esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 3 Datasets and Experiments
### Quality of the Signals  
#### 3.2.1 Multipath Errors for Pseudo Range  
As a linear combined observation, the pseudo range multipath (MP) combination can reflect the quality of pseudo range observations and observation environments through its noise level, which is defined as [33]:  
\[MP_{i}=p_{i}-\frac{f_{i}^{2}+f_{j}^{2}}{f_{i}^{2}-f_{j}^{2}}\phi_{i}+\frac{2f_{j} ^{2}}{f_{i}^{2}-f_{j}^{2}}\phi_{j} \tag{8}\]  
where \(MP_{i}\) denotes the pseudo range MP of the frequency \(i\); the subscript \(i\), \(j\) denotes different frequency.  
In the pseudo range MP combination, the atmosphere delays and geometry-based parameters are eliminated by the MP linear combination such that the remaining part of the MP combination contains the linear combination of the phase ambiguities, hardware delays, as well as multipath effects. When no cycle slip occurs, the ambiguity and the hardware delay are considered a constant and can be calculated by averaging over epochs. Thus, a subsection mean method [8] can be applied to extract the residuals of the MP combination, which are mainly influenced by the pseudo range MP in the continuous tracking of signals.  
By extracting the residual MP, we compute the root mean square (RMS) of the MP for different signals with an elevation interval of 2\({}^{\circ}\). Figure 2 plots the RMS of MP vs. elevation of all the signals in BDS-3, GPS, and Galileo. It is apparent that the RMS of MP decreases as the elevation increases, which indicates that the quality of signals may be higher at high elevation. For specific comparison, in BDS-3, the highest RMS of MP occurs in B1I, whose value is over 0.8 m at low elevation and over 0.2 m at an elevation higher than 80\({}^{\circ}\). B2a and B3I perform similarly, whereas B2b received the lowest RMS of MP. Despite B1C has the higher RMS of MP of B2a, B2b, and B3I, it performs better than B1I, whose RMS of MP varies from about 0.45 m (at an elevation of 10\({}^{\circ}\)) to about 0.15 m (near the zenith). In GPS, the RMS of MP in L2 decreases fast from about 0.78 to 0.1 m with increasing elevation. L5 receives a lower RMS of MP than L1 over all elevations. In Galileo, the order of the values of the RMS of the MP in different signals is E5b, E5, E5a, E1, and E6. By comparing the RMS of the MP in different signals, we found that the overlapping frequency B2a/L5/E5a corresponds to a lower RMS value of MP than B1C/L1/E1 in BDS-3, GPS, and Galileo over all elevations.
## 5 Discussion and State-of-the-Art Comparison
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Entry Type** & **Consensus** & **Key Points** & **Limitations** \\ \hline \multirow{4}{*}{[OUR]} & Debate & & Based on the subjectivity of & \\  & transcription & Based on sentiment & language. Uses Z-numbers to & \\  & from social & analysis similarity. & represent preferences with a & \\  & media. & & reliability measure based on text & \\  & media. & & polarity. Inclusion of subjectivity & \\  & media. & & and reliability in automatic & \\  & & & analysis. & \\ \hline \multirow{4}{*}{[18]} & Natural & Optimisation of & Analysis of aggressiveness, & \\  & language & consensus between & non-aggressiveness, positivity & \\  & comments. & clusters. & and negativity. & \\ \hline \multirow{4}{*}{[19]} & Fuzzy & Consensus over & & With large numbers of \\  & preference & preferences with & Includes weighting operators & members, heterogeneous \\  & matrix. & feedback to experts. & and subjective weighting. & granularity does not \\ \hline \multirow{4}{*}{[26]} & Internet & & Uses hierarchical AHP with & Does not employ group \\  & reviews. & & AutoML. & consensus measures. \\ \hline \multirow{4}{*}{[39]} & Opinions from & & Uses Aspect-Based Sentiment & \\  & social media. & Calculated over the & Analysis (ABSA). Uses & Does not analyse other \\  & aspect sentiment polarity & sentiment analysis, polarity & subjectivity-related \\  & matrix. & analysis and consensus to detect & \\  & & echo chambers. & \\ \hline \multirow{4}{*}{[40]} & Tweets and & Uses proximity function & Uses the Induced Ordered & Needs further work in \\  & reviews. & (NPF) to the neutral & Weighted Averaging (IOWA) & testing the robustness of \\  & & point. & operator for aggregation. & the measures. \\ \hline \multirow{4}{*}{[41]} & \multirow{2}{*}{Z-numbers.} & \multirow{2}{*}{Uses extended OWA.} & Includes information from & Computational \\  & & & Z-numbers in calculations. Uses & complexity of \\  & & & OWA operator for aggregation. & Z-numbers. \\ \hline \multirow{4}{*}{[42]} & \multirow{2}{*}{Z-numbers.} & Uses overlap & & \\  & function-based Z-number & With overlapping functions and & Functions can be difficult \\  & weighted Bonferroni & aggregation using OZWBM. & to interpret by experts. \\ \cline{1-1}  & & mean (OZWBM). & & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Group decision-making-related methods analysis.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 3 Sparse Surface Array and Material Design
### Sparse Planar Array Material Design
The unique electromagnetic characteristics and physical properties of metamaterials provide opportunities to realize low-profile array antennas, reduce array volume and weight, regulate electromagnetic wave polarization, broaden bandwidth, and achieve high gain and low element coupling. Additionally, the ease of fabricating metamaterials via methods such as PCB technology, LTCC technology, and 3D printing technology further enhances the potential for metamaterials in sparse array design.
## 5 Uncertainty Estimation  
Retrieval uncertainties of the developed UV-visible algorithm are estimated using a reference test. In the test, the inversion (i.e., the retrieval algorithm) is applied to simulated UV-visible radiances by the forward model under known reference conditions. Then, retrieval uncertainties are analyzed by comparing the results with the reference conditions.  
In the simulation, VZA, RAA and surface reflectance are fixed at 12\({}^{\circ}\), 33\({}^{\circ}\) and 0.06, respectively and other measurement geometry and aerosol conditions were assumed to be as follows:  
- SZA: 0\({}^{\circ}\)-56\({}^{\circ}\) in 7\({}^{\circ}\) intervals,  
- AOD at 443 nm: 0.3, 0.8 and 1.5,  
- SSA at 443 nm: 0.89, 0.92 and 0.97,  
- ALH: 1.0, 2.5, 4.0 and 5.5 km, and  
- Surface elevation: 0 km.  
Figure 10: Comparison of (**a**) GEMS and AERONET SSA, (**b**) GEMS and OMAERUV SSA, (**c**) OMAERUV and AERONET SSA and (**d**) GEMS a priori and OMAERUV SSA (443 nm). Colors indicate the aerosol type distinguished by the satellite algorithm shown on the \(y\)-axis. Black: HAF (smoke for OMAERUV), red: dust and blue: NA. Dashed and dotted lines indicate an uncertainty envelope of \(\pm\)0.05 and \(\pm\)0.03 in SSA, respectively. Black solid lines are 1:1 lines.
## 4 Numerical Model and Materials
### Refined Model of Anchor Joint
#### 4.2.3 Contact Relationships  
Given that the anchor joint is actually embedded during the fabrication of the concrete segment in practice, the anchorage zone of the joint is tied with its adjacent concrete, whereas the surface-to-surface contact formulation is employed for the other integrations. The penalty coefficient of interface between anchor joint and concrete is set to be 0.25, while that between different components of the anchor joint is 0.5 to reproduce the fastening effect according to [40].
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 2 Diabetic Retinopathy  
Diabetic retinopathy is one of the World Health Organization's priority eye diseases [17]. DR is a leading cause of visual impairment in working-age adults in most developed countries. Early detection of retinopathy is critical in preventing vision loss [18]. In diabetes, the hyperglyic state damages small retinal vessels, retinal cells, and components of the blood-retina-barrier. The damage manifests as microaneurysms, intraretinal hemorrhages, intraretinal microvascular abnormalities, venous heading, cotton wool spots, hard exudates, intraretinal cysts, subretinal fluid, and neovascularization (Figures 1A and 3A) [14; 19]. Sight-threatening complications of diabetes include diabetic macular edema (DME), macular ischemia, and proliferative disease associated with vitreous hemorrhage, traction retinal detachments, and neovascular glaucoma [20; 21].
# Indoor Carrier Phase Positioning Technology Based on OFDM System
## 3 Positioning Algorithm
### Extended Kalman Filter
#### 3.2.3 Interaction with the Ambiguity Resolution Block  
The EKF estimated float DD carrier-phase ambiguities would be sent to ambiguity resolution block to get integer DD carrier-phase ambiguities to improve positioning accuracy. For this purpose, after each EKF step \(k\), the float solution of the DD carrier-phase ambiguities \(\mathbf{\tilde{N}}(k\mid k)\) and the corresponding to covariance matrix \(\mathbf{P}_{\mathbf{N}}(\mathbf{k})\) are provided to the ambiguity resolution block for searching the DD integer ambiguities \(\mathbf{\overline{N}}(k\mid k)\). To fix integer ambiguities, we use MLAMBDA, a modified LAMBDA method for integer least squares ambiguity determination [34; 35].  
DD integer ambiguities \(\mathbf{\overline{N}}(k\mid k)\) can be used to update \(\mathbf{\tilde{N}}(k\mid k)\). However, the EKF performance may be degraded if unreliable \(\mathbf{\overline{N}}(k\mid k)\) is used to update \(\mathbf{\tilde{N}}(k\mid k)\). Thus, before using \(\mathbf{\overline{N}}(k\mid k)\) to update \(\mathbf{\tilde{N}}(k\mid k)\), there is a need to test the reliability of the DD integer ambiguities \(\mathbf{\overline{N}}(k\mid k)\).  
The following approach is used to test the reliability of DD integer ambiguities \(\mathbf{\overline{N}}(k\mid k)\).  
* Initialization: Set a predefined threshold for the ratio test: \(\epsilon>0\), e.g., \(\epsilon=0.5\). Set a predefined maximum count \(n_{max}\), e.g., \(n_{max}=5\). Set counter \(n(0)=0\).
* Step 1: For each epoch \(k\), requesting the MLAMBDA to output two sets of the DD integer ambiguities. With the request, MLAMDA will return one group of the best estimates and one group of the second-best of the DD integer ambiguities and together with the corresponding residuals, say \(r_{1}(k)\) and \(r_{2}(k)\).
* Step 2: Calculate the ratio of \(r_{1}(k)/r_{2}(k)\), and compared it with a predefined threshold \(\epsilon\). The smaller \(r_{1}(k)/r_{2}(k)\) indicates that the best DD integer ambiguities estimates and the second-best estimates are close. If \(r_{1}(k)/r_{2}(k)<\epsilon\), the counter \(n(k)\) is increased by 1, i.e., \(n(k)=n(k-1)+1\). Otherwise, set \(n(k)=0\).
* Step 3: If \(n(k)>n_{max}\), declared that the \(\overline{\mathbf{N}}(k\mid k)\) is reliable DD integer ambiguity resolution. Once reliable DD integer ambiguity resolution \(\overline{\mathbf{N}}(k\mid k)\) is obtained, it can be used to update the EKF.
## 2 Method
### Data Pattern and Filtration  
An example of the relationship between the rotational speed measurement of the gearbox output shaft and the calculated CI-values, before normalization, can be seen in Figure 3(a) where the enveloped FFT CI-value results before the defect develops in failure 1 are used. Included in this is CI-values from measurements taken under two different scenarios. As this specific type of turbine has been at standstill, it will need to reach a rotational speed above 880 rpm before the generator starts to produce. Thereafter, it can be allowed to decrease in rotational speed to roughly 750 rpm before the generator stops producing again. However, the limitations set on the monitoring system regarding when to store a vibration measurement does not take this into account. Instead, limits have been set on the rotational speed between 700-1300 rpm and the rotational speed variance during the measurement to no more than 30 rpm. Thereby, measurements will exist in  
Figure 3: Normalized condition indicator CI-values from FFT spectra on 0-5 kHz and 0–10 kHz frequency range enveloped measurements in blue and red respectively from failure case 3.  
Figure 2: Normalized CI-values from FFT spectra in blue and WPT in black on enveloped measurements from failure case 1 in (**a**) and failure case 2 in (**b**).  
the database where the resistance in the drivetrain from the generator will be much lower, and this will in turn affect the general amplitude level of the vibration spectra. Additionally, the three measurements taken during the highest rotational speed of the gearbox output shaft do not follow the established pattern. It was shown that the variance in speed as these measurements was relatively high at 13.7, 8.96 and 17.8 rpm respectively.  
Therefore, another input data filtering operation was added where measurements taken with more variance in rotational speed of 8 rpm were eliminated from the dataset. In Figure 3(b), the enveloped FFT CI-values dependent on the variance in rotational speed during the measurement is presented. Therefore, the invalid CI-values, seen in red when taken during generator not producing and in black respect to the high variance in rotational speed in Figure 3(a),b, has been identified in the failure case 1 and 3 datasets and removed,  
Figure 4: Absolute CI-values dependent on rotational speed in (**a**) and variance in rotational speed in (**b**). CI-values filtered out with respect to the generator not producing in red and CI-values filtered out with respect to high variance in rotational speed in black.  
leaving the valid CI-values in blue. For the planet bearing failure in failure case 2 however, the variance in rotational speed of that bearing was only at most 4.3 rpm due to the downshift in rotational speed compared to the tachometer measurement position. Thereby, no further data elimination with respect to rotational speed variance was made in that case.
## 2 Materials and Methods
### Resazurin-Based Broth Microdilution MIC Test  
The assay was performed according to (CLSI) protocol with modification following the protocol reported by [PERSON] et al. [38]. Two concentrations of C-CaOH\({}_{2}\) NP stock solutions were prepared in DMSO: 75 mg/mL for aerobic strains and 500 mg/mL for the anaerobic bacteria. The bacterial inoculums were diluted 1:150 and adjusted to 0.5 McFarland. Each sterile 96-well U-bottom plate was labeled such that each row represented a bacterium while each column was composed of the following: (1) column 1 for broth sterility (C1); (2) column 2 for C-CaOH\({}_{2}\) NP sterility (C2); (3) column 3 for antibiotic control (C3); (4) columns 4 to 11 were the serial dilution of C-CaOH\({}_{2}\) NP samples (C4-C11); and (5) column 12 for growth control (C12). The final working volume of each well was 100 \(\mu\)L. The final C-CaOH\({}_{2}\) NP concentrations tested after serial dilution were 50-0.39 mg/mL for aerobes and 250-1.95 mg/mL for anaerbes. Then, the bacterial inoculums were added to C3 (95 \(\mu\)L), C4-C11 (50 \(\mu\)L), and C12 (100 \(\mu\)L) and mixed well. Lastly, 5 \(\mu\)L of ciprofloxacin was added to C3 as positive control. All plates were incubated at 37 \({}^{\circ}\)C (24 h for aerobes and 48 h for anaerbes). Afterwards, 0.15 mg/mL of resazurin powder was dissolved in sterile phosphate-buffered saline to prepare a 0.015% resazurin solution. The solution was then vortexed and filter sterilized using a Millipore membrane 0.22 \(\mu\)m filter. After incubation, 30 \(\mu\)L/well of resazurin was added to all wells and plates were incubated for 4 h. The observed color change (blue: inhibition; pink: growth) was recorded. The MIC values were the lowest concentrations at which the color change started.
## 4 Results and Discussion
### Thermal Energy System
#### 4.1.2 Challenges and Limitations  
Despite these benefits, the system faces challenges tied to high initial and maintenance costs, reflecting the energy and resource-intensive manufacturing processes [33]. Two life cycle hotspots constrain the overall performance. First, fossil energy embedded in flat-glass and mirror fabrication accounts for 46% of the CED. Second, the Therminol-55 inventory dominates freshwater-europhication potential because of aromatic-solvent synthesis [30]. Another key limitation is space or land-area requirements, which may restrict large-scale deployment in high-density regions [34]. The land occupation was modest (5.8 m\({}^{2}\) yr crop-eq/kWh) but remains higher than the PV alternative. These factors collectively limit scalability in dense urban scenarios indicating a need for future design improvements to lower the overall environmental footprint.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 2 Requirement Analysis
### System Delay  
For safety-relevant driver assistance applications, a fast response and high dynamics are required. This is important for a warning system to give timely alarms and for a controlling system be able to respond smoothly to changes in the relative position. For this, the information from the relative position device has to be processed in a short time period and be output at a sufficiently high rate. In platooning, a fast and reliable control of the speed and steering of the vehicle is pursued [10]. While today's ACC applications require between a 10 Hz to 20 Hz update rate [11], future collision detection and pre-crash applications require update rates up to 50 Hz [12].  
The output rate of measurements is one important factor. However, the delay in the measurement, i.e., the time elapsed since a physical event occurs until it is output to the application, is significant, since it causes a delayed detection for FCA or an unstable controlling in platooning applications. For instance, radar sensors and laser scanners are not sensitive to changes in velocity. Thus, they can only estimate acceleration by looking at consecutive measurements and, consequently, suffer from an increased delay. A cooperative solution that directly transmits sensor information from the vehicles will overcome this limitation. However, a propagation and communication system delay will be introduced by the V2V communication [13].
## 3 SOA-Enabled HONoC Architecture
### Optical Router Model  
The \(5\times 5\) general optical router for mesh-based HONoC, as shown in Figure 3, consists of the following five ports: Injection/Ejection, North, East, South and West. The ONoC parameters in this analysis are based on the study conducted by [PERSON] et al. [19], which presented the formal analytic models of insertion loss and crosstalk noise for mesh-based HONoCs.  
Optical signals can traverse from the \(i\)th port to the \(j\)th port and the value of \(i\) and \(j\) varies from 0 to 4: 0 denotes the Injection/Ejection port and 1-4 denote the North, East, South and West ports, respectively. \(P_{in}^{i}\) denotes the optical power coming into the \(i\)th port and \(L_{i,j}(x,y)\) is the insertion loss from the \(i\)th port to the \(j\)th port in the optical router located at \((x,y)\) in the mesh-based HONoC. \(P_{i,j}(x,y)\), defined in (2), is the optical power when going from the \(i\)th port to the \(j\)th port in the optical router \(R(x,y)\).  
\[P_{i,j}(x,y)=\ P_{in}L_{i,j}(x,\,y)\,i,\,j\in\{0,\,\ldots,\,4\},\ \ x\in\{1,\,\ldots,\,m\},\ \ y\in\{1,\,\ldots,\,n\} \tag{2}\]  
\(N_{i,j}(x,y)\), calculated in (3), denotes the crosstalk noise introduced into the optical router located at \((x,y)\) in the mesh-based HONoC and \(K_{i,j,k}\) is the crosstalk noise introduced into the \(k\)th port when the optical signal travels from the \(i\)th port to the \(j\)th port.  
\[N_{i,j}(x,y)=\ P_{in}^{0}(x,y)K_{i,j,0}+\ P_{in}^{1}(x,y)K_{i,j,1}+P_{in}^{2} (x,y)K_{i,j,2}+P_{in}^{3}(x,y)K_{i,j,3}+P_{in}^{4}(x,y)K_{i,j,4} \tag{3}\]  
The SNR is the ratio of the signal power to the noise power and can be written as  
\[\text{SNR}=10\log\left(\frac{P_{S}}{P_{N}}\right), \tag{4}\]  
where \(P_{S}\) is the power of the optical signal and \(P_{N}\) is the power of noise.  
Figure 3: General optical router model.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 5 Discussion
### Clustering Analysis  
The clustering of smart city indices has been analyzed using several methods. In particular, according to the elbow method, k-means clustering divides the cities into five distinct clusters. Cities within each cluster are relatively close to each other, indicating similar smart city indices. This method is straightforward but may not capture complex relationships between cities.  
The dendrogram shows a hierarchical relationship between cities, providing a detailed view of similarities and differences. It is useful for understanding the nested structure of clusters but can be complex to interpret for large datasets. This method can identify more nuanced relationships but may be less intuitive than k-means. SOM clusteringuses a neural network approach to map high-dimensional data into a lower-dimensional space. It effectively visualizes complex relationships and provides clear boundaries using convex hulls, but the interpretation of clusters can be less direct. Moreover, it accounts for overlapping clusters.  
The feature importance values provide insight into the relative contribution of each feature to the models' predictions. The results show that smart living is by far the most important feature, with an importance of 0.259014. This suggests that smart living contributes significantly more to the model's ability to predict the target variable compared to the other features. Its dominant importance may indicate that this aspect of smart city development has the most direct impact on the overall Smart City Index.  
Smart mobility comes in second, with an importance of 0.170147. While its contribution is lower than smart living, it still plays an important role in the model's predictions, indicating that mobility infrastructure and services are key factors in the development of a smart city.  
Smart environment follows closely behind, with an importance of 0.163159. This suggests that environmental factors are an important driver in the Smart City Index, although less impactful than smart living and mobility.  
Smart economy, with an importance of 0.149919, indicates that economic factors contribute meaningfully to the model's predictions, though not as much as smart living, mobility, and environment. Economic factors play a supporting role in determining the Smart City Index.  
Smart people has a importance of 0.137956, suggesting that human capital and social elements of the city have a moderate influence on the prediction of the target variable, slightly less than the economic aspects.  
Smart government has the lowest importance, with a value of 0.119805. This indicates that governance factors, while still relevant, are less critical in the overall prediction of the Smart City Index compared to the other features. Its relatively low importance could imply that the data being modeled place less emphasis on governance in determining smart city performance.