esa-sceva/satcom-chunk-collection huggingface dataset:
# A High-Precision Energy-Efficient GPS Time-Sync Method for High-Density Seismic Surveys
## 4 Results and Discussion  
High-density seismic array surveys need to be deployed in a large-scale exploration environment and a long exploration period, which places strict requirements on the time synchronization performance between multiple nodes. The time synchronization performance is significantly affected by sensor node localization, the number of nodes, and the exploration time. In high-density seismic array surveys, it is crucial to adopt an energy-efficient data time stamp system and time synchronization method.  
We propose the T3I method for time synchronization based on the GEIWSR-III system in this paper. According to the structure of the time synchronization platform and the energy-efficient requirements, we analyzed the clock architecture of the data acquisition system and different performance indicators of the clock. Finally, we determined the method of using GPS intermittent timing and TCXO continuous timing for time stamping. At the time when a data point is not missed, multi-node time synchronization is achieved by recording and updating GPS time in the SD card.  
The performance evaluation shows that the accuracy of this method only depends on the accuracy of the GPS and a clock period of the data acquisition system. The synchronization stability test proves that the stability of the time synchronization proposed in this paper does not change with time and space during the working time of the instrument, which is in line with the original intention and expectations. Field Measurement I shows that the hammer signals are kept in sync when they are placed in a pile. Field Measurement II shows that the hammer signals between nodes still have strong time synchronization performance when maintaining the same node spacing. Due to the time synchronization accuracy of the proposed method in this paper only depends on the accuracy of GPS and local TCXO, the synchronization performance of nodes has nothing to do with the distance between nodes and the exploration time, so that the synchronization method proposed in this paper can be applied to high-density seismic array surveys.
# A Psychoacoustic-Based Multiple Audio Object Coding Approach via Intra-Object Sparsity
## 2 Proposed Compression Framework
### NPTF Allocation Strategy
We group \(l_{1}^{q},l_{2}^{q},\ldots,l_{NPTF_{q}}^{q}\) into a vector \(\mathbf{I}_{q}\equiv\big{[}l_{1}^{q},l_{2}^{q},\ldots,l_{NPTF_{q}}^{q}\big{]}\), in the meantime, a new vector containing all extracted TF instants \(\mathbf{\hat{S}}_{q}\equiv\big{[}S_{q}\Big{(}n,l_{1}^{q}\Big{)},S_{q}\Big{(}n,l_{ 2}^{q}\Big{)},\ldots,S_{q}\Big{(}n,l_{NPTF_{q}}^{q}\Big{)}\big{]}\) is generated. Finally, both \(\mathbf{I}_{q}\) and \(\mathbf{\hat{S}}_{q}\) should be stored locally and sent into the Downmix Processing module. ```
Input:\(Q\)\(\triangleright\) number of audio objects Input:\(\left\{S_{q}(n,l)\right\}_{q=1}^{Q}\)\(\triangleright\) MDCT coefficients of each audio object Input:\(\left\{f_{i}^{q}\right\}_{i=1}^{L}\)\(\triangleright\) reordered frequency index by psychoacoustic model Input:\(BPA\)\(\triangleright\) lower limit used in dichotomy part Input:\(BPB\)\(\triangleright\) upper limit used in dichotomy part Input:\(BPM\)\(\triangleright\) median used in dichotomy part Output:\(K\)\(\triangleright\) desired NPTF allocation result
1 Set \(\mathbf{K}=\mathcal{O}\)
2 for\(q=1\) to \(Q\)do
3 for\(k=1\) to \(L\)do
4 Calculate IOER function \(f_{\text{IOER}}(k,q)\) using \(\left\{S_{q}(n,l)\right\}_{q=1}^{Q}\) and \(\left\{f_{i}^{q}\right\}_{i=1}^{L}\) in Formula (12). 5 endfor
6
7 Initialize \(BPA=0\), \(BPB=1\), \(BPM=0.5\)-(\(BPA+BPB\)), \(STOP=0.01\) chosen based on a series of informal experimental results. 8 while (\(BPB-BPA>STOP\))do
9 Find the index value corresponding to \(BPM\) value in IOER function (i.e.,\(f_{\text{IOER}}(k_{q},q)\approx BPM\)), denoted by \(k_{q}\). 10 if\(\underset{q=1}{Q}\)\(k_{q}>L\)then
11\(BPB=BPM\),
12\(BPM=[0.5\)-(BPA + BPB)]. 13 else
14\(BPA=BPM\),
15\(BPM=[0.5\)-(BPA + BPB)]. 16 endif
17 endwhile
18\(\mathbf{K}=\left\{k_{q}\right\}_{q=1}^{Q}\)
19 return\(\mathbf{K}\)
```  
**Algorithm 1**NPTF allocation strategy based on bisection method
# Fixed Flexible Spiral Wire Retainers and Unwanted Tooth Movements: A Case Report
###### Abstract  
This case report presents a study of unwanted tooth movements during the retention phase after orthodontic treatment. The early recognition of these unwanted tooth movements is paramount for patients and clinicians to prevent the associated negative consequences. A 21-year-old male presented with aesthetic complaints regarding his upper front teeth. He underwent orthodontic treatment at the age of 9 years and 11 months and finished his treatment 2 years and 11 months later. Flexible spiral wires (FSW) were bonded to the anterior segment of the upper and lower jaws to stabilize the end result. The failure of the fixed retainers had never occurred previously. The diagnostic assessment demonstrated a previously orthodontically treated class I malocclusion with excessive angulation and torque differences in the maxillary anterior segment. To correct the position of the maxillary anterior segment and prevent further misalignment, the patient received orthodontic re-treatment. Thereafter, the result was retained with fixed braided-rectangular-wire (BRW) retainers located at 12-22 and 33-43 and a vacuum-formed retainer (VFR) in the maxilla. The end result appeared to be stable after 28 months of retention. Unwanted tooth movements can occur during the orthodontic retention phase and might result from the use of fixed flexible spiral wire retainers. Follow-up appointments are recommended to monitor the stability and recognize these movements.  
orthodontics; dentistry; adverse effects; stability; fixed retainers; retention; unwanted tooth movements +
Footnote †: journal: Medical Care  
1
Footnote 1: email: {[PERSON], [PERSON], [PERSON], [PERSON].  
may also fail or have adverse effects. Recently, a systematic review was conducted to evaluate the available evidence on the failure of fixed retainers and reported that fixed retainers fail in a range of 7.3% to 50%, according to which detachment at the adhesive-enamel interface was the most commonly reported type of failure [10]. Other observed adverse effects of fixed retainers in the orthodontic literature are detachment at the adhesive-wire interface [11], wire fracture [12], wire untwisting [13], and calculus accumulation [14; 15]. Wire untwisting may lead to unwanted changes in the tooth position and can be associated with the development of gingival recessions [13; 16; 17]. Other causes of unwanted tooth movements can be tongue thrust or personal habits [18; 19]. A clinical case of unwanted tooth movements during the retention phase is presented in this case report. This case showed that the unwanted tooth movement was the direct result of an untwisting FSW, because the maxillary teeth were displaced in a different direction from the original tooth position (Figure 1), which clearly showed that this was not a case of simple relapse. The early recognition of these unwanted tooth movements is paramount for patients and clinicians to prevent the associated negative consequences [20].
# Estimating Significant Wave Height from SAR with Long Integration Times
## 2 Estimating Significant Wave Height from SAR with Long Integration Times
### Pre-Processing  
In order to avoid the interference of artificial targets targets on the sea surface, a sub-block data is chosen from the SAR SLC data at first. The sub-block is selected according to the following two constraints: (1) the sub-block needs to contain ocean waves; and (2) the sub-block does not include other interference information, such as ship, oil slicThen, two operations need to be performed for the sub-block data. One operation is that the sub-block amplitude data is calculated for azimuth cutoff calculation. The other is that a fast Fourier transform (FFT) along the azimuth is performed, obtaining the Range-Doppler domain data, which is used for ocean wave refocusing.
# A Cluster-Based Optimal Computation Offloading Decision Mechanism Using RL in the IIoT Field
## 5 Conclusions  
In this study, we proposed an algorithm that determines the optimal offloading strategy of tasks in terms of the energy consumption of devices and task execution delay using  
Figure 5: Performance comparison according to cluster types when using same-target MECS and D2D device selection methods with the different optimal offloading strategy (the number of devices is 52 and task arrival rate is 0.8): (**a**) task blocking rate; (**b**) task completion rate within delay constraints; (**c**) total energy consumption(_J_); (**d**) throughput.  
Q-learning in an IIoT environment. Because we clustered the nearby devices together, and each CH determined the optimal offloading strategy for the tasks that occurred within its cluster, we effectively reduced the time and space complexity of determining the optimal strategy. The algorithm outperforms the compared algorithms in terms of energy consumption of devices, task completion rate, task blocking rate, and throughput with different cluster types and task arrival rates per device. This is because the load is considered when selecting the MECS and D2D device for task offloading. Future research will focus on the optimal offloading strategy for multi-hop computation offloading.  
Conceptualization, S.K. and Y.L.; Methodology, S.K.; Software, S.K.; Writing-Review Editing, S.K. and Y.L.; Supervision. Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea Government(MSIT) (No.2021R1F1A1047113).  
Not applicable.  
The authors declare no conflict of interest.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 1 Introduction
### Importance of Chaotic Time Series Forecasting
#### 1.2.3 Chaotic Time Series Forecasting in Hydrological Systems  
Hydrological forecasting plays a critical role in reducing future flood impacts, also helps produce more benefits for hydropower production, and enhances water resource management [18]. It is worth noting that predicting the destiny of a river inflow is an essential concern for water quality management [19].
## 4 Results and Discussion
### Performance Comparison with Other Works  
A comparison of the proposed seven-segment digit detection model with other works is presented in Table 8. Overall, the developed model successfully detected seven-segment digits automatically and accurately without requiring manual ROI selection. The model presented in this study achieved higher accuracy (99.28%) and F1-score (96.48%) compared to other works [8; 10; 26]. Additionally, it detected blood pressure measurement images containing shadow reflections and illumination effects by leveraging data variability and augmentation techniques. This demonstrates that the developed model has effectively addressed the challenges faced by [PERSON] et al. [8] and [PERSON] and [PERSON] [10]. Furthermore, the model has been compressed to INT8 precision, resulting in a smaller model size and inference time. Therefore, this model can run efficiently with low latency on edge devices such as mobile phones. Consequently, there is no need to deploy the model on servers like the implementation by [PERSON] et al. in their research [26].  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & **YOLOv8s** & \multicolumn{2}{c}{**RT-DETR**} & \multicolumn{2}{c}{**YOLOv11s**} \\ \cline{2-6}  & **Value** & **Value** & **\(\Delta\)(\%)** & **Value** & **\(\Delta\)(\%)** \\ \hline Accuracy (\%) & 99.70 & 98.27 & \(-1.44\) & 99.78 & +0.08 \\ Precision (\%) & 97.77 & 90.13 & \(-7.81\) & 98.97 & +1.23 \\ Recall (\%) & 98.40 & 98.88 & +0.49 & 98.63 & +0.23 \\ F1-Score (\%) & 98.08 & 94.31 & \(-3.85\) & 98.80 & +0.73 \\ Size (MB) & 21.51 & 63.10 & +193.35 & 18.30 & \(-14.92\) \\ Inference (ms) & 974.50 & 2391.70 & +145.43 & 952.40 & \(-2.27\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation metrics for the training results of three deep learning models.  
Figure 9: Confusion matrix of the test results of 3 models.
## 2 Materials and Methods
### Methodology
#### 2.2.3 Determination of Fire Ignition Points  
Various datasets were employed to evaluate and analyze potential fire ignition points. These datasets include the Digital Elevation Model (DEM) and its related derivatives such as aspect and slope, ERA-5, and METEO data capturing temperature, wind characteristics (velocity and direction), and rainfall, as well as the adjusted Corine Land Use/Land Cover product from the Copernicus Initiative. Additionally, satellite-based spectral indices like NDVI and EVI were incorporated. Selecting appropriate ignition points is a critical aspect of simulating fire scenarios, as it allows for the assessment of potential fire spread patterns and their potential impact on residential areas.  
For this purpose, BEYOND's Machine Learning model of daily fire risk forecasting was utilized [30]. In order to study the worst-case wind scenarios in relation to potential wildfires in the area, classes were created based on wind intensity, and extreme wind intensities were examined in conjunction with prevailing directions. As evident from the wind rose diagram (Figure 3), in this region, north, northwest, and northeast winds dominate, while wind intensities greater than 7 Beaufort often include south and southeast winds. An important observation is the absence of east winds with intensities exceeding 6 Beaufort. For various wildfire spread scenarios and for each ignition point, only those directions that could potentially threaten settlements and points of high interest were considered.
## 2 Spacecraft Modeling and Problem Formulation  
_Spacecraft Attitude Control Modeling_  
This paper considers the rigid spacecraft, whose attitude is represented in terms of unit quaternion [32]. The quaternion \(Q=(q_{0},\ q)\in\mathbb{R}^{4}\) has the scalar \(q_{0}\in\mathbb{R}\) and the vector \(q=[q_{1},\ q_{2},\ q_{3}]^{T}\in\mathbb{R}^{3}\). The quaternion satisfies the unity operational law, e.g., \(q_{0}^{2}+q^{T}q=1\).  
Then, the ACS of a rigid spacecraft, including the unit quaternion-based attitude kinematics and attitude dynamics, can be characterized as [33]:  
\[\begin{cases}\dot{q}_{0}=-\dfrac{1}{2}q^{T}\omega\\ \dot{q}=\dfrac{1}{2}(q^{\times}+q_{0}I_{3})\omega\end{cases} \tag{1}\]  
and  
\[(J_{s}+\Delta I)\dot{\omega}+\omega^{\times}(J_{s}+\Delta I)\omega=T_{c}+T_{d}, \tag{2}\]  
where \(\omega=[\omega_{x},\ \omega_{y},\ \omega_{z}]^{T}\in\mathbb{R}^{3}\) represents the attitude angular velocity; \(T_{c}=[T_{cx},\ T_{cy},\ T_{cz}]^{T}\in\mathbb{R}^{3}\) represents the control torque provided by all the actuators; and \(T_{d}=[T_{dx},\ T_{dy},\ T_{dz}]^{T}\in\mathbb{R}^{3}\) represents the external space disturbance torque. \(D\) denotes torque coefficient matrix, while \(J_{s}\) and \(\Delta I\) denote the nominal inertia moment matrix and its uncertainty, respectively.  
Considering the inertia uncertainty as external disturbance, (2) can thus be transformed into:  
\[J_{s}\dot{\omega}+\omega^{\times}J_{s}\omega=T_{c}+u_{f}+u_{d}, \tag{3}\]  
where \(u_{fb}\) denotes the bias actuator fault, and \(u_{d}=-\Delta I\dot{\omega}+\omega^{\times}\Delta I\omega+T_{d}\) represents the jointed disturbance.  
**Assumption 1**.: _There exist nonnegative constants \(u_{ldmax}\) and \(u_{dmax}\) such that the jointed disturbances \(u_{ld}\) and \(u_{d}\) are respectively bounded by \(\|u_{ld}\|\leq u_{ldmax}\) and \(\|u_{d}\|\leq u_{dmax}\)._  
**Assumption 2** ([27]).: _Nonlinear term \(\omega^{\times}|\omega\) in (2) satisfies:_  
\[\|\omega^{\times}J\omega-\hat{\omega}^{\times}J\hat{\omega}\|\leq\eta\|\omega -\hat{\omega}\| \tag{4}\]  
_with a positive scalar \(\eta\)._
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### Digital Modulation Based Systems  
The digital modulation-based FD systems are developed for the analog SI cancellation and digital SI cancellation. These are divided into two categories such as FD-digital self-interference cancellation based and frequency shift keying-based FD-UWAC systems, as discussed below.
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
## 2 Related Work
[PERSON] considers the heterogeneous maximum bandwidths of nodes in IEEE 802.11 ax-based networks, and OFDMA resource allocation is formulated as a utility maximization problem in terms of the performance of both the uplink OFDMA and the downlink MU-MIMO [25]. Unfortunately, most of them are limited to single-hop networks, and the simultaneous use of OFDMA and MU-MIMO is also assumed.
## 3 Results and Discussion
### Photophysical Evaluation  
The stability of the organic compounds is an important characteristic regarding their practical application. Under normal conditions, oxidation, particularly photooxidation, is the major factor contributing to molecular degradation. This is why the present work also involved studying the photostability of **4a-c** and **6a-c**. For this purpose, solutions of **4a-c** and **6a-c** in methanol, acetonitrile, and DMSO at concentration \(5\times 10^{-5}\) mol/L were subjected to irradiation with UV light in a SUNTEST instrument (AMETEK, Linsengerich-Altenhasslau, Germany). The kinetics of photostability were monitored spectrophotometrically. All compounds under study showed negligible changes in their absorption spectra during irradiation in all three solvents. Figure 2 shows the absorption spectra of compound **6a** in DMSO after irradiation, which are similar to those of the other compounds. The results clearly demonstrate the high stability of the studied benzimidazoles under the experimental conditions used in this work, which is consistent with previous reports for compounds of the same nature [25] and their suitability for future biological applications.
## 2 Materials and Methods
### Instrumentation  
A freeze-dryer (lyophilizer), BK-FD10P, from Biobase (Jinan, China), was utilized to freeze-dry the pulp and peels of OM. An Analysette 3 PRO (Fritsch GmbH, Oberstein, Germany) was employed for the sieving process. A Biobase UCD-150 ultrasonic (US) cell disrupter from Jinan, China, with a 150 W maximum nominal power and a 6 mm diameter probe tip (emitting surface), was utilized for all ultrasonication-assisted extractions. A Heidolph stirring hotplate (Heidolph Instruments GmbH & Co. KG, Schwabach, Germany) was employed to conduct the stirring process. In order to conduct the assays, the samples were heated in an Elmasonic P70H ultrasonic bath (Elma Schmidbauer GmbH, Singen, Germany). A Shimadzu UV-1900i UV/Vis spectrophotometer (Kyoto, Japan) was used for photometric analyses. A Lovibond CAM-System 500 colorimeter (The Tintometer Ltd., Amesbury, UK) was used to measure CIELAB coordinates (_L_*, _a_*, and _b_*) from the extracts. The supernatant was extracted from the liquid samples by centrifuging them using a NEYA 16R (Remi Elektrotechnik Ltd., Palghar, India). Individual polyphenols were quantified through high-performance liquid chromatography (HPLC) by employing a Shimadzu CBM-20A liquid chromatograph and a Shimadzu SPD-M20A diode array detector (Shimadzu Europa GmbH Duisburg, Germany). The compounds were separated through a Phenomenex Luna C18(2) column (Torrance, CA, USA) maintained at 40 \({}^{\circ}\)C (100 A, 5 \(\upmu\)m, and 4.6 mm \(\times\) 250 mm).
## 4 Shield Tunnel Load-Bearing Performance Analysis by Full-Scale Test
### Measurement Point  
Based on the analysis in Section 3, 400 mm is selected as the thickness of the test segment. In order to study the load-bearing performance of the large inner diameter shield tunnel under excavation unloading conditions, the measurement content of the full-scale test includes the tunnel convergence deformation, internal force, longitudinal joint opening, circumferential joint dislocation, and curved bolt stress.  
Figure 19: Horizontal loading test method for the shield tunnel: (**a**) distribution of loading point, (**b**) horizontal loading beam, and (**c**) horizontal loading instrument photo.  
Figure 20: Vertical load device on the tunnel: (**a**) distribution of loading points, (**b**) loading beam, and (**c**) device photo in the test.  
In the test, the middle ring of the tunnel is selected as the displacement observation object. The displacement sensor is fixed on the support rod, and the support rod is fixed on the center column. A total of eight sensors (D1\(\sim\)D8) are used to analyze the variation in the tunnel displacement, as shown in Figure 21. A total of 11 sections (S1\(\sim\)S11) are set as the measurement areas for the internal force testing of the shield tunnel. The angle distribution of these sections is shown in Figure 22. The stress of the segment rebar is measured by vibrating wire stress gauges, and the strain of the segment concrete is measured by resistance strain gauges.  
The bolts stress in the middle ring of the tunnel is measured by resistance strain gauges, and the grooves are made on the inner and outer sides of the screw before installing the strain gauge, as shown in Figure 23. There are a total of 12 measuring points for the longitudinal seam bolt and 8 measuring points for the circumferential seam bolt. Both the longitudinal seam opening and the circumferential seam dislocation are measured by rod displacement sensors, as shown in Figures 24 and 25. There are a total of 12 points to measure the longitudinal seam opening and 4 points to measure the circumferential seam dislocation.  
Figure 21: Arrangement of displacement measurement for shield tunnel structure: (**a**) distribution of displacement measurement points, (**b**) support rod, and (**c**) displacement sensor.  
Figure 22: Arrangement of interface measurement for shield tunnel structure: (**a**) distribution of interforce measurement points, (**b**) vibrating wire stress gauge, and (**c**) resistance strain gauge.
## 5 Conclusions
Researchers have attempted experiments for precipitation enhancement using various equipment in addition to atmospheric aircraft, such as helicopters, rockets, and drones [22; 27]. Precipitation enhancement technology may represent the only method for increasing water supply in its natural state and developing reserve water resources in situations where natural water resources are limited [63]. Accordingly, continuous precipitation experiments and efforts for objective and systematic technology development are required. In addition, identifying the occurrence and distribution of seedable clouds can enhance the seeding effect [2; 30]. Continuous precipitation enhancement experiments and related studies can be conducted by identifying seedable clouds, observing cloud characteristics in extensive areas via satellite, establishing a plan for precipitation enhancement experiments, and exploring areas of interest with high seedable cloud occurrence frequencies. Meteorological satellite data is essential to perform these studies, and satellites such as GK-2A and Himawari-8 that have a high spatiotemporal resolution can be used to analyze cloud characteristics and examine their distribution more accurately. Conceptualization, B.-Y.K. and J.W.C. ; methodology, B.-Y.K., J.W.C., and A.-R.K.; software, B.-Y.K. ; validation, B.-Y.K. ; investigation, B.-Y.K., W.J., and A.-R.K.; writing--original draft preparation, B.-Y.K. ; writing--review and editing, B.-Y.K., J.W.C., A.-R.K., W.J., and J.-C.H. ; visualization, B.-Y.K. ; supervision, J.W.C., project administration, J.-C.H. ; All authors have read and agreed to the published version of the manuscript. This research was funded by the Korea Meteorological Administration (KMA), grant number KMA2018-00222. This work was funded by the Korea Meteorological Administration Research and Development Program \"Development of Application Technology on Atmospheric Research Aircraft\" under Grant (KMA2018-00222). The authors declare no conflict of interest.