esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Modeling of the Extraction Process and Test of Significance  
Analysis of variance (ANOVA) was used to analyze the significance of the developed model equation, the results of which are shown in Table 3. Two important indicators for ANOVA were the \(F\)-value and \(p\)-value. The model \(F\)-value of 107.09 implied that the model was significant. A \(p\)-value less than 0.001 also indicated that the model was significant [28]. It also could be seen from Table 3 that the linear co-efficients (X\({}_{3}\)) and a quadratic term co-efficient (X\({}_{1}\)X\({}_{2}\), X\({}_{2}\)X\({}_{3}\)) were significant, with small \(p\)-values (\(p<0.05\)). The other term co-efficients were not significant (\(p>0.05\)). Therefore, X\({}_{3}\), X\({}_{1}\)X\({}_{2}\) and X\({}_{2}\)X\({}_{3}\) were important factors in the SWE process of RES.
## 3 Results and Discussion of Data Analysis  
### Case Study of a Single Event  
Taking the C2.1-class solar flare event that occurred on 3 May 2023 (local time: 11:39-11:53) as a case study, we performed a qualitative analysis to explore the relationship between low-frequency signal field strength and XRA data. As shown in Figure 2, the variations in field strength and XRA data during this period are presented. In the field strength variation graph (Figure 2, top), the blue curve represents the raw field strength data, while the red curve corresponds to the smoothed data. It is evident that the field strength exhibited a significant upward trend during the solar flare, which likely reflects ionospheric disturbances induced by the flare, leading to enhanced signal propagation strength. Concurrently, the XRA data variation graph (Figure 2, bottom) shows dynamic changes in X-ray flux intensity, displaying a characteristic dip followed by a rise. This pattern aligns with the typical temporal evolution of C-class solar flare events. Based on observations of over 150 similar cases, we hypothesize that variations in XRA data may correlate with trends in field strength.  
As depicted in Figure 3, the cross-spectral density (CSD) between field strength and XRA data is shown as a function of frequency. The results indicate that the two datasets exhibit strong correlations in the low-frequency range (<0.1 Hz), as evidenced by high cross-spectral density values. However, as the frequency increases, the cross-spectral density decreases, suggesting weaker correlations at higher frequencies. The high-density region in the low-frequency range is likely associated with ionospheric electron density changes induced by solar flares. The high-energy X-rays released by solar flares rapidly affect the ionosphere, causing a systemic change in signal propagation paths. Such changes are typically gradual and sustained, predominantly reflected in the low-frequency range. In contrast, the lower density observed in the high-frequency range may be attributed to rapid, localized disturbances, such as instrumental noise or environmental interference.  
Figure 3: Cross-Spectral Analysis Between Field Strength and XRA Data During a C2.1-Class Event.  
Figure 2: Comparison of Field Strength and XRA Data During a C2.1-Class Event.  
Overall, the time-domain signal observations and cross-spectral analysis collectively reveal a potential correlation between field strength and XRA data. Detailed quantitative results and further statistical analysis are presented in Sections 3.2 and 3.3.
###### Abstract  
In this paper, a Novel Active Disturbance Rejection Control (N-ADRC) strategy is proposed that replaces the Linear Extended State Observer (LESO) used in Conventional ADRC (C-ADRC) with a nested LESO. In the nested LESO, the inner-loop LESO actively estimates and eliminates the generalized disturbance. Increasing the bandwidth improves the estimation accuracy which may tolerate noise and conflict with H/W limitations and the sampling frequency of the system. Therefore, an alternative scenario is offered without increasing the bandwidth of the inner-loop LESO provided that the rate of change of the generalized disturbance estimation error is upper bounded. This was achieved by the placing of an outer-loop LESO in parallel with the inner one that estimates and eliminates the remaining generalized disturbance originating from the inner-loop LESO due to bandwidth limitations. The stability of LESO and nested LESO was investigated using Lyapunov stability analysis. Simulations on uncertain nonlinear single-input-single-output (SISO) system with time-varying exogenous disturbance revealed that the proposed nested LESO could successfully deal with a generalized disturbance in both noisy and noise-free environments, where the Integral Time Absolute Error (ITAE) of the tracking error for the nested LESO was reduced by 69.87% from that of the LESO.  
nested extended state observer; generalized disturbance; system uncertainties; linear extended state observer; active disturbance rejection control; Lyapunov stability +
Footnote †: journal: Applied Sciences
## 2 Basic Theory
### Spectral Kurtosis
The optimal center frequency \(f_{c}\), bandwidth \(B_{w}\), and envelope signal \(c_{0}(n)\) can be obtained when \(K(f_{i},\Delta f_{k})\) is the maximum: \[[f_{c},B_{w},c_{0}(n)]=\text{argmax}\left\{K(f_{i},\Delta f_{k})\right\}\] (15) where \(argmax\) represents the corresponding independent variable parameters when \(K\) is the maximum, that is, the \(argmax\) corresponds to the optimal center frequency \(f_{c}\), bandwidth \(B_{w}\), and complex envelope signal \(c_{0}(n)\).
## 4 Experimental Results
### Displacement Distance Test  
Smart wheelchairs were performance measured in mobility tests with a load of up for support no more than 100 kg with a distance of up to 3250 m. The test results for the distance of the wheelchair with two 12V/5 A dry batteries are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Distance (m)**} & \multicolumn{6}{c}{**Time**} \\ \cline{2-7}  & **Weight 50 kg** & **Weight 60 kg** & **Weight 70 kg** & **Weight 80 kg** & **Weight 90 kg** \\ \hline
10 & 86.20 & 75.10 & 66.85 & 56.80 & 45.90 \\
20 & 58.80 & 60.30 & 53.40 & 45.10 & 36.80 \\
30 & 51.60 & 45.32 & 40.10 & 34.10 & 27.60 \\
40 & 34.40 & 30.10 & 26.90 & 22.60 & 18.50 \\
50 & 17.20 & 15.10 & 13.40 & 11.30 & 9.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Electric-powered wheelchair speed test.  
Figure 8: Incline comparison chart.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Distance (m)**} & \multicolumn{6}{c}{**Time**} \\ \cline{2-7}  & **Weight 50 kg** & **Weight 60 kg** & **Weight 70 kg** & **Weight 80 kg** & **Weight 90 kg** \\ \hline
10 & 127.60 & 100.50 & 86.60 & 72.50 & 62.90 \\
20 & 102.90 & 80.80 & 69.40 & 58.60 & 50.90 \\
30 &
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 2 Materials and Methods
### Problem Formulation
#### 2.2.1 Objective Function
To enable trajectory planning for residual VoI maximization, let \(\mathcal{C}_{i}\) denote a horizontal circle of radius \(\rho_{i}\) around each CH, centered at \(\left(x_{CH_{i}},y_{CH_{i}},z_{d}\right)\), for each \(i=1,2,\ldots,M\). This circle defines the horizontal communication region within which the AUV can successfully transmit data to CH \(i\) at the horizontal plane \(z=z_{d}\). Furthermore, define \(\mathcal{B}_{i}\) as the corresponding closed disk with the same center and radius. It is assumed that for any given index \(i\), there exists at least one index \(\alpha\
eq i\), with \(\alpha\in\{1,2,\ldots,M\}\), such that:  
\[\mathcal{B}_{i}\bigcap\mathcal{B}_{\alpha}=\phi. \tag{10}\]  
Let \(\mathcal{Q}_{i}=\left\{q_{1_{i}},q_{2_{i}},\ldots,q_{L_{i}}\right\}\), where \(q_{l_{i}}=\left(x_{q_{l_{i}}},y_{q_{l_{i}}},z_{d}\right)\) for \(l=1,2,\ldots,L\), be a finite set of \(L\) candidate waypoints uniformly distributed along the circumference of the horizontal circle \(\mathcal{C}_{i}\). Each \(q_{l_{i}}\in\mathcal{C}_{i}\) lies at a horizontal distance \(\rho_{i}\) from the center. The AUV must visit one waypoint per CH, and the sequence in which the CHs are visited significantly affects the overall performance due to information decay over time. Let \(\hat{\Phi}\) denote the set of all permutations of the index set \(\{1,2,\ldots,M\}\), which contains \(M!\) elements. Each permutation \(\Phi=(s(1),s(2),\ldots,s(M))\), where \(\Phi\in\hat{\Phi}\), represents a unique visiting sequence of the \(M\) circles. For each permutation \(\Phi\), the set of all possible combinations of candidate waypoints, one per CH, is defined as the Cartesian product:  
\[\mathcal{Q}_{\Phi}=\mathcal{Q}_{s(1)}\times\mathcal{Q}_{s(2)}\times\cdots \times\mathcal{Q}_{s(M)}. \tag{11}\]  
This results in \(L^{M}\) candidate paths. Each element \(\mathcal{P}\in\mathcal{Q}_{\Phi}\) is a candidate trajectory comprising one waypoint per CH, ordered according to the sequence \(\Phi\):  
\[\mathcal{P}=\left(q_{l_{s(1)}},q_{l_{s(2)}},\ldots,q_{l_{s(M)}}\right), \tag{12}\]  
where \(q_{l_{s(i)}}\in\mathcal{Q}_{s(i)}\) is the selected waypoint for CH \(s(i)\) in the sequence. Building on the above definitions, the primary objective function is formulated as follows:  
\[\underset{\Phi,\mathcal{P}}{\text{max}}\sum_{i=1}^{M}V_{r_{i}}(t_{c})=\sum_{ i=1}^{M}\mathcal{V}_{int_{i}}(1-\beta\;\mathcal{I}_{i})^{t_{c}}. \tag{13}\]  
The AUV trajectory planning problem is thus defined as the task of selecting the optimal visiting sequence of CHs, along with their corresponding data collection waypoints, in order to maximize the objective function in (13). It is assumed that the AUV has complete knowledge of the VoI associated with each CH at the start of the mission from its initial surface position \(S_{s}\).
# All-Fiber Hyperparametric Generation Based on a Monolithic Fiber Fabry-Perot Microresonator
## 4 Conclusions  
In conclusion, we demonstrated all-fiber hyperparametric generation based on a fiber Fabry-P\(\acute{\text{e}}\)rot microresonator, which is compatible with today's widely used optical fiber systems. The high-quality FFP microresonator ensures strong self-phase modulation in a wide spectral range and eventually excites cascaded stimulated Raman scattering, which further extends the spectral bandwidth to over 400 nm. The low transmission loss of HNLF enables a long cavity length of 148 mm, resulting in fine line-to-line spacing of 667 MHz. This result may find broad application prospects in the fields of optical communication and spectroscopy. In the current situation, we demonstrated hyperparametric generation for the presentation of the FFP microresonator and all-fiber setup. In future work, we may further obtain a mode-locked frequency comb state and even a Stokes soliton comb with refined design of the cavity dispersion.  
Conceptualization, K.J. and X.W.; methodology, K.J.; software, K.J.; validation, K.J.; formal analysis, K.J.; investigation, K.J.; resources, [PERSON] and [PERSON]; data curation, [PERSON], [PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, G.Z., Z.X. and S.Z.; project administration, G.Z., Z.X. and S.Z.; funding acquisition, G.Z., Z.X. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2019 YFA0705000, 2017 YFA00303700), Key R&D Program of Guangdong Province (2018B030329001), Leading-edge technology Program of Jiangsu Natural Science Foundation (BK20192001), and National Natural Science Foundation of China (51890861, 11690031, 11621091, 11627810, 11674169, 91950206). The authors declare no conflict of interest.
## 4 Results and Discussion
### Origin of Overpressure
#### 4.2.2 Bowers Method  
In addition to the logging curve chart, the Bowers method serves as a crucial approach for determining the cause of overpressure [11; 16; 34]. This method relies on effective stress along with sonic or density logging data to construct loading and unloading curves (Figure 6a,b). It is understood that the overpressure response due to disequilibrium compaction is manifested in the loading curve, whereas fluid expansion, pressure transmission, and tectonic compression are represented in the unloading curve [8; 37; 43]. Utilizing vertical effective stress, sonic velocity, and DEN data, loading and unloading curves for Z201, GS17, and WY1H (Figure 6c-h) were constructed to elucidate the causes of overpressure in the Qiongzhusi Formation. The results indicate that the overpressure points in the Qiongzhusi Formation predominantly align with the unloading curve, suggesting that pressure transmission and fluid expansion are the primary drivers of overpressure in this formation. Furthermore, in the plots depicting DEN vs. vertical effective stress, a decrease in vertical effective stress corresponds to a reduction in the density of the shale in the overpressure section, indicating that disequilibrium compaction also plays a role in contributing to the overpressure of the Qiongzhusi Formation.  
Figure 6: Plate of Bowers method and overpressure cause identification of Qiongzhusi Formation: (**a,b**) Plate of Bowers method; (**c,d**) overpressure cause identification of Z201; (**e,f**) overpressure cause identification of GS17; (**g,h**) overpressure cause identification of WY1H.  
Previous studies have demonstrated that shale gas migrates within a shale reservoir [18; 20]. Consequently, there is a contributory effect of overpressure transmission and fluid pressurization within the Qiongzhusi Formation. In summary, the overpressure in the Qiongzhusi Formation results from disequilibrium compaction, fluid expansion, and overpressure transmission.
## 4 Numerical Results
### Relative Displacement between Superstructures  
Relative displacement time history between superstructures, without viscous damper and with viscous damper, is presented in Figure 11. The results with viscous dampers (D300 and D2000) are only presented here because the results with other viscous dampers exhibit a similar response. The dashed red line presents the stopper value. A positive relative displacement of the expansion joint corresponds to opening of the joint gap, whilst a negative relative displacement corresponds to a closing. The viscous damper can greatly reduce the relative displacement between superstructures. Maximum relative displacement obviously decreases as the resistance force of viscous damper increases. However, maximum positive relative displacements present the same value with stopper value. Given that the positive relative displacement (outward) is larger than the stopper value, bounding between roller bearing (B2) and stopper occurs except the case of D2000. The stopper prevents the increase in maximum positive relative displacement. The resistance force of D2000 is the largest; thus, it could obviously reduce positive and negative relative displacements. Figure 12 shows the viscous damper force time history. D2000 presents the largest resistance force that can reduce relative displacement between superstructures. The case of D0 presents no viscous damper. Thus, the relative displacement between superstructures is the largest.  
Figure 11: Relative displacement between superstructures.  
Figure 10: Possibility of deck unseating damage.
Taking into account the issues inherent to visual-based systems, such as LoS, occlusion cases [4] and the problems related to the expensive robot assisting devices, which are cumbersome and require specialized medical staff, the alternative of lower cost, small-size sensor nodes to be used in motion tracking [3, 8] was risen. In sensor-based integrations, such nodes are worn by the patient and gather, in an unobtrusive way, motion, position and physiological state data, without interfering with the patient's normal behavior. This low cost technology is motivated by the several benefits regarding self organization, flexibility and the ability to provide long-term monitoring. The technical challenges of WSN within this application field of rehabilitation are presented in [9].
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 1 Introduction
On the other hand, in order to reduce the end to end delay, we abstract the data forwarding model to M/M/1/S/FCFS (First Come, First Served), and calculate the average waiting time, then present the traffic distribution algorithm based on the greedy idea. The remainder of this paper is organized as follows. Section 2 introduces the basic working principle of RPL, the related work on multipath routing, energy-aware routing, and end-to-end delay. A new adaptive multipath data distribution algorithm based on RPL is put forward in Section 3. Section 4 evaluates the performance of the algorithm on the real test bed, including the network load distribution, network lifetime, energy consumption, end-to-end delay and other network performance at different network sizes and nodes with different cache sizes. Finally, a conclusion together with an outlook is addressed in Section 5.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 7 Experimental Results
### DOA Estimation and Navigating to the BLE Beacon
#### 7.2.1 Experimental Setup 1  
For the first experiment, we considered a two-room space divided by a wall with a combined size of approximately 700 sqft, as shown in the layout diagram in Figure 16. The BLE beacon was placed on a table at one end of a room, and the first measurement was started from the far end of another room (labeled as measurement 1) using a pre-calibrated scanner device.  
At the first location (two scans) the DOA was estimated by our algorithm as \(\hat{\theta}_{1}=194.325^{\circ}\), and after moving in that direction by a few feet, we made another measurement (labeled as measurement 2). The new DOA estimated at this location was \(\hat{\theta}_{2}=175.06^{\circ}\), which pointed us to the right room. By moving a few feet with the scanner pointed at the DOA, we made another measurement followed by two more with \(\hat{\theta}_{3}=255.276^{\circ}\), \(\hat{\theta}_{4}=215.026^{\circ}\), and \(\hat{\theta}_{5}=14.96^{\circ}\) each taking us closer to the BLE beacon. After measurement 5, the RSS values recorded were higher due to the proximity of the scanner device to the BLE beacon, which allowed the scanner application to suggest to the user that the beacon was within reach. In this experimental setup, we were able to navigate towards the BLE beacon, which was located in a different room and roughly 40 feet away, by making 5 measurements (10 scans). The results for this experiment are summarized in Table 1.  
Figure 16: Layout of the room showing the BLE beacon’s location and all measurement locations.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 8 Privacy-Preserving Patient Monitoring
### Addressing Challenges: The Balancing Act  
Privacy-preserving patient monitoring plays a crucial role in ensuring both data security and patient well-being. As discussed in this section, encrypted health data streams strengthen Data Protection Mechanisms, while secure aggregation techniques enhance Privacy-Preserving Data Sharing. By integrating anomaly detection methods with differential privacy, these systems also support User Awareness and Empowerment, allowing patients and healthcare providers to receive critical alerts without unnecessary data exposure.  
This approach maintains a delicate balance, ensuring that healthcare systems provide accurate and meaningful insights while respecting patient privacy. Wearable IoTdevices, though powerful, face challenges like limited resources and the need for real-time responsiveness, making edge computing for privacy preservation a vital component.  
Ultimately, the success of privacy-preserving patient monitoring depends on seamlessly blending encryption, secure data aggregation, and intelligent anomaly detection. This combination not only enables continuous, real-time monitoring but also ensures that privacy is never compromised. Addressing challenges like data accuracy, system efficiency, and processing constraints requires a thoughtful, patient-centered approach that aligns with the evolving landscape of digital healthcare.
## 3 Research Method  
To address the comprehensive monitoring challenges of SDAs during the construction of HSRs in the context of a circular economy, this study proposes a research framework based on 3S techniques (Figure 1). GPS provides spatial positioning and reference information for the research data, serving as the foundation for buffer analysis, spatial overlay, and imagery geolocation in GIS. RS is primarily used for data collection, including data from the Gaofen (GF)-1 satellite, UAVs, and Sentinel-1 satellite. GIS are mainly used for processing RS data and developing models in conjunction with other methods. Essentially, georeferenced RS imagery integrates both RS and GPS techniques--RS provides the structural information of the imagery, while GPS supplies the positional data, supporting the structural framework. GIS then completes the series of processes for data processing, analysis, and decision-making based on the RS imagery. Through the integrated application of these 3S techniques, this study focuses on analyzing disturbance boundaries, soil and water conservation measures, and spoil volume changes within SDAs of HSRs to achieve comprehensive monitoring objectives. Additionally, a case study is presented to validate the application and effectiveness of these 3S techniques in the comprehensive monitoring of SDAs in HSRs.