esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Optimization of Buckling Problems for Panel Structures with Stiffening Characteristics
## 3 Optimization of the Panel with Ribs and Frames
### Method of the MMCs  
Due to the need for weight reduction, flight vehicles are designed with a large number of shell structures. When subjected to external forces, the panel shell structure can effectively disperse the force through its unique geometric shape, whether it is tension or pressure. However, complex stress conditions are generated during the execution of missions by flight vehicles, and pressure is coupled with inertial forces, which can cause structural buckling and failure. It is necessary to improve buckling resistance of the structure by topology optimization.  
Traditional implicit topology optimization methods have some numerical problems that have not been thoroughly solved. The optimization models established through these methods often have a huge number of dimensions. The MMC (moving morphable component) method presented by [PERSON] et al. [19] is an explicit topology optimization method that fundamentally avoids some of the numerical problems in implicit methods and reduces the number of dimensions in the optimization model.  
The basic concept of the MMC method is to use components as the basic modules that make up the structure, and to construct the optimal topology configuration similarly to when assembling building blocks. Figure 10 shows the process of building components in the ribs of the standard panel.
## 5 Statistical Quantative Parameters  
The results obtained with the proposed STATCOM operation of a seven level VSC with binary-weighted transformers is compared with the existing approaches shown in Table 3. With low harmonics for 11 kV, a 50 Hz distribution system used a single DC-Link capacitor with eight switches and two transformers. The proposed methodology is applied for a 132 kV, 50 Hz high voltage and high-power transmission system with less harmonic distortion achieved with the minimum number of switches and binary integrated transformers with a unique DC-Link capacitor. With a cascade H-bridge multilevel inverter, less harmonic distortion was achieved by compromising the number of switches to 12 and one extra-low voltage and low-frequency transformer. The harmonic distortion is minimized with a hybrid structure combination of two cells and H-bridges multilevel inverter with flying capacitors for a 400 V, 50 Hz distribution system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Current** & **Operation** & **\% Total Harmonic Distortion (THD)** \\ \hline System & Under inductive & 1.89 \\  & Under capacitive & 2.88 \\ \hline Load & Under inductive & 0.90 \\  & Under capacitive & 1.43 \\ \hline STATCOM & Under inductive & 2.80 \\  & Under capacitive & 4.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: THD at various currents.  
Figure 9: Performance of seven level VSC based STATCOM at terminal voltage variations.
## 2 Related Works
### Approaches  
All human pose estimation methodologies are classified into two parts--single-person and multiple-person estimation approaches. Single-person estimation approaches detects the pose of the subject (person) in an image; on the other hand, multiple-person estimation approaches detect the pose of all the subjects (persons) available in an image.  
Single-person estimation includes the following: CNN-based approaches, RNN-based approaches, hybrid approaches, and key point-based approaches. CNN-based approaches use convolutional neural networks (CNNs) to classify poses. A common method is to extract features from an image using a CNN and to then use a fully connected layer to classify the pose. One example of this approach is the work of [PERSON] et al. in 2016 [36], who used a CNN to classify yoga poses from images. RNN-based approaches: These approaches use recurrent neural networks (RNNs) to classify poses. This can be useful for classifying dynamic poses, or poses that change over time. One example of this approach is the work of [PERSON] et al. in 2017 [37], who used an RNN to classify dance poses from videos. On the other hand, Hybrid approaches: These approaches combine CNNs and RNNs to classify poses. This can be useful for classifying both static and dynamic poses. One example  
Figure 1: (**a**) COCO and MPII Keypoints; (**b**) Pairwise between key points.  
of this approach is the work of [PERSON] et al. in 2018 [38], who used a hybrid CNN-RNN model to classify poses from images and videos. Lastly, Key point-based approaches: These approaches use key point detection to classify poses. This can be useful for classifying poses from images and videos. One example of this approach is the work of [PERSON] et al. in 2018 [39], who used key point detection to classify poses from images and videos.  
For multiple-person estimation, two (2) common approaches are: Top-down approaches and Bottom-up approaches. Top-down approaches start with the processing from low to high resolutions, using a bounding box object detector to follow the detection of the subject/s (person/s) in the image then focuses on determining their succeeding poses [40; 41; 42]. However, the disadvantage of this approach is that when the early detection fails, then it will not be able to recover from it also, multiple subjects (persons) detection shows signs of vulnerability of this approach. Moreover, the more number of subjects/persons in an image, the higher the computational cost. This is because for every detection, a single-person estimator runs.  
On the other hand, bottom-up approaches processing from high to low resolutions. The first step of this approach is to locate all the key points in an image and then group these key points [43; 44; 45; 46; 47]. The disadvantage of this approach is when there is an overlapping of multiple subjects/persons.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 6 Discussion
### Future Development Trends
#### 6.4.3 Combination with Big Data  
In a construction project, there is a huge variety of available data. For example, monitoring records and various images taken at the construction site; files, records, data, and models generated in the early stage; information that can be further collected, such as the movement tracks of workers and equipment; data of other projects; and so on. However, the reality is that a large amount of useful information was collected and then discarded due to the inability to process the data promptly. How to use these abundant data at the same time to make the best judgment is a challenge.  
Big data technology is a technology that quickly obtains valuable information from various types of data. Many new technologies have emerged in the field of big data, and they have become powerful weapons for big data collection, storage, processing, and presentation.  
In the field of construction process monitoring, the data presents the following characteristics. First, there is a wide variety of information. In addition to imaging and laser scanning, many other technologies have also been applied for construction progress monitoring, such as barcoding, RFID, UWB, GIS, and GPS. Rich information will help reflect the actual state of the construction site in more detail. Second, images and videos occupy more memory than type data, with smaller density of useful information. It is very difficult to store these data reasonably and extract useful information from them. Third, there are obstacles in data sharing between different enterprises and different projects. In addition, it is attractive to summarize the general rules used in other projects from these daily images. With these characteristics, the stage is set for big data technology.  
Currently, due to the complexity of image processing, the application of big data in the field of AEC is still very weak. However, it is certain that employing big data could move the state of the art in the domain of construction progress monitoring to the next level [19]. In addition, big data analytics will enable massive data to be processed in time to reflect daily changes and update the BIM model and construction schedule accordingly.
# Chloroplasts at the Crossroad of Photosynthesis, Pathogen Infection and Plant Defense
## 2 The Chloroplast is a Major Synthesis Site for Many Plant Hormones
### The Chloroplast is Involved in the Synthesis of SA
While JA protects plants against necrootrophs and insect herbivores, SA is predominantly involved in plant defense againstotrophs and hemibiotrophs [10; 99; 100]. SA influences the production and signal transduction of other hormones (Figure 1). The activation of SAR by SA directly suppresses the expression of ABA biosynthetic and responsive genes [97]. This suppression is likely mediated by NPR1 or signals downstream of NPR1 [97]. In line with this hypothesis, NPR1 overexpression in rice significantly negated the enhancement of blast susceptibility by ABA [90]. As a hormone for defense against biotrophs, SA suppresses JA-ET defense responses and thus increases plant susceptibility against necrotrophs [10, 100, 99]. For example, SA and aspirin (the acetylated form of SA) were found to suppress ET biosynthesis and expression of JA/ET-inducible wounding-responsive genes [166, 170, 171, 172, 173]. Recent studies showed that SA suppresses JA-ET signaling downstream of the E3 ubiquitin ligase Skip-Cullin-F-box complex SCFCOII [174, 175, 176]. Ubiquitination is a strategy used by both the plant and the pathogen [177, 178, 179]. From the plant's point of view, ubiquitination-triggered degradation of pathogen proteins (e.g., effectors) is protective [177, 178, 179]. On the other hand, pathogens developed mechanisms to utilize or evade ubiquitination, to manipulate plant responses [177, 178, 179]. SA influences the production and signal transduction of non-hormone signaling molecules (Figure 1). A number of studies showed that SA application promotes ROS accumulation [180, 181, 182]. Consistent with this observation, genetic and pharmacological analyses demonstrated that high and low SA contents were strictly correlated with high and low foliar H\({}_{2}\)O\({}_{2}\) concentrations, respectively [163, 183]. How does SA induce ROS accumulation? SA was found to inhibit ROS scavenging enzymes, such as catalase and ascorbate peroxididases [180, 182, 183, 184]. Interestingly, evidence also showed that SA may promote ROS scavenging under stress conditions [163, 185, 186]. This is accomplished by inducing the accumulation of glutathione and reducing power [163, 187]. Taken together, SA has an ambivalent effect on ROS accumulation and scavenging [188], depending on treatments and growth conditions. SA promoted \(\cdot\)NO production by a \(\cdot\)NO synthase-like enzyme in _Arabidopsis_ on a dose-dependent manner [189]. SA treatment induced the increase in cytosolic Ca\({}^{2+}\) concentration in tobacco suspension culture cells [190, 191].
# Deep Learning Methods in Image Matting: A Survey
## 4 Deep Learning Methods
### Trimap-Free Methods
#### 4.2.1 Methods for Automatically Generating Trimaps  
Due to the complexity involved in creating trimaps, it is unfriendly to users that do not have knowledge of matting. As a result, many researchers have begun exploring methods for automatically generating trimaps to avoid user interaction. This approach makes image matting techniques more accessible and user-friendly.  
[PERSON] et al. [8] proposed a method for automatic portrait matting. The authors regarded the trimap prediction as a semantic segmentation task with three classes (i.e., background, foreground, and transition regions), using FCN [59] for prediction and facial landmark points to align the mask. Additionally, they proposed a matting layer that can perform forward and backward propagation, achieving an end-to-end automatic portrait matting system. [PERSON] et al. [8] only used a CNN model in the trimap generation stage, whereas [PERSON] et al. [10] utilized CNN separately for both the trimap generation and matting tasks. The authors also introduced a fusion module to preserve the semantic information of the foreground and background, allowing the results generated by two distinct networks (semantic segmentation and image matting) to blend with each other. [PERSON] et al. [22] proposed a new portrait matting method that differs from the sequential approach used by [8; 10]. The authors used Resnet50 [60] to serve as the backbone, which was coupled with the ASPP [61] module for the extraction of multi-scale features. These features were subsequently fed into the human pose network, trimap network, and matting network individually. With the semantic information generated by the human pose network assisting the trimap and matting networks, the authors achieved better results. The resulting alpha matte was more refined compared with previous methods. As trimap-free methods heavily rely on expensive alpha matte annotations, most of the existing methods are targeted at portrait matting. Therefore, [PERSON] et al. [31] proposed a framework for salient object matting that utilizes the U2 Net [62] network to generate a trimap for any salient object present in an image. This trimap is then passed to the matting model for further refinement, effectively solving the problem of previous trimap-free methods being only capable of single-object matting. As a result, this approach expands the application scenarios of trimap-free models.
# A Feasibility Study of the Use of PZT Actuators for Active Control to Enrich Engine Sound
## 3 Experimental Set-Up
### Implementation of the PZT-Plate Actuator for Sound Radiation  
The three PZT actuators were bonded on a component plate of the front cross member at the roof of the car, as shown in Figures 2 and 6. As shown in Figure 6, the front cross member used in this study was a bent metal structure with the dimensions of about 1000 mm \(\times\) 90 mm \(\times\) 2 mm (length \(\times\) width \(\times\) thickness). The component plate has an almost square shape with sides about 80 mm long and is positioned in the middle of the front cross member which is the mounting location of the rear mirror.  
This plate with the PZT was chosen after considerable inspection. In the process of choosing a proper PZT location, several critical factors were scrutinized. These included a sufficient magnitude of the structure-borne sound power output to satisfy the required target sound level, a suitable response of the secondary path and whether the causality condition was satisfied.  
As shown in Figure 6, one large (40 \(\times\) 20 \(\times\) 1 mm) and two small (30 \(\times\) 20 \(\times\) 1 mm) rectangular actuators were used to cover the component plate (this was because the middle part of the plate was dented.), and they were electrically wired to work as a single actuator. Their electromechanical specifications can be found in reference [24].
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 8 Prediction Analysis  
### Modeling Analysis  
The CatBoost and XGBoost models demonstrated strong performance when predicting the cycle for the test dataset, as evidenced by the actual versus predicted plots (Figure 12). The majority of the points are closely clustered around the ideal fit line, which represents perfect predictions.  
This indicates that the model accurately treated the underlying patterns in the data and made reasonable predictions across a wide range of values, particularly in the interval between 200 and 800, where the predictions of the models closely matched the actual values (Figures 12-15). However, outside this interval, it is remarkable how much less ability there is to predict, as shown by the models that can be improved by conducting a more accurate fatigue test and increasing the amount of data that can effectively provide the machine learning system with the means to train the models, thereby enhancing their predictive ability and reducing deviations from the ideal fit line.  
In the above figures the blue dots represent actual vs predicted values, where each dot corresponds to a test sample. The red line is the best-fit regression line, indicating perfect predictions if all points lie on it. The shaded region around the line represents confidence intervals or model uncertainty. If the blue dots are close to the red line, the model performs well; deviations indicate prediction errors. All models seem accurate but show some errors at higher N Cycle values, possibly due to overfitting or difficulty predicting extreme cases.  
Figure 12: Prediction plot for the best model with test data vs. actual data.  
Figure 14: Prediction plot for the best models with train data vs. actual data (Random Forest, Decision, Tree, XGBoost and CatBoost).  
Figure 13: Prediction plot for the best models with Val data vs. actual data (Random Forest, Decision, Tree, XGBoost and CatBoost).  
To further enhance the model's accuracy, particularly at these extremes, it would be beneficial to investigate the data points with the largest errors and assess whether they are outliers or represent unique patterns. Additionally, further hyperparameter tuning and feature engineering--including creating new interaction terms or applying transformations to the existing features--might help the model generalize better across the full range of N cycle values.  
Overall, the XGBoost and CatBoost regressor models show good predictivity power despite the quantity and the complexity of the dataset, offering an effective tool for fatigue life prediction tasks.
# Physico-Chemical Characterization of Sesame/Rapeseed Oil Mixtures
## 3 Results
### UV Spectral Analysis  
UV spectroscopy can provide information on the quality of the oil, its state of preservation and changes brought about by technological processes. The UV spectra for sesame oil, rapeseed oil and their mixtures used in the study are shown in Figure 3.  
It is known from the literature [42] that edible oils were characterized by more pronounced peaks between 230 nm and 290 nm due to the presence of conjugated dienes and trienes of unsaturated fatty acids. They are rich in linoleic and linolenic acids, which are oxidized by isomerized conjugated dienoic and trienoic acids. The conjugated dienes exhibit strong absorption at around 232 nm, while conjugated triene shows an absorption band of approximately 270 nm in vegetable oils.  
The results presented in Figure 3 show analog results. It is clearly seen from the figure that UV spectra for all investigated oils are characterized with two peaks at wavelengths 232 nm and approximately 280 nm. The hydroperoxides and the conjugated diene, which may result from its decomposition, show a strong absorption band at approximately 232 nm. The conjugated triene and the secondary oxidation products, particularly diketones, show an absorption band at approximately 280 nm. The similar results are obtained in [43].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Refractive Indices**} & **SO** & **5/95** & **10/90** & **30/70** & **50/50** & **70/30** & **90/10** & **RSO** \\ \cline{2-9}  & 1.4729 \(\pm\) & 1.4711 \(\pm\) & 1.4711 \(\pm\) & 1.4711 \(\pm\) & 1.4713 \(\pm\) & 1.4720 \(\pm\) & 1.4724 \(\pm\) & 1.4718 \(\pm\) \\  & 0.0001 \({}^{\mathrm{d}}\) & 0.0002 \({}^{\mathrm{a}}\) & 0.0002 \({}^{\mathrm{a}}\) & 0.0003 \({}^{\mathrm{a}}\) & 0.0002 \({}^{\mathrm{a}}\) & 0.0001 \({}^{\mathrm{c}}\) & 0.0001 \({}^{\mathrm{c}}\) & 0.0002 \({}^{\mathrm{b}}\) \\ \hline \hline \end{tabular}  
* The results were Mean \(\pm\) SD (n = 6). The small letters in a row mean differences in the results with a significant level of \(p\) < 0.05 (Duncan test).  
\end{table}
Table 6: Refractive indices measured by Abbe refracometer at 20 \({}^{\mathrm{\circ}}\)C.  
Figure 3: Spectra of absorbance of sesame and rapeseed oils and their mixtures.  
The results obtained also show that the values of the absorbance at 232 nm changed from 0.57 (sesame oil) and 0.36 (rapeseed oil) to the following values from studied mixtures: 0.37 (mixture 5/95), 0.38 (mixture 10/90), 0.40 (mixture 30/70), 0.61 (mixture 50/50), 0.64 (mixture 70/30) and 0.76 (mixture 90/10). It was established that the values of the absorbance increased with the mixing of the oils. They increased with increasing concentrations of sesame oil. This behavior occurs because the oxidation of polyunsaturated fatty acids is accompanied by the displacement of isolated double bonds for conjugated double bonds [44].
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 2 Problem Description
### Method Framework  
The main improvements introduced in this article are primarily focused on two aspects: 1. Improvement of the local search process in tabu search; 2. Improvement of the data transmission path planning method based on observation solutions. The method framework and the improvements in the method are reflected in Figure 2, and the process is as follows:  
1. Generate an initial solution using the initial solution generation method, and use this initial solution as the current solution. The process of the initial solution is described in Section 3.2;
2. Generate observational neighborhood solutions based on the current solution and the local search rules;
3. Determine the attitude maneuver constraint satisfaction of observation neighborhood solutions and generate the attitude maneuver history of neighborhood solutions. Calculate the inter-satellite data transmission time windows and satellite-ground data transmission time windows. Generate inter-satellite data transmission solutions \(y_{ik,l}\) and satellite-ground downloading solutions \(z_{kbm}\) through the data transmission plan method based on the observation neighborhood solutions. Calculate the satellite cluster's status and keep all solutions that satisfy all constraints. The data transmission path plan method is described in Section 3.4. The data transmission path plan method is also used in the initial solution generation method to generate the data transmission solution of the initial observation solution;
4. Evaluate the neighborhood solutions using the objective function value \(J\). If there is a historically optimal solution in the neighborhood solutions, it is selected as the current solution and updated in the algorithm; otherwise, evaluate the neighborhood solutions using the satellite cluster profit-state evaluation function and use the historically optimal solution or current non-tabu best solution based on profit-state evaluation values as the current solution. Details related to the improvement of the observation local search process are described in Section 3.3;
5. Update the tabu list;
6. Repeat steps 2-5 until the algorithm termination condition is met. Output the historically optimal solution.