esa-sceva/satcom-chunk-collection huggingface dataset:
# Yield and Quality of Essential Oils in Hemp Varieties in Different Environments
## Materials and Methods
### Apparatus, Sample Preparation, Identification and Quantification of Terpenes in EOs  
EOs composition was determined by GC-MS (Agilent Technologies GC-MS 5977E, USA) and the amount of each compound was expressed as relative percentage by peak area normalization. One microliter of each EO sample was diluted in 50 uL of \(n\)-hexane in an amber vial. The gas chromatograph was fitted with a 30 m HP-5 MS capillary column (Agilent Technologies, Santa Clara, CA, USA). Helium was used as carrier gas (1.2 mL min\({}^{-1}\) constant flow). Oven settings were as follows: 40 \({}^{\circ}\)C for 3 min, steps of 4 \({}^{\circ}\)C/min up to 280 \({}^{\circ}\)C and then holding at 280 \({}^{\circ}\)C for 10 min with a run time of 73 min. One uL of sample was injected in split mode and the split ratio was 1:20. Data acquisition was performed in scan mode by MassHunter software (Agilent Technologies, Santa Clara, CA, USA). The EO compounds were identified by a comparison of their Mass spectra with those reported in the Mass Spectral Library Nist 14 and by means of their Linear Retention Indices (LRI) relative to a series of n-alkanes (C\({}_{8}\)-C\({}_{30}\)) on the same capillary column. LRI obtained were compared with values reported in the literature [26; 27; 35; 36; 37; 38; 39; 40].
## 4 Multimedia-Based Cross-Layer Optimization in VSNs  
Modern visual sensor networks are strongly influenced by the adopted coding technique. The source transmission rate, the energy consumption over the current path(s) and the error-resilience of the communication depend on the way still images and video are encoded and decoded. What can be seen in the academic community in the last few years is a strong relation between visual coding and the communication architecture, resulting in multimedia-based cross-layer optimizations. In this paper, we classify the multimedia-based cross-layer optimization solutions in visual sensor networks in two categories:  
* _Image-based_: Images are snapshots retrieved by video-based sensors, respecting the field-of-view of the embedded camera at each sensor. The image monitoring applications can be delay-unconstrained or require real-time transmission, directing impacting the adopted communication architecture. Another key aspect is that images typically require less bandwidth than video media, also consuming less energy due to lower transmission rates. The quality of the received image will also vary according to the application requirements: Grayscale low-resolution images may be suitable for some types of monitoring, while colored high-resolution images are expected for high visual definition applications or when resource-rich sensors are deployed.
* _Video-based_: Some applications directly benefit from video media to provide an enhanced understanding of the monitored target or scene. The same constraints in terms of delay and quality considered for image-based solutions are valid for video, with addition of some extremely relevant characteristics, such as the number of transmitted frames per second. The nature of video media imposes more stringent requirements for delay and jitter, also demanding more bandwidth than images. In such context, design issues as multipath routing achieve even more relevance.  
Besides the type of media, we can classify the cross-layer optimization in visual sensor networks according to where the coding is performed:  
* _Source processing_: When raw visual data are fully processed by the sensor which collected them, the compression is said to be performed locally at the source node. For that, the source must have sufficient processing and memory resources to properly execute the encoding algorithms. Source processing potentially reduces the end-to-end delay, but may demand considerable energy consumption from the source node.  
* _In-network processing_: When source nodes do not have sufficient resources to encode a large volume of data, when the communication architecture define specialized tasks for intermediate nodes or if it is desired to save energy of source nodes, in-network processing is performed. It must be specified some protocol to share the original data among the nodes which will compress the data. In such case, the energy is saved in source nodes at the cost of additional complexity.  
The next subsections survey multimedia-based cross-layer optimization considering all the previously presented design issues.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning
#### 4.3.6 V2V  
In [119], a DRL algorithm is adopted to map the correlation between observation and optimal resource allocation in V2V systems. The proposed model satisfies the latency constraints on V2V links and is able to minimize any interference in the V2V system. In [120], a RL-based approach for sum rate optimization in V2V systems is being introduced. The model is a reinforcement distributed Resource Allocation (RA) algorithm, modeled as a multi-agent system. Furthermore, a double deep Q-learning algorithm is applied to jointly train the agents and maximize the sum-rate. Simulation results show that the proposedRL-based algorithms achieve close to optimal performances, while ensuring limited latency and accurate packet delivery in the V2V link.
# Spaceborne GNSS-R Observation of Global Lake Level: First Results from the TechDemoSat-1 Mission
## 3 Results and Discussions  
### Overall Evaluations and Comparisons  
Comparisons between TDS-1/CryoSat-2 and TDS-1/Hydroweb using summer data are performed in this section. Data from summer is applied to avoid the influence of lake ice, and it is set as a duration from August to October in the Northern hemisphere and from February to April in the Southern hemisphere [25]. Figure 3 shows the correlations of the global lake level results derived from TDS-1, CryoSat-2 and Hydroweb data from 2015 (a1, a2) and 2016 (b1, b2). The results from 2017 are dismissed since there is only one-day data (i.e., 3/10/2017) publicly available within the selected time window. The scattered points in Figure 3 denote the water levels of overlapped lakes derived from TDS-1/CryoSat-2 (a1, b1) and TDS-1/Hydroweb (a2, b2). For TDS-1, the water level of each lake is the mean value of all the specular points within the lake boundary. For CryoSat-2, the water level of each lake is determined using the rules described in Section 2.2. Note that the TDS-derived lake levels have strong correlations with those from CryoSat-2 and the Hydroweb data, with R\({}^{2}\) greater than 0.95 for both 2015 and 2016. However, the overall root-mean-square error (RMSE) (the reference lake level  
Figure 2: Principle of lake water level measurement using TechDemoSat-1 (TDS-1) data. (**a**) Geometry of lake water level measurement. The solid reflected line minus the direct path is \(\tau_{obs}\), and the dotted reflected line minus the direct path is \(\tau_{ref}\); (**b**) calculation of the observed time delay. \(\tau_{window}\) represents the delay of the delay-Doppler map (DDM) window and \(\tau_{sp}\) represents the delay of the specular point within the DDM window.  
data are treated as ground-truth data) is large (approximately tens of meters), and the data show a smaller RMSE in 2015 than that in 2016. The large RMSE of the data indicates that although TDS-1 has relatively comparable results, it cannot guarantee incredibly high accuracy of the lake level estimates. Possible contributing factors will be discussed in the following Section 3.3.
## 2 Materials and Methods
### Satellite Data Pre-Processing
#### 2.3.3 Sentinel-2 Processing  
For Sentinel-2 (S2), the data containing clouds or noisy data were filtered using the Scene Classification Map (SCL) band provided with the dataset. To do this, all pixels classified with values 1 (Saturated), 2 (Dark), 3 (Cloud shadow), 7 (Cloud_low), 8 (Cloud_medium), 9 (Cloud_high), 10 (Cirrus), and 11 (Snow/Ice) were removed. In addition, the cloud mask band (QA60) provided with the dataset was used to remove opaque and cirrus cloud noise.
## 5 Conclusions  
Even within managed crop systems, we show there is considerable and important within-field variation in LAI at scales finer than the resolution of current satellite imagers. We show that UAV multispectral observations at the cm scale, acquired from a sensor designed to match Sentinel-2 spectral bands, improve interpretation of the satellite signal. Furthermore, the fine-scale resolution of the UAV sensor provides a tool for accurately upscaling LAI ground measurements, which were collected in coordination with the UAV flights, to satellite resolution. The within-field variance in spectral data resolved from the UAV observations was linked to wheat growth stage. Consequently, the Sentinel-2 and UAV platform data were more comparable at the later growth stages, when the vegetation canopy appeared more homogeneous due to a reduced influence of bare soil. Calibrating models used to retrieve LAI from Sentinel-2 observations directly from ground measurements performed poorly and were unable to explain the variance in LAI throughout the growing season. On the other hand, our novel two-stage model calibration, involving the use of upscaled UAV LAI estimates, demonstrated a clear improvement in the accuracy of LAI retrievals from Sentinel-2 data, reducing bias strongly. It would be anticipated that our approach would also improve the retrieval of additional biophysical variables for other arable crop types and a broader range of vegetation cover types. Repeating our analysis at additional field sites would be necessary in order to test the robustness of the two-stage calibration. Nonetheless, our study has highlighted the value of UAV observations for effectively providing a link between point measurements on the ground and 20-m resolution multispectral observations made from the Sentinel-2 satellite.  
Conceptualization, A.R., A.M. and M.W.; methodology, A.R., A.F. and M.W.; software, A.R.; validation, A.R. and M.W.; formal analysis, A.R. and M.W.; investigation, A.R.; resources, A.M. and S.H., data curation, A.R. and A.F.; writing--original draft preparation, A.R. and M.W.; writing--review and editing, A.R., A.F., A.M., S.H., R.R. and M.W.; visualization, A.R.; funding acquisition, A.M. and M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the joint Biotechnology and Biological Sciences Research Council (BBSRC) and National Environment Research Council (NERC) Sustainable Agricultural Research and Innovation Club (SARIC) initiative (grant numbers: BB/P004628/1 and BB/P004458/1) and NCEO.  
We would like to thank the farmers on Farms 1 and 2 for consenting to the field survey campaigns carried out across their properties. Technical advice was provided by [PERSON] from the University of Edinburgh's Airborne GeoSciences Facility. We are also grateful for the support received from the NERC National Centre for Earth Observation (NCEO) Field Spectroscopy Facility. The authors declare no conflict of interest.
# Copenhagen Adduction Exercise to Increase Eccentric Strength: A Systematic Review and Meta-Analysis
## 1 Introduction  
Muscle injuries are common in many sports, such as basketball [1], rugby [2], football [3], tennis [4], track and field [5]. Muscle injury can represent more than 40% of total injuries in football [3, 6], and track and field [5]. Ice hockey and football are susceptible to many adductor muscle strains [7]. In this regard, the adductor injury is the second most common muscle injury among football players [3] and is associated with lost competition time [8]. Therefore, intervention programs aimed to reduce the incidence of adductor muscle injuries are recommended.  
The low level of adductor muscle strength has been linked to the possibility of suffering an injury for instance in ice hockey, where the muscle strength ratio between adduction and abduction has been reported as a risk factor [9]. [PERSON] et al. also found that adduction strength was lower (18%), during the preseason in players who sustained groin strains compared with uninjured players [9].  
Furthermore, large addductor eccentric contraction demands in sports training and competition seem to be one of the reasons to have a high prevalence of adductor injuries [10]. Thus, many different exercises have been included in preventive programs [11; 12] focused on improving adductor strength, in order to avoid injuries and limit the amount of losing time in competition [13] including, among others, adduction partner, hip adduction with an elastic band, isometric adduction, sliding hip abduction/adduction exercise, side-lying adduction and the Copenhagen adduction (CA) exercise.  
The CA exercise is a dynamic high-intensity exercise that does not require equipment and can be easily performed on-field and at any training facility [14]. It is an eccentric partner exercise performed to outer range of motion, the player who is exercising is in a side-lying position using the forearm as support on the floor, the other arm is placed along the body, the upper leg is held by the partner, approximately at the height of his hip, with one hand on the ankle and the another one on the knee. Then, the player must raise the body from the floor, around 3 s concentric hip adduction, until the body is in a straight line, so the feet are touching each other. Then the body is lowered halfway to the ground, around 3 s eccentric adduction, until touching the floor without using it for support [12].  
Although the high prevalence of adductor injuries and the potential benefits of the CA exercise, to our knowledge there are no meta-analyses aimed to analyze the effect of CA on eccentric hip adduction strength (EHAD). Therefore, the purpose of this study was to systematically review the scientific literature about the effect of CA on EHAD and to provide practical recommendations for training. Besides, a mini meta-analysis was carried out to determine the effect sizes of CA on EHAD.
## 3 Method
### Neighborhood Microimage Selection Under Multiple Constraints
#### 3.1.2 Geometric Consistency Constraints  
In stereo matching scenarios, geometric constraints are employed to avoid the inefficiencies and mismatches associated with brute-force searching, thereby reducing the search range and improving the resulting matching accuracy. Owing to the consistent direction of a microlens array, microimages naturally possess epipolar rectification properties.  
For the matched points between the two images \(l_{1}\) and \(l_{2}\) in Figure 2, with the two camera centers being \(O_{1}\) and \(O_{2}\), respectively, and for a point P in space, it corresponds to a pixel \(p_{1}\) in image \(l_{1}\) and a pixel \(p_{2}\) in image \(l_{2}\). At this point, the three points \(O_{1}\), \(O_{2}\), and \(P\) determine a plane, known as the epipolar plane, and \(l_{1}\)\(l_{2}\) is the epipolar line. Given the pixel position of \(p_{1}\), the pixel position of \(p_{2}\) must lie on the epipolar line. For the matched points in the two micro images, their connecting line should be parallel to the epipolar line, which is the Epipolar Geometry; otherwise, they are considered as mismatched and will be eliminated. By comparing the slopes, mismatched point pairs that are not parallel can be identified and removed, which is called polar correction [31].  
To determine the baseline range for the neighborhoods of light field microimages, we considered the nearest surrounding microimages to the reference microimage as the neighborhood for feature point matching. On the basis of the disparity results obtained from feature point matching, a maximum baseline is initially set, along with various baseline search ranges. As shown in Figure 3, the feature matching and disparity analysis processes begin within the range starting from the minimum baseline, gradually defining the effective neighborhood range.  
Figure 2: Epipolar constraint. (**a**) Epipolar Geometry and (**b**) polar correction.  
Given two microimages, A and B, we obtained their center points \(\rm{Center_{A}}\)(x,y) and \(\rm{Center_{B}}\)(x,y). For each pair of matching points \(\rm{(P_{Ai}(x,y)\,P_{Bi}(x,\ y))(i\ \in\ n)}\), we calculated the slope \(\rm{k_{Pi}}\) of the line connecting these points, where \(\rm{P(x,y)(0\ \leq x\ \leq R,\ 0\ \leq y\leq R)}\) represents the pixel coordinates of the current microimage and \(R\) is the pixel radius of the microimage. The slope \(\rm{k_{Pi}}\) of the epipolar line produced for the microimages was also computed, and points were filtered out when the absolute difference between the slopes of the two lines exceeded a predefined error threshold T.  
\[\rm{k_{Pi}}=\frac{\rm{P_{Bi}.y-P_{Ai}.y}}{\rm{P_{Bi}.x-P_{Ai}.x}}(i\in n), \tag{1}\]  
\[\rm{\xi(P_{Ai}(x,y),\ P_{Bi}(x,y))\ =\ \left\{\begin{array}{ll}1&\mbox{if
# Adaptive Optimal Control of UAV Brake Based on Parameter Estimation
## 1 Introduction
In the classical critic-actor-based ADP framework [30], the complex ADP structure of multiple NN approximators increases the computational burden in the implementation. Thus, a simple and effective ADP structure based on a single critic NN is proposed in [31; 32], in which the convergence of critic NN weight can be retained so that control actions can be calculated directly through the weight of a critic NN. Due to the fact that the approximate error of the NN cannot be compensated for, the steady-state error can only be guaranteed to converge to a small set near zero. A new error sign robust integral (RISE) control is developed in [33] to compensate for bounded uncertainties and perturbations. The key feature of the RISE method is that asymptotic stability can be proved by using an integral of the sign of the error feedback term. In addition, compared with the traditional Sliding Mode Control (SMC) method [34], this scheme generates continuous control signals, thus avoiding the requirement of infinitely fast control switching. Motivated by the above discussions, the aim of this article is to develop a new adaptive law to ensure convergence of estimation parameters and tracking errors in UAV braking systems. A performance index function of error and input is established for the braking system, considering the balance criterion of control resources and control effect, and the optimal solution is updated online with ADP. Moreover, the improved adaptive optimal control strategy includes robust feedback items. Their purpose is to compensate bounded disturbance, modeling uncertainty, and NN estimation error, minimizing the cost function while tracking slip rate, reducing the use of control resources, and improving control performance. Comparative simulations are given to validate the effectiveness. The contributions of this paper are as follows. 1. A parameter-updating law driven by parameter estimation error is designed for UAV braking systems with unknown parameters. The contradiction between system robustness and parameter convergence is solved, and the tracking error and estimation error converge simultaneously. 2. The performance index function about the error and input of the UAV braking system is established. The adaptive dynamic programming based on a single critic NN is adopted to solve the function so that the control energy consumption, dynamic tracking error, and filtering error in the braking process are optimized comprehensively.
# A New Prediction Model of Annular Pressure Buildup for Offshore Wells
## 3 Wellbore Annulus Volume Model
### Deformation of Casing Sealing Section  
The deformation of the casing sealing section must consider the deformation of the external cement sheath and the formation under the influence of thermal expansion and stress. As shown in Figure 3, assuming that the cement sheath and the formation are regarded as a stressed whole, the cement sheath formation can be divided into a hot area affected by temperature and a cold area not affected by temperature according to the temperature influence range. The inner radius of the casing is \(r_{0}\), with an outer radius of \(r_{1}\). The outer radius of the cement ring is \(r_{2}\). The area where the formation is affected by the temperature is the hot area with a radius of \(b\), and the region where the temperature cannot be swept is the cold area with a radius of \(c\).  
For casing deformation, Equation (10) can also be used for its calculation. The pressure in the casing is \(q_{0}\), the pressure at the cementation surface of the casing and cement sheath is \(q_{1}\), the pressure at the cementation surface of the cement sheath and the formation hot zone is \(q_{2}\), and the pressure at the interface of the hot and cold areas is \(q_{3}\).  
When \(r=r_{0}\), the \(q=q_{0}\), \(r=r_{1}\), \(q=-q_{1}\), the radial deformation of the casing is as follows:  
\[\left\{\begin{array}{l}u_{1i}=c_{1i}^{1}q_{0}-c_{1i}^{2}q_{1}+u_{T1i}\\ u_{1o}=c_{1o}^{1}q_{0}-c_{1o}^{2}q_{1}+u_{T1o}\\ \end{array}\right. \tag{12}\]  
When \(r=r_{1}\), the \(q=q_{1}\), \(r=r_{2}\), \(q=-q_{2}\), the radial deformation of the cement sheath is as follows:  
\[\left\{\begin{array}{l}u_{2i}=c_{2i}^{1}q_{1}-c_{2i}^{2}q_{2}+u_{T2i}\\ u_{2o}=c_{2o}^{1}q_{1}-c_{2o}^{2}q_{2}+u_{T2o}\\ \end{array}\right. \tag{13}\]  
Figure 3: Schematic diagram of casing-cement sheath formation system.  
When \(r=r_{2}\), the \(q=q_{2}\), \(r=b\), \(q=-q_{3}\), the radial deformation of the formation hot area is as follows:  
\[\left\{\begin{aligned} u_{3i}&=c_{3i}^{1}q_{2}-c_{3i}^{2 }q_{3}+u_{T3i}\\ u_{3o}&=c_{3o}^{1}q_{2}-c_{3o}^{2}q_{3}+u_{T3o}\end{aligned}\right. \tag{14}\]  
When \(r=b\), the \(\sigma_{1}=q_{3}\), considering the assumption that the formation is infinite, the radial deformation of the formation's cold area is as follows:  
\[u_{4i}=\frac{q_{3}}{k_{b}} \tag{15}\]  
where  
\[k_{b}=\frac{(1-2v)\Big{(}\frac{c^{2}}{b^{2}}\Big{)}+1}{(1-2v)\Big{[}\Big{(} \frac{c^{2}}{b^{2}}\Big{)}-1\Big{]}}\frac{E}{(1+v)b} \tag{16}\]  
According to the principle of coordinated deformation, when \(r=a\) or \(r=b\), the deformation at the junction of the hot and cold zone meets Equations (14) and (15) at the same time, and the deformation of the casing sealing section can be obtained by combining Equation (12) with Equation (16).
# Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
## 3 Methodology  
### Optimal Tuning of the Fopid Controller for a Buck Converter  
The objective is to optimally tune the parameters of an FOPID controller, \(PI^{\lambda}D^{\mu}\) applied to a buck converter for minimization of a suitable objective function \(J\). In this paper, the performance indices of ISE, ITSE, ITAE, and IAE have been tested as objective functions for finding the optimal values of \(K_{p}\), \(K_{i}\), \(K_{d}\), \(\lambda\), and \(\mu\) using the CI algorithm. A performance index is based on the error signal and gives a quantitative measure used for evaluating the performance of a closed loop control system. In optimal control, the system parameters are adjusted so as to minimize this index [49]. The ISE is a performance index which gives more weightage to penalization of larger errors, and ignores smaller errors [50], and has been found to have more practical significance [51]. It is defined as:  
\[J_{1}=ISE=\int_{0}^{t}e^{2}(t)dt \tag{9}\]  
Here the error e is the difference between the reference voltage \(V_{ref}\) and the output voltage \(V_{o}\) of the converter, and can be evaluated from the circuit as \(e(t)=V_{ref}-V_{o}\). The time t can be chosen equal or greater to the settling time of the system. Similarly, the other indices are defined as:  
\[J_{2}=ITSE=\int_{0}^{t}te^{2}(t)dt \tag{10}\]  
\[J_{3}=ITAE=\int_{0}^{t}t\mid e(t)\mid dt \tag{11}\]  
\[J_{4}=IAE=\int_{0}^{t}\mid e(t)\mid dt \tag{12}\]