esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method for Rapid Optimization Design of a Subsonic Tandem Blade
## 2 CFD Setup and Procedure
### Setup for Double-Stage Transonic Compressor with Tandem Blades  
A 3D RANS simulation was conducted for a double-stage transonic compressor with tandem blades at 100%, 90%, and 80% design speeds. The baseline tandem stator design parameters are listed in Table 2. It should be noted that the optimization was conducted on the tandem stator only, while the inlet stage and the tandem rotor were kept fixed in this paper. The FB and AB airfoils of 15 blade elements along the radius were kept the same as the 2D model mentioned above.  
The total grid number of the tandem stator was approximately 2.1 million (the number along the axial; circumferential and spanwise: \(381\times 71\times 77\)). The first nondimensional wall distance was specified to ensure that y+ was less than 5, and the grid expansion ratio near the wall was kept at less than 1.2. The mesh topology and some other installations were the same as the 2D tandem case shown in Figure 6, so no additional explanation is needed.  
Standard atmosphere conditions (Pt\({}_{11}\) = 101325 Pa, T\({}_{11}\) = 288.15K, and axial flow direction) were imposed as the inlet reference plane. Additionally, outlet average static pressure was increased gradually to achieve the compressor characteristics in 100%, 90%, and 80% design speeds.
# A New Workflow to Generate Monoclonal Antibodies against Microorganisms
## 3 Results  
### Selection of B-Cell Epitopes from Reference Genome E. coli O157:H7 Using bioinformatic Workflow  
The bioinformatic workflow shown in Figure 2 was used to identify potential proteins that are unique for _E. coli_ O157:H7 compared to nine other MOs, as well as the pan-genome of _E. coli_ listed in Table 1. Out of 84,201 genes tested, 1206 genes have been identified that distinguish the reference strain _E. coli_ O157:H7 from other bacteria species. The selected genes were further analyzed with regard to the localization of the corresponding proteins predicted with the help of PSORTb server version 3.0. To generate mAbs specific for living bacteria in food or drinking water, the potential target proteins need to be localized on the cell surface. Out of the 1206 genes of our genome analysis result, it was revealed that 53 of the corresponding proteins were located at the outer membrane or extracellular (Supplementary Table S2). The extracellular proteins were further restricted according to the criterion of being connected or anchored to the cell surface. Out of these 53 possible candidates, we selected FimH and OmpG as model targets. Their crystal structure has already been investigated in previous studies [17; 18] and allows a more precise verification of the predicted B-cell epitopes on the protein surface.  
The IEDB Emini surface accessibility tool was used for B-cell epitope prediction. According to the criteria, all peptides that were present as a linear B-cell epitope were filtered. Table 2 shows an overview of the determined epitopes. Figure 3 (first row) shows the five chosen B-cell epitope sequences and visualizes their localization in the prospected target proteins (1 st row). The B-cell epitope sequences were used to produce epitope-engineered VPs for immunization.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
###### Abstract  
Many researchers have reported on the preparation and characterization of thin films. The prepared thin films could be used in lasers, cathodic ray tubes, solar cells, infrared windows, ultraviolet light emitting diodes, sensors, supercapacitors, biologic applications, and optoelectronic applications. The properties of these thin films strongly depend on the deposition techniques. Throughout the years, many investigations into the production of various types of thin films (by using the successive ionic layer adsorption and reaction (SILAR) method) were conducted. This method attracts interest as it possesses many advantages when compared to other deposition methods. For example, large area depositions could be carried out in any substrates at lower temperatures via inexpensive instruments; moreover, a vacuum chamber is not required, it has an excellent growth rate, and the unique film properties could be controlled. In this work, metal sulfide, metal selenide, metal oxide, and metal telluride were deposited on substrates by using the SILAR method. According to the findings, both thick and thin films could be synthesized under specific conditions during the experiment. Additionally, the results showed that the number of deposition cycles, rinsing times, immersion times, and concentrations of the precursors affected the crystallinities, grain sizes, film thicknesses, surface roughness, and shapes of the obtained films. These films could be used in solar cell applications with high power conversion efficiency due to the appropriate band gap value and high absorption coefficient value.  
SILAR method; thin films; semiconductor; band gap; solar cell applications +
Footnote †: journal: Applied  
1
Footnote 1: _Applied_  
Received: 2 July 2022  
Accepted: 2 August 2022  
Published: 16 August 2022  
**Problemsier's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Problemsier's Note:** The original version of this paper is available at [[https://github.com/journals/article/pdf/](https://github.com/journals/article/pdf/)]([https://github.com/journals/article/pdf/](https://github.com/journals/article/pdf/)).
## 5 Discussion
### Validation of the Results
#### 5.1.1 Validation of Linear Static Results  
The linear static analysis was performed on DFF and PERIH to find the structure's critical load combination and associated maximum vertical and lateral displacement. AS 3600 and ACI were used to check the maximum permissible vertical and lateral displacements, respectively.  
According to Table 2.4.2 of AS 3600-2001, it is recommended to have a total vertical deflection limit of L/250 for all structural members. Since DFF has a span of 8.1 m, the maximum recommended vertical deflection according to AS 3600 is 0.032 m. Figure 22 compares DFF's maximum vertical displacement for each load combination with the suggested deflection limit.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Analysis** & **Main Objective** & **Relevant Standards** \\ \hline \multirow{2}{*}{Linear Static} & Vertical and lateral & AS 3600 \\  & displacement & ACI \\ Natural Frequency & Structural vibration & AS 3600 \\  & & EN 1991-1 [58] \\ Spectral Response & Mass participation factor & EN 1998-1 [59] \\ \hline \hline \end{tabular}
\end{table}
Table 11: Validation of the analyses, in accordance with the available standards.  
Figure 21: Temperature variation of the structure.  
All the deflections were observed to comply with the deflection limit specified by AS 3600, with an average difference of approximately 174%.  
Similarly, a total floor deflection limit of 0.06 m was obtained for PERIH, based on a floor span of 15 m. As shown in Figure 23, the maximum floor deflection in all the load combinations is below the suggested limit by approximately 127%.  
For the lateral deflection, ACI recommends a total limit of H/500 for all structural members. This equates to a lateral deflection limit of 0.0084 m for DFF, based on the height of 4.2 m used in each cassette. Figure 24 compares the maximum lateral displacement with the suggested limit.  
It was observed that the maximum lateral deflection of the structure complies with the ACI limit, with an average difference of 101.3%.  
Figure 23: Deflection limit and the maximum vertical deflection for each load combination for PERIH.  
Figure 22: Deflection limit and the maximum vertical deflection for each load combination for DFF.  
Overall, DFF and PERIH had a maximum displacement below the recommended limits. This indicates the compliance with the standards ULS and SLS.
## 3 Results  
### Effective Antecedent Precipitation Analysis  
The optimal distribution models of EAP at the meteorological stations revealed distinct spatial variations (Figure 4a). The generalized extreme value distribution and lognormal distribution dominate the study area, with the former primarily prevailing in the northern coastal region of Zhejiang, while the latter is more prevalent in the southern coastal region of Zhejiang. Frequency cumulative distribution graphs for various optimal distribution functions are presented in Figure 4b-e, which show good fits between the selected models and the data, indicating statistical validity and minimal prediction errors. Figure 5 presents a comparison of probability density histograms for various fitted distributions at the four representative stations shown in Figure 4. Based on the optimal distribution functions for each station, the EAP for different return periods was calculated and visualized through ANUSPLIN interpolation.  
The results reveal substantial regional variation in the EAP distribution across Zhejiang (Figure 6). The north-central region shows relatively low EAP values, whereas the western, southwestern, and coastal areas exhibit relatively high EAP values, which are consistent with the province's precipitation patterns. The mountainous and hilly terrain in the western and southwestern regions leads to enhanced uplift and condensation of moisture due to the complex topography, resulting in increased precipitation. Additionally, coastal areas, including the Zhoushan Islands, Ningbo, and Taizhou, benefit from their proximity to the ocean, where marine moisture transport and topographic factors further increase precipitation. In contrast, northern Zhejiang, predominantly the Hangjiahu Plain, experiences less precipitation due to minimal topographic influence and greater distance from the ocean. As the return period increases, the cumulative range and intensity of the EAP significantly increase, with maximum EAP values reaching 426.65 mm for the 50-year return period.  
Figure 4: (**a**) The optimal distribution functions of each meteorological station. Example plots are shown for the **(b)** Weibull cumulative frequency, (**c**) gamma cumulative frequency, (**d**) generalized extreme value (GEV) distribution cumulative frequency, and (**e**) Logmoral cumulative frequency.  
Figure 5: Probability density histogram comparisons of various fitted distribution functions for the four representative stations shown in Figure 4b-e. Subplots (i)–(iv) correspond to the stations in Figure 4b–e, respectively. Each subplot displays the observed frequency histogram along with the fitted curves of multiple candidate distributions, allowing for the visual evaluation of the goodness of fit at each site.  
Figure 6: Spatial distribution of EAP under different return periods: (**a**) 5 years, (**b**) 10 years, (**c**) 20 years, and (**d**) 50 years.
# Method to Control the Deformation of Anti-Slide Piles in Zhenzilin Landslide
## 6 Conclusions  
The Zhenzilin landslide occurred in Luding County located at southwest of the Sichuan province in China. Anti-slide piles were installed in the landslide area comprising weak upper, hard middle, and weak lower strata. Based on an in situ investigation, the displacement of the pile head of the Zhenzilin landslide was 13 cm. A new calculation method for controlling this large deformation is proposed in this paper based on setting the reasonable embedded length of the anti-slide pile. The reasonable embedded length of the anti-slide pile in the Zhenzilin landslide should be 10.5 m based on the above method, which can ensure that the displacement of the pile head does not exceed 10 cm. However, in the original design of the Zhenzilin landslide, the embedded length of the anti-slide pile was 8.5 m, which led to a 12.8 cm displacement at the pile head.  
Some factors influencing the deformation and internal forces of the pile were also studied. The increasing thickness of the upper weak bedrock layer result in greater maximum shear force of the pile in the embedded bedrock and displacement of the pile head. Furthermore, reduction of the modulus of subgrade reaction of the first weak bedrock can also cause increasing maximum shear force of the pile and displacement of the pile head. If the deformation of the pile head is excessively large, cracks develop in the landslide body behind the pile, thereby providing a channel for surface-water infiltration, which reduces the strength of mudstone and increases the long-term risk of landslide occurrence.  
Conceptualization: H.W., P.W., H.Q., J.Y., and J.Z.; Methodology: J.Y. and J.Z.; Software: H.W.; Validation: H.Q., J.Z., and J.Y.; Formal analysis: P.W.; Investigation: H.W.; Resources: H.W.; Data curation: P.W.; Writing--Original draft preparation: [PERSON], Writing--Review and editing: [PERSON], [PERSON], H.Q., [PERSON], and [PERSON]; Visualization: P.W.; Supervision: [PERSON]; Project administration: [PERSON]; Funding acquisition: J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded jointly by the Key Research and Development Project of Henan Province (Science and Technology Research Project) (grant No. 192102310466), Key projects of universities in Henan Province (grant No. 20A570002) and Teaching Reform Project of Henan University Minsheng College (MSJG2017014).  
All authors thank the anonymous reviewers and the editor for the constructive comments on the earlier version of the manuscript.  
The authors declare no conflict of interest.
## 2 Methods
### Data Collections and Data Processing Procedures
#### 2.4.3 G-LiHT  
G-LiHT Thermal Sensor  
Surface temperature data products from G-LiHT's LWIR thermal imager were generated for both individual flight lines and other mapped areas. This imager with its 384 cross track pixels and a 30\({}^{\circ}\) FOV has the narrowest swath and lowest cross track sampling of G-LiHT's sensors and frequently dictates the flight line spacing to achieve continuous coverage. However, the surface is oversampled along track and typically 100 (along track) lines are extracted from each frame of the 25 Hz video sequence. Again, the fixed relationship between LiDAR scan angles and pixel centers is exploited through backwards projection to populate each co-registered raster grid cell of the final data product. Data were collected at the ground calibration site with a hand held IRT device and a portable calibration black body, as described above, to verify the accuracy of G-LiHT's surface temperature products.  
Customized LiDAR data processing code was written in IDL (EXELIS Visual Information Solutions, Boulder, CO, USA) to ingest the raw swath-based unfiltered LAS files from G-LiHT. Algorithms apply a grid-based bottom-up Delaunay triangulation process with a progressive morphological filter16. Lowest elevation points (i.e., ground returns) are isolated and used to create a Triangulated Irregular Network (TIN) from which the digital terrain model (DTM) is generated through interpolation to uniform raster grid spacing. The grid cell size is selected so each cell has a sufficient population of LiDAR returns which is proportional to flight altitude, laser repetition rate, surface complexity and reflectance at 1550 nm. Typical flight altitudes and laser repetition rates for G-LiHT are 300 m/300 kHz or 600 m/150 kHz, which yield 1 m or 2 m data samples, respectively. G-LiHT DTM products include ground slope and aspect, reported in degrees. Next, triangulation is used to determine the base elevation of every non-ground return and the Canopy Height Model (CHM) is created by selecting the greatest vegetation return height for every grid cell.  
Commonly used statistical metrics are derived from the remaining LiDAR returns which fall between the modeled top of canopy and ground surfaces. These are subdivided into shrub and tree classes based on a threshold height (above ground value, 1.37 m). Statistical metrics are calculated based on a 14 m\({}^{2}\) grid size such that a sufficient sample population is achieved for each grid cell. A total of 35 LiDAR metrics are included in the standard G-LiHT distributed data products which include: mean and standard deviation of all tree and shrub heights; tree fractional cover (fraction of first returns intercepted by tree); and tree return percentiles and deciles in 10% increments. A complete list of G-LiHT plot scale metrics are archived [20].
###### Abstract  
This study evaluated the effect of clinical and patient-related factors on marginal bone loss. The sample was composed of individuals who were treated at two dental schools in southern Brazil. The variables evaluated were divided into individual (age, sex, diabetes, hypertension and antihypertensive use), and implant levels (arch, position, brand, protection, torque, time). After implant installation and following the second stage, periapical radiographs were obtained to measure the distance between the peri-implant bone crest and implant platform. Measurements were performed using ImageJ software. Data were analyzed using linear and Poisson multilevel regression. Fifty-eight patients with 113 implants were evaluated. The mean marginal bone loss was 0.45 \(\pm\) 0.48 mm. Considering the linear multivariate model, age, diabetes, torque and time between surgery and reopening were found to be significant (_p_ < 0.05). In the dichotomous model (bone loss \(<\)0.2 mm and \(\geq\)0.2 mm), only high torques resulted in higher marginal bone loss (_p_ = 0.033). Marginal bone loss occurred before the second surgical stage and was greater in implants with high torque. Torque below 20 N, reopening performed after six months, diabetic status and young age all resulted in higher marginal bone loss, but these values are probably not clinically significant. These variables must be better explored in future studies.  
Keywords:dental implants; marginal bone loss; insertion torque +
Footnote †: journal: Medical Imaging
# Effect of the Insulating Layer on the Properties of SMC Inductors
## 4 Experimental Results  
Several parameters are considered to assess the performance of SMC inductors. Commonly, various permeability values (initial, complex and maximum magnetic permeability) are referred to as principal data. On the other hand, the parameter that is required to be considered is the quality factor \(Q\). This is a measure of the performance of an inductor  
Figure 5: Toroid sample preparation for the test: winding process and test setup.  
Figure 6: Rod sample preparation for the test: winding process and size.  
and is useful information when considering a material's implementation in high-frequency applications. It is represented through the following equation:  
\[Q=\frac{\omega L}{R}=\frac{\mu^{\prime}}{\mu^{\prime\prime}}=\frac{1}{tan\delta} \tag{1}\]  
where \(R\) and \(L\) are resistance and inductance value, respectively, and \(\omega\) indicates the pulsation corresponding to the operating frequency. The factor's representation is made through complex permeability [23], where \(\mu^{\prime}\) and \(\mu^{\prime\prime}\) are the real and imaginary parts, respectively, and obtain the \(\mu_{r}\) relative permeability, which is depicted mathematically as:  
\[\mu_{r}=\mu^{\prime}-j\mu^{\prime\prime} \tag{2}\]  
It helps to understand how energy is dissipated through a phase shift of \(\delta\) (loss angle) between magnetic polarization J (t) and magnetic field H (t), mainly, real permeability represents the inductive component, and the imaginary part describes the loss component. Within a low-frequency applied magnetic field H, the induced magnetic flux B would be in phase with this field; however, with increasing frequency, there is an observed delay between the two. Notably, the magnetic flux cannot stay in phase with the magnetic field due to a phenomenon called domain wall resonance, and this phase delay is expressed through \(\delta\). The loss tangent is deducible to:  
\[tan\delta=\frac{\mu^{\prime\prime}}{\mu^{\prime}} \tag{3}\]  
The relation of the quality factor, through the loss tangent, provides an indication of how much power is lost over how much is stored within the material.
## 1 Introduction  
Due to the increased demand for wireless communication services and the adoption of a fixed spectral allocation policy that designates a specific band for primary systems in a given geographical region and on a long-term basis, spectral scarcity has become of main concern. The spectral scarcity is one of the greatest obstacles to the deployment of existing and new wireless communication systems and services. With the advent of the concept of cognitive radio (CR) [1], cognition-based dynamic access techniques to spectral bands have arisen to contribute to solving the problem of spectrum congestion and scarcity. This is done by allowing for the opportunistic occupation of an underutilized portion of the spectrum by secondary CR networks.  
Spectrum sensing is the fundamental task performed by a CR in order to gain access to a band of interest. As the name suggests, it is the task of monitoring a given band of interest in order to find spectral holes for subsequent opportunistic occupation. CRs with spectrum sensing capability have to identify spectrum holes efficiently and avoid harmful interference to primary users by either switching to an unoccupied band or keeping the interference below a maximum acceptable level [2].  
Third-generation (3G) broadband systems are mostly based on direct-sequence spread spectrum (DSSS), such as Evolution-Data Optimized (EV-DO) or High-Speed Packet Access (HSPA). Fourth-generation (4G) systems, however, predominately use multicarrier systems, like orthogonal frequency division multiplexing (OFDM), combined with or without its access counterpart, the orthogonal frequency division multiple access (OFDMA) [3, 4]. A reason for the adoption of OFDM is that it has some advantages in delivering high speed data, especially in a multipath, frequency selective fading channel [3]. As OFDM/OFDMA is being adopted as the scheme of choice in broadband communication systems, it is important for CR networks to sense this kind of signal.  
Due to the high importance of OFDMA signals in wireless communication systems and the high importance of spectral sensing in the context of cognitive radio networks, ongoing researches are proposing new methods of wideband spectral sensing and existing ones are being combined to improve the performance of cognitive radio networks. This paper aims at contributing to this research effort by proposing a new approach to the spectrum sensing of OFDMA and other wideband signals.