esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Reliability Method with Multiple Shape Parameters Based on Radial Basis Function
## 3 The Proposed Approach: MSRBF-MCS  
In the Kriging model-based sequential sampling, researchers always aim to find the candidate sample points that are the closest to the LSF and have a high local uncertainty  
\begin{table}
\begin{tabular}{c c} \hline
**Type Name** & **Expression** \\ \hline Gaussian & \(\phi(r)=e^{-\left(r/c\right)^{2}}\) \\ Multiquadric & \(\phi(r)=\sqrt{1+\left(r/c\right)^{2}}\) \\ Inverse multiquadric & \(\phi(r)=\left(1+\left(r/c\right)^{2}\right)^{-1/2}\) \\ \hline \end{tabular} Note: \(c\) is the shape parameter, and its value range is (0, 1).  
\end{table}
Table 1: Common types of kernel functions.  
at the same time [25]. However, the prediction based on the RBF model does not offer the prediction variance. Hence, it is necessary to launch multiple predictions to obtain the prediction variance [25,26]. To the best of our knowledge, the method to launch multiple predictions by creating multiple RBF models with different shape parameters \(c\) has not been presented yet. By setting different values of \(c\), different RBF models are created. The global uncertainty of each RBF model is calculated via cross-validation, and the weighting coefficients of each model are obtained accordingly. Then, the weighted prediction's mean, variance, and the distance-related factors are used to formulate the learning function.
## 5 Discussion
### Limitations and Future Work  
This study has several limitations. First, it was based on a single company case within a specific domain. The participants of the study were company employees (trainers and installation technicians) with advanced or expert skills in elevator maintenance and installation, which results in both advantages and drawbacks. On one hand, the expert perspective and domain knowledge is critical when testing future technological adoptions which they will possibly be using. On the other hand, the effects on situated learning and knowledge transfer might be lower for novice maintenance trainees due to a lack of existing experience and knowledge. Therefore, future work should include more investigation with novice learners to make a complete statement on the usefulness of asymmetric VR for remote training.  
Co-located use of asymmetric VR during face-to-face training could also be explored further, similar to [33]. A hybrid system incorporating multiple connected devices with traditional displays and HMDs could improve the viewing experience for the content which is not adequately displayed by mirroring the HMD view; this solution especially makes sense for groups when there is not enough time for everyone to try the VR system individually and is more cost-effective than simply scaling the number of HMDs. Whenexperimenting with hybrid and distributed systems for training purposes, attention should be paid to the evaluation of the transfer and learning effects, similar to [36].  
In our study, the \"Research Panel\" data could easily be analyzed manually. Future work could explore automated methods and different visualizations, for example, video overlays or subtitles, which may be required for larger amounts of data. Subjective feedback on lessons may have a positive bias due to social factors; future work could concentrate on ways to detect and correct a possible bias by including new types of indicators. These should be synchronized with other session recordings as was conducted in our research panel implementation.
## 3 Results
### Average Number of Two-Spotted Spider Mite (TSSM) Eggs Per Leaf
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & **Sampling Height** & \\ \hline  & & **h1** & **h2** & **h3** \\ \hline
10 July & Control & 0.06 \(\pm\) 0.02 Aa & 0.83 \(\pm\) 0.35 Aa & 0.57 \(\pm\) 0.41 Aa \\  & Biological & 8.53 \(\pm\) 3.82 Be & 2.93 \(\pm\) 1.25 Bab & 5.8 \(\pm\) 2.29 Bab \\  & Acaricide 1 & 1.03 \(\pm\) 0.83 Aa & 0.23 \(\pm\) 0.14 Aa & 0.23 \(\pm\) 0.14 Aa \\  & Acaricide 2 & 0.30 \(\pm\) 0.20 Aa & 0.53 \(\pm\) 0.36 Aa & 0.23 \(\pm\) 0.16 Aa \\
17 July & Control & 2.37 \(\pm\) 0.91 Ab & 4.93 \(\pm\) 2.25 Ac & 5.33 \(\pm\) 1.84 Abb \\  & Biological & 3.40 \(\pm\) 1.14 ABab & 2.87 \(\pm\) 1.24 Aa & 3.40 \(\pm\) 0.99 Aa \\  & Acaricide 1 & 3.43 \(\pm\) 1.50 Bb & 4.27 \(\pm\) 1.39 Ab & 10.57 \(\pm\) 3.49 Be \\  & Acaricide 2 & 2.43 \(\pm\) 1.35 Ab & 3.73 \(\pm\) 1.25 Ac & 7.17 \(\pm\) 2.03 Abc \\
29 July & Control & 8.37 \(\pm\) 3.66 Bd & 4.63 \(\pm\) 1.87 ABb & 11.30 \(\pm\) 3.60 Ce \\  & Biological & 3.13 \(\pm\) 0.75 Aa & 5.27 \(\pm\) 1.36 Bab & 8.40 \(\pm\) 2.36 Bb \\  & Acaricide 1 & 3.03 \(\pm\) 0.97 Aa & 7.36 \(\pm\) 1.47 Cc & 8.23 \(\pm\) 1.36 Bb \\  & Acaricide 2 & 3.80 \(\pm\) 0.93 Ac & 3.07 \(\pm\) 0.88 Ab & 2.74 \(\pm\) 0.69 Ab \\
9 August & Control & 5.50 \(\pm\) 1.25 Bc & 7.00 \(\pm\) 1.19 Abc & 18.94 \(\pm\) 5.51 Cd \\  & Biological & 4.77 \(\pm\) 1.28 Bab & 6.03 \(\pm\) 1.10 Ab & 13.50 \(\pm\) 2.26 Be \\  & Acaricide 1 & 3.97 \(\pm\) 0.76 ABb & 7.90 \(\pm\) 1.07 Bc & 8.50 \(\pm\) 0.98 Ab \\  & Acaricide 2 & 3.00 \(\pm\) 0.72 Ab & 16.06 \(\pm\) 2.78 Cd & 19.93 \(\pm\) 4.95 Cd \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average number of two-spotted spider mite eggs per leaf according to different sampling heights. Figure 1: Average number of two-spotted spider mite eggs per leaf per treatment (for comparison between dates of evaluation within specific treatments; df = 3, 359; for control treatment, F = 6.66, \(p\) < 0.05; for biological control treatment, F = 3.35, \(p\) < 0.05; for Acaricide 1, F = 4.20, \(p\) < 0.05; for Acaricide 2, F = 22.51, \(p\) < 0.05); for comparison between treatments on a specific date of evaluation (uppercase letters; df = 3,359; for 10 July, F = 9.23, \(p\) < 0.05; for 17 July, F = 1.35, \(p\) = 0.2580; for 29 July, F = 3.21, \(p\) < 0.05; for 9 August, F = 11, \(p\) < 0.05).
## 4 Applications of Microextraction Techniques to the Determination of Organic Micropolultants
### UV Filters Including Benzotriazoles  
Organic ultraviolet (UV) filters have been employed for decades during the formulation of personal-care products (PCPs). Although they were initially designed for sunscreen formulations, they are added to other daily cosmetic products to prevent the harmful effects of UV exposure [69].  
Benzotriazole UV stabilisers (BUVSs) are one of the most commonly employed types of UV filters. These derivatives of benzotriazole absorb the full spectrum of UV light including UV-A (320-400 nm) and UV-B (280-320 nm); they are used in many PCPs, as well as for several other purposes, such as corrosion inhibitors in dishwasher detergents and UV-light stabilisers in plastics or dental restorative materials [69].  
Some of these compounds have been identified in List 3 by the United States Environmental Protection Agency (USEPA) as ingredients of unknown toxicity [70].  
Stir bar solid extraction (SBSE) is the most common microextraction technique, as shown in Table 2. [PERSON] _et al._[64] developed a SBSE method with liquid desorption and UHPLC-(ESI)MS-MS to extract and analyse four UV filters (2,2-dihydroxy-4-methoxybenzophenone, benzophenone-3, octocrylene, and octyldimethyl-_p_-aminobenzoic acid). The method was sensitive enough to determine these compounds at trace levels in environmental waters. In river waters, benzophenone-3 ranged from 6 to 28 \(\mathrm{\SIUnitSymbolMicro}\mathrm{L}^{-1}\). Benzophenone-3 ranged from 75 to 127 \(\mathrm{\SIUnitSymbolMicro}\mathrm{L}^{-1}\) in the influent sewage and fell below 25 \(\mathrm{\SIUnitSymbolMicro}\mathrm{L}^{-1}\) in the effluent sewage.  
[PERSON] _et al._[71] optimised a SBSE-based method using polydimethylsiloxane (PDMS Twister(r)) with liquid desorption to extract benzotriazole UV stabilisers from water samples for analysis by ultra-high performance LC with MS/MS detection. The optimised method was applied to seawater and wastewater samples with good selectivity, high sensitivity and limits of quantification ranging from 61.5 to 184 ng\(\cdot\)L\({}^{-1}\). Recoveries between 68.4% and 92.2% were achieved for the more polar compounds.  
Recently, [PERSON] _et al._[72] optimised SBSE methods coupled with liquid chromatography tandem mass spectrometry (LC-MS/MS) using two new commercially available polar coatings consisting of polyacrylate (PA) with polyethyleneglycol (PEG) (Acrylate Twister\({}^{\circledR}\)) or PEG-modified silicone (EG Silicone Twister\({}^{\circledR}\)); these materials were compared to the classic coating based on polydimethylsiloxane (PDMS Twister\({}^{\circledR}\)) for the extraction of a group of pharmaceuticals and personal care products (PPCPs), including UV filters, from wastewater samples. The EG Silicone coating extracted some of the polar compounds more efficiently while improving the sorption of nonpolar compounds compared to the other two coatings.
## 2 Experiment Details  
### Sample Preparation and Corrosion Testing Procedures  
Electrochemical experiments and weight loss tests were designed to study the corrosion inhibition performance and inhibition mechanism of solid corrosion inhibitors. L80 carbon steel was selected as the corrosion sample, and its chemical composition is shown in Table 1. The surface of L80 carbon steel (40 mm \(\times\) 13 mm \(\times\) 2 mm) was polished step by step using SiC sandpaper (280#, 500#, 1000#, and 2000#) until a mirror finish was achieved. The surface was cleaned sequentially with acetone, deionized water, and anhydrous ethanol and then dried with cold air. The sample was then connected to a wire. A 10 \(\times\) 10 mm\({}^{2}\) area was reserved on the surface, and the remaining area was sealed with red adhesive. The test solution was prepared by soaking the solid corrosion inhibitor in a 3.5 wt.% NaCl solution under different conditions for varying time periods.
# Analysis of Development Coordination Levels between Skywalk Systems and Urban Spatial Environments
## 3 Materials and Methods
### Experimental Design
#### 3.2.3 Experiment 3: Cluster Analysis of Coordination Evaluation Results  
In this study, a cluster analysis is used to classify urban skywalk systems in North America and Asia into different types based on the coupling results, and the characteristics of and differences between each type are analyzed. In a cluster analysis, data with similar characteristics are grouped together, and when studying the relationship between skywalk systems and urban environments, it helps identify the correspondence between different types of skywalk systems and different urban environments to better understand their construction in urban environments [61].  
This study utilized K-mean clustering to divide the dataset into K clusters so that the data points within each cluster were as similar as possible and the data points between different clusters were as different as possible. The measure of similarity is usually the distance between the data points, whereby the closer the distance, the higher the similarity. The process of a K-means cluster analysis involves iteratively updating the centroids of the clusters and the division of the clusters until certain stopping conditions are met, such as the maximum number of iterations being reached, or the change in the clusters being less than a certain threshold value [62].  
In the K-means program, a simple and widely used squared error cost function is used to measure the distance. It is defined as follows [63]:  
\[\mathrm{E}=\sum_{j=1}^{k}\sum_{i=1}^{N}\left(\parallel v_{i}-c_{j}\parallel \right)^{2} \tag{11}\]  
where N and k represent the data and the number of centers, respectively; and \(v_{j}\) is a sample of the data, in this case, the location (coordinates) of the ith collision belonging to the center \(c_{j}\). The Euclidean norm is typically used, but other distances such as Manhattan distance or road network distance can also be employed. During the clustering process, the centers are adjusted according to specific rules to minimize the total distance inequality (11) by searching for the center \(c_{j}\) when the data are presented. The Euclidean distance between the data samples and all centers is calculated, and the nearest center is updated according to [63]:  
\[\Delta C_{z}(t)=\eta(t)[v(t)-c_{z}(t-1)] \tag{12}\]  
where z denotes the nearest center to the data v(t); note that the center and the data are represented at time t, where \(c_{z}(t-1)\) denotes the center position at the time of the previous clustering step. The adaptation rate, \(\eta(t)\), can be chosen in several ways. [PERSON] [63] set \(\eta(t)=1/n_{z}(t)\), where \(n_{z}(t)\) is the number of data samples that have been assigned to the center up to time t.
# Review and Categorization of Digital Applications in the Energy Sector
## 1 Introduction  
Digitalization is not a recent phenomenon but started decades ago. First, commercial computers, as well as tests with artificial intelligence, date back to the 1950s [1, 2]. Due to the exponential development speed of individual digital technologies ([PERSON]'s \"law\") and the effect of mutual acceleration, the use of digital applications has increasingly accelerated and is expected to continue to accelerate for decades. Many areas of industry and society have already been fundamentally changed by digitalization. The most prominent examples are digital photography and online commerce. According to [3] today, each day 36 million Amazon purchases are conducted and 3.3 billion digital photos are taken [4]. The German energy sector (in particular the electricity sector) has undergone significant changes since the year 2000 [5] mainly due to the liberalization of the electricity market and the introduction of the Renewable Energy Sources Act. Some of the changes have been caused, enabled or accompanied by digital applications. However, the principles of the value chain have not fundamentally changed. In the coming decades, digital applications have the potential to cause significant changes in the energy sector, even affecting the value chain itself.  
Good knowledge of the expected digital applications and how benefits and potential downsides affect different stakeholders is an essential basis for a broad societal and political discussion to set targets and guidance for digital transformation. Furthermore, this knowledge is relevant for the development of new business models. Therefore, the benefits, as well as potential risks and bottlenecks from the perspective of different stakeholders, need to be analyzed early on to develop solution options for pitfalls and ensure that the full benefits can be utilized.  
As a first step, transparency on which digital applications can be expected in the electricity sector and why they will/might be implemented needs to be created. Therefore, this article presents the results of a literature review of ten publications with the following three objectives:  
1. Identify and categorize the digital applications in the energy sector;
2. Identify the expected benefits attributed to the applications;
3. Identify the impacted stakeholders.  
In a second step the identified digital applications need to be holistically analyzed. Therefore, as an outlook, a potential methodology to holistically assess and evaluate digital applications in the electricity sector is suggested. While the methodology is outlined in this article, a follow-up article will present a detailed description, an applied use case of the methodology and a critical reflection of the approach.
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 5 Experimental Results
### Algorithm Performance Analysis
#### 5.2.2 Algorithm Defense Effect Comparison  
The false alarm probability is set to 0.1, the perturbation value of FGSM and PGD adversarial samples is per \(\in[0.06,0.1]\), and the detection probabilities of the USDAA algorithm, USDAA-F algorithm and DAED algorithm on FGSM and PGD adversarial samples are shown in Figure 5 and Figure 6, respectively. Without Attack indicates the detection probability for clean samples by the target model \(F_{\beta}\).  
The following can be seen from Figures 5 and 6:  
1. The slight perturbations of the input samples cause the detection performance of the target model to decline sharply. In the case of the absence of attack, when the \(SNR\) is \(-12\) dB, the detection probability of the target model \(F_{\beta}\) for clean samples is 94.6%, but the detection probability of the model for FGSM adversarial samples and PGD adversarial samples is 3.1% and 1.6%, respectively, and the detection effect deteriorates sharply. This is because the target model trained only with clean samples is not robust to the adversarial samples.
2. The DAED algorithm uses 42000 labeled samples and the USDAA algorithm only uses 3840 labeled samples. Even though the USDAA algorithm uses only 11% of the labeled samples of the DAED algorithm, the defense effects of the USDAA algorithm and the USDAA-F algorithm are better than those of the DAED algorithm.When the false alarm probability is 0.1 and the \(SNR\) is \(-10\) dB, the detection probabilities of the USDAA, USDAA-F and DAED for FGSM adversarial samples are 88.1%, 84.8% and 64.3%, respectively; accordingly, the detection probabilities for PGD adversarial samples by the three defense models are 88.5%, 88.7% and 68.8%, respectively. This is because the USDAA algorithm and the USDAA-F algorithm extract the features of clean samples and adversarial samples by using the contrast loss and the reconstruction loss training model in the unsupervised pre-training stage; however, the denoising filter of the DAED algorithm can only remove part of the adversarial perturbations in the adversarial samples, so the defense capability of the DAED algorithm is limited.
3. The defense effect of the USDAA algorithm is better than that of the USDAA-F algorithm. This is because the USDAA algorithm fine-tunes all network layer parameters, including the parameters of the feature extractor, while USDAA-F fine-tunes only the classification layer parameters.
# Topological Defect Evolutions Guided by Varying the Initial Azimuthal Orientation
## 2 Materials and Methods  
The LC device is formed by an LC layer [8 CB (NCLCP, Nanjing, China), isotropic--40.5 \({}^{\circ}\)C-N-33.5 \({}^{\circ}\)C-SmA] sandwiched between a photoalignment agent [SD1 (NCLCP, Nanjing, China), dissolving in dimethylformamide (DMF) at a concentration of 0.3 wt.%.]-covered substrate and a PDMS (Polydimethylsiloxane, Dow Corning, Midland, MI, USA) layer coated with a superstrate [Figure 1a; the molecular structures of SD1, PDMS, and LC 8 CB are shown in Figure 1b]. The former gives planar anchoring, while the latter provides homeotropic alignment, and they work together to generate a hybrid alignment. The specific sample fabrication and characterization are presented in Appendix A. A periodic square lattice of \(+1\) and \(-1\) defects is imposed in the SD1 layer using a multistep partly overlapping photoalignment technique [25], with directors varying gradually among adjacent defects (Figure 1), and the detailed processes are given in Appendix B. The director field is depicted as follows [26]:  
\[\alpha(x,y)=\sum_{i}s_{i}\mathrm{arctan}\frac{y-y_{0i}}{x-x_{0i}}+\alpha_{0}, \tag{1}\]  
where topological charge is set as \(s_{i}=\pm 1\), (\(x_{0i}\), \(y_{0i}\)) are the coordinates of the singularities, and \(\alpha_{0}\) (the angle between the easy axis and the \(x\)-axis) is the initial azimuthal orientation. Here, the cell gap is fixed as \(h=8.5\) um, and the lattice constant is set as \(l=30\) um. The LC director follows the guidance of the photopatterned substrate, and the undefined director orientation on the alignment singularities leads to the generation of \(\pm 1\) point defects. \(\alpha_{0}\) can be freely varied owing to the flexibility of photoalignment. Figure 1a-d show the latticeswith \(\alpha_{0}=0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\), respectively. The director fields surrounding the alignment singularities change according to the variance of \(\alpha_{0}\).
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 6 Conclusions  
In this paper, we propose a novel one-step multi-view subspace clustering method, which integrates the self-expression model and graph learning to simultaneously exploit the local and global information of subspace representations from multi-view data. Moreover, to further exploit the hidden relationships between different steps to achieve an end-to-end clustering, our method integrates the subspace representation learning, multi-view information fusion, and clustering tasks into a joint framework. Experimental results on nine popular multi-view data sets confirm the effectiveness of our method by comparing with many baseline methods.  
In the future, we have two directions to improve our method. First, since our method is a linear model, we will consider expanding our model to non-linear cases to deal with complex multi-view data. Second, for better fitting for large-scale multi-view subspace clustering, we will adopt anchor-based ideas to improve our method.  
Conceptualization, Y.D. and H.Y.; methodology, H.Y. and C.S.L.; software, Y.D.; experiment, validation and analysis, Y.D. and H.Y.; investigation, Y.D. and H.Y.; resources, H.Y.; data curation, Y.D.; writing--original draft preparation, Y.D. and H.Y.; writing--review and editing, Y.D., H.Y., C.S.L. and L.L.L. All authors have read and agreed to the published version of the manuscript.  
Figure 6: The parameter sensitivity of \(\lambda_{1}\) on nine multi-view data sets. (**a**) 3 sources, (**b**) 100 leaves, (**c**) BBC, (**d**) Caltech101, (**e**) COIL-20, (**f**) NottingHill, (**g**) Webkb, (**h**) Cornell, (**i**) Wikipedia.  
**Funding:** This research was funded by National Natural Science Foundation of China under Grant 61903091; Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515010801).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Autonomous Orbit Determination of LLO Satellite Using DRO-LLO Links and Lunar Laser Ranging
## 2 Measurement Model
### Inter-Satellite Ranging Model
 \tag{2}\]  
where \(\rho_{\text{D}}^{\text{L}}\) and \(\rho_{\text{L}}^{\text{D}}\) are the geometric distances in the ranging signals received by the DRO satellite and the LLO satellite; \(D_{\text{com}}=c\cdot\left(D_{\text{D}}^{\text{R}}+D_{\text{D}}^{\text{S}}+D _{\text{L}}^{\text{R}}+D_{\text{L}}^{\text{S}}\right)\) is the sum of the receiving delay and sending delay of bidirectional link devices; \(\Delta\delta\tau_{\text{L}}\) is the clock bias variation value of the LLO satellite during the time period \(t_{\text{L}}\sim\widetilde{t}_{\text{D}}\); and \(\Delta\delta\tau_{\text{D}}\) is the clock bias variation value of the DRO satellite during the time period \(t_{\text{L}}\sim\widetilde{t}_{\text{D}}\). The magnitude of the measurement system delay is related to the ambient temperature of the measurement payload. Under the condition of a slow temperature change, the system delay (\(D_{\text{com}}\)) can be modeled as a constant within a certain period of time. Considering that the clock bias changes in the DRO and LLO satellites in a short period of time are very small (measured atomic clock drift ranges from \(10^{-10}\) s/s to \(10^{-11}\) s/s), \(\Delta\delta\tau_{\text{L}}\) and \(\Delta\delta\tau_{\text{D}}\) can be ignored, and Equation (2) can be further expressed as follows:  
\[z_{\text{com}}=\frac{1}{2}\cdot\left[\rho_{\text{D}}^{\text{L}}+\rho_{\text{L }}^{\text{D}}+c\cdot D_{\text{com}}+\varepsilon_{\text{D}}+\varepsilon_{\text {L}}\right] \tag{3}\]  
In filtering, system delay and clock bias are treated as known parameters.