esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 2 Related Work
### Recognition
#### 2.3.2 Place Recognition  
Visual place recognition (VPR) plays a vital role in achieving long term autonomy for mobile robotic applications [1]. Several studies have shown that features extracted by CNN models outperform traditional vision-based approaches such as SIFT. This success of deep learning-based CNN models has motivated the researchers to use CNN based approaches for the VPR task. Generally, VPR methods are divided into image acquisition, feature extraction, and image search [50].  
In a study, [PERSON] et al. [51] proposed an approach based on 3D geometric and semantic understanding of real-world environment by training the generative model on the semantic scene to improve the descriptor learning for missing observations. [PERSON] et al. [52] developed a set of semantics and appearance-based methods for efficient VPR in a challenging scenario by extracting convolutional features from a dense semantic segmentation network using the proposed Local Semantic Tensor (LoST) descriptor of images. [PERSON] et al. [53] generated condition and viewpoint-invariant features by training two CNN models to recognize specific places with a multi-scale feature encoding method. Ref. [54], used CNN to perform the task of recognizing revisited locations under different environmental variations and mapping visual information to low dimensional space with Euclidean distance that corresponds to place dissimilarity.
## 6 Open Problems and Challenges
(3) The semantic understanding of concept layer ignores spatial location information and globality. Whether it is visual-feature-based or object-sematic-based method, it is difficult to obtain an accurate global description of spatial location relationships and geographic scene characteristics at the same time. Thus, it is not conducive to an accurate understanding of the geographic scene. (4) Fusion application of abundant multi-source data  
In the big data era, the accessibility of various types of data has been broken through, creating conditions for the geographic scene understanding. On the one hand, the promotion of big data technology in the field of remote sensing has promoted the arrival of the era of remote sensing big data. Multi-source remote sensing data collaboration can integrate the advantages of various remote sensing observation methods and make up for the insufficient single sensor, which is one of the important research directions for the breakthrough of remote sensing image processing. On the other hand, the remote sensing image data record the natural environment of the surface, but the perception of changes in the social environment is scarce. The fusion of multi-source data is not only limited to HSR remote sensing data itself, but also needs to combine different types of data resources to make up for the deficiency of remote sensing monitoring. Social media data represented by Twitter, Facebook, Sina Weibo and Internet maps represented by GeoNames, GNIS, OpenStreetMap, etc., have become important sources of data for depicting the scenes of humanities and society. The fusion of HSR remote sensing images and multi-source data provides a new idea and method for geographic scene understanding.
## 6 Simulation Results and Discussion
### Simulation Parameters
#### 6.1.2 Impact of DA on EC  
Figure 17 illustrates EC during DA in benchmark protocols. During any change in the location of the sensor node, some percentage of energy is consumed. This EC decreases with the increase in nodes density. Two important things for the accountability of this EC are the network type (i.e., sparse or dense  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Number of Sensor Nodes & 150 to 450 \\ Number of Sinks & 9 \\ Initial Energy of the Nodes (J) & 100 \\ Network Area (m\({}^{3}\)) & \(1500\times 1500\times 1500\) \\ Transmission Range of Nodes (m) & 250 \\ Transmission Power (W) & 2 \\ Reception power (W) & 0.1 \\ Idle State Power (mW) & 10 \\ Data Size (Kpps) & 50 \\ Packet Size (bytes) & 200 \\ Noise of Ship (db) & 0.2 \\ Bandwidth (KHz) & 4 \\ Beacon (bites) & 50 \\ Wind (m/s) & 5 \\ Node Speed in the Horizontal Direction (m/s) & 2 \\ Layers & 6 \\ Frequency & 914 \(\times\) 10\({}^{6}\) Hz \\ Energy for DA (mJ/m) & 1500 \\ Mobility Model & Random Walk 2D Mobility Model \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
deployment) and nodes density (number of nodes). If the IoT-UWSN is spare, then the Probability of void hole increases. Decreasing behavior of EC is clearly depicted in Figure 17. The probability of void hole occurrence decreases with the increase in nodes density. This decrease in void hole results in few DAs, which lead the UWSN towards less EC. Energy consumed on DA in GEDAR and LETR is shown Figure 17. It is clear from the figure that during initial stages the EC of both aforementioned protocols was a bit high. Afterward, it reduces with increase in nodes density in IoT-UWSN.
## 4 Conclusions  
In the present study, a custom-built impact test apparatus for a corrugation specimen is developed in order to simulate the jet flow impact on the primary barrier, and to quantitatively evaluate the impact characteristics of the primary barrier. The main conclusions are as follows:  
* Temporal patterns measured in the experiment and actual sloshing test were compared. In the force profile, the ratio of rise time and duration is similar to the actual sloshing phenomenon, and thus the effectiveness of the impact test method was confirmed. The effect of the impact energy and the number of repetitions on reaction force time-history and deformation of primary barrier were investigated. As the repetitive impact was applied, the force profile changed to a uniform shape.
* Small corrugations have less impact resistance than large corrugations, but there was no significant difference in displacement increment for each corrugation. With repetitive impact loading, corrugation reached the collapse phase where it lies down on the bottom. When the corrugation collapses, the fulcrum phenomenon appears. If the collapsed primary barrier is subjected to a sloshing load, the plywood under the primary barrier can be damaged due to the geometric shape of the fulcrum concentrating the load.
* Experimental results of the primary barrier behavior under impact conditions are needed to perform LNG CCS assessment under sloshing with a probabilistic analysis. Given that it is very difficult to define failure of a thin-walled structure made of stainless steel through the experimental results, it is necessary to develop a computational analysis methodology based on failure theory in order to define failure criteria of the primary barrier under a sloshing load.  
The stainless steel used in the primary barrier exhibits outstanding mechanical performance in terms of the yield and tensile strength under cryogenic temperatures rather than at room temperature. Therefore, the experimental conditions are much more conservative than the actual conditions in an LNG cargo tank. However, in the cryogenic environment, the elongation of stainless steel decreased and, thus, further study is required to investigate the dynamic behavior of a primary barrier under a cryogenic environment.  
Conceptualization, M.-S.K.; Formal analysis, M.-S.K.; Investigation, J.-H.K. and S.-K.K.; Methodology, M.-S.K.; Project administration, J.-H.K. and S.-K.K.; Supervision, J.-M.L.; Writing--original draft, M.-S.K.; Writing--review & editing, J.-M.L.  
The authors received no financial support for the research, authorship, and/or publication of this article.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (No. 2018R1A2B6007403). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) through GCRC-SOP (No. 2011-0030013).  
The authors declare no conflict of interest.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 4 Performance Analysis
### Multipath Error Envelope  
The signal multipath error envelope is defined as the minimum and maximum code tracking errors caused by the multipath interference as a function of the multipath delay, when only one multipath signal exists [15]. The amplitude of the multipath signal is often assumed to be half of the amplitude of the direct signal. The multipath error envelope is usually plotted within one chip, because the multipath delays larger than one chip are rare and their errors usually negligibly small.
## 4 Methodologies and Implementation
### Comparison with Other Segmentation Methods  
The comparison between the segmentation algorithm combining DDPM and Deep Learning-Based Segmentation provides a nuanced understanding of their respective strengths, limitations, and potential contributions to medical image segmentation. We will explore how the integration of DDPM with deep learning methods, exemplified by DeepSegDDPM, compares with traditional deep learning-based segmentation approaches.
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 2 Materials and Methods  
### Coherent Wind Measurement Lidar System of Wind Profile  
A diagram of the Lidar system is shown in Figure 1. The wind profile coherent wind measurement Lidar system is composed of a laser transmitting module and a receiving module. In this study, the transmitting and receiving modules of the telescope are integrated. In the laser transmitting module, the seed source is used to excite the laser to produce linearly polarized light with a central frequency of f\({}_{0}\). The linearly polarized light is divided into a transmitting signal and a local oscillator (LO) signal after the beam splitter. The transmitted signal is modulated by an acousto-optic modulator into a pulse of light and generates a frequency shift of f\({}_{\text{m}}\). Again, using the power amplifier, the final exit occurs after the circulator in the telescope. Due to the Doppler effect, the echo signal will produce a Doppler frequency shift of f\({}_{\text{d}}\) compared with the outgoing laser in the space atmospheric wind field [34]. The backscattered radiation from the aerosols entrained in the atmosphere is collected by the same telescope. At this time, the frequency of the backscattered echo signal received by the telescope is f\({}_{0}+\)f\({}_{\text{m}}+\)f\({}_{\text{d}}\). In the laser receiving module, the backscattered echo signal is coherently mixed with the LO signal, converted to intermediate frequency electrical signals with a frequency of f\({}_{\text{m}}+\)f\({}_{\text{d}}\), and focused onto the balance detector. After sampling using the acquisition card, the echo signal is divided into numerous continuous distance gates according to the time sequence. In the digital processing circuit part, the power spectrum of the sampled echo signal is estimated, and the Doppler frequency shift is extracted. Finally, the wind speed information is retrieved through the Doppler frequency shift in the echo signal [35; 36].  
In this study, we used a pulsed Doppler Lidar system manufactured by Technovo Lidar High Tech Co. Ltd, Hefei, China (model KC-WL-2006 Lidar). The wind profile Lidar used the pulse trigger mode, and the laser wavelength was 1550 nm bands, which has the advantages of a high efficiency, being relatively safe for human eyes, and having a compact structure [37; 38]. The all-fiber single-mode laser, which has a pulse width of 200 ns and a repetition frequency of 10 kHz, was used to measure the wind speed within a range of 3 km. The main parameters of the wind profile coherent wind measurement Lidar system are shown in Table 1.
## 2 Materials and Methods
### Method
#### 2.3.4 Machine Learning Methods
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Formula** \\ \hline Normalized Difference Vegetation Index (NDVI) & (Rnir \(-\) Rred)/(Rnir \(+\) Rred) \\ Ratio Vegetation Index (RVI) & Rnir/Rred \\ Difference Vegetation Index (DVI) & Rnir-Rred \\ Optimized Soil-Adjusted Vegetation Index (OSAVI) & \(1.16\times(\)Rnir \(-\) Rred)/(Rnir \(+\) Rred \(+\) 0.16) \\ Optimal Vegetation Index (VIOPT) & \(1.45\times(\)RniR\({}^{2}\) + 1) \(\times(\)Rred \(+\) 0.45) \\ Normalized Difference Water Index (NDWI) & (R860 \(-\) R1240/(R860 \(+\) R1240) \\ Water Index (WI) & R900/R970 \\ Transformed Chlorophyll Absorption Ratio (TCARI) & 3\(\times\) ((R700 \(-\) R670) \(-\) 0.2 \(\times\) (R700 \(-\) R550) \(\times\) (R700/R670)) \\ Chlorophyll Index Red Edge (CI red edge) & (Rnir/Rred edge) \(-\) 1 \\ Red-Green Vegetation Index (RGVI) & Rred/Rgreen \\ Red-Blue Vegetation Index (RBVI) & Rred/Rblue \\ Green-Blue Vegetation Index (GBVI) & Rgreen/Rblue \\ Misra Green-Red Vegetation Index (MGRVI) & (R\({}^{2}\)green \(-\) R\({}^{2}\)red)/(R\({}^{2}\)green \(+\) R\({}^{2}\)red) \\ Relative Green-Blue Vegetation Index (RGBVI) & (R\({}^{2}\)green \(-\) Rblue \(\times\) Rred)/(R\({}^{2}\)green \(+\) Rblue \(\times\) Rred) \\ Green Leaf Area (GLA) & (2 \(\times\) Rgreen \(-\) Rred \(-\) Rblue)/(2 \(\times\) Rgreen \(+\) Rred \(-\) Rblue) \\ Excess Red (EXR) & 1 \(\times\) Rred \(-\) Rgreen \\ Excess Green (EXG) & 2 \(\times\) Rgreen \(-\) Rred \(-\) Rblue \\ Excess Green Minus Excess Red (EXGR) & 2 \(\times\) (Rgreen \(-\) Rred \(-\) Rblue) \(-\) 1.4 \(\times\) (Rred \(+\) green) \\ Color Index of Vegetation (CIVE) & 0.441 \(\times\) Rred \(-\) 0.881 \(\times\) Rgreen \(+\) 0.3856 \(\times\) Rblue \(+\) 18.79 \\ Visible Atmospherically Resistant Indices (VARI) & (Rgreen \(-\) Rred)/(Rgreen \(+\) Rred \(-\) Rblue) \\ \hline \hline \end{tabular} Note: R represents the spectral reflectance of the band.  
\end{table} 
Table 1: Description of vegetation indices.
## 3 Results  
In the results, the initial electronic search identified 122 results, of which 54 were duplicates, and 62 were eliminated based on the exclusion criteria after screening the titles. Two authors (AR, FF) independently screened the titles and abstracts based on the listed criteria. From the remaining six abstracts, full-text articles were obtained for all agreed upon titles, and disagreements were resolved by discussion. After the analysis of the full text two more articles were excluded because they did not meet the inclusion criteria [66; 67]. Finally, four studies were included in this review. The selection process is explained in detail and graphically summarized in Figure 2, and the included studies are briefly reported in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **N\({}^{\circ}\) Cases** & & & & \\
**(First** & **(Disease)** & **Sample** & **Bacteria** & **Study Type** & **Sample Analysis** \\
**Author + Year)** & **and Controls** & & & & \\ \hline Arduino 2017 [62] & 14 MMP and 33 controls & Subgingival & _F. mucleatum_ & Cross-sectional & PCR technique \\  & affected by PG & & plaque samples & _Capnocytophaga_ spp. & study & \\ \hline Gireeva 2021 [63] & 30 PV & & _P. intermedia,_ & & \\  & & & _T. detricola,_ & Observation & \\  & & & _T. forythensis,_ & longitudinal & \\  & & & and _P. gingivalis_ & study & \\ \hline Scaglione 2020 [64] & 7 PV & Oral cavity swabs & Firmicutes & Cross-sectional & NGS-based \\  & & & Fusobacteria & study & technologies \\ \hline Zorba 2021 [65] & 15 PV and 15 healthy controls & Oral smear & _Fusobacterium nucleatum_ & Case–control & NGS-based \\  & & & _Campocytophaga leadbetteri Parvinomas micra_ & study & technologies \\ \hline \hline \end{tabular}
\end{table}
Table 1: Brief report of main data extracted from included papers. Mucous membrane pemphbigoid (MMP); pemphigus vulgaris (PV); plaque-induced gingivitis (PG); polymerase chain reaction (PCR); next generation sequencing (NGS).  
Figure 1: Summary of the risk of bias assessment.
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 2 Background  
### Evolution of Electronic Navigational Charts  
The S-57 standard played a foundational role in digital chart production, but its fixed structure limited support for both dynamic updates and the integration of increasingly diverse maritime information. To address these limitations, the IHO developed the S-100 UHDM [2]. S-100 is designed with a flexible and modular structure to more effectively represent marine geospatial information, and it can accommodate a variety of technical data types including static data, time-series, grid-based, and three-dimensional data. One of the key features of S-100 is its registry-based architecture, which supports the systematic creation and maintenance of documents that define maritime data elements and their portrayal. This structure facilitates easier updates across systems and enhances interoperability. Recognizing the benefits of S-100, the IMO has announced that all new ECDIS installations must comply with S-100-based systems starting from 1 January 2029 [14]. This regulatory shift reinforces the importance of transitioning to S-100 and underlines the need to develop data models that can fully leverage its capabilities.  
S-100 provides a foundational framework for handling marine geospatial information, enabling each domain to define data structures, encoding formats, and visualization methods tailored to their specific needs. Building on this framework, a wide range of domain-specific data models--such as those for aids to navigation, navigational warnings, and marine environmental data--have been developed [15]. These models enable a more integrated and layered representation of navigational information within digital chart systems, thereby enhancing situational awareness and voyage planning for navigators.  
Among the various data models developed under the S-100 framework, one that is particularly relevant to the subject of this study is the 'Navigational Warnings' model designed to support the delivery of real-time navigational safety information [16]. This model enables the structured and spatially referenced provision of hazard alerts, such as warnings related to maritime accidents, military exercises, or severe weather. The Australian Maritime Safety Authority (AMSA) has implemented this model in testbed services, where real-time navigational warnings are transmitted, structured, and visualized directly on ENCs [17]. These services demonstrate how S-100-based data models can be applied in operational environments to enhance situational awareness by overlaying time-sensitive safety information on digital charts in an intuitive and spatially meaningful way. Figure 1 shows a screenshot of AMSA's testbed system, where real-time navigational warnings are displayed on the chart along with a list of corresponding messages, illustrating how such information is integrated and portrayed in practice.  
Figure 1: AMSA testbed displaying navigational warnings on ENCs [17].
# Further Investigations into the Capacitive Imaging Technique Using a Multi-Electrode Sensor
## 4 Experiments of the Multi-Electrode Sensor
### Defects on the Surface of the Fiberglass Board  
As shown in Figure 8, the non-conducting specimen (fiberglass board) contained three surface defects and the three surface defects from left to right were abbreviated to S1, S2 and S3. The diameters of these three defects were all 10 mm and the depths of these three defects from left to right were 3 mm, 2 mm and 1 mm respectively. In addition, the distance between any two adjacent defects was 80 mm and the length of the line scans was 220 mm, leaving 20 mm on each side of the boundary to avoid edge effects. The red line shown in Figure 8 was the inspection path using the multi-electrode sensor and the lift-off was 0.1 mm.  
Figure 7: Setup of basic experimental system.  
Through position compensation, the experimental results of the multi-electrode sensor detecting three surface defects were shown in Figure 9a-c. For any single result, it could be seen that the depths of three surface defects were decreased from left to right in turn. Except that, it was difficult to acquire approximately quantitative information about the depths of these three surface defects through any single result. For different depths of surface defects, different plate pairs (e.g., E21, E31 and E41) showed different results, which could be used to approximate the depths of three surface defects. Compared with these three line scans of S1 and S2, there were one trough in Figure 9a,b and two obvious troughs in Figure.9c, so S1 and S2 were approximate with the sensing area of E31. Compared with these three line scans of S3, there were one trough in Figure 9a and two obvious troughs in Figure.9b,c, so S3 was approximate with the sensing area of E21. For S1, S2 and S3 in Figure 9c, the two troughs of each defect were unequal, which were all caused by the MSDs asymmetry induced by the floating potential in actual experiments.
## 3 Results
### Classification
#### 3.4.2 Classification: Alpha  
We observed no significant differences among no jitter (\(Mdn=0.557\)), small (\(Mdn=0.554\)), or large jitter (\(Mdn=0.539\)) mean balanced accuracy estimates from calibrations, \(\chi^{2}(2)=4.750\), \(p=0.093\). With window parameter tuning, there again were no notable differences among balanced accuracy distributions in the no jitter (\(Mdn=0.548\)), small (\(Mdn=0.557\)), or large jitter (\(Mdn=0.560\)) calibrations, \(\chi^{2}(2)=0.583\), \(p=0.747\). Regardless of whether we used default or tuned windows, balanced accuracy estimates from the alpha classifier did not correlate with alpha attenuation effects measured offline in Brain Vision (all absolute \(r_{\rm s}(22)\) values \(\leq 0.373\); all \(p\) values \(\geq 0.073\)).