esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Impact Behavior of STS304L Steel Plate Under Cryogenic Temperature
## 2 Experiment Details
### Environmental Chamber and Temperature Control  
To maintain stable testing conditions, the specimen was securely clamped using bolts and frames to minimize disturbances during the impact test, as shown in Figure 3. Additionally, low-thermal-conductivity glass wool was placed between the specimen and jig to minimize temperature rise due to heat conduction from the jig.  
A custom-built environmental chamber was installed to maintain consistent low-temperature conditions. As illustrated in Figure 4, the chamber consisted of spray polyurethane foam, plywood, and expandable polystyrene, which provided insulation and prevented external heat ingress. Liquefied nitrogen was injected through an injection valve until the interior reached the desired test temperature. A digital control system regulated the nitrogen flow, while expandable polystyrene was used to seal the chamber and prevent nitrogen leakage. Cryogenic silicone (HARDEX 650 \({}^{\circ}\)F RED Gasket Maker, Hardex Corporation Sdn Bhd, Kuantan, Malaysia) was applied to seal the gaps between the jig and environmental chamber, further preventing nitrogen leakage.  
Figure 3: Schematic of the impact test setup.  
Figure 2: (a) Schematic of drop weight impact test apparatus (INSTRON CEAST 9340) and (b) photograph and schematic drawing of hemispherical impactor.  
The temperature-time history was recorded using a T-type thermocouple mounted behind the specimen to verify thermal stability. As shown in Figure 5, the time required to achieve thermal equilibrium was 1640 s at \(-100\)\({}^{\circ}\)C and 2281 s at \(-193\)\({}^{\circ}\)C. Once equilibrium was reached, the temperature remained stable throughout the test, confirming the effectiveness of the chamber in maintaining cryogenic conditions.
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 5 Experimental Study
### Experiment Settings  
Considering the primary objective of this study is to enhance the accuracy of machinery fault diagnosis in various few-shot scenarios, we conduct experiments through simulations on a single device using multiple processes. Table 3 summarizes the device configurations used in our simulation. In our experimental setup, we consider three distinct scenarios based on the relationships between the source domain and the target domain, as well as the relationships among different source domains. These scenarios are summarized in Table 4 as follows:  
* SCDF (Same Working Condition with Different Fault Type): In this scenario, the working conditions (WC) of both the source and target domains are the same, while the fault types (FT) differ.
* DCSF (Different Working Condition with Same Fault Type): In this scenario, we have multiple source domains, each with different working conditions, but they share the same fault types in the target domain.
* DCDF (Different Working Condition with Different Fault Type): This scenario has multiple source domains, each with different working conditions. However, the source domains have the same fault type, while the target domain presents a new fault type.
# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 3 Results  
### Seabed Classification Results  
Experiments shown that the adopted technique (HBT) exploits IOPs and AOPs to perform an unsupervised seabed classification (Figure 8) according to the relationships of their bio-optical characteristics, as modelled according to Table 1 below.  
The statistical behavior of the above-computed bio-optical parameters in the study coastal area is presented in Table 2. The diffuse attenuation coefficient (\(K_{d}\)) of Sentinel-2A band B4 (\(K_{d}\)(B4)) reaches minimum values in areas with either a high load of suspended particles or with coverage of sparse flora (meadows), due to increased scattering. Furthermore, in areas with suspended particles, the total absorption coefficient reaches a minimum in band B3 (\(\alpha\)(B3)) and maximum in band B1 (\(\alpha\)(B1)), respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Seabed Class** & **Bio-Optical Characteristics** \\ \hline \multirow{4}{*}{**Suspended Particles**} & \(\begin{array}{c}\text{Degl}(\text{B3})>\text{Degl}(\text{B2})>\text{Degl}( \text{B1})>\text{Degl}(\text{B4})\\ \text{ }b_{b}(\text{B1})>b_{b}(\text{B2})>b_{b}(\text{B3})>b_{b}(\text{B4})\\ \alpha(\text{B1})>\alpha(\text{B4})>\alpha(\text{B2})>\alpha(\text{B3})\\ \text{ }K_{d}(\text{B1})>K_{d}(\text{B2})>K_{d}(\text{B3})>K_{d}(\text{B4})\\ \end{array}\) \\ \hline \multirow{4}{*}{**Seaweed**} & \(\begin{array}{c}\text{Degl}(\text{B3})>\text{Degl}(\text{B2})>\text{Degl}( \text{B1})>\text{Degl}(\text{B4})\\ \text{ }b_{b}(\text{B1})>b_{b}(\text{B2})>b_{b}(\text{B3})>b_{b}(\text{B4})\\ \alpha(\text{B4})>\alpha(\text{B1})>\alpha(\text{B2})>\alpha(\text{B3})\\ \end{array}\) \\ \cline{2-3}  & \(\begin{array}{c}\text{Sparse Meadows}\\ \text{ }\text{The backscattering coefficients (\(b_{b}\)(B\({}_{\rm i}\)) have a similar fluctuation in all the classified seabed types, reaching their maximum in band B1 and gradually decreasing to their minimum in band B4. In areas covered by submerged flora, the reflectance of the green band (B3) dominates, since it is the main parameter that characterizes these coastal regions. Offshore waters are purely defined by the combination of the rate of image sunlight correction in bands B1 and B2, and the variation of diffuse attenuation coefficient (\(K_{d}\)) in spectral bands B2 and B4.
# Use of Image Correlation to Measure Macroscopic Strains by Hygric Swelling in Sandstone Rocks
###### Abstract  
Some materials undergo hygric expansion when soaked. In porous rocks, this effect is enhanced by the pore space, because it allows water to reach every part of its volume and to hydrate most swelling parts. In the vicinity, this enlargement has negative structural consequences as adjacent elements support some compressions or displacements. In this work, we propose a normalized cross-correlation between rock surface texture images to determine the hygric expansion of such materials. We used small porous sandstone samples (\(11\times 11\times 30\) mm\({}^{3}\)) to measure hygric swelling. The experimental setup comprised an industrial digital camera and a telecentric objective. We took one image every 5 min for 3 h to characterize the whole swelling process. An error analysis of both the mathematical and experimental methods was performed. The results showed that the proposed methodology provided, despite some limitations, reliable hygric swelling information by a non-contact methodology with an accuracy of 1 micron and permitted the deformation in both the vertical and horizontal directions to be explored, which is an advantage over traditional linear variable displacement transformers.  
rock swelling; image processing; DIC; macroscopic strain measurement +
Footnote †: journal: Applied Sciences
## 2 Models and Data
### Radiative Transfer Model: AccuRT
#### 2.2.1 Atmospheric Gases  
The _earth_atmospheric_gases_ material allows the user to choose from six model atmospheres. For our purposes, we have chosen the US 1976 standard [32] model. All of the models are based on the best information available when they were published and contain altitude profiles of temperature, pressure, and concentrations of the bulk atmospheric molecules (N\({}_{2}\) and O\({}_{2}\)), as well as many radiatively significant trace gases including H\({}_{2}\)O, CO\({}_{2}\), O\({}_{3}\), CH\({}_{4}\), and NO\({}_{2}\). From this material, we obtain the specifications of absorption coefficients for the radiatively significant atmospheric gases based on the Low-Tran/ModTran band model. Molecular (Rayleigh) scattering coefficients are also provided by the _earth_atmospheric_gases_ material.
# Radar Target Localization with Multipath Exploitation in Dense Clutter Environments
## 4 Simulation Results
The proposed algorithm performs well in both scenes, and the RMSEs are less than 1 m. However, the performance of the referenced algorithm decreases significantly in the presence of cluttered measurement, and the algorithm is not applicable in dense clutter environments.
# Buffeting Response Prediction of Long-Span Bridges Based on Different Wind Tunnel Test Techniques
## 4 Test Arrangement
### Three-Component Force Test  
Through the static sectional model test of the stiffened beam, the three-component force coefficients were measured at different attack angles, and the results were provided for the subsequent prediction and analysis of the buffeting response of the structure. The sectional model of the stiffening beam, which was made of high-quality wood, adopted a geometric scale ratio of 1:50. The model was 2.095 m long, 0.61 m wide and 0.061 m high.  
Stiffening beams were made of high-quality wood, and auxiliary facilities such as railings and maintenance tracks were carved from plastic plates as a whole according to the size of the drawings. The test was conducted in the XNJD-1 wind tunnel. The test section was equipped with a side wall support and a force balance system specially used for the static three-component force test of the bridge sectional model, and the attitude angle of the model (the angle of attack of the incoming flow relative to the model) was controlled by the computer. A three-component strain balance was used to measure the static three-component force. The sectional model ends were mounted directly on the three-component force test balance. In order to ensure the two-dimensional flow, end plates were set at both ends of the stiffening girder model to avoid the flow field being disturbed. The model installed in the wind tunnel is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Turbulence Field**} & \multicolumn{2}{c}{**Turbulence Intensity**} & \multicolumn{2}{c}{**Turbulence Integral Scale**} \\ \cline{2-5}  & \(l_{u}\) & \(l_{w}\) & \(L_{u}\) & \(L_{w}\) \\ \hline XNJD-1 & 14.2 & 12.3 & 0.153 & 0.092 \\ XNJD-3 & 15.7 & 11.3 & 1.187 & 0.650 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Turbulence characteristics parameters.  
Figure 7: Longitudinal and vertical turbulence spectra in XNJD-3 spire turbulence field. (**a**) Longitudinal; (**b**) vertical.
## 3 Methodology
### Random Forest and Ridge Regression Ensemble Algorithm (RFRR)
#### 3.4.2 Ridge Regression  
Ridge regression is an improved linear regression method used to handle datasets with multicollinearity (high correlation among the input variables). It reduces model complexity and prevents overfitting by adding an L2 norm penalty term (regularization term) to the loss function.  
The key feature of ridge regression lies in the introduction of the regularization term. The regularization term is the sum of the squares of the model coefficients, multiplied by a regularization parameter (\(\lambda\)). Adding the regularization term helps make the model coefficients smoother, and in the presence of multicollinearity, it effectively reduces the model's sensitivity to noise and improves its generalization ability. Given the potential strong correlation between multiple environmental factors, ridge regression stabilizes the estimation of these variables, enhancing the model's robustness while preventing overfitting.  
In the chlorophyll concentration model based on ridge regression in the experiment by [PERSON] and [PERSON] (2018), ridge regression significantly improved the model's stability. However, due to the difficulty of ridge regression in handling complex relationships between data features and target variables, no improvement was observed in the estimation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware Type** & **Hardware Information** \\ \hline CPU & 13 th Gen Intel(R) Core(TM) i9-13900H 2.60 GHz \\ GPU & NVIDIA GeForce RTX 4060 Laptop GPU \\ RAM & 16.0 GB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table of hardware specifications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**NO.** & **Parameter** & **Setting Value** \\ \hline
1 & n\_estimators & 10 \\
2 & n\_jobs & 10 \\
3 & min\_samples\_split & 2 \\
4 & min\_samples\_leaf & 1 \\
5 & max\_features & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of hyperparameters for Random Forest used in stacking.  
of CHLS for water-stressed leaves. Therefore, it is necessary to complement and stack ridge regression with the Random Forest algorithm.  
The software environment for the ridge regression algorithm is the same as that for the Random Forest algorithm mentioned above. Table 5 lists the main hyperparameters of the ridge regression model, while the other parameters not listed were set to their default values.
###### Abstract  
This study aimed to investigate the effects of exercise on aerobic capacity and quality of life (QOL) in heart failure (HF) patients and to identify the optimal exercise prescription for improving aerobic capacity and QOL. A comprehensive search was conducted in Embase, PubMed, Cochrane Library, Web of Science, and Scopus covering data published up to 9 October 2024. The Population, Intervention, Comparison, Outcome (PICO) framework was used to define the inclusion. (a) Population: patients with HF; (b) Intervention: randomized controlled trials (RCTs) with subjects randomly assigned to either the intervention or control group; (c) Comparison: studies that measured peak oxygen uptake (VO2 peak), maximal oxygen uptake (VO2 max), or the Minnesota Living with Heart Failure Questionnaire (MLHFQ) at baseline and compared results post-intervention; (d) Outcome: the primary outcomes were aerobic capacity and QOL. The Physiotherapy Evidence Database (PEDro) scale and the Cochrane Risk of Bias 2 (RoB-2) tool were used to assess the quality of the included studies. Weighted mean difference (WMD), standardized mean difference (SMD), and 95% confidence interval were used to pool data. A total of 47 studies met the inclusion criteria. Exercise significantly improved aerobic capacity (WMD, 2.85, \(p\) < 0.00001) and QOL (SMD, \(-\)0.79, \(p\) < 0.00001) in HF patients. Subgroup analyses indicated that combined exercise, session duration \(\geq\) 60 min, \(\geq\)3 sessions per week, \(\geq\)180 min per week, and supervised exercise showed more significant improvements in aerobic capacity and QOL. Additionally, exercise had greater benefits in middle-aged patients. To improve aerobic capacity and QOL, the optimal exercise prescription for HF patients involves engaging in supervised combined exercise at least three times per week, with each session lasting at least 60 min, to achieve a total weekly duration of 180 min. These recommendations are particularly relevant for middle-aged patients, who may experience greater benefits from exercise interventions. The protocol has been registered on PROSPERO with the registration number CRD420250632915.  
exercise; heart failure; aerobic capacity; quality of life ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN
## 2 Materials and Methods
### Wavelet Transform
#### 2.1.2 Stationary Wavelet Transform  
SWT and DWT both employ separable filter banks to perform two-dimensional wavelet decomposition along the row and column directions of an image [23]. However, SWT distinguishes itself by eliminating downsampling operations, which yields distinct signal representation properties as formulated in Equation (3). Unlike DWT, which reduces spatial resolution via dyadic downsampling at each decomposition level, SWT maintains constant image dimensions across all scales, preserving full spatial resolution.  
SWT adopts a non-decimated framework that retains complete sampling density throughout each decomposition stage. This redundancy-based approach, with a redundancy factor of 3J + 1 for J decomposition levels, ensures translation invariance--guaranteeing consistent feature representation irrespective of spatial positioning. The preserved resolution  
Figure 2: Schematic of DWT: (**a**) First-order wavelet transform; (**b**) LL; (**c**) LH; (**d**) HL; and (**e**) HH.  
characteristics render SWT particularly advantageous for applications demanding simultaneous detail preservation and multiscale analysis, such as image fusion and denoising.  
\[SWT\left\{\begin{array}{l}LL^{k}=\sum\limits_{n}\sum\limits_{m}h(n)h(m)LL^{k-1} (x,y)\\ LH^{k}=\sum\limits_{n}\sum\limits_{m}h(n)g(m)LL^{k-1}(x,y)\\ HL^{k}=\sum\limits_{n}\sum\limits_{m}g(n)h(m)LL^{k-1}(x,y)\\ HH^{k}=\sum\limits_{n}\sum\limits_{m}g(n)g(m)LL^{k-1}(x,y)\end{array}\right. \tag{3}\]  
Figure 3 illustrates the result of applying SWT to Figure 2b. This transformed image was generated through multi-level SWT decomposition, which preserves spatial information while capturing frequency domain characteristics essential for denoising.  
The Inverse Stationary Wavelet Transform (ISWT) reconstructs the original image by combining sub-band coefficients through inverse filtering and weighted synthesis operations, as formulated in Equation (4).  
\[\begin{array}{l}LL^{k}=\begin{array}{l}\sum\limits_{n}\sum\limits_{m}h(n)h(m )LL^{k+1}(x,y)\\ \hskip 28.452756 pt+\sum\limits_{n}\sum\limits_{m}h(n)g(m)LH^{k+1}(x,y)\\ \hskip 28.452756 pt+\sum\limits_{n}\sum\limits_{m}g(n)h(m)HL^{k+1}(x,y)\\ \hskip 28.452756 pt+\sum\limits_{n}\sum\limits_{m}g(n)g(m)HH^{k+1}(x,y)\end{array} \tag{4}\]
# Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations
###### Abstract  
Small capacity and passively cooled battery packs are widely used in mild hybrid electric vehicles (MHEV). In this regard, continuous usage of electric traction could cause thermal runaway of the battery, reducing its life and increasing the risk of fine incidence. Hence, thermal limitations on the battery could be implemented in a supervisory controller to avoid such risks. A vast literature on the topic shows that the problem of battery thermal runaway is solved by applying active cooling or by implementing penalty factors on electric energy utilization for large capacity battery packs. However, they do not address the problem in the case of passive cooled, small capacity battery packs. In this paper, an experimentally validated electro-thermal model of the battery pack is integrated with the hybrid electric vehicle simulator. A supervisory controller using the equivalent consumption minimization strategy with, and without, consideration of thermal limitations are discussed. The results of a simulation of an MHEV with a 0.9 kWh battery pack showed that the thermal limitations of the battery pack caused a 2-3% fuel consumption increase compared to the case without such limitations; however, the limitations led to battery temperatures as high as 180 \({}^{\circ}\)C. The same simulation showed that the adoption of a 1.8 kWh battery pack led to a fuel consumption reduction of 8-13% without thermal implications.  
Keywords:mild hybrid electric vehicle; battery electro-thermal model; equivalent consumption minimization strategy; fuel consumption; thermal limitation; battery-pack sizing +
Footnote †: journal: _aciences_
# Statistical Analysis Applied to the Production of Mirto Liqueur
## 3 Results
### Multivariate Analysis
#### 3.1.3 Volatiles Content
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Df** & **Mean Square** & **F-Ratio** & _p-Value_ \\ \hline A: Time & 4.89908 \(\times\) 10\({}^{11}\) & 1 & 4.89908 \(\times\) 10\({}^{11}\) & 0.88 & 0.3907 \\ B: v \(\times\) w & 1.6667 \(\times\) 10\({}^{13}\) & 1 & 1.6667 \(\times\) 10\({}^{13}\) & 30.01 & 0.0028 \\ C: Temperature & 5.67396 \(\times\) 10\({}^{12}\) & 1 & 5.67396 \(\times\) 10\({}^{12}\) & 10.22 & 0.0241 \\ D: \% EtOH & 3.33726 \(\times\) 10\({}^{13}\) & 1 & 3.33726 \(\times\) 10\({}^{13}\) & 60.10 & 0.0006 \\ AB & 1.36951 \(\times\) 10\({}^{12}\) & 1 & 1.36951 \(\times\) 10\({}^{12}\) & 2.47 & 0.1771 \\ AC & 9.89477 \(\times\) 10\({}^{10}\) & 1 & 9.89477 \(\times\) 10\({}^{10}\) & 0.18 & 0.6905 \\ AD & 5.71015 \(\times\) 10\({}^{12}\) & 1 & 5.71015 \(\times\) 10\({}^{12}\) & 10.28 & 0.0238 \\ BC & 1.68895 \(\times\) 10\({}^{10}\) & 1 & 1.68895 \(\times\) 10\({}^{10}\) & 0.03 & 0.8684 \\ BD & 1.73622 \(\times\) 10\({}^{11}\) & 1 & 1.73622 \(\times\) 10\({}^{11}\) & 0.31 & 0.6002 \\ CD & 1.62712 \(\times\) 10\({}^{13}\) & 1 & 1.62712 \(\times\) 10\({}^{13}\) & 29.30 & 0.0029 \\ Total error & 2.77652 \(\times\) 10\({}^{12}\) & 5 & 5.55304 \(\times\) 10\({}^{11}\) & & \\ Total (corr.) & 8.26203 \(\times\) 10\({}^{13}\) & 15 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of variance for the volatiles content. Figure 3: Pareto plot for the volatiles content. Also, in this case, through a multiple regression procedure, it is possible to generate a mathematical equation that describes the variation of the volatiles content (Equation (2)). \[ \begin{array}{c}\text{volatile content = $-1.11981E7$ + 180,528 \times\text{ }\%$ EtOH $-$ 59,550.2 \times\text{ Temperature }$ $-$ \\ 17,498.3 \times\text{ Time + 2.04126E6 \times\text{ v }\times\text{ w}}.\end{array}  \tag{2}\]  
Since the \(p\)-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between the variables at the 95.0% confidence level. This guarantees the viability of Equation (2), which is then statistically affordable for describing the volatile content in terms of the EtOH%, temperature, time and v \(\times\) w.
# Towards a Heat-Resistant Tethered Micro-Aerial Vehicle for Structure Fire Sensing
## 6 Flight Tests and Experimental Measurements  
Multiple test flights were conducted in a controlled scenario (laboratory environment). A controlled heat source was placed on the floor between drawers and shelves to test the camera's ability to distinguish a potential fire source. An overlay capture of the thermal imaging sensor during flight can be found in Figure 10 to show the UAV's perspective and flight capacity. The MAV performed as expected and the heat source was successfully identified at all times during testing.  
The only downside found during testing was that the MAV suffered oscillations (especially over yaw) due to its reduced mass compared to the tether mass when hovering at higher altitudes or on taut flights. Multiple authors have extensively studied this phenomenon [37; 38; 39; 40], and the potential solution falls outside the scope of this paper. However, the problem was partially fixed with adequate PID tuning [41].  
Another significant limitation is that without external cooling, the maximum operating time will always be defined by the heat dissipation caused by inner electronics. A possible future solution may involve injecting coolant through the tether, increasing the complexity and weight of the proposed system, or making use of thermoelectric cooling [42].  
To evaluate the fabricated assembly, the full-scale prototype was placed in a heat chamber at 100 \({}^{\circ}\)C, removing the electronics to make room for the sensor and data logger. Similarly, the simulated thermal model was run with the electronics removed. The evolution of the measured temperature for 300 s is plotted in Figure 11 along with the simulated model. The measurements were taken at intervals of 5 s. Both curves exhibit a similar response, although the experimental data show slightly higher values than the simulated temperature. This can be attributed to heat losses due to small faults or imperfections in the assembly (e.g., small holes in the side felt that allow hot air to pass through). In any case, the temperature after 300 s, both in the simulated case and the experiment, remains below 50 \({}^{\circ}\)C, complying with the design specifications. It is important to note that the heat generated by the electronic components will slightly increase the temperature since the consumption of the flight controller, video transmitter, and cameras is higher than that of the temperature logging system.  
Figure 9: Photographs of the final assembly.