esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Different Operational Settings Using the Scratch Device  
As a low roughness value was desired, a closer differentiation of the influencing parameters on the mechanically produced scratch borders was carried out. A classification of all experimental data into the varied parameters via box plot (Figure 4) revealed that there are distinct settings for direction, force, and angle that lead to lower mean and median values for optimum scratch performance.  
Figure 3: Comparison of manually performed scratch assays versus assays performed with the scratch device (thick lines). Nearly independent from its operation mode, the scratch device achieves scratch borders that show a lower roughness than many conventional methods that allow cell harvesting.  
As the order of classification plays an important role [61; 62], the three classification parameters (angle, direction, and force) have been checked for the lowest roughness median value in all six possible decision tree permutations (Figure 5).  
A subsequent evaluation of the step-wise refinement for the ideal parameters (Figure 6) shows a probability for achieving a low scratch border roughness.  
Figure 4: Roughness values obtained during the scratch performances, unfiltered and classified into their respective configuration (force, direction, and angle), displayed as a box plot (symbols: \(\Box\) = mean value, x = min/max values). It is possible to visually identify operational modes that have enabled reliable scratch borders with a low border roughness.  
Figure 5: Construction of the decision trees for the classification of the roughness data. The order of the refinement parameters is permuted to obtain all possible outcomes independently from each other.  
When the highest force is applied, a good scratch is deemed the most probable. A chosen angle of \(-45^{\circ}\) also leads to a desirable outcome of the scratch border. Concerning the scratching direction, no preference was observed.  
As to the refinement of the scratching parameters, the outcome represents a probability to achieve a good scratch border (meaning a low roughness value). As the outcome of the scratch direction shows a 50% distribution for every direction, the influence can be considered inessential. A different picture can be drawn when the applied force and scratching angle are considered. In four out of six decision trees, the best outcomes have been recorded for the highest force applied. This is in accordance with the manual scratch techniques, which also obtained smoother scratch borders when a reasonably high force was applied. In the range of the results observed, the application of 0.5 N force per scratch had to be avoided.
## 3 Map Construction Method
### Road Centerline Extraction
#### 3.1.3 Road Centerline Generation  
The road centerline is composed of the road triangle centerlines. For type I triangles, the midpoint of one neighboring side and the midpoint of the longer side of the other two sides are calculated (e.g., \(P_{1}\) and \(P_{2}\) in Figure 6a). The two points are then connected to form the triangle centerline. For type II triangles, the midpoints of two neighboring sides are identified (e.g., \(P_{3}\) and \(P_{4}\) in Figure 6b). Then, \(P_{3}\) and \(P_{4}\) are connected to form the triangle centerline. For type III triangles, the gravity center of the triangle is first calculated (as \(P_{6}\) in Figure 5b), and the midpoint values of the three common sides are extracted (as \(P_{5}\), \(P_{7}\), and \(P_{8}\) in Figure 6b), and then, the center of gravity is connected to the other three points to form the centerline.  
The algorithm starts by traversing the adjacent triangles from any one of them and determines the next triangle to be visited based on the common edges. If the next triangle is a type II triangle, the midpoints of the two common edges are extracted and connected. If the next triangle is a type III triangle, extract the center of gravity and the midpoint of three sides of that triangle. When all the type I triangles have been searched once as the start or terminal points, and all the type III triangles have been searched three times as the start or terminal points, the network diagram construction is completed.
## 1 Introduction
In recent years, the accuracy and efficiency of such techniques have improved significantly [22], making it also possible to apply some of the methods in real-time applications [23]. Some of the proposed approaches utilize feature matching applied for orthorectification and velocimetry, such as airborne feature matching velocimetry (AFM) [18], whereas the applicability of the optical-flow methods has also been verified in experimental irrigation stations. It led to promising results, with average relative errors less than 6.5%, applying the time-averaged surface velocity of water flows based on the Farneback optical flow method [24]. With an appropriate measuring environment, it is possible to prepare spatial distributions of the flow in the tank [25]. Unfortunately, breeders are usually opposed to the excessive installation of sensors that can injure fish, so the most commonly available information is the amount of water leaving the water pump supplying the fish-farming tank. The spatial distribution is constant for the static configuration of the tank, that is: the number and settings of nozzles, pump performance and the shape of the tank. It changes as the amount of fish and floating biomass increases, which is problematic if it is not possible to place the sensors in the water together with the fish. The video measurement method developed and presented in the article is non-invasive, with the quality of results comparable to those obtained by sensors immersed in the tank and a flow meter measuring the performance of the pump feeding the tank.
## 4 Simulation Results and Attack Test
### Key Space Analysis  
Broadly speaking, for the encryption system, with the growth of the key space, the security of the algorithm will be relatively strengthened. In this scheme, the core part is the hyperchaotic system, whose attribute is vitally decided through the initial parameters, and the provided initial values \(\mu_{0}\), \(\lambda_{0}\), \(x_{0}\), and \(y_{0}\). Moreover, these values are derived through the hash value, which is generated from the plaintext image and securely transmitted by using the ECIES for encryption. The key space of spec256k1 ([[https://en.bitcoin.it/wiki/Secp256k1](https://en.bitcoin.it/wiki/Secp256k1)]([https://en.bitcoin.it/wiki/Secp256k1](https://en.bitcoin.it/wiki/Secp256k1)), accessed on 31 March 2021), Advanced Encryption Standard-256-Galois/Counter Mode (AEC-256-GCM) in the ECIES, and the hash value from the plaintext image are all 2\({}^{256}\). With the consideration of the computing power of current devices, it is impossible for attackers to use either public channel attack or brute force attack to steal the key from users. As a result, the analysis demonstrates that the key space of this novel scheme is secure enough.
# Damage Analysis of Reinforced Concrete Columns Based on Fiber Beam-Column Element
## 6 Conclusions  
Based on the RSAPS platform developed previously, this paper combined the fiber element model with the damage constitutive model of concrete and steel bars. The method of damage analysis of reinforced concrete members was established on the basis of the fiber element model. The RSAPS platform was used to simulate the quasi-static test of a reinforced concrete column. It was found that the fiber element model can simulate the degradation of the strength and stiffness of the column. The obtained hysteresis curve was in good agreement with the test results. The damage distribution of the simulated specimen was consistent with the experimental results. Through the simulation analysis of the column in the shaking table test, it was found that the fiber element model can effectively simulate the nonlinear dynamic behavior and damage distribution of the reinforced concrete column. The simulated displacement peak values and the corresponding moments in the X and Y directions were basically consistent with the experimental results. Furthermore, the simulation model can predict the process of the column damage evolution and intuitively describe weak parts of the column with high computational efficiency and precision, providing a practical analysis method for the analysis of the seismic collapse process of building structures. The method proposed in this paper can quickly predict the possible damage degree and location of the columns, which can provide a basis for the rapid evaluation of the columns. In addition, this method can reproduce the post-earthquake damage of the columns and provide an important reference for the post-earthquake repair work of the columns.  
However, in this model, the bond-slip between concrete and steel bars and the buckling of the longitudinal steel bars were not considered. This leads to an overestimation of the stiffness of the specimen in the simulation.  
Conceptualization, Y.G. and Y.H.; methodology, Y.G. and L.Z.; software, Y.G. and L.Z.; writing--original draft preparation, Y.G. and Z.Z.; writing--review and editing, Y.G. and Y.H.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 52108481) and the Fundamental Research Program of Shanxi Province (Grant No. 202103021223211 and Grant No. 202203021212119).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Analysis
### Characteristics of Spatiotemporal Variation in NPP
#### 4.2.2 Spatial Distribution Characteristics  
In the study area, there was a clear pattern of regional variation in the multi-annual NPP averages, with a general spatial trend showing a decrease from the southeast coast to northwest inland (Figures 9 and 10). High NPP regions were focused in the eastern and southern portions of the mainland, mainly in southwestern Hainan Province, Fujian Province, and most of Taiwan Province. These regions have low latitudes, sufficient heat, and abundant precipitation, which are conducive to the accumulation of organic compounds in vegetation. Low NPP regions were clustered in the western and northern parts of the continent, mainly in China's Inner Mongolia Autonomous Region, Xinjiang Uygur Autonomous Region, Gansu Province, Qinghai Province, Ningxia Hui Autonomous Region, and Tibet Autonomous Region. These areas have an arid climate, poorer soils, and low vegetation cover, which are not beneficial for the accumulation of organic material in vegetation.  
Figure 8: Graph of the monthly NPP averages in 2001–2020.  
The number of NPP values in the domain of 0-150 g C/m\({}^{2}\) was the highest, with 56.03%, followed by 300-450 g C/m\({}^{2}\), 450-600 g C/m\({}^{2}\), and 150-300 g C/m\({}^{2}\) with 22.82%, 12.90%, and 6.36%, respectively, and finally \(>\)600 g C/m\({}^{2}\) with the lowest percentage of 1.89%. In terms of the statistical histogram by province (Figure 9), Taiwan Province had the largest mean multi-annual NPP values of 984.44 g C/m\({}^{2}\), followed by Hainan Province (724.04 g C/m\({}^{2}\)) and Fujian Province (638.46 g C/m\({}^{2}\)). The mean multi-year NPP value in Xinjiang Uygur Autonomous Region was the smallest, at 30.79 g C/m\({}^{2}\).
# Impact of Patient Size and Positioning on Radiation Dose in Multiphase Liver CT Examinations
## 3 Results
#### 3.1.1 Group with Abdominal Imaging Only  
The analysis of the 'Abdomen' group, encompassing 53 patient observations, reveals the following insights. We found a moderate positive correlation between BMI and total DLP (Spearman r = 0.54, _p_-value < 0.00001), as well as a significant, albeit weaker, correlation with DLP during the liver phase (Spearman r = 0.28, _p_-value = 0.030). These findings suggest that higher BMI is associated with increased radiation dose, but the effect is more pronounced in the total DLP compared to the liver phase (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Group** & **Variable** & \(n\) & **Mean \(\pm\) SD** & **Median (IQR)** \\ \hline Abdomen & Height (m) & 80 & 1.69 \(\pm\) 0.09 & 1.70 (1.63–1.76) \\ Abdomen & Weight (kg) & 80 & 80.4 \(\pm\) 14.1 & 79 (69–90) \\ Abdomen & BMI (kg m\({}^{-2}\)) & 80 & 28.0 \(\pm\) 4.2 & 27.9 (25.1–30.8) \\ \hline Abdomen + Pelvis & Height (m) & 178 & 1.68 \(\pm\) 0.09 & 1.68 (1.62–1.74) \\ Abdomen + Pelvis & Weight (kg & 178 & 73.8 \(\pm\) 13.9 & 72 (63–82) \\ Abdomen + Pelvis & BMI (kg m\({}^{-2}\)) & 178 & 26.3 \(\pm\) 4.3 & 25.5 (23.0–28.9) \\ \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics for anthropometric variables in the two study cohorts.  
\begin{table}
\begin{tabular}{c c c} \hline
**Group** & **BMI Category** & _n (\%)_ \\ \hline Abdomen & Underweight (\textless{}18.5 kg m\({}^{-2}\)) & 1 (1.3\%) \\ Abdomen & Normal (18.5–24.9) & 17 (21\%) \\ Abdomen & Overweight (25.0–29.9) & 40 (50\%) \\ Abdomen & Obese (\(\geq\)30) & 22 (28\%) \\ \hline Abdomen + Pelvis & Underweight (\textless{}18.5 kg m\({}^{-2}\)) & 2 (1.1\%) \\ Abdomen + Pelvis & Normal (18.5–24.9) & 52 (29\%) \\ Abdomen + Pelvis & Overweight (25.0–29.9) & 80 (45\%) \\ Abdomen + Pelvis & Obese (\(\geq\)30) & 44 (25\%) \\ \hline \end{tabular}
\end{table}
Table 2: Distribution of BMI categories in the two study cohorts.
# All-Optical Tuning of Fano Resonance for Quasi-BIC and Terahertz Sensing Applications
## 2 Materials and Methods  
Figure 1a shows a schematic diagram of the proposed THz metastructure. A typical section of the metasurfaces was fabricated on PolyTetraFluoroEthylene (PTFE), and the inset shows the enlarged geometry of the unit cell. A single resonator is 640 \(\times\) 640 \(\upmu\)m, the length and width \(l_{0}\) of the metal-aluminum structure are both 400 \(\upmu\)m, and the array size is 400 \(\times\) 400 \(\upmu\)m. There are two rectangular openings on the metal aluminum, and  
the upper part is a fixed opening with a size of 360 \(\times\) 170 \(\upmu\)m. In order to verify the structural asymmetry, the lower inner boundary is filled with aluminum or photosensitive silicon material with a width of 0-40 \(\upmu\)m. As shown in Figure 1b, when red photosensitive silicon with width of 40 \(\upmu\)m is introduced into the lower rectangular opening, the internal length and width are \(l_{3}\) = 280 \(\upmu\)m and \(l_{4}\) = 90 \(\upmu\)m, respectively. Both Si and Al material parameters can be referenced from the Palik Refractive Index database values so that the optical properties of this structure can be simulated and analyzed. In order to excite a significant resonant response, the polarization direction of the incident light must be along the asymmetric axis [24]. The parameters of the materials in the model are shown in the following Table 1.  
The aluminum material as a perfect electrical conductor with conductivity about 10\({}^{7}\) S/m in the THz band. The optical excitation uses the optical pump and terahertz probe (OPTP) method to control the modulate process by applying femtosecond pulsed laser stimulation (wavelength 800 nm, repetition rate 1 kHz, pulse width 100 fs). The photosensitive silicon material acts as the active medium. When the photon energy (1.55 eV) is larger than the silicon band gap (1.1 eV) of external optical excitation pumping, the light-induced free carriers can change the electrical conductivity of the silicon resonator surface, enabling the modulate from semiconductor-like to metal-like. The conductivity of silicon in the absence of illumination is set to \(\sigma\) = 0 S/m in the simulation. With the increase of pump light intensity, the conductivity increases from 0 to 5 \(\times\) 10\({}^{5}\), correspondingly.  
To validate the response of the designed metamaterial, numerical simulations based on the Ansys 3D High Frequency Simulation Software (HFSS) is employed. Periodic boundary conditions are employed along the \(x\) and \(y\)-directions and the perfectly matched boundary condition is used in the \(z\)-direction in the free space. The normal-incidence \(x\)-polarized THz wave is applied as the excitation. Furthermore, this design is realized on the assumption that the incident terahertz wave is the plane wave [26]. According to the equivalence principle, the field and source inside the periodic boundary can be replaced by the only field and source outside, so the Maxwell equations are solved on the boundary [27].
## 3 Results and Discussion
### Groups NR-III and NR-IV
Instead, we observed NAD and ADH domains--in some cases followed by a Peptidase S9 (PFAM: PF00326) domain. Peptidase S9 belongs to proteolytic enzymes that consume serine in their catalytic activity, and they are ubiquitously found in viruses, bacteria, and eukaryotes [92]. We also observed a helix-turn-helix domain (HTH) being inserted after one or two ACP domains in several groups. The HTH domain was previously found in PKSs in fungi but it has not been shown in the arrangement of the NR-PKSs of lichen-forming fungi before. Proteins with an HTH motif are, for example, involved in DNA repair, RNA metabolism, and protein--protein interaction [93], and could thus be involved in developmental or morphogenic processes. Group NR-VI  
Characterized PKSs in this group include PKS18 and PKS19 genes from _Botryotinia fuckeliana_. Genes from this group were suggested to be involved in usnic acid biosynthesis [71], and according to [PERSON] et al. [29] they belong to PKS8. Our results show that this group is divided into two clades. The first clade contains genes of known usnic acid producers such as _Ramalina intermedia_, _R. peruviana_, _Cladonia metacorallifera_, _C. macilenta_, _C. uncialis_, _Evernia prunastri_, _Alectoria sarmentosa_, _Letharia columbiana_, and _Usnea hakonensis_, interspersed with genes from lichen-forming fungi known not to produce usnic acid. The second clade includes genes mostly from lichen-forming fungi not producing usnic acid, including a gene from _B. rubella_ (bacurbpred_000551). However, sequences from _Cladonia macilenta_, _C. uncialis_, and _Evernia prunastri_ were also recovered in this clade. Group NR-VII  
The only genes characterized in this group are PKS17 from _Botryotinia fuckeliana_ and PKS21 from _Cochliobolus heterostrophus_. These genes have been proposed to produce citrinin or lovastatin [14; 94; 95]. The other identified genes from the lichen-forming fungi in this group had 50 to 70% BLAST similarity to genes involved in the biosynthesis of monascourbin, citrinin, conidial yellow, azanigerone A, and stipiatic acid (see also discussion on monascourbin above). As mentioned above, the biosynthetic gene from _B. rubella_ (bacurbpred_007642) has a 100% identity to the monascorubrin biosynthetic gene. The recently recognized group NR-IX [29] contains several predicted PKS23 sequences, involved in the biosynthesis of atranorin (see sections on 3.1.1 Atranorin and 3.1.2 Homosekikaic acid above) and methylated orselmitic acid derivatives. In contrast to the groups NR-VI and NR-VII, NR-IX sequences always contain an ADH and lack a NAD domain after CMET, while Peptides S9 is only sporadically present. It is necessary to note that only one ACP domain was present in the PKS23, including atranorin producers. In addition, other characterized PKSs of this group are from _Cochliobolus heterostrophus_ (PKS22 and PKS23) and _Botryotinia fuckeliana_ (PKS16 and PKS20).
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 5 Evaluation and Results
### Mission 3  
The dataset for Mission 3, illustrated in Figure 9, consists of 84.9 % authentic signals and 15.1% attack signals. The attacks are categorized as 3.5% simplistic, 4.1% intermediate, and 7.5% sophisticated attacks. Figure 10 demonstrates the DELTA_TOW values for this mission.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature** & **Decision Tree** & **Random Forest** & **XGBoost** \\ \hline PRN & 0.057747 & 0.083850 & 0.291810 \\ DO & 0.056479 & 0.132609 & 0.124330 \\ PD & 0.486443 & 0.367965 & 0.270811 \\ DELTA\_RX & 0.000000 & 0.000000 & 0.000000 \\ DELTA\_TOW & 0.000000 & 0.000029 & 0.000000 \\ CP & 0.054414 & 0.127390 & 0.100905 \\ EC & 0.000116 & 0.009787 & 0.002693 \\ LC & 0.000000 & 0.007440 & 0.000228 \\ PC & 0.000110 & 0.016208 & 0.000288 \\ PIP & 0.000000 & 0.001881 & 0.000341 \\ PQP & 0.000000 & 0.001683 & 0.000337 \\ TCD & 0.305553 & 0.141088 & 0.153162 \\ C/N\_0 & 0.039137 & 0.110069 & 0.055094 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Feature importance scores for Mission 2.  
Figure 7: Continuous DELTA_TOW values in Mission 2.  
Figure 8: Feature importance ranking from most to least significant—Mission 2.  
As shown in Figure 11, the features PD, TCD, PRN, DO, CP, and C/N0 are the most significant among the others for the decision tree model. In random forest, the feature importance parameter displays PD, TCD, DO, CP, C/N0, and PRN with the highest importance scores. Furthermore, XGBoost's best features are described as follows: PRN, PD, TCD, DO, CP, and C/N0. The scores obtained for each model can be found in Table 11.  
The decision tree model achieved an accuracy of 99.990%, precision of 99.978%, recall of 99.969%, and F1-score of 99.974%. The random forest model provided the highest performance, with an accuracy of 99.995%, precision of 99.991%, recall of 99.984%, and F1-score of 99.988%. Meanwhile, XGBoost demonstrated the lowest performance, with 99.894% accuracy, 99.769% precision, 99.764% recall, and 99.766% F1-score.  
Figure 10: Continuous DELTA_TOW values in Mission 3.  
Figure 9: Mission 3 class distribution.  
Figure 11: Feature importance ranking from most to least significant—Mission 3.