esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study aimed to optimize the mechanical properties of PA6/GF composites in terms of process conditions, such as process type, resin type, and fiber weight content. Based on the thermal and rheological properties of PA6 resins, PA6/GF composites were fabricated at temperatures below 270 \({}^{\circ}\)C. Compared to the pultrusion process, the direct extrusion process has a shorter fiber length because the fibers and resin are cut while entangled with the screw. Therefore, composites prepared via the pultrusion process have better mechanical properties than those prepared via the direct process because of their longer mean fiber length. The optimal combination of resins and fibers to prepare composites is directly related to the mechanical properties of the composite through its interface features. The results of the single-fiber pullout test demonstrated that the 1011 BRT resin is better than the GP 1100A (W) resin in our system. The composites prepared using the 1011 BRT resin were observed to have excellent mechanical properties. The fiber efficiency results of both process methods are higher in the pultrusion process than  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **P-B-40** & **P-B-50** & **D-B-40** & **D-B-50** \\ \hline \(\lambda_{\sigma}\) & 0.2155 & 0.1933 & 0.1956 & 0.1572 \\ \(\lambda_{\text{E}}\) & 0.6176 & 0.6401 & 0.6774 & 0.6121 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fiber efficiency factors of 1011 BRT.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **P-G-40** & **P-G-50** & **D-G-40** & **D-G-50** \\ \hline \(\lambda_{\sigma}\) & 0.2181 & 0.1548 & 0.1490 & 0.1273 \\ \(\lambda_{\text{E}}\) & 0.6738 & 0.5394 & 0.5380 & 0.5140 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fiber efficiency factors of GP 1100A (W).  
in the direct extrusion process. These results indicate that the pultrusion process is a more efficient process in terms of mechanical properties. This study provides guidance on the types and processes of resins that can be altered to achieve the desired mechanical properties of PA6/GF.  
Conceptualization, S.-E.K., D.-H.P., and D.-H.C.; methodology, S.-E.K. and J.-G.A.; validation, J.-G.A., S.A., J.-C.L., and K.-Y.K.; formal analysis, S.-E.K.; investigation, D.-H.P. and D.-H.C.; resources, H.-I.Y. and K.-Y.K.; data curation, S.-E.K. and K.-Y.K.; writing--original draft preparation, S.-E.K. and J.-G.A.; writing--review and editing, H.-I.Y. and K.-Y.K.; visualization, S.-E.K.; supervision, H.-I.Y.; project administration, K.-Y.K.; funding acquisition, K.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Technology Innovation Program (Parts and Materials Technology Development) (20011408, Development of thermoplastic composite material and part molding process technology for lightweight hydrogen electric vehicle electrical parts) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Republic of Korea).  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.
# Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction
## 2 Materials and Methods
### Maximum Likelihood Expectation Maximization Method (MLEM)
#### 2.2.3 Initial Guess  
As mentioned in Section 2.2.1, the MLEM method does not consider the X-ray tube characteristic peaks, nor the detector response function discontinuities. Typically, a general X-ray energy spectrum which already includes characteristic lines is therefore inserted in MLEM calculations as an initial guess. To generate such an initial guess, an X-ray spectrum that only includes the characteristic peaks of the tungsten anode (W-K\(\alpha\) = 59.3 keV, W-K\(\beta\)1 = 67.2 keV, W-K\(\beta\)2 = 69.1 keV, W-L\(\alpha\)1 = 8.3 keV) was generated for each tube voltage using the electronic version of the Institute of Physics and Engineering in Medicine (IPEM) [39]. The generated X-ray spectrum obtained by using the electronic version of IPEM (report no. 78) for a tube voltage of 100 kV is shown in Figure 6.  
Figure 3: Schematic view of the polyvinyl chloride (PVC) step wedge.  
Figure 4: Experimental setup for acquiring 2D radiographs of a PVC step wedge at different tube voltages.  
Figure 5: Normalized projections of the PVC step wedge when the center of the beam was set in the center of step number 11: (**a**) 60 kV, (**b**) 100 kV, (**c**) 180 kV.  
In the second phase, the energy absorption response function of the GadOx scintillator was calculated by using several individual Monte Carlo simulations. A mono-energetic photon beam was simulated in front of the GadOx scintillator layer (see Section 2.1.1 for scintillator parameters), after which, energy deposition in the scintillator was registered for each energy interval. The energy deposition in the scintillator versus the energy of the incident photon beam, the so-called energy absorption response function, is shown in Figure 7. The discontinuity around 50 kV is caused by the gadolinium (Gd) K-edge. The product of the general X-ray spectrum (obtained by IPEM report number 78) for each tube voltage multiplied with the normalized absorption response function of the GadOx scintillator, was then used as an initial guess for the MLEM calculations.
## 2 Literature Review
Then, the projected data are split into multiple orthographic grid data blocks. Each image block is registered frame by frame using Efficient Second-order Minimization (ESM) [19]. Finally, the robot motion is estimated by the weighted average of all block motions. This approach uses an RGBD camera; thus, 3D ground information helps to find accurate robot motion. [PERSON] et al. [20] integrate the gyroscope and wheel encoder information into visual odometry to reduce the odometry drift problem during planar motion. They use the gyroscope information to compensate for the orientation from the wheel encoder. In addition, the ground plane from camera images is segmented to extract the image features on the ground. As the backend of the odometry measurement, the graph optimization is used to minimize the measurement residuals of wheel encoder, gyroscope, vision, and ground feature constraint, respectively. [PERSON] et al. [21] introduce a SLAM technique called StreetMap. For the localization and mapping from a mobile robot, they use a downward-facing camera to utilize the ground textures. They categorize the ground textures into two groups: feature based and line based. In each category, they proposed different localization mapping methods. They used the downward-facing camera to obtain the ground texture images. However, no image transformation, such as ortho-rectification, is used. [PERSON] et al. [22] address the self-localization of a mobile robot using a single catadioptric camera. The geometric property of the catadioptric camera projection model is utilized to obtain the intersection of vertical lines and the ground plane. From the catadioptric camera image, the ground plane is segmented and followed by the vertical line extraction. Using the geometry of the catadioptric camera projection model, the 3D line equation corresponding to the extracted vertical image line is calculated. Then, the robot motion between two consecutive frames is estimated using the relationship of the 3D vertical lines. In our previous investigation, we design a pallet-type mobile robot and mount a fisheye camera at the front of the robot to calculate the visual odometry [23]. The fisheye image is first converted to a cubemap and next a ground plane image to track the image pattern on the ground. Using the motion of the feature tracking, the visual odometry of the robot is calculated.
# Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection
## 4 Results and Discussion
The relatively complete diagonal recursion structure of the RP of the pathological voice in each channel indicates that the vocal folds of the patient had lesions, but it still maintained approximately periodic \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Accuracy (\%)** & **Kappa** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline RQMs [27] & 91.56 & 0.773 & 0.919 & 0.916 & 0.917 & 0.953 \\ RQMs [28] & 87.11 & 0.658 & 0.878 & 0.971 & 0.874 & 0.921 \\ MFCC & 92.00 & 0.772 & 0.919 & 0.920 & 0.919 & 0.963 \\ LLE\&CD\&HE\&SE & 0.334 & 0.760 & 0.756 & 0.758 & 0.786 \\ MRQMs & **99.56** & **0.988** & **0.996** & **0.996** & **0.996** & **0.995** \\ (proposed method) & \multicolumn{1}{c}{} & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of selected studies conducted for detection of pathological voice in MEEI database with 10-fold cross-validation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifiers** & **Accuracy** & **Kappa** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline BN & 86.57\% & 0.799 & 0.884 & 0.866 & 0.866 & 0.972 \\ SVM & 87.56\% & 0.813 & 0.899 & 0.876 & 0.878 & 0.929 \\ RF & 89.05\% & 0.836 & 0.899 & 0.891 & 0.890 & 0.962 \\ LWL & 87.56\% & 0.813 & 0.890 & 0.876 & 0.876 & 0.961 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Multiclass classification of MRQMs. vibration. According to the density of the diagonal, it can be known that the vibration cycle of the vocal folds disease in this case is larger than that of the normal voice vibration cycle, which may be caused by the slow vibration of the vocal folds disease. In addition, the diagonals of the RP of pathological voice were burr, indicating that vertical or horizontal recursion, different from diagonal recursion, existed near the diagonal. Compared with normal vocal folds vibration, rapid collision and separation occurred in patients with pathological voice. Therefore, pathological voice had more dense diagonal recurrence structure than normal voice, which reflected the phenomenon that its vibration period was shorter than normal voice. Periodic tensioning of vocal folds determines the fundamental frequency of voice signals. For different vocal folds diseases, the air pressure and muscle tension at the glottal diaphragm and the instability of the vocal folds state results in different voice signal waveforms. However, different vocal folds diseases also had similar recurrence characteristics directly, so the classification and detection of vocal folds diseases had not achieved ideal results.
# Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement
## 3 Materials and Methods
### Classification Based on Subcategory-Similarity Measurement
#### 3.2.1 Generate Fuzzying Similarity Matrix  
We use B-CNN combined with weakly supervised localization to get the feature representation of training images. For the specific image \(I\) in training set, we extracted bilinear feature \(f\) and classified the image into \(k\) subcategories with softmax, where \(k\) is the number of subcategories in the dataset. Linking \(k\) return values to a new feature vector \(f_{s}\), where \(f_{s}(i)\) is the return value of the image classified as category \(L_{i}\). For reducing the influence of intraclass differences, the expectation \(\tilde{f}_{s}=E(f_{s})\) of the return values of softmax for all samples belonging to the same subcategory were calculated. The fuzzying similarity matrix denotes as \(SeR^{k\times k}\):  
\[S_{i,j}=\Xi(\tilde{f}_{s}) \tag{2}\]  
where \(\Xi(\cdot)\) means in the joint of \(k\)\(k\)-dimensional vectors into \(k\times k\) dimensional matrix, \(S_{i,j}\) indicates the probability that the image marked as \(L_{i}\) is classified as \(L_{j}\).  
As illustrated in Figure 2, we propose fuzzying similarity matrix-driven deep metric learning to learn the differences between highly similar subcategories. By optimizing the specifically designed triplet-loss function, the network can effectively increase interclass differences and reduce intraclass differences. However, if the triplets [29; 30; 31] are randomly selected, their loss function values are mostly 0, which makes back propagation have little effect on loss convergence in the training stage. Therefore, we adaptively sample the triplets that contribute more to triplet loss according to the  
Figure 4: Architecture of VGG-16. The blue layer represents the pooling layer while the white layer represents the convolution and activation layer.  
fuzzying similarity matrix. We initialize the triplet sampling distribution matrix \(C=M\), and sets \(C=C+\epsilon\) to ensure that the sampled triplets can cover all subcategory pairs, where \(\epsilon=min(M)/2\) in our experiments. Aiming at the disadvantage that three images of triplets may come from the same subcategory, the principal diagonal elements of the fuzzying similarity matrix are extracted to form a diagonal matrix \(diag(S_{11},S_{22},\ldots,S_{kk})\), and then obtain the triplet-sampling distribution matrix \(C\):  
\[C=S-diag(S_{11},S_{22},\ldots,S_{kk}) \tag{3}\]  
Normalizing all the elements of the triplet sampling distribution matrix \(C\), given \(t\) as the total number of triplets, the final amount of triplet tuples for subcategory \(L_{i}\) and \(L_{j}\) is \(t\times C_{ij}\), where \(C_{ij}\) represents the probability that the triplet tuples is consist of \(L_{i}\) and \(L_{j}\). Thus, two subcategories with higher similarity are oversampled to improve the discriminative ability of the model, while other subcategories are normally sampled to ensure that the model can also distinguish them.
## 3 Classification and Synthesis of Bimetallic Nanoparticles
### Simultaneous Methods
#### 3.3.2 Thermal Decomposition  
As highlighted in the preceding sections, noble metal nanoparticles can be prepared at room temperature through the reduction of the corresponding metal salts. However, for the synthesis of transition metal nanoparticles, higher temperatures are required due to the inability of the particles to crystallize at room temperature [123]. To employ this synthetic method successfully, the reaction must be conducted at or near the decomposition temperature of the metal precursors. The metal precursor that has lower decomposition temperature will form single-component metal nanoparticles, with the second metal precursor still in the solution. The second metal will be formed on the surface of the first metal nanoparticle when the temperature increases to the decomposition temperature of the second metal precursor.  
Regarding specific examples, thermal-decomposition methods have been used for the synthesis of CoPt\({}_{3}\) and FePt nanocrystals from cobalt and platinum precursors as well as iron and platinum precursors, respectively [116]. To control the size and shape of the nanoparticles, experimental conditions, such as, temperature, concentration of the stabilizing agents, and the ratio between the metal precursors are systematically varied. Additionally, [PERSON] et al. demonstrated that FePt particles can be dispersed in organic solvents and water in addition to having good biocompatibility [105]. Furthermore, the thermal decomposition method has been used for the preparation of FeCo, FeNi, PtCo, and FeAu alloy nanoparticles [40; 61; 110; 114; 116]
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 2 Methodology  
This section describes the two algorithms used in this work.
# Circular Regression Applied to GNSS-R Phase Altimetry
## 4 Experimentation
### Experimental Setup  
In our implementation, we use an experimental setup that allows recording the direct and the reflected signals using only one channel receiver. This original setup is presented Figure 6. In the proposed setup, the reflected GNSS signal is delayed using a fiber optic coil. The delay, denoted \(\tau^{L}\), depends on the length of the coil and is known by the user. The reflected signal is summed with the direct signal before digitization using a signal combiner. The fiber optic coil does not affect the linear behavior of the phase delay. In our approach, the aim of this delay line is to separate the direct and the reflected signal in time in order to be able to track these two signals independently using a single channel receiver. Such a system is less complex and less expensive than a classical multi-channel data grabber for GNSS reflectometry and also requires less memory. However, the recording of both signals in a single channel reduces the \(C/N_{0}\) by approximately 3 dB-Hz: the noise associated with the combination of satellite signals sensed affects the recorded reflected signal and vice versa.  
Figure 5: Standard deviation of the estimated height as a function of the satellite elevation rate and for various initial elevations.  
The composite signal (sum of the direct and reflected GNSS signals) is recorded by a Syntony \"SiFEn-R-one by one\" data grabber. This front-end receiver has a sampling frequency up to 200 MHz and a quantization up to 8 bits. It can be used for any GNSS signal, with adjustable frequency down-conversion. For the experimentation, a sampling frequency of 25 MHz and a quantization of 1 bit were used. The down-conversion frequency is fixed for the frequency of the GPS-L1 band. The additional code delay is fixed at \(\tau^{L}=5.12\) us (1 km of optical fiber), which is sufficient for separating the direct and reflected signals' correlation peaks for a ground-based experiment with satellites at any observable elevation. Figure 7 presents a Delay-Doppler Mapping (DDM) of a composite signal sensed during the experimentations. The composite signal is processed using the architecture described in Figure 2. The additional \(\tau^{L}\) delay is taken into account in the local code replica in Equations (7) and (8) by adding it to \(\tau_{s}^{D}\).
## 3 Discussion
It is recognized that cAMP is one of the most effective signaling molecules for stabilizing the endothelial cell barrier. The two main signaling pathways downstream of cAMP are the activation of PKA and the exchange protein directly activated by cAMP [46]. These pathways activate guanine nucleotide exchange factors (GEFs), such as Tiam1 and Vav2, thereby stabilizing the endothelial barrier with AJs mediated by Rac1 and improving the cortical actin cytoskeleton. In this study, we demonstrated that IMD, in vitro, directly increased cAMP levels, elevated Rac1 levels, activated the cAMP/Rac1 pathway, and mitigated iohexol-induced damage to adhesion and connections between HUVECs. Additionally, IMD restored HUVEC viability, which was compromised by iohexol, and attenuated iohexol-induced HUVEC apoptosis. We further validated our findings using a rat CIAKI model, where IMD treatment resulted in increased Rac1 and VE-cadherin expression in the kidneys of CIAKI rats. Compared to the CIAKI group, IMD-treated CIAKI rats exhibited significantly enhanced renal CD34-positive staining and reduced renal tubular injury scores. Transmission electron microscopy revealed that IMD significantly mitigated the destruction of the PTC endothelial barrier compared to the CIAKI group; however, this protective effect was abolished when Rac1 was blocked by NSC23766. These results indicate that IMD protects intercellular junctions in PTCs, stabilizes the endothelial barrier, and reduces CIAKI through activation of the cAMP/Rac1 pathway. IMD has promising clinical application prospects across various pathological conditions. This includes its role in the tumor microenvironment and its application in clinical studies of anti-tumor drugs [47]. Additionally, IMD shows potential for use in the treatment of sepsis and renal ischemia-reperfusion injury [48]. Studies have demonstrated that intermedin alleviates renal ischemia-reperfusion injury and enhances neovascularization in Wistar rats [29]. Future research should further explore the therapeutic potential of IMD in clinical applications. There are some limitations to this study. We utilized HUVECs instead of PRECs, which may not fully simulate the physiological state of RPECs. We plan to validate our findings in future studies using RPECs or other kidney-specific endothelial cell models to further enhance the translational relevance of our results. Additionally, IMD can bind to and activate various G proteins, exerting a range of biological effects through different signaling pathways. Consequently, we cannot entirely rule out the possibility that IMD may play a protective role in the kidney through other mechanisms. In conclusion, our findings demonstrate that IMD protects the endothelial junctions of PTCs, stabilizes the endothelial barrier, and preserves renal function via the cAMP/Rac1 pathway in the CIAKI model. Therefore, we propose that IMD holds potential as a novel preventive agent for CIAKI, and its clinical significance warrants further investigation.
## 3 Results and Discussion
### Microstructural Characterization
#### 3.1.1 Phase Identification via X-Ray Diffraction (XRD) Analysis
As the atomic radius of Cu (0.128 nm) is smaller than that of Al (0.143 nm), the Cu substitution induces local lattice distortion and impedes the dislocation motion, thereby contributing to enhanced mechanical strength through the formation of internal stress fields [20; 21]. In contrast, Alloy B retained pronounced Mg\({}_{2}\)Si diffraction peaks even after the T6 treatment, indicating that Mg predominantly remained in a precipitated form rather than dissolving into the Al matrix. This behavior is consistent with the fact that the Mg content in Alloy B exceeded the maximum solubility limit of Mg in Al (\(\sim\)1.8 wt.% at 450 \({}^{\circ}\)C), resulting in the stabilization of Mg\({}_{2}\)Si as a secondary phase under the given thermal conditions. Therefore, in Alloy B, the dominant strengthening mechanism is likely governed by precipitation hardening rather than solid solution strengthening. Moreover, after T6 the composite peak at \(\sim\)40\({}^{\circ}\) 20 (overlapping Al (111) and Si (111)) intensified markedly in Alloy A, whereas Alloy B showed only a modest increase (see purple-boxed inset in Figure 1b). This marked increase can be understood as the result of two successive, Cu-mediated processes: first, during the solutionizing step at 530 \({}^{\circ}\)C, the high Cu concentration accelerates the recovery of the Al matrix by promoting the vacancy-assisted dislocation climb and annihilation, thereby reducing the lattice microstrain and sharpening the \(\alpha\)-Al (111) reflection; second, in the aging stage at 175 \({}^{\circ}\)C, the same enhanced diffusion kinetics facilitate the rapid coalescence of fragmented eutectic Si into larger, well-oriented spheroids, which constructively add to the overlapping \(\alpha\)-Al (111) signal. Together, these sequential effects of strain relief followed by Si spheroidization amplify the 40\({}^{\circ}\) 20 peak intensity in the Cu-rich Alloy A relative to Alloy B. Furthermore, thermodynamic simulations (Figure S1) and EPMA maps (Figure 2) elucidate the contrasting Mg\({}_{2}\)Si behavior: Alloy A (Cu:Mg = 3:1) dissolves the Q-phase (Al\({}_{5}\)Cu\({}_{2}\)Mg\({}_{8}\)Si\({}_{6}\)) nearly completely, sequestering Mg in a solid solution, whereas Alloy B (1:3) retains the residual Q-phase, leaving excess Mg to precipitate as coarse Mg\({}_{2}\)Si during aging. Consequently, Alloy B exhibits stronger Mg\({}_{2}\)Si reflections at (200), (220), and (400), while Alloy A forms only fine Mg\({}_{2}\)Si, which is consistent with the precipitation hardening in Alloy B and the solid solution strengthening in Alloy A.