esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Real-Time Precise Point Positioning in Arctic Northeast Passage
## 2 Materials and Methods  
### GNSS Signal Quality  
The quality of satellite signals can greatly affect positioning accuracy. However, little work has been performed to analyze GNSS signal quality in the Arctic NEP. To address this issue, we first discuss the corresponding quality metrics, including the maximum satellite elevation, NSAT, PDOP, SNR, and multipath errors.  
Both tropospheric and ionospheric errors have considerable impacts on GNSS positioning, and lower altitudes usually result in larger atmospheric and multipath errors. In addition, the satellite elevation angle at high latitudes is usually low; thus, it is necessary to discuss the satellite elevation in the NEP.  
The NSAT and the DOP represent certain spatial geometry features of GNSS satellites. The PDOP reflects positional geometry in the X, Y, and Z directions [20], which can be calculated as below:  
\[\delta P_{m}=Bdx+e_{m}dt_{r}+e_{p} \tag{1}\]  
where the vector of \(\delta P_{m}\) denotes the observed pseudo-range minus computed values; \(B\) denotes the corresponding design matrix; and \(dt_{r}\) is the estimated receiver clock offset. \(e_{m}\) is the m-th row vector with all unit values of 1. The vector of \(e_{p}\) denotes the pseudo-range observation noises.  
\[A=\left(B^{T}B\right)^{-1}=\left\{\begin{matrix}A_{11}&A_{12}&A_{13}\\ A_{21}&A_{22}&A_{23}\\ A_{31}&A_{31}&A_{33}\end{matrix}\right\} \tag{2}\]  
\[PDOP=\sqrt{A_{11}+A_{22}+A_{33}} \tag{3}\]  
The receiver SNR is related to factors such as residual atmospheric and multipath errors, antenna gains, and the internal circuit design of the receiver, which reflects the data quality and the observation noise level.  
The signal received by the GNSS receiver includes not only the direct signal, but also the reflected signal from nearby objects (such as the sea surface); thus, multipath errors should be considered due to the interference of these two signals. The multipath error \(M^{\rm s}_{r,j1}\) of the \(j1\) signal can be approximated as [21]:  
\[MP^{\rm s}_{r,j1}=P^{\rm s}_{r,j1}-\frac{f^{\rm s}_{r,j1}+f^{\rm s}_{r,j2}}{f^ {\rm s}_{r,j1}-f^{\rm s}_{r,j2}}\lambda_{j1}\varphi^{\rm s}_{r,j1}+\frac{2f^{ \rm s}_{r,j1}}{f^{\rm s}_{r,j1}-f^{\rm s}_{r,j2}}\lambda_{j2}\varphi^{\rm s}_{ r,j2}-B^{\rm s}_{r,j1} \tag{4}\]  
\[B^{\rm s}_{r,j1}=\frac{f^{\rm s}_{r,j1}+f^{\rm s}_{r,j2}}{f^{\rm s}_{r,j1}-f^{ \rm s}_{r,j2}}\lambda_{j1}N^{\rm s}_{r,j1}+\frac{2f^{\rm s}_{r,j1}}{f^{\rm s}_ {r,j1}-f^{\rm s}_{r,j2}}\lambda_{j2}N^{\rm s}_{r,j2}+d_{r,j1}-d^{\rm s}_{j1} \tag{5}\]  
where \(s\), \(r\), and \(j(j1,j2)\) denote the satellite PRN (pseudo random noise), receiver, and carrier frequency; \(P^{\rm s}_{r,j1}\) and \(\varphi^{\rm s}_{r,j1}\) are the pseudo-range and carrier-phase observation, respectively; \(d_{r,j1}\) and \(d^{\rm s}_{j1}\) are the code biases; \(\lambda_{j}\) is the wave-length, and \(f^{\rm s}_{r,j}\) is the frequency.
## 2 Materials and Methods
### Olfactometer Bioassays  
A dual-port Y-tube glass olfactometer (stem, 110 mm; ports, 90 mm at a 130\({}^{\circ}\) angle from the stem; internal diameter, 10 mm) was used to assess the olfactory response of horn flies to dung volatiles, according to [PERSON] et al. [41], with modifications.  
The following compounds tested here were purchased from Sigma-Aldrich (St. Louis, MO, USA): \(\alpha\)-pinene (\(\geq\)97.5% purity (GC); CAS N\({}^{\circ}\): 80-56-8; product number: 147524), \(\beta\)-caryophyllene (\(\geq\)98.0% purity (GC); CAS N\({}^{\circ}\): 87-44-5; product number: 22075), \(D\)-limonene (\(\geq\)96.5% purity (GC); Enantiomeric excess (e.e.) \(\geq\) 97.5%; CAS N\({}^{\circ}\): 5989-27-5; product number: 183164), and \(p\)-cresol (\(\geq\)98.5% purity (GC); CAS N\({}^{\circ}\): 106-44-5; product number: C85751). \(\beta\)-pinene (\(\geq\)95.0% purity (GC); CAS N\({}^{\circ}\): 127-91-3; Product number: PHL89335) was purchased from PhytoLab GmbH & Co. KG (Vestenbergsgreuth, Deutschland).  
A piece of filter paper (0.5 cm \(\times\) 5 cm) was individually soaked on 10 \(\upmu\)L of the following samples: (a) Fresh and aged cattle dung; (b) \(D\)-limonene, \(\alpha\)-pinene, \(\beta\)-pinene, \(\beta\)-caryophyllene, and \(p\)-cresol at 0.05 \(\upmu\)L\({}^{-1}\) in \(n\)-hexane. For blanks, filter papers were soaked on 10 \(\upmu\)L of \(n\)-hexane. These pieces of filter paper were placed in the middle of Pasteur pipettes, and each port of the Y-tube was connected to these pipettes, containing either sample or blank. The olfactometer was horizontally put over a white LED flat panel light, and it was connected from the base of the stem to a vacuum pump generating a purified air flow (200 mL min\({}^{-1}\)) to carry the volatile stimuli from the ports to the stem. The air was purified through activated carbon.  
To test the horn fly responses to samples during the photophase, 24 h-starved male and female flies of unknown age were individually released into the olfactometer through a hole at 2 cm of the base of the stem and immediately afterward sealed in with a Teflon cap. Three minutes were given to each fly to choose between treatment or blank, and its preference in the treatment or blank port (>half of the port) was recorded. Olfactory responses were recorded as the percentage of flies inside the treatment or blank ports. Non-responding flies were not considered for the purpose of this calculation. Each sample was tested with 10 different flies per sex, and the bioassay was replicated 6 times until 60 flies per sample were evaluated.  
The order of the Y-tube ports was reversed, and the olfactometer was rinsed and air-dried after two flies were tested. After the homogeneity of variance and normality of the data were confirmed, olfactory responses of flies among treatments were analyzed using parametric Student \(t\)-test (\({}^{\star}p\leq\) 0.05, **\(p\leq\) 0.01, ***\(p\leq\) 0.001), followed by a Tukey test using the statistics program StatsDirect 3.1 (StatsDirect Ltd., Cheshire, UK).
# Deep-Feature-Based Approach to Marine Debris Classification
## 2 Materials and Methods
### Machine Learning Classifiers
#### 2.3.3 Support Vector Machines  
For linearly separable training data \(\mathcal{D}=\{(x_{1},y_{1}),\ldots,(x_{n},y_{n})\}\subseteq\mathbb{R}^{m}\times\{ \pm 1\}\), the support vector machine (SVM) [58] classifier aims to find optimal hyperplane \(\langle\mathbf{w},\mathbf{x}\rangle+b=0\), \(\mathbf{w}\in\mathbb{R}^{m},b\in\mathbb{R}\), \((\langle\cdot,\cdot\rangle:\mathbb{R}^{m}\times\mathbb{R}^{m}\to\mathbb{R}\) denotes the inner product on a vector space \(\mathbb{R}^{m}\) given by \(\langle\mathbf{x},\mathbf{y}\rangle=\sum\limits_{i=1}^{m}x_{i}y_{i}\), \(\forall\mathbf{x},\mathbf{y}\in\mathbb{R}^{m}\).), i.e., an affine subspace of dimension m-1, which maximizes the margin of separation between two classes. The corresponding decision function is given by \(f(x)=sign(\langle\mathbf{w},\mathbf{x}\rangle+b)\). Since the weight vector \(\mathbf{w}\) of the optimal hyperplane is a linear combination of training examples \(\mathbf{x}_{i}\)[58], for \(f(\mathbf{x})\) calculation, it is only necessary to compute inner products \(\langle\mathbf{x},\mathbf{x}_{i}\rangle\). When dealing with nonlinearly separable data, SVM first maps original data into high-dimensional (inner-product) feature space \(\mathcal{F}\) via a nonlinear map \(\mathbf{\phi}:\mathbb{R}^{m}\to\mathcal{F}\) and applies the linear method to the obtained data in \(\mathcal{F}\). Inner products \(\langle\mathbf{\phi}(\mathbf{x}),\mathbf{\phi}(\mathbf{x}_{i})\rangle\) can be effectively calculated using kernel function \(k:\mathbb{R}^{m}\times\mathbb{R}^{m}\to\mathbb{R}\); the function that satisfies \(k(\mathbf{x},\mathbf{y})=\langle\mathbf{\phi}(\mathbf{x}),\mathbf{\phi}(\mathbf{y})\rangle,\forall x, y\in\mathbb{R}^{m}\). In this paper, we use radial basis function (RBF) as the kernel function and extend the binary SVM classifier to the multiclass problem according to the _one-versus-one_ approach, i.e., training \(\frac{k(K-1)}{2}\) binary classifiers, one for each pair of \(K>2\) possible classes, and using majority voting to obtain the final prediction.
# Potential of Waste Oyster Shells as a Novel Biofiller for Hot-Mix Asphalt
## 3 Results
### Fourier Transform-Infrared Spectroscopy (FT-IR)  
Possible interactions between the oyster shell powder (OSP) and the bitumen matrix were analyzed using FT-IR spectroscopy. The OSP, base asphalt, and OSP-filled asphalts were examined for this purpose. The FT-IR spectra of OSP, and unfilled and OSP-filled asphalts, are given in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Span & 4.273 \(\upmu\)m \\ Weighted residual & 0.12\% \\ Specific surface area & 0.7582 \(\upmu\)m\({}^{2}\) g\({}^{-1}\) \\ D [3; 2] & 7.91 \(\upmu\)m \\ D [4; 3] & 117 \(\upmu\)m \\ Dv (10) & 2.98 \(\upmu\)m \\ Dv (50) & 71.10 \(\upmu\)m \\ Dv (90) & 307 \(\upmu\)m \\ Mode & 162 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Particle-size distribution (PSD) data of oyster shell powder (OSP) achieved using a dispersive air pressure 1 bar.  
Figure 3: Typical particle size distribution for oyster shell powder (OSP) after dry ball milling process.  
As can be seen, all samples are characterized by various bands ranging from 400 to 4000 cm\({}^{-1}\). Referring to the IR spectra of plain bitumen (i.e., AP-5 OSP 0 wt %) and filler-asphalt mastics, the absorption bands at 722 cm\({}^{-1}\) correspond to the rocking vibration of the group (-CH\({}_{2}\)-CH-)\({}_{n}\). The absorption bands at 1376 cm\({}^{-1}\) are assigned to the symmetric bending vibration of CH\({}_{3}\) functional group, whereas, the absorption bands at 1457 cm\({}^{-1}\) are due to the asymmetric bending vibration of -CH\({}_{3}\) groups and the scissor bending vibration of -CH\({}_{2}\)- groups. The absorption bands at 2851 cm\({}^{-1}\) and 2921 cm\({}^{-1}\) result from the symmetric stretching of -CH\({}_{3}\) and the asymmetric stretching of -CH\({}_{2}\)-, respectively. In the OSP spectrum, the small peak located at 1794 cm\({}^{-1}\) may correspond to the combination vibrations of -NH- _in_-plane bending and C=O stretching. The shoulder of carbonate compounds (e.g., bicarbonate ion, HCO\({}_{3}^{-}\)) occurs at 2513 cm\({}^{-1}\). On the other hand, the intensities of the C-O bands of oyster shell powder between 500 cm\({}^{-1}\) and 1500 cm\({}^{-1}\) are the strongest. The multiple bands observed at 712, 874, 1159, and 1396 cm\({}^{-1}\) are ascribed to the _in_-plane bending vibration, the _out_-of-plane bending vibration, the symmetric stretching, and the asymmetric stretching of carbonate ion (i.e., CO\({}_{3}^{2-}\)), respectively [45; 46]. From the foregoing data, it is evident that no new characteristic absorption bands of OSP/asphalt blends was generated, thereby demonstrating that no chemical reaction took place between the oyster shell powder and the asphalt cement.
## 2 Methodology
### Topic Analysis  
The topic selection is carried out by analyzing the abstracts of all retrieved documents, involving a three-stage process. In the first stage, a numerical and contextual representation is generated for each term. This representation is achieved using a pre-trained neural network model called Bidirectional Encoder Representations from Transformers (BERT) [22]. BERT is designed to improve the understanding of context and semantics in language, enabling more accurate performance across a wide range of NLP tasks such as sentiment analysis, named entity recognition, question-answering, and machine translation. The key innovation in BERT is its bidirectional training approach, which allows the model to process text simultaneously from both the left and the right context. This is in contrast to traditional NLP models that typically process text in a unidirectional manner, either left-to-right or right-to-left. By considering the context from both directions, BERT can capture a more accurate understanding of the relationships between words and their meanings within a sentence or text. BERT embeddings are highly effective in language comprehension, as they capture the semantic relationships between words.  
After embedding the words as vectors, a dimensionality reduction process is necessary to analyze and group the concepts meaningfully, while various techniques can be employed for this purpose, the Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) technique [23] is used in this case, as it effectively preserves both global and local components of the data space. The UMAP algorithm leverages the concepts of simplices from algebraic topology and manifold theory to perform dimensionality reduction. It functions by approximating the high-dimensional data manifold in a lower-dimensional space while preserving the relationships between data points. After completing the dimensionality reduction, the next step is to perform clustering to identify similarities that enable topic extraction. Building upon the work presented in [20], the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm [24] is employed at this stage to generate the topics. HDBSCAN works by first constructing a density-based minimum spanning tree for the dataset. It then uses a hierarchical clustering approach to identify clusters, allowing for the extraction of clusters with varying densities. This method also allows for noise points to be identified, which are not assigned to any specific cluster.  
Figure 1: Illustration of the semi-automated literature review methodology, demonstrating the process and key steps involved.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 1 Introduction
We studied five robust machine learning models of increasing complexity and compared their performance with the threshold-based methods used in previous studies. To validate the developed pipeline, we compared the estimated, based on the segmentation results, amount of NO\({}_{2}\) to the theoretically derived ship emission proxy [11]. The rest of this paper is organized as follows: In Section 2, we start with an overview of the related literature. We then provide a description of the data sources used in this study and introduce the developed methodology (Section 3). In Section 4, the reader can find the results of the study, which are followed by the conclusions in Section 5 and discussion in Section 6.
# The Evolution and Development Trends of LNG Loading and Unloading Arms
## 3 Development of Land-Used LNG Loading and Unloading Arms
### Overall Structure of the Land-Used Loading Arm
At the same time, due to the flammable nature of LNG, the generation of static electricity and sparks should be avoided during the operation of the loading arm, and anti-static and fire-and-explosion-proof measures should be well implemented. Swivel joints and the emergency disconnection device remain key components of land-based LNG loading arms.
# Partial Retraining Substitute Model for Query-Limited Black-Box Attacks
## 4 Experimental Evaluation
### Real-World Attack on Google Automl Vision  
We attack Google AutoML Vision (GAV) [23] to demonstrate the applicability of the proposed method. GAV provides custom models for image classification using an automated training process. If a user submits training data and labels, a trained model classifies images via the custom labels. The network architecture and parameters of the model are hidden. Prediction results include top-3 labels and probabilities; however, we use only a top-1 label according to our black-box environment. Based on the ImageNet dataset, the experimental settings are the same as those specified in Section 4.4 in order to compare the results between simulations and real attacks. ResNet50 is used as the target model in previous experiments and is added to the set of substitute models in this section because we do not know the target model's architecture.  
Table 5 shows the attack results against GAV. The upper bound of the number of queries is limited to 10,000 to compare with the results in Table 4. The performance of the real-world attack is slightly worse than the simulation results. We cannot analyze the reasons because the information regarding the GAV model is hidden. We postulate that some possible reasons are unique network architecture, different data pre-processing, further treatments for improving classification accuracy, or defense techniques.  
Figure 9 shows the query efficiency when the substitute model is ResNet50, which provided the best result. Note that the maximum upper bound of queries is 16,000. The attack success ratio reaches 95.84% with the ResNet50 substitute model, I-FGSM algorithm, and 16,000 queries. The proposed method requires less than 6000 queries to reach a 30% attack success ratio but the previous method needs more than 16,000 queries.  
Figure 8: Attack success ratio according to the upper bound of the number of queries (InceptionV3, ImageNet).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Attack** & \multicolumn{2}{c}{**Previous Method**} & \multicolumn{2}{c}{**Proposed Method**} \\
**Architecture** & **Algorithm** & **\% Succ.** & **\# Query** & **\% Succ.** & **\# Query** \\ \hline VGG16 & I-FGSM & 9.3\% & 7637 & 66.3\% & 6236 \\ VGG16 & C\&W & 6.8\% & 8913 & 60.6\% & 5743 \\ InceptionV3 & I-FGSM & 14.1\% & 7495 & 69.1\% & 5123 \\ InceptionV3 & C\&W & 11.2\% & 8138 & 64.9\% & 5564 \\ ResNet50 & I-FGSM & 14.6\% & 6812 & 72.3\% & 4981 \\ ResNet50 & C\&W & 12.4\% & 7448 & 67.2\% & 5218 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Real-world attack results on Google AutoML Vision (ImageNet, query limit = 10,000).
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 4 Results and Discussion  
The presented algorithm was implemented in C++23 language (ISO/IEC 14882:2024 [38]). The result is a program that allows marking and verifying the authenticity of any glb file containing at least one vertex mesh. Of course, depending on the number of bits used, the mesh must consist of a set of vertices of the appropriate size. The required number of vertices can be calculated using the following formula:  
\[V_{num}=\lceil B/L\rceil, \tag{6}\]  
where \(B\) is the number of bits in the HMAC code (i.e., the watermark) and \(L\) is the number of LSBs used to embed data in a single coordinate of a vertex attribute.  
Moreover, if the watermark is to be placed in the selected attribute, the model's vertices must have this attribute; otherwise, the watermark will not be applied. Opening the watermarked file in a 3D graphics processing program and saving it again, even without making any modifications, can result in a change of bits and thus a violation of the watermark.  
The authors tested the method on several models. Some of the most popular metrics, PSNR, SNR, RMSE, and HD, were used to evaluate and compare the results with other works. A study was also conducted on the relationship between the number of modified bits in attributes other than position and the changes occurring in the model.  
Further implementation details and the source code can be found in the Supplementary Materials section.
# Understanding How GitHub Supports
## 4 Results
### Curation Repository versus Software Repository
#### 4.1.2 Comparison of User Participation for Curation and Software Repositories  
In addition to the comparison of popularity, in order to characterize how GitHub features are leveraged in curation repositories, we compared the curation repositories and software repositories in terms of different types of user activities.  
The number of activities performed in each repository in our sample was aggregated and log-transformed [19]. Then, independent samples _t_-tests were performed on each type of activity introduced above to compare curation repositories with software repositories. Since eight _t_-tests were performed among the two groups (including comparing the numbers of stars for popularity), a Bonferroni-Holm (1979) correction for multiple testing was applied to avoid the issue of multiple comparisons [20]. The results are shown in Table 1. All Bonferroni-Holm corrected \(p\) values were significant. Therefore, curation repositories resulted significantly different from software repositories for all seven types of activity. Specifically, curation repositories resulted to have higher numbers of _Fork_, _Push_, and _Pull Request_ events and lower number of _Create_, _Delete_, _Issue Comment_, and _Issues_ events.  
GitHub records several types of activity logs ([[https://developer.github.com/v3/activity/events/types](https://developer.github.com/v3/activity/events/types)]([https://developer.github.com/v3/activity/events/types](https://developer.github.com/v3/activity/events/types))). The important and representative activities include the following: _Create_ and _Delete_ events indicate a change in the repository structure, such as creating or deleting a Git branch; _Issue Comment_ and _Issues_ events indicate user participation in issue tracker; _Fork_ event happens when a GitHub user clones a repository to his/her own space, which is a prerequisite for a _Pull Request_; a _Pull Request_ event indicates that a GitHub user makes some code changes of a repository and wants the owners of the repository to merge the changes, and, thus, it is an indicator of contributions from others; a _Push_ event refers to the event of committing a code change in a repository.  
The lower frequencies on _Create_ and _Delete_ events for curation repositories implies that curation repositories are less likely to change the repository structure. The lower frequencies of _Issues_ and _Issue Comment_ events indicates that curation repositories are less active on issue trackers, which possibly means that they have fewer bug reports and feature requests. The higher frequency of _Push_ events shows the active development of curation repositories. The higher frequencies of _Pull Request_ and _Fork_ events indicate that curation repositories receive more units of contributions from the community.
## 5 Simulations and Performance Analyses
### Selection of the Number of Snapshots \(D\)  
First, we present the SINR loss performance of the proposed joint optimization algorithm versus the number of snapshots in the range-Doppler dictionary (the value of \(D\)). Figure 3 gives the simulation results. It is seen that the SINR loss performance depends on the value of \(D\). As \(D\) increases, more atoms are used to construct a global dictionary and match MP clutter. Hence, the MP clutter estimation error is decreased and the SINR loss performance is enhanced. On the other hand, an increasing dimension in the range-Doppler dictionary leads to higher computational complexity. Therefore, the value of \(D\) is selected by making a trade-off between the computational cost and SINR loss performance. Based on the experimental results, \(D\) is set at 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of spatial elements & 10 \\ Number of temporal pulses & 10 \\ Antenna array & Side-looking array \\ inter-channel spacing & \(\lambda/2\) \\ Pulse repetition interval & 2.5 ms \\ Platform velocity & 100 m/s \\ Number of clutter patches in each range cell & 181 \\ Range cell index of target & 1000 \\ Normalized Doppler frequency of target & \(-\)0.28 \\ Signal-to-noise ratio of target & 0 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of an airborne passive radar system.