esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Methodology for Calculating Fatigue Damage
#### 2.2.3 Wind Field Simulation  
An important step in the creation of a dynamic aeroelastic simulation of a wind turbine rotor is the generation of a time-varying wind field, large enough to cover the whole rotor. Using stochastic wind fields, as opposed to a homogeneous wind speed over the swept rotor area, makes a critical difference for the purposes of fatigue assessments. As shown below, because of the finite (non-zero and not one) spatial coherence of real-life wind fields, the periodic rotation of the wind turbine translates into a time-varying stochastic wind speed signal with periodic components at any given location along the rotor blade. These periodic components correspond to the fundamental rotor frequency and its harmonics. Evidently, such periodic loading has a significant impact on fatigue loading.  
QBlade has a computational tool for the creation of such wind fields based on stochastic temporospatial simulation. The following parameters were used in the present work:  
* Simulation time (s): All simulations were performed for 600 s (10 min)
* Rotor radius (m): This parameter must be slightly larger than the rotor radius, to avoid convergence problems in the FAST simulation; therefore, \(R\) was set to 4 m
* Hub height of the turbine (m): 15 m
* Average speed (m/s): This parameter was varied from 3 m/s-20 m/s in order to assess the fatigue damage of each of the turbines for a large range of wind speed classes. The final set of simulations used for this work was limited to the range between 7 m/s and 20 m/s since the damage for lower wind speeds was found to be practically zero
* Turbulence intensity (\(\%\)): Two sets of Turbulence Intensities (TI) were assumed, one corresponding to \"high\" and another to \"low\" TI. Given that TI depends on the average speed, the following formula, taken from the standard IEC61400-2, was used to adjust TI for each run:  
\[\sigma_{v}[\text{m/s}]=\frac{15+aV_{\text{Hub}}}{1+a}\times I_{15}, \tag{7}\]  
where TI = \(\sigma_{v}/\sigma\), \(a=2\), and \(I_{15}=0.16\) for low turbulence intensity, as well as \(a=3\) and \(I_{15}=0.18\) for high TI.  
Figure 6 shows a spatial wind field map for one instant taken from the simulation of 600 seconds with an average speed of 10 m/s and a high level of turbulence intensity (corresponding to 21% according to Equation (7).
## 3 Results
### Cloning and Sequence Analysis of RgGES in _R. glutinosa_  
Given that geraniol synthase plays an important role in the production of catalpol, it is necessary to identify the corresponding gene from _R. glutinosa_ and analyze its function. Cluster-11149.40193, which was annotated as _GES_, was selected for further analysis. As a result, two homologous _GES_ genes (_RgGES1_ and _RgGES2_) with several mutations were  
Figure 7: qRT-PCR analysis of hydroxylase candidate genes in _R. glutinosa_. Asterisks indicate a significant difference (* \(p\) < 0.05; ** \(p\) < 0.01; *** \(p\) < 0.001).  
obtained by the PCR method (Supplementary Figure S6). The ORFs of _RgGES1_ (GenBase Accession No. C_AA047852.1) and _RgGES2_ (GenBase Accession No. C_AA047853.1) were 1749 bp, which encoded 582 amino acids.  
The phylogenetic analysis of the RgGESs with the known terpene synthases from other plants revealed that RgGES1 and RgGES2 were classified into the TPS g group [39], and the RgGES proteins were most closely related to the geraniol synthase from _Ocimum basilicum_ (Figure 8). The multiple sequence alignment showed that RgGES1 and RgGES2 contained several highly conserved domains, similar to the GESs from _C. roseus_ (AFD64744.1) and _O. basilicum_ (AAR11765.1), including DDxxD, RxR, and NSE/DTE (Figure 9). The DDxxD and NSE/DTE motifs are important for the fixation of the pyrophosphate substrate, while RxR, which is located about 35 amino acids ahead of the DDxxD motif, is involved in the complexation of the diphosphate [40].
# Performance-Enhancing Sulfur-Doped TiO2 Photoanodes for Perovskite Solar Cells
## 3 Results
### X-ray Diffraction (XRD) Analysis  
Changes in the crystalline phase of the S-TiO\({}_{2}\) photo-anode thin film were analysed using X-ray diffraction. The X-ray diffractograms of the un-doped and S-doped TiO\({}_{2}\) photo-anode thin film sintered at 450 \({}^{\circ}\)C are shown in Figure 2. Each sample had defined TiO\({}_{2}\) diffraction peaks, which were correlated with the characteristics of nano-crystalline anatase TiO\({}_{2}\) peaks with several FTO diffraction peaks. According to a previous study, the characteristic peaks of anatase TiO\({}_{2}\) are found at the angles of 27.0\({}^{\circ}\), 38.2\({}^{\circ}\), 55.1\({}^{\circ}\) and 62.0\({}^{\circ}\) corresponding to the (1 0 1), (1 0 4), (1 0 5) and (2 0 4) planes [23]. Ruelle-phase TiO\({}_{2}\) was not found in the sintered TiO\({}_{2}\) photo-anode thin-film sample, which could be related to the sintering temperature of 450 \({}^{\circ}\)C. The sintering temperature is an important factor in producing the S-doped TiO\({}_{2}\) photo-anode thin film because a higher temperature could lead to the formation of un-desired, less photo-active rutile phase and could reduce the incorporation of S (boiling point: 444.6 \({}^{\circ}\)C) in the thin-film lattice [15]. The XRD diffractogram of the S-doped TiO\({}_{2}\) was compared with that of pure TiO\({}_{2}\), and the two diffraction patterns were almost similar with no S characteristic peak. This observation indicated that the S ions had entered the lattice structure of TiO\({}_{2}\) or the concentration of the S compounds were lower than the detection limit of the equipment, as observed in a previous study [24]. In addition to the diffraction pattern, the data in the XRD pattern could be used to calculate the average particle size by using the Scherrer equation as follows:  
\[\mathrm{D_{hkl}}=\frac{0.89\uplambda}{\beta\cos\theta} \tag{1}\]  
where \(\uplambda\) is the wavelength of the Cu K\(\alpha\) laser used (0.1541 nm), \(\theta\) is the diffraction angle of the peak and \(\beta\) refers to the full width of the peak measured at half maximum intensity (FWHM) [25]. Thus, the average particle sizes of both samples were calculated according to Equation (1), resulting in 23.8 nm and 18.0 nm for the un-doped and S-doped TiO\({}_{2}\) photo-anode, respectively. Thus, S-doping had a direct effect on the average particle size of the sintered TiO\({}_{2}\) photo-anode. A previous study has reported that doping via substitution of a lattice atom with a dopant would reduce the grain size compared with the un-doped sample, as observed in this study [26].
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## 5 Conclusions  
The Mc6 LAI product was improved when comparing the differences from in situ LAI (LAI-RM) from those recorded by Mc5. LAI-RM comparison with MODIS LAI showed improvement with the newer collection where the Mc5 overestimate of +2.22 LAI was reduced to +0.97 LAI with the Mc6. The EPIC LAI simulations compared favorably to both MODIS LAI collections across all three of four research sites and all 13 dates when evaluating the overall mean difference. On three of the four sites, the EPIC/MODIS LAI comparison at 12.0 km resolution grid showed similar weighted mean LAI differences (LAI 1.29-1.34), with both Mc5 and Mc6 exceeding EPIC LAI across most dates. For all four research sites, both MODIS collections showed a positive bias when compared to EPIC LAI, with Mc6 (LAI = 0.40) aligning closer to EPIC than the Mc5 (LAI = 0.61) counterpart. However, there was larger variability and directionality when viewing these differences on a site by site basis. EPIC LAI potentially could augment MODIS LAI data feeds at the EPIC coarser resolution as more forest types are calibrated and validated and as other inputs are improved. EPIC shows promise for forecasting future meteorological alternative management applications, where the potential of providing a dynamic range of LAI values from EPIC may supersede current LAI inputs found in many atmospheric models.  
The following are available online at [[http://www.mdpi.com/2072-4292/12/17/2764/s1](http://www.mdpi.com/2072-4292/12/17/2764/s1)]([http://www.mdpi.com/2072-4292/12/17/2764/s1](http://www.mdpi.com/2072-4292/12/17/2764/s1)), Figures S1 and S2: Appomattorx (Site location, forest composition), S3 and S4: Hertford (Site location, forest composition), S5 and S6: Fairystone (Site location, forest composition), S7 and S8 (Site location, forest composition), Table S1: Forest stand structural attributes for 4 sites.  
**Author Contributions:** [PERSON] coordinated this research, establishing cooperation from private and public entities for the collection of the in situ data. He has analyzed the MODIS-EPIC relationship and has written the majority of this paper. [PERSON] ran the smoothing algorithm on the MODIS time-series data. He also wrote the section on the smoothing algorithm. [PERSON] provided the EPIC LAI simulations for this evaluation. She wrote the EPIC methods and contributed to the results. [PERSON] assisted in the in situ data collection and the writing of the in situ LAI collection techniques. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank [PERSON] for his initial input into this MODIS LAI work. [PERSON], [PERSON], and [PERSON] were involved in the initial in situ data collections at these research sites.  
The authors declare no conflict of interest.  
The views in this manuscript are those of the authors and do not represent EPA policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
## 3 Residual Displacement Ratios
### Analysis Methodology  
The methodological framework of displacement-based seismic design has been profoundly shaped by advancements in constant-ductility spectral analysis. Seminal works by [PERSON] and [PERSON] [48] established the theoretical basis for relating structural displacement ductility demands to spectral characteristics, a paradigm further refined through the nonlinear static procedures of [PERSON] [49]. Recent extensions of these principles--such as [PERSON] et al.'s [50] adaptation for masonry structures, [PERSON] and [PERSON] [51] exploration of period-dependent inelastic spectral ordinates, and [PERSON] et al.'s [52] strength-ductility relationship formulations--have collectively reinforced the validity of constant-ductility spectra as a robust tool for displacement demand quantification.  
Figure 6: Scatter density plots of residual displacements versus elastic spectral displacements and peak inelastic displacements: (**a**) scatter plots of \(d_{\rm r}\)–\(S_{\rm d}\); and (**b**) scatter plots of \(d_{\rm r}\)–\(d_{\rm m}\).  
In this investigation, the constant-ductility approach is adopted in computing residual displacement demands of SDOF oscillators. The tangent stiffness-proportional damping model with an initial damping ratio of 5% is employed in NTHA. The bilinear hysteresis model is selected as shown in Figure 7. This model provides a valid approximation for simulating steel-framed structural systems. As shown in the figure, the initial elastic stiffness \(k_{\mathrm{i}}\) can be determined in terms of the yield displacement \(d_{y}\) and the yield force \(F_{y}\), that is written as follows:  
\[k_{\mathrm{i}}=\frac{F_{y}}{d_{y}} \tag{5}\]  
The displacement ductility demand \(\mu\) can be defined as follows:  
\[\mu=\frac{d_{\mathrm{m}}}{d_{y}} \tag{6}\]  
where \(d_{\mathrm{m}}\) is the maximum displacement that the structure reaches.  
To reflect the strain hardening or softening after yielding, the post-yield stiffness ratio is defined as follows:  
\[r=\frac{k_{\mathrm{p}}}{k_{\mathrm{i}}} \tag{7}\]  
where \(k_{\mathrm{p}}\) is the slope of the second branch of the skeleton force-displacement relation.  
The study also considers bilinear SDOF oscillators incorporating five post-yield stiffness ratios (\(r=0.00\), \(0.01\), \(0.03\), \(0.05\), and \(0.10\)), \(30\) natural periods (\(T=0.1\)-\(3.0\) s, with an interval of \(0.1\) s), and five levels of displacement ductility (\(\mu=2\)-\(6\), with an interval of \(1\)). To examine the effect of aftershock intensity, eight levels of relative intensity ratio (\(\kappa=1/5\), \(1/4\), \(1/3\), \(1/2\), \(2/3\), \(4/5\), \(1\), and \(4/3\)) are considered in scaling sequence ground motions.
## 4 Research on Critical Technologies
### Modulation and Demodulation Technologies
#### 4.1.1 IMDD Communication System  
IMDD systems have become the preferred choice for most of today's short- and medium-distance optical communications or low-capacity communications due to their simple system structure. A complete IMDD system can be divided into three parts: the transmitter, the transmission channel, and the receiver. Since a simple single PD is required for optical signal reception at a single receiver in an IMDD system, the difference lies mainly in the system structure at the transmitter side, the fiber type, and the DSP at the transmitter/receiver side. According to the different structures of the transmitter side, IMDD systems can be divided into two categories: systems based on directly modulated lasers (DMLs) and systems based on external modulators [20].
## 2 Introduction to Related Methods  
### Improved U-Net Model  
The U-Net is a widely utilized deep learning model for image segmentation [14]. This architecture comprises a symmetric encoder (compression path) and a decoder (expansion path). It effectively captures rich contextual information through the establishment of cross-level connections. This distinctive structure allows the U-Net to excel in managing detailed information while preserving global contextual features.  
The encoder comprises multiple residual modules that gradually downsample the input signal while extracting relevant features [15]. Through successive convolutional and pooling layers, it captures local feature information, such as changes in the time-frequency-domain signal, during the continuous downsampling process. The feature maps generated in the encoder are linked to the decoder via a skip residual connection structure, allowing for the reconstruction of the extracted signals through upsampling, which aids in preserving the detailed information of the signal. The noise component of the original signal is eliminated through interpolation calculations, resulting in an approximate restoration of the original time-frequency-domain signal.  
In this paper, the two-dimensional convolutional layers of the U-Net are restructured as one-dimensional convolutional layers. Additionally, adaptive convolution kernels are introduced to dynamically adjust the size of the convolution kernel based on the characteristics of the signal. This approach allows the model to employ distinct processing strategies for various frequency bands of the signal, thereby enhancing the quality of the generated output. Figure 1 shows the structural diagram of the improved U-Net model.
# An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
## 6 Results and Discussions  
### Dataset Description and Evaluation Metrics  
The SDN dataset was created using the Mininet simulator, which was used to classify traffic using machine and deep learning methods [34]. This study began with ten topologies in the Mininet emulator, each of which connected these switches to a single Ryu controller. The network simulators used ICMP, UDP, and TCP traffic, with hostile traffic consisting of a combination of UDP Flood, TCP Sync, and ICMP attacks. The dataset contained a total of 23 features, some of which were collected from switches and others that were estimated. The retrieved features (duration in nan seconds) included Byte count, switch ID, duration, packet count, and duration (in n sec). The sum of duration-sec and duration-nsec was equal to the entire duration. The total number of bytes commuted from the switch port was determined by the destination IP, source IP, port number, and Tx-bytes. Rx-bytes was the total number of bytes received over the switch port. The time and date were transformed into numbers in the dt field, and flow was monitored at 30 s intervals. Furthermore, calculated features such as packet per-flow and byte per-flow counted packets as a single flow. The packet rate was calculated by dividing the total packets per flow by the total number of packets sent per second. Tx-kbps and Rx-kbps were data reception and transmission rates, respectively, and the sums of port bandwidth were Tx-kbps and Rx-kbps. The class labels present in the columns refer to whether the type of traffic was malicious or benign. However, labels 0 and 1 respectively indicate benign and malicious traffic. The duration of the network simulation was 250 min and the row of data possessed was 104,345.  
Along with the detailed interval, the simulation of data was authenticated. The dataset was split into two halves for testing and training: 30 and 70%, respectively. The full training process took 42 s to complete [35; 36; 37].  
The proposed platform was MATLAB 2020a, along with system requirements, which are listed in Table 2.
## 3 Proposed Method
### Procedure of the Proposed Method  
The essence of mechanical fault diagnosis is a pattern recognition problem and includes data acquisition, feature extraction, and fault classification. In the past, vibration signals have been widely used for the fault diagnosis of machines. In this paper, the vibration signals of multi-sensors are also used for bearing fault diagnosis, and the main procedure of the presented method is plotted in Figure 1.  
Step 1: Install the considered acceleration sensors on the bearing housing, for example, on the top or sides. Then, vibration signals are collected by multi-sensors and the data acquisition system. In this paper, they are all bought from professional data collection companies to make the signals accurate and effective.  
Step 2: The traditional EEMD algorithm is used to decompose the collected vibration signals into a set of IMFs. For this task, it is carried out offline in this paper, because a lot of computation time is need to decompose many data segments to extract features to train the SVM model.  
Step 3: The proposed sensitive IMFs selection method is applied to find useful IMFs which include abundant fault information to reconstruct a new vibration signal. Then, an original feature space including features in a time domain and frequency domain is produced from the reconstructed signal. For each sensor, there is a original feature space.  
Step 4: The proposed feature fusion method is performed. Here, weights are assigned by the principle defined in Section 3.2, and the so-called fused features are extracted by the obtained weights and original feature space.  
Figure 1: Main procedure of the presented method.  
Step 5: Part fusion features are randomly selected to train the standard SVM model to construct a classifier for bearing fault diagnosis, and the rest of these fusion features are used to test the proposed method.
## 2 Research Background
### Constructability Review  
According to [5], the term \"constructability\" refers to the optimum use of knowledge and experience of construction practitioners in planning, design, procurement, and field operations to achieve the overall project objectives. As awareness of the potential benefits of constructability reviews increased, many construction companies began conducting such reviews at different design stages to improve the reliability of the design and facilitate the construction process [22]. This trend has led to the need for more engaging contractors in the design process to provide input on constructability, allowing designers to make more informed decisions with fewer design iterations [23].  
A lack of constructability insights can lead to significant challenges during construction. [PERSON] et al. [6] conducted a detailed study and identified basic constructability issues that should be addressed during design, including:  
* Unfamiliarity with construction procedures, construction details, site conditions, acceptable tolerances, and rules and regulations
* Failure in understanding site and building conditions, O&M needs, and 3D information
* Failure in coordinating among consultants, i.e., architectural, structural, mechanical, electrical, and plumbing (MEP), and scheduling
* Failure related to receiving crucial information too late and the omission of essential details  
These constructability issues can be aggravated by certain project-related factors that may vary from one project to another. For instance, [PERSON] et al. [24] identified project planning issues, site planning issues, and major construction methods, as factors that can impact constructability issues in a project. Additionally, ref. [25] consider project schedule (construction time), project budget, required resources, and the quality of the final product as vital factors for constructability performance. Nevertheless, most of the constructability issues identified by [6] can be attributed to shortcomings in considering experts' knowledge during the design phase, which is consistent with [26]'s findings. In other words, in the constructability review process, integrating experts' knowledge to resolve design and construction issues is critical. Identifying what expert knowledge is useful in addressing constructability challenges requires an understanding of the nature of constructability knowledge and how it can be effectively captured and applied in practice.