esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Emission Spectroscopy and Analysis
McCumber formalism provides an accurate estimate of the emission cross section as well as its spectral distribution for narrow band emission [38]. On the other hand, the Fuchtbauer-Ladenburg formalism is proven to be accurate for low ion-doping concentrations, where the reabsorption of the emitted photons is negligible and the analysis is applicable to relatively broadband emissions in comparison with the case in McCumber approach, since there is no assumption of the band shape. In Table 3, the emission cross sections obtained by using both formalisms are shown for the two emission bands of the tellurite glasses at different Tm\({}_{2}\)O\({}_{3}\) concentrations. By using the Fuchtbauer-Ladenburg equation for the TeNbTm sample, we obtained average stimulated emission cross section values of 2.62 \(\pm\) 0.02 \(\times\) 10\({}^{-21}\) and 5.63 \(\pm\) 0.02 \(\times\) 10\({}^{-21}\) cm\({}^{2}\) for the 1460-nm and 1860-nm bands, respectively. As can be further seen from the table for the sample containing zinc as a glass modifier (TeZnTm), the average emission cross sections were determined to be 2.78 \(\pm\) 0.39 \(\times\) 10\({}^{-21}\) and 6.33 \(\pm\) 0.34 \(\times\) 10\({}^{-21}\) cm\({}^{2}\) for the 1460- and 1860-nm bands, respectively. These results compare well with values reported for other tellurite hosts [9; 39] and it can also be concluded that for the TeZnTm sample, we obtained higher stimulated emission cross sections (as high as 6.33 \(\pm\) 0.34 \(\times\) 10\({}^{-21}\) cm\({}^{2}\)), especially for the 1860-nm emission band. As can be seen from Table 3, the TeNbKTm glass host has the highest average absorption cross section (8.18 \(\times\) 10\({}^{-21}\) cm\({}^{2}\)) among the three hosts, at 794 nm. The TeZnTm glass host also has a reasonably high absorption cross section (7.85 \(\times\) 10\({}^{-21}\) cm\({}^{2}\)). Furthermore, at low doping concentration levels, the quantum efficiencies for both transitions are quite high in comparison with the other hosts, suggesting that the TeZnTm glasses are the most promising laser gain media among the materials explored in this study. Figure 3: **(a)** Emission spectra of the two transitions of the Tm\({}^{3+}\) ion originating from \({}^{3}\)F\({}_{4}\) and \({}^{3}\)H\({}_{4}\) levels to the ground level for the glass host TeZnTm with Tm\({}_{2}\)O\({}_{3}\) concentrations of 1.0 and 0.25 mol %; **(b)** Emission spectra of the three glass hosts TeNbKTm, TeZnTm and TeNbTm, each with 1.0 mol % Tm\({}_{2}\)O\({}_{3}\) doping. As can be seen from Table 3, both formalisms agree reasonably well for all glasses investigated in this study. Figure 4 further shows the absorption and emission cross sections as a function of wavelength for the TeNbKTm samples with 1.0 mol % doping concentration, based on the McCumber analysis. As can be seen, the emission cross section has a broad, smooth peak, which makes the glass system a promising candidate for ultrafast laser applications.
## 4 Experiments
### Dataset  
We adopt the multi-object tracking benchmarks MOT17 to evaluate our tracking performance. It consists of several challenging pedestrian tracking sequences, with a significant number of occlusions and crowded scenes, varations in angle of view, sizes of objects, camera motion, and frame rates. MOT17 has the same video sequences as the lastest MOT16 [23] challenge benchmark, but provides more accurate ground truth in the evaluation. In addition to DPM [24], Faster-RCNN [25] and scale dependent pooling (SDP) [26] detections are also provided for evaluating the tracking performance. The number of trajectories in the training data is 546 and the number of total frames is 5316. The complexity of the tracking problem requires several metrics including multiple object tracking accuracy (_MOTA_), most tracked (_MT_), ID F1 score (_IDF1_) and so on. Specifically, the _MOTA_ and _IDF1_ scores quantify two of the main aspects: object coverage and identity. Also, we perform all the experiments using the public detections provided by the MOT Challenge for fair comparison. The single object tracker of our method is only initialized and online trained from public detection bounding boxes.
## 2 Theoretical Background  
The cylinder liner deformation can be represented by a Fourier series [20]. The deviation \(\Delta R\) of the liner bore from the perfect circular shape can be written as:  
\[\Delta R=A_{0}+A_{1}\cos\theta+B_{1}\sin\theta+A_{2}\cos 2\theta+B_{2}\sin 2 \theta+\ldots+A_{n}\cos n\theta+B_{n}\sin n\theta \tag{1}\]where \(A_{n},B_{n}\) are the Fourier's coefficients, \(\partial\) is the angular position, and \(n\) is the deformation order. For each \(n\)th order, the maximum bore deformation \(U_{max,n}\) and the phase angle \(\varnothing_{n}\) can be calculated with  
\[U_{max,n}=\sqrt{A_{n}^{2}+B_{n}^{2}} \tag{2}\]  
\[\varnothing_{n}=\frac{1}{n}\tan^{-1}\frac{B_{n}}{A_{n}} \tag{3}\]  
Using the harmonic series, the \(\Delta R\) can be rewritten as:  
\[\Delta R=A_{0}+\sum_{n=1}^{i}\left[\frac{U_{max,n}}{2}\cos[n(\theta-\varnothing _{n})]\right] \tag{4}\]  
Different deformation orders describe different distortion patterns. The zeroth order describes the round bore, the first order describes the eccentric bore, the second order describes the oval bore, third order describes the three-lobe-shaped bore, and the fourth order describes the four-lobe-shaped bore. Generally, the first four deformation orders dominate the total liner deformation, while the higher orders deformations are commonly neglected [13].
## 4 Numerical Results and Discussion
### Parametric Study
Figures 7 and 8 present the nondimensional displacement, \(w^{\star}\), of [0\({}^{\circ}\)/90\({}^{\circ}\)] and [0\({}^{\circ}\)/90\({}^{\circ}\)/0\({}^{\circ}\)] spherical panels with the change of R/\(a\) and \(a/h\) ratios, respectively. The impact of transverse shear deformation and the action of membrane owing to curvature on \(w^{\star}\) is observable. The curvature effect on transverse displacement, \(w^{\star}\), also plays a critical role in the thinner shell regime, specifically beginning from the ratio R/\(a\) < 40. Bending-stretching coupling is inherent in antisymmetric laminations, and it directly impacts the interaction of membrane action with the beam-column/tie-bar effect. The membrane action due to curvature has a complicated interaction with the stated mechanism. This interaction must be considered for the prior design of composite shells. Figures 9 and 10 display the variations of the normalized stresses, \(\sigma_{x}{}^{*}\), with \(a/h\) and R/\(a\) ratios for antisymmetric [0\({}^{\circ}\)/90\({}^{\circ}\)] and symmetric [0\({}^{\circ}\)/90\({}^{\circ}\)/0\({}^{\circ}\)] spherical panels, respectively. The membrane action has similar effect in both the thick and the thinner panel regime, particularly for R/\(a\)\(\leq\) 40, as mentioned above. Figure 6: (**a**)Variation of non-dimensional displacement, \(w^{\star}\), and (**b**) non-dimensional stresses, \(\sigma_{x}{}^{*}\), of moderately thick (\(a/h\) = 20) curved panels (one spherical and two cylindrical) with an R/\(a\) ratio for symmetric [0\({}^{\circ}\)/90\({}^{\circ}\)/0\({}^{\circ}\)] lamination. Figure 7: Nondimensional displacement, \(w^{\star}\), of (0\({}^{\circ}\)/90\({}^{\circ}\)) spherical panels with the change of R/\(a\) and \(a/h\) ratios. Figure 8: Nondimensional displacement, \(w^{\star}\), of \((0^{\circ}/90^{\circ}/0^{\circ})\) spherical panels with the change of \(\mathrm{R}/a\) and \(a/h\) ratios. Figure 9: Nondimensional stresses, \(\sigma_{x}{}^{\ast}\), of \((0^{\circ}/90^{\circ})\) spherical panels with the change of \(\mathrm{R}/a\) and \(a/h\) ratios. Variations of the non-dimensional displacements, \(w^{*}\), and stresses, \(\sigma_{x}{{}^{*}}\) of a moderately thick (\(a/h=20\)) shell panel (R/\(a=10\)) of antisymmetric [0\({}^{\circ}\)/90\({}^{\circ}\)] cross-ply spherical panels, computed at the whole surface are presented in Figures 11 and 12, respectively. Non-dimensional displacements, \(w^{*}\), and stresses, \(\sigma_{x}{{}^{*}}\), reached their maximum value at the center of the panel. It can be observed on the graphs that the boundary conditions were exactly satisfied by the boundary discontinuous double Fourier series method.
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 4 Experimental Results and Discussion
### Model Evaluation  
We used F1-scores to evaluate the generated models. The F1-score is a widely used metric that quantitatively represents the degree of alignment or correspondence between the true values and the predicted values in a classification task. The F1-score is calculated by following formulation:  
\[F1\;score=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{2}\]  
Figure 16: Heatmap examples: (**a**) original constellation; (**b**) cloned constellation generated by the DC-GAN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(-51\) GHz & \(-26\) GHz & 0 & \(+26\) GHz & \(+51\) GHz \\ \hline SD-FEC & \(1.37\times 10^{-3}\) & \(8.87\times 10^{-4}\) & \(9.11\times 10^{-4}\) & \(8.97\times 10^{-4}\) & \(1.29\times 10^{-3}\) \\ Post-FEC & \(0.00\) & \(0.00\) & \(0.00\) & \(0.00\) & \(0.00\) \\ BER & \(0.00\) & \(0.00\) & \(0.00\) & \(0.00\) & \(0.00\) \\ \hline \end{tabular}
\end{table}
Table 3: The measured BERs.  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
Here, TP (True Positive), FP (False Positive), and FN (False Negative) represent the number of validation data points that were predicted as Positive or Negative, corresponding to their actual True or False states. In this experiment, for example, for the No-Shift data, the F1-score would be higher if it were not detected with other labels (\(-\)51 GHz, +51 GHz).
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 5 Execution of the Proposed Model
### Problem Instances  
The performance of PolySCIP, heuristic algorithms (simple and iterative) and NSGA-II algorithms is compared on three real instances; LU1, LU2, and MU1 as shown in Table 4. LU1 is a small portion of Luxembourg city containing 63 street nodes and 47 buildings. LU2 consists of 2 districts of Luxembourg city that contain a total of 2026 street nodes and 1063 buildings. MU1 is an inner part of the city of Munich, which contains 16,075 street nodes and 21,816 buildings.  
For the LU1 instance, the radius of the carsharing station (\(r\)) is set to zero and the maximum walking distance for users (\(w\)) is 150 m for all evaluated algorithms. The number  
Figure 6: LU2 instance. Blue nodes represent possible locations for carsharing stations. Yellow nodes represent residential buildings.  
of stations is set to four. The LU1 setup is ideal for observing and understanding the operational details of the evaluated algorithms.  
The size of the LU2 graph reflects two city districts and is the smallest portion for real-world planning (deemed by the business expert). The maximum acceptable walking distance depends on the selected mode of transportation. [PERSON] and [PERSON] [53] show that people are willing to walk significantly longer to take a train than a bus as long as they deem it worthy. For carsharing, a realistic walking-to-the-car distance (\(w\)) is around 500 m [68]. The population is selected to match the real numbers reported by the city of Luxembourg [69] and is distributed uniformly in residential buildings. The area covered by a carsharing station has a radius (\(r\)) of 100 m, while the number of stations is set to 10.  
The MU1 instance aims to evaluate the performance of algorithms on a city-wide scale. The population in each district is taken from municipalities and is distributed uniformly in residential buildings. We estimated the number of carsharing users at 2% of the total population. The station radius (\(r\)) and walking distance (\(w\)) are the same as in the LU2 instance, but the number of stations is increased to 100 stations. In all three instances, carsharing users are distributed uniformly among available buildings on the map. All of these settings have been defined by domain experts based on the study and real deployment plan.
## 4 Experimental Validation
### Case 2
#### 4.2.6 Ablation Study  
Ablation experiments were conducted on the JNU dataset, and the results are shown in Table 17. It can be observed that by adding a single module, both Method 2 and Method 4 significantly improved the accuracy, and Method 3 also showed some improvement. Methods 6 and 7 indicate that the overall performance is further enhanced when different  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **0-1** & **0-2** & **1-0** & **1-2** & **2-0** & **2-1** & **Average** \\ \hline MCR-Vgg16-TLDAF & 94.333 & 92 & 97.333 & 97.667 & 70.333 & 71 & 87.111 \\ MCR-ResNet18-TLDAF & 100 & 99.333 & 100 & 100 & 99.333 & 94.333 & 98.833 \\ MCR-2D-CNN-TLDAF & 99 & 98.333 & 100 & 100 & 100 & 99 & 99.389 \\ MCR-KAResNet-TLDAF & 99.667 & 100 & 100 & 100 & 99.667 & 100 & **99.889** \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparative experimental results of different models on the JNU dataset.  
Figure 15: Average accuracy of fault diagnosis for different models on the JNU dataset.  
modules are used in combination. Method 5 shows a noticeable improvement over Method 1, but a decline compared to Method 4, possibly due to increased model complexity and interactions between modules.  
Although Methods 6 and 8 achieved a similar accuracy, Method 8 exhibited a more stable performance across various testing conditions, without significant degradation due to changes in specific conditions. This demonstrates that on the JNU dataset, the synergistic effects of multiple modules in Method 8 not only improved model performance but also enhanced stability and generalization ability. These results validate the effectiveness and superiority of the proposed method in bearing fault diagnosis.
# Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach
## 3 Aviation Accident Case Research  
### Aviation Accident Data Collection  
Based on the constructed ACAKG model, this paper selects a total of 437 accident cases from 2018 to 2022. The accident dataset mainly comes from the Aviation Safety Network (ASN), which is a statistical website of aviation accident data belonging to the Flight Safety Foundation, providing complete information on aircraft accidents, and the sources of information mainly come from government agencies, aviation accident investigation committees, and civil aviation agencies [32]. The NTSB, UKAB, JTSB, BFU, etc., are the governmental agencies of the United States, the United Kingdom, Japan, Germany, and other countries, which are responsible for publishing their own national aviation accident investigation reports. In response to missing data, duplications, and errors in the collection, the data were calibrated against the original data published by national organizations. The final aviation accident database was formed, including structured data fields such as accident time, location, type, consequence (including fatalities and aircraft damage), nature of the flight, flight phase, etc., and unstructured data fields such as accident description, causation, consequence, improvement measure, etc.  
According to the research needs, only four data fields, namely, accident consequence, flight phase, accident type, and accident causation, are extracted for the causation-related analysis of aviation accidents. Among them, accident consequence and flight phase correspond to only one data point in each accident, accident causation and accident type correspond to one data point or multiple data points, and there is a causal or juxtaposition relationship between multiple data points, though only the causal relationship is analyzed in this paper.  
The ACAKG consists of 31 accident causation nodes (897 causal factors were identified, including 497 human factors, 119 aircraft factors, 100 environment factors, and 166 management factors), nine flight phase nodes from P01-P9, eight accident-type nodes from T01-T08, and 12 accident consequence nodes, including fatality nodes from RH01-05 and aircraft damage nodes from RA01-07.  
In addition, the operating type distribution of 437 accidents is shown in Figure 3, and it should be noted that the aircraft are capable of carrying at least 12 passengers.
## 4 Experiment and Result
### Embedding and Classification
The training and validation loss of the frozen and unfrozen models are shown in Figure 7. Figure 7 shows that the AugFake-BERT model has a high loss value when the weights are frozen, indicating that it is unable to correctly predict the class in new test data in the original dataset due to imbalanced data. Thus, we further trained the BERT model from scratch with our augmented dataset. The results on the augmented dataset are illustrated in Section 4.4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Parameters Value** \\ \hline Maximum Sequence Length & 80 \\ Epochs & 5 \\ Batch Size & 15 \\ Activation & ReLU, SoftMax \\ Learning Rate & \(2\times 10^{-3}\) \\ Optimizer & AdamW \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimized hyperparameters adapted for AugFake-BERT. Figure 6: The impact on accuracy of the number of batches and epochs: (**a**) The impact of batch size on accuracy and (**b**) The impact of the number of epochs on accuracy.
## 1 Introduction  
The rapid development of the energy, aerospace, and nuclear industries, resulting from the growing energy demand, necessitates the development of new and improvement of existing engineering materials capable of operating in extreme environmental conditions, such as high temperatures, aggressive atmospheres, or long-term mechanical loads [1, 2, 3].  
One such material is a nickel-chromium-iron-molybdenum alloy, also known as Inconel HX(r) (UNS N06002), which has excellent mechanical strength and oxidation resistance at temperatures up to 1200 \({}^{\circ}\)C. These properties make it an ideal material for applications in gas turbines, industrial furnaces, heat treatment equipment, and nuclear reactor components [4, 5, 6].  
The physicochemical properties of HX alloy are due to its precisely developed chemical composition and microstructure, which provide a balance between mechanical strength, ductility, weldability, and thermal stability [7, 6, 8]. Adding molybdenum and chromium strengthens the matrix through a solid-solution hardening mechanism, and chromium also increases corrosion resistance at high temperatures. The alloy also exhibits good hot-forming properties and is relatively easy to weld [8, 9].  
Due to its unique properties, HX alloy is of interest in the context of advanced technologies. Numerous studies are being conducted on its microstructure, mechanical properties,and resistance to degradation [9; 10; 11; 12; 13; 14]. Particular attention is paid to analyses of material behavior under additive processing (AM) conditions, where techniques such as selective laser melting (SLM) significantly affect the microstructure, unlike traditional manufacturing methods. The SLM structures have a pronounced anisotropy of mechanical properties and increased susceptibility to intergranular corrosion. [PERSON] et al., on the other hand, proposed the use of solid-solution hardening as a strategy to reduce the formation of microcracks typical of additively manufactured materials [15].  
Given the growing role of additive technologies in the design and manufacture of components from heat-resistant nickel superalloys designed to operate in extreme conditions, there is a clear need for a comprehensive analysis of the mechanical properties of NickelAloy HX. The aim of this paper is to investigate the effect of the technological parameters of the LPBF sample manufacturing process on the microstructure and tensile strength at ambient temperature. An important aspect of the work is also the assessment of the influence of the direction of powder melting with a laser beam on the mechanical properties of the obtained samples. Additionally, it seeks to establish correlations between process parameters and anisotropic mechanical behavior through comprehensive microstructural analysis. Ultimately, the goal is to provide practical guidelines for achieving targeted property profiles tailored to specific application requirements.
## 3 Results
### Effects of CWP on Concrete Durability Properties
#### 3.3.3 Thermal Insulation and Fire Resistance  
The inclusion of CWP in concrete enhances its thermal insulation and fire resistance properties, which are critical factors for improving structural durability in high-temperature environments.  
[PERSON] et al. 2024 [44] found that concrete with up to 20% CWP replacement demonstrated a reduction in thermal conductivity by 11.2% and thermal diffusivity by 13.6%. This improved thermal insulation is particularly advantageous for buildings exposed to high temperatures or those requiring energy efficiency, as it reduces heat transfer through the structure and maintains a stable internal environment.  
Additionally, the study reported that CWP-based concrete exhibits superior fire resistance. It did not undergo spalling or develop major cracks during fire exposure, demonstrating its ability to maintain structural integrity under extreme heat. This resilience is attributed to the capacity of CWP to create a denser matrix with fewer capillary pores, which reduces permeability and the risk of explosive spalling caused by the rapid vaporisation of trapped moisture under high heat.  
[PERSON] et al. [82] supported these findings by highlighting that a denser microstructure, achieved with CWP incorporation, minimises the ingress of moisture and aggressive ions, which indirectly contributes to fire performance.  
The combination of enhanced thermal insulation and fire resistance underscores the value of the CWP in modern concrete formulations. CWP not only contributes to structural safety in fire-prone environments but also aligns with sustainability goals by reducing the energy requirements for climate control in buildings. These benefits position CWP as a sustainable and efficient material for concrete construction, emphasising its role in achieving durability and environmental resilience.