esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 2 Subspace Identification
### General Instruction for User-Defined Parameters
#### 2.2.2 Estimation of System Order  
The dimension of \(\mathbf{S}_{1}\) is another user-defined parameter representing the estimation of system order, and it is crucial while implementing SSI. In the case of complex structures or field applications, the distinguishment between non-zero but small singular values and zero singular values is vague. An obvious number of spurious modes can be generated if a larger value is selected. However, if a smaller value is assumed as the representing system order, the modes with relatively low energy may be lost during realization.  
Estimating system order was first addressed in the 2000s in the context of sub-space methods [45]. Three different approaches can be utilized; two use the information hidden in the singular values, while the third employs estimated innovation variance. For example, one approach estimates system order according to the magnitudes of singular values while dealing with a time-varying system [46]. Following a similar thought, another approach estimates it by adjusting an adequate ratio of the first (largest) singular value as a criterion [47]. A slightly more advanced approach exploits the difference between each singular pair, and this approach compares the information with the signal-to-noise ratio (SNR), providing a more realistic way for choosing a system order [44], despite the fact that this approach is more sophisticated. One recent approach uses variance accounting for (VAF) as a criterion to estimate system order, and this approach ensures that reconstructed responses under the estimated system order can be easily retrieved [41].
# A Study of Landfast Ice with Sentinel-1 Repeat-Pass Interferometry over the Baltic Sea
## 4 Results and Discussion
### Interferogram Analyses
The vertical displacement due to ice growth and changes of freeboard must be less than 1 cm at the top, which is much smaller than what we measured. The lateral displacement could be due to thermal expansion. The linear thermal expansion coefficient is of the order of \(10^{-4}\)\({}^{\circ}\)C\({}^{-1}\). However, the form of the fringes does not suggest thermal expansion has taken place. Precipitation is not expected to produce the observed gradients of changes over the study area. We do not have water current measurements in the study area, but water currents beneath the landfast ice should be small. Winds are the principal reason of lateral displacements. The wind was from northwest compressing the drift ice on the land fast ice boundary had a minor influence on the inner part of the fast ice zone where the fringes are shown. There was simultaneous dilatation along the boundary, as typically takes place in such forcing conditions [23]. The physical realization of the small deformation was opening and closing of cracks. As seen in the SAR images in Figure 3, further out in the unstable outer part of fast ice boundary, heavier deformation with ridging has taken place. This area is away from the landfast ice boundary suggesting that the main load from the northwest storm has not reached our study area. Southwest winds with high speed, in order of 15 ms\({}^{-1}\) pushed the drift ice and put pressure on the landfast ice to northeast on 10, 16, and 17 February (Figure 4b, mean wind speed and wind direction). Movements were gradual over the landfast ice. There were some small jumps in the fringe pattern, which shows shearing and cracking (Jumps in Figure 9a). However, these cracks were not so strong for changing the fringe pattern and did not change fringe shape. One research question is the low coherence and backscatter lines in the study area (black lines in Figures 3 and 7). Some of them are recognizable as jumps in Figure 9a. These lines reach 600 meters in some areas. There are at least two possibilities to explain their presence: 1) They can be fractures due to landfast ice displacement, or 2) They can be routes on the ice. We overlaid one of the SAR backscatter images on the Google Earth map, and it was seen that the lines could be routes between islands that people use for fishing. Looking at Krassinletto Island (fishing island) shows that most of the black lines finished there. There are also some tracks that do not seem to reach an island, but that might be just because not all of the islands necessary appear on the Google Earth map or they can be some fishing camps on the ice. We have some areas with high fringe density demonstrating high displacement activity. The smoother transitions are more around islands especially if several islands locate near each other (Figure 9a) [11]. Figure 10: Sea level and sea level differences in Kemi and Oulu stations during 6–18 February 2015.
## 2 Related Work
### Physical Model-Based Restoration Methods  
Sandstorm image restoration algorithms based on the atmospheric scattering model [22] typically require additional prior knowledge to accurately estimate atmospheric light and transmission, enabling the inversion of the model to obtain a clear image. Inspired by the dark channel prior (DCP) [23], [PERSON] et al. [11] proposed a modified DCP method to reduce halo effects and suggested performing color correction in the Lab color space. While this method reduces the halo effect, it introduces new color distortions. Building on the concept of the red channel prior, [PERSON] et al. [24] introduced an inverted blue channel prior for sandstorm image restoration. However, the excessive blue channel caused a bluish tint in the restored images. [PERSON] et al. [25] developed a novel color shift classifier and designed an adaptive atmospheric light refinement method for the correction of color bias, but the restored images still exhibited significant color distortions. [PERSON] et al. [12] introduced a color correction algorithm based on consistent chromatic variance, using gamma correction to estimate the transmission map. This method effectively removed the color veil in dust storm images, but the dust removal effect remained unsatisfactory. Although physical model-based methods can achieve more significant dust removal results, achieving a balanced trade-off between color correction and dust removal remains challenging.
###### Abstract  
Indoor positioning systems (IPS) have been recently adopted by many researchers for their broad applications in various Internet of Things (IoT) fields such as logistics, health, construction industries, and security. Received Signal Strength (RSS)-based fingerprinting approaches have been widely used for positioning inside buildings because they have a distinct advantage of low cost over other indoor positioning techniques. The signal power RSS is a function of the distance between the Mobile System (MS) and Access Point (AP), which varies due to the multipath propagation phenomenon and human body blockage. Furthermore, fingerprinting approaches have several disadvantages such as labor cost, diversity (in signals and environment), and computational cost. Eliminating redundancy by ruling out non-informative APs not only reduces the computation time, but also improves the performance of IPS. In this article, we propose a dimensionality reduction technique in a multiple service set identifier-based indoor positioning system with Multiple Service Set Identifiers (MSSIDs), which means that each AP can be configured to transmit N signals instead of one signal, to serve different kinds of clients simultaneously. Therefore, we investigated various kinds of approaches for the selection of informative APs such as spatial variance, strongest APs, and random selection. These approaches were tested using two clustering techniques including K-means and Fuzzy C-means. Performance evaluation was focused on two elements, the number of informative APs versus the accuracy of the proposed system. To assess the proposed system, real data was acquired from within the College of Engineering and Applied Sciences (CEAS) at the Western Michigan University (WMU) building. The results exhibit the superiority of fused Multiple Service Set Identifiers (MSSID) performance over the single SSID. Moreover, the results report that the proposed system achieves a positioning accuracy \(<\)0.85 m over 3000 m\({}^{2}\), with an accumulative density function (CDF) of 88% with a distance error of 2 m.  
Indoor Positioning System; WLAN; C-Means; K-Means; Access Point Selection; RSS-fingerprint 1
## 3 Methodology
### Analysis Methods
#### 4.1.2 Alarm-Dismiss Usage  
**Task Completion Time.** We considered _avg_task_completion_time_ and _sd_task_completion_time_ in the usage category of the task completion time. First, our Kruskal-Wallis \(H\) test results on the _avg_task_completion_time_ revealed significant and strong differences among wake-up tasks [\(H(3)=14,4454.19\), \(p<0.001\), \(\epsilon^{2}=0.683\)]. The task completion time of _normal_task_ was approximately 14 s on average, while those of _picture_task_ (\(Mean=40.05\), \(SD=15.63\)) and _math_task_ (\(Mean=40.81\), \(SD=14.67\)) were the longest. _shake_task_'s completion time was approximately 26 s.  
There is also significant and moderate difference in the variability of the task completion time among the tasks (_sd_task_completion_time_) [\(H(3)=14,281.84\), \(p<0.001\), \(\epsilon^{2}=0.067\)]. _normal_task_ tended to be more consistent in the task completion time compared to the other tasks (vs. _picture_task_: \(d=0.594\), vs. _shake_task_: \(d=0.163\), vs. _math_task_: \(d=0.461\)). In the variability of the completion time, the magnitude of the difference between _math_task_ and _picture_task_ was not great (\(d=0.156\)).  
**Snooze.** Regarding snooze use, _avg_freq_snooze_ and _sd_freq_snooze_ were compared among the wake-up tasks. We found significant difference in the number of snoozes (_avg_freq_snooze_), but its magnitude was small [\(H(3)=277.76\), \(p<0.001\), \(\epsilon^{2}=0.001\)]. _picture_task_ has the largest number of snooze use per alarm (\(Mean=0.332\) and \(SD=0.507\)) among the wake-up tasks. However, the standard deviation of snooze use with the _picture_task_ was too large, so the Kruskal-Wallis \(H\) test accepted the null hypothesis. Next, we found that there was a significant but weak difference in the consistency of snooze use (\(sd\_freq\_snooze\)) [\(H(3)=16.18\), \(p=0.001\), \(e^{2}<0.001\)]. The standard deviation of the snooze use per alarm was not considerably different among the wake-up tasks.
## 2 Design and Transfer Function Acquisition of MRB
### Magnetic Circuit Simulation of MRB  
In order to reduce the amount of calculation in simulation, the factors that have little influence on calculation such as bolts, sealing rings and chamfering in the transmission device were ignored. The simplified structure is shown in Figure 3, where different colors represent different areas, and loop reluctance can be analogous to [PERSON]'s law.  
The closed loop of the magnetic circuit consists of a left shell, input disk, MRF, brake disk and right shell. However, some of the magnetic flux will be lost in the components and magnetic resistance. Loop reluctance can be determined analogous to Ohm's Law:  
\[R_{m}=\frac{F_{m}}{\Phi}=\frac{N_{c}l}{\Phi}=\frac{L_{c}}{\mu A} \tag{7}\]  
where \(R_{m}\) is the magnetic resistance, \(F_{m}\) is the magnetic potential, \(\Phi\) is the magnetic flux, \(N_{c}\) is turns, \(I\) is the electric current, \(L_{c}\) is the length of the magnetic path, \(\mu\) is the magnetic conductivity, and \(A\) is the Magnetic flux area.  
The function of the excitation coil is to provide the magnetic field needed for the normal operation of the MRB, and considering coil heating, the resistance of the excitation coil should be as low as possible.  
When the coil of excitation has poor cooling performance, the current density \(J\) allowed to pass over a long period of time shall not exceed 3-5 A/mm\({}^{2}\). When the transmission device has good heating dissipation structure, the value should not exceed 5-7 A/mm\({}^{2}\)[24]. The calculation formula is as follows:  
\[J=\frac{4\,I}{\pi d^{2}} \tag{8}\]  
where \(d\) is the diameter of the excitation coil.  
Figure 3: Magnetic circuit structure mode of MRB.  
According to the abovementioned requirements, the design coil diameter was 0.96 mm and turns were 1000. When the maximum working current was 2 A, the current density could be calculated according to Formula (8) to be 2.77 A/mm\({}^{2}\), which met the working requirements.  
Ansoft Maxwell (16.0) was adopted to simulate and analyze the electromagnetic field of MRB, where the number of turns of the excitation coil was 1300. As shown in Figure 3(a), when the single-turn current of the coil was 2 A, the highest magnetic induction of about 2.1 T appeared at the junction of the left shell and the right shell with the outer shell, close to the magnetic saturation strength of No. 20 steel [25]. The magnetic flux density in the gap between the non-magnetic material and the air was the smallest, equal to about zero, indicating that it plays a good role in limiting the envelope of the magnetic field lines and can meet the experimental requirements. From Figure 3(b), the magnetic field in the working area was basically perpendicular to the direction of the working surface, and the effective magnetic field lines accounted for more than 90% of the total magnetic field, which met the design requirements of MRB [26].  
Figure 4: Magnetic field simulation results. (Working current of 2 A).
## 5 Numerical Simulations
In the first part of the maneuver, a single obstacle is considered. A PWM behavior can be observed in the thrust variations and the sample time of the thrusters switching on is set equal to 1 s, to reduce the fuel consumption due to the combination of APF and SMC. Usually, the SMC frequency is very high (close to infinity) to obtain good performance, but, in our case, we reduce the frequency of SMC to take into account the hardware limitations and to reduce the fuel consumption. Concerning Figure 7c, the relative quaternion has peaks and discontinuities because the APF algorithm changes the desired attitude, in accordance to Equation (17), when the Chaser is close to the obstacle. In the second phase, Figure 8, two obstacles with different safety radii are included, to evaluate the performance of the APF algorithm. In that case, we can observe that the Chaser is moving few meters inside the area of the first obstacle of the maneuver delimited by the safety radius. This is due to the value of the repulsive gain \(k_{r,i}\) of Equation (12) related to the obstacle. Even if the maneuver is executed in safely manner, a more suitable tuning of this gain is required to completely satisfy the requirements. If an adaptive gain is chosen, better performance could be observed in proximity of obstacles. In the final approach maneuver, Figure 9, the desired positions are defined in accordance to the cone geometry where the maximum amplitude of the cone is \(r_{0}=1\) m at the beginning of the maneuver and the final \(R_{bar}\) is \(r_{f}=0.05\) m. No obstacles are considered because the Chaser is too close to the Target. Different switching frequencies are chosen to reduce the fuel consumption. All the constraints are satisfied, even if a reduced frequency is considered in the first part of the forced motion. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Obstacle Id.** & **Radius (m)** & **Position (m)** \\  & & \([x,y,z]_{LVLH}\) \\ \hline OBS \#1 & 650 & \([-10,000,\ 0,\ 1500]\) \\ OBS \#2 & 150 & \([-2800,\ 0,\ 0]\) \\ OBS \#3 & 50 & \([-2100,\ 0,\ 200]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Obstacles LVLH position. Figure 7: _Cont._
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 4 Network Model  
Figure 2 depicts the workflow of the proposed scheme, which comprised four major entities: medical sensing devices or IoMT devices, central authority (CA), personal digital assistant (PDA), and application providers (AP). The CA is a trusted third party whose responsibility is to establish the system by producing the parameters transmitted to the other three network entities. Wearables, smart belts, smart thermometers, smart oximeters, smart chairs, smart glucose gadgets, and smart sphygmomanometers are some examples of implanted IoMT devices. These devices continually monitor personal health information (PHI) by measuring the pulse rate, heart rate, body temperature, body movement, oxygen level in the body, blood pressure, breathing rate, and glucose levels, among others. In addition to data collection, connecting IoMT devices is a crucial duty for which wireless communication technologies such as Zigbee are utilized. Depending on the network's distance and characteristics, Bluetooth 4.0, Wi-Fi, and 5G can also be used. The communication methods are appropriate for wireless sensor networks due to their low cost and low power consumption [25; 26]. The IoMT devices produce a dual signature and transfer it with the encrypted PHI to the PDA. When a PDA receives a message with a dual signature,  
it checks the dual signature before forwarding the message to application providers (AP). The application provider (AP) is responsible for continually and effectively monitoring the patient's condition. When receiving a letter from a PDA, the application provider checks the dual signature and then decrypts the PHI-containing message. The doctors and nurses who are part of the application provider and linked to the hospital server act depending on the PHI received.
## 3 Analysis of Test Results
### Process of Crack Propagation and Specim Failure  
#### 3.3.1 Recognition and Feature Extraction of Surface Cracks  
Currently, several methods can be used to study the damage process in impact dynamics, and the most popular method is high-speed (HS) photography to record the entire damage process, including fractal analysis [40,41] and deep learning [42,43]. However, in the analysis of the images, more scholars tend to analyse them qualitatively but fail to quantify them. Because the generation of the cracks in the image is very complex, the extraction of all cracks is difficult. Therefore, in this study, we used the Ratsnake annotation software [44] to extract the cracks from each original image during the fracture process. Ratsnake can quickly segment the image with annotations and customise the annotation. RGB images captured using the HS camera were converted to black-and-white binarised images; this improved the resolution and accuracy [45]. Due to space limitations, in this paper, raw rock samples at different temperatures and saturations were selected as examples, and deformation was generated during the destruction process until fragments were ejected during the camera recording. The shooting frequency of the high-speed camera was 100,000 frames/s, and each set of data was intercepted every 1 \(\upmu\)s. The final damage image and the crack extension process are shown in Figure 12a,b. Figure 12c is last frame of the results extracted from the original crack images and Ratsnake software. The dry rock at 20 \({}^{\circ}\)C (DR-20) is taken as an example; its impact velocity is 17.96 m/s. It can be seen that the sample has developed large cracks with debris ejection.  
Figure 11: Variation of peak intensity of water-saturated coal and rock with temperature. (**a**) Coal; (**b**) rock.
## 6 Conclusions  
This paper presents a novel method for stability and velocity control of a two-wheeled robotic wheelchair. In this method, a movable pendulum-like movable mechanism is added to the wheelchair mainly for stability control. The Euler-Lagrange equation is used to establish the equation of motion of the system and a quasi-sliding mode control scheme is used in the controller design. The simulation results show that the proposed method achieves better stability and velocity control with less input power than the conventional methods only relying on the motions of the wheels. It is also robust against external disturbances. The future work is to implement the proposed method on a two-wheeled robotic wheelchair system under development in our lab.  
Figure 19: Input torque of the movable mechanism in the proposed method (PM) in Case 2.  
Figure 20: Involular power of the movable mechanism in the proposed method (PM) in Case 2.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] developed the idea covered in the paper; [PERSON] did simulations, drafted and revised the paper under the guidance of [PERSON] and [PERSON]; [PERSON] is the corresponding author to revise and finalize the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The project is funded by the Institute of Biomedical technologies at the Auckland University of Technology, New Zealand.  
**Acknowledgments:** The authors would like to acknowledge [PERSON] and [PERSON] for their advice and guidance.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Appendix A. Dynamic Model Components of Proposed Method**  
The \(\mathbf{M(q)}\), \(\mathbf{H(q,\dot{q})}\) and \(\mathbf{G(q)}\) elements in proposed method are as below:  
\[M_{11}=m_{b}l^{2}+m_{p}b^{2}+m_{p}l^{\prime 2}-2m_{p}bl^{\prime}\cos\theta_{p}+J _{b}+J_{p},\quad M_{12}=m_{b}rl\cos\theta_{b}+m_{p}rb\cos\theta_{b}-m_{p}rl^{ \prime}\cos(\theta_{b}+\theta_{p}),\]  
\[M_{13}=m_{p}l^{2}-m_{p}bl^{\prime}\cos\theta_{p}+J_{p},\quad M_{22}=(m_{b}+m_{ p}+2m_{w})r^{2}+2J_{w},\]  
\[M_{23}=-m_{p}rl^{\prime}\cos(\theta_{b}+\theta_{p}),\quad\ \ M_{33}=m_{p}l^{ \prime 2}+J_{p},\quad\ H_{1}=m_{p}bl^{\prime}(\theta_{p}^{2}+2\theta_{b} \dot{\theta}_{p})\sin\theta_{p},\]  
\[H_{2}=-(m_{b}l+m_{p}b)r\theta_{b}^{2}\sin\theta_{b}+m_{p}rl^{\prime}(\theta_{b }+\dot{\theta}_{p})^{2}\sin(\theta_{b}+\theta_{p}),\quad\ H_{3}=-m_{p}bl^{ \prime}\theta_{b}^{2}\sin\theta_{p},\]  
\[G_{1}=-m_{b}gl\sin\theta_{b}-m_{p}gb\sin\theta_{b}+m_{p}gl^{\prime}\sin( \theta_{b}+\theta_{p}),\quad\ G_{2}=0,\quad\ G_{3}=m_{p}gl^{\prime}\sin( \theta_{b}+\theta_{p}).\]