esa-sceva/satcom-chunk-collection huggingface dataset:
# Case Study of an Integrated Design and Technical Concept for a Scalable Hyperloop System
## 4 Functional Definition
### Boarding  
To enter or exit the system, the vacuum environment must be overcome. Generally, there are two options to achieve this: either the pod is removed from the vacuum via an air lock, or the pod connects its interior to the outside using an air dock.  
Air locks offer the advantage that low-speed parts of the track, as well as the stations, can be operated at atmospheric pressure, thereby reducing costs. On the other hand, air docks are expected to be faster in operation, more energy-efficient, and prevent cyclic loads on the cabin.  
A common air lock with two gates and reasonably dimensioned pumps takes at least about 10 min to pump down to 10 mbar, which undermines much of the hyperloop use case. There are other theoretical proposals, such as using a column of water as an air lock that the pod has to pass through. Another option is to feature a combination of a blower that reaches a pressure of 300 mbar relatively fast, then releasing the rest of the air into the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Car** & **Rail** & **Hyperloop** & **Plane** \\ \hline Best-case overhead time [min] & 0 & 15 & 20 & 115 \\ Worst-case overhead time [min] & 10 & 45 & 60 & 175 \\ Best-case average speed [\(\frac{\text{km}}{\text{h}}\)] & 120 & 250 & 600 & 750 \\ Worst-case average speed [\(\frac{\text{km}}{\text{h}}\)] & 60 & 60 & 460 & 600 \\ Best-case route length factor & 1.1 & 1.1 & 1.1 & 1.0 \\ Worst-case route length factor & 1.2 & 1.2 & 1.2 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Assumed data to compare modes of transportation. Results are presented in Figure 3.  
Figure 3: Travel time and distance comparison of different modes of transportation based on the data presented in Table 2. Between the black dots, the hyperloop offers the shortest average travel time compared to other modes of transport.  
tube. This design, however, involves substantial energy consumption. Additionally, all pods have to come to a full stop twice in the journey, prolonging travel time. Moreover, depending on the sound insulation, loud noises could reduce passenger comfort.  
Conversely, air docks directly bridge the gap between the pod door and tube and allow for quick passenger boarding and deboarding operations. A pump system is required at the station to depressurize only the small volume between the pod door and station. Inflatable sealings are a favored way to implement this without lateral movement of the pod or the station interface. Due to space constraints in the cabin, the pod door should open outward while still providing airtight, safe, and reliable docking.  
Given the advantages explained above, passenger boarding and deboarding using an air dock is selected. However, air locks will still be required at given locations--for example, at maintenance facilities--to move the pods into and out of the vacuum environment.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Output and Audit Module  
The output and audit module are the final component of this method, handling the Hamming distance result shares from the approximate matching module, aggregating them to produce the matching results, and ensuring computational correctness and security through audit verification via smart contracts and validation nodes. Blockchain and IPFSare utilized to achieve output and traceability, allowing participants to access only the final results without exposure to masks or intermediate data, with masks generated by the mask distribution contract. Validation nodes randomly sample and recompute results to maintain credibility and node reputation. All actions and data are recorded on the blockchain and stored on IPFS, ensuring security, efficiency, and transparency.
## 2 Design
In order to have high velocity and impedance matching, n\({}_{\rm eff}\) and Z\({}_{\rm o}\) should be kept near n\({}_{\rm op}\) and 50 \(\Omega\), respectively. As evident in Figure 2, the n\({}_{\rm eff}\) varies around an obtained n\({}_{\rm op}\) between 2.228 and 2.24, while Z\({}_{\rm o}\) varies between 44 \(\Omega\) and 56 \(\Omega\). Another critical factor deciding the performance metric of the MZI is \(V_{\pi}L\) which depends on w\({}_{\rm g}\), overlap factor (\(\tau\)), the extraordinary refractive index of LiNbO\({}_{3}\) (n\({}_{\rm e}\) = 2.138), and optical wavelength (\(\lambda_{o}\)) and is given by  
\[V_{\pi}L=\frac{\lambda_{o}w_{g}}{2\pi n_{e}^{2}\tau_{33}\tau} \tag{1}\]  
where \(\tau\) is given by  
\[\tau=\frac{w_{g}}{V}\frac{\iint\mid E_{op}\mid^{2}E_{rf}dS}{\iint\mid E_{op} \mid^{2}dS} \tag{2}\]  
\(\tau\) depends on the RF and optical field along the z-axis and the rib distance D from the trace electrode. Using the RF and optical fields, the overlap integral is calculated using Equation (2) for varying rib distance from the trace electrode and is plotted in Figure 3a. Results suggest that as we move away from the trace electrode, the value of \(\tau\) decreases toward the center of the electrode gap, and then starts increasing gradually as we move towards the ground due to the increase in RF field toward the ground electrode. As we place the rib closer to the trace electrode, the metal-induced losses in the optical mode increase. This issue is addressed by depositing a SiO\({}_{2}\) buffer layer between the electrodes and the LN layer. Variation of \(V_{\pi}L\) with D is plotted in Figure 3b. From the results, the value of D was determined to be 1.6 \(\upmu\)m; at this position, the value of \(\tau\) is 0.87.
## 4 Results and Discussion
### Practical Implications  
The findings of this study have significant practical implications for the oil and gas industry, particularly in preventing costly NPT due to stuck pipe incidents. Through the utilization of real-time drilling data and a LSTM autoencoder, operators are empowered to proactively detect the first signs of stuck pipe events and, consequently, take appropriate measures in good time to evade risks. The predictive capability provides enhanced operational safety, drilling efficiency, and reduced financial losses for pipe retrieval operations. Furthermore, the automation potential of the model can also support decision-making  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Remarks** \\ \hline K-Nearest Neighbors (KNN) & 85.20\% & 86.00\% & 81.50\% & 83.70\% & \begin{tabular}{c} Performance stable but sensitive \\ to outliers \\ \end{tabular} \\ \hline Support Vector Machine (SVM) & 88.40\% & 89.10\% & 84.20\% & 86.60\% & \begin{tabular}{c} Good for high-dimensional data, \\ less adaptive \\ \end{tabular} \\ \hline Decision Tree (DT) & 84.10\% & 85.00\% & 80.40\% & 82.60\% & \begin{tabular}{c} Interpretable but may overfit on \\ small sets \\ \end{tabular} \\ \hline Random Forest (RF) & 90.60\% & 91.50\% & 87.30\% & 89.30\% & \begin{tabular}{c} Balanced performance, ensemble \\ strength \\ \end{tabular} \\ \hline Artificial Neural Network (ANN-Deep, 3 Layers) & 93.10\% & 92.00\% & 89.80\% & 90.90\% & \begin{tabular}{c} Better feature learning but slower \\ convergence \\ \end{tabular} \\ \hline Convolutional Neural Network (CNN) & 94.60\% & 93.50\% & 91.20\% & 92.30\% & \
\begin{tabular}{c} Strong pattern extraction, needs \\ more computation \\ \end{tabular} \\ \hline Proposed LSTM Autoencoder (Current Study) & 99.06\% & 97.12\% & 91.34\% & 94.15\% &
\begin{tabular}{c} Real-Time Compatible, Trained \\ on Normal Only \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Balanced performance comparison with traditional ML methods.  
through the capability of real-time anomaly detection with minimal dependence on manual observation and experience. As industries move towards digital transformation, the integration of machine learning-based anomaly detection with existing drilling control systems can empower smarter and more autonomous drilling operations.
###### Abstract  
Nested named entity recognition (NER) is a task that identifies hierarchically structured entities, where one entity can contain other entities within its span. This study introduces a nested NER model for few-shot learning environments, addressing the difficulty of building extensive datasets for general named entities. We enhance the Biaffine nested NER model by modifying its output layer to incorporate label semantic information through a novel label description embedding (LDE) approach, improving performance with limited training data. Our method replaces the traditional biaffine classifier with a label attention mechanism that leverages comprehensive natural language descriptions of entity types, encoded using BERT to capture rich semantic relationships between labels and input spans. We conducted comprehensive experiments on four benchmark datasets: GENIA (nested NER), ACE 2004 (nested NER), ACE 2005 (nested NER), and CoNLL 2003 English (flat NER). Performance was evaluated across multiple few-shot scenarios (1-shot, 5-shot, 10-shot, and 20-shot) using F1-measure as the primary metric, with five different random seeds to ensure robust evaluation. We compared our approach against strong baselines including BERT-LSTM-CRF with nested tags, the original Biaffine model, and recent few-shot NER methods (FewNER, FIT, LPNER, SpanNER). Results demonstrate significant improvements across all few-shot scenarios. On GENIA, our LDE model achieves 45.07% F1 in five-shot learning compared to 30.74% for the baseline Biaffine model (46.4% relative improvement). On ACE 2005, we obtain 44.24% vs. 32.38% F1 in five-shot scenarios (36.6% relative improvement). The model shows consistent gains in 10-shot (57.19% vs. 49.50% on ACE 2005) and 20-shot settings (64.50% vs. 58.21% on ACE 2005). Ablation studies confirm that semantic information from label descriptions is the key factor enabling robust few-shot performance. Transfer learning experiments demonstrate the model's ability to leverage knowledge from related domains. Our findings suggest that incorporating label semantic information can substantially enhance NER models in low-resource settings, opening new possibilities for applying NER in specialized domains or languages with limited annotated data.  
deep learning; natural language processing; nested named entity recognition; label embedding; few-shot learning +
Footnote †: journal: Applied sciences
## 4 Experimental Characterization of Si-SC Structures with SLARC  
The performance of a single-layer anti-reflection coating (SLARC) is critically influenced by its refractive index (\(n\)) and thickness (\(d\)). Optimal SLARC design necessitates a refractive index that is the geometric mean of air (\(n_{0}\approx 1\)) and silicon (\(n_{Si}\approx 3.5\)) for the visible spectrum. Theoretically, the ideal SLARC thickness is a quarter-wavelength (\(\lambda\)) of the target light, as expressed below:  
\[\mathbf{d}=\frac{\lambda}{4n} \tag{1}\]  
However, achieving optimal performance across the entire solar spectrum often requires deviations from this theoretical thickness. To this end, a comprehensive analysis of refractive index and optical reflectivity was conducted for SiO\({}_{2}\) and Si\({}_{3}\)N\({}_{4}\) SLARCs with varying thicknesses across the wavelength range of 350 nm to 1000 nm. These measurements provide essential insights into the relationship between SLARC properties and their impact on solar cell performance.
# Analysis of Vertical Earth Pressure Acting on Box Culverts through Centrifuge Model Test
## 3 Centrifuge Model Tests
### Ground Composition  
#### 3.3.1 Material Properties of Soil  
Most of the centrifuge model tests that have been performed thus far have used poorly graded sand as the model ground to obtain stabilized results. However, the actual ground is mostly composed of granite weathered soil, which is classified as well-graded sand under the USCS (Unified Soil Classification System). Therefore, the model ground was constructed using well-graded sand. For this purpose, four types of poorly graded sands with specific particle sizes were mixed in a specific ratio. Table 2 shows the approximate particle size and mixing ratio of each poorly graded sand.  
A sieve test was conducted as prescribed in ASTM D2487 [37] to determine the particle size distribution of the mixed well-graded sand, and the results are shown in Figure 4a. The mixed sand has an even particle size distribution compared to that of the constituent poorly graded sands. The coefficient of uniformity (\(C_{u}\)) and coefficient of curvature (\(C_{c}\)) of the model soil as calculated from the particle size distribution curve are 4.09 and 1.18, respectively. These values do not correspond to the acceptance criteria prescribed in the USCS (\(C_{u}>6\), and \(1<C_{c}<3\)). However, the particle size of the mixed sand is close to that of well-graded sand than to that of poorly graded sand, which is the standard sand (e.g., Ottawa sand and Toyoura sand) used in existing centrifugal model tests.  
In addition, a laboratory compaction test was performed to determine the degree of compaction of the ground composed of well-graded sand according to ASTM D698 [38]. Figure 4b shows the compaction curve obtained from the laboratory compaction test. According to this curve, the maximum dry unit weight of sand and its optimum water content were calculated as 1.715 t/m\({}^{3}\) and 8%, respectively.
# Quadratic Curve Fitting-Based Image Edge Line Segment Detection: A Novel Methodology
###### Abstract  
In the field of computer vision, edge line segment detection in images is widely used in tasks such as 3D reconstruction and simultaneous localization and mapping. Currently, there are many algorithms that primarily focus on detecting straight line segments in undistorted images, but they do not perform well in detecting edge line segments in distorted images. To address this quandary, the present study introduces a novel method of line segment identification founded on the principles of quadratic fitting. The method proposed utilizes the inherent property of a linear projection in a three-dimensional space, whereby it appears as a quadratic curve in a distorted two-dimensional image. This approach applies an iterative estimation process to ascertain the optimal parameters of the quadratic form that aligns with the edge contour. This process is facilitated by implementing an assumption and validation mechanism. Upon deriving the optimal model, it is then employed to identify the line segments that are encompassed within the edge contour. The experimental assessment of this novel method incorporates its application to both distorted and distortion-free image datasets. The method eliminates the necessity for preliminary processing to discarding distortions, thereby making it universally applicable to both distorted and non-distorted images. In addition to this, the experimental results based on the dataset indicate that the proposed algorithm in this paper achieves an average computational efficiency that is 27 times faster than traditional ones. Thus, this research will contribute to line segment detection in computer vision.  
line segment detection; quadratic fitting; image distortion; computer vision Article
# A Probabilistic Hyperspectral Imagery Restoration Method
## 3 Revisited Low-Rank based HSI Denoising from Linear Mixed Model Perspective
### Analyze HSI Restoration as a LRMA Problem  
We use \(rank(\bullet)\) to denote the rank number of a given matrix. It is realized that noise, i.e. \(rank(\mathbf{S})\), is always a full matrix [29]. Since the row and column dimensionalities of \(\mathbf{S}\) are \(n_{p}\) and \(n_{b}\), \(rank(\mathbf{S})=\min(n_{b},n_{p})\).  
Since \(\mathbf{L}=\mathbf{M}\mathbf{H}^{\mathsf{T}}\), the rank of noise-free HSI \(\mathbf{L}\), denoted as \(rank(\mathbf{L})\), is \(\min(rank(\mathbf{M}),rank(\mathbf{H}))\). \(n_{e}\) represents the number of endmembers, which is related with the materials captured in the HSI image. Since there are always several materials in the HSI image [29], \(n_{e}\) is far smaller than the number of spectral bands \(n_{b}\) or \(n_{p}\), which is always more than one hundred or ten thousand for an HSI image. Thus, \(rank(\mathbf{L})\leq\min(rank(\mathbf{M}),rank(\mathbf{H}))\leq n_{e}\).  
The rank of \(\mathbf{X}\), denoted as \(rank(\mathbf{X})\), is no larger than \(rank(\mathbf{L})+rank(\mathbf{S})\) since \(\mathbf{X}=\mathbf{L}+\mathbf{S}\). According to the fact that \(n_{e}\) is far smaller than \(n_{p}\) and \(n_{b}\), \(rank(\mathbf{L})<<\min(n_{p},n_{b})=rank(\mathbf{S})\), which makes \(rank(\mathbf{L})<<rank(\mathbf{X})\approx rank(\mathbf{S})\).  
According to above analysis and the fact that only several endmembers are always contained in one HSI in reality [29], HSI denoising can be casted into recovery a low-rank matrix \(\mathbf{L}\) from \(\mathbf{X}\).
## Appendix B Calculation of Vertical Gradient of Refractive Index  
Vertical height range of \((i,j)\)th sub volume lies between its uppermost point \(h_{U(i,j)}\) and lowermost point \(h_{L(i,j)}\), given from [7] as follows:  
\[\begin{array}{ll}h_{L(i,j)}=h_{1}+\frac{1}{2n}R_{t(i,j)}^{2}+\left\{\theta_ {1}+(i-1)\cdot\Delta\alpha+(j-1)\cdot\left(\frac{\omega}{M}\right)\right\} \cdot R_{t(i,j)}&\text{m}\\ h_{U(i,j)}=h_{1}+\frac{1}{2n}R_{t(i,j+1)}^{2}+\left\{\theta_{1}+(i-1)\cdot \Delta\alpha+j\cdot\left(\frac{\omega}{M}\right)\right\}\cdot R_{t(i,j+1)}& \text{m}\end{array} \tag{102}\]  
Value of refractivity \(N\) at a given point in troposphere can be calculated from ITU-R Recommendation P452-15 [25], as follows:  
\[N=\frac{77.6}{T}\left(P+4810\ \frac{e}{T}\right)\ \ \text{N-units} \tag{103}\]  
where \(P\) is the atmospheric pressure in millibars, \(T\) is the atmospheric temperature in degree Kelvin, and \(e\) is the atmospheric vapor pressure in millibars, measured at the given point. Atmospheric vapor pressure can be obtained from [26] as follows:  
\[e=\left(T_{d}+d^{\prime}\right)^{a^{\prime}}\cdot 10^{\left[C^{\prime}+\left[ \frac{b^{\prime}}{T_{d}+d^{\prime}}\right]\right)}\ \ \ \text{mbar} \tag{104}\]  
\(T_{d}\) is the dew point temperature in degree kelvin measured at the given point. Values of constants \(a^{\prime}\), \(b^{\prime}\), \(c^{\prime}\), and \(d^{\prime}\) are given in Table 19[26]. Values of \(P\), \(T\), and \(T_{d}\) have been obtained for different tropospheric heights from IMD observatories of Ahmedabad and Guwahati. The value of \(N\) has been calculated using these values for different heights lying in the height range of the given sub volume. \(\frac{\delta N}{\delta z}\) is then calculated for successive heights and averaged to obtain a single value, \((\frac{\delta N}{\delta z})_{(i,j)}\) for the given sub volume. Average values of \(\delta n/\delta z\) for the \((i,j)\)th sub volume have been calculated from [27] as follows:  
\[\left(\frac{\delta n}{\delta z}\right)_{(i,j)}=\left(\frac{\delta N}{\delta z }\right)_{(i,j)}\times 10^{-6} \tag{105}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constant** & **Value** & **Value** \\  & **(\(T_{d}>0\ ^{\circ}\)C)** & **(\(T_{d}\leq 0\ ^{\circ}\)C)** \\ \hline \(a^{\prime}\) & \(-4.9283\) & \(-0.32286\) \\ \hline \(b^{\prime}\) & \(-2937.4\) & \(-2705.21\) \\ \hline \(c^{\prime}\) & \(23.5518\) & \(11.4816\) \\ \hline \(d^{\prime}\) & \(273\) & \(273\) \\ \hline \hline \end{tabular}
\end{table}
Table 19: Values of constants for calculation of vapor pressure from dew point.
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## 3 Prediction of Long-Term Settlement
### Statistics of Trough Width Parameter  
Based on the data points of 59 cross sections, the relationship between trough width \(i\) and buried depth \(H\) for different soil types, in comparison with different reference lines of trough width parameter \(K\), is shown in Figure 5a. For a common presentation, the buried depth \(H\) and trough width \(i\) were normalized by the tunnel diameter \(D\). Based on linear regressions, the average value of trough width parameter \(K\) for each soil layer was estimated as follows: silt: \(K\) = 0.40, slit with silty sand: \(K\) = 0.52, silty sand: \(K\) = 0.51, and silty clay: \(K\) = 0.42. As can be observed, most data points are located in the range of 0.30-0.70.  
For a better overview of the parameter \(K\), Figure 5b presents the histograms distribution of the trough width parameter \(K\). In comparison, cohesion soils (silty clay or silt) show a little lower \(K\) than cohesionless soils (silty sand or silt with silty sand), but also with a higher scatter of results. This may be attributed to the intrinsic unpredictability of the parameter \(K\) for the cohesion soil in Changzhou, as well as the limits of the database used in this study. For cohesionless soils (slit with silty sand and silty sand), the \(K\) value ranges mostly between 0.40-0.60. In the case of cohesion soils (Silty clay or silt), the \(K\) value is primarily in the range of 0.30-0.60.  
The observed statistics on \(V_{I}\) and \(K\) can serve as a foundation for more reasonable, statistically driven predictions for long-term ground settlement for future EPB shield tunneling in similar water-rich soft strata.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 3 Methodology
### Platform Independent Computational Model
#### 3.2.1 Update  
The update service modifies the notebook's contents, but only on one page and one field at a time. This service is represented by the structural pattern given by the CPN marked in green as shown in Figure 2a,b. The request originates from the service request place S with the token \"UPDATE (COM, SI)\" indicating to make the update in the page identified by \"COM\" for the field \"PRESENCE\" with the value \"YES\", meaning that someone is in the dining room as shown in Figure 2a. This request is left in place \"\(P\)\" where the presence update requests are attended.  
The structural pattern of the CPN that represents this operation consists of two transitions U and NU, both having as input places the notebook H and the request container \(P\) for receiving update notes. In Figure 2a, note \"(COM, SI)\" can be seen in \(P\) and the corresponding page \"(COM, NO, BAJ, NO, NO, NO, NO)\" to be modified in H. The \"u\" field corresponds to the first field in the last expression, while the \"\(p\)\" field corresponds to the second.  
As described in the informal model, the update is carried out only when the note contains a value that is not already registered in H. The above is expressed with the restriction \"\([p<p^{\prime}]\)\" noted on the transition U. The conditions of qualification annotated on the transitions U and NU are mutually exclusive among themselves since they indicate the criteria to carry out the update, depending on the values of \"\(p\)\", taken from the note in \(P\), and of \"\(p\)\", taken from the sheet identified by \"COM\" in H, as shown in Figure 2b.  
In the case of the update note \"(COM, NO)\", the NU transition is enabled while the U does not, so the actions noted for the NU transition do not cause any change in H. This representation preserves the number of tokens in H and the correspondence and uniqueness between tokens and rooms.