esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## 3 Robust Localization via RTS Smoothing
### Segmented RTS Smoothing Algorithm  
Assuming the duration of a dynamic system is \(k=1,2\ldots\), \(N\), the segmented RTS smoothing algorithm involves forward filtering and inverse smoothing of N observations in segments. The segment length is \(L\), where \(1<L<N\). In other words, the RTS inverse smoothing process is performed immediately after obtaining the filtering results for the segmented period, without waiting for subsequent filtering results. This approach significantly reduces the lag time of the inverse smoothing process. Additionally, the segmentation length can be varied, avoiding the issue of poor real\(-\)time performance associated with RTS smoothing. In this study, the segmented RTS smoothing algorithm is applied to the potential probability assumption density filtering algorithm, effectively addressing the problem of poor real\(-\)time performance resulting from the use of RTS smoothing.  
Step 1 Output variables are cleared: \(\textit{ffit}\hat{x}_{k}=[\ ],\hat{p}_{k}=[\ ]\).  
Step 2 Local filtering result variable clearing \(\textit{ffit}\hat{x}_{k}=[\ ],\hat{p}_{k}=[\ ]\). The filtered result variable after local association is cleared \(x_{k}=[\ ]\), \(Q_{k}=[\ ]\), the segmented smoothing counter _count_ is cleared to zero.  
Step 3 If the tracking of the target is not finished, action continues down the track, otherwise the smoothing result \(\textit{ffix}_{k},\hat{p}_{k}\) is output; then, the entire segmented RTS smoothing algorithm ends.  
Step 4 Upon receiving the current filtering result, it is stored in the variables \(\textit{ffix}_{k}\) and \(\hat{p}_{k}\). the Hungarian algorithm is used to correlate the trajectories and estimates, obtaining the filter value for each target. The correlated filter result is saved in variables \(x_{k}\) and \(Q_{k}\), and they are counted using a counter called count.  
Step 5 If \(count==L\), after performing local correlation using \(x_{k}\) and \(Q_{k}\) for each target's filtering results, the inverse smoothing process uses the RTS smoothing algorithm. The results of the smoothing process are stored in variables delta \(\textit{ffix}_{k}\) and \(\hat{p}_{k}\) before being transferred to Step 2. In order to significantly reduce the lag time of the inverse smoothing process, it is carried out immediately following the acquisition of the filtered data without waiting for subsequent data. Real\(-\)time performance of the fusion algorithm can be improved by decreasing the segment length \(L\).  
To correct the position, velocity, and attitude values that have been calculated in the forward filtering, it is recommended to use the state error smoothing values. These smoothing values can be utilized to derive the final optimal smoothed values for these states using the following equations.
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
## 3 Analysis of Numerical Simulation Results
### Beam Wandering Effect
(4) Under the same turbulence strength, the beam wander phenomenon becomes more severe as the weight coefficient of \(Z_{25}\) aberration increases. Compared to the no-aberration state, the effect of \(Z_{25}\) aberration on the far-field centroid drift is not significant, with the maximum difference being 0.11 cm. Therefore, in weak turbulence and short-range laser transmission over the sea, the beam wander caused by these four aberrations is not significant and can be neglected.
## 2 Mathematical Modeling and Methods
### Pump-Motor Hydraulic System Model
#### 2.1.4 Variable Pump-Fixed Motor System  
The variable pump-fixed motor system is the main executive part of the hydraulic system. The PM could adjust the output speed of the hydraulic system by the displacement ratio. The mathematical model is as follows:  
\[\left\{\begin{array}{l}D_{p}=K_{p}\gamma\\ q_{p}=D_{p}\omega_{p}-C_{ip}(p_{1}-p_{r})-C_{ep}p_{1}\\ q_{p}=C_{im}(p_{1}-p_{r})+C_{em}p_{1}+D_{m}\frac{d\theta_{m}}{dt}+\frac{V_{0}}{ \theta_{e}}\frac{dp_{1}}{dt}\\ D_{m}(p_{1}-p_{r})=I_{t}\frac{d\theta_{m}}{dt^{2}}+B_{m}\frac{d\theta_{m}}{dt} +G\theta_{m}+T_{L}\end{array}\right. \tag{4}\]  
where \(K_{p}\) represents the displacement gradient of the variable pump; \(D_{p}\) represents the displacement of the variable pump; \(\gamma\) represents the swash plate swing angle of the variable mechanism; \(q_{p}\) represents the output flow of the variable pump; \(\omega_{p}\) represents the speed of the variable pump; \(C_{ep}\) represents the internal leakage coefficient of the variable pump; \(C_{ep}\) represents the external leakage coefficient of the variable pump; \(p_{1}\) represents the pressure on the high pressure chamber side; \(p_{r}\) represents the low pressure pipeline pressure; \(\theta_{m}\) represents the shaft rotation angle; \(C_{im}\) represents the internal leakage coefficient of the motor; \(C_{em}\) represents the external leakage coefficient of the motor; \(V_{0}\) represents the working chamber of the pump and the motor and the total volume of the connecting pipe; \(D_{m}\) represents the motor displacement; \(\beta_{e}\) represents the effective volume the amount of elastic film; \(I_{t}\) represents the total moment of inertia of the hydraulic motor and the load (converted to the motor shaft); \(B_{m}\) represents the total viscous damping coefficient of the hydraulic motor and the load (converted to the motor shaft); \(G\) represents the torsional spring stiffness of the load; \(T_{L}\) represents the acting on the motor external load torque on the shaft.
## 6 Conclusions  
In this study, we aimed to control the transmission rates of data packets in CSMA/CA wireless networks more accurately using MAB algorithms. We proposed the use of the logarithmic values of the transmission rates of MCS levels instead their nominal values in the computation of their expected throughput estimations. To evaluate the effectiveness of the proposed method, we introduced two MAB algorithms which adopt the logarithmic rates and a sliding window over time. The proposed MAB algorithms were also designed to support frame aggregation schemes available in wireless network standards for high throughput such as IEEE 802.11 ax.  
The experimental results using the event-driven network simulator, ns-3, demonstrated that the proposed MAB algorithms outperformed their counterparts that do not adopt the logarithmic rates in both the stationary and non-stationary scenarios. Additionally, the proposed MAB algorithms achieved better performance than their counterparts when frame aggregation was enabled. Furthermore, the proposed MAB algorithms achieved comparable performance to non-MAB-based transmission rate control algorithms such as Minstrel in rapidly changing wireless channel environments.  
We expect that the use of the logarithmic rates of the transmission rates can be adopted to improve the performance of MAB algorithms that involve other types of wireless communications such as cellular networks. However, by comparing the experimental results of the proposed algorithms with those of the Ideal algorithm in the IEEE 802.11 ax network, we can see that there is room for improvement. As a future work, we consider the adoption or development of techniques to reduce the size of the search space to improve the performance particularly when the search space is large. In addition, we would like to investigate whether the adoption of sophisticated change detection techniques could result in better performance than using the sliding window.  
This work was supported by 2021 Hongik University Research Fund.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 2 System Architecture  
The proposed system architecture integrates 6G satellite and terrestrial networks to support unmanned aerial vehicles (UAVs) in a range of operational scenarios, from commercial deliveries to emergency services. This architecture is designed to leverage the strengths of both satellite and terrestrial technologies, ensuring seamless connectivity, high throughput, and low latency. It includes several key components: a satellite constellation, terrestrial base stations, UAVs as mobile nodes, and a network management system. The system has the following components:  
* Satellite constellation: A constellation of low-Earth-orbit (LEO) satellites provides wide coverage and high-bandwidth communication links. These satellites are equipped with advanced beamforming capabilities to dynamically focus connectivity on specific areas or UAVs, enhancing signal strength and reducing interference [27; 28; 29; 30].
* Terrestrial network: Comparising 6G base stations, this network layer offers high-speed, low-latency connections in urban and suburban areas [29]. The terrestrial network serves as the primary means of communication where available, with the capability of handing over to the satellite layer when UAVs move out of terrestrial coverage [31].
* UAVs as mobile nodes: UAVs are equipped with dual-mode communication systems capable of connecting to both satellite and terrestrial networks. These systems automatically switch between satellite and terrestrial links based on the signal quality, network load, and predefined operational param.
* Network management system (NMS): This central system coordinates between the satellite and terrestrial networks. It manages resource allocation, monitors network health, and orchestrates handovers between satellites and terrestrial nodes. The NMS uses predictive algorithms to optimize routes and connectivity for UAVs based on their flight plans and network conditions [32].
# Through-Floor Vital Sign Searching for Trapped Person Using Wireless-Netted UWB Radars
## 1 Introduction  
In the personnel rescue scenarios, personnel positioning methods have attracted extensive interest in scientific research and engineering applications. In this context, ultra-wideband (UWB) radar can detect the vital signs caused by breathing or micro movements of people under ruins, which plays an important role in detecting trapped living persons and shows significant advantages in strong anti-interference ability, high penetrability, and range resolution.  
Currently, the period estimation for respiration or similar vital signs, the removal of the stationary/nonstationary clutter, and radar system design [1, 2, 3, 4, 5, 6, 7] have lately received great attention for the detection and rescue of life signal by the UWB radar. For the radar system, the more observation points, the greater the possibility for finding the trapped person. If no vital signs are detected at a fixed observation point, selecting subsequent observations can increase the probability of detection. However, vital sign search methods based on different observation points are facing challenges and it is worthwhile devoting much effort to this. In this way, rescuers can thoroughly analyze radar echoes at multiple observation points to extract hidden vital signs of trapped persons [8, 9, 10, 11, 12]. Hence, the vital sign searching strategy based on different observation points has emerged as an interesting candidate because of its impressive ability to detect trapped persons in some complex scenes, such as through walls.  
Further, the multiview and netted radar systems were found to improve the signal-to-noise ratio (SNR) and increase rescue efficiency in [13, 14]. The development of UWB radar vital sign detection techniques that allow multipoint observation and data association processing is becoming a priority, motivating the design of multiobservation point detection and decision systems that increase rescue efficiency.  
In this paper, a novel multiple-observation-points-based vital sign searching strategy for rescuing trapped persons is introduced. Controlled by a wireless network, four UWBlife-detection radars are set up on a 2D observation aperture and connected to form a group. The main contribution of this paper is the multiple-observation-points-based vital sign searching strategy, including the design of the searching path and decision procedure, which has not been previously conceived nor demonstrated.
## 1 Introduction
The application of TD-NMR has been extended to the studies on the enclathration of CO\({}_{2}\) or CH\({}_{4}\) within hydrates by [PERSON] et al., [PERSON] et al., and [PERSON] et al. [47; 48; 49]. [PERSON] et al. [50] demonstrated the feasibility of TD-NMR spectroscopy to quantify the crystal content of the tetra-n-butyl ammonium bromide (TBAB) hydrates. Although NMR is relatively reliable, it is expensive. Therefore, the accumulated knowledge regarding the phase fraction measurement of slurry for research purposes does not provide the tool applicable for in situ monitoring of the hydrate slurry solid fraction. The present study sets out to devise a nondestructive yet cost-effective method to monitor the solid fraction of hydrate slurries with minimal apparatus, which can be easily aligned with the industrial setup. The flow loop equipped with square-edged orifice and slash plate pump was constructed, in which R134a hydrate slurry of the specified solid volume fraction was formed and circulated. The measured pressure differences were analyzed to develop empirical correlation, with which the solid volume fraction of R134a slurry can be determined from the measured pressure drop across the orifice and the flow rate, without iteration.
## 3 Results and Discussion  
### HNS Propagation Behaviors  
Figure 2 shows the time-dependent propagation behavior of the HNS mass fraction field. This study determined the interfaces between the HNS and seawater with a mass fraction of 100 ppm taken as the criterion with reference to the exposure limit for styrene, as selected by the Occupational Safety and Health Administration [21]. During propagation over time, there was little difference between the side and bottom crack cases, noticing that the crack location has practically negligible effect. This indicates that the current velocity and HNS property may be important parameters in HNS propagation.  
Figure 3a compares the HNS propagation characteristics with respect to the current velocity for different crack locations. As the current velocity increases, the propagation becomes faster owing to higher convective motions, resulting in relatively lower diffusive behavior and rapid propagation in the streamwise direction. As shown in Figure 3b, it is interesting that even for a heavier HNS, a big difference is not observed, indicating that for the floating material, such as styrene, convective motion becomes dominant. Under the present scenario, the spilled HNS interacted with the vertical directional (\(z\) direction) momentum only by the buoyancy force. When the HNS moves towards sea level because of floating behavior, the vertical motion rapidly changes owing to the streamwise motion, which is dominant. In addition, from Figure 3, it can be seen that there is no clear difference in the propagation behaviors according to the crack location, except for the asymmetric propagation in the side crack cases.  
Figure 2: The transient evolution of hazardous and noxious substance (HNS) propagation: (**a**) Side crack and (**b**) bottom crack in the case that \(v_{c}=1.028\) m/s and \(\rho_{HNS}=800\) kg/m\({}^{3}\).
## 3 Case Study
### Collection of Processing Data Based on CM
#### 3.2.1 Evaluation Standard Cloud Determination  
Prior to computing membership using the CM, it is imperative to convert the initial evaluation state into evaluation standard clouds for precise quantitative interpretation. The standard cloud characteristic parameters (\(Ex^{\prime},\ En^{\prime},\ He^{\prime}\)) for the states of the root nodes in Table 2 are defined by Equation (6), as listed in Table 5.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Nodes** & \multicolumn{4}{l}{**Eight Expert Scores**} \\ \hline X5 & 0.8 & 0.7 & 1.0 & 0.9 & 1.2 & 1.5 & 1.0 & 1.5 \\ X6 & 1.0 & 0.8 & 0.7 & 0.8 & 0.9 & 0.7 & 0.8 & 0.5 \\ X7 & 8.2 & 8.5 & 8.0 & 8.5 & 8.5 & 8.0 & 8.2 & 8.0 \\ X8 & 6.0 & 5.0 & 6.0 & 5.5 & 6.0 & 5.0 & 6.5 & 5.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Expert scores for nodes X5–X8.  
Figure 8: Monitoring data for nodes X1–X4.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Nodes** & **S0** & **S1** & **S2** \\ \cline{2-6}  & **(\(Ex^{\prime}\), \(En^{\prime}\), \(He^{\prime}\))** & **(\(Ex^{\prime}\), \(En^{\prime}\), \(He^{\prime}\))** & **(\(Ex^{\prime}\), \(En^{\prime}\), \(He^{\prime}\))** \\ \hline X1 & (1.000, 0.333, 0.100) & (3.500, 0.500, 0.100) & (7.500, 0.833, 0.100) \\ X2 & (1.000, 0.333, 0.100) & (3.500, 0.500, 0.100) & (7.500, 0.833, 0.100) \\ X3 & (9.000, 0.333, 0.100) & (6.500, 0.500, 0.100) & (2.500, 0.833, 0.100) \\ X4 & (9.000, 0.333, 0.100) & (6.500, 0.500, 0.100) & (2.500, 0.833, 0.100) \\ X5 & (277.500, 7.500, 1.000) & (322.500, 7.500, 1.000) & (367.500, 7.500, 1.000) \\ X6 & (5.000, 1.667, 0.200) & (14.000, 1.333, 0.200) & (22.000, 1.333, 0.200) \\ X7 & (20.000, 6.670, 0.500) & (55.000, 5.000, 0.500) & (85.000, 5.000, 0.500) \\ X8 & (47.500, 5.833, 0.500) & (75.000, 3.330, 0.500) & (92.500, 2.500, 0.500) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Standard cloud characteristic parameters for different states of root nodes.
## 6 Conclusions  
We successfully designed, fabricated, and tested a conduction-cooled 3 T HTS magnet system with a compact size and good portability. With the GA, an optimization theory, the 3 T HTS magnet composed of eight double-pancake coils was optimally designed considering the mechanical hoop stress, critical current, and magnetic field distributions. A metallic form of insulation was adopted for quench protection without a charging delay. After the fabrication process, the magnet was cooled to the target temperature in one day. At the target temperature (<20 K), the system generated 3 T at the target current without any problems and survived a sudden discharge at about 45 A (half of the operating current) of current due to the power supply voltage limit. A thermal analysis with the roughly calculated thermal contact resistances was also implemented to verify the temperature trends of the magnet, and the simulation results were found to be similar to the measured data. From these results, we demonstrated that the 3 T liquid-helium-free compact magnet system can be a proper property measurement system for convenient property measurement tests. We expect that the system will be operated as a new user facility at KBSI after lengthy operating tests.  
Design, J.Y.J.; fabrication, M.S.K. and Y.J.H.; test and data acquisition, M.S.K., Y.J.H., S.S., J.Y.J., and Y.C.; writing--original draft preparation, J.Y.J.; writing--review and editing, Y.S.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Korea Basic Science Institute (KBSI), specifically by grant number D110200.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 12: FEM simulation result of the 3 T system.
# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
## 5 Simulation and Analysis
Here we extract the specific total amount of package transmission within the setting rounds for the three algorithms in the two different scenarios, which is shown in Table 8. It can be seen that EEDCF and DFLC are better than EADEEG in data transmission efficiency due to both using of fuzzy logic mechanism and the additional consideration of the nodes density, which can effectively alleviate the unnecessary energy consumption caused by \"isolate points\" problem. Figure 14 indicates the data transmission result of the three algorithms in Scenario 2. We compare the final data transmission amount of the three algorithms in both scenarios in Table 8 and find that the relationship of the results in the two scenarios is very similar. In Scenario 1, the final result of data transmission in EEDCF is \(14.7\times 10^{4}\) Bytes, which is 44.3% better than the result in DFLC, and 123.2% better than the result in EADEEG. In Scenario 2, the final result of data transmission in EEDCF is \(22.9\times 10^{4}\) Bytes, which is 80.3% better than the result in DFLC, and 193.6% better than the result in EADEEG. Because the node's density of Scenario 2 is higher than Scenario 1, it is reasonable, as shown in Table 8, that the data transmission amounts of the three algorithms in Scenario 2 are all higher than that in Scenario 1. Moreover, in the case of higher nodes density in the network, EEDCF algorithm improves the performance of the system even far better compared with DFLC algorithm and EADEEG algorithm, respectively. Certainly, the performance of DFLC is
# Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP
## 4 Experiments and Analysis  
To test the performances of the proposed PIH scheme, extensive experiments are conducted on many standard images with a 256 \(\times\) 256 size, which can be obtained from the CVG-UGR (Computer Vision Group, University of Granada) image database [41]. The normalized Hamming distance was adopted in our experiments in order to measure the similarity between two hashes.  
\[Dis\Big{(}H_{f},H_{f}^{\prime}\Big{)}=\frac{1}{m}\sum_{i=1}^{m}\Big{|}H_{f}(i)-H_{ f}^{\prime}(i)\Big{|}, \tag{16}\]  
where \(H_{f}\) and \(H_{f}^{\prime}\) are two hash sequences, and where \(m\) is the hash length.  
In the experiments, the parameters of the resized image size \(M\), sub-block size \(b\), LBP radius \(R\), LBP pixel number \(P\) of the neighbor, and final perceptual hash length \(m\) are set to 256, 4, 1, 8 and 500, respectively.  
All the experiments are tested on a laptop, the Intel Core i-3630 QM 2.66 GHz CPU (Intel Corp., Santa Clara, CA, USA), with an 8 GB memory and running MATLAB 2016a (Mathworks Inc., Natick, MA, USA). The average time cost is computed on test images with a 256 \(\times\) 256 size, and the average running times of the different PIH methods [13; 24; 26] are listed in Table 1. Our method and [PERSON] et al.'s method [13] need more time cost than the other two methods [24; 26] because of the use of the LRR operations.
# The Quanta Image Sensor: Every Photon Counts
## 5 Jot Device
### Color and Polarization Filters  
For many applications, color filter arrays (CFAs) are needed to enable color imaging. In this case, the bit planes can be separated by color into groups and processed independently, and then re-fused for a full color image. Generally, cubic sizes for the color groups need not be the same and may facilitate particular improvements in image quality. Color processing could also be performed for each combined bit plane followed by cubic processing. The options are certainly broad but mostly unexplored.  
For SDL jets, one can consider microlenses and color filters that cover multiple jets (e.g., \(2\times 2\)) since diffraction will likely result in optical resolution lower than the jet pitch [1; 64]. Color crosstalk was analyzed in [20] for example, and a new color filter array pattern to ameliorate the impact of color crosstalk was proposed and analyzed in [12]. Polarization filter gratings can also be applied to jets, or groups of jets to select particular polarization of photons [24]. For example, 4 polarization filters formed by gratings, corresponding to \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\) polarization selection angles can each be placed over a group of jets, e.g., \(4\times 4\) jets under each filter. Color filters can also be adjacent to the polarization filters to form a \(3\times 3\) super-kernel of filters for polarization and color as shown in the inset to Figure 15. Thus, both color and polarization information can be obtained from the \(12\times 12\times t\) super-cubic of jets, with accuracy dependent on exposure and cubic size.