esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths
## 4 Multi-Frequency RTK Positioning Accuracy Analysis  
### Number of Satellites and PDOP Values  
The number of visible satellites refers to the number of satellites that can be observed by the rover and base-station at the same time. The increase in visible satellites can improve the ambiguity fixing rate and shorten the ambiguity correct fixing time, which is an important factor affecting the positioning accuracy. The PDOP value can express the satellite geometric configuration, and the smaller its value, the better the degree of satellite distribution and the higher the positioning accuracy [19]. Positioning analyses were performed for the baseline TGBU-TGCU on DOY221 data, and the number of satellites and PDOP values is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Station** & **Antenna Type** & **Receiver Type** \\ \hline BORJ & LEIAR25.R4 & JAVADTRE,3 DELTA \\ TGBF & LEIAR25.R4 & LEICA GR50 \\ TGD2 & LEIAR25.R4 & LEICA GR50 \\ TGCU & LEIAR25.R4 & LEICA GR50 \\ TGBU & LEIAR25.R4 & LEICA GR50 \\ TGDA & LEIAR25.R4 & LEICA GR50 \\ METG & TRM59800.00 & SEPT POLARX5 \\ MET3 & JAVRINGANT\_DM & JAVAD TRE\_3 DELTA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of the selected stations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Baseline** & **Baseline Length** & **Integer Ambiguity Resolution [18]** \\ \hline BORJ-TGBF & 6 km & Continuous \\ METG–MET3 & 3 km & Instantaneous \\ TGCU–TGBU & 30 km & Continuous \\ TGDA-TGCU & 96 km & Continuous \\ TGD2-TGCU & 128 km & Continuous \\ \hline \hline \end{tabular}
\end{table}
Table 2: Baseline information.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Option** & **Setting** \\ \hline Elevation Mask & 15\({}^{*}\) \\ Satellite Ephemeris & Broadcast \\ Tropospheric zenith wet delay & Estimated as random walk parameter \\ Tropospheric zenith hydrostatic delay & Saastamoinen model \\ Initial ionospheric delay & Klobuchar model \\ Residual ionospheric delay & Estimated as random walk parameter \\ Earth Rotation & Model Correction \\ Relativistic Effects & Model Correction \\ Integer Ambiguity Resolution & Partial Fixing/LAMBDA \\ Parameter Estimation & Kalman Filter \\ \hline \hline \end{tabular}
\end{table}
Table 3: Date processing strategy.  
From Figure 2, the number of available satellites of BDS-2+BDS-3 varies between 8 and 14, while the number of available satellites of BDS-3 varies between 5 and 9, and the number of available satellites of GPS varies between 5 and 10. The PDOP value of BDS-2+BDS-3 is stable between 0.5 and 1.5, while the PDOP value of GPS and BDS-3 varies between 1 and 3 and is greater than BDS-2+BDS-3. It shows that the current number of available satellites and satellite geometry of BDS-2+BDS-3 is significantly better than GPS and the satellite geometry of BDS-3 is roughly equivalent to GPS.
## 2 Materials and Methods
Initially, the samples were machined to the desired diameter using a rough pass, followed by a lathe operation with a fixed depth of cut value of 0.5 mm (see Figure 3a) to prepare them for measuring surface roughness. Surface roughness, a key parameter in our study, was meticulously measured using a Mahr (Goettingen, Germany) brand MarSurf PS1 surface roughness measuring device, as shown in Figure 3b. To ensure the utmost accuracy, at least five measurements were taken for each sample, and outliers--specifically the highest and lowest values--were discarded. The average of the remaining three measurements was then calculated, ensuring the precision of our results. Graphs were created to depict the relationship between the working parameters and the measured surface roughness values. Finally, the effects of the processing parameters on surface roughness were analyzed and discussed based on the gathered data. Figure 3: (**a**) Turning process of brass material; (**b**) Mahr-MarSurf PS1 device for measuring surface roughness. Figure 2: Cutting-edge angle values are (**a**) 70\({}^{\circ}\), (**b**) 80\({}^{\circ}\), and (**c**) 90\({}^{\circ}\).
# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 2 Standard Planes Recognition with AI
### Organ Identification  
Organ identification in ultrasound images is a similar problem to object detection in natural images in computer vision. Therefore, some algorithms from computer vision have been successfully translated to detect and identify organs in ultrasound imaging [13], and they are also used as the first step in organ segmentation.  
Traditional ML methods performed organ classification with multi-way linear classifiers (e.g., SVM) and preselected features extracted from US images. However, these methods were susceptible to spurious factors such as image deformation and specific imaging processing steps. Therefore, deep neural networks (DNNs) were proposed for these tasks, as they can learn abstract features and provide individual prediction labels for each US image. They are particularly useful for identifying pathological organs, tumors, lesions, and nodules.  
In practice, sonographers know which organs are being scanned, as the protocols define different acoustic window areas for each organ in adult patients. However, there are some fields where organ identification may be very useful, such as obstetrics, as the fetus may be in many possible positions and can move during the ultrasound scan. For instance, [PERSON] and [PERSON] [36] proposed using rotation-invariant features and SVM for the localization of the fetal heart in short videos. They later extended the method for localizing three cardiac views, taking into account the temporal structure of the data [15].
# Identifying Human Daily Activity Types with Time-Aware Interactions
## 5 Discussion  
### Effect of Word Vector Size and Sliding Window Size  
We performed two sets of experiments to evaluate the influence of different embedding dimensions and sliding window sizes.  
The sliding window size defines the number of words associated with each word. If the sliding window size is too small, this model cannot capture enough information; if the sliding window size is too large, this model may introduce excessive noise. In the first set of experiments, the word vector dimension was set to 200, and the sliding window size ranged from 5 to 20. As shown in Table 5, the change in sliding window size slightly influences on the final experimental results, and it is only slightly increased. This finding suggests that our model is insensitive to the sliding window size.  
The dimension of the word vector will directly determine the number of parameters in the hidden layer. When the word vector dimension is small, the trained model may fit poorly; when the word vector dimension is large, the model is likely over-fitted. In this set of experiments, the sliding window size is 20, and the word vector dimension ranges from 100 to 300. As shown in Table 6, the model performs best at 200 dimensions and has slightly improves compared with other dimensions.  
In a word, the results are insensitive to the word vector dimension and sliding window size when the two hyperparameters are set to the values commonly used in existing studies, suggesting that the proposed model is robust.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **100-Dimension** & **200-Dimension** & **300-Dimension** \\ \hline Mic-Precision & 0.9219 & **0.9259** & 0.9229 \\ Mac-Precision & 0.8007 & 0.8877 & **0.8915** \\ Mic-F1 & 0.9219 & **0.9259** & 0.9229 \\ Mac-F1 & 0.7333 & **0.7774** & 0.7522 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Precision and F1 score in different dimension word embedding for 20 sliding window size.  
Figure 4: Curves of (**a**) accuracy (**b**) loss of different models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Sliding Window Size = 5** & **Sliding Window Size = 10** & **Sliding Window Size = 15** & **Sliding Window Size = 20** \\ \hline Mic-Precision & 0.9224 & 0.9232 & 0.9232 & **0.9259** \\ Mac-Precision & 0.8661 & 0.8631 & 0.8641 & **0.8877** \\ Mic-F1 & 0.9224 & 0.9232 & 0.9232 & **0.9259** \\ Mac-F1 & 0.7539 & 0.7662 & 0.7541 & **0.7774** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Precision and F1 score in different sliding window sizes for 200-dimension word embedding.
# Seismic Risk Assessment of Chania, Greece, Using an Integrated Computational Approach
###### Abstract
[23] provided an overview and implementation of the disruption index, illustrating how it can be used to quantify the state of disorder due to the disruption of the urban structure and its functions after a severe earthquake, considering problem dimensions and affected population needs. The first step of the process is the definition of the seismic input based on historical seismicity information and seismogenic uncertainty. The second step is the calculation of the seismic impact on the buildings and infrastructure of the region under investigation. Finally, urban vulnerability information and a GIS computational platform are used to organize data for structures and lifelines. For this purpose, the aforementioned computational tool that considers such issues (i.e., QuakeIST) is applied in the city of Chania, which is the second-largest city on the island of Crete, Greece. Chania is selected due to its historical significance and status as an increasingly prosperous tourist destination. More importantly, the island is located on the edge of active plate boundaries, where numerous damaging earthquakes have occurred since ancient times. Consequently, this investigation aims to integrate seismic sources, seismotectonic deformation, ground motion attenuation, and local site conditions to accurately assess earthquake hazard, which is combined with structures and their vulnerability to derive a realistic seismic risk assessment at an urban scale. Herein, the focus is on buildings taking into account all the main parameters involved (including the function/ownership of buildings, i.e., public (schools, etc.) and private), since the city center does not have any major infrastructure (bridges, airport, harbor), while the structural \"wealth\" (in terms of history, culture, architecture, and tourism) of the city is mainly the buildings in the old Venetian harbor. The seismic vulnerability of the water pipeline network of Chaniawas examined in a previous GIS-based study [26]. A few relevant studies have presented attempts to perform Chania's seismic risk assessment [21; 27]. However, this work deals with this critical and complex problem using a more holistic framework, exploiting the advanced capabilities of QuakeIST software (Instituto Superior Technico, University of Lisbon, Portugal). The main aim is to parameterize the seismic hazard problem realistically, determine the spatial variability of earthquake amplification due to geological and other local factors, and accurately predict possible impacts (damage and losses). This study attempts to formulate a practice-oriented methodology that authorities could easily use as an effective decision-making tool in order to implement appropriate techno-economic planning and seismic protection measures to minimize potential damage and losses (primarily human, but financial as well) in future earthquakes.
## 4 Discussion
### Study Limitations  
Firstly, the study's sample size could have been larger, which is the first limitation. Participants were evaluated in a state of completely relaxed rest, rather than during actual sleep, limiting the clinical application of these findings to asymptomatic adults. Additionally, only a medium-firm hybrid mattress was used, and while conventional pillows were of similar height, the viscose pillow's height differed due to production standards, which is another limitation. Sleep health includes both subjective and objective parameters, and although the study objectively demonstrated spine-related comfort and support parameters, these data alone cannot explain sleep quality and comfort levels. Future studies should include subjective outcome measurements taken from individuals using the self-report method to support the results. Despite having clear exclusion criteria, we did not ask the participants about ear diseases or breathing problems, which could affect their posture and sleep, indicating another limitation of the study.
## 4 Experiment
### Measurement Results and Discussions
If we correlate the results shown in Figure 6a-c with \(\varphi\) reported in Table 4, the relative lager oscillations observed in Figure 6a can be attributed to the azimuthal angle setting error. Since the RIM is much larger when recording time \(t\) is 40s, even though the reconstructed azimuthal angle is as small as 0.3869\({}^{\circ}\), relative more significant anisotropy, i.e. nonzero off-diagonal elements, can be observed. In order to further demonstrate the fidelity of the proposed method, we further investigated the time-dependent volume fraction changes during holography. With the benefit of the distribution function of the refractive index, the spatiotemporal concentration function of ZnS nanoparticles expressed in terms of position \(x\) and time \(t\), \(C(x,t)\) defined as Equation (24) [23] can be fitted directly. \[C(x,t)=C_{0}+(C_{max}-C_{0})\sin(\frac{2\pi}{\lambda}x)\Big{(}1-e^{-(2\pi/ \lambda)^{2}Dt}\Big{)}, \tag{27}\]  
where \(C_{\max}\) and \(C_{0}\) are the maximum and average nanoparticles concentration, respectively. The fitted curve can be achieved as shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**time(s)** & \(C_{0}\) & \(C_{A}\) & \(H\) (\(\mu\)m) & \(\theta\) (\({}^{\circ}\)) & \(\varphi\) (\({}^{\circ}\)) & \(MSE\) \\ \hline
5 & & 0.055 & 8.2052 & 30.5508 & \(-3.6431\) & 4.8585 \\
10 & & 0.086 & 9.2286 & 23.8851 & \(-0.0558\) & 4.6035 \\
15 & & 0.108 & 8.4533 & 26.7676 & \(-1.22687\) & 6.1378 \\
20 & & 0.113 & 9.375 & 28.3695 & \(-0.7261\) & 14.0584 \\
25 & & 0.109 & 9.8429 & 25.6022 & 1.7953 & 21.8013 \\
30 & & 0.104 & 8.2554 & 26.5644 & \(-2.9308\) & 2.9309 \\
35 & & 0.113 & 6.5781 & 25.2278 & \(-0.9119\) & 6.004 \\
40 & & 0.120 & 8.763 & 26.7677 & 0.3869 & 6.7657 \\ \hline \end{tabular}
\end{table} 
Table 4: Measured results of volume gratings with different exposure time. Figure 7: Fitting result of spatiotemporal concentration of ZnS nanoparticles. The apparent diffusion coefficient, \(D_{a}\), achieved from the curve fitting is 2.18\(\times\)10\({}^{-15}\) m\({}^{2}\) s\({}^{-1}\), which agree to the previous results 2.0\(\times\)10\({}^{-15}\) m\({}^{2}\) s\({}^{-1}\) we reported. Such an apparent diffusion coefficient achieved using a different model exhibits again that the apparent diffusion coefficient is 3 orders lower than the initial diffusion coefficient (3.4 \(\times\) 10\({}^{-12}\) m\({}^{2}\) s\({}^{-1}\)) predicted by the Stokes-Einstein diffusion equation, which has been interpreted using the rapid increase of the mixture viscosity during polymerization [31].
# Prediction of Cotton Yarn Quality Based on Attention-GRU
## 2 Cotton Yarn Quality Prediction Model
### Attention-GRU Prediction Model
(5) Utilizing the loss function, the model's output \(\overline{y}\) is subjected to loss calculation in comparison to the actual quality \(y\). The Adam optimizer is then chosen to optimize the model's parameters. [PERSON] achieves parameter optimization by computing both the first and second moments of gradients, which facilitates the design of independent adaptive learning rates for different parameters. This mechanism enables the neural network's weights to be iteratively updated based on training data, ultimately steering the output value of the loss function towards its optimal state. The model employs the mean squared error (MSE) algorithm as its loss function, as represented in Equation (7). \[E_{mse}=\frac{1}{n}{\sum_{i=1}^{n}}\left(\overline{y}_{i}-y_{i}\right) \tag{7}\]  
where: \(n\) denotes the number of samples; \(\overline{y}_{i}\) and \(y_{i}\), respectively, represent the predicted value and the actual value for the \(i\)-th sample; and \(E_{mse}\) signifies the degree of loss. (6) An adaptive loss change threshold is utilized to dynamically determine the optimal number of iterations for the model across different datasets. This method involves recording the positive loss change values obtained from the initial 'a' training iterations of the model. These values are then employed to calculate the loss change threshold tailored to the corresponding training dataset. With the loss change threshold as a reference, the training iteration count for the model is dynamically determined. The formula for calculating the adaptive loss change threshold and the training iteration positioning benchmark is presented in Equation (8). The procedure for computing the adaptive loss change threshold for the model is depicted in Figure 6.  
\[\left\{ \begin{array}{l}\Delta e\ =\ loss_{b+1}-loss_{b}\\ P_{d}\ =\ \frac{1}{a}\cdot\sum\limits_{i=1}^{a}\Delta e_{i}\\ t_{o}\ :(loss_{b+a+1}-loss_{b+a})>P_{d}\end{array} \right. \tag{8}\]  
where: \(loss_{b}\) represents the loss value for the model at the 'b'-th training iteration, and \(\Delta e\) denotes the positive loss change value corresponding to the 'b + 1'-th training iteration of the model where \(\Delta e\) is greater than zero. \(P_{d}\) signifies the loss change threshold calculated for the initial 'a' training iterations of the model, and \(t_{o}\) indicates the optimal iteration count for training determined by positioning the model based on the loss change threshold tailored to the specific dataset. Figure 6: Adaptive loss change threshold calculation process.
## 2 Experimental Study
### Time-Frequency Analysis  
The frequency-domain analysis is widely used to research the frequency distribution of the signals. However, it is only suitable to the stationary process, not for the non-stationary process. In other words, the frequency domain analysis illustrates the frequency components of the signals but fails to figure out the moment that a certain frequency component appears.  
The time-frequency analysis expresses the signal as a function of both time and frequency using the local transform. It helps understand changes in energy with both frequency and time upon the occurrence of a certain frequency. The methods usually adopted for time-frequency analysis are short-time Fourier transform, wavelet transform, Hilbert Huang transform, and so on.
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 5 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Type of Paper**} & \multicolumn{2}{c}{**Inertial**} & \multirow{2}{*}{**Doppler Velocity**} & \multirow{2}{*}{**Other Positioning**} & \multirow{2}{*}{**Target Applications**} \\  & & & & & & \\ \hline
[53] & Journal & ✓ & ✓ & Dead Reckoning & Observation/Monitoring/ \\  & & & & & & Offshore Inspections \\ \hline
[107] & Conference Paper & x & x & LiDaR (Light Detection and Ranging) & Deep Underwater Life Inspection \\ \hline
[29] & Conference Paper & ✓ & x & - & Oil Spill Surveillance \\ \hline
[110] & Conference Paper & ✓ & ✓ & Hydrostatic Pressure Sensors, Long Baseline & Scientific Purposes and Research \\ \hline
[93] & Journal & ✓ & x & Visual Controller & Underwater Imaging and \\  & & & & & & Object identification \\ \hline
[105] & Journal Article & x & x & Ultra-Short Baseline & Underwater Navigation \\  & & & & & with accuracy \\ \hline
[109] & Conference Paper & ✓ & - & Light Detection and Ranging & Marine Science \\ \hline
[38] & Conference Paper & - & - & - & Offshore Work \\ \hline
[73] & Conference Paper & x & x & Electromagnetic Positioning & Underwater Navigation \\ \hline
[21] & Conference Paper & ✓ & ✓ & Global Positioning System (on the water surface) & Underwater Ice Ridge Exploration \\ \hline
[69] & Book & - & - & Photogrammetry & Archaeology Research \\ \hline
[62] & Conference Paper & x & x & Geomagnetic Positioning & Underwater Navigation \\ \hline
[16] & Review & - & - & - & Oil/Gas Operations, Underwater Maintenance, Subsea Installations \\ \hline
[79] & Conference Paper & ✓ & x & Fiber Optic Gyroscope & Marine and Underwater Purposes \\ \hline
[27] & Conference Paper & x & x & Long Baseline and & Environmental Monitoring \\  & & & & Ultra-Short Baseline & and Mapping \\ \hline
[86] & Conference Paper & x & ✓ & Synthetic Aperture Radar & Underwater Topography \\ \hline
[59] & Journal & x & x & Acoustic Modern System & Various Applications \\ \hline
[49] & Review & ✓ & x & Acoustic Systems & Marine Exploration and Monitoring \\ \hline
[20] & Conference Paper & - & - & - & Subsea Oil and Gas Operations \\ \hline
[87] & Conference Paper & x & x & Biomimetic Navigation & Shipwreck Penetration \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._on various factors, the third column highlights the reasons for accuracy degradation in each study. The fourth and final column presents the accuracy errors observed in the systems, as detailed in the respective papers. This structure allows for a clear understanding of how different conditions affect the performance of positioning systems, as well as the potential errors that may arise.
## 2 Methodology
### Denoise Methods  
#### 2.5.1 CEEMDAN-NSPCA Denoising Algorithm  
The interpretation of the feature dimensions in PCA is somewhat ambiguous, lacking the very clear explanation of the original sample features. Non-principal components with small variance may still hold important information about differences among samples. Consequently, dimensionally reducing and discarding these components could impact subsequent analyses directly. If there is noise in the signal, especially the noise signal with a small standard deviation, it may be amplified in the process of standardization. This is because the standardization process is based on the standard deviation; the smaller the standard deviation, the more concentrated the data distribution and the better the central trend. The larger the standard deviation, the more dispersed the data distribution and the worse the central tendency, and when the standard deviation is small, even small noise may be significantly amplified after standardization. Therefore, the standard deviation is used to determine whether the signal needs standardized processing, and the standard deviation of 0.1 usually means that the data is less discrete. In this paper, the signals containing noise are all less than 0.1, and no standardized processing is required. Generally, the low-frequency component contains most of the energy and main information in the signal, and the high-frequency component contains the details and noise in the signal. By retaining the low-frequency component, the main information can be ensured such that the main information is not lost, and the noise removal of the high-frequency component can effectively remove the noise of the details. Therefore, a denoising algorithm is proposed in the flowchart in Figure 2 based on the combination of CEEMDAN and PCA without standardized treatment (CEEMDAN-NSPCA) to acquire the information. The IMF components through CEEMDAN with high-frequency IMF components are selected for the following dimensional reduction by PCA to preserve as much of the effective information as possible. The principal component contribution rate is defined as the variance of one principal component to the sum of variances, and it is used to measure the principal component's contribution. The cumulative contribution rate of 0.85 is applied in this work. Finally, the denoised signal is reconstrued by the reserved principal components (orthogonalized IMF components) and the remaining low-frequency IMF components.  
Figure 2: Flowchart of the CEEMDAN-NSPCA denoising algorithm.