esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps
## 2 Literature Review
### Material Efficiency and Turbine Life Extension by Blade Retrofit  
[PERSON] et al. [40] point out that global stocks for most materials are enough to meet anticipated demand for buildings, infrastructure, equipment, and products, but that environmental impacts of materials production and processing, particularly those related to energy, are becoming critical. These impacts can be mitigated by process efficiencies, but the increase in overall impacts will be unacceptable unless material production and processing is reduced through longer-lasting products, modularization and remanufacturing, component re-use, and designing products with less material.  
[PERSON] [41] noted that wind turbines produce low carbon energy but contain large amounts of materials. For maximum resource effectiveness, strategies should be adopted for extending the lifetime of wind turbines, including retrofitting same length blades to restore output or longer blades made from carbon/glass hybrids to increase output. A refurbished wind turbine must have the same performance in terms of engineering specifications and power generation that the turbine had before refurbishment. Turbine lifetime is also determined by other factors, while economic lifetime is often shorter than technical lifetime.  
[PERSON] [42] defined the capacity factor as the ratio of electricity produced by a turbine over a period divided by the amount of output produced had it operated at full nameplate capacity for the same period. The capacity factor must be at a certain minimum level for wind turbines to operate feasibly. [PERSON] observed mean values below 21% in 2009 [42], while more recent observed values are between 37% and 40% [43]. [PERSON] and [PERSON] [44] observed that various issues can affect the capacity factor, for example, turbine downtime, which can result in a 4-7% fall in output. [PERSON] [45] noted that the capacity factor declines significantly as the turbine ages, with declines of 0.9% per year for the first 10 years of operation from a starting point of 24% to 11% in year 15. [PERSON] and [PERSON] [44] observed a decline of 0.45% per year and noted a decline rate two to three times higher for turbines built before 2003. [PERSON] [46] found that performance is adversely affected by dirty or pitted blades. [PERSON] [47] observed that General Electric (2014) replaced 77 m blades with a 91 m retrofit, boosting annual electricity production by 20%.
# Robust Image Embedded Watermarking Using DCT and Listless SPIHT
## 2 Related Work  
SPIHT is a tree-based image compression algorithm. It scans the transformed coefficients in a depth first search. Set partitioning embedded block (SPECK) is block based image compression algorithm [37]. However, it scans the coefficients using a breadth-first approach. The listless variants of SPIHT and SPECK are called Listless SPIHT (i.e., NLS) and Listless SPECK (LSK), respectively [36; 38]. These listless algorithms have been proposed to overcome the memory management problems associated with SPIHT and SPECK. Listless SPIHT and LSK can compress images from a lossy to a lossless format. These algorithms generate a bit string in order of importance. That means that the lower part of the bit string contains more information of the image than higher part of the bit string. The user can truncate at any point of the compressed bit string to reconstruct an image, which is a compressed representation of the original image. However, none of these algorithms are used in image/video watermarking schemes in the literature. Most of the works in literature use JPEG/JPEG 2000 for compression of the watermarked images. Compression using listless SPIHT and LSK has superior performance than JPEG and is highly competitive with JPEG 2000 [38]. JPEG 2000 is computationally intensive because of context modelling and adaptive arithmetic coding. Listless SPIHT and LSK are much less complex and take less memory for compression and reconstruction. The authors considered a modified listless SPIHT for compression of the watermarked image from lossy to lossless, because compression is considered as one of the attacks in the watermarking scheme [39; 40].  
CRT-based encryption technique provides additional security and robustness against the existing schemes. [PERSON] et al. proposed DCT-based CRT techniques that provide better robustness against various attacks [25; 26]. In order to achieve the merit of both CRT and listless SPIHT, we proposed a blind watermarking scheme which uses modified listless SPIHT (NLS) that combines with DCT coefficients and CRT encryption technique.
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 1 Introduction
### Contributions  
Indeed, motivated by the above-mentioned factors, in this paper, we jointly consider the influence of channel state information and transmission bit rate and propose a channel and bit adaptively power control (APC) strategy for uplink NOMA VLC systems. Since the received constellation at the photodiode (PD) receiver has a nice structure under our proposed APC strategy, we develop a low-complexity maximum likelihood (ML) demodulation and decoding algorithm. Simulation results show that our proposed APC strategy outperforms the GRPA, FPA, and TDMA schemes.
# High-Efficiency Navigation of Nonholonomic Mobile Robots Based on Improved Hybrid A* Algorithm
## 5 Conclusions and Further Work  
The nonholonomic robot with autonomous navigation was developed in this paper. The improved hybrid A* algorithm with Chebyshev weighting was applied in the path planning process to increase the efficiency of the navigation. The development of the self-path planning method was conducted in the following processes: (1) the construction of maps via the Robot Operating System (ROS), (2) the self-positioning of the robot via the adaptive Monte Carlo (AMCL) algorithm, (3) the global path planning via the improved hybrid A* algorithm, and (4) the local path planning via the model predictive control (MPC) strategy. By testing the performance of the algorithm in the simulation and practical environments, it was found that the improved hybrid A* algorithm outperformed the conventional A* algorithm in both search time and search length. These improvements make it an ideal choice for mobile chassis with nonholonomic constraints. Compared with the conventional A* algorithm, the average search time was reduced by 12.62-24.5%, and the average search length was reduced by 9.25-12% at different obstacle rates. In the practical navigating tests, the average time of search was reduced by 18-24%, and the average length of search was reduced by 10.3-12%, while the overall path was smoother. These results demonstrate the superior performance of the improved algorithm in various working environments.  
Consideration for moving obstacles is an inevitable aspect of future work. With the evolution and increasing accessibility of autonomous navigation technologies, the rapid detection and avoidance of mobile objects become an imperative concern [35]. Future research focus will be on the development of new sensors, construction of more accurate obstacle-prediction models, and implementation of faster path-planning algorithms. Furthermore, applying artificial intelligence and deep learning in navigation algorithms can result in more precise prediction and avoidance of moving obstacles. The widespread implementation of these technologies will advance the maturity and expansion of robotics.  
Conceptualization, Z.C. and G.L.; methodology, Z.C.; validation, G.L.; investigation, Z.C. and Q.H.; resources, Z.Y. and Q.W.; data curation, Q.H.; writing--original draft, Z.C.; writing--review & editing, G.L. and S.D.; visualization, Z.Y., Q.W. and G.L.; supervision, G.L. and S.D.; funding acquisition, S.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
Figure 14: Path generated via different algorithms in the practical navigation experiment: (**a**) conventional A* algorithm and (**b**) improved hybrid A* algorithm.
# Proxy-Based Semi-Supervised Cross-Modal Hashing
## 4 Experiments
### Sensitivity to Hyper-Parameters  
\(\alpha\) and \(\beta\) represent the relative importance of two types of loss in the total loss. Specifically, \(\alpha\) denotes the importance of the loss \(\mathcal{L}_{h}^{l_{d}}\), which constrains the relationship between data and data. Meanwhile, \(\beta\) indicates the importance of the loss \(\mathcal{L}_{h}^{l_{p}}\), which constrains the relationships between data and categories. We investigate the sensitivity of the parameters \(\alpha\) and \(\beta\). We set their ranges to 0.001, 0.01, 0.05, 0.1, 0.5, 0.8, 1. As shown in Figure 4, when either \(\alpha\) or \(\beta\) is close to 0, the retrieval performance decreases. When \(\alpha\) and \(\beta\) are set to 0.5 and 1, respectively, the model performs optimally. The model is still quite sensitive to hyper-parameters \(\alpha\) and \(\beta\). This also validates that, during model training, it is essential to  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**MIRFLICKR-25K**} & \multicolumn{4}{c}{**NUS-WIDE**} & \multicolumn{4}{c}{**MS COCO**} \\ \cline{2-11}  & & **16 Bits** & **32 Bits** & **64 Bits** & **16 Bits** & **32 Bits** & **64 Bits** & **16 Bits** & **32 Bits** & **64 Bits** \\ \hline \multirow{4}{*}{Img2 Txt} & PSSCH-1 & 0.783 & 0.789 & 0.801 & 0.661 & 0.669 & 0.674 & 0.641 & 0.672 & 0.711 \\  & PSSCH-2 & 0.711 & 0.723 & 0.732 & 0.604 & 0.615 & 0.624 & 0.610 & 0.623 & 0.644 \\  & PSSCH-3 & 0.764 & 0.771 & 0.784 & 0.643 & 0.652 & 0.659 & 0.638 & 0.664 & 0.697 \\  & PSSCH (Ours) & **0.794** & **0.797** & **0.818** & **0.666** & **0.678** & **0.684** & **0.644** & **0.689** & **0.723** \\ \hline \multirow{4}{*}{Txt2 Img} & PSSCH-1 & 0.771 & 0.776 & 0.786 & 0.665 & 0.673 & 0.682 & 0.645 & 0.684 & 0.709 \\  & PSSCH-2 & 0.703 & 0.709 & 0.713 & 0.594 & 0.603 & 0.609 & 0.604 & 0.618 & 0.625 \\ \cline{1-1}  & PSSCH-3 & 0.752 & 0.758 & 0.767 & 0.641 & 0.647 & 0.658 & 0.630 & 0.662 & 0.689 \\ \cline{1-1}  & PSSCH (Ours) & **0.774** & **0.787** & **0.803** & **0.671** & **0.683** & **0.692** & **0.657** & **0.702** & **0.728** \\ \hline \hline \end{tabular}
\end{table}
Table 4: mAP results of PSSCH and its variants on MIRFLICKR-25K, NUS-WIDE, and MS COCO. The best results are in bold font.  
consider both the relationship between data and data and the relationships between data and categories.
## 4 Results
### Environmental Isotopes  
Oxygen \(\delta^{18}\)O and hydrogen \(\delta^{2}\)H are ideal tracer isotopes that can be used to determine groundwater recharge and mixing sources. They form part of a water molecule that does not contribute to geochemical reactions; therefore, they provide good insights into the physical processes affecting groundwater, such as groundwater mixing and evaporation [49; 50]. Based on isotopic data from groundwater samples collected in November 2018 and other historical records [23; 24; 51], isotope analysis results show that Oxygen \(\delta^{18}\)O in Quaternary groundwater ranged from \(-5.51\)% (well 54) to \(+4.7\)% (well 28), from \(-5.9\) % (well 26)  
Figure 6: Piper diagram [46] showing the major ion water types of all water samples. Chemistry of the rainwater sample [48].  
to +4.9% (well 38) in PNA groundwater, and from \(-\)9 % (well 4) to \(-\)4.81 % (well 103) in NSA groundwater. The hydrogen \(\delta^{2}\)H isotopes in the Quaternary aquifer ranged from \(-\)40.87 % (well 54) to 37% (well 32), from \(-\)58% (well 26) to 43% (well 38) in the PNA, and from \(-\)71% (well 4) to \(-\)33.22% (well 93) in the NSA. The \(\delta^{18}\)O-\(\delta^{2}\)H relationship (Figure 7a and Table 2) shows that most groundwater samples fell close to the global meteoric water line (GMWL, [52]), indicating that they were mainly of meteoric origin.  
Figure 7: (a) \(\delta^{18}\)O (%) versus \(\delta^{2}\)H (%) and CI\({}^{-}\) (mg/L) (b) \(\delta^{18}\)O (%) versus CI (mg/L) for groundwater samples tapping different aquifers in the Qena Basin, (GMWL, from [51]). Isotopic data serials greater than 48 are from [23; 24; 51]; the rainwater sample is from [53].
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 4 Performance Evaluation of Improved ACA
### Advantages of Multilevel Adaptive Path Planning  
The multilevel adaptive path planning scheme consisted of the primary, secondary, and tertiary path-planning algorithms. As the applied area differed, the environment modelling and path planning in these algorithms varied as well. Figure 20 shows the meaning of multi-level adaptive path planning.  
The multilevel adaptive path-planning model had the following advantages.  
1. The model reduced computing overheads. For a given area to be studied, the computing overhead was proportional to the computing accuracy. Nearly unbearable computing overheads are required to plan a path with a meter-level accuracy in an off-road environment with a magnitude of several hundred meters by traditional methods. The multilevel adaptive method, however, divided the path planning task into three steps, which dispensed with the need for rigorous environment modeling and hence balanced computing accuracy with computing difficulty.
2. The model was applicable to off-road areas of different sizes. In the multilevel adaptive level, the primary and secondary path-planning algorithms did not consider the problem of road networks, but in the tertiary planning, the highway information was considered. Therefore, paved roads were prioritized in path planning to achieve universality for different off-road environments.  
Figure 19: The route achieved by tertiary path planning.  
Figure 20: Path planning by the multilevel adaptive model.
# Exosomal miR-92a Concentration in the Serum of Shift Workers
## 2 Materials and Methods
### Determination of Serum Exosomal miR-92a
#### 2.3.1 MiRNAs Extraction  
Exosomes were extracted from 1 mL of serum using a \"Total Exosome Isolation (from serum)\" Kit (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA) according to the manufacturer's instructions. Exosomal proteins were then quantified through the Bradford method and 20 \(\upmu\)g of exosomal protein were diluted in 250 \(\upmu\)L of PBS for the following RNA extraction process. We used the same quantity of exosomal protein to prevent differences in miR-92a related to the total amount of exosomes. Before RNA isolation, and to avoid further technical errors during the subsequent quantification of miRNAs by qRT-PCR, 60 fmol of a synthetic spike-in small RNA from _Caenorhabditis elegans_ (cel-miR-39) were added to the samples as external normalizer [51]. Total RNA was then extracted by adding 750 \(\upmu\)L of Tri-Reagent BD (Sigma-Aldrich, Saint Louis, MO, USA) and 200 \(\upmu\)L of chloroform. The Phase Lock Gel (Eppendorf, Hamburg, Germany) was used to enhance the phase separation. Finally, miRNAs were isolated using the Purelink miRNA Isolation Kit (Invitrogen; Thermo Fisher Scientific, Inc.) and eluted in a final volume of 40 \(\upmu\)L of RNase-free water, centrifuging twice 20 \(\upmu\)L to improve the yield. Samples were stored at \(-80\)\({}^{\circ}\)C until reverse transcription.
# Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees
## 2 Materials and Methods
### Experimental Procedures  
We evaluate two major applications of the technique. First, we show that the generated samples can be used for debugging the ML model through data inspection, resembling tasks T1-T4 from [8]. Second, we examine the quality of the downstream ML model trained directly on synthetic samples, thus demonstrating a possibility of solving T5 (data labelling/annotation) as well.  
In the debugging experiment, we attempt to detect a more subtle bug compared to [8]: an incorrect image rotation that yields lower model performance. While the pixel intensity inversion can be easily spotted using low-fidelity synthetic samples, image rotation requires higher fidelity to be detected.  
Downstream learning experiments are set up as follows:  
1. Train the generative model (_teacher_) on the original data under privacy guarantees.
2. Generate an artificial dataset by the obtained model and use it to train ML models (_students_).
3. Evaluate students on the held-out real test set.  
We use two image datasets, MNIST [35] and Fashion-MNIST [36]. Both have \(60,000\) training and \(10,000\) test examples, where each example is a \(28\times 28\) size greyscale image. The task of MNIST is handwritten digit recognition, while for Fashion-MNIST it is clothes type recognition. Although these datasets may not be of particular interest from the privacy viewpoint, this choice is defined by the ability to compare to prior work.  
Our evaluation is implemented in Python and Pytorch ([[http://pytorch.org](http://pytorch.org)]([http://pytorch.org](http://pytorch.org)), accessed on 29 May 2022). Source code is included in Supplementary Material; it will be released publicly upon publication. For the generative model, we experimented with variations of Wasserstein GAN [37] and WGAN-GP [34], but found the former to produce better results, probably because gradient clipping is already a part of the privacy mechanism. Our critic consists of three convolutional layers with SELU activations [38] followed by a fully connected linear layer with another SELU and then a linear classifier. The generator starts with a fully connected linear layer that transforms noise (and possibly labels) into a 4096-dimensional feature vector which is then passed through a SELU activation and three deconvolution layers with SELU activations. The output of the third deconvolution layer is down-sampled by max pooling and normalised with a tanh activation function.  
All models are trained using Adam with the learning rate 0.0001. The clipping threshold for gradients of discriminators is set to 0.5, no clipping is performed for generators,and the noise standard deviation is 0.01 for MNIST and 0.02 for Fashion-MNIST. The reported accuracy and privacy were achieved with 400 epochs for both datasets.  
Although we use centralised setting throughout this section, the results are readily transferable to federated scenarios. Previous work suggests that neither GAN sample quality [24] nor BDP guarantees [32] should be significantly affected.
# Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model
## 5 Supplementary Analysis--Kurtosis  
One of the statistical quantificators providing the information on the distribution of the results is the kurtosis. With the mentioned parameter, the results obtained of the variable deformation are recalculated for the variable eccentricity output (Figure 9). As the dynamic response has a sinusoidal character, the value of kurtosis for sine and cosine function is close to 1.5 [87]. As the influence of the nonlinear effects increases with the eccentricity, the value of kurtosis is getting bigger within. Kurtosis is increasing linearly till the \(ecc=0.25\), what is the effect of increasing nonlinearities introduced into the system. The moment when the bearing is getting into the chaotic motion is for the \(ecc=0.35\), when the value of kurtosis is twice higher than for pure periodic function. When the value kurtosis = 3 or higher, it is the information about the intermittences in the analyzed system [88]. The general character of the kurtosis is similar to the course of recurrence quantificators based on the vertical lines.
## 3 Results and Discussion  
Unit cell and array metamaterial analyses are discussed in this section. Parametric studies, such as substrate type and metamaterial structure design, are also presented in order to compare and identify the optimal performance of the metamaterial structure. The effective medium parameters and S-parameter results from simulation and experiment are thoroughly discussed to determine whether the proposed oval-square-shaped SRR metamaterial-based structure can be classified as having left-handed metamaterial properties suitable for satellite and radar applications.
## 3 Results and Discussion
### Most Productive Authors  
The information obtained from WoS on neurotoxicants between 2011 and 2020 indicated that 1467 researchers authored the 321 documents retrieved for this analysis. Similarly, the authors per document in this study was 4.57, thus, showing the strength of collaboration exhibited by researchers in this field. Table 2 shows the 20 most productive authors in this research area, with an h-index ranging from 2 to 10 and citations ranging from 44 to 835. The h-index has always been used to assess the worth of scientific outputs [43]. It is also used to estimate how impactful (by citations) and productive an author or group of authors is/are within an institution or a country [44]. Scientists, journals, institutions, and countries are usually compared using the h-index value, which is based on a list of published articles arranged in order by the times cited count, calculated using the principle that h articles are cited h times at minimum [45]. However, the comparison among researchers using the h-index must be within a particular field because it is considered unfit for such purpose across lines of discipline. Notwithstanding, it is a very important tool in bibliometric analysis, as it always accurately reflects the attainment and contribution to scientific knowledge of individual authors [46; 47].  
In the present study, the relevance of authors, institutions, and countries with respect to their contributions to knowledge concerning neurotoxicants, their environmental effects, and accompanying neurological disorders were assessed. The impact of research documents is determined most by the number of times research documents are cited by other researchers. Although a citation may not be the perfect measure of impact, it provides some key information that would help to reach a reasonable conclusion on the matter [48; 49]. As indicated in Table 2, which contains the top 20 authors, [PERSON] and [PERSON] contributed 14 (4.36%) and 13 (4.05%) articles for each of the 321 documents retrieved from WoS, respectively, and had the highest h-index of 10 and 9, correspondingly. However, [PERSON] (h-index = 4), who was ranked 14 th on the list, had the highest number of citations, with 835 citations, compared to the top two authors, with 331 and 460 citations, respectively, despite his fewer number of published articles in the field. This showed that citations are not only influenced by the number of articles produced by a researcher and/or h-index but also by the publication year [31].  
Figure 1: Number of publications (N) and total citation per year from 2011 to 2020.
# The Impact of Space-Based AIS Antenna Orientation on In-Orbit AIS Detection Performance
## Materials and Methods
### Analysis Method
#### 2.3.2 Individual Message Reception Performance
Finally, from the number of received messages on a certain area of interest, the information regarding which antenna orientation has the better performance on that area for a particular ship will be discovered.