esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### OC and EC Fractions  
The average mass concentration of carbonaceous components (OC and EC) in PM\({}_{1}\), PM\({}_{2.5}\) and PM\({}_{10}\) were 11.37 and 5.87, 19.24 and 7.36 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\), and 27.04 and 10.27 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\) during the whole sampling days, respectively (Figure 3). For the four PEs, both the mean OC and EC concentrations of PM\({}_{1}\), PM\({}_{2.5}\) and PM\({}_{10}\) in PE3 were the lowest, with values in range of 6.59 to 12.39 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\) and 3.60-6.41 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\), respectively. While both OC and EC values were comparable in other three PEs, with values approximately double that in PE3 (Table 2). The proportion of TAC (TAC = OM + EC, OM (organic matter) = 1.6 \(\times\) OC [39]) in PM\({}_{1}\), PM\({}_{2.5}\) and PM\({}_{10}\) ranged from 25.4% to 59.7%, 17.2% to 58.1%, and 11.1% to 38.7%, with an average proportion of 43%, 36% and 26%, respectively. The decreasing trend with particle size increasement was consistent to that in previous study, which reported TAC accounted for 59.2% of PM\({}_{0.1}\), while it was 44.6% and 43.8% of PM\({}_{2.5}\) and PM\({}_{10}\) in Vietnam [40]. The variation in size-dependent OC and EC concentration in these four PEs are discussed in the following three sections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pollution Episode** & **1** & **2** & **3** & **4** \\ \hline Period & 12/20–12/24 & 12/24–1/3 & 1/3–1/8 & 1/10–1/17 \\ Duration time (day) & 5 & 11 & 6 & \textgreater{}8 \\ Average growth rate of PM\({}_{1}\) (\(\mathrm{\SIUnitSymbolMicro m^{-3}}\)-day\({}^{-1}\)) & 25.0 & 25.2 & 11.6 & 2.1 \\ Average growth rate of PM\({}_{2.5}\) (\(\mathrm{\SIUnitSymbolMicro m^{-3}}\)-day\({}^{-1}\)) & 42.3 & 31.6 & 17.7 & 26.0 \\ Average growth rate of PM\({}_{10}\) (\(\mathrm{\SIUnitSymbolMicro m^{-3}}\)-day\({}^{-1}\)) & 56.8 & 34.8 & 10.2 & 14.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic of the four pollution episodes.
## 4 Receiver-Triggered Handshake Protocol
### Receiver-Triggered Handshake Protocol
#### 4.2.1 Communication between a Victim Terminal and a Carrier (or Shelter) Terminal  
To incorporate (2) and (6) in Section 4.1, a victim terminal that sent an \(\mathrm{R_{M}}\) and has received an \(\mathrm{A_{m}}\) from a carrier terminal does not send any more bundles. If it has not received an \(\mathrm{A_{M}}\), it attempts to send an \(\mathrm{R_{M}}\) to a carrier terminal to which it is next connected. The algorithm for communication between a victim terminal and a carrier terminal is as follows:  
Step 1: Carrier terminal, \(\mathrm{C_{i}}\), moves around the disaster area while transmitting \(\mathrm{S_{M}}\) every T seconds.  
Step 2: Victim terminal, V, receives an \(\mathrm{S_{M}}\) from \(\mathrm{C_{i}}\), and sends back an \(\mathrm{R_{M}}\) to \(\mathrm{C_{i}}\).  
Step 3: \(\mathrm{C_{i}}\) receives the \(\mathrm{R_{M}}\) from V and sends back an \(\mathrm{A_{m}}\).  
Step 4: 4-1: If V receives the \(\mathrm{A_{m}}\), go to Step 5.  
4-2: If V receives no \(\mathrm{A_{m}}\), go back to Step 1.  
Step 5: V does not send any more \(\mathrm{R_{M}}\).  
This communication sequence is shown in Figure 4.
# Smooth Fractional Order Sliding Mode Controller for Spherical Robots with Input Saturation
## 4 Fractional Order Hierarchical Sliding Mode Controller
### A Novel Fractional PI\({}^{\lambda}\)D\({}^{\mu}\) Sliding Mode Controller  
Equation (18) uses the fractional differential term in the sliding surface; this term leads to faster response and lower overshoot. However, it may lead to a high controller output when the desired object varies in impulse form due to the explosion of the derivation pertaining to the desired speed, \(x_{2d}\). The large output of the controller will cause the actuator to saturate rapidly. It also makes the shell subsystem tracking the desired speed become wave-like, with an opposite speed response at the start stage. Therefore, inspired by backstepping method m [49], this study applies a filter to resolve this problem. Below, the aforementioned results are extended to deal with the input saturation problems of the spherical robot system and to achieve smoother dynamic response.  
Let \(x_{2d}\) pass the filter \(\overline{x}_{2d}\). Then, the new desired state is redefined in Equation (31)  
\[\dot{\varepsilon}\dot{\overline{x}}_{2d}+\overline{x}_{2d}=x_{2d} \tag{31}\]  
where \(\varepsilon\) is a designed positive constant, and the initial value of the filter is \(\overline{x}_{2d}=0\). Then, the error \(e_{2}\) may be redefined as:  
\[e_{2}=x_{2}-\overline{x}_{2d} \tag{32}\]  
The new equivalent control law of each subsystem is updated as:  
\[\dot{\tau}_{1}=-{b_{1}}^{-1}(\lambda_{11}D^{\lambda}e_{2}+\lambda_{12}D^{\mu+ 1}e_{2}+f_{1}+g_{1}\tau_{f}-\dot{\overline{x}}_{2d}) \tag{33}\]  
\[\dot{\tau}_{2}=-{b_{2}}^{-1}(\lambda_{2}D^{\lambda}e_{4}+\dot{x}_{4d}-f_{2}-g_ {2}\tau_{f}) \tag{34}\]  
In order to handle the input saturation, an auxiliary system was designed. The total control law needs to include some terms of the auxiliary system. Hence, the new total control law \(\dot{\tau}\) can be updated as:  
\[\dot{\tau}=\frac{-\alpha\text{sign}(S)-\beta S+\eta b_{1}\dot{\tau}_{1}+\xi b_ {2}\dot{\tau}_{2}-\hat{S}}{\eta b_{1}+\xi b_{2}} \tag{35}\]  
\[\dot{\hat{S}}=\left\{\begin{array}{l}-\left[\left(S(\eta b_{1}+\xi b_{2}) \Delta\tau+\frac{1}{2}\Delta\tau^{2}\right)/\hat{S}\right]-\chi\hat{S}+\Delta \tau_{f}\\ 0,\end{array}\right|\hat{S}\right|\geq\rho\\ \left|\hat{S}\right|<\rho \tag{36}\]  
where \(\chi\) is a positive constant, and \(\rho\) is a positive constant whose value is small.
## 1 Introduction  
Extracellular vesicles (EVs) have emerged as a major research focus due to their critical role in inter-organ communication in healthy and pathological stages [1]. Amongextracellular vesicles, exosomes, which range from 30 to 200 nm in size and are composed of phospholipids and proteins, play essential roles in cellular signaling and recognition processes [2].  
Exosomes have been shown to contain a diverse array of cargo molecules, which vary according to their tissue of origin [3]. These cargo molecules include proteins, lipids, metabolites, and nucleic acids, with a significant presence of microRNAs (miRNAs). Once secreted and, depending on the uptake mechanism, exosomes can influence target tissues either by transferring their cargo molecules or by interactions between exosomal membrane proteins and receptors on the plasma membrane of target cells [4].  
Exosomes can be captured and internalized by distant tissues through fusion with the plasma membrane; these particles are vital in transporting molecules between tissues using an endocrine mechanism. Understanding the mechanisms of exosome-mediated signaling is increasingly critical in metabolic diseases, including type 2 diabetes mellitus, cardiovascular conditions, and obesity. Notably, the interactions of miRNA-bearing exosomes with metabolically active tissues, such as skeletal muscle, adipose tissue, small intestine, liver, and pancreatic beta cells [5], are particularly significant.  
Pancreatic beta cells play a central role in metabolic regulation, primarily by secreting insulin in response to glucose levels. Once released, insulin binds to receptors in tissues such as the liver and skeletal muscle, modulating metabolic activity. Notwithstanding, exomes targeting beta cells promote potential mechanisms parallel to insulin signaling [6]; evidence indicates the presence of broader mechanisms possibly extending to other metabolically active tissues. Adequate nutritional therapy, based on energy balance and supplementation with active compounds capable of improving physiology, could modify the pattern of exosomal secretion in tissues with high metabolic activity [7]. These mechanisms could be a basis for developing new therapies based on their biological responses by modulating metabolic regulatory proteins or optimizing controlled release systems.
## 5 Simulation Experimental Results and Discussions
### Comprehensive Balance Method Analysis
#### 5.4.1. Hexahedral Mesh  
Factor C: According to the analysis of \(R\)-values, factor C is the most important factor for the three indicators--compliance, maximum stress, and computation time--but it is a less significant factor for the weight reduction ratio indicator. Therefore, the main factors should be given priority when determining the optimal level. From the \(k\)-value analysis, the values of \(k_{1}\), \(k_{2}\), and \(k_{3}\) of factor C for compliance and the weight loss ratio are not substantially different, whereas, for maximum stress, the values of \(k_{2}\) and \(k_{3}\) demonstrate a slight difference. From the analysis of \(p\)-values, factor C exhibits a very substantial impact  
Figure 9: Trend graph of the conducted orthogonal experiments: (**a**) hexahedral mesh and (**b**) tetrahedral mesh.  
on computation time. Considering the importance of factor C for different indices and the need to reduce computational costs, C3 is selected.  
Factor D: According to the analysis of \(R\)-values, factor D represents a minor factor for compliance, the weight loss ratio, and computation time, but it is a secondary factor for maximum stress. In determining the optimal level, priority should be given to the maximum stress indicator. From the analysis of \(k\)-values, the \(k_{2}\) value is selected for all indicators. From the analysis of \(p\)-values, factor D possesses no significant impact on any of the four indicators. Based on the majority principle and the importance of factor D for different indices, D2 is selected.  
Factor E: According to the analysis of R-values, factor E represents the main factor for the weight reduction ratio, secondary for compliance and computation time, and a minor factor for maximum stress. From the analysis of \(k\)-values, the values of \(k_{1}\), \(k_{2}\), and \(k_{3}\) for factor E are not substantially different for compliance. From the analysis of \(p\)-values, factor E has no significant effect on any of the four indicators. Based on the importance of factor E for different indicators, E2 is selected.  
Based on the above analysis, the optimal solution for the hexahedral mesh is obtained as C3D2E2, which corresponds to a mesh size of 2.5 mm, a filter radius of 4.8 mm, and a penalty factor of 3.5. This optimal solution corresponds to test 9 in Table 10, and the finite element stress map is presented in Figure 10.
## 4 Materials, Exergy Storage Tool, and Methodology
### Exergy Storage Terminology--The Gas Storage Experience  
The technology behind current and future storages for electrical energy based on compressed air, H\({}_{2}\), or SNG storages is derived largely from tried and tested storage technologies developed for the storage of natural gas [4]. A terminology has emerged to define operations and refer to the volumes of gas in an underground gas storage facility, which we adopt here when defining the exergy stored and explain below.  
Underground gas storages generally operate by compressing the storage gas during injection and decompressing the gas again during withdrawal. The total gas storage capacity or volume is the maximum volume of natural gas that can be stored at the storage facility. This is governed by several physical factors such as the reservoir volume, engineering, and operational procedures including minimum and maximum pressure ranges and injection rates, which are determined from rock mechanical studies. The total storage volume comprises two elements:  
* Working gas' volume, which represents the available gas that can be used between the maximum and the minimum operating storage pressures
* Cushion gas' volume, representing that below minimum operating pressure that is not available and which must remain permanently in the storage to provide the required minimum pressure to maintain the geomechanical stability of the storage. In the case of porous rock storage, it also provides some of the drive, but it is irretrievable, being effectively lost in the porosity.  
The working gas volume represents the'static', one-fill gas capacity and does not reflect multiple filling cycles. Thus, it is representative of a seasonal storage, similar to most traditional aquifer and depleted field storages. Of course, gas storages may be cycled many times during a year, which gives rise to what is described as a 'dynamic working gas volume' [39], which is greater than the static one-fill working gas volume.  
Thus, exergy storage estimates are here referred to as the 'working exergy' (that available for work) and the 'cushion exergy' (that portion that must remain in the salt cavern/storage). The exergy tool was set up to calculate the static 'working exergy' (available) volume (see below). After introducing the static one fill 'working exergy' storage, we describe how, through a series of filters, attempts are made to derive realistic static 'working exergy' storage estimates from the total theoretical storage calculated (Figures 2 and 3a,b). These are based on cavern sizes and percentages of the total number of caverns, including that based upon the number of gas storage caverns in any particular basin (Figures 4-8).  
However, as with gas storage caverns, the static 'working exergy' storage capacity is increased by multiple cavern-filling cycles. Therefore, also described and calculated are 'dynamic working exergy storage' capacity estimates, which are based upon multiple cavern cycles per year. The yearly cycle numbers are derived from different injection and withdrawal rates, which are informed by both CAES and UGS experience (S2, S3).
## 4 Conclusions  
For the application of CO\({}_{2}\)-WAG used in non-homogeneous, low-permeability reservoirs and the machine learning-assisted prediction method of CO\({}_{2}\)-WAG, some conclusions can be drawn from this work as follows:  
(1) Compared to water flooding and continuous CO\({}_{2}\) flooding, CO\({}_{2}\)-WAG can effectively improve oil recovery. In addition, compared to CO\({}_{2}\) flooding, CO\({}_{2}\)-WAG can reduce the CO\({}_{2}\) production rate, which is conducive to the storage of CO\({}_{2}\) for the reduction of greenhouse gas emissions.  
(2) The CO\({}_{2}\)-WAG cycle time has a slight influence on oil production. Both CO\({}_{2}\) storage amount and CO\({}_{2}\) storage efficiency decrease with the cycle. On the premise that the reservoir formation is not fractured, the oil production increases but CO\({}_{2}\) storage efficiency decreases with the fluid injection rate. For low-pressure reservoirs, the oil production increases with the water-gas ratio, but CO\({}_{2}\) storage efficiency decreases with the water-gas ratio.  
(3) The random forest regression algorithm in machine learning has better fitting accuracy in predicting the results of CO\({}_{2}\)-WAG development. Therefore, it can be used to predict the oil production and CO\({}_{2}\) storage results under different combinations of injection parameters.  
(4) Compared to numerical simulations, using machine learning algorithms to predict results avoids the need to build models and run data files, which will save a lot of time for subsequent parameter optimization.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app122110958/s1](https://www.mdpi.com/article/10.3390/app122110958/s1)]([https://www.mdpi.com/article/10.3390/app122110958/s1](https://www.mdpi.com/article/10.3390/app122110958/s1)).  
Conceptualization, H.L.; Data curation, C.G.; Formal analysis, C.G.; Funding acquisition, H.L., J.X. and S.L.; Investigation, C.G.; Methodology, H.L. and C.G.; Project administration, H.L. and S.L.; Software, C.G.; Supervision, S.L.; Writing--original draft, H.L. and C.G.; Writing--review & editing, H.L., S.L., J.X. and G.I All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [52074337, 51906256, 52174052, 51904323], the Shandong Provincial Natural Science Foundation [ZR2021]Q18 and Fundamental Research Funds for the Central Universities [21 CX06033A].  
Not applicable.  
All data is involved in the text.  
This research is supported by the National Natural Science Foundation of China, the Shandong Provincial Natural Science Foundation and Fundamental Research Funds for the Central Universities, which are gratefully acknowledged.  
The authors declare no conflict of interest.
###### Abstract
Regarding intertidal bedforms, visual image analysis raised the assumption of bedform movement, therefore the positions of bedform structures in the upper flats of Norderney were further analyzed using the extensive time series of satellite images available for this study. For this purpose, the water-covered bedform troughs were extracted from the TerraSAR-X images using an automated method developed by Ref. [19]. The studies presented here were part of the German research project WIMO (Scientific monitoring concepts for the German Bight) with subproject TP 1.4 (Application of high resolution SAR-data (TerraSar-X) for monitoring of eulittoral surface structures and habitats). In addition, data from the DeMarine-1 and DeMarine-2 projects with the subprojects TP4 (Integration of Optical and SAR Earth Observation Data and in situ Data into Wadden Sea Monitoring) and SAMOWatt (Satellite data for Monitoring in the Wadden Sea) have been included in the investigations.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 5 Discussion  
The CB-Gardner algorithm proposed in this paper opens up several avenues for future research and discussion, as described below.  
(1) In this paper, the first sampling point was set as the exact first symbol instant. However, in a practical communication system, it is challenging to locate the first symbol instant with such precision. One potential solution is to use pilot signals to detect the arrival time of the signal;  
(2) CB-Gardner requires knowledge of the phase of each symbol instant to locate the error-detection point. However, in a multipath environment, the phase of the symbol instant may be random, making it impossible for the algorithm to locate the error-detection point. Although we have explored alternative methods to estimate phase, they also eliminate the phase variation caused by Doppler, leading to error-detection failure. Therefore, CB-Gardner is currently unsuitable for multipath environments, especially complex underwater acoustic environments, but it may be useful in wireless communication with fewer channel taps;  
(3) In our simulations, CB-Gardner had a high BER for QPSK modulated signals. Several factors may have contributed to this result. First, the phase variations in QPSK are more diverse, resulting in a waveform at the symbol instant that is less like a cosine waveform. Second, the error detection Equation (12) may fail, causing the localized symbol instant to drift. Third, the constellations of the QPSK signal are relatively dense and require a higher SNR. These factors can increase the self-jitter of error detection, resulting in a higher BER.
## 1 Introduction
[15] proposed an adaptive fault-tolerant control based on event-triggering. The control scheme can still guarantee the semi-global uniform boundedness of the system tracking error in the case of actuator faults. [PERSON] et al. [16] studied the position-constrained ship dynamic positioning output feedback control of thruster system dynamics, using a fixed-time extended state observer to compensate for the total disturbance composed of unknown disturbances and unknown dynamics. In addition, a finite-time auxiliary power system was used to handle the input saturation problem of thrusters. Finally, a robust control term was introduced to address the errors generated in controller design. The verification results indicated that the designed control strategy can enable the ship to reach and maintain the required position and heading and continuously stay within the specified operating area under the condition that all signals from the closed-loop control system are consistent and ultimately bounded. Inspired by the above studies, this article investigates the dynamic positioning control problem of ships with unknown system dynamics, thruster faults, and external ocean disturbances. Firstly, the ship dynamic positioning control model is established considering the thruster faults. A fixed-time, non-singular sliding mode surface is designed with the sliding mode variable structure control technology and the fixed-time stability theory. On this basis, a fixed-time adaptive fault-tolerant control scheme is proposed, which can make the system stable in a fixed time. The main contributions of this article are threefold:  
1. By creating a novel integral sliding mode surface, the fixed-time integral sliding mode controller naturally eliminates the singularity issue, overcoming a significant limitation of traditional fixed-time terminal sliding mode control. 2. By adopting the parameter adaptive control to estimate the square of the upper bound of the total uncertainty, the designed controller is smooth with no obvious chattering phenomenon and does not require any information on the upper bound of the total uncertainty. 3. The practical fixed-time stability of the closed-loop system is proven. The dynamic positioning errors under the designed controller can be reduced to about zero in the minor fields within a fixed time, even when subject to model uncertainties, external disturbances, and actuator faults. The remainder of this work is arranged as follows: Section 2 shows the problem formulation and preliminaries. Section 3 introduces the main results. Section 4 gives the findings of the numerical simulation. Finally, Section 5 draws conclusions and recommendations for future research.