esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Method
### Interface Parameters  
Angles of interface strength adopted for the local soil trench wall-backfill interface, pipe-Beckfill interface, and the EPS-backfill interface and corresponding interface strength reduction factors (Rinter) are given in Table 4. Interface strength reduction factors were obtained from the literature review [75; 76; 77].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Soils** & \(\mathbf{\gamma_{n}}\) & \(\mathbf{E_{50}}\) & \(\mathbf{E_{50}^{ref}}\) & \(\mathbf{E_{ur}^{ref}}\) & \(\mathbf{c}\) & \(\mathbf{\varphi}\) & \(\mathbf{\overline{v}}\) \\  & **(kN/m\({}^{3}\))** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(kPa)** & **(°)** & **(°)** \\ \hline Cr. S. (96\% RC) & 22.19–23.81 & 90 & 89.2 & 267.6 & 69 & 45 & 15 \\ \hline Sand (86\% RC) & 17.70–18.3 & 9.7 & 9.5 & 28.5 & 0 & 37 & 7 \\ \hline Sand (96\% RC) & 19.35–19.95 & 36 & 35.5 & 106.5 & 0 & 45 & 15 \\ \hline Bedding Cr. S. & 18 & - & 32 & 96.0 & 20 & 40 & 10 \\ \hline Bedding Sand & 16 & - & 6.3 & 18.9 & 0 & 33 & 3 \\ \hline Native Soil Clay & 20.4 & - & 20 & 60.0 & 34.5 & 24 & 0 \\ \hline Embankment Fill Clay & 20.4 & - & 5.21 & 15.6 & 34.5 & 15 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardening Soil (HS) Model parameters for the soils.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Pipe Profile Types** & **A** & **B** & **C** & **E** & **F** \\ \hline Pipe diameter (m) & 0.762 & 0.762 & 0.762 & 1.067 & 1.524 \\ \hline Pipe Rigidity (kN/m/m) & 302 & 650 & 490 & 413 & 230 \\ \hline Normal Stiffness, EA(kN/m) & 32,620 & 35,550 & 8335 & 11,960 & 18,190 \\ \hline Flexural Stiffness, EI (kNm\({}^{2}\)/m) & 2.490 & 5.390 & 4.050 & 9.360 & 15.220 \\ \hline Equivalent Thickness, d (m) & 0.030 & 0.043 & 0.076 & 0.097 & 0.100 \\ \hline Poisson’s Ratio, v & 0.300 & 0.300 & 0.450 & 0.450 & 0.450 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of pipe types.
# Analysis of Distributed Measurement Method for Array Antenna Position
###### Abstract  
The measurement of the phase center of the airborne array antenna can directly affect the accuracy of the Earth observation system. However, the relationship between the relative motion of each sub-antenna cannot be accurately measured because of the adverse environment of the airborne platform. Therefore, it is necessary to find a suitable method to measure the motion parameters of distributed antennas and the phase center of each element antenna accurately in order to improve the imaging resolution of the Earth observation system. Distributed position and orientation system (POS) technology has high precision, but its measurement error will accumulate with time. So it needs to transfer and align continuously to achieve high-precision measurement. The paper introduces the distributed measurement method of measuring the array antenna position based on the combination of fiber Bragg grating (FBG) sensing technology and POS technology on the aircraft wing. The paper first introduces the technical scheme and principle, then carries out the structural design and method analysis. Next, the structural strength of the experimental model is checked and summarized.  
FBG sensing technology; POS technology; array antenna; measurement Article
## 4 Conclusions  
We have measured PM\({}_{2.5}\) and its chemical components in Fukuoka in spring 2020. The variations in chemical components are similar to that of PM\({}_{2.5}\) concentration except PAHs. We extract days with high concentrations. The paths taken by air masses were examined by backward trajectory using HYSPLIT. For the days with high concentrations for almost all components, the backward trajectory suggests that transboundary air pollution from the Asian continent influences the Fukuoka air quality in addition to the local sources. For high sulfate days, influences of active volcanoes, which emit SO\({}_{2}\), were suggested. For high PAHs days, the influence of local emissions in Fukuoka and Kitakyushu was suggested in addition to the transboundary air pollution. Some of chemical components showed the different concentration variations from PM\({}_{2.5}\) and other chemical components, which indicates that it is necessary to examine the health effects of each chemical component, and to study the source and formation processes of each chemical component in the atmosphere.  
Conceptualization, A.Y. and A.T.; methodology, A.Y., A.T., K.H., K.-O.P. and N.T.; software, A.Y., A.T.; validation, A.Y., A.T., K.H. and N.T.; formal analysis, A.Y., A.T., K.H., K.-O.P. and N.T.; investigation, A.Y., A.T., A.S., K.S., K.H. and N.T.; resources, A.Y., A.T., A.S., K.S., K.H., N.T., A.H., H.N. and H.O.; data curation, A.Y., A.T., K.H., K.-O.P. and N.T; writing--original draft preparation, A.Y. and A.T.; writing--review and editing, A.Y., A.T., A.S., K.S., K.H., N.T., A.H., H.N. and H.O.; visualization, A.Y. and A.T.; supervision, A.T.; project administration, H.N., H.O., K.H. and A.T.; funding acquisition, H.N., H.O., K.H. and A.T.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Environment Research and Technology Development Fund (JPMEERF20195051, JPMEERF20202003, JPMEERF2025M02) of the Environmental Restoration and Conservation Agency Provided by the Ministry of Environment of Japan.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors are grateful to the technical staff at Department of Pediatrics, National Hospital Organization Fukuoka National Hospital for replacing the filters.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 3 Tracking Error Analysis of Delta Doppler Aided PLL
### Oscillator Phase Error  
For second-order PLL, the variance of oscillator phase error \(n_{\theta}^{osc}\) can be expressed as [22]:  
\[\mathrm{var}[n_{\theta}^{osc}]=2\pi^{2}f_{L}{}^{2}[\frac{\pi^{2}h_{-2}}{\sqrt{ 2}\omega_{n}{}^{3}}+\frac{\pi h_{-1}}{4\omega_{n}{}^{2}}+\frac{h_{0}}{4\sqrt{ 2}\omega_{n}}] \tag{30}\]  
where the oscillator parameters \(h_{-2}[s^{-1}]\), \(h_{-1}[dimensionless]\) and \(h_{0}[s]\) are related to the Allan deviation and \(f_{L}\) is the carrier frequency.  
Oscillator phase error is a reflection of oscillator's characteristics, it can be considered as an input noise of the loop. Then the variance of the steady-state phase tracking error \(\Psi_{ss}^{osc}\) due to the oscillator phase error can be expressed as:  
\[\mathrm{var}[\Psi_{ss}^{osc}]=c_{2}\mathrm{var}[n_{\theta}^{osc}] \tag{31}\]
# Joint Invariants of Linear Symplectic Actions
## 5 From Joint to Differential Invariants
### Contact Discretization  
Now we use joint invariants to obtain differential invariants of curves in contact 3-space \(W=\mathbb{R}^{3}(x,y,u)\) with respect to the group \(G=\operatorname{GL}(2,\mathbb{R})\), acting as in SS4.3. The curves will be given as \(y=y(x),u=u(x)\) and their jet-space is \(J^{k}(W,1)=\mathbb{R}^{2k+3}(x,y,u,y_{x},u_{x},\ldots,y_{x,x},u_{x,x})\). The differential invariants are generated in the Lie-Tresse sense ([7], SS8.1) as  
\[\mathcal{A}_{G}=\langle I_{1},I_{2}\,;\,\
abla\rangle.\]  
where  
\[I_{1}=\frac{u_{x}-y}{xy_{x}-y}\,,\quad I_{2}=\frac{(xy-2u)^{2}}{(xy_{x}-y)^{3} }\,y_{xx}\,,\quad\
abla=\frac{xy-2u}{xy_{x}-y}\,\mathcal{D}_{x}.\]  
Instead of exploiting the absolute rational invariants \(T_{i},T_{ij}\) we will work with the relative polynomial invariants \(R_{i},R_{ij}\) from Section 4.3. To get absolute invariants we will then have to pass to weight zero combinations.  
Consider three close points \(\hat{A}_{i}=(x_{i},y_{i},u_{i})\), \(i=0,1,2\), lying on the curve. We again omit indices for the middle point, so \(x_{0}=x-\delta\), \(x_{1}=x\) and \(x_{2}=x+\epsilon\). Using the [PERSON] decomposition as in the preceding subsection, we obtain  
\[R_{1}=xy-2u, R_{0}-R_{1}=\delta(2u^{\prime}-y-xy^{\prime})+o(\delta),\] \[R_{01}=\delta(xy^{\prime}-y)+o(\delta), R_{02}=(\epsilon+\delta)(xy^{\prime}-y)+o(|\epsilon|+|\delta|),\] \[R_{12}=\epsilon(xy^{\prime}-y)+o(\epsilon), R_{01}+R_{12}-R_{02}=\tfrac{1}{2}\epsilon\delta(\epsilon+\delta)y^{ \prime\prime}+o((|\epsilon|+|\delta|)^{3})\]  
as well as  
\[\overrightarrow{A_{0}A_{1}}=\delta(\partial_{x}+y^{\prime}\partial_{y}+u^{ \prime}\partial_{y})+o(\delta).\]  
Passing to jet-notations, we obtain the limit formulae for basic differential invariants:  
\[I_{1}=\lim_{A_{0}\to A_{1}}\frac{R_{0}-R_{1}}{2R_{01}}+\frac{1}{2}=\lim_{A_{0 }\to A_{1}}\frac{T_{0}-1+T_{01}}{2T_{01}},\] \[\frac{1}{2}I_{2}=\lim_{A_{0},A_{2}\to A_{1}}\frac{R_{1}^{2}(R_{01}+R_ {12}-R_{12})}{R_{01}R_{02}R_{12}}=\lim_{A_{0},A_{2}\to A_{1}}\frac{T_{01}+T_{1 2}-T_{12}}{T_{01}T_{02}T_{12}},\] \[\
abla=\lim_{A_{0}\to A_{1}}\frac{R_{1}}{R_{01}}\overrightarrow{A_{ 0}A_{1}}=\lim_{A_{0}\to A_{1}}\frac{\overrightarrow{A_{0}A_{1}}}{T_{01}}.\]  
These formulae straightforwardly generalize to invariants of jets of curves in contact manifolds of dimension \(2n+1\), \(n>1\), in which case there are also other generators obtained by higher order discretizations.
# Satellite Image Categorization Using Scalable Deep Learning
## 3 Materials and Methods  
### Data Description  
Satellite Image Classification Dataset-RSI-CB256 is used for this research. This is a publicly available dataset and can be found in [48]. Figure 1 shows sample images of this dataset. This dataset comprises four distinct classes acquired from sensors and Google map snapshots. The size of each image is 256 \(\times\) 256. Figure 2 shows the category spread of the data. It also shows the dataset's inter-class similarities and intra-class variational challenge through t-distributed stochastic neighbor embedding (T-SNE) visualization. T-SNE visualization allows for the mapping of higher-dimensional data points to two- or three-dimensional spaces. T-SNE preserves the pairwise similarities between the high-dimensional data points as much as possible in the lower-dimensional space. In other words, the data points that are related in the high-dimensional space will be near each other in the low-dimensional space, while the data points that are disparate in the high-dimensional space will be apportioned in the low-dimensional space. Visualization shows the challenging gravity of a dataset.
# Influence of Cutter Head on Cavitation of Non-Jammed Submerged Grinder Pump
## 4 Numerical Results and Analysis
### Optimization of Cutter Head Shape  
Through the above research, it can be seen that the cavitation near the cutter head of the GSP-22 pump had two components: the clearance cavitation between the radial clearance of the dynamic and static cutter discs and the airfoil cavitation caused by the poor shape of the cutter head. Therefore, it is of great significance to optimize the shape of the cutter head and to control the airfoil cavitation near the cutter head.  
In order to control the cavitation of the cutter head, the most ideal cutter head shape is streamlined. To ensure the sufficient strength of the cutter head, its shape was designed based on the 791 airfoil, which has good hydraulic performance. The top section of the cutter head was 0.75 times the root section, narrowing evenly from the root to the top. The final design of the moving cutter head is shown in Figure 17.  
The cavitation of the submersible grinder pump GSP-22 with two, three and four cutter heads was studied, respectively, and the performance of the submersible grinding pump before the optimization of the cutter head was compared and analyzed. The inlet pressure was set to 1.1 atm and the outlet flow to 22 m\({}^{3}\)/h.
###### Abstract  
This paper presents a low-noise potentiostat readout circuit with a chopper fully differential difference amplifier (FDDA) for glucose monitoring. Glucose monitoring is necessary for the early diagnosis of diabetes complications and for health management. Ammeter electrochemical sensors are widely used for glucose detection, and in general, a three-electrode structure of a reference electrode (RE), a counter electrode (CE), and a working electrode (WE) is implemented with a potentiostat structure. A low-noise characteristic of the readout circuit is essential for highly accurate glucose monitoring. The chopping technique can reduce low-frequency noises such as 1/f noise and can achieve the required low-noise characteristic. The proposed potentiostat readout circuit is based on a low-noise chopper FDDA with a class-AB output stage. The implementation of the chopper FDDA scheme of the potentiostat readout circuit can decrease the number of amplifiers in the control part of the potentiostat, with reduced power consumption and a wide dynamic output range. The negative feedback loop of the inverting amplifier scheme with the FDDA maintains the voltage between the _WE_ and _RE_ constants. The negative feedback loop tracks the reference voltage of the _RE_ with an input voltage of the _WE_. The proposed potentiostat readout circuit is designed in the standard 0.18 \(\upmu\)m CMOS process, and the simulated current consumption is 48.54 \(\upmu\)A with a 1.8 V power supply. The simulated input-referred noise level was 8.53 pArms.  
potentiostat readout circuit; glucose monitoring; chopper amplifier; fully differential difference amplifier  
2022
## 4 Results
### Classification of Papers According to Study Categories
#### 4.1.1 Application Environments  
In the \"Application Environment\" category, 191 papers were labeled from the selected papers. Most papers refer to the marine environment with 89 papers. Applications for lacustrine and coastal environments are in second and third place with 22 and 20 papers, respectively. The fluvial environment occupies the fourth place with 18. Finally, there is a group of papers that refers to applications in environments such as ponds, reservoirs, and wetlands, which contributes 42 papers. In 28 papers, two or more application environments were identified. On the other hand, a total of 78 papers were not tagged, as the environment information was not found explicitly. In this case, it is considered that most of these works are carried out on topics applicable to any of the environments. However, further in-depth study is required to perform an adequate classification. The distribution of the number of categorized papers according to the application environment is shown in Table 9.
## 3 Classification with Machine Learning and Results
### Results
The improvement in the accuracy corresponds to the increased training dataset in the results, which is acceptable with our expectations. The accuracy of around 90% can be inferred by using our smallest training set of 1500 data samples, only one-fifth of the total inputs. The small variations in all results point to the robustness of the SVM algorithm. The DNN model was also executed 20 times independently for each dataset comparison as in Figure 8b. It can clearly be seen that the performance of SVM using the lowest amount of data is higher and more stable than that of the DNN model. In contrast, the DNN utilizes a high percentage of datasets better as most of the accuracies are over 98%. Moreover, Figure 8c demonstrates that both the proposed models have significantly  
Figure 8: The classification accuracy versus the number of running times: (**a**) SVM algorithm, (**b**) DNN algorithm, and (**c**) decision tree algorithm. higher abilities than the decision tree algorithm, which represents a linear classification at all percentages of the dataset which means that both of these classification models are reasonable choices for the non-linear problem of this study. Figure 9 shows two testing results of each classification model, with 20% and 70% datasets on a logarithmic scale including the correct and incorrect prediction rates. All of the incorrect predictions classify the thicknesses into the wrong classes, which are adjacent to the actual thicknesses. Fewer misclassifications from the SVM model with the 20% dataset can be observed due to the low incorrect prediction rate in Figure 9a compared to that of the DNN in Figure 9b. In contrast, the DNN obtains the higher correct prediction rate when employing a 70% dataset as in 9d, although the SVM model also accomplishes good results with only two thicknesses having incorrect predictions, as in Figure 9c. Finally, we inferred the material thickness by relating back to the conventional method as in Figure 1a for comparison. The NIST iterative method employed the same dataset for extracting the thickness from each set of input features. The obtained thicknesses were evaluated with the actual ones to determine the average accuracy of each thickness. These results were then compared to those attained from two proposed ML models whose  
Figure 9: The classification testing results of **(a)** SVM with 20% dataset **(b)** DNN with 20% dataset **(c)** SVM with 70% dataset and **(d)** DNN with 70% dataset. training phases utilized 70% of the dataset. Table 3 reveals that two trained models work more effectively than the traditional approach as they provide a higher accuracy rate for all thicknesses.
## 5 Numerical Results
### IL and RL  
As shown in Figure 10, the IL and RL are losses at Nyquist frequency, and the model's predictions of IL are more accurate than its predictions of RL. This is because IL is primarily influenced by the dielectric loss and input features, including dielectric loss-related parameters. Conversely, RL is affected by additional factors such as impedance mismatches and complex trace shapes, complicating its prediction. Therefore, the accuracy of IL prediction is better than that of RL prediction. The relative error of RL prediction is 17.4%, but this is sufficient to support the accurate prediction of eye diagrams and COM values.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 4 Model Performance and Results Analysis
### Comparison of Theta Estimates Between 1 PL-IRT and Time-Aware Models  
To evaluate how time-aware models influence ability estimation, we compared the theta (\(\theta\)) values obtained from the standard 1 PL-IRT model with those derived from each of the six proposed models that incorporate response time. Two statistical measures were used: Pearson correlation coefficient (r) to assess the consistency of examinee rankings and Mean Absolute Error (MAE) to evaluate average estimation deviation.  
Table 3 presents the results. Subtractive models (TP-IRT and DTA-IRT) demonstrate high agreement with the 1 PL model, showing strong correlations (r > 0.99) and low MAE values (\(\approx\)0.22). This suggests that these models retain the structural integrity of ability estimates while incorporating temporal behavior. In contrast, some multiplicative models (e.g., ART-IRT and NRT-IRT) exhibit lower correlation and higher divergence. Notably, STP-IRT had the highest MAE (1.60), suggesting potential over-penalization of slow responses.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**MODEL** & \multicolumn{2}{c}{**First Approach**} & \multicolumn{3}{c}{**Second Approach**} & \multicolumn{3}{c}{**Third Approach**} \\ \hline
**TIF** & \begin{tabular}{c} **Log-** \\ **Likelihood** \\ \end{tabular} & **Item Fit** & **TIF** & \
\begin{tabular}{c} **Log-** \\ **Likelihood** \\ \end{tabular} & **Item Fit** & **TIF** &
\begin{tabular}{c} **Log-** \\ **likelihood** \\ \end{tabular} & **Item Fit** \\ \hline
**1 PL-IRT** & 4.5490 & \(-\)799.1263 & 0.1461 & 5.9506 & \(-\)678.2216 & 0.1599 & 0.1770 & \(-\)678.2216 & 0.1553 \\
**TP-IRT** & 4.8375 & \(-\)806.1206 & 0.1467 & 5.9416 & \(-\)678.0587 & 0.1598 & 0.1770 & \(-\)678.0587 & 0.1553 \\
**TWD-IRT** & 5.658 & \(-\)882.1208 & 0.2981 & 6.3143 & \(-\)756.0996 & 0.3593 & 0.2297 & \(-\)756.0996 & 0.3153 \\
**STP-IRT** & 4.6812 & \(-\)810.4032 & 0.1489 & 5.9505 & \(-\)724.0715 & 0.1599 & 0.1754 & \(-\)724.0715 & 0.1558 \\
**NRT-IRT** & 5.0883 & \(-\)858.6678 & 0.1593 & 7.5000 & \(-\)950.4710 & 0.2501 & 0.1229 & \(-\)950.4710 & 0.4016 \\
**DTA-IRT** & 4.2493 & \(-\)806.4547 & 0.1485 & 5.9505 & \(-\)677.8257 & 0.1599 & 0.1773 & \(-\)677.8257 & 0.1552 \\
**ART-IRT** & 4.7244 & \(-\)858.5565 & 0.1598 & 7.5000 & \(-\)893.0601 & 0.2501 & 0.1868 & \(-\)893.0601 & 0.3582 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different IRT models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Theta Correlation with 1 PL** & **Theta MAE vs. 1 PL** \\ \hline
**TP-IRT** & 0.998 & 0.224 \\
**DTA-IRT** & 0.992 & 0.216 \\
**TWD-IRT** & 0.908 & 0.435 \\
**NRT-IRT** & 0.809 & 0.505 \\
**STP-IRT** & 0.868 & 1.603 \\
**ART-IRT** & 0.784 & 0.493 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of theta estimates relative to 1 PL model.
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
###### Abstract  
Sea ice adversely impacts nautical, logistical and scientific missions in polar regions. Ship navigation benefits from up-to-date sea ice analyses at both regional and local scales. This study presents a satellite-based sea ice navigation system (SatSINS) that integrates observations and scientific output from remote sensing and meteorological data to develop optimum marine navigational routes in sea ice-covered waters, especially in areas where operational ice information is usually scarce. The system and its applications are presented in the context of a decision-making process to optimize the routing of the RV _Xuelong_ during her passage through Prydz Bay, East Antarctica during three trips in the austral spring of 2011-2013. The study assesses scientifically-generated remote sensing ice parameters for their operational use in marine navigation. Evaluation criteria involve identification of priority parameters, their spatio-temporal requirements in relation to navigational needs, and their level of accuracy in conjunction with the severity of ice conditions. Coarse-resolution ice concentration maps are sufficient to delineate ice edge and develop a safe route when ice concentration is less than 70%, provided that ice dynamics, estimated from examining the cyclonic pattern, is not severe. Otherwise, fine-resolution radar data should be used to identify and avoid deformed ice. Satellite data lagging one day behind the actual location of the ship was sufficient in most cases although the proposed route may have to be adjusted. To evaluate the utility of SatSINS, deviation of the actual route from the proposed route was calculated and found to range between 165 m to about 16.0 km with standard deviations of 2.8-6.1 km. Growth of land-fast ice has proven to be an essential component of the system as it was estimated using a thermodynamic model with input from a meteorological station.  
sea ice; remote sensing; marine navigation; Antarctica +
Footnote †: journal: Remote Sensing