(T_{f}=T_{c}\) and \(T_{f}=5T_{c}\): Above—normed _iae_ values (69), middle—\(TV_{1}(u)\); below—normed \(TV_{0}(y)\).
## 3 Set Up Buildings' Settlement Prediction Model
### Accuracy Validation of LSTM Neural Network Model  
In order to verify the correctness of the constructed LSTM neural network model, the settlement monitoring data from the aforementioned 10 measurement points were sequentially input into the network for training, and the monitored and predicted values of the maximum settlement were obtained as shown in Figure 5.  
As can be seen in Figure 5, the prediction accuracy of the model is more than 82% when predicting the maximum settlement value of a single point of the building, among which, the largest error between the predicted value and the monitoring value occurs at the measurement point F148 (the predicted value is \(-2.46\) mm, the monitoring value is \(-2.99\) mm, and the error of both of them is 17.73%), the evaluation indexes of RMSE, MAE, and \(R^{2}\) are 0.68, 0.54, and 0.83, respectively, the error of the proposed model is small, and the error of the constructed model is small. Therefore, the buildings adjacent to the foundation pit can use the LSTM model constructed in this paper for settlement prediction.
## 2 Materials and Methods
### Validation Data  
Two sea ice datasets are used to evaluate the performance of the proposed sea ice monitoring approach. The sea ice edge (SIE) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF) are used as the reference data [46; 47]. The OSISAF SIE data is generated with a grid resolution of 10 km using a Bayesian approach based on the combination of ASCAT (Advanced Scatterometer) and SSMIS (Special Sensor Microwave Imager Sounder) data with different channels (e.g., 19, 37 and 91 GHz). It is worth noting that the OSISAF data has quality flags which indicate the quality of the sea ice products. The data are divided into five levels according to the confidence levels. The confidence level of 0 means unprocessed, 1 means erroneous, 2 means unreliable, 3 means acceptable, 4 means good and 5 means excellent. The data with a minimum confidence data level of 3 are adopted in this study [46].  
The sea ice concentration (SIC) data generated through the Arctic radiation and the turbulence interaction study Sea Ice (ASI) algorithm using AMSR-2 (Advanced Microwave Space Radiometer-2) data are also used as the reference data [4]. This SIC map was obtained from the online sea ice data platform www.meereisportal.de [48]. The reference SIC data are used to generate daily maps in the polar stereographic coordinates with a grid resolution of 6.25 km. The TDS-1 DDMs can be matched with the SIC maps using the location of specular point and date of data collection, which are contained in the data Level 1b. The DDM with a SIC value above 15% is regarded as sea ice, otherwise as sea water [25].
# _remote sensing_
## 3 Related Data and Correction Models in Cal/Val of Altimeters
### Mass
#### 3.4.2 Mean Sea Surface Models  
The above in situ sea surface can make connections between the HY-2B footprints and the ATG of Zhi'wan island in the Cal/Val of altimeters. However, there are no such models for other altimeter calibrations (Figure 2). Therefore, the global MSS models of DTU2021, CLS2015, and EGM2008 subtracted by CLS_MDT_2018 (EGM_CLS) were used.  
It is a difficult problem to validate the reliability and accuracy of MSS models, as altimeter provides the most accurate SSH measurements and nearly all available altimeter data have already been used in the derivation of the MSS [37; 38]. Two methods were used to validate the precision of the three MSS models in WSCS (113.5-115\({}^{\circ}\)E, 21.5-22.5\({}^{\circ}\)N). Firstly, we make comparisons between the three models. Results showed that the standard deviation were 6.78 cm, 3.74 cm, and 6.24 cm of CLS2015 vs. DTU2021, CLS2015 vs. EGM, and DTU2021 vs. EGM_CLS, respectively (Figure 7). The MSS accuracy of CLS2015 and EGM_CLS is equivalent. Secondly, the HY-2A altimeter (GDR product derived from NSOAS in 2018), which is not fused in the derivation of the MSS, was used to evaluate the precision of the models. This satellite launched on August, 2011 by NSOAS of China, in order to measure the SSH, WS, sea surface temperature, SWH, etc. [39]. The standard deviation of the SSH of HY-2A was \(\sim\)7.0 cm compared with the Jason-2 data, which showed good accuracy for validation of MSS models (Figure 8) [39]. The descending pass No. 190 and ascending pass No. 203 flew over this area (red lines in Figure 8). The SSH of the altimeter was derived using the methods in Section 2.2. The standard deviations between HY-2A and DTU2021, CLS2015, and EGM_CLS were 2.14 cm, 2.08 cm, and 2.20 cm, respectively.  
Figure 7: Error distributions between the in situ sea surface measurements and DTU 2021 (**a**), EGM2008 plus CLS_MDT_2018 (**b**), and CLS 2015 (**c**).
## 3 Energy-Efficient Routing in WSN  
In a WSN, the finite energy [32] resources available to nodes necessitate strategic energy conservation measures to extend the network's overall lifetime. The challenge lies in determining when to transition nodes into a sleep state during periods of inactivity to minimize power consumption. To address this, the ECP-LEACH protocol [33] has been developed for WSNs. The protocol employs a threshold mechanism to determine the opportune moments for transitioning nodes into sleep mode. At each round, the consumed energy of nodes is compared to a predetermined threshold value, and nodes exceeding this threshold are put to sleep.  
The sleep scheduling module and the threshold monitoring module form the foundation of the ECP-LEACH protocol. Figure 1 describes the workflow of the ECP-LEACH protocol. By closely monitoring each node's power usage during a duty cycle, the threshold monitoring module serves as a monitor for the network's nodes. This module assesses each node's power consumption with a predetermined threshold. The module puts the node to sleep if its power consumption rises above this threshold. The main goal of this method is to figure out how much energy each node uses in each duty cycle.  
Figure 1: ECP-LEACH protocol.  
At the same time, the sleep scheduling module plays a role in coordinating the sleep of the nodes. This module employs a scheduling technique when it receives sleep signals from the threshold monitoring module. The nodes' current power levels and communication histories are used to determine which nodes will enter sleep mode and remain there until the duty cycle concludes. This method of controlling a node's sleep and active states helps to save power usage without sacrificing operational effectiveness.  
The ECP-LEACH protocol's adaptation to noncluster master nodes is one of its most notable features. This strategy's justification stems from the function of cluster master nodes in controlling network traffic and data distribution. Applying sleep modes to these nodes arbitrarily may result in poor network performance and connectivity problems. Therefore, the protocol strikes a compromise between energy conservation and a reliable network connection by only applying the threshold and sleep scheduling method to noncluster master nodes. This helps keep the network from being congested and makes sure everything works properly.  
ECP-LEACH is a paradigm shift in WSN energy consumption management. The protocol creates a balance between node states by combining a sleep scheduling module and a threshold monitoring module and applying it consistently to noncluster master nodes. It guarantees substantial energy savings and prolongs the life of the network without sacrificing performance or connectivity. Notably, the ECP-LEACH works well for applications that require energy efficiency like environmental monitoring and forest fire detection [34].
## 2 Proposed Position Tracking System Based on Composite Sensors  
The UWB wireless technology is applied in telecommunication using a significantly wide frequency band over several gigahertz in baseband without using a carrier wave. This system does not entail mutual interference with existing mobile communication systems by using a very narrow pulse of several nanoseconds based on a very low spectrum power, such as the noise of an existing wireless system. The proposed position tracking system based on a composite sensor consists of a positioning tag attached on a moving object for measuring the object position and an anchor installed on the predetermined position for UWB communication with the tag, as shown in Figure 1a.
## 3 Experiment and Result Analysis
### Analysis of Density and Molten Pool Morphology

Table 4: Relative density of Inconel 718 samples formed by SLM under different laser powers. Figure 4: Optical microscope images of Inconel 718 samples formed by SLM under different laser powers: (**a**) P = 260 W, (**b**) P = 285 W, (**c**) P = 310 W, (**d**) P = 335 W, (**e**) P = 360 W.  
Figure 5: Morphology of melt pool parallel to the forming direction during SLM formation of Inconel 718 under different laser powers. (**a**) P = 310 W, (**b**) P = 335 W, (**c**) P = 360 W.
## 3 Push-Out Tests and Analysis of Results
### Experimental Method and Measurement Conditions  
In the push-out tests, a vertical load was applied to the steel upper part of the specimens in a static loading fashion using a 5 MN dynamic universal testing machine (UTM). Load control was applied at a rate of 50 kN/min, and displacement control was applied at a rate of 1.5 mm/min. Figure 7 provides an overview of the push-out tests performed at the Myongji University Hybrid Structure Testing Center. Monotone increasing displacement control was performed on specimens #1 and #2 for each experimental case. For specimen #3 in each case, 5-40% of the expected ultimate load was applied 25 times at a rate of 50 kN/min, and then monotone increasing displacement control was performed until failure. Figure 8 depicts the overall loading process. Regarding the sensor placement, six strain gauges were installed on each of the specimens at the bodies of the embedded shear connectors, and four strain gauges were installed on the surface of the concrete, as shown in Figure 9. To measure the slip displacement caused by the loading, linear variable differential transformers (LVDTs) were installed at both the front and back to measure the relative displacement between the steel girders and slab concrete. As shown in Figure 9a, the LVDT was restrained with a magnetic mount on the lower part of the web of the H-beam and the relative displacement with the rigid base floor was measured. The LVDT displacement value was simultaneously measured with the UTM control load using data acquisition equipment, and the load-displacement graph presented in the analysis of the experimental results was used by averaging these two displacement values.  
Figure 6: Shear specimen fabrication process.  
Figure 7: Push-out test set-up.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 4 Design of Joint Positioning Algorithm
### Time Difference Estimation  
To estimate the time difference between different TRPs, the core work is the accumulation of correlation power and the time domain sample position of the valid estimated RSRP. Then, RSRP at \(i\)th TRP can be obtained as  
\[P_{i}=\frac{1}{N_{\text{R}}N_{\text{symb}}^{\text{SRS}}}\left(\sum_{r\in N_{ \text{R}}}\sum_{l\in\mathcal{L}}|\mathbf{y}_{i}|^{2}\right)-\alpha\phi^{2}, \tag{6}\]  
where \(P_{i}\) is the estimated power at \(i\)th TRP; \(\mathbf{y}_{i}{=}\text{IFFT}\big{(}\mathbf{Y}_{i}\mathbf{r}_{\text{SRS}- \text{Pos}}^{\text{T}}\big{)}\) with a dimension of \(N_{\text{R}}\times N_{\text{FFT}}\) and denotes the IFFT operation output of the frequency domain correlation between received signal \(\mathbf{Y}_{i}\) and corresponding SRS-Pos sequence \(\mathbf{r}_{\text{SRS}-\text{Pos}}\); \(N_{\text{FFT}}\) is the number of points in the Fourier transform; \(\mathcal{L}\) is the set of SRS-Pos OFDM symbol index; \(\alpha\) is an empirical value to adjust the impact of noise; \(\hat{\phi}^{2}\) is estimated noise power according to  
\[\begin{split}\hat{\phi}^{2}&=\frac{1}{N_{R}N_{ \text{symb}}^{\text{SRS}}N_{\text{RE}}^{\text{SRS}}}\\ &\sum_{r\in N_{\text{R}}}\sum_{k\in\mathcal{K}}\sum_{l\in \mathcal{L}}\big{|}\mathbf{\hat{H}}_{i}(k,l)-\mathbf{\hat{H}}_{i}(k+1,l) \big{|}^{2},\end{split} \tag{7}\]  
where \(\mathbf{\hat{H}}_{i}(k,l)\) denotes the estimated channel response; \(\mathcal{K}\) denotes the set of occupied REs, and the set size is \(N_{\text{RE}}^{\text{SRS}}\).  
Figure 4: Signal Processing flow of joint UL-AoA and UL-TDOA positioning.  
Therefore, the estimated distance between the device and \(i\)th TRP can be obtained by searching maximum RSRP  
\[\hat{d}_{i}=\frac{c\tau_{i}}{\text{SCS}*_{\text{NFFT}}} \tag{8}\] \[\text{s.t.}\tau_{i}=i_{\text{max}}\big{|}\text{max}\big{(}\hat{P}_ {i}\big{)}\,,\]  
where \(c\) is the light speed.  
From (8), the transmission delay between the device and \(i\)th TRP can be obtained. With the increase in NR subcarrier spacing, especially within the higher frequency range, the resolution of \(\tau_{i}\) can be improved, i.e., the positioning accuracy can be enhanced. That is why the requirement for IIoT sensors is quite strict, for which higher frequency bands can be applied without considering the coverage.
## 2 Methods
### Construction of Grayscale Image Dataset of sEMG Signals  
In the field of sEMG gesture recognition, when a deep-learning model is utilized for image recognition tasks, it is often necessary to convert one-dimensional sEMG signals into two-dimensional sEMG grayscale images for input into the deep model. In this paper, we form the sEMG matrix using a sliding window, and this matrix is transformed into an sEMG image via a mathematical mapping equation. The size of the generated sEMG image is detailed in Equation (1).  
\[\text{Image}\in S^{C\times W\times H} \tag{1}\]  
In this paper, we utilize original sEMG signals where the number of picture channels (\(C\)) equals the number of features, with \(C=1\) for original sEMG. [PERSON] et al. [12] demonstrated that a 150-250 ms window for sliding sampling of sEMG signals was optimal. Considering the computational power of computers and the requirements for real-time processing, the picture height (\(H\)), which is the total number of sliding windows in a 200 ms time period, is set to 20 for original sEMG; the picture width (\(W\)), representing the number of sampled electrode channels, is 10. We used only the original sEMG signals for grayscale image transformation without feature extraction. This approach simplifies the training process and reduces the number of parameters involved.  
Each group of sEMG signals is filtered, and the filtered data are then mapped to the grayscale interval [0, 1], resulting in the creation of an sEMG grayscale image. Figure 5 presents a schematic diagram of the signal-to-image conversion process. The equation for maximum-minimum normalization is shown in Equations (2) and (3).  
\[X_{[0,1]}=F(X_{(i,j)}) \tag{2}\]  
\[F(X_{(i,j)})=(X_{(i,j)}-X_{\text{min}})/(X_{\text{max}}-X_{\text{min}}) \tag{3}\]  
where \(X_{[0,1]}\) is the sEMG matrix after mapping conversion, with element values ranging from 0 to 1; \(F(\ )\) is the mapping function that converts one-dimensional signals to two-dimensional images; \(X_{(i,j)}\) represents the value at row \(i\) and column \(j\) in the sEMG matrix, formed by a sliding window, where \(0<i\leq H\) and \(0<j\leq W\); \(X_{\text{max}}\) is the largest value within the sEMG matrix formed by the sliding window. Similarly, \(X_{\text{min}}\) is the smallest value in the matrix.  
Figure 4: Comparison of sEMG signals before and after Butterworth filtering.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 8 Performance Analysis
### Security Feature Comparison  
This work compares the following security features (SCFs) of the proposed protocol with those of related protocols. We considered (SCF1) \"impersonation attack\", (SCF2) \"physical drone capture attack\", (SCF3) \"stolen mobile device attack\", (SCF4) \"offline password guessing attack\", (SCF5) \"ephemeral secret leakage attack\", (SCF6) \"session key  
\begin{table}
\begin{tabular}{c c c c} \hline
**Protocol** & \(E_{cp}\) **(mJ)** & \(E_{cm}\) **(mJ)** & **Total (mJ)** \\ \hline
[25] & 0.01932 & 0.5324 & 0.55172 \\
[26] & 0.01932 & 0.8712 & 0.89052 \\
[27] & 6.32296 & 2.892 & 9.21496 \\
[28] & 0.01932 & 0.8712 & 0.89052 \\
[13] & 3.12606 & 1.2568 & 4.38286 \\
**Proposed** & 0.1078 & 0.89 & 0.9978 \\ \hline \end{tabular}
\end{table}
Table 8: Energy consumption comparison.  
Figure 6: Communication cost comparison with [13; 25; 26; 27; 28].  
disclosure attack\", (SCF7) \"replay attack\", (SCF8) \"MITM attack\", (SCF9) \"user anonymity\", (SCF10) \"user untraceability\", (SCF11) \"mutual authentication\", and (SCF12) \"formal security verification\". Table 9 presents a comparison of the security features. Compared with related protocols in IoD environments, the results show that the proposed protocol provides better security.