esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Whole-spacecraft launch-vibration isolation systems are attractive for achieving the goal of better, faster, cheaper, and lighter small satellites by reducing the design-load and vibration-test specifications for on-board components. In this study, a three-axis passive launch-vibration isolation system, based on superelastic shape memory alloy (SMA) technology, was developed to significantly attenuate the dynamic launch loads transmitted to a small satellite. This provides a superior damping characteristic, achieved by superelastic SMA blades stiffened by multilayered thin plates with viscous lamina adhesive layers of acrylic tape. The basic characteristics of the proposed isolation system with various numbers of viscoelastic multilayers were obtained through a static load test. In addition, the effectiveness of the design was validated through a launch environment simulating sine and random vibration tests.  
superelastic shape memory alloy; whole-spacecraft vibration isolation; small satellite; launch environment  
15 June 2021  
24 July 2021  
1 Dec 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
superelastic shape memory alloy; whole-spacecraft vibration isolation; small satellite; launch environment
## 3 The Hybrid Distributed Topology Control Scheme (HDTC)  
This section presents an inter-vehicle topology control protocol, HDTC, which combines the concept of the cone-based scheme [8] and the proposed time-slot scheme to govern a group of vehicles in a vehicular sensor network and to decide the manner of data exchange among vehicles with on-board sensors. The proposed HDTC consists of three phases. In Phase I, the modified cone-based scheme and the time-slot scheme are proposed for the initial selection of group members. Afterwards, a hybrid solution is introduced for selection refinement and forming a communication group. Note that the transmission range of a sensor defines the communication area in which it can interact with other sensor devices. On the basis of Phase I, a distributed routing scheme is developed in Phase II. In order to keep the local network structure stable, in Phase III, a scheme for reforming the communication/routing group is presented. Figure 2 shows the conceptual flow diagram of the HDTC scheme, where \(G_{u}\) and \(R_{u}\) are the sets of communication/routing groups for vehicle \(u\) and UB and LB are the upper and lower bounds of \(|G_{u}|\), respectively. Note that the function of the upper and lower bounds of \(|G_{u}|\) is to maintain the local communication structure, especially for a sparse network.  
Figure 2: Flow diagram of the hybrid distributed topology control (HDTC) scheme. UB, upper bound; LB, lower bound.  
The main assumptions are: (1) all sensors are homogeneous with the same transmission range; (2) the sensors have sensing and communication capability; (3) the position information of the source and destination vehicles is known; (4) the trajectory characteristics of the destination vehicle are known; and (5) periodic messages are transmitted at regular intervals to inform the neighboring vehicles. Note that there are no base stations to coordinate or supervise activities among sensors/vehicles.
## 3 Results and Discussion
### Carotenoid Profiling by UPLC-Q-TOF-MS  
To achieve the precise identification and quantification of the carotenoids present in _Monoraphidium_ sp., complete carotenoid profiling was performed. The major carotenoids identified are presented in Figure 4 and Table 5. In the extracted fraction of carotenoids, astaxanthin (Peak A), violaxanthin (Peak B), lutein (Peak C), 9-cis- \(\beta\) caroten (Peak D), and \(\beta\)-carotene-5,6-epoxide (Peak E) were detected with retention times of 28.07, 30.41, 31.76, 33.67, and 33.92 min, respectively. The existence of astaxanthin, violaxanthin, lutein, 9-cis-\(\beta\) caroten, and \(\beta\)-carotene-5,6-epoxide was confirmed by mass spectrometry and their presence was estimated to be 14.8%, 3.61%, 45.12%, 7.62%, and 24.21%, respectively, in the cells grown under the control conditions.  
Figure 4: UPLC-QTOF-MS chromatogram with ESI, positive ion mode of carotenoids in control (autotrophic cultivation under BBM medium) and optimized cultivation conditions. The identified carotenoids in samples are (A) astaxanthin, (B) violaxanthin, (C) lutein, (D) 9-cis-\(\beta\) caroten, and (E) \(\beta\)-carotene-5,6-epoxide. Detector response recorded in AU (absorption units) represented on y axis of chromatogram. The retention time is indicated by the numbers above each peak.  
Under optimized conditions, the type of carotenoids detected was the same as those in the control cells, but with variation in their quantity. Peaks A and B of the chromatogram showed that astaxanthin and violaxanthin had increased by 1.19- and 1.34-fold. Similarly, 9-cis-\(\beta\) caroten (Peak D) and \(\beta\)-carotene-5,6-epoxide (Peak E) showed a modest increase of 1.07-fold and 1.08-fold, respectively. However, lutein (Peak C) showed a decline of 1.32-fold under optimized conditions compared to the control. Similar to our findings, studies have shown that the concentrations of violaxanthin, \(\beta\)-carotene, and lutein were enhanced in _Tetruselmis striata_ CTP4 grown under intense heat and light stress [40]. _Asteracys quadricellularum_ PUMCC 5.1.1 also yielded 47.0, 28.7, 15.5, and 14.0 \(\mu\)g \(\beta\)-carotene, lutein, astaxanthin, and canthaxanthin mg\({}^{-1}\) dry biomass, respectively, under optimized culture conditions [41]. Many studies have shown that astaxanthin is a valuable secondary carotenoid produced under stress situations [42]. _Ankistrodesmus_ sp. has also shown an increased production of violaxanthin and astaxanthin when grown under nitrogen and light stress [43]. Additionally, the _Bracleacocus aggregates_ BMS5/15 strain produced sataxanthin and beta-carotene when grown in glass bubble-column photobioreactors [44]. The synthesis of carotenoids in microalgae is greatly influenced by the optimization of both nutrients and light [45]. As stated by several recent publications, further research on optimizing the culture conditions of inadequately characterized microalgae is necessary [18]. This study demonstrates that _Monoraphidium_ sp. carotenoids have potential applications in the health sector when grown under highly specialized, optimized culture conditions.
## 2 Materials and Methods
### The Methodology
#### 2.1.2 Feature Extraction and Selection
\[r_{XY}=\frac{Cov(X,Y)}{\sqrt{Var(X)Var(Y)}}=\frac{\sum_{i}(x_{i}-\overline{x}) (y_{i}-\overline{y})}{\sqrt{\sum_{j}\left(x_{j}-\overline{x}\right)^{2}\sum_{k }\left(y_{k}-\overline{y}\right)^{2}}} \tag{1}\]  
where \(X\) and \(Y\) are two random variables, \(Cov(X,Y)\) is the covariance of the two variables, \(Var(X)\) and \(Var(Y)\) are the variance of the variable \(X\) and \(Y\), respectively, \(x_{i}\) and \(y_{i}\) are two observations of \(X\) and \(Y\), respectively, and \(\overline{x}\) and \(\overline{y}\) are their mean values. The Pearson correlation coefficient, \(r_{XY}\), is always included in the range \([-1,\ 1]\), where \(-1\) indicates that the two variables are negatively correlated, 1 indicates that the variables are positively correlated, and 0 that the variables do not correlate. After the correlation matrix construction, an iterative process for feature elimination is conducted. Hence, for each column, and for each row, if \(r_{XY}\leq-0.9\) or \(r_{XY}\geq 0.9\), the column is eliminated. The Pearson correlation analysis does not consider the effect of each feature on the classification accuracy. For this reason, the RFE, which is a wrapper model, is also applied to evaluate the selected features' goodness. The model adopted within the RFE is the Decision Tree.
## 1 Introduction
In [22], simultaneous processing of images and signals from inertial sensors was employed to estimate six degree-of-freedom head movements. In the above-mentioned studies, the first processing step in analyzing IMU signals is to parametrize the signals for predefined analysis time windows [17; 18]. The extracted parameters are then used to build appropriate classifiers that recognize given types of movements. In a recent work [23], novel methods for recognizing human physical activity that are based on the so called symbolic representation algorithms were presented. Using three database sets, the authors have shown that their approach performs best in terms of accuracy, processing time, and memory consumption in comparison to other classic approaches that were based on supervised classification techniques. In another very recent work [23], IMU were applied to measure the 3D range of motion of the trunk and lower limb joints. The results of this study show that inertial sensors can be successfully applied to investigate maladaptive movement strategies. In our study, we propose a hands-free interface enabling blind persons to control the menu of a navigation device by means of head movements. We also envision that such an interface can serve as a communication aid for people with serious motor disabilities, e.g., for the paraplegic individuals. The main contribution of our work, which has not been addressed in earlier studies [17; 18; 19; 24], is the comparison of the parametric and the time domain representations of IMU signals on which different classifiers are trained to recognize four head movement patterns. Similarly to the work reported in [25], in which IMU were used to recognize types of physical activities, we studied the dependence of different lengths of time windows on the parametric representation of inertial signalsto recognize different types of head gestures. In our work, we show that time domain approaches to analysis of inertial signals can outperform parameter-based ones and are less computationally demanding. The latter feature is particularly important for mobile implementations of the interface. The ultimate motivation for our research is to develop user-friendly and efficient electronic travel aids that will help the visually impaired retain orientation and mobility in unfamiliar environments [26; 27]. The remainder of this paper is organized as follows. In Section 2, we describe the experimental methods that we used for data recording and data pre-processing. We also shortly review the applied data classifiers. In Section 3, we present the head gesture recognition results for the parametric and time domain representations of IMU signals. Finally, in Section 4 we apprise the achieved results, show the envisioned applications of our study, and point out limitations of the presented work.
## 3 Results
### The Dominance of Water Stress to Vegetation Growth in Global Drylands
#### 3.3.2 Differences in the Dominance of SW at Different Depths to Vegetation Growth  
Further, this study explored the differences in the contributions of SW to LAI changes in global drylands during 1982-2018 at different soil depths. The findings showed that, at various soil depths, the contributions of SW to LAI changes were spatially consistent across global drylands from 1982 to 2018 (Figure 7). Decreasing SW negatively contributed to LAI changes in most global drylands at different soil depths, primarily distributed in middle latitude areas of the northern hemisphere, southern South America, and eastern Australia. The area proportion for SW-stressed ecosystems accounted for approximately 64.22%, 64.26%, 62.99%, and 55.93% of global drylands at soil depths of 0-7 cm (Figure 7a), 7-28 cm (Figure 7b), 28-100 cm (Figure 7c), and 100-289 cm (Figure 7d), respectively, as presented in Table 3. Correspondingly, SW at different soil depths positively contributed to LAI changes in over 35% of vegetated areas in global drylands, defining non-SW-stressed ecosystems, mainly covering western India, southern Africa, the southern Tibetan Plateau, and northwestern Australia. Intriguingly, area proportions of SW-stressed ecosystems gradually decreased to the lowest levels with increasing soil depths across global dryland during 1982-2018, which further revealed that decreasing SW on the second (7-28 cm) and third (28-100 cm) soil depths produced more significant water constraints to vegetation growth in global drylands compared to SW at the 100-289 cm soil depth.  
Figure 6: Spatial patterns of the contributions in LAI changes caused by (**a**) rising CO\({}_{2}\) concentration, (**c**) decreasing SW, and (**e**) increasing atmospheric VPD. Their latitude differences in the contributions are shown in (**b**), (**d**), and (**f**), respectively.  
Satellite-Observed and Process-Based Models Simulated Offsets in the Contribution to Vegetation Growth Between CO\({}_{2}\), SW, and VPD
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 5 Simulation Results  
In this section, we test the performance of ASPF based on the trace obtained from a small power system in China. In order to validate the proposed algorithm, we apply it in the original forecasting PV power which is predicted by radial basis function NN and Multiple Linear Regression (MLR). The algorithms including the improved _k-means_, LARS, BPNN and ASPF are verified via simulations and comparisons.
# Technology Behavior Model--Impact of Extended Reality on Patient Surgery
## 2 Literature Review
### Image  
Extended reality, a hybrid application of virtual and augmented reality, is a concrete manifestation of extended reality, which combines physical reality with virtual reality in 3D technology [26]. Smart glasses are connected to a computer PDM system through a parametric XR system to ensure command recognition [27]. The system treats each marker as a uniquely identified part by planar coordinates, facilitating the assignment of the next step while facilitating the compensation of errors within the model. By processing data analysis and information compensation, the values of multiple components are superimposed to form a three-dimensional image. For missing data, a secondary check is performed to ensure continuous data transfer from the data model [28]. During mixed reality data acquisition, information connection and data exchange between real objects and corresponding PDM elements are marked with different colors and semi-transparent codes to achieve one-to-one correspondence. In constructing stereo images of the human body, the raw 3D data obtained from the dataset are derived from the skeletal structure of the human body, combined with actual data collected by CT, NMR, and NMR thin real-intensity scans [29]. The system outlines the nerves, arteries, veins, and endothelium of organs in 3D, facilitating sectioning tools for segmentation and observation of regions of interest and image processing of lesions. The problem of poorly defined, partially deconstructed edges can be resolved by identifying 2D CT and MR images, and the repetitive jump from CT slices to XR visualized structures is enhanced by system inscriptions [30]. The holographic estimation of the model is performed by different color coding. Computer-aided software allows for 3D processing and editing, converting fragmented and scattered individual decomposition images into a collection of STL files for uploading to the smart glasses display [31]. A spatial model is created based on the multiplication of planar and surface coordinates, and with interactive control, the translucent model can be superimposed on the patient's body to form a superimposed image that determines the exact location of the data assignment and improves surgical efficiency. To precisely determine the exact location and direction of motion in the enclosed organ, the target is measured using different signals, using satellite sensor data for inertial localization, combined with the annotation of optical methods. This is an important hybrid dynamics basis for mixed reality optical imaging, enhancing the imaging clarity of the image. The 3D human structure spatial coordinates are overlaid by a reference coordinate system capable of coinciding with spatial locations and times that are not visible to the real-world naked eye, resulting in a gaze vector that enhances the visibility of the human eye.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 3 Results and Discussion
### Energy Awareness and Wind Resilience
#### 3.3.2 Energy Awareness Simulation  
The energy-aware decision-making process for the selection of the first charging visit along the path was assessed under the proposed throttle simulation conditions for an assumed station coordinate of \((270,140)\). All three possible conditions discussed in the Section 2 were implemented and are shown in Figure 15.  
Figure 16 represents the function \(F(n)\) based on the optimization of \(n\), where the points of interest \(j\) along the path satisfy  
Figure 14: Throttle and true air speed along path at a ground speed of 15 mph.  
Figure 15: Distance to charging station, estimated range and points of interest along the trajectory.  
\[\min_{n=j}F(n)\text{ s.t. }F(n)>0.\]  
The plot shows that the above condition is satisfied in the first 747 steps.
# Wideband Vibro-Acoustic Modulation for Crack Detection in Wind Turbine Blades
###### Abstract  
Wind turbines (WT) are a popular method used in energy production, but blade failure and maintenance costs pose significant challenges for the industry. Early detection of blade defects is vital to prevent collapse. This paper examines the modulation of blade vibrations via low-frequency blade rotation, mirroring the vibro-acoustic modulation (VAM) method. Specifically, we study the modulation of blade vibrations, which are generated via blade interactions with air turbulence and have a wide frequency range. These vibrations are modulated by the alternating bending stress experienced during blade rotation. For the simulation of VAM, we employ a simple breathing crack model, which considers a mechanical oscillator with parameters that are periodically changed in response to low-frequency blade rotation. The modulation of the wideband signal by blade rotation can be extracted using the detection of envelope modulation on noise (DEMON) algorithm. This model was applied for the estimation of the modulation of a large (52-m-long) WT blade. Steel specimens have been used in laboratory experiments to demonstrate the feasibility of VAM using a probe broadband noise signal. This paper presents the first work to experimentally and theoretically apply wideband signals in VAM. It further explores the analysis of the use of natural vibrations within VAM for the SHM of WT blades.  
widespread vibration; vibro-acoustic modulation (VAM); crack detection; wind turbine blade +
Footnote †: journal: Applied Physics Letters  
12 July 2023  
Revised: 4 August 2023  
Accepted: 17 August 2023  
Published: 24 August 2023
# CPM-OFDM Performance over Underwater Acoustic Channels
## 3 System Architecture  
In this section, we briefly explain the various components of the CPM-OFDM transceiver and the continuous phase modulation.
###### Abstract  
In this study, a particle swarm optimization with a sigmoid increasing inertia weight (SIPSO) algorithm is proposed for structural damage identification based on the optimization of structural vibration response constraints. In view of the existing problems for particle swarm optimization algorithms used for structural damage identification, such as low accuracy of damage identification and easy misjudgment of damage location, the sigmoid increasing inertia weight is introduced to improve the global and local search ability of the algorithm. Simulation results show that the parameters of the sigmoid increasing inertia weight have a significant effect on the performance of the SIPSO algorithm for structural damage identification. Compared with similar improved particle swarm optimization algorithms, the SIPSO algorithm has some advantages of fast convergence speed, high identification accuracy, and strong robustness ability in structural damage identification.  
structural damage identification; particle swarm optimization; inertia weight; identification accuracy
# Droop Control Strategy of Utility-Scale Photovoltaic Systems Using Adaptive Dead Band
## 2 Reactive Power-Voltage Droop Control Method
### Proposed Reactive Power-Voltage Droop Control Method
\[Q_{req}=\frac{\Delta Q_{n}}{\Delta|V_{n}|}\Big{(}V_{POL,ref}-V_{POL,max}\Big{)} \tag{11}\]  
From the result obtained from the power flow calculation, Equation (12) is used to determine the voltage at the point where the limit value of the reactive power, \(V_{\text{L,i}}\), begins, as well as to derive the maximum power from the result of the network data. The maximum reactive power at a specific point in time is derived from Equation (4), and \(V_{\text{L,i}}\) is calculated from the voltage value of the slack bus, \(V_{\text{slack}}\), and the admittance matrix value, \(Y_{\text{pv,slack}}\), between the PV and slack buses. The dead band value for each hour is calculated using the values of \(Q_{max}\), \(V_{L}\), \(V_{op}\), and droop coefficients calculated from the defined formula. \[Q_{max,i}=-V_{L,i}^{2}\big{|}Y_{pv,slack}\big{|}+V_{slack}V_{  
PV, admittance matrix, historical PV output, and load curve, is used to calculate the power flow and identify the bus voltage, phase angle, and real/reactive power injection of the system. Second, a procedure for checking the value of reactive power-voltage control according to the dead band range is followed. In case of the first iteration, this process proceeds with the initially set dead band range and information on the reactive power value according to the voltage change is checked by the Jacobian matrix, and a 3D graph of the voltage change and reactive power change over time are extracted. However, if the constraint condition is not met in contingency analysis, the reactive power-voltage control is readjusted by changing the dead band by modifying the reference voltage or droop coefficient value according to Equation (13). Third, contingency analysis is then checked whether the system is stable against disturbance, and a detailed description of the disturbances is provided in Section 3.2. In evaluating the system stability and constraints, it is verified whether the range of reactive power capability is satisfied and whether the degree of overshoot and rise time of the voltage profile, called the performance indicator, satisfy the limited set values. Thus, the dead band value of the plant controller connected to the large-scale PV every hour is set such that the system operation in response to disturbance becomes possible.