esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Monostatic-Bistatic Geometry for Stereo-Radargrammetric Reconstruction
### 2.2 Methods for Monostatic-Bistatic DEM Generation
#### 2.2.1 Projection of Bistatic Parameters  
The projection of bistatic parameters, such as baseline \(\vec{B}\), slant range \(\vec{r}^{\star}\), sensor position \(\vec{R}^{\star}\), onto the range-elevation plane of the monostatic antenna allows three-dimensional monostatic-bistatic geometry to be accounted [32], and generates an explicit formulation for height determination [23]. The projection can be carried out considering the unit vector normal to the first antenna range elevation plane (Figure 5):  
\[\vec{n}=\frac{\vec{R}^{\prime}\times\left(\vec{V}^{\prime}_{\oplus}\times\vec {R}^{\prime}\right)}{\left|\vec{R}^{\prime}\times\left(\vec{V}^{\prime}_{ \oplus}\times\vec{R}^{\prime}\right)\right|} \tag{4}\]  
and, by means of this vector, calculating the projected parameters as follows:  
\[B_{\perp}=\sqrt{B^{2}-\left(\vec{B}\cdot\vec{n}\right)^{2}} \tag{5}\] \[r^{\star}_{\perp}=\sqrt{r^{\star 2}-\left(\vec{r}^{\star 2}\cdot \vec{n}\right)^{2}}\] (6) \[R^{\star}_{\perp}=\sqrt{\left|\vec{R}^{\prime}\right|^{2}- \left(\vec{R}^{\prime}\cdot\vec{n}\right)^{2}} \tag{7}\]  
Figure 5: Three-dimensional viewing geometry of the monostatic-bistatic stereo-radargrammetric survey (not to scale for clarity).  
where \(\vec{R}^{\prime}\) is the monostatic sensor position, \(\vec{V}^{\prime}_{\oplus}\) is the monostatic antenna velocity with respect to the Earth-centred-Earth-fixed (ECEF) reference frame. Finally, within the monostatic range-elevation plane, target altitude, \(h\), can be derived as follows  
\[h=\sqrt{{R^{\prime}}^{2}+{r^{\prime}}^{2}-2R^{\prime}r^{\prime}\cos\theta^{ \prime}}-r_{\oplus} \tag{8}\]  
where \(r_{\oplus}\) is the spherical Earth radius and \(\theta\)' is the monostatic radar-target off-nadir angle, that can be expressed as  
\[\theta^{\prime}=\cos^{-1}\left(\frac{{R^{\prime}}^{2}+B_{\perp}^{2}-{R^{ \prime}}_{\perp}^{2}}{2R^{\prime}B_{\perp}}\right)-\cos^{-1}\left(\frac{{r^{ \prime}}^{2}+B_{\perp}^{2}-{r^{\prime}}_{\perp}^{2}}{2r^{\prime}B_{\perp}}\right) \tag{9}\]  
For the sake of simplicity a spherical Earth is assumed, but the method can be applied to more general zero relief regular surfaces.
## 4 Conclusions  
In summary, the microchip laser developed in this paper emits the laser with a wavelength of 1064 nm, pulse duration of 410 ps, and single pulse energy of 16.8 uJ. The accompanying electronic drive system allows for continuous adjustment of the operating frequency between 100-1000 Hz and temperature control precision of \(\pm\)0.1 \({}^{\circ}\)C. The total power consumption of the entire system, including all electronic components, is less than 3 W. This microchip laser serves as a miniaturized, high-power plasma excitation source for deep space exploration TOF mass spectrometers. In subsequent work, it will be integrated and jointly tested with the TOF mass spectrometer currently under development. TOF mass spectrometers are characterized by their lightweight, compact, and low-power nature, making them suitable for onboard component analysis in planetary exploration missions and as portable devices for mineral resource exploration, thus offering broad application prospects.  
Conceptualization, W.L. and B.L.; methodology, W.L.; software, Y.L. and P.S.; validation, H.W., W.L. and Y.C.; formal analysis, W.L.; investigation, P.S.; resources, Y.L.; data curation, H.W.; writing--original draft preparation, W.L.; writing--review and editing, Y.C.; visualization, Y.C.; supervision, B.L.; project administration, W.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
Figure 16: The impact craters on the aluminum foil generated by laser irradiation observed under a microscope.  
Figure 15: Laser excitation of microchip laser for the generation of plasma in stone.  
**Funding:** This research was funded by Civil Aerospace Technology Pre Research Project, grant number 20211176.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Background
### Syntax and Semantics of Gherkin  
The class diagram in Figure 1 shows the syntactic structure of keywords of Gherkin. At the top level is the keyword _feature_, which represents a unit of functionality that the software under development will have upon completion [27], such as transferring money between two accounts in a banking application, booking a room in a hotel application, and so on. A feature contains a number of _scenarios_, where a scenario is a concrete example that describes how the feature plays out under the specific circumstance. For example, the behaviors of transferring money will be different, respectively, depending on the source account having a sufficient or an insufficient balance before a transfer.  
A scenario contains several _steps_ which are interpreted _sequentially in the order of appearance_. A step begins with one of the keywords _Given_, _When_, or _Then_:  
* A _Given_ step gives the precondition or the initial context of the scenario;
* A _When_ step gives the event or action that sets the scenario in motion;
* A _Then_ step gives the post-condition reflecting the effect of the event or action.  
If a scenario contains several successive steps all beginning with the same keyword, each of the subsequent keywords can be replaced by an _And_ or a _But_ for better readability without changing its meaning; see Section 3 for an example. A feature and its constituent scenarios are stored in a plain text file with the.feature extension.
# Microbes in Health and Disease: Human Gut Microbiota
## 5 Gut Microbiota-Targeted Therapies
### Fecal Microbiota Transplantation (FMT)  
FMT involves transplanting the gut microbiota of a healthy donor to the colon of a diseased subject. The transplantation routes include upper gastrointestinal routes (e.g., nasogastric/nago jejun tube, endoscopy, and oral capsules) and lower gastrointestinal routes (e.g., colonoscopy) [135]. The U.S. Food and Drug Administration (FDA) classifies fecal microbiota products that are used to diagnose, prevent, treat, or cure a disease or condition in humans as biological products and has oversight of their safety and effectiveness [136]. The screening of donor and donor stool involves rigorous, multiple-stage protocols (e.g., questionnaires, laboratory stool tests, and blood tests) to ensure the safety of recipients [137]. To date, in clinical trials, FMT has been utilized mainly to treat recurrent _C. difficile_ infection (CDI), which is associated with dysbiosis of the gut microbiota caused by the use of broad-spectrum antibiotics, with an overall 91% effect rate at week eight [138]. Very recently, the FDA approved two fecal microbiota products for the treatment of recurrent _C. difficile_ infection: Vowst (fecal microbiota spores, live-brpk that contains Firmicutes spores from human feces) and Rebyota (fecal microbiota, live-jslm that contains a broad consortium of microbes prepared from human feces), both of which presented higher success rates for the treatment of recurrent _C. difficile_ infection than did the placebo [139; 140]. FMT has also been investigated for the treatment of other gastrointestinal (GI) disorders (e.g., irritable bowel syndrome, inflammatory bowel diseases, etc.) and various non-GI disorders (diabetes, obesity, Parkinson's disease, autism, Alzheimer's disease, etc.) [141; 142; 143], with promising and mixed results observed, for example, in the treatment of irritable bowel syndrome [141]. Notably, concerns of safety and risk exist in the use of FMT for the treatment of diseases regarding the transmission of infectious diseases, acquisition of noninfectious diseases, and other potential long-term adverse effects [145].
###### Abstract  
All flood hazard and risk assessment suffer from a certain degree of uncertainty due to multiple factors, such as flood frequency analysis, hydrodynamic model calibration, or flood damage (magnitude-damage functions) models. The uncertainty linked to the flood frequency analysis is one of the most important factors (previous and present estimation point to 40%). Flood frequency analysis uncertainty has been approached from different points of view, such as the application of complex statistical models, the regionalization processes of peak flows, or the inclusion of non-systematic data. Here, we present an achievable approach to defining the uncertainty linked to flood frequency analysis by using the Monte Carlo method. Using the city of Zamora as the study site, the uncertainty is delimited by confidence intervals of a peak flow quantile of a 500-year return period. Probabilistic maps are derived from hydrodynamic results, and further analysis include flood hazard maps for human loss of stability and vehicle damage. Although the effect of this uncertainty is conditioned by the shape of the terrain, the results obtained may allow managers to achieve more consistent land-use planning. All those Zamora city results point out the probable underestimation of flood hazard (the higher hazard areas increase around 20%) and risk when the uncertainty analysis is not considered, thus limiting the efficiency of flood risk management tasks.  
flood hazard; Monte Carlo analysis; flow frequency analysis; probabilistic maps; Zamora city 40.1
# A Preliminary Analysis on the Insecticidal Effect of Cyantraniliprole against Stored-Product Pests
###### Abstract  
While existing insecticides are becoming obsolete, the need for research and application of alternative substances is growing. Cyantraniliprole is a second-generation ryanodine receptor with the ability to bind and activate the ryanodine receptors in insect striated muscle cells, causing continuous muscular contraction, paralysis, and death. Many studies indicate its effectiveness on a variety of insects, but its action on storage pests has not yet been reported. We successfully conducted experiments for the first time on adult _Tenchrio molitor_, _Tribolium confusun_, _Alphitobius diaperinus_, _Rhyzopertha dominica_, and _Trogoderma granarium_ with application of cyanraniliprole, causing dose-dependent mortality. Bioassays were carried out in the laboratory, where experimental adults were sprayed with six concentrations of cyanraniliprole. Mortality was recorded at 7, 14, 21, and 28 days after exposure. Mean mortality, survival concentration, and survival time were estimated for each species. The concentrations with both the highest mortality and the lowest survival rate were 2500 and 3000 ppm. Our results indicate that the tested insecticide was effective against _T. confusum_ adults and is a promising pesticide for use in storage facilities.  
storage pests; coleoptera; mortality; chemical control Article
# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 5 Conclusions  
This study presented an automated multiple concrete damage detection method called Mask R-CNN that is based on a state-of-the-art instance segmentation deep learning model. Mask R-CNN consists of three stages: region proposal, classification, and segmentation. This method is implemented with a backbone network ResNet-101 followed by an RPN and mask branch. The mask branch in this study had an enhanced resolution of 56 \(\times\) 56 to improve the performance of the segmentation of amorphous concrete damage. Using a total of 765 images with annotations of damage (319, 196, 249, and 237 images for cracks, efflorescence, rebar exposure, and spalling, respectively), the training of Mask R-CNN occurred for 50 epochs on a workstation with four GPUs with image augmentation implemented.  
The validation of the trained Mask R-CNN was carried out on 25 actual images that contained cracks, efflorescence, rebar exposure, and spalling with their collocation, along with various environmental objects. Two types of metric were proposed to validate the performance of localization and segmentation. For test images, the average precision was 90.41%, and the average recall was 90.81% for localization purposes. For segmentation, the trained Mask R-CNN achieved an average of 87.24% for precision and 87.58% for recall, which shows a good accuracy in the field environment. It was also suggested that the performance of the network can be improved by increasing the depth and resolution of the network architecture or by optimizing the network for slender-shaped objects such as cracks and rebar exposure. The experiment results showed the potential of the Mask R-CNN model to replace the existing visual inspection of concrete structures with high accuracy.  
In future studies, the classes of the structural damage can be extended to not only the four concrete damages, cracks, efflorescence, rebar exposures, and spalling in this study, but also damages such as separated aggregate and corrosion of steel structures. In addition, since it requires heavy computational costs to find small concrete cracks on massive civil structures, it is needed to investigate for a deep learning model that has a less parameters or a capability to process larger area at a time. Also, localization and quantification of multiple damages in higher accuracy needs to be performed in the future.  
B.K. and S.C. conceived and designed the methods; B.K. developed the software; B.K. performed the validation and visualization of results; B.K. and S.C. wrote the paper; S.C. supervised the research. S.C. acquired the research funding. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF).  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 018R1C1B6006600).  
The authors declare no conflict of interest.
# LED Nonlinearity Estimation and Compensation in VLC Systems Using Probabilistic Bayesian Learning
## 5 Results and Discussion
For the fast PBL regression model adopted here, PBL regression is initialized with the bias only and the basis functions are iteratively added, updated or deleted to increase the marginal likelihood [35]. Since adding basis functions requires most of the computations, the worst case is that a new basis function is added at each iteration and the worst-case computational complexity is given by \(O(N_{it}M^{2})\) with \(N_{it}\) being the iteration number and \(M\) being the number of basis functions. However, the worst case scenarios are highly impossible to occur due to the inherent sparsity of PBL regression. As introduced in [31], for common cases, an approximation of the computational complexity is about \(O(N_{it}N_{nz}^{2})\) with \(N_{nz}\) being the number of non-zero elements in the weight vector \(\mathbf{w}_{\text{MP}}\). More specifically, by using the fast marginal likelihood maximization method, only three elements out of totally \(M\) = 65 elements in \(\mathbf{w}_{\text{MP}}\) are non-zero, i.e., \(N_{nz}\) = 3. Consequently, the proposed PBL-based LED nonlinearity estimation and compensation scheme is computational efficient which is suitable for the implementation in practical VLC systems with computing capability-limited user terminals.
# Kinematic Modeling of a Trepanation Surgical Robot System
## 2 Methods
### Forward Kinematics  
In this section, the forward kinematics (FK) solution of the tripod effector is discussed. In order to produce the loop-closure constraints, the closed-chain mechanism is modeled as three open chains starting from the fixed frame \(\{B\}\) to the body frame \(\{E\}\) via frames \(\{C_{1}\}\), \(\{B_{2}\}\), and \(\{B_{3}\}\) (see Figure 5). In order to find a closed-form solution, a simplification is made, assuming that all the joints are rotational according to the frames of Figure 5. The modified D-H (Denavit-Hartenberg) parameters [34] are shown in Tables 1-3.  
The angles \(q_{21}\) and \(q_{31}\) that appear in Table 1 are used for the rotations around the \(Z_{B1^{\prime}}\) and \(Z_{C1}\) axes, respectively (see Figure 5). Similarly, the angles \(q_{22}\) and \(q_{32}\) that appear in Table 2 are used for the rotations around the \(Z_{B2^{\prime}}\) and \(Z_{C2}\) axes, respectively. Finally, the angles \(q_{23}\) and \(q_{33}\) that appear in Table 3 are used for the rotations around the \(Z_{B3^{\prime}}\) and \(Z_{C3}\), axes respectively.  
The loop-closure equations are the following:  
\[{}^{B}T_{1}\ {}^{1}\ T_{B_{1}^{\prime}}\ {}^{B_{1}^{\prime}}\ T_{C_{1}}\ {}^{C_{1}}\ T_{E} = {}^{B}\ T_{2}\ {}^{2}\ T_{B_{2}^{\prime}}\ {}^{B_{2}^{\prime}}\ T_{C_{2}}\ {}^{C_{2}}\ T_{E}\] \[{}^{B}T_{1}\ {}^{1}\ T_{B_{1}^{\prime}}\ {}^{B_{1}^{\prime}}\ T_{C_{1}}\ {}^{C_{1}}\ T_{E} = {}^{B}\ T_{3}\ {}^{3}\ T_{B_{3}^{\prime}}\ {}^{B_{3}^{\prime}}\ T_{C_{3}}\ {}^{C_{3}}\ T_{E} \tag{22}\]  
Using the position part of the loop-closure equations, we are able to determine five out of six variables (\(q_{21}\), \(q_{32}\), \(q_{33}\), \(q_{22}\), and \(q_{23}\)). The variable \(q_{31}\) can be found from the orientation part. The forward kinematics set of solutions is the following:  
\[q_{21} = \cos^{-1}\frac{b}{\|CE\|}\] \[q_{32} = \cos^{-1}\frac{(t_{1}-t_{2}-\|CE\|\cdot\sin q_{21})^{2}-\|C_{2}E \|^{2}-\|B_{2}C_{2}\|^{2}}{2\|C_{2}E\|\cdot\|B_{2}C_{2}\|}\] \[q_{33} = \cos^{-1}\frac{(t_{1}-t_{3}-\|CE\|\cdot\sin q_{21})^{2}-\|C_{3}E \|^{2}-\|B_{3}C_{3}\|^{2}}{2\|C_{3}E\|\cdot\|B_{3}C_{3}\|}\] \[q_{22} = \tan^{-1}\frac{\|C_{2}E\|\cdot\cos q_{32}+\|B_{2}C_{2}\|}{\|C_{2 }E\|\cdot\sin q_{32}}\] \[q_{23} = \tan^{-1}\frac{\|C_{3}E\|\cdot\cos q_{3}3+\|B_{3}C_{3}\|}{\|C_{3 }E\|\cdot\sin q_{33}}\] \[q_{31} = \sin^{-1}(\frac{\sqrt{2}}{2}\sin(q_{22}+q_{32})) \tag{23}\]
## 4 Conclusions  
In the present study, the mechanical properties and water purification performance of porous concrete mixed with (D)PAOs and zeolite were measured and analyzed in an effort to improve the water purification performance of rivers and streams.  
1. Preliminary characteristics experiments were conducted to evaluate the effect of the addition of (D)PAOs on the fluidity and strength of mortar. As a result, the mixing ratio of (D)PAOs was set to 10%. When zeolite was added to (D)PAOs-added mortar, the strength decreased but was still 1.0-1.9 MPa higher than that of plain mortar.
2. (D)PAOs/zeolite-added porous concrete showed similar trends to those observed in the mortar tests. To improve the water purification performance of porous concrete, the optimal mixing ratio of zeolite was determined to be 10%.
3. The T-N and T-P removal rates were 11.0% and 17.8% higher, respectively, in the (D)PAOs-added porous concrete than in the typical porous concrete. The COD and BOD concentrations were 65.74 mg/L and 1.60 mg/L lower, respectively, in the (D)PAOs-added porous concrete. When 10% (D)PAOs and 10% zeolite were added together, the water purification performance was improved even further.
4. In conclusion, when 10% (D)PAOs and 10% zeolite were added together, the porous concrete exhibited the best physical properties with respect to strength and water purification performance. The porous concrete products developed are expected to be applied for various applications, such as revetment blocks, anchor-fixing blocks, and retaining=wall blocks.  
Data curation, Y.-I.J., J.-W.L. and B.-J.L.; Investigation, Y.-I.J. and J.-W.L.; Methodology, Y.-I.J.; Writing--original draft, Y.-I.J. and J.-W.L.; Writing--review and editing, Y.-I.J. and B.-J.L.  
This research was financially supported by research fund of Chungnam National University.  
The authors declare no conflict of interest.  
Figure 10: Microorganisms observed in the water purification test.  
Figure 9: Increased flocs during test.
# Comparison of Functional Connectivity Analysis Methods in Alzheimer's Disease
## 3 Functional Connectivity Analysis Methods
### Data-Driven Methods
#### 3.2.1 Principal Component Analysis (PCA)  
PCA is a statistical approach for linearly transforming many correlated variables into a smaller number of uncorrelated variables that capture the majority of the variation in the original set of variables. It is also known as the Karhunen-Loeve transform [33]. One of the main goals of PCA is to reduce the dimensionality of the original data set. A limited number of uncorrelated variables is considered to reflect the underlying sources for observations. A smaller set of correlated variables is more computationally efficient in subsequent analysis. As a result, PCA is usually applied as a preprocessing step before applying other data-driven analysis methods such as clustering and ICA. For the investigation of FC, principal component analysis is helpful.
## Nomenclature  
The following abbreviations and notations are used in this manuscript:  
\begin{tabular}{l l l l} \(ODE\) & Ordinary Differential Equation & \(MHD\) & Magnatohydrodynamics \\ \(BVP\) & Boundary value problem & \(k(\hat{\mathcal{T}})\) & Thermal conductivity \\ \(IVP\) & Initial value problem & \(k_{\infty}\) & Free stream conductivity \\ \(m\) & Velocity power index parameter & \(K_{r}\) & Reaction rate parameter \\ \(\alpha\) & Wall thickness parameter & \(\mu\) & Temperature dependent viscosity parameter \\ \(E\) & Activation energy & \(n\) & Fitted rate constants \\ \(C_{f}\) & Local skin friction & \(\lambda,H\) & Fluid parameters \\ \(C_{p}\) & Specific heat & \(\delta\) & Temperature relative parameter \\ \(g\) & Dimensionless velocity & \(St,Sc\) & stratification parameters \\ \(w_{m}\) & Local wall couple stress & \(Nb\) & Brownian motion parameter \\ \(Nu_{x}\) & Local Nusselt number & \(Nt\) & Thermophoresis parameter \\ \(Pr\) & Prandtl number & \(Le\) & Lewis number \\ \(Kr\) & Reaction rate parameter & \(L_{t}\) & Thermal Slip parameter \\ \(q_{w}\) & Wall heat flux & \(M\) & Magnetic parameter \\ \(Sh_{x}\) & Sherwood number & \(\eta\) & Plate surface \\ \(Re\) & Reynolds number & \(\varepsilon\) & Variable thermal conductivity \\ \(\tau\) & Thermo-phoretic parameter & \(d\) & Temperature dependent viscosity parameter \\ \(\hat{T}_{\infty}\) & Ambient temperature & \(\hat{T}_{w}\) & Wall temperature \\ \(u\) & Velocity component in \(x\)–direction & \(v\) & Velocity component in \(y\) –direction \\ \(\mu\) & Viscosity & \(B_{0}\) & Magnetic induction \\ \(\xi\) & Boltzmann constant & & \\ \end{tabular}