esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical accelerometers for Detecting Low-Frequency Micro-Vibrations
## 5 Optical Phase Modulation
Extending the length of the resonant cavity of FPI by a \(45^{\circ}\) reflector can also enhance the sensitivity of the accelerometer. This method allows the sensitivity of the accelerometer to be increased without affecting its resonant frequency [61; 62]. Efforts must also be exerted to improve other properties of FPI optical accelerometers apart from their sensitivity. Multi-axis vibration detection is one research direction. [PERSON] et al. designed a 3D optical accelerometer that was implemented by orthogonally mounting three 1D FPI optical accelerometers [63]. This accelerometer has a sensitivity of \(48\)\(\mathrm{ng/Hz^{1/2}}\) and a bandwidth of \(160\) Hz. Micro-opto-electro-mechanical technology has developed rapidly in recent years, and phase-modulated accelerometers based on this technology have also gained the attention of researchers. In particular, grating interferometric accelerometers are developing rapidly. The core sensing unit of the grating interferometer (GI)-based accelerometer is the cavity of the grating interferometer. It is a micro-cavity composed of a movable diffraction grating. The cavity length is varied by modulating the optical range difference to change the intensity of the emitted light. However, the intensity modulation is susceptible to interference from low-frequency fluctuations in light intensity and changes in environmental conditions. To improve the signal quality, [PERSON] et al. proposed a grating interferometric accelerometer based on the demodulation method of phase and intensity dual modulation [64; 65]. The optical accelerometer uses a micromechanical silicon structure based on a double-mask process as the elastic component and a photodetector with a sensitivity of \(0.45\) A/W for sensing. The sensitivity of the accelerometer has reached \(619\) V/g. A similar study was done by [PERSON] et al. with good results [66; 67; 68; 69; 70], and the specific parameters are shown in Figure 11:0  
In summary, optical accelerometers based on optical phase modulation exhibit the advantages of high sensitivity, compact structure, and simple optical path. The theoretical performance of optical accelerometers with optical phase modulation can be extremely high, and the corresponding acceleration measurement sensitivity can reach the sub-\(\upmu\)g level. However, the performance of this type of optical accelerometer is directly related to the machining and assembly accuracy of its structure, and thus, such accelerometers require high micromachining accuracy and high cost but exhibit poor repeatability. They are suitable for high-precision military navigation or precision vibration isolation systems for precision instruments that are not cost-sensitive. The parameters of the structures and the optical accelerometer based on optical phase modulation (i.e., resonant frequency, working frequency band, and sensitivity) are provided in Figure 11:1  
Figure 11:2 Schematic of a V-beam FPI-based accelerometer. Reprinted with permission from Ref. [59]. Copyright 2014 Elsevier.
## 7 Conclusions  
In this paper, the relationships between the achievable sum-rate of IA and quantization bits under different quantization strategies are analyzed, and the theoretical closed-form formulas of the average IA sum-rates with RVQ-based CSI feedback, SQ-based CSI feedback, and mixed CSI/DI feedback are separately derived. The simulation results have confirmed the validness of the approximated sum-rate results, whose normalized approximation errors are less than 5% in general IA scenarios. The proposed sum-rate analysis method is more accurate than those rate loss-based performance analyses and obtains concise enough sum-rate expressions, achieving a trade-off between accuracy and complexity. The theoretical and simulation results demonstrate that the popular VQ-based feedback may be more applicable to IA scenarios with fewer receiving antennas and low SNR regimes, while the mixed SQ-based CSI and VQ-based DI feedback strategy can have a wider application range. The proposed IA performance analysis method can act as a guidance to determine the optimal feedback overhead in practical channel conditions. In the future, we may extend the analysis results by combining neutrosophic statistics in several directions, such as considering a more complicated neutrosophic Rayleigh model in the wireless channel [32], and reducing the approximation error by analyzing the distribution characteristics of different kinds of quantization errors [33; 34; 35].  
Conceptualization and methodology, L.S.; formal analysis, validation, and writing--original draft preparation, F.L.; writing--review and editing, L.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61901388) and the Guangdong Basic and Applied Basic Research Foundation (2020A1515110757).  
Not applicable.  
The authors would like to thank the editor-in-chief, the editor and the anonymous reviewers for their valuable reviews.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IA & Interference alignment \\ MIMO & Multi-input multi-output \\ DoF & Degree of freedom \\ IC & Interference channel \\ CSI & Channel state information \\ DI & Decoding information \\ VQ & Vector quantization \\ SQ & Scalar quantization \\ RVQ & Random vector quantization \\ SNR & Signal-to-noise ratio \\ LFB & Limited feedback \\ CDF & Cumulative distribution function \\ PDF & Probability distribution function \\ FLOP & Floating-point operation \\ \end{tabular}
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 4 Multi-Modal Data Collection Analysis
### Problem Analysis  
The primary goal of the mobile data collection in this paper is to minimize both the weighted average AoI and the energy consumption. The factors that influence the AoI include the AUV trajectory, the data transfer time and the importance of the data. Consequently, to minimize the weighted average AoI, the optimization problem can be expressed as  
\[\min \frac{1}{N}\sum_{i=1}^{N}A_{i}+\beta\sum_{i=1}^{N}e_{i},\] (9) s.t. \[x_{i,t}^{ac}\in\{0,1\}, \tag{9a}\] \[x_{i,t}^{op}\in\{0,1\},\] (9b) \[\sum_{t=1}^{T}x_{n,t}^{ac}+x_{n,t}^{op}=1,\;\forall n\in N\] (9c) \[\text{E}_{AUV}<energy_{AUV},\] (9d) \[P_{0}=(x_{0},y_{0}), \tag{9e}\]  
where \(A_{i}\) denotes the final result of AoI when the data from \(c_{i}\) reach the sink node, \(e_{i}\) denotes the energy consumption by \(c_{i}\) during data collection, and \(x_{i,t}^{ac}=1\) indicates that the AUV has reached the acoustic communication range of CH \(c_{i}\) and receives data through UAC. Otherwise, \(x_{i,t}^{ac}=0\) holds. Similarly, \(x_{i,t}^{op}\) represents an optical communication indicator. \(E_{AUV}\) is the energy consumption of the AUV in data collection, and \(energy_{AUV}\) indicates the initial total energy of the AUV. The constraint in (9c) is used to ensure that each node can select just one communication option during data collection. The constraint in (9d) is to guarantee that the AUV cannot consume all of its energy. Finally, the constraint in (9e) is to determine the initial position of AUV.  
The optimization problem (9) is a non-linear integer programming problem, which is intractable due to the presence of binary variables and non-convex objective function. In the following section, we model this as a Markov decision process (MDP) to be solved by leveraging the DRL approach.
## 1 Introduction
Not only does this bandwidth range have detrimental effects on the SNR, but it also drives the size and price of the system. ECOPS, likewise, employs two femtosecond lasers. In contrast to ASOPS, however, only one laser has a fixed repetition rate. The repetition rate of the second laser is tunable, and is modulated around that of the first laser. The net effect is equal to a mechanical delay, just significantly faster, with data acquisition rates well in the kHz range [23]. Scan range and scanning speed can be selected according to the requirement of the experiment. [PERSON] et al. demonstrated that ECOPS achieved a better dynamic range than ASOPS at the same measurement speed, or vice versa, a drastically (\(\sim\)50x) higher measurement speed for the same dynamic range [22]. Recently, the same group applied an ECOPS system to three-dimensional terahertz imaging [26]. In this work, we demonstrate the usage of ECOPS for terahertz-based thickness measurements at unprecedented speed. In the following sections of this paper, we first describe the ECOPS setup and its main components. We then discuss the system performance in terms of time-domain dynamic range, signal-to-noise ratio and terahertz bandwidth, including the effect of pulse-trace averaging and the interplay between measurement speed and scan range. Finally, we apply our ECOPS instrument to thickness measurements of a piece of a silicon wafer, and compare the results to values obtained with a conventional TDS system and with a mechanical micrometer gauge. Simulating the scenario of an industrial production line, we examine the aforementioned wafer with the sample itself moving at 700 mm/s.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Precision
#### 3.1.4 SWIR2 Band  
Tables 8-10 provide the results for the SWIR2 band. For this band, there is repeatability for individual systems and bright targets from \(\pm\)0.01 to 0.02, while for dark targets the repeatability is from \(\pm\)0.001 to 0.002. Across all teams, the repeatability is the same as for individuals. That is, it does not matter if only one instrument/operator combination or multiple instrument/operators are available because the repeatability is essentially the same. Unfortunately, the UAS surface reflectance measurements in the SWIR2 band experienced difficulties. The small human-made targets only provided a few pixels resulting in large uncertainties in those measurement extractions (indicated by the asterisks in Table 10). UAS hyperspectral data were acquired using two separate optical systems. Due to the SWIR system's larger GSD, nearly twice that of the VNIR system, extracting SWIR channel data from UAS imagery was difficult and unreliable. Therefore, UAS SWIR channel data for the mystery panels were omitted from the analysis. Interestingly, the Permaflect targets were somewhat larger and reflectance estimates from them were quite reliable. In fact, they were quite similar to the ASD estimates.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{6}{|c|}{UAS Reportedly– Percent Band} \\ \hline \multicolumn{6}{|c|}{High Band Foreability} \\ \hline  & Permaflect 55\% & RIT 30\% & Asphalt & Mystery & Average & Std. \\  & & & & Gray & & Dev. \\ \hline Reflectance & 0.52 & 0.31 & 0.14 & 0.46 & & \\ \hline AM Difference & 0.019 & 0.008 & 0.015 & 0.058 & 0.021 & 0.019 \\ \hline Noon Difference & 0.010 & 0.006 & 0.004 & 0.046 & & \\ \hline \multicolumn{6}{|c|}{Full Forest Foreability} \\ \hline  & Permaflect 5\% & RIT 2\% & Alfafa & Mystery & Average & Std. \\  & & & & Black & & Dev. \\ \hline Reflectance & 0.06 & 0.02 & 0.06 & 0.04 & & \\ \hline AM Difference & 0.001 & 0.003 & 0.008 & 0.017 & 0.005 & 0.005 \\ \hline Noon Difference & 0.001 & 0.001 & 0.008 & 0.003 & & \\ \hline \end{tabular}
\end{table}
Table 4: Green band precision results for UAS.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \multicolumn{6}{|c|}{ASD Representative Across All Terms-NIN End} \\ \hline \hline  & & \multicolumn{3}{c}{Mean} & \multicolumn{3}{c}{Mean} & \multicolumn{3}{c}{RIT} & \multicolumn{3}{c}{RIT} & \multicolumn{3}{c}{Mystery} & \multicolumn{3}{c}{Gray} \\ \hline \multicolumn{6}{|c|}{Permaflect 55\%} & \multicolumn{3}{c}{RIT} & \multicolumn{3}{c}{RIT} & \multicolumn{3}{c}{RIT} & \multicolumn{3}{c}{RIT} & \multicolumn{3}{c}{Mystery} & \multicolumn{3}{c}{Gray} \\ \hline \multicolumn{6}{|c|}{Reflectance} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{Std. Dev.} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{Std. Dev.} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{Std. Dev.} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{Std. Dev.} \\ \hline All & AM & 0.547 & 0.009 & 0.390 & 0.013 & 0.542 & 0.019 & 0.502 & 0.017 \\ \cline{
## 3 Predefined-Time Backstepping Control  
Define the virtual desired trajectory \(\eta_{g}\) as  
\[\eta_{g}=\eta_{1d}+J_{1}M \tag{34}\]  
where \(M=[\rho_{1},\rho_{2},\rho_{3}]^{T}\), \(\rho_{1},\rho_{2},\rho_{3}\) is the positive parameter to be designed d. The virtual desired trajectory error \(\eta_{ge}\) is  
\[\eta_{ge}=\eta_{1}-\eta_{g} \tag{35}\]  
Convert the virtual desired trajectory error \(\eta_{ge}\) into the Earth coordinate system:  
\[e_{ge}=J_{1}^{T}\eta_{ge} \tag{36}\]  
Define the virtual trajectory tracking error \(z_{1}\) as  
\[z_{1}=e_{ge}+M \tag{37}\]  
Substituting (34)-(36) into (37) gives  
\[z_{1}=J_{1}^{T}\eta_{ge}+M=J_{1}^{T}(\eta_{1}-\eta_{1d}-J_{1}M)+M=J_{1}^{T}\eta _{e} \tag{38}\]  
Since \(J_{1}^{T}\) is an orthogonal matrix and the determinant is not zero, it follows from (38) that \(\eta_{e}=0\) when \(z_{1}=0\). Therefore, the control objective of this paper converts into how to make \(z_{1}\) stable in a predefined time.  
According to (30), (34)-(38), the derivative of \(z_{1}\) with respect to time is solved to be equal to  
\[\begin{array}{ll}\dot{z}_{1}&=-\omega T_{1}^{T}\eta_{e}+\dot{J}_{1}^{T}\dot{ \eta}_{e}+\dot{M}\\ &=-\omega z_{1}-\omega M+v_{1}-J_{1}^{T}\dot{\eta}_{g}+\dot{M}\\ &=-\omega z_{1}+\Lambda\upsilon+v_{1}^{*}-J_{1}^{T}\dot{\eta}_{g}+\dot{M}\\ \end{array} \tag{39}\]  
where \(\Lambda=\begin{bmatrix}1&\rho_{3}&-\rho_{2}\\ 0&0&-\rho_{1}\\ 0&\rho_{1}&0\\ \end{bmatrix}\) is the system connection matrix, which simplifies the controller design; \(\upsilon=\begin{bmatrix}u,q,r\end{bmatrix}^{T}\) is the velocity vector in the direction of the driving degrees of freedom; and \(v_{1}=\begin{bmatrix}u,v,w\end{bmatrix}^{T},v_{1}^{*}=\begin{bmatrix}0,v,w \end{bmatrix}^{T}\) is the intermediate matrix such that the virtual control error is \(\upsilon_{e}=\upsilon-\alpha,\alpha\), which is the virtual control law to be designed.  
By (34)-(39), the underactuated AUV system is linked to the drive subsystem, so the control law of the underactuated AUV can be solved by solving the control law of the drive subsystem and substituting it into the main system to solve the control law of the underactuated AUV, which facilitates the derivation of the control law without the model parameters.
## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### Ground Truth Data for Enhanced Coastal Monitoring
In sediment-rich environments, such as those studied by [PERSON] et al. [70]. GTD have been key in calibrating shoreline models to consider sediment plumes. This calibration, in turn, improved the delineation of shorelines under adverse optical conditions, hence reducing errors in areas difficult to analyze earlier. GTD have also proved to be of immense help in the historical analysis of shorelines. [PERSON] et al. [70] applied GTD to georeference legacy datasets such as aerial photography, which enabled valid long-term shoreline change studies over decades. In seasonal monitoring, [PERSON] et al. [56] integrated GTD to calibrate NDWI-based indices in Antalya's coastal zones for a more accurate shoreline position with an increase in accuracy of up to 18%, and this provided important information on how to manage the areas under tourism pressure. In vegetation-dense areas, [PERSON] et al. [53] integrated GTD to enhance the subpixel shoreline detection methods and achieved an RMSE of less than 6 m, while models without integrating GTD had an RMSE of 20 m.  
The above-mentioned applications and reviewed studies have shown the crucial role of GTD in the monitoring of coastlines: from calibration in tidal areas to noise reduction in sediment-laden areas and georeferencing during historical analysis, GTD enhance reliability and precision for methodologies involving remote sensing, forming the fundamental basis for achieving high accuracy with respect to coastal monitoring and management.
## Three-Plane Five-Element Microphone Array Model
### Establishment of a Three-Plane Five-Element Microphone Array Model  
The model of the established three-plane five-element microphone array is shown in Figure 1.  
The array consists of seven microphones: M0, M1, M2, M3, M4, M5, and M6. The first single-plane five-element microphone array is composed of four microphones in the X0Y plane--M1, M2, M3, and M4--and M0. The second single-plane five-element microphone array is composed of four microphones in the X0Z plane--M1, M3, M5, and M6--and M0. The third single-plane five-element microphone array is composed of four microphones in the Y0Z plane--M2, M4, M5, and M6--and M0. All three five-element microphone arrays use M0 as the reference microphone. The microphone M0 is located at the origin (0,0,0). It is assumed that the distances between the remaining six microphones and the origin of the coordinates are all a; then, the coordinates of each microphone can be expressed as M1 (a, 0, 0), M2 (0, a, 0), M3 (-a, 0, 0), M4 (0, -a, 0), M5 (0, 0, a), and M6 (0, 0, -a). It is defined that the time that it takes for S to propagate to microphones M0, M1, M2, M3, M4, M5, and M6 is \(t_{0}\), \(t_{1}\), \(t_{2}\), \(t_{3}\)  
Figure 1: Three-plane five-element microphone array.  
\(t_{4}\), \(t_{5}\), and \(t_{6}\), respectively. Based on the model, the relative time-delay values of six groups are set as \(\tau_{10}=t_{1}-t_{0}\), \(\tau_{20}=t_{2}-t_{0}\), \(\tau_{30}=t_{3}-t_{0}\), \(\tau_{40}=t_{4}-t_{0}\), \(\tau_{50}=t_{5}-t_{0}\), and \(\tau_{60}=t_{6}-t_{0}\). The coordinates of the sound source S are S (x, y, z) in the Cartesian coordinate system and S(r,\(\theta\),\(\varphi\)) in the spherical coordinate system. The distance between the sound source S and M0 is \(r\), the projection point of S on the X0Y plane is S\({}^{\prime}\), the elevation angle SOS\({}^{\prime}\) is \(\theta\), and the horizontal angle X0S\({}^{\prime}\) is \(\varphi\). The target sound source S generates sound waves propagating in the form of spherical waves with a propagation speed of c.
# Anatomical Characteristics of the Masseter Muscle in
## 2 Materials and Methods
### 3D CT and Segmentation  
A 3D analysis was performed using a SOMATOM Definition Dual Source CT (DSCT; Siemens, Forchhelm, Germany) with the following imaging conditions: field of view, 20.8 cm; 100 kV; 76 mAs; scanning time, 1 s; and 0.5 mm thickness. The participants were instructed to be static and maintain centric occlusion to avoid changes in the masseter volume [19]. All cross-sectional CT images were stored in Digital Imaging Communication in Medicine (DICOM) format, and the reconstruction was performed using Mimics 10.0 (Materialize n.v., Leuven, Belgium). Subsequently, the superficial and deep layers of the masseter muscle and skull were outlined.  
Outlines of the skull were automatically drawn using the 'threshold' method in Mimics. The outlines of the superficial and deep layers of the masseter muscle were drawn manually for each participant. Per five serial sections, one outline was drawn in the horizontal, sagittal, and coronal planes individually. As a result, approximately 20 outlines were drawn in each plane.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## Appendix B State-of-the-Art Anomaly Detection Models
### Depth-Based Models
#### b.2.2 Local Outlier Factor (LOF)  
The LOF algorithm [44; 45] tries to assess the isolation of one observation relative to the rest of the data set before flagging it as an anomaly. This model relies on the concept of local density. The anomaly score of an observation \(x\) is its local outlier factor, which quantifies how dense the location area of \(x\) is compared to that of its neighbours. Hence, for each observation, \(\text{LOF}_{k}(x)\approx 1\) means that the density of observations around \(x\) is similar to that of its neighbours; therefore, \(x\) could not be considered an isolated observation. \(\text{LOF}_{k}(x)\gg 1\), instead, shows that the density of \(x\) is lower compared to its neighbours; hence, \(x\) should be flagged as an anomaly.
## 3 Results and Discussion  
In an open outdoor area with no obstructions, the BTTT is tested with the Beidou antenna facing south at 45\({}^{\circ}\), as shown in Figure 7. The shore base communication terminal contains a superior computer and a shipborne Beidou terminal. Communication between the buoy main control board and the terminal is achieved through a serial port connection to the superior computer using serial port debugging software.
## 3 Results and Discussion
### Effect of Ionic Strength on the Adsorption of Cimetidine onto BRIS  
The effect of ionic strength on the adsorption of cimetidine onto WRB, GWRB, and HWRB is presented in Figure 8. The results showed that the ionic strength affected the adsorption greatly and the increasing ionic strength restrained the adsorption of cimetidine onto all three bricks. Compared with the inner-sphere complexes, the outer-sphere complexes adsorption is expected to be more vulnerable by the ionic strength. Therefore, the adsorption of cimetidine onto WRB, GWRB, and HWRB conjectured to be outer-sphere surface complexes.  
As shown in Figure 8, the effect of ionic strength on the adsorption could be divided into two regions. With the ionic strength increase from 0 to 0.02 mol/L, the adsorption capacity of cimetidine decreased sharply from 92.7, 119.4, 108.6 to 14.2, 17.5, and 4.6 ug/g for WRB, GWRB, and HWRB, respectively. The competition between cimetidine and Na\({}^{+}\) may induce the decrease in cimetidine adsorption. Resemble results could be reported by the adsorption of antibiotics on adsorbents [36]. With the ionic strength further, the adsorption capacity increased slightly for all bricks. This phenomenon was possible derived from the charge screening effect that can reduce the repulsion between cimetidine and bricks, which was also observed by the adsorption of triclosan onto activated carbon, kaolinite, and montmorillonite [37].