esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Quality Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Phenolic Compounds** **(g/100 g dw)**} & \multirow{2}{*}{**Undried**} & \multicolumn{3}{c}{**Dried at**} \\ \cline{3-5}  & & **60 \({}^{\circ}\)C** & **70 \({}^{\circ}\)C** & **80 \({}^{\circ}\)C** \\ \hline \hline
**Phenolic acids**  Gallic acid & 16.62 a & 14.41 b & 17.66 c & 15.29 d \\  Salicylic acid & 1.23 a & 0.96 b & 0.73 c & 0.94 b \\  Chlorogenic acid & 12.31 a & 11.69 a & 9.20 b & 6.11 c \\  Coumaric acid & 0.03 a & 0.14 b & 0.02 a & 0.03 a \\ **Flavonoides**  Quercetin & 14.61 a & 11.66 b & 8.23 c & 5.03 d \\  Kaempfer & 1.61 a & 0.93 b & 0.43 c & 0.43 c \\  Catechin & 5.77 a & 3.56 b & 2.45 b & 1.25 c \\  Naringine & 8.36 a & 7.73 a & 6.58 b & 6.34 b \\  Epicatechin & 2.68 a & 2.31 a & 2.48 a & 2.11 a \\ \hline \hline
**Total** & 63.22 a & 53.39 b & 38.58 c & 37.53 c \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Phenolic compounds contents of undried and dried _Arbutus unedo_ L. fruits (Results are expressed as mean values of three determinations; Significant differences at \(p<0.05\) (Tukey’s test) are indicated with different letters). declined and could not be detected at higher temperatures. These various effects on drying on single phenolics were supported by other authors [78; 79]. They reported that the effect of oven drying temperatures were different on numerous individual phenolic compounds. These can be attributed to the difference in the molecular structures of these phenolic molecules, which may or may not be subjected to enzymatic degradation/co-pigmentation, or oxidation reactions at specific drying temperatures. Drying method can vary in their effect on phenolic compounds, and this is mainly attributed to the drying temperatures. [PERSON] et al., (2021) reported a double fold increase in phenolic compounds content in _Myrtus communis_ L. [80] when dried _via_ microwave than in air dried samples, whereas, oven drying at higher temperature (100 and 120 \({}^{\circ}\)C) resulted in significant reduction (30% and 60% reduction at 100 and 120 \({}^{\circ}\)C, respectively). Also, [PERSON] and [PERSON] (2008) showed that applying low temperatures (<100 \({}^{\circ}\)C) to dry citrus peels reduced the destructive power on the most key phenolic compounds as compared to higher temperature (150 \({}^{\circ}\)C) [81].
## 3 Distortions Induced by Low Cost  
In short-reach optical links based on PAM4 modulation, the cost is one of the most important factors to be considered for commercial implementation. Low-cost components are highly desired in practical application, while they also bring great challenges and serious distortions such as linear and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Tx** & **Bandwidth** & **Chip** & **Cost** & **Reach** & **Fiber** & **Power Fading** & **Wavelength (nm)** \\ \hline VSCEL & low & high & low & \textless{}300 m & MMF & N/A & Mostly 850 \\ DML & low & high & fair & \textless{}80 km & SSMF & low & 1310/1550 \\ EML & high & low & high & \textless{}80 km & SSMF & high & 1310/1550 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of different transmitters [54; 55; 56; 57; 58; 59].  
Figure 1: Typical configuration for short-reach PAM4 transmission system.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Rx** & **Cost** & **Footprint** & **Sensitivity** & **Reach** \\ \hline PD + TIA & low & low & low & low \\ APD + TIA & fair & low & fair & fair \\ OA + PD & high & high & high & high \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of different receivers [1; 7; 26; 39].  
nonlinear impairments. In this section, the major distortions induced by low cost will be illustrated in short-reach optical communications.
[34] investigated the use of aluminium oxide nanocoolant jets in drilling operations with vegetable oil as the base fluid using minimum quality lubrication. They found that aluminium oxide nanocoolant significantly reduced the thrust force and torque during drilling operations and also improved the tool life. The present work is an extension of the work performed by [PERSON] et al. [35] on the thermal performance of nanocoolants during turning operations. [PERSON] et al. [35] performed numerical simulations using single nanofluid pairs, and the present study considers the effects of different hybrid nanocoolant pairs on thermal performance in drilling operations. It is observed from the literature that many studies have been conducted on nanofluid MQL machining processes; most of the existing literature has not investigated the thermal performance of hybrid nanoparticles mixed with cutting fluids. Some researchers have undertaken CFD analysis of nanofluid spray with the MQL method; however, no work has been documented on drilling operation, to the best of the authors' knowledge. Moreover, only a few have studied the machinability of titanium alloy Ti-6A1-4V. Understanding thermal dispersal in the cutting tool is always a detailed study using existing experimental and numerical techniques. Hence, comprehending the thermal performance of metal-oxidehybrid nanocoolants in comparison with other hybrid nanocoolants, such as oxide-oxide and CNT-oxide, needs further insight. The primary objective of this investigation is to compare and contrast the thermal performance of different hybrid nanocoolants such as Cu-Al2O3, TiO2-Al2O3, SWCNT-Al2O3, and MWCNT-Al2O3 hybrid nanoparticles dispersed in mineral oil coolant used in drilling operations. The motive for using hybrid nanofluids is to proliferate the heat transfer and pressure drop attributes by a reciprocation between benefits and drawbacks of individual suspension, attributed to adequate thermal network, synergistic effect, and exemplary aspect ratio of nanomaterials. However, most studies have been conducted on a single jet with nanofluids. A critical interface is the rake of the flute, i.e., the chip-tool interface. Accordingly, the implementation of coolant is expected to boost heat removal, thereby fostering the overall heat transfer rate. There are few studies on the use of multiple-jets-assisted machining of titanium alloy, affirming that it enhances forced convection and sustainability; there is no study of hybrid nanofluids concerning thermal performance, especially for drilling operations. Concerning the gaps discovered in the literature regarding the absence of discussions on titanium alloy machining under multiple jets hybrid nanofluids, this investigation concentrates on the thermal performance of hybrid nanoparticles in the drilling operation of Ti-6 Al-4V superalloy under duplex jets.
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 4 Conclusions  
In this paper, the dynamic behavior of the twin spool rotor-bearing system with looseness-rub coupled failure is investigated based on the proposed simulation model, and the conclusions are as follows:  
The impact of looseness failure on the system varies depending on the speed. At low speeds, the system is susceptible to multiplier components, but the centerline orbit remains mostly unchanged. At high speeds, the system tends to generate a complex motion spectrum with noticeable low-frequency components. As the degree of looseness increases, the system may transition into chaotic motion once the loosening stiffness diminishes to a specific threshold value. If the case stiffness is low or the amplitude is near the touching gap, the system may experience complete rub. As the rub impact stiffness increases, the system will transition from complete rub to local rub, resulting in the appearance of frequency components below the main frequency in the spectrum.  
Based on the results of the single failure vibration characteristic analysis, the influence of the looseness-rub coupled failure on the twin-spool rotor-bearing system vibration characteristic is further considered. The frequency components that remain constant regardless of rotational speed are observed in the frequency spectrum around 50\(\sim\)80 Hz during the occurrence of the looseness-rub coupled failure. The rub impact noticeably amplifies the presence of 50\(\sim\)80 Hz in the spectrum.  
Further research in this field should extend to (1) the comprehensive experiments for the failure twin-spool rotor-bearing system and (2) the more general dynamic simulation model with a case, squeezed-film damper, suspension component, and bolted disk-drum structure.  
Conceptualization, H.Z. and K.L.; methodology, H.Z.; software, H.Z.; validation, H.Z.; formal analysis, H.Z. and R.L.; investigation H.Z. and R.L.; resources, H.Z., K.L., R.S., D.L. and R.L.; data curation, H.Z.; writing--original draft preparation, H.Z. and R.L.; writing--review and editing, K.L. and X.G.; visualization, H.Z.; supervision, K.L.; project administration, K.L.; funding acquisition, K.L. and X.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant Number 12072263); State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures (Grant Number KF2020-26).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All necessary and relevant data are included in this paper.  
**Acknowledgments:** The authors thank the editor and reviewers for their valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# 2 \(\times\) 2 Matrices: Manifolds, Realizations, Applications
## 3 Canonical Integral Transforms and the Universal \(2\times 2\) Matrix Covers
### Bargmann Parameters for the Metaplectic Group
\begin{array}{c}\overline{\mathbb{SL}}(2,R)\overset{\infty:1}{=}\mathsf{SL} ^{(m)}(2,R)\overset{\frac{1}{2}\pi:1}{=}\mathsf{M}\mathsf{p}(2,R)\overset{2:1 }{=}\mathsf{SL}(2,R)\overset{2:1}{=}\mathsf{SO}(2,1).\\ \theta\in(-m\pi,m\pi]\end{array}  \tag{42}\]  
The metaplectic group \(\mathsf{M}\mathsf{p}(2,R)\), the two-fold cover of the group of \(2\,\times\,2\) matrices, is the group of linear canonical transforms in quantum mechanics and wave optics. In higher dimensions \(N\), the symplectic groups \(\mathsf{Sp}(2N,R)\) will follow a pattern similar to the \(N=1\) case reported here. The Bargmann decomposition (34) of \(\mathsf{Sp}(2N,R)\) is now into unitary \(\mathsf{U}(N)\) and positive definite \(N\times N\) symmetric matrices [21]; the former are further decomposed as \(\mathsf{U}(1)\otimes\mathsf{SU}(N)\), with the \(\mathsf{U}(1)\) circle covered by a parameter range over chosen periodicity intervals from the universal cover \(\overline{\mathsf{Sp}}(2N,R)\) to the metaplectic \(\mathsf{M}\mathsf{p}(2N,R)\), and down to its double covering of the pseudo-orthogonal group \(\mathsf{SO}(3,2)\).
# A Survey of Planning and Learning in Games
## 3 Learning in Games
### Learning Techniques  
#### 3.1.1 Supervised and Unsupervised Learning  
The goal of Supervised Learning (SL) is to learn a predictive model that can be used to make predictions about future unseen data; see Figure 1. More formally, given a training dataset composed of \(N\) input-output pairs \(\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{N},y_{N})\}\), where \(x_{i}\) is a feature vector in \(\mathbb{R}^{d}\) and \(y_{i}\) is the desired output or label obtained via an unknown function \(y=f(x)\), the objective of SL is to derive a function \(h\) that approximates the true function \(f\). When the labels take discrete values (e.g., {yes, no}) the task is referred to as a classification task, otherwise if the labels take continuous values the task is referred to as regression. Decision Trees and K-Nearest Neighbors (KNNs) are two examples of SL algorithms. Unsupervised Learning (UL), on the other hand, works with unlabeled data (i.e., the dataset consists only of the \(x_{i}\) feature vectors). The goal of UL is to try to extract meaningful information from this data (e.g., patterns). K-Means is an example of an UL algorithm. Other examples of learning algorithms, which can be used to solve both SL and UL tasks, include support vector machines (SVMs) and artificial neural networks (NNs). NNs are discussed in the next subsection.  
SL and UL have found a plethora of applications in the domain of game AI research, ranging from classical board games to video games. A well-known example of this in the domain of classical board games is Logistello [11; 52], the computer program that defeated the human Othello world champion in 1997. Logistello uses the Generalized Linear Evaluation Model (GLEM) to automatically find significant features to represent the board positions and derive the evaluation function used to evaluate them (i.e., assign appropriate weights to those features), via SL.  
Concerning video games, SL and UL have been used to address game AI-related tasks as diverse as control, player characterization, and content recommendation. Examples of work related to control include General Video Game Playing [53], learning from demonstration in both its active (or online) [37; 54] and passive (or offline) [55] variants, and opponent modeling for both offline learning, via the use of gameplay traces in RTS games [29; 56] and online learning during actual gameplay in a fighting game [57]. Regarding the characterization of players, these learning techniques have also been used with the objective of predicting aspects of the players as diverse as playstyle [32], playing behavior [33], real world characteristics (e.g., gender and personality traits) [34], playing performance [24], and preferences [27] in order to, for example, adjust the game's difficulty dynamically (e.g., [24]), model player retention (e.g., [58; 59]), or implement game content recommendation systems (e.g., [27]).
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
## 2 Attitude and Orbit Determination Model
### State Model
\begin{array}{cc}0&-\omega_{z}&\omega_{y}\\ \omega_{z}&0&-\omega_{x}\\ -\omega_{y}&\omega_{x}&0\end{array} \right] \tag{11}\]  
The four quaternion elements satisfy the following normalization constraint:  
\[\mathbf{q}^{\rm T}\mathbf{q}=\mathbf{q}_{13}^{\rm T}\mathbf{q}_{13}+q_{0}^{2}=1 \tag{12}\]  
Combining the elements for the above orbits and attitude, the state vector to be estimated is:  
\[ \begin{split}&\mathbf{X}=\left[\mathbf{X}_{1}^{\rm T},\mathbf{X}_{2}^{\rm T}, \mathbf{q}^{\rm T},\mathbf{\omega}^{\rm T}\right]^{\rm T}\\ &=\left[x_{1},y_{1},z_{1},\dot{x}_{1},\dot{y}_{1},\dot{z}_{1},x_{2},y_{2},z_ {2},\dot{x}_{2},\dot{y}_{2},\dot{z}_{2},q_{0},q_{1},q_{2},q_{3},\omega_{x}, \omega_{y},\omega_{x}\right]^{\rm T}\end{split}  \tag{13}\]  
The state model is given by:  
\[\mathbf{X}=\mathbf{F}(\mathbf{X})=\left[\mathbf{X}_{1}^{\rm T},\mathbf{X}_{2}^{\rm T},\dot{\mathbf{q}}^ {\rm T},\dot{\mathbf{\omega}}^{\rm T}\right]^{\rm T}=\left[\mathbf{f}(\mathbf{X}_{1})^{\rm T },\mathbf{f}(\mathbf{X}_{2})^{\rm T},\mathbf{g}_{1}(\dot{q})^{\rm T},\mathbf{g}_{2}(\mathbf{\omega} )^{\rm T}\right]^{\rm T} \tag{14}\]
## 2 Experimental Setup and Processing Methods
### AVD Conversion Method
Then, the corrected acceleration (\(a(t)\)) is integrated in order to calculate the new velocity, (\(v(t)\)), and the displacement, (\(d(t)\)). Finally, the displacement (\(d(t)\)) is cosine-corrected. Using the cosine Fourier series transform, the discrete input data is calculated as the sum of the cosine functions. \[v(t)=G\exp(i\omega t) \tag{2}\]9 Flowchart for the conversion of acceleration data using Method 1. \[\dot{v}(t)=i\omega G\exp(i\omega t) \tag{3}\]0 Flowchart for the conversion of acceleration data using Method 2. The third method uses the least-square fitting and Butterworth filters for digital strong-motion data, as proposed by [PERSON] (1998). Figure 7 shows the flowchart for the conversion of acceleration data using Method 3, wehreby \(a_{0}(t)\) is the initial acceleration, \(a(t)\) is the corrected acceleration, \(\omega\) is natural frequency, \(\omega_{c}\) is cutoff frequency, and \(G_{0}\) is a frequency response at zero frequency. The least-squares fitting in the first step removes the linear trend of the acceleration. The long-term measurement of the acceleration of a high-rise building incorporates the errors and drift of the baseline. These are caused by errors in the measuring equipment, along with background error. The correction method for their removal consists of the following three steps. First, the baseline drift of the acceleration is removed using the least-squares method, and the acceleration in the time domain is converted into the frequency domain by Fourier transform. Next, low-frequency error is removed using the fourth-order Butterworth filter. The Butterworth filter does not generate a ripple in the passband; it has better performance than the Bessel filter, and the bandpass filter can simultaneously remove low-frequency and high-frequency noise. Because of these advantages, passband Butterworth filters are used in the PEER (Pacific Earthquake Engineering Research Centre) strong-motion database ([PERSON], 1998). The Butterworth filter is also used in the Strong Motion data (PRISM) software developed by the US Geological Survey (USGS) ([PERSON], 1998). The accelerometer installed in the high-rise building is an analogue system, and a typical analogue Butterworth filter is used. The last step required is to remove the linear trend that arises from having a nonzero initial velocity. When the strong-motion instrument records seismic data, those data may come from the effect of the background noise. To ensure that the strong motion records start from zero, the records should be processed by means of baseline initialization ([PERSON], 1998). Furthermore, motions with very long periods must be sacrificed, and a combination of baseline correction and low-cut filtering can be used to produce motions that are accurate representations of the true ground motions obtained by many earthquake modelers ([PERSON], 1998).
# Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network
## 5 Conclusions  
Aiming at the influence of the foresight distance, driving speed and yaw rate for autonomous vehicles on the target point tracking control, a tracking controller based on the LSTM network is proposed in this paper. The foresight distance is obtained by the fuzzy control algorithm, whose inputs are the present velocity, yaw rate of the vehicle and output is an appropriate foresight distance. This distance is an important value with reference to the key parameter in pure pursuit algorithm. The two-layer LSTM network controller's function is to regulate the autonomous vehicle's speed and yaw rate simultaneously and intelligently to arrive at the target point, which is gained by a geometrical calculation based on the foresight distance. It is obvious that the foresight distance, driving velocity and yaw rate are interrelated. This method avoids the complicated process of traditional calculation and reduces the tracking error of point sequence tracking significantly.  
The simulation experiments are carried out under the conditions of straight path as well as multiple curvature path, its results show that the proposed algorithm can adaptively adjust vehicle's control parameters on the corners or straight segments to track different reference paths quickly and effectively. In order to verify the actual effectiveness of this algorithm, it is also applied to the tracking control of the cleaning vehicle in a subway station. The cleaning vehicle has a precise route and the swinging amplitude of the wheels is moderate during the running process in field experiment. Besides, the proposed controller is freedom from the impacts of path noise points, this phenomenon means that the proposed method can generate better speed and yaw rate for the path tracking in a broader field. Therefore, the proposed algorithm can not only reduce the labor costs, but also greatly improve the work efficiency and quality of the cleaning vehicle. It provides a reference for the research of target points tracking control of autonomous vehicles.  
Conceptualization and Methodology, X.C. and H.W.; Validation, X.C.; Experiments manipulation, X.C. and Z.M.; Writing--Original Draft Preparation, X.C.; Writing--review and editing, X.C., H.W. and Z.M.  
This work was supported by the National Natural Science Foundation of China (No. 51375293, No. 31570998).  
The authors declare no conflict of interest.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 2 Materials and Methods
### Data Acquisition
#### 2.2.2 Experiment One: Individual Bunches under Laboratory Conditions  
The 21 individual bunches were imaged using the RGB camera and Kinect sensor under laboratory conditions; i.e., the laboratory was illuminated with white fluorescent lights and natural light entered through the windows. Each bunch was suspended from a tripod against a green background to maximise image contrast (Figure 3a):  
1. _RGB imagery:_ The camera was placed parallel to the suspended bunch at a distance of 60 cm. A single image per bunch was captured for image processing. A ruler was included in each image for length reference (Figure 3b).
2. _RGB-D (Kinect) imagery:_ The Kinect sensor was placed 60 cm proximal to the target and captured individual meshes per bunch, resulting in a solid 3-D model of the bunch. The Kinect sensor, coupled with the Kinect Fusion software (part of Microsoft's Software Development Kit 1.8 for Windows [25]) running on a laptop, can capture individual meshes. Meshes were captured in._stl_ format with a resolution of 640 voxels/m, and a voxel resolution of \(256\times 256\times 256\) voxels. A white surface was positioned directly behind the bunch to improve contrast and depth determination, as illustrated by the mesh seen in Figure 3c. During mesh capture, the entire bunch system was rotated, providing different angles of view.  
Figure 3: Data acquisition under laboratory conditions. (**a**) Experimental setup for image capture; (**b**) RGB image of an individual bunch with a ruler for reference length; and (**c**) RGB-D (Kinect mesh) of an individual bunch.