esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relative Sensitivity of the Major Vegetation Types  
Due to varied responses of the five vegetation communities to drought and increasing temperatures/aridity, the regression slopes between NDVI anomalies and PDSI varied among vegetation types. The relative sensitivity of the vegetation also displayed seasonal differences (Figure 10). Compared to developed land, grassland and CSS showed significantly increased drought sensitivity in winter and spring, and a mild increase of drought sensitivity in summer and fall (Figure 10 and Table 3). Grassland had the largest seasonal fluctuations in response to drought stress (Table 3). Chaparral showed the highest drought sensitivity in the warm seasons (median \(p<0.05\), Figure 10 and Table 3). Forest displayed an insignificant increase in drought sensitivity, compared to developed land, in the cold seasons, but a significantly increased sensitivity from summer to fall (Figure 10).  
The seasonal correlation coefficients between NDVI anomalies and PDSI reflected the general water stress variations of the five vegetation types throughout the year (Table 2). Winter (summer) showed the lowest (highest) median correlation coefficients, indicating less severe (extreme) drought stress due to favorable (limited) water conditions. Spring and fall displayed intermediate correlation coefficients, suggesting moderate drought stress. Moreover, the NDVI-PDSI correlations also displayed slight differences among the five ecosystems (Table 2). Chaparral and CSS showed relatively higher correlation coefficients in all seasons, indicating that the two communities had a more consistent response to drought in time, as compared to forest, grass, and developed land.
# Evaluation of Online Teaching Quality Based on Facial Expression Recognition
## 4 Discussion  
With the development of modern information technology, the concept of educational evaluation is also gradually changing. It advocates paying more attention to students' emotions and attaching importance to the means and effects of teaching quality evaluation. In such an era, this our study applies face detection and expression recognition technology to online teaching evaluation, and it obtains students' listening status so as to provide teachers with students' listening effects in a timely manner. The MTCNN face detection technology used in our study cascades three shallow networks together; the accuracy of each level is gradually improved, the coordinate information of the face frame is determined, and the face image is cut out from the picture. Using the VGG16 algorithm with ECANet for expression recognition, the accuracy of the test on the FER2013 data set can reach 67.4%, and the accuracy on the CK+ data set can reach 99.18%, which can more accurately identify the emotions of students. Using the model in our study to conduct classroom evaluations on nine students who took online classes, the emotional scores of the nine students were obtained as 0.593, 0.799, 0.797, 0.600, 0.026, 0.872, 0.800, 0.454, and 0.540. It can be concluded that the students with a score between 0.5 and 0.6 were in a normal state of listening, the students with a score above 0.7 were in an active learning state, and the two students with a score below 0.5 were in a negative learning situation. Of course, there are still some problems in the model of our study. Although the MTCNN algorithm we adopted has certain advantages for small target detection, the running speed of the algorithm is relatively slow. Moreover, our study lacks the corresponding student classroom face data set to train the expression recognition network, which leads to a decline in the accuracy of the model. In the following research, the corresponding data set can be produced and the MTCNN algorithm can be optimized to improve the running speed of the algorithm. Additionally, a new computer vision-based student attention evaluation criteria can be added, making classroom teaching evaluation more objective and robust.  
Figure 11: Expression recognition results.
## 3 Structural Model of PZT Actuator and Foil Structures
### Constitutive Model of PZT Stack  
An external load acting on the stacks and the piezoelectric deformation act only in the direction of axis 3 in Figure 2. In general, the shear stresses acting on the PZT material can be omitted [15]. Assuming the electric field in axis 3, the constitutive relation for a PZT material with regard to the linear inverse piezoelectric effect is expressed as:  
\[\varepsilon_{3}=\frac{1}{E_{p}}\sigma_{3}+\frac{d_{33}}{t_{p}}V_{3} \tag{1}\]  
where \(\varepsilon_{3}\) and \(\sigma_{3}\) represent the normal strain and stress of the PZT material in axis 3. \(E_{p}\) and \(t_{p}\) are the elastic modulus and thickness, respectively, of the PZT material, one layer of the PZT stack. \(V_{3}\) is the driving voltage of the PZT material along axis 3. \(d_{33}\) indicates the inverse piezoelectric coefficient.  
The total deformation of the PZT stack with \(N_{p}\) materials can be expressed by multiplying both sides of Equation (1) with the total PZT material thickness of \(t_{p}N_{p}\):  
\[\delta_{3}=\frac{1}{k_{p}}\sigma_{3}+D_{33}V_{3}. \tag{2}\]  
The first term of the right-hand side indicates the mechanical deformation of the PZT stack caused by the normal stress \(\sigma_{3}\), and is simplified using the structural stiffness per unit area of the PZT stack \(k_{p}\). The second term depicts the piezoelectric deformation caused by the inverse piezoelectric effect with the piezoelectric coefficient \(D_{33}\) (\(=N_{p}d_{33}\)), and it is equal to the \(\delta_{3}\) value for the no-load condition. Note that the linear piezoelectric deformation can be extended to a higher order polynomial function when nonlinearity is significant [16]. In this study, a second order polynomial of \(D_{33,1}V_{3}^{2}+D_{33,2}V_{3}\) is adopted.  
Figure 2: Schematic diagram of a PZT stack and a PZT element in axial configuration: a stack of ceramic PZT materials separated by metallic electrodes, an element with a PZT material and electrodes.
# An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments
## 2 Proposed Method
### New Map Points Processing Strategy  
The algorithm in this section mainly deals with new map points. In the general ORB-SLAM3 algorithm, new map points may have similar descriptors to existing map points, or they may be influenced by a mismatch in the dynamic environment or two frames that are too close together in triangulation, which will lead to generating a wrong coordinate. To deal with this problem, we propose a new map points processing (called NMPP) strategy. In this strategy, for each new map point, we perform the following two operations, namely a distance check and a repeated existing map points elimination operation.
###### Abstract  
Light scattering by non-spherical particles is an important factor influencing atmospheric radiative transfer. To accurately simulate the scattering properties of non-spherical particles, the Invariant Imbedded T-matrix method (IIM T-Matrix) is developed by combining the Lorenz-Mie theory and invariant imbedding technique. In this model, the non-spherical particle is regarded as an inhomogeneous sphere and discretized into multiple spherical layers in the spherical coordinate system. The T-matrix of the inscribed sphere is firstly calculated by the Lorenz-Mie theory, and then taking it as the initial value, the T-matrix is updated layer by layer by using the invariant imbedding technique. To improve the computational efficiency, the model is further parallelized by the OpenMP technique. To verify the simulation accuracy of the IIM T-Matrix method, the results of the model are compared with those of the EBCM (Extended Boundary Condition Method) T-Matrix method, DDA (Discrete Dipole Approximation) and MRTD (Multi-Resolution Time Domain). The results show that the scattering phase matrix simulated by the IIM T-Matrix method closely agrees with that of the well-tested models, indicating that the IIM T-Matrix method is a powerful tool for the light scattering simulation of non-spherical particles. Since the IIM T-Matrix method is derived from the volume integral equation, compared to the T-Matrix method which is based on surface integral principles (i.e., \"EBCM\" or the \"null field method\"), it can be applied to the scattering calculations of particle with arbitrary shapes and inhomogeneous compositions, which can greatly expand the application scope of the T-Matrix method.  
Keywords:light scattering; non-spherical particles; invariant imbedding T-Matrix method; Lorenz-Mie theory; non-rotationally symmetric particle +
Footnote †: journal: Applied Physics Letters
## 3 Experts' Knowledge System
### FHR Score  
Baseline FHR and decelerations were quantitatively analyzed in 5 min, and the following data were utilized to calculate the FHR score: baseline FHR (bpm), dip duration (min), dip amplitude (bpm), nadir FHR (bpm), recovery time (sec) to return baseline, lag time (bpm), and the presence of acceleration. The work looks complicated in visual analysis, but it was easy in computer analysis (Figure 2) [3].  
Figure 2: Quantified analysis of FHR changes for computerized experts’ knowledge system [3]. Each deceleration was analyzed if there were multiple decelerations in 5 min, where sum of the scores of all decelerations were the FHR score in 5 min. The items of above analysis was evaluated by the evaluation score determined by the percentage of low Apgar score lower than 7, _i.e._, it was four points when low Apgar appeared in 100% of that FHR change, three points if low Apgar was 70%, two points for 40% and one point for 20% (Table 1). A statistician allowed the evaluation as a goodness score. The sum of evaluation scores in 5 min was the FHR score.  
Fetal state was abnormal if the FHR score was 10-19, and highly abnormal if the score was 20 or more. The FHR score was comprehensive evaluation of fetal status [11].  
The FHR score in the first stage of labor (X) correlated to 1 min Apgar score (Y) and umbilical arterial blood pH (Z). Regression equations were;  
\[\mathrm{Y=9.361-0.335X},\,R^{2}=0.84,\,p<0.05\]  
\[\mathrm{Z=7.31-0.01X},\,R^{2}=0.85,\,p=0.024\] [4].  
As expected Apgar score = 4 (severe asphyxia) and pH = 7.16, if FHR score was 15 according to regression equations, rapid delivery will be indicated to avoid fetal or neonatal compromise if the FHR score was high [4].  
The down-hill amplitudes (bpm) of each long term variability were measured and averaged. The averaged variability \(<\)5 bpm was reported as \"reduced variability\", and \(<\)1 bpm was \"loss of variability\". Frequency spectrum analysis also detected the loss of variability [7].
## 2 Research Methodology
### Data Extraction and Analysis
#### 2.3.1 Classification Framework
Effectiveness evaluation is designed to measure the system's capability of getting the desired result for a specific task or activity. For example: reducing assembly time, enhancing productivity, etc. [30]. Usability evaluation utilizes expert evaluations, needs analysis, behaviors measures, user interviews, surveys, etc. to measure the ease of adaption of AR-based systems. Thus, the system flaws can be identified at the early stages of development [194]. **Layer 4 Industry adoption**  
This layer considers whether an AR prototype/application is tested in industry or not. A prototype/application can be classified into two classes depending on its industry adoption status, which are \"Tested in the industry\" and \"Novel stage\". If the field experiment is carried out for a prototype, it is classified into the \"Tested in industry\" class. The \"novel stage\" is relevant to applications, which focus on solving specific issues of AR technology such as tracking, calibration, etc., rather than finding holistic solutions for real industrial case studies, or are only tested in a laboratory environment. A pilot project solves real case studies and has the potential to be applied in the manufacturing environment, but there were no in-depth experiments carried out for it to verify/validate the results. Thus, pilot projects are also classified into the \"novel stage\" category. **Layer 5 Intelligent AR solution**  
To support this layer, an article should satisfy at least one of the following questions. * Does the prototype or application integrate with another industry 4.0 technology such as AI, IoT, CPS, Digital Twin, etc.? * Does the solution/concept potentially establish the fundamental base for the further integration of AI, IoT, etc. in AR environment to support manufacturing? * Do the algorithms try to solve a limitation in AI-supporting AR systems? * Categories of current AR assisted quality sector All the AR-based solutions for the quality sector can be classified into 3 groups:
* AR supporting quality as a virtual Lean tool for error prevention (virtual Poka Yoke)
* AR-based applications for metrology
* AR-based solutions for in-line quality control (process, product, machine, human)  
Metrology: Applied metrology is a subset of metrology and a measuring science created to ensure the appropriateness of measurement devices, as well as their calibrationand quality control, in manufacturing and other operations. Nowadays, measurement technologies are utilized not only for assuring the completed product, but also for proactive management of the entire production process. With AR's superimposition advantage and metrology's power, metrology integrated AR might be a promising research area for the long-term success of quality 4.0.
## 3 CubeSats and Inter-Satellite Links (ISLs)
### Future OISL Payloads
The payload contains three main sections: (i) the top section contains the transmitter and receiver telescopes supported by a chassis (red structure); (ii) the middle section contains the amplifiers of the laser and the controller for the fast-steering mirror, held together by a cradle (grey structure); and (iii) the bottom section is where the Optical Interface Assembly (OIA) PCB stack is located. The primary function of this section is to manage the payload electronics, which include important components such as power management modules, data processing boards, and control electronics. The stack also controls the telescope actuation in addition to driving the high-speed communications. The actuators provides line-of-sight stabilization, micro-scanning, tracking, and fine pointing, essential for the PAT process.
## 3 Analyzing Experimental Phenomena and Results
### Load-Strain Curve  
At the beginning of test loading, the specimen deflection increases rapidly with the increase in load, there is no relative slip deformation on both sides, and the strains on the steel and ECC surfaces continue to increase. The load-strain curve on the ECC surface maintains a continuous increase in microstrain at lower load levels due to the unique strain-hardening-force-deformation characteristics of the ECC, and the strain on the ECC surface increases with load only after cracks appear on the surface layer. The surface load-strain curve of the reinforcement remained linear without significant transitions until yielding and was relatively stable after reaching the peak. The load-strain curves for the same ECC thickness combination are very similar, and the difference in load capacity is not significant.  
As shown in Figure 7, by comparing the load-strain curves of the ECC and the reinforcement, the tensile strain on the surface of ECC was 1800-2000 \(\mu\varepsilon\) and the surface strain of steel was 900-1000 \(\mu\varepsilon\) before the cracks appeared in the ECC layer, and with the formation of the initial cracks in the members, the load-strain curves of ECC and reinforcement changed significantly. In terms of microstrain growth, both the ECC surface strain and the reinforcement surface strain are basically in linear growth, which is mainly due to the strain-hardening property of the ECC, which allows both strains to increase smoothly at low-loading levels. After the measured strain value of the reinforcement reaches 6000 \(\mu\varepsilon\) and the measured strain value of the ECC concrete bottom surface exceeds 12,000 \(\mu\varepsilon\), the reinforcement yields and the specimen gradually loses its load-bearing capacity due to concrete cracking. At the yielding stage, the load-strain curve approaches a horizontal line, indicating the good ductility of the member.  
Figure 6: Load-deflection curve. (**a**) 1/2 span; (**b**) 1/4 span.
## 3 UAV Power Supply and Charging Mechanisms
### Battery Powered UAVs
#### 3.1.1 Battery Swapping  
The swapping process consists of recharging or replacing the UAV batteries and can be done conventionally or via hot swapping. In the conventional swapping method, the UAV whose battery is depleted has to leave its service location to the charging station and be replaced by an already charged UAV. The challenge with this method is that it requires several backup UAVs to be able to replace the UAVs whose batteries are depleted; as pointed out in [46], up to two standby UAVs may be needed per UAV to ensure continuous coverage for a commercial UAV that has a charging power of 180 V. However, the exact number of backup UAVs would depend on the UAV downtime, which comprises both the time taken for the UAV to fly to and from the charging station as well as that taken to recharge the battery to full capacity [43].  
In the hot swapping approach, the UAVs do not have to be powered off or remain in the charging station until the battery is recharged; instead, as soon as they reach the charging station, new batteries are quickly inserted into them so that they can return to their operating station immediately. The limitation of hot swapping is that it requires human involvement to replace depleted batteries with new ones. To address this challenge, automated battery swapping mechanisms have been developed whereby a robotic actuator can be used to remove and replace UAV batteries [47]. For swapping to be implemented effectively, a battery recharging station is required to recharge or replace depleted batteries. This recharging station can be located on cellular towers, footops of buildings, and specialized standalone pylons. There is also the need for multiple UAVs to ensure continuous service provision. In addition, a management system must be put in place to coordinate the battery recharging and replacement cycle of the swarm of UAVs [48].
## 2 Materials and Methods  
### Overall Framework  
We employed a supervised machine learning to fit a series of tree-based ensemble regression models to a set of domain concepts. This approach enabled familiarity prediction based on lexical features. Compared with other types of regression, tree-based ensemble models allow for more rapid computation and are appropriate when processing tabular data; this method yields results that are easy to explain [20; 21; 22]. Tree-based ensemble models are also insensitive to missing values, and our out-of-vocabulary problem in a pretrained word embedding model occasionally failed to vectorize a domain concept. Figure 1 depicts the flow of the research process in response to research questions.  
Initially, a collection of domain concepts was manually obtained from an online course and assembled into raw datasets. Then, we used advanced natural language processing (NLP) techniques to generate lexical features for the collected domain concepts and employed domain experts to determine these domain concepts' familiarity ratings. Consequently, an experimental dataset was established for predicting domain concepts' familiarity. To address RQ1, we examined lexical features' distribution characteristics and computed the Pearson's correlation coefficient (\(r\)) to test features' independence. With regard to RQ2, we evaluated how well our ensemble regression models predicted familiarity ratings using three steps. First, the experimental dataset was partitioned into a training dataset and a testing dataset in a ratio of 1:2. Second, to identify the best regression model, we tested six ensemble tree-based regression models [23] on the training dataset: adaptive boosting (AdaBoost), gradient boosting (GBoost), extreme gradient boosting (XGBoost), a light gradient boosting machine (LightGBM), categorical boosting (CatBoost), and a random forest (RF). We specifically used ten-fold cross validation with the randomized-search method to assess candidate models and tune their hyperparameters for model fitting and evaluation. We adopted common evaluation metrics [24] to compare these models' performance. Third, based on hyperparameter settings that led to the highest average validation scores, we retrained the optimal regression model on the entire training dataset.  
Figure 1: Workflow of our methodology for predicting domain concepts’ familiarity.  
Prediction outcomes were also collected for the test dataset to verify the chosen model's performance. To improve results' reliability, we again calculated Pearson's correlation coefficient (\(r\)) between domain concepts' predicted and human-judged familiarity ratings. This correlation coefficient signals the accuracy of predicted familiarity ratings [18]. To answer RQ3, the Shapley additive explanation (SHAP) method [25] was leveraged to illustrate the contributions of lexical features in model predictions and provide interpretability for familiarity prediction of the best regression model.
## 3 Autonomous Mapping and Navigation with Satellite Imagery and Cadastral Information
### Second Stage: Path Planning
#### 3.2.2 Determining the Reference Points  
Reference or control points contain well-known points and parts of the grove, e.g., the vertex of the orange tree lines or the middle of paths where farmers walk. A set of reference points will be used as intermediate points in the entire path that a robotic system should follow.  
Firstly, the vertexes of the orange tree rows are extracted. For each extracted line, the vertexes correspond to the first and last points that belongs to the path according to the _Path binary map Path_.  
Then, Hough transform is applied again to detect the two border paths perpendicular to the orange rows. With these two lines, their bisection is used to obtain additional reference points. These last points correspond to the centre of each path row (the path between two orange rows). Concretely, the intersection between the bisection line and the _Path binary map_ is used to calculate the points. After obtaining the intersection, the skeleton algorithm is applied to obtain the precise coordinates of the new reference points.  
However, under certain conditions, two central points have been detected for the same row, e.g., if obstacles are present at the centre of the path, the skeleton procedure can provide two (or more) middle points between two orange tree rows. In those cases, only the first middle point is selected for each path row.  
Finally, all the reference points can be considered basic features of the orange grove since a subset of them will be used as intermediate points of the collision-free path that a robotic system should follow to efficiently navigate through the orange grove. For that purpose, we applied Algorithm 8 to extract all the possible middle points in a parcel.  
```
Vertexes = Extract reference points from \(SortedTreeLines\) MiddlePaths = Extract reference points that belong to middle of path rows Return:Vertexes and MiddlePaths
```  
**Algorithm 8** ReferencePoints[Path SortedTreeLines]