esa-sceva/satcom-chunk-collection huggingface dataset:
# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 2 Materials and Methods
### Coherence Estimation
#### 2.5.1 SNR Decorrelation  
\(\gamma_{SNR}\) is caused by thermal noise of the system. In most cases, when the signal-to-noise ratio is higher than 10 dB, this decorrelation term can be ignored. However, due to the relatively weak interaction between grassland vegetation and the C-band signal, this term is not negligible in this study and has to be taken into account. \(\gamma_{SNR}\) is defined as [35]:  
\[\gamma_{SNR}=\frac{1}{\sqrt{\left(1+\frac{1}{SNR_{sat}}\right)\left(1+\frac{1 }{SNR_{sat}}\right)}} \tag{3}\]  
where \(SNR_{sat}\) is calculated for each of the two images in the interferometric pair according to:  
\[SNR_{sat}=\frac{\sigma_{sat}^{0}-NESZ_{sat}}{NESZ_{sat}} \tag{4}\]  
where \(\sigma_{sat}^{0}\) is the backscattering coefficient of the area under investigation in the respective acquisition, and \(NESZ_{sat}\) is a range-dependent noise parameter that can be calculated using look-up tables provided in the Sentinel-1 metadata. The parameters in Equation (4) are in linear scale.  
In this study, the mean \(\gamma_{SNR}\) value for VH acquisitions was 0.91, and was 0.98 for VV. The minimum values were 0.66 for VH and 0.94 for VV.
# A New Type Bionic Foldable Wing Design for High Maneuverable Unmanned Aerial Vehicle
## 3 Folding Wing Structure Design and Validation
### Dynamic Simulation Analysis of the Virtual Prototype  
During the actual flight, the unsynchronized movement of the wings will cause the center of gravity to change, which will affect the maneuverability and stability of the aircraft. Therefore, the change range of the center of gravity should be small. When the wing moves symmetrically, the transverse center of gravity of the bionic foldable wing is unchanged, but the change in the position of the center of gravity along the direction of the fuselage will affect its longitudinal stability. The center of gravity of the folding mechanism was calculated. The steering gear driving rod rotates at a uniform speed of 30\({}^{\circ}\)/s. When the driving rod rotates 90\({}^{\circ}\), the wing is folded or unfolded. As shown in Figure 14, 1.25-4.25 s is the process of the wing from full spread to full retraction, and the variation range of the center of gravity in this process is controlled within 4 cm.  
According to the design criteria of the center \(x_{0}\) of gravity range, the center of gravity of the whole machine should conform to [27]:  
\[0.282\mathrm{b}\leq x_{0}\leq 0.348\mathrm{b} \tag{16}\]  
b--chord of the wing. In this scheme, the chord length is 20 cm, so the range of gravity center of the whole machine is as follows:  
\[x_{0}=\frac{m_{1}\Delta x_{1}}{m_{1}+m_{2}}\leq 1.3\ \mathrm{cm} \tag{17}\]  
m\({}_{1}\)--wing weight, m\({}_{2}\)--fuselage and onboard equipment weight.  
Therefore, when the weight of the fuselage and airborne equipment is 2.07 times the weight of the wing, the center of gravity meets the design standard. The wing weight is 0.63 kg, and the fuselage is expected to carry more than 5 kg. The proportion of wing weight meets the requirements, and the center of gravity design meets the expected standards.  
As shown in Figure 15, the change process of the midpoint coordinate of the wing rib can be regarded as the approximate change in the wingspan. When the steering gear drives the driving rod to rotate at a uniform speed of 30\({}^{\circ}\)/s, the change range is 27.5 cm. When both wings move perfectly symmetrically, the maximum wingspan is 54 cm.  
In the structural model, rotary joint 1 connects the inner wing skeleton with the fuselage, rotary joint 6 connects the inner wing skeleton with the outer wing skeleton, and rotary joint 4 connects the fuselage with the connecting rod 3. Under a torque drive of 3 N\(\cdot\)m, the impact force on rotary joint 1 is shown in Figure 16, the impact force on rotary joint 6 is shown in Figure 17, and the impact force on rotary joint 4 is shown in Figure 18. The wing is in full extension state at moment 0, and the wing is in full retractable state at the end position of 0.28 s. The impact force is less than 50 N, far less than the performance threshold of material failure, meeting the design requirements.  
Figure 14: Change in center of gravity of folding mechanism.
## 2 Results and Discussion
### Results
#### 2.1.4 COG (Clusters of Orthologous Groups) Functional Classification  
In our study, all annotated uninges were aligned to the COG database to further assess the completeness of transcriptome library and the effectiveness of annotation process. Out of the 71,095 all-unigenes, 19,986 were assigned to 25 COG categories (Figure 4, Table S2), where \"general function prediction only\" category represented the largest group (6308; 31.56%) followed by \"transcription\" (3459; 17.31%), \"replication, recombination and repair\" (3275; 16.39%) and \"posttranslational modification, protein turnover, chaperones\" (2672; 13.37%). The following categories: \"extracellular structures\" (22 unigenes) and \"nuclear structure\" (43 unigenes) represented the smallest groups. In addition, 1029 unigenes were assigned to \"secondary metabolites biosynthesis, transport and catabolism\" suggesting that these important processes might take place in _P. ginseng_ adventitious roots.
## 3 Monitoring Data Collecting for the Forth Bridge
### Sky Plots  
Figure 3 shows the sky plots for the various satellites systems at station shm1 on 2 May 2018. The satellite corresponding azimuth and elevation are denoted by the circumference and radii. It can be found that 30 GPS satellites and 14 Galileo satellites can be observed at the station in one day. Because the station is located at 56\({}^{\circ}\) north latitude, the GPS and Galileo satellites generally appear in the south of the station and an approximately circular hollow appeared in the north part of each sky plot.
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
###### Abstract  
When image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information about the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces a structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher-quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. In order to facilitate the model's ability to extract both global and local features, we introduce a multi-scale feature fusion module. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases.  
no-reference image quality assessment; multitask learning; image restoration; multi-level features +
Footnote †: journal: Medical Image Analysis  
1
Footnote †: journal: Medical Image Analysis  
1
Footnote 1: email: {[[https://doi.org/10.3390/app13116802](https://doi.org/10.3390/app13116802)]([https://doi.org/10.3390/app13116802](https://doi.org/10.3390/app13116802))}
## 3 Multi-Source Information Fusion Method
### Internal and External Leakage Diagnosis Based on Support Vector Machine
#### 3.2.2 Support Vector Machine  
SVM is a data-driven machine learning algorithm. The special advantages of the SVM algorithm in small sample, high dimensional, and nonlinear problems have made it a popular fault diagnosis method in recent years. SVM contains two typical algorithms: Support Vector Classification (SVC) and Support Vector Regression (SVR). In this paper, the SVC algorithm [23] is adopted as the diagnosis method.  
For a sample training dataset, \(D=\{(x_{i},y_{i})|i=1,2,\cdots n\}\), \(x_{i}\in R^{n},y_{i}\in\{\pm 1\}\), \(n\) is the sample size. The nonlinear mapping maps the input vector \(x\) from the original space \(R^{n}\) to a high-dimensional space \(Z\).  
Constructing the optimal linear classification hyperplane \(y=\left(w^{T}\cdot\varphi(x_{i})\right)+b\) in high-dimensional space.  
The classification constraint is  
\[s.t.\left\{\begin{array}{c}y_{i}\left[\left(w^{T}\cdot\varphi(x_{i})\right)+ b\right]-1+\varepsilon_{i}\geq 0\\ \varepsilon_{i}\geq 0\end{array}\right. \tag{15}\]  
\[\underset{w,b,\varepsilon}{min}[\frac{1}{2}w^{T}w+C\sum_{i=1}^{n}\varepsilon _{i}] \tag{16}\]  
where \(w\) is the normal of the hyperplane, \(b\) is the intercept, \(\varphi(x_{i})\) is the nonlinear mapping, \(\varepsilon_{i}\) is the slack variable, and \(C\) is the penalty factor.  
Define the Lagrangian function as follows:  
\[L=\frac{1}{2}w^{T}w+C\sum_{i=1}^{n}\varepsilon_{i}-\sum_{i=1}^{n}\beta_{i} \varepsilon_{i}-\sum_{i=1}^{n}\alpha_{i}\Big{\{}y_{i}\Big{[}\left(w^{T}\cdot \varphi(x_{i})\right)+b\Big{]}-1+\varepsilon_{i}\Big{\}} \tag{17}\]  
where \(\alpha_{i}\), \(\beta_{i}\) are Lagrange multipliers.  
Obtaining partial derivatives of \(w,b,\varepsilon_{i}\) and setting them as 0, and replacing the inner product \(\left[\varphi^{T}(x_{i})\cdot\varphi(x_{j})\right]\) with the kernel function \(K\left(x_{i},\ x_{j}\right)\):  
\[\underset{\alpha}{max}\sum_{i=1}^{n}\alpha_{i}-\frac{1}{2}\sum_{i=1}^{n}\sum _{j=1}^{n}\alpha_{i}\alpha_{j}y_{i}y_{j}K\left(x_{i},\ x_{j}\right), \tag{18}\]  
\[s.t.\left\{\begin{array}{c}\sum\limits_{i=1}^{n}y_{i}\alpha_{i}=0\\ 0\leq\alpha_{i}\leq C\end{array}\right. \tag{19}\]  
The optimal classification function can be obtained by solving Equations (18) and (19):  
\[f(x)=sgn[\sum_{i=1}^{n}\alpha_{i}y_{i}\ K\left(x_{i},\ x_{j}\right)+b] \tag{20}\]  
Based on the SVC algorithm above and the SVM toolbox in MATLAB (Matrix Laboratory), and given a set of training instances marked as belonging to one or the other of the two categories, the SVM training algorithm creates a model that assigns a new instance to one of the two categories. Since the SVM original classifier is only suitable for a two-class problem; the \"One vs One\" method is adopted to achieve three pattern recognition in this paper, and the eight time-domain characteristic parameters of the pressure signal are used as the input of the SVM to establish the training and testing datasets. The relay valve leakage fault diagnosis implementation process based on SVM is shown in Figure 4.
_Energies_**2013**, \(6\), 1568-1589; doi:10.3390/en6031568  
_energies_ ISSN 1996-1073  
www.mdpi.com/journal/energies  
Catalytic Upgrading of Bio-Oil by Reacting with Olefins and Alcohols over Solid Acids: Reaction Paths via Model Compound Studies  
**[PERSON] 1,2, [PERSON]. 2,*, [PERSON] 1, [PERSON] 1 and [PERSON] 1,***  
* MOE Key Laboratory of Bio-based Material Science and Technology, Northeast Forestry University, Harbin 150040, China; E-Mails: [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (J.S.)
* Department of Chemistry, Mississippi State University, Mississippi State, MS 39762, USA
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (C.U.P.); [EMAIL_ADDRESS] (Q.W.); Tel.: +1-662-325-7616 (C.U.P.); Fax: +1-662-325-7611 (C.U.P.); Tel./Fax: +86-451-82191993 (Q.W.).  
_Received: 5 January 2013; in revised form: 1 February 2013 / Accepted: 26 February 2013 / Published: 11 March 2013_  
**Abstract:** Catalytic refining of bio-oil by reacting with olefin/alcohol over solid acids can convert bio-oil to oxygen-containing fuels. Reactivities of groups of compounds typically present in bio-oil with 1-octene (or 1-butanol) were studied at 120 \({}^{\circ}\)C/3 h over Dowex50 WX2, Amberlyst15, Amberlyst36, silica sulfuric acid (SSA) and Cs\({}_{2.5}\)H\({}_{0.5}\)PW\({}_{12}\)O\({}_{40}\) supported on K10 clay (Cs\({}_{2.5}\)/K10, 30 wt. %). These compounds include phenol, water, acetic acid, acetaldehyde, hydroxyacetone, d-glucose and 2-hydroxymethylfuran. Mechanisms for the overall conversions were proposed. Other olefins (1,7-octalidene, cyclohexene, and 2,4,4-trimethylpentene) and alcohols (_iso_-butanol) with different activities were also investigated. All the olefins and alcohols used were effective but produced varying product selectivities. A complex model bio-oil, synthesized by mixing all the above-stated model compounds, was refined under similar conditions to test the catalyst's activity. SSA shows the highest hydrothermal stability. Cs\({}_{2.5}\)/K10 lost most of its activity. A global reaction pathway is outlined. Simultaneous and competing esterification, etherfication, acetal formation, hydration, isomerization and other equilibria were involved. Synergistic interactions among reactants and products were determined. Acid-catalyzed olefin hydration removed water and drove the esterification and acetal formation equilibria toward ester and acetal products.  
keywords:bio-oil upgrading; model compound reactions; solid acid; reaction pathways; olefins; alcohols +
Footnote †: journal: Journal of the American Chemical Society
## 4 Blending Ensemble Learning-Based Landslide Susceptibility Mapping and Risk Assessment
### Application and Evaluation of Blending Ensemble Model
#### 4.3.3 Analysis of the Results  
The experimental results indicate that the introduction of the ten-day rainfall factor significantly enhances the predictive performance of the model. The Blending ensemble model improves both the overall predictive performance and generalization capability in landslide prediction. Although the CatBoost model achieves the highest ROC value and prediction accuracy, its recall rate is relatively lower. By employing a multi-model fusion approach, the ensemble model not only attains a higher ROC value and the highest prediction accuracy but also achieves the highest recall rate, demonstrating its superior effectiveness in predicting landslide events and more robust predictive performance. Particularly in the binary classification task of landslide occurrence, the ensemble model fully leverages the strengths of each base learner, reducing potential errors that may arise from a single model in specific scenarios.  
Overall, ensemble learning methods have effectively improved the accuracy of landslide prediction. By combining multiple base learners and the meta-learner logistic regression, the ensemble model exhibits its notable advantages in complex and dynamic data environments, delivering more reliable predictive performance.
## 3 Results
### Evaluation of Multi-Source Model on External Datasets
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **Prevalence** & **Sensitivity (Recall)** & **Specificity** & **PPV (Precision)** & **NPV** & **F1-score** & **ROC AUC** & **AP (PR-AUC)** \\ \hline \multirow{2}{*}{Preumothorax} & 7.6\% & 0.862 & 0.945 & 0.566 & 0.988 & 0.683 & 0.961 & 0.825 \\  & (4081/53,426) & (0.852–0.872) & (0.943–0.947) & (0.554–0.577) & (0.987–0.989) & (0.673–0.693) & (0.957–0.964) & (0.814–0.834) \\ \hline \multirow{2}{*}{Support devices} & 14.2\% & 0.014 & 0.999 & 0.785 & 0.86 & 0.028 & 0.763 & 0.449 \\  & (7959/53,426) & (0.012–0.017) & (0.999–1.0) & (0.721–0.855) & (0.856–0.863) & (0.023–0.033) & (0.757–0.769) & (0.437–0.461) \\ \hline \multirow{2}{*}{No finding} & 72.1\% & 0.558 & 0.646 & 0.803 & 0.361 & 0.658 & 0.653 & 0.814 \\  & (38.527/53,426) & (0.553–0.563) & (0.639–0.654) & (0.798–0.808) & (0.356–0.367) & (0.654–0.663) & (0.648–0.658) & (0.81–0.819) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of the model trained on for labels. Figure 4: Confusion matrices for pneumothorax classification in the different test datasets and the overall performance in all test datasets (individually and combined). Percentages are row-normalized.
# An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
## 6 Conclusions  
A dual-carrier NFC, attached to an inductive WPT link, has been proposed and optimized for simultaneously transmitting power and bi-directional data via the same ultra-weak coupled coil pair. To demodulate the weak uplink signal under strong power carrier interference, a dedicated circuit is designed and introduced to the inductive link. The parameters of the dedicated circuit are optimized to minimize the interactions between the NFC and the WPT. We use our implantable sensor system for glaucoma treatment as an application example to present the dual-carrier NFC system and to elaborate its optimization procedure. The prototype achieves reliable (BER = 1 \(\times\) 10\({}^{-7}\)) communication even though the coupling coefficient is as low as 0.008, and the NFC coexists well with the WPT with only 15% PTE loss and \(\sim\)5% voltage ripple. The proposed NFC is very suitable for the battery-less implantable biomedical sensor systems with strict limits on coil size and coil number. The proposed NFC scheme and its optimization procedure could also be useful for other small-sized implantable biomedical sensor applications.  
This work is supported by NSFC (National Natural Science Foundation of China) under the National Major Projects for Instrument Development of China 31427801. The authors would like to thank the cooperative hospital (Peking University Third Hospital) for providing medical requirements on the implantable sensor system for glaucoma treatment. Thanks also go to Beijing Open Lab of Keysight Technologies, for the support of test instruments.  
[PERSON] designed the NFC system and circuits, performed the experiments and wrote the paper. [PERSON] provided the initial idea of this research and many useful comments and co-wrote the paper. [PERSON] optimized the WPT circuits and coil parameters. [PERSON] contributed to the data collection and analysis. [PERSON] contributed to the system modeling. All of the authors read and approved the final version of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & [32] & [33] & [34] & [35] & **This Work** \\ \hline Coil Number & 2 & 2 & 3 [FOOTNOTE:3]Footnote 3: They used two coils at the implant side to prevent the LSK transmitter from interfering with the inbound BPSK data signal transfer and power transfer. 4 The 5-mW PDL is our system requirement. The 10 Kips data rate is sufficient for our implantable sensor system for glaucoma treatment. 5 The PTE is the power transfer efficiency of the coupling link excluding the efficiency of the power amplifier and the efficiency of the rectifier. 5 OOK(On-off keying); PPSK (Passive phase shift keying). 6 Simulated by using ANSYS HFSS from the coil parameters. 6 Equivalent to a 4 \(\times\) 10\({}^{-5}\) packet error ratio (our packet size is 24 bits). & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with Other Single-inductive-link Designs.
# Grasp Analysis for the Robot-Based Manipulation of Pre-Assembled Cables with Electrical Connectors
## 3 Methodology
### Broader Context  
The overall goal of this methodology is to reduce the time, effort, and the required expertise during the design of gripper systems for ECs. In detail, this is to be achieved by simulatively estimating the required grasp force, quantifying the dynamic cable influence, and comparing surface pairs with each other. Thus, the required effort for iterative prototyping with time-consuming physical test series should be reduced.  
In the context of production engineering, the methodology is applied at the stage of assembly planning. The EC CAD data, the product to be assembled, and the cable specifications are assumed to be known. The latter may already be known from the company's own product development, obtained from the cable manufacturer, or determined on the basis of existing cable libraries.  
Since cables have to be mated on both sides in the majority of industrial applications, this methodology considers the two-sided grasping, handling, and mating of pre-assembled ECs. Therefore, two robot systems and two gripper systems are envisaged. For each cable, they aim to simultaneously grasp the two plugs, handle and mate them into two sockets of a product. The methodology covers two-jaw parallel gripper systems. A gripper system consists of a gripper base and two gripper jaws. While commercially available systems are envisaged as the gripper base, the gripper jaws are to be designed individually for each plug resulting in force-closure surface contacts.
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 2 Related Work  
This section first reviews traditional methods for change detection in remote sensing, summarizing their progress and challenges. Next, we discuss the application of Siamese networks in change detection, highlighting their advantages and limitations. Finally, we introduce foundation vision models (VFMs), including SAM, and analyze their potential and adaptation challenges in remote sensing change detection.
## 1 Introduction
The position and alignment of the rear fins yielded 10 cases with different transverse and axial fin positions and different AoA (between \(0^{\circ}\) and \(45^{\circ}\)). The geometry of the fins is obtained from the commercial _FCS Accelerator_ fins. Additionally, for the commercial 4-fin configuration, we applied four different surf velocities of 5 m/s, 7.5 m/s, 10 m/s, and 18 m/s, representing different wave heights. Our primary goal is to provide flow field data around a 4-fin configuration with both steady-state Reynolds-averaged Navier-Stokes (RANS) equations and unsteady fluid flow using unsteady RANS (URANS) simulations [16; 17]. URANS has been shown to be essential for resolving the time-dependent fluid flow structures at high angles of attack [11]. Both RANS models were used in combination with the SST (shear stress transport) \(k-\omega\) turbulence model [18; 19]. [PERSON] and [PERSON] [20] showed that URANS, in combination with the SST \(k-\omega\) turbulence model, provides a very good consistency to Large Eddy Simulations (LES) of wing profiles. The SST \(k-\omega\) turbulence model has proven its efficiency and correctness towards experimental data in the field of airfoils [21; 22] and hydrofoils [23]. Thus, RANS and URANS turbulence models, in combination with the SST \(k-\omega\) turbulence model, are widely applied and appropriate for the simulation of wing and wing-like profiles to analyze lift and drag forces [24; 25; 26] and are also used in the field of wind turbines [27; 28]. Our hypotheses regarding the effects of varying the positions of the rear fins and the surf-velocity on our 4-fin configuration are as follows:  
1. In comparison to the 3-fin configuration, the 4-fin configuration is expected to be slower because more drag force is produced, and the surfer will have to apply more work to perform a turn maneuver because of the higher lift force. Fluid flow velocity will have a significant influence on the lift and drag forces and will therefore have an immense effect on the surfer. 3. The various positions of the rear fins along the axial and transverse direction of the surfboard potentially have a massive impact on the lift and drag forces of the entire configuration, on the stall point, and consequently on the surfing. Our results may also cross-fertilize fields of application that use lifting bodies that are similar to surfboard fins, e.g., sailboat keel fins [29] and kitefoils [30].