esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### DNA Extraction and SNVs Genotyping  
Venous blood samples from patients and control group individuals were collected into vacutainer tubes containing EDTA. Genomic DNA from the blood was extracted by using a QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany), according to the manufacturer's instructions and DNA samples were stored at \(-20\)\({}^{\circ}\)C for further analyses. The genetic variants were determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay as previously described [15; 28; 29; 30]. The primers for _FOLR1_ gene variants rs2071010 and rs630074 were designed using the NCBI-SNV database and Primer3 Plus [31]. In Table 2, the primers and restriction enzymes used for  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Gene ID**} & \multirow{2}{*}{**rs Number**} & \multirow{2}{*}{**Position** (**GRCh38p14)**} & \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Ref. Allele**} & \multirow{2}{*}{\begin{tabular}{c} **Exonic** \\ **Function** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Clinical** \\ **Significance** \\ **(**line**) \\ \end{tabular} } \\  & rs2071010 & chr11.72189920 & intronic & G & — & \begin{tabular}{c} Uncertain \\ significance \\ \end{tabular} \\ \hline FOLR1 & rs630074 & chr11.72193893 & intronic & C & — & Not reported \\ \hline FOLH1 & rs61886492 & chr11.49164722 & exonic & G & \begin{tabular}{c} H (His) \textgreater{} Y \\ (Tyr) \\ \end{tabular} & Not reported \\ \hline GGH & rs11545078 & chr86.63026205 & exonic & G & T (Thr) \textgreater{} I & Not reported \\ \hline GGH & rs3758149 & chr86.63039169 & upstream & G & — & Not reported \\ \hline SLC19A1 & rs1051266 & chr21.45837880 & exonic & T & \
\begin{tabular}{c} H (His) \textgreater{} R \\ (Arg) \\ \end{tabular} &
\begin{tabular}{c} Uncertain \\ significance \\ \end{tabular} \\ \hline GRCh38.p14. & & & & & \\ \end{tabular}
\end{table}
Table 1: Characteristics of selected genes and polymorphisms.  
SNV genotyping are presented. Products were electrophoresed on agarose gels with Midori Green Advanced DNA Stain (Nippon Genetics, Duren, Germany). To verify the genotyping quality for each polymorphism, 20% of random samples were genotyped. All results confirmed the preceding genotyping outcomes.
###### Abstract  
Deep learning has recently been used to study blind image quality assessment (BIQA) in great detail. Yet, the scarcity of high-quality algorithms prevents from developing them further and being used in a real-time scenario. Patch-based techniques have been used to forecast the quality of an image, but they typically award the picture quality score to an individual patch of the image. As a result, there would be a lot of misleading scores coming from patches. Some regions of the image are important and can contribute highly toward the right prediction of its quality. To prevent outlier regions, we suggest a technique with a visual saliency module which allows the only important region to bypass to the neural network and allows the network to only learn the important information required to predict the quality. The neural network architecture used in this study is Inception-ResNet-v2. We assess the proposed strategy using a benchmark database (KADID-10k) to show its efficacy. The outcome demonstrates better performance compared with certain popular no-reference IQA (NR-IQA) and full-reference IQA (FR-IQA) approaches. This technique is intended to be utilized to estimate the quality of an image being acquired in real time from drone imagery.  
image quality assessment (IQA); visual saliency; Inception-ResNet-v2; saliency map; no-reference IQA (NR-IQA)
## 4 Results and Discussion
### Model Limitations and Future Work  
Although the positive contributions of the introduced VHRShips dataset and proposed HieD approach are explained in the previous sections, our approach has some shortcomings that need to be considered. More research can be conducted on the below captions to improve the performance further.  
* An additional sub-stage can be designed to detect and eliminate false positive samples for the localization, recognition, and identification stages, if further performance is needed. This design can also reduce the effects of the error cycle created by false positive outcomes.
* The distribution of unbalanced classes can be improved by implementing different over/under-sampling approaches, and the number of samples can be increased for the classes with limited data.
* Introducing more images with different classes will strengthen the dataset for the future.
* Different CNN networks can be integrated to increase the performance of the stages.  
Figure 4: Comparative visuals of HieD (left column) and YOLOv4 (right column) for inshore regions (blue labels are the ground truth and yellow labels with a circle at the center of bBox are the results of HieD or YOLOv4).
## 1 Introduction
High-frequency subband filtering is used for noise reduction, and adaptive enhancement is used to highlight detailed information. Finally, the image is synthesised, which can effectively ensure that the synthesised image has a good enhancement effect. Wavelet transform has a multi-resolution analysis ability, which can effectively deal with a high dynamic range and detailed information, and also has good time-frequency localisation. In addition, SWT2 performs image processing without sampling operations, which can effectively avoid image deformations in space and reduce the probability of offset or blurring phenomena of contour, texture, etc. [17].
## 3 Results and Discussion
### Column Tests
(2009) [13] also reported that the amounts of 2,4-dinitrophenol or benzoic acid as polar organics adsorbed on the loess were reduced when they existed simultaneously, due to their competitive adsorption, but their transport characteristics were not discussed. Contrarily, the results produced by [PERSON] et al. (2017) [37] and [PERSON] et al. (2021) [18] revealed that Pb\({}^{2+}\) adsorption could create more binding sites for NH\({}_{4}\)\({}^{+}\) adsorption, and NH\({}_{4}\)\({}^{+}\) adsorption increased the surface area of loess when Pb\({}^{2+}\) and NH\({}_{4}\)\({}^{+}\) coexisted. Eventually, their adsorption became stronger and mobility was inhibited due to the existence of each other [42]. This suggests that their interaction will lead to the environmental behaviors of pollutants varying in soil-water systems. It is well known that there might be TNT, RDX, HMX and their derivatives (i.e., aniline, nitrobenzene and Pb\({}^{2+}\)) in firing/training ranges, and their adsorption performances depend on the chemical structures and properties of the adsorbents and coexisting substances. In this regard, the present results indicate that the transport of TNT and aniline could be underestimated if the interaction is not considered, and the coexistence of pollutants can increase the risk of deep soil and even groundwater pollution [43].
## 4 Simulation and Discussion
### Reconstruction Performance versus the Sparsity Level  
Then, we compared reconstruction performance of FRSVssMP with the existing algorithms versus the sparsity level \(K\). In this simulation, for both synthetic and real sparse signals, signal length \(N\) and measurement length \(M\) were set to \(N=256\) and \(M=128\). And the parameters \(S\), \(\lambda\), \(\alpha\) for FRSVssMP were set equal to \(S=\lfloor k_{0}/2\rfloor\), \(\lambda=10^{-6}\), \(\alpha=0.5\) throughout our experiments, where \(k_{0}\) was the estimated initial sparsity level. The empirical results suggest that the step-size transformed parameter \(\eta\) is critical to the performance. For synthetic sparse signals, \(\eta\) was set to \(\eta=0.2\), while we set \(\eta=0.25\) for real sparse signals.
# Fuzzy Model Predictive Control for Unmanned Helicopter
## 3 Control Methodology
### Set of Fuzzy Rules  
In the presented research, each of the fuzzy rules from the local regulators can be expressed as follows:  
**Rule 1:** If \(V_{x}=0\) m/s, then \(u_{1}(k)=u_{0,1}+(\Delta\mathbf{U}_{1})_{1:n}\) (27)
**Rule 2:** If \(V_{x}=10\) m/s, then \(u_{2}(k)=u_{0,2}+(\Delta\mathbf{U}_{2})_{1:n}\) (28)
**Rule 3:** If \(V_{x}=20\) m/s, then \(u_{3}(k)=u_{0,3}+(\Delta\mathbf{U}_{3})_{1:n}\) (29)  
Each control increment vector \(\Delta\mathbf{U}_{i}\) (for \(i=1,2,3\)) is computed as the solution to the local MPC optimization problem:  
\[\Delta\mathbf{U}_{i}=\Big{[}\bar{\mathbf{R}}_{i}+\mathbf{Z}_{i}^{\top}\bar{\mathbf{Q}}_{i}\mathbf{ Z}_{i}\Big{]}^{-1}\mathbf{Z}_{i}^{\top}\bar{\mathbf{Q}}_{i}\big{(}\bar{\mathbf{r}}_{p}-\mathbf{W}_{i} \mathbf{x}(k)\big{)} \tag{30}\]  
where  
* \(u_{0,i}\) is the control input at the operating point associated with rule \(i\);
* \(\mathbf{x}(k)\) is the current state vector;
* \(\bar{\mathbf{r}}_{p}\) is the extended reference trajectory vector over the prediction horizon;  
Figure 3: Membership functions \(\mu_{i}(V_{x})\) used in the proposed reasoning.  
* \(W_{i},Z_{i}\) are the state and input prediction matrices for local model \(i\);
* \(\bar{Q}_{i},\bar{R}_{i}\) are the block-diagonal weighting matrices for the state and input errors.  
Since \(\Delta\mathbf{U}_{i}\) contains the full sequence of future control increments, only the first \(n\) elements are used to compute the current control input for each local regulator.  
Finally, the global control signal is obtained by aggregating the local control outputs using fuzzy membership weights \(\mu_{i}(V_{x})\) corresponding to each operating point:  
\[u(k)=\sum_{i=1}^{3}\mu_{i}(V_{x})\cdot u_{i}(k) \tag{31}\]  
where \(\mu_{i}(V_{x})\in[0,1]\) and \(\sum_{i=1}^{3}\mu_{i}(V_{x})=1\).
## 4 Results  
### Variation in Observed Top-Of-Canopy Reflectance  
Figure 2a shows the temporal variation in the mean TOC reflectance extracted from the HLS data within the target area. The highest variation was observed in the NIR band with increasing NIR reflectance from the start of the vegetation growing seasons, which then stabilized mid-season, and decreased at the end of vegetation growing season. Other bands exhibited the same pattern, but with smaller variations in the magnitude of reflectance compared to the NIR band.  
Figure 2: **(a)** Temporal variation in mean top-of-canopy (TOC) reflectance in each band extracted from harmonized Landsat-8 and Sentinel-2 (HLS) data within the target area defined by flux footprint (delineated in Figure 1); **(b)** mean and standard deviation (SD) of TOC reflectance on 31 August 2016 extracted from HLS and airborne data within the target area. The dotted line with triangular symbols is the airborne reflectance resampled to the HLS wavelengths. NIR: near infrared, SWIR: shortwave infrared.  
The observed temporal variation in the TOC reflectance likely did not occur due to the variation in _LAI_ because the evergreen Norway spruce species can be expected to have stable _LAI_ (Section 4.3) throughout the year. Instead, change in leaf age distribution caused temporal variation in the reflectance. Both young and mature leaves contribute to the TOC reflectance when captured from the top of the crown by the sensors of HLS data. Young leaves, in particular, start becoming greener from the beginning of the vegetation growing season, and then they stay sufficiently green until September and then start browning. This phenological development of the young leaves was likely the cause of variation in the TOC reflectance observed in this study.  
Figure 2b compares the mean and standard deviation, showing the variability of TOC reflectance within the target area, extracted from the HLS and airborne data acquired on 31 August 2016. To facilitate comparison, we also plotted the airborne reflectance resampled at the band centers of the HLS data. Both the HLS and airborne data exhibited similar magnitudes of mean TOC reflectance, with slightly higher NIR reflectance (0.19) in the HLS compared to airborne data (0.16). We found that both the HLS and airborne data exhibited similar variability within the target area even though the stand variability was poorly captured in the HLS data with coarse spatial resolution compared to the airborne data.
# An Optical Differential Method for Underwater Wireless Communication in Turbid Environments
## 2 Methodology
An effective extinction coefficient is defined for a particle with the diameter of \(d\) and excited by light with the normalized spectrum of \(l(\lambda)\), which is described as follows:  
\[c_{ext}(d)=\sum_{\lambda=0.38}^{0.78}c_{ext}(d,\lambda)I(\lambda). \tag{4}\]  
\(c_{ext}(d,\lambda)\) is the attenuation coefficient of visible light at different wavelengths for spheres with different particle sizes and is calculated using the Mie scattering model, and \(I(\lambda)\) is the normalized spectrum. Thus, \(c_{ext}(d)\) is superimposed from \(c_{ext}(d,\lambda)\), and the weight is normalized spectrum \(I(\lambda)\). Using Equation (4), we can calculate the effective extinction of different particle sizes for white light. Figure 1c shows the effective extinction coefficient as a function of sphere diameter. The effective extinction coefficient decreases as the diameter increases, although there are diminishing oscillations. It infinitely approaches a constant value (\(\sim\)2.04) as the diameter. In equation (3) is obviously satisfied for two white lights since \(c_{1}=\max(c_{ext}\ (d))=2.54\), and \(c_{2}=\min(c_{ext}\ (d))=2.04\), where \(\max(x)\) and \(\min(x)\) return the maximum and minimum of \(x\), respectively. Figure 1: Principle of ODCM. (**a,b**) Emission spectra of two consisting light sources. (**c**) Effective extinction coefficients as functions of the diameter of particles excited by the two consisting light sources. (**d**) Signal stability of functions of light propagating distance. The reliability of the measured signal, \(\varphi\), is defined as follows:  
\[s=1-E_{r}=1-\frac{|\varphi_{0}-\varphi_{d}|}{|\varphi_{0}|} \tag{5}\]  
where \(\varphi_{0}\) and \(\varphi_{d}\) are the measured signals after propagating distances of 0 and \(d\), respectively. For conventional intensity modulation, the signal \(\varphi\) is measured in illuminance (lx), and for chromaticity modulation, the signal \(\varphi\) is the CIE 1931 color coordinates, so it is unitless. \(E_{r}\) represents the relative error of the signal. The measured quantity could be the intensity or chromaticity of the transmission light. It is worth noting that \(\varphi\) could be a vector if the measured quantity is multidimensional; for instance, chromaticity coordinates in the CIE 1931 color diagram are two-dimensional. In such cases, the numerator of \(E_{r}\) implies the Euclidean distance between the theoretical and the measured chromaticity. Figure 1d compares theoretical signal reliabilities as a function of propagating distance if the two white LEDs (shown in Figure 1a,b) are used as the light sources (see more details on theoretical derivations in the Supplementary Materials). It can be clearly seen that the light intensity decays much faster than the chromaticity for both kinds of light sources. The exponential decay coefficient of intensity is more than 15 times larger than that of chromaticity, indicating the superiority of chromaticity over conventional intensity in long-distance underwater light communications.
## 3 Results  
### Comparison between Simulation and Experiment  
Figure 3a,b are examples of single-frame images that can be obtained from the HEXITEC detector and the simulation detector, respectively, where the simulated image was obtained by framing the data using the number of protons per frame in Equation (2). These images show the detector output in a span of 625 \(\upmu\)s when a 150 MeV proton beam is incident on the water phantom, producing secondary particles that will be seen by HEXITEC. Both images depict the interactions between incoming particles and the sensor, where particle tracks and hits are visualised in the form of pixel clusters. Such clustering happens when a particle deposits its energy and the charge produced in the sensor diffuses through more than one pixel. Due to this, the study assumes that a single cluster in a frame corresponds to a single particle entering the detector. This suggests that a characterisation of clusters can be performed such that the cluster size can be related to the particle type, as evidenced from Figure 3b, where the true particle-type information can be retrieved. Whilst electrons produce clusters with a high number of pixels, neutrons and gammas are represented by smaller and more compact clusters.  
Data clustering was performed for both scenarios using the Python DBSCAN function from the Scikit-Learn [34] package, where the frame number and the position of the events were chosen as the clustering parameters to get the overall total flux in the detector. Figure 4 shows the cluster flux in the detector during the whole experiment for both scenarios. Note that both curves exhibit a similar behaviour that matches the shape of the curve in Figure 2, as expected.  
Although the comparison suggests that the model accurately simulates the experimental setup in terms of secondary particle flux, some dissimilarities are evidenced, especially for the beam energies with the highest beam currents. A definite cause for this is the increase in the background flux in the hospital, which is not taken into account in these measurements, suggesting that a profound characterisation of the background noise should be part of future studies. Additional sources of dissimilarity between UCLH data and the simulation can be attributed to the fact that the latter does not account for the effect of backscattering inside the detector ASIC or housing, as the detector is simplified as a pixellated CZT crystal, and it also does not model the charge transport within the sensitive volume, as this is beyond the scope of the Geant4 software.  
Figure 4: Comparison plot showing the total number of clusters per second found in both scenarios.  
Figure 3: Single frame image produced by the HEXITEC detector during the data acquisition time. Secondary particles produced by a 150 MeV proton beam are detected and their energy deposition can be seen in a 2D histogram. By evaluating the particle type in the simulations, electrons are represented by long track-like clusters, whereas gammas and neutrons have smaller cluster sizes. (**a**) Image acquired at UCLH. (**b**) Image simulated in Geant4.
## 4 Evaluation of Seismic Performance
### Stiffness Degradation  
Stiffness is an important coefficient to evaluate the seismic behavior of the shear walls. Under reversed cyclic loading, both traditional and precast RMSWs behaved non-linearly after yielding and experienced accumulated damage in the test, which was indicated by the degradation of stiffness. In this paper, secant stiffness was used to investigate the specimen's stiffness degradation in cyclic loading. The secant stiffness (\(K_{\mathrm{s},i}\)) of the specimen at each loading cycle can be defined by the equation below.  
\[K_{\mathrm{s},i}=\frac{|+F_{\mathrm{max},i}|+|-F_{\mathrm{max},i}|}{|+D_{i}|+| -D_{i}|} \tag{2}\]  
where \(+F_{\mathrm{max},i}\) and \(-F_{\mathrm{max},i}\) are the maximum loads of the first \(i\)th cycle in the push and pull loading direction, respectively; \(+D_{i}\) and \(-D_{i}\) are the displacements corresponding to \(+F_{\mathrm{max},i}\) and \(-F_{\mathrm{max},i}\), respectively. Then the calculated secant stiffness, \(K_{\mathrm{s}}\), was normalized with respect to the corresponding initial stiffness, \(K_{0}\), which was defined as the stiffness of the specimen working at the elastic stage.  
Figure 13 shows the variation of normalized stiffness \(K_{\mathrm{s}}/K_{0}\) with respect to the lateral displacement and drift of each specimen. As observed from the figure, the normalized wall stiffness of both traditional and precast test walls declined with a similar power law as displacement increased. A similar observation of traditional RMSWs was reported by [PERSON] [28]. The normalized secant stiffness of test walls decreased rapidly from the initial loading up to about 14 mm (0.5% drift) and then degradation occurred gradually until the termination of the test. The relevant results indicate that the wall stiffness degraded to 50% of the initial stiffness at about 0.2% drift. At 0.5% drift, the test wall exhibits about 21% of the initial stiffness.
## 4 Response Reconstruction for an Actual Continuous Bridge
### Data Analysis
#### 4.3.3 Result Analysis  
Figure 11(a) presents the reconstruction outcome for Case-1. The strain data reconstructed at 04-S01 closely aligned with the actual high-frequency bridge strain observed through monitoring. Furthermore, Figure 11(b) illustrates the time history of the reconstruction error, indicating that the discrepancies between the reconstructed results and the monitoring data were relatively minor throughout the entire monitoring time frame.  
Figure 13 shows the reconstruction result of Case-2. The reconstruction result aligned well with the monitoring data. Moreover, the reconstruction outcome reflected a scenario with multiple vehicles, indicating that the simulated relationship was applicable under random traffic conditions involving several vehicles. This further supported the notion that the relationship between the variables remained time-invariant and constant, regardless of fluctuating traffic conditions. Additionally, considering that the input training data were constrained to only 20 min, the validation in Case-2 underscores that the proposed method was viable for practical engineering applications, enabling the use of short-term monitoring data for the reconstruction of long-term data.  
Additionally, this study endeavored to delve into the reconstruction of torsion modes as part of future research. Figure 14 offers a visual comparison of strain data across different sides of S02. In deviation from the linear relationship depicted in Figure 10, the strains measured on opposing sides did not maintain a linear correlation. This finding implies that torsion modes require consideration in the analysis. Nonetheless, subsequent evaluations revealed that the existing strain gauge configuration in the field experiment was not adequate to capture torsion modes. This issue will be discussed in a future study.  
Figure 12: Comparison between reconstructed and measured strain time histories at 04-S01 in Case-1. (solid curve, reconstruction result; dashed curve, high-pass filtered monitoring data).  
Figure 13: Reconstruction and monitoring data in the second case (solid curve, reconstruction result; dashed line, high-pass filtered monitoring data).