esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of the Bearing Capacity of Bridge Foundation on Rock Masses
## 3 Numerical Analysis  
This section describes the numerical method used to solve the study problem, including the hypotheses used and the calculation cases carried out. Calculations were developed to model accurately the bearing capacity of real bridge foundations and to compare to the described analytical solution. To do so, numerical simulations were run using FLAC software (Itasca, Version 7) (Hacaca, Version 7) (Later, the load due to the self-weight of the rock mass (unit weight of 26 \(\mathrm{kN/m^{3}}\)) was incorporated in the numerical calculations to know the influence of considering the foundation dimension. Therefore, it is necessary to incorporate the effect of the self-weight of the ground, typical of slope stability analysis in rock [50; 51], in order to study the influence of the foundation geometry.  
In the numerical analysis it was assumed that the bearing capacity was reached when the continuous medium did not stand more load, because an internal failure mechanism had formed. The load in FLAC was applied through velocity increments on the nodes that must be previously fixed, and the bearing capacity was known from the relation between stresses and displacements of one of the nodes: for this study we considered the central node of the foundation.  
In order to compare the results obtained numerically (FDM) with the analytical solution [15], the same boundary conditions were used in the rock-foundation contact. However, the hypothesis of a rigid or flexible foundation depends on the relative deformability of the shallow foundation regarding the supporting ground. Therefore, this consideration cannot be included in the analytical solution since the bearing capacity is independent of the ground deformability. The conditions of uniform stresses and displacements at the contact points cannot be simultaneously imposed, and therefore, it is necessary to previously investigate how to consider the rigidity of the foundation using the FDM method. The best approximation of the numerical result with the analytical solution was observed assigning a constant deformation velocity for the contacting points of the foundation and the rock.  
A convergence study was carried out controlling the value of the bearing capacity obtained under different increments of the applied velocity. Figure 3 shows the dependence of the results of the bearing capacity in relation to the velocity increments applied on the nodes, and how, with the decrease in the value of velocity increments, the result converges towards the final value. This convergence study was carried out for each case with a different combination of geometrical and geotechnical parameters.  
Figure 2: The 2D model employed in the numerical calculation (\(x\)-axis and \(y\)-axis units in meters).
## 3 Results and Discussion
### Structure of Common PR and B-Containing PR  
The chemical structures of common PR and B-containing PR prepared under optimal process parameters were investigated by FTIR spectroscopy. The FTIR spectra of all samples were displayed in Figure 4. From the FTIR spectra, the characteristic absorption peaks at 3326 cm\({}^{-1}\) for common PR and 3217 cm\({}^{-1}\) for B-containing PR ascribe to the presence of hydroxyl; the characteristic absorption peaks of benzene skeleton occur at 1594 and 1510 cm\({}^{-1}\) for common PR and B-containing PR. The characteristic absorption peaks around 1237 cm\({}^{-1}\) for common PR and 1222 cm\({}^{-1}\) for B-containing PR correspond to the C-O bond of phenolic hydroxyl, and the stretching vibration peak of C-O bond for residual phenol hydroxyl is weakened visibly. The characteristic absorption peak of the C-O bond for benzyl hydroxyl is located at about 1100 cm\({}^{-1}\)[27; 28]. Notably, the B-containing PR exhibits a new strong absorption peak at about 1455 cm\({}^{-1}\), which is attributed to the characteristic peak of the B-O bond compared with that of common PR, indicating successful incorporation of the B element by the current simple method [13; 27]. In addition, there are two characteristic peaks of para-substituted and ortho-substituted of benzene for common PR at about 812 and 756 cm\({}^{-1}\), while the characteristic peak  
Figure 3: The thermal stabilities of (**a**) common PR and (**b**) B-containing PR.  
at 812 cm\({}^{-1}\) is weakened significantly (almost disappeared), suggesting the high ortho-substituted characteristic of the as-prepared B-containing PR [29; 30; 31].
## 3 Result and Discussion
### Identification of Fungal Growth Days by Support Vector Machine (SVM)  
#### 3.4.1 Full-Spectrum SVM Models  
Nine circular ROIs in the same size were randomly selected manually using ENVI software on each hyperspectral image of inoculated Petri dish. The average spectrum of each pixel in one ROI was used as a sample to build SVM model for the identification of fungal growth days. The location of the nine ROI on a Petri dish was shown in Figure 7. Mean reflectance spectra of each ROI were acquired. Considering the three replicates, there were 27 samples (3 replicates \(\times\) 9 ROIs) for each fungi in each day. Thus, for each fungus there were 162 samples in total.  
The noise on spectral curves was reduced by Savitzky-Golay smoothing with the 2 nd order polynomial and 5-point smoothing window. Then, PCA was applied to reduce the dimension of the spectral data. In each type of fungi, the samples were randomly divided into calibration and prediction set with a 2:1 division. There were in 108 samples in the calibration set, and 54 samples in the validation set. Based on the result of PC score images, the first 2 to 6 PCs were taken as the input of SVM classifier. Additionally, 5-fold cross validation method was used to verify the stability of the models. For _A. flavus_, the classification model can differentiate fungi with different days with overall accuracy of 93.52% in calibration set, overall accuracy of 92.59% in validation set, and overall accuracy of 90.74% in the cross validation, while for _A. parasiticus_, the classification results reached 100% both in calibration, validation set, and cross validation.  
Figure 6: (**a**) PC\({}_{2}\) to PC\({}_{6}\) score plot of _A. flavus_; (**b**) PC\({}_{2}\) to PC\({}_{6}\) score plot of _A. parasiticus_; (**c**) classes projected onto image of _A. flavus_; and (**d**) classes projected onto image of _A. parasiticus_.  
Figure 7: Location of regions of interest (ROI) on a Petri dish.
## 4 Evaluation  
We evaluate the performances of ASE coding in the aspects of compression ratio and the hardware implementation. Here let us describe four evaluations: the entropy coding ability, the performance effect of the near neighbor entry exchange, the optimization effects, and the hardware resource impacts. To compare the compression ratio, we used the entropy coding by the Shannon's information entropy based on eight bit and 16 bit symbol, LZ77 [18], the Deflate of 7-zip [19], and LCA-DLT [17] of connecting two compression modules for eight-bit symbol. The evaluation in this section uses the ASCII data sequences of [20] (Gene DNA sequences, Protein sequences, English texts, MIDI pitch values, XML, Linux source code). We will use the first 10 Mbyte of each data file available from the website. We will also use 4K-size YUV 420 video frames for the evaluations. The 100 th frame is taken from the videos [21] and are saved to 12 Mbyte file, in which Y, U and V data are stored in the order. We use the videos; Bosphorus, ReadySetGo and Beauty. As evaluating ASE coding, we use default parameters of eight-bit symbol size, 256 look-up table entries, four time hit for entropy culling, and one entry distance for the near neighbor entry exchange. Subsequently, we evaluate effects of those parameters in the following section against compression performances by varying the parameters.
## 4 Materials and Methods  
### Plant Material and Chemicals  
All the flowers of the _Tillandsia_ species used for this study come and are available from the _Tillandsia_ PROD plant nursery located in Le Cailar (Occitanie, France, 43\({}^{\circ}\)41\({}^{\prime}\)31.98\({}^{\prime}\) N, 4\({}^{\circ}\)14\({}^{\prime}\)34.85\({}^{\prime}\) E) and were harvested from April to September 2021. We used 14 species, hybrids and forms: _T. aeranthos_, _T. bergeri_, _T. aeranthos x T. bergeri_ (hybrids of both species), _T. aeranthos_ '_uruguay_' (a purple foliage form of _T. aeranthos_), _T. albertiana_, _T. ixioides_, _T. ionantha_, _T. tentiifolia_, _T. paleacea_, _T. cacticola_, _T. lorentziana_, _T. didistica_, _T. bandensis_, and _T. recurrata_. For _T. tenifolia_, 8 plants were used, providing 2 flowers for each analysis. For _T. albertiana_, _T. ixioides_, _T. paleacea_, _T. recurrata_, and _T. lorentziana_, 5 plants were used, providing 2 flowers each (except for _T. recurrata_ which 3 flowers were provided). For _T. ionantha_, 12 plants were used, providing 2 flowers each. For _T. aeranthos_, _T. bergeri_, and _T. aeranthos x T. bergeri_, respectively 7, 18, and 17 plants were used, providing 2 flowers for each. For _T. cacticola_, 10 plants were used, providing 2 flowers for each. For _T. didisticha_, 6 plants were used with 2 flowers each. For _T. bandensis_, 9 plants were used, providing 3 flowers each. For _T. aeranthos 'uruguay'_, 4 plants were used, providing 2 flowers each. The chemicals used for the identification of the compounds were ordered from Sigma-Aldrich (St. Louis, MO, USA): \(\alpha\)-pinene (98.5% purity), \(\beta\)-pinene (98.5%), \(\beta\)-myrcene (90%), limonene (99%), eucalypt (99%), \(\beta\)-ocimene (95.4%), \(\gamma\)-terpinene (98.5%), \(\alpha\)-terpineol (90%), 1,4-cineole (98.5%), terpionolene (94%), \(\alpha\)-ionone (96%), benzaldehyde (99.5%), phenylacetaldehyde (95%), methyl phenylacetate (97%), methyl benzoate (99.5%), benzyl acetate (99.7%), benzyl alcohol (99.9%), 2-phenylethanol (99%), 2-methoxy-4-vinylphenol (98%), methyl salicate (99%), phenylethyl acetate (97%), eugenol (99.6%), methyl eugenol (98%), benzyl benzoate (98%), hexyl acetate (99.7%), methyl nicotinate (98%), isoamyl alcohol (99.8%), isoamyl acetate (99.7%), 2-pentylfuran(98%), indole (99.9%), nonanoic acid (99.5%), pentanol (99.8%), hexanol (99.9%), heptanol (99.9%), octanol (98%), hexanal (95%), heptanal (95%), 2-heptenal (95%), octanal (98%), 2-ocrenal (97%), nonanal (97%), 2-nonenal (95%), decanal (95%), and undecanal (98%).
# Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
###### Abstract  
This study presents a pressure variation model (PVM) derived from the regression analysis of dynamic pressure computed through numerical analysis to estimate the velocity of underwater vehicles. Furthermore, the drift angle estimation algorithm was developed using predicted velocities from PVM and pressure sensor differences. This approach estimates the single-motion states of underwater vehicles, such as straight, turning, and gliding. Furthermore, it confirms the viability of state estimation even in multiple motions involving turning and gliding motion with a drift angle and spiral motion. The comparison with numerical analysis results validated prediction accuracy within 15%.  
artificial lateral line system (ALLSs); pressure variation model (PVM); pressure sensor array; unmanned underwater vehicle (UUV); computational fluid dynamics (CFD) +
Footnote †: journal: Applied sciences
# Compressive Sensing Imaging Based on Modulation of Atmospheric Scattering Medium
## 5 Conclusions  
In this paper, we have confirmed that the multiple scattering medium of the atmosphere can be used for CS long-distance imaging. The stochastic and time-varying characteristics of the scattering medium were helpful in obtaining a series of modulation patterns, which form the measurement matrix. At the same time, the random laser transmission direction was designed to ensure the randomness of the modulation process. By using the MC method, atmospheric modulation patterns were simulated and the measurement matrix was obtained. First, it was proved that the generated matrix satisfies the RIP condition. Then, the numerical simulation of CS imaging was performed using the atmospheric measurement matrix, and reconstructed images were obtained. Finally, the atmospheric measurement matrix was loaded onto the DMD for optical experiments to verify the proposed method.  
For realistic applications, our method still faces a huge challenge, as the atmospheric modulation patterns should be well-characterized. In our current research, the atmospheric modulation of light is simulated by the MC method, which differs from the real scenario. As mentioned in Reference [40], the simulation results are not guaranteed to be fully reproducible and scattering phase function should be considered carefully for the high degree of asymmetry in the turbid atmosphere. By now, the asymmetry factor in the aerosol scattering phase function is used to improve the accuracy of the simulation results. Yet detailed quantitative assessment is not available. Moreover, the information of the laser transmission direction and the optical thickness profiles of molecules and aerosols are required to compute or simulate the atmosphere modulation pattern. This will be investigated in our future work.  
Conceptualization, Z.Y. and X.P.; methodology, X.M. and X.L.; software, Y.L., X.L. and W.N.; validation, X.L. and X.M.; formal analysis, X.L. and X.M.; investigation, X.L. and X.M.; data curation, X.L. and X.M.; writing--original draft preparation, X.L. and X.M.; writing--review and editing, X.M.; supervision, Z.Y. and X.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the key Cultivation Foundation of the National Space Science Center, Chinese Academy of Sciences.  
The authors declare no conflict of interest.
## 3 Results
### In Vitro Antibacterial Activity Using Disc Diffusion Method  
Five _E. hirta_ extracts were investigated to evaluate their antibacterial activity against pathogenic bacteria including two strains of Gram-positive bacteria (_S. pneumoniae, B. subtilis,_ and _S. aureus_) and three strains of Gram-negative bacteria (_E. coli, S. typhi, S. flexneri, P. aeruginosa,_ and _V. cholerae_) using the agar disc diffusion method. The results for the antibacterial activity of the five _E. hirta_ extracts are displayed in Table 3. The antibacterial activity of extracts stopped the growth of most of the colonies of bacteria by forming significant inhibition zones (inhibition zone 10 mm).  
The results reveal that three extracts (EH-PE, EH-Ch, EH-EA) are potentially effective in suppressing bacterial growth with variable potency. EH-PE extract was effective against the growth of _S. typhi_ and _B. subtilis_ at concentrations of 25 mg/mL, 50 mg/mL, 100 mg/mL, 200 mg/mL, 400 mg/mL. With EH-Ch extract, at concentration of 400 mg/mL, there are inhibition zones on six pathogenic strains, including _B. subtilis, S. aureus, S. pneumoniae, S. typhi, V. cholerae,_ S. flexneri. In addition, EH-Ch extract exhibited an inhibitory effect against three pathogenic strains at concentration of 200 mg/mL including _V. cholerae, B. subtilis, S. pneumoniae._ Moreover, at a concentration of 100 mg/mL, this extract was effective against _Bacillus subtilis_. With EH-EA extract, at concentration of 100 mg/mL, 200 mg/mL and 400 mg/mL, there are inhibition zones on four pathogenic strains, including _S. typhi, B. subtilis, S. flexneri, V. cholerae._ At a concentration of 400 mg/mL, EH-EA extract was able to inhibit _S. aureus, S. pneumoniae_ and at a concentration of 200 mg/mL, this extract exhibited an inhibitory effect against _S. pneumoniae._  
For the remaining extracts, they have weak effectiveness in the inhibition of investigated bacteria. Both EH-Me extract and EH-Bu extract just exhibited significant antibacterial activities against two strains (_V. cholerae_ and _B. subtilis_) at a high concentration of 400 mg/mL.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Samples** & \
\begin{tabular}{c} **Total Phenolic Content** \\ **(mg GAE/g Dried Extract)** \\ \end{tabular} &
\begin{tabular}{c} **Total Flavonoid Content** \\ **(mg QE/g Dried Extract)** \\ \end{tabular} \\ \hline EH-Me & 109.86 \(\pm\) 1.38 \({}^{\text{a}}\) & 18.92 \(\pm\) 1.33 \({}^{\text{f}}\) \\ EH-PE & 90.89 \(\pm\) 1.45 \({}^{\text{b}}\) & 8.48 \(\pm\) 1.16 \({}^{\text{g}}\) \\ EH-Ch & 55.86 \(\pm\) 0.66 \({}^{\text{c}}\) & 16.41 \(\pm\) 1.44 \({}^{\text{h}}\) \\ EH-EA & 254.96 \(\pm\) 10.05 \({}^{\text{d}}\) & 27.66 \(\pm\) 0.73 \({}^{\text{i}}\) \\ EH-Bu & 70.90 \(\pm\) 0.65 \({}^{\text{e}}\) & 12.43 \(\pm\) 1.66 \({}^{\text{j}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total phenolic and flavonoid contents in _E. hirta_ extracts. Each value is the average of three analyses \(\pm\) standard error of mean (n = 3). Values with different superscripts in the columns are significantly different based on one-way ANOVA followed by Dunnett’s test.
# Research on Phase Stabilization Algorithm of Femtosecond Timing System
###### Abstract
The first approach is based on pulsed mode-locked lasers, detecting delay variations through cross-correlation functions and optical compensation, achieving femtosecond-level precision [13]. This method has been applied in multiple accelerator facilities. However, it is sensitive to external interference and difficult to actively compensate for temperature drift. The second approach is based on continuous-wave lasers, detecting delay variations through the Michelson interferometer method and electronic compensation, achieving a phase stability of 10 femtoseconds [14]. This technology offers advantages such as strong anti-interference capabilities and convenient system setup. This work focuses on constructing a femtosecond timing system prototype based on continuous-wave lasers. In this system prototype, a high-frequency reference signal phase compensation system based on a PID control algorithm was designed and implemented. The algorithm development and communication between the host computer and the key devices controlling the phase were completed. In the process of achieving communication, an FPGA was innovatively programmed as a critical interface converter. This autonomous development enabled effective communication between the host computer and the optical fiber stretcher, showcasing a novel approach in the integration and application of FPGA technology within the system. This research achieved phase stability control of the high-frequency reference signal. Under laboratory conditions, the long-term phase drift of the high-frequency reference signal was controlled within 20 femtoseconds (R.M.S. ).
## 3 Results
### Resource Utilization  
Efficient utilization of FPGA resources is crucial for optimizing DL models, especially in resource-constrained environments. For both VGG16 and VGG19 implementations, resource utilization was similar, as the default settings of the DPU were used for both models. Table 2 summarizes the FPGA resource usage in this work.  
Both VGG16 and VGG19 utilized approximately 90.6% of the available DSP resources, reflecting the computationally intensive nature of CNN operations. LUT usage was efficient, at 54.5%, demonstrating effective use of FPGA's logic resources for the models' operations. BRAM utilization was moderate, at 58.3%, indicating a balanced use of on-chip memory. Flip Flops (FFs) were used effectively, at 41.7%, suggesting optimal distribution of tasks across available logic elements. Finally, RAM utilization was minimal, at 8.1%, highlighting the efficient memory management within the FPGA.  
The resource utilization results indicate a moderate LUT usage (54.5%) compared to higher DSP utilization (90.6%) for both VGG16 and VGG19. This allocation reflects the optimization goals of reducing inference latency and maintaining accuracy. DSPs are heavily utilized to accelerate computationally intensive matrix operations, such as convolutions, which are critical for CNN performance. Meanwhile, LUT utilization remains moderate because the design leverages the FPGA's parallel processing capabilities to distribute logic tasks efficiently, avoiding overloading specific resources. This balance ensures scalability while prioritizing latency reduction and accuracy retention.  
While the resource usage was identical for both VGG16 and VGG19 due to the default DPU configuration, these settings can be adjusted for other models to improve efficiency further. The results demonstrate that our design effectively leverages available resources, achieving high performance while maintaining low resource consumption, which is crucial for real-time, edge device applications.
# High-Throughput Bit-Pattern Matching under Heavy Interference on FPGA
## 5 Pattern Matching under Interference and Simulations
### Error-Free and Error-Present Input Generation
Then, an input stream of 100,000 of these patterns was created by picking any one of them randomly (entry 0, entry 63, entry 4, entry 23, etc.). In all, 100,000 randomly generated patterns were added to the stream, to account for misfires for a total of 200,000 patterns. Without applying the error model, the input stream was used for the error-free case. For the error-present case, multiple values of \(P\) and \(r\) (Gilbert-Eliot model) were created and, for each value, the error model was applied separately to the error-free input stream, creating as many test input streams as the number of generated values of \(P\) and \(r\) for the same original error-free input stream. The rate of missed patterns is the average rate of missed patterns of all those input streams.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 5 Simulation Results  
To evaluate the performance of the proposed HBF algorithms, simulation results are presented in this section. In the simulations, it is assumed that the SNRs between the relay-destination link and the source-relay link are identical. Unless otherwise mentioned, the number of antennas at the source, relay and destination nodes are \(N_{\text{T}}=64\), \(N_{\text{R}}=48\) and \(N_{\text{D}}=32\), respectively. The number of RF chains and data streams are set as \(N_{\text{RF}}=N_{\text{S}}=2\). Due to the limited scattering characteristics of mmWave channels, we assume \(L=10\). The Rician factor is chosen as 20 dB. All the AoDs and AoAs are independently and uniformly distributed in \([0,2\pi]\). Moreover, we define the same source and relay transmit power, i.e., \(P_{\text{B}}=P_{\text{R}}=2\). In this paper, all simulation results are averaged over 1000 mmWave channels, which are generated randomly.
# IGBT Overvoltage Protection Based on Dynamic Voltage Feedback and Active Clamping
## 5 Experimental Analysis  
To test the influence of the parameters of the dynamic voltage feedback circuit on the voltage spike and gate voltage during the turn-off transient of the IGBT, the experimental platform shown in Figure 13 was built according to Figure 3 for the experiment, and the IGBT model GF10 NC60 KD was selected for test comparison and analysis. Its rated voltage is 600 V, collector current \(i_{c}\) = 20 A (\(T_{j}\) = 25 \({}^{\circ}\)C), saturation voltage drop \(V_{CE(sat)}\) = 2.2 V, threshold voltage \(V_{G(th)}\) = 4.5 \(\sim\) 6.5 V, gate drive voltage \(V_{GE}\) = \(\pm\)20 V. The input signal of the push-pull circuit selects 24V DC power supply. IGBT test input DC bus voltage \(V_{d}\) = 300 V, and selects 300 V DC power supply with power of 1500 W as its input.