esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Purpose of Grounding Systems
#### 2.1.3 Reduction in Equipment Damage Due to Transient Overvoltages  
Transient overvoltages originate from events such as lightning strikes or switching surges [8]. These overvoltages can cause insulation breakdown, leading to equipment damage. Grounding systems reduce this risk by offering a path for the transient currents to flow safely into the earth, thereby preventing equipment damage.  
The voltage rise due to transient overvoltages can be quantified using the equation for the transient voltage surge,  
\[V_{transient}=\frac{L\cdot I_{transient}}{R_{ground}} \tag{3}\]  
where \(I_{transient}\) is the transient current, \(L\) is the inductance of the system, and \(R_{ground}\) is the grounding resistance. By providing a direct path for these surges to dissipate, grounding systems help protect sensitive equipment from such transient events.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on High-Spatial Resolution Images
#### 3.1.1 Main Methods
With the application of deep learning techniques in road extraction, many data-driven methods such as patch-based CNN models, FCNs, deconvolution networks, GAN models and graph-based methods have been proposed for road extraction [34; 71]. The patch-based CNN method can exploit a large image and predict a small patch of labels through the CNN models based on structured and refined CNNs; however, this method is time-consuming and computationally inefficient [34]. The FCN method predicts images by replacing connected layers with output labels and classifies images at the pixel level using the FCN-32 and U-shaped FCN models, thereby improving the road extraction accuracy [44; 45]. A variant of FCN--DenseNets--including SegNet, DeepLab, RCFs, Y-net and U-Net decoder [49; 51; 52; 54] can extract hierarchical features from images, especially \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** & **References** & **Precision** \\ \hline Patch-based DCNN & Weight sharing, less parameter & Inefficiency, large-scale training & [168], 2016 & 0.905 \\  & Arbitrary image size, end to end & Low fitness, low position accuracy, & & [38], 2017 & 0.917 \\ FCN-based & training & lack of spatial consistency & [36], 2016 & 0.710 \\ DeconvNet-based & Arbitrary image size, end to end & High cost of computing and & [49], 2017 & 0.858 \\  & training, better fitness & storage & [51], 2018 & 0.919 \\  & & Non-convergence, & [65], 2017 & 0.841 \\ GAN-based & More consistent & gradient vanishing, and model & [66], 2017 & 0.883 \\  & & collapse & [69], 2018 & 0.835 \\ Graph-based & High connectivity & Complex graph reconstruction and & [170], 2020 & 0.823 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different data-driven methods on Massachusetts road dataset. high-resolution images. The GAN method includes a generator and discriminator to segment images and distinguishes between forged and real images, respectively [179]. The graph-based method realises the vector representation of road maps through iterative road tracking and polygon detection strategies.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 3 The Architecture Proposal
### Architecture Components
#### 3.2.1 Network Administrator  
The _Network Administrator_ component is responsible for performing the authentication and authorization of connection requests from the _Customer AS_. It also provides information to the _AS app_ regarding multiple paths and the bandwidth utilization for a given prefix, and verifies the quantity of network traffic destined for the prefixes of the _Customer AS_. If a change in the RIB_IN occurs  
Figure 3: The proposed architecture described by the application, control, and data planes of SDN architecture.  
Figure 2: AS domain composed of only OpenFlow switches.  
(for example, a drawback prefix network), the _Network Administrator_ component notifies the changes to the _AS app_ and informs the _Policy Manager_ and _Path Computer_ components to update the new network state.  
To avoid two (or more) _AS apps_ making conflicted decisions about the routing, each _AS app_ is associated with a customer AS, and each customer has their own prefixes registered inside the Routing Registry (RR). Thereby, verifying whether a given prefix belongs to a customer AS is conducted through querying the _RR_ database and, with that information, it is possible to avoid the routing objective of one _AS app_ negatively affecting the routing objective of the other.
# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
## 2 Methodology  
In this section, the underlying theories and developed method are described, including the PCA algorithm, LSTM networks, and the hyperparameter selection based on DE, as well as the prediction framework proposed in this paper.
# Defining Feasible Joint and Geometric Workspaces Through Boundary Functions
## 3 Theoretical Framework and Methodology
### Case Study: Planar Robot  
The planar robot was analyzed as a case study to illustrate the kinematic and collision constraints in robotic manipulators. Its structure consists of a two-degree-of-freedom mechanism, where \(q_{1}\) and \(q_{2}\) correspond to rotations about the \(y\) axis. This configuration allows for a simplified representation of kinematic interactions while preserving essential characteristics for evaluating workspace feasibility and self-collision conditions. Figure 2 presents an isometric view of the robot, depicting its geometric arrangement and joint distribution.  
This model is particularly useful for demonstrating the role of boundary conditions in defining feasible workspaces. The kinematic relationships between its links provide a structured approach for verifying self-collision criteria, as they depend on the relative positioning of consecutive elements in the manipulator's chain. In this context, the feasible workspace is determined by considering the relative position vector between key reference points along the structure.  
The kinematic chain of the planar robot, depicted in Figure 3, provides a structured representation of the positional relationships and rotational axes of its joints. The degrees of freedom are defined as follows:  
* \(q_{1}\): Represents a rotational motion about the \(y\) axis at the base.
* \(q_{2}\): Represents an additional rotation about the \(y\) axis at the second joint, determining the final positioning of the end effector.  
By formulating the manipulator's motion in terms of relative position vectors and transformation matrices, it is possible to define a set of constraints that delineate the boundary of the feasible workspace. The self-collision condition is verified through a function that depends on the joint variables \(\mathbf{q}\), ensuring that the relative displacement between reference points remains within allowable limits. The analysis of this system provides insight into the general methodology used to evaluate collision-free trajectories in robotic mechanisms. To support this analysis, Table 1 presents the geometric parameters that define the relative positioning of the robot's joints, while Table 2 details the recursive formulation of the direct kinematics equations, describing the position and orientation of each joint in terms of preceding transformations.
## 4 The Proposed Model
### Feature Extraction  
After completing the preprocessing process outlined above, the SBERT model is applied to the cleaned and preprocessed texts in order to obtain word embeddings, or in other words, vector representations of the texts. These word embeddings are derived from large transformer models within the SBERT model. These larger transformer models contribute significantly to the accuracy of word embeddings by leveraging vast amounts of data and sophisticated architectures to understand the context and nuances of language. They are trained on diverse corpora, enabling them to grasp subtle semantic relationships and the contextual use of words in various linguistic settings. By processing text through layers of attention mechanisms and NNs, they generate embeddings that capture the meaning of individual words and how those meanings change in different sentences. Using these large transformer models, the BERT model first obtains contextual embeddings for each token in each text. Then, these contextual embeddings from BERT are pooled using pooling strategies to form a single fixed-size sentence embedding. This pooling process condenses complex data into a manageable form, ensuring that critical semantic insights are retained within a compact representation and thereby balancing efficiency with depth of understanding.  
Ultimately, the process of generating structured data, or more precisely, probabilities of semantic similarities, is facilitated by these word embeddings. This is achieved through a comparison of the angle between two embedding vectors, a method known as cosine similarity. This measure provides a quantifiable metric of how closely related the meanings of two sentences are, even when their surface structures differ. In essence, the similarity score reflects the semantic similarity between the two sentences, indicating that high similarity scores suggest similar meanings, even if different words are used. This feature is particularly beneficial for applications with the objective of finding semantic similarity, such as semantic search, text clustering, and information retrieval.  
The proposed model leverages SBERT to predict the SPs based on the similarities of semantically similar sentences. Since similar user stories often require tasks with similar sizes and complexities, it is assumed that the amounts of effort needed for their implementation should be close.
## 2 Materials and Methods
### Repair of Cycle Slips Using the STG Combinations  
Any combination of Equations (10)-(13) alone can be used to detect cycle slips except for some special cycle slip pairs (CSPs) \((dN_{1},dN_{2})\). For instance, when the CSPs are \((60k,77k),k=\pm 1,\pm 2,\ldots\), the detected cycle slip value in Equation (10) is 0, because these CSPs do not result in a change in \(dN_{1}-\lambda_{1}/\lambda_{2}dN_{2}\). Similarly, special CSPs, such as \((k,k)\), do not show jumps in the combination of Equation (11), and Equation (12) cannot detect CSPs such as \((77k,120k)\).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Equations** & \(i\) & \(j\) & \(1\)+\(\alpha_{j}^{i}\) & \(1\)+\(\frac{j}{j}\) \\ \hline Equation (10) & 1 & \(-\frac{f_{2}^{2}}{f_{1}^{2}}\) & 0 & 0.3928 \\ Equation (11) & 1 & \(-\frac{f_{2}}{f_{1}}\) & \(-0.2833\) & 0.2208 \\ Equation (12) & 1 & \(-0.5\) & 0.1765 & 0.5 \\ Equation (13) & 1 & 0 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ionospheric and geometric distance residuals with the different coefficients \(i\) and \(j\).  
Fortunately, the joint STG combinations can complement individual drawbacks. For instance, for the CSP (1, 1), Equation (11) is insensitive, but Equation (10) has a value of 0.22 cycles, and Equation (12) has a value of 0.3583 cycles, while Equation (13) has a value of one cycle, which can be detected easily. Rows 3-6 in Table 3 refer to some special CSPs that are insensitive for some of STG combinations.  
What is more, although Equation (13) seems to be suitable for estimating cycle slips for single-frequency GPS observations alone, the ionospheric residual in Equation (13) is larger than the other combinations. Thus, it is suggested to adopt all the combinations in Equations (10)-(13) together to detect and repair cycle slips for dual-frequency LEO satellite GPS observations.  
When cycle slips are detected using combinations from Equations (10)-(13), the values of cycle slips \((dN_{1},dN_{2})\) can be uniquely determined from the following equations:  
\[\begin{array}{c}dN_{1}-\lambda_{1}/\lambda_{2}dN_{2}=a\\ dN_{1}-dN_{2}=b\\ dN_{1}-0.5\lambda_{2}/\lambda_{1}dN_{2}=c\\ dN_{1}=d\end{array}, \tag{16}\]  
where \(a,b,c,d\) are the detected cycle slip values, corresponding to Equations (10)-(13), respectively. Based on Equation (16), the float cycle slip solutions \((dN_{1,f},dN_{2,f})\) are firstly solved and then the integer solutions are determined, e.g., using the LAMBDA method proposed by [PERSON] [36].
###### Abstract  
We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and a fuzzy machine with the ability to think and to reason. It has the advantages of both models. General ANFIS rule generation methods include a method employing a grid division using a membership function and a clustering method. In this study, a rule is created using CFCM clustering that considers the pattern of the output space. In addition, multiple ANFISs were designed in an incremental tree structure without using a single ANFIS. To evaluate the performance of ANFIS in an incremental tree structure based on the CFCM clustering method, a computer performance prediction experiment was conducted using a building heating-and-cooling dataset. The prediction experiment verified that the proposed CFCM-clustering-based ANFIS shows better prediction efficiency than the current grid-based and clustering-based ANFISs in the form of an incremental tree.  
adaptive neuro-fuzzy inference system (ANFIS); context-based fuzzy C-means (CFCM) clustering; fuzzy trees; incremental fuzzy inference system +
Footnote †: journal: Applied Sciences
## 4 Discussion  
In endoscopic sinus surgery (ESS) in chronic sinusitis patients, an image-guided surgery (IGS) system is considered to be a valuable tool for surgeons to make important decisions during surgery [29]. The IGS system has been commonly used for ESS because it provides important topological information about the paranasal sinuses during surgery. The IGS system provides spatial and visual information to the surgeons by visualizing the patient's preoperative medical images, such as from CT and MRI, on the screen of the system during surgery [30; 31]. In addition, the IGS system relieves the operator's mental and physical stress during ESS surgery, which leads to reduction of surgical risk to the patient [32; 33]. IGS technology has improved dramatically over the last few decades, with smaller and more portable platforms, quick setup time, and user-friendly interfaces. One of the most important elements of the IGS system is meeting clinically acceptable accuracy levels by performing accurate matching between the patient's physical space \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Registration Time \({}^{\mathbf{a}}\)**} & \multicolumn{2}{c}{**Image Loading Time \({}^{\mathbf{a}}\)**} & \multicolumn{2}{c}{**FRE**} \\ \cline{3-10}  & & **M** & **S** & **M** & **S** & **M** & **S** \\ \hline
1 & KYK & 28.71 & 66.00 & 1.20 & 15.78 & 3.21 & 0.54 \\
2 & CYS & 34.92 & 66.00 & 1.60 & 18.40 & 2.85 & 0.34 \\
3 & YSY & 34.27 & 104.9 & 1.10 & 18.20 & 3.73 & 0.62 \\
4 & PWY & 32.33 & 85.95 & 1.40 & 18.10 & 2.84 & 0.54 \\
5 & KDH & 46.87 & 65.55 & 1.89 & 21.59 & 4.74 & 0.42 \\
6 & KYS & 33.21 & 51.39 & 1.30 & 18.50 & 2.08 & 0.64 \\
7 & KDS & 34.58 & 131.19 & 1.41 & 17.90 & 3.29 & 0.41 \\
8 & CCH & 38.13 & 122.45 & 1.32 & 18.41 & 2.24 & 0.51 \\
9 & KYH & 38.42 & 114.12 & 1.40 & 22.46 & 3.33 & 0.50 \\
10 & ISH & 35.59 & 82.86 & 1.10 & 17.50 & 3.40 & 0.41 \\
11 & KJB & 39.43 & 92.42 & 1.40 & 25.16 & 3.49 & 0.38 \\ \hline \multirow{2}{*}{Mean} & \multicolumn{2}{c}{36.04 \(\pm\)} & \multicolumn{2}{c}{89.35 \(\pm\)} & \multicolumn{2}{c}{1.37 \(\pm\)} & \multicolumn{2}{c}{19.27 \(\pm\)} & \multicolumn{2}{c}{3.20 \(\pm\)} & \multicolumn{2}{c}{0.48 \(\pm\)} \\  & & 4.7 & 26.2 & 0.23 & 2.68 & 0.72 & 0.10 \\ \hline \multicolumn{2}{c}{\(p\)-value} & \multicolumn{2}{c}{0.000} & \multicolumn{2}{c}{0.000} & \multicolumn{2}{c}{0.000} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of registration time, image loading time, and fiducial registration errors (FREs) between the developed (Medigator®, Gmeditec, Incheon, Korea) and the commercialized (Scopis® Hybrid Navigation System, Stryker, Kalamazoo, MI, USA) systems.
# REGNet: Ray-Based Enhancement Grouping for 3D Object Detection Based on Point Cloud
## 4 Experiments and Discussions  
### Dateset  
We evaluated our REGNet on two large datasets, ScanNet V2 and SUN RGB-D, both of which have been captured from real 3D scans. ScanNet V2 is a well-known public indoor scene dataset that comprises 1513 indoor scenes with detailed annotations, including instance points, semantic labels for 40 categories, and 3D bounding boxes categorized into 18 categories.  
SUN RGB-D contains 10,335 indoor RGB and depth images with per-point semantic labels and object bounding boxes. The data in this dataset were captured by four consumer-grade depth cameras, namely Intel Realsense, Asus Xtion, Kinect v1, and Kinect v2. A total of 5285 pairs of data are used for training, and the rest are used for validation. Prior to training and validation, each RGB-D scene is first converted into a point cloud representation by aligning the RGB and depth images and then projecting the depth information onto the RGB image.
## 4 Conclusions  
Gear transmissions with a continuously changing gear ratio are finding more and more applications in practice. A suitable design of the pitch curve shape for the required range of the changing gear ratio is the first crucial step for the successful solution of a problem. For the required continuously changing gear ratio, the elliptical shape of the pitch curve was designed. The gear set was designed as a pair of identical elliptical gears. The center of rotation is in one of the foci of the pitch ellipse, they are eccentrically placed elliptical gears. The active curve of the tooth profiles is an involute and it is different for the active and passive side of a tooth. These are gears with an asymmetrical tooth profile. Unlike the involute portion of teeth profiles of \"standard\" circular gears, where the evolute of the involute is the base circle, in this case the evolute of the involute is the ellipse. Each of the twelve teeth of the gear is different and the other twelve teeth of the same gear are the same.  
The gear ratio of the designed elliptical gear set is not constant, but is continuously changing in the range from 0.5 through 1.0 to 2.0 and back. This is how the gear ratio varies during one revolution.  
Unlike standard circular gears, where the angular speed of the driving and also of the driven gear is constant, in this case, the angular velocity of the driven eccentrically placed gear is not constant, but it varies depending on the continuously changing gear ratio.  
In the case of the designed elliptical gear set, the magnitude of the circumferential velocity in the central point of mesh is different for each intermeshing pair of teeth. The direction of circumferential velocities of individual pairs of intermeshing teeth is identical, their position between the centers of rotation changes.  
In further research, the authors plan to deal with the problem of the deformation and stiffness of the created gearing. The deformation of the designed elliptical, eccentrically placed gear set differs from the deformation of a standard gearing. The overall course of the deformation changes during the mesh and it is different for each pair of intermeshing teeth.  
Conceptualization, S.M.; methodology, S.M. and G.F.; investigation, S.M. and M.U.; data analysis, S.M. and S.S.; writing--original draft preparation, S.M. and V.M.; writing--review and editing, M.U., G.F. and V.M. All authors have read and agreed to the published version of the manuscript.  
This work is a part of these projects VEGA 1/0403/18, VEGA 1/0600/20, VEGA 1/0528/20, KEGA 012 TUKE-4/2019, KEGA 013 TUKE-4/2019, APVV SK-SRB-18-0053.  
The authors declare no conflict of interest.
# Advances in the Noninvasive Diagnosis of Dry Eye Disease
## 7 Meibography  
Meibography allows the observation of the morphological structure of the MG in a two-dimensional plane. The technique was introduced for the first time in 1977 by [PERSON], who used transillumination from the palpebral skin with an illuminating probe to capture images of MGs. The approach was then refined by other methodologies that allow the visualization of MG on black-and-white film, on infrared film with a near-infrared CCD (charge-coupled device) camera, or with an infrared CCD camera [52]. Non-contact techniques based on an infrared filter and infrared CCD camera that made possible recording the transillumination image from the conjunctival side of the eyelid were developed in the last decade [53]. Non-contact meibography is based on the autofluorescence of a healthy meibum when illuminated with infrared light, which can be detected by an infrared charge-coupled device camera; thus, the glands appear as light areas against a darker background and any alterations in meibum or loss of acinar tissue appear as a dark area [54]. Recent technology has led to the development of several mobile, handheld, pen-shaped and multi-functionality (slit lamp-based, mobile and topography-equipped) systems with infrared light-emitting diodes fixed to infrared cameras that allow the capture of videos and images of MG and increase the feasibility of outpatient care assessment [55; 56].  
In clinical practice, the grading systems of MG structures can be used to document the presence, progression, and treatment response to MGD [57]. The most useful systems are the Meiboscore and the Meibograde; both feature a score based on the percentage of loss of the glandular area, although the latter offers a higher sensitivity and should be able to detect minor changes in MG [58]. [PERSON] et al. demonstrated that OSDI score, BUT and lissamine green staining were significantly correlated with MGD grade and MG area loss [59]. Although functional and morphological changes in MG are often thought to be well correlated [60], meibography alone does not appear to be sufficient for reaching the diagnosis of MGD, but it should be considered in the context of other clinical parameters (e.g., BUT, lid margin examination) or other imaging techniques (e.g., IVCM).