esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[26] developed a spatio-temporal monitoring model of the center of mass based on a least-squares support vector machine by introducing the initial coordinates of the center of mass. [PERSON] et al. [27] used the kriging interpolation method to construct a high-precision horizontal space-time gradient expansion model of the core wall of the earth-rock dam, which effectively reflected the three-dimensional deformation trend of the dam. [PERSON] et al. [28] combined Kalman filtering and kriging spatial interpolation to construct a spatio-temporal model of dam deformation, efficaciously filtering out the noise of deformation data in time and space. [PERSON] et al. [29] proposed a geographically and temporally weighted regression (GTWR) model which improved the accuracy of data repair during the missing period of measuring points. These methods are limited by monitoring technology and analysis theory, and mainly consider the geometric location of measuring points, and cannot reasonably consider the influence of important environmental factors on the number of monitoring effects. The accuracy and physical meaning of the model need to be improved [30], and also there are relatively few application cases in dam safety monitoring data restoration. To sum up, a cokriging spatial model for repairing data based on variable importance for the projection method was proposed in this paper. The model could identify the important environmental impact factors of different parts, and combined with the spatial layout characteristics of the same monitoring effect variables to construct a one-dimensional, two-dimensional, or three-dimensional spatial model with the measured values of the measuring points as the main variables and the important environmental impact factors as the covariates. The expansion of dam safety monitoring data to the whole spatial area of the dam effectively solves the problem of the existing spatial model being unable to take into account the influence of environmental quantities and the selection of environmental impact factors relying on experience, which can significantly improve the accuracy of repairing abnormal or missing data and ensure that safety monitoring data can truly reflect the safe operation of the dam.
## 2 Materials and Methods
### Methodology  
#### 2.5.1 ML Methods  
Four ML methods, including random forest (RF), support vector machine (SVM), convolutional neutral network (CNN), and extreme learning machine (ELM), were applied and compared in this study.  
RF [67] is a machine learning algorithm that introduces randomness based on bagging ensemble learning [68]. It is widely used in the GPP estimation field [69; 70; 71]. The bootstrap method [72] is adopted to randomly and repeatedly extract training sample sets to form a forest containing \(m\) decision trees, thus producing \(m\) different results. The result of the final model is determined according to the voting method. The samples that are not extracted are called out-of-bag data, which are used to calculate out-of-bag error (OOB) and evaluate the model's generalization ability. The generalization error of an RF model comes to reach the minimum as the number of trees increase. RF is also able to rank the importance of model input variables. As the importance value increases, the input variable has a greater impact on the output.  
SVM is a tool for multi-dimensional function estimation and widely used in regression and classification [73; 74; 75]. Its principle is to map an input vector to a high-dimensional space to transform nonlinear regression into linear regression. Its superior generalization ability compared to traditional statistical methods [76] may make it more feasible to achieve satisfactory results in cross-site validation. This study chooses the penalty factor (\(c\)) and the parameter of radial basis kernel function (\(g\)) as the model parameters to set up the model.  
CNN is a deep learning algorithm composed of input, convolutional, pooling, and fully connected layers [77]. The convolutional layer contains multiple convolutional kernels: a one-dimensional (1-D) convolutional kernel is used for ordinal data such as time series data, a 2-D convolutional kernel is used for images, and a 3-D kernel is used for video and 3D images. CNN's hierarchical structure gives it the flexibility and applicability for a variety of complex regression tasks [78]. Compared to ANN, CNN demonstrates quicker learning of complex problems through weight sharing, enabling greater parallelization [79]. However, the accuracy of CNN's learning relies on a large amount of data, often in the hundreds or thousands of data points. Fortunately, we have enough training data (N = 4208) to make up for that.  
ELM is a new type of feedforward neutral network algorithm, consisting of input, hidden, and output layers [80]. The connection weight between the input layer and the hidden layer as well as the neuronal threshold value of the hidden layer are not adjusted after setting. To optimize the model, just the number of neurons in the hidden layer and the activation function need to be adjusted. Additionally, ELM converges significantly faster than traditional algorithms because there is no need for iterative learning [81]. In comparison to traditional networks requiring training all parameters, ELM simplifies the attainment of a global optimal solution using random parameters [81].
## 2 Materials and Methods  
Video classification involves the assignment of one or more labels to a video based on its content. The utilization of NNs for video classification has gained popularity due to their ability to learn intricate patterns within the data. CNNs are particularly suitable for video classification tasks as they can effectively capture spatial and temporal features. CNNs consist of multiple layers of neurons that are trained to extract relevant features from images or video frames.  
Typically, NNs used for video classification are trained on a large dataset of labeled videos. These videos are partitioned into training and validation sets to assess the performance of the model. To increase the diversity of the training data, various data augmentation techniques, such as flipping, rotating, and scaling, can be employed. Additionally, transfer learning can expedite the training process by utilizing pre-trained models that were previously trained on different datasets.  
However, video classification poses challenges due to variations in lighting conditions, camera angles, and object appearances. Domain adaptation techniques can address these challenges by transferring knowledge from a source domain to a target domain.  
The application of video classification using NNs spans various fields, including surveillance, entertainment, and healthcare. In healthcare, video classification can aid in the analysis of medical videos such as endoscopic videos, facilitating the detection of abnormalities and assisting in diagnosis. The evaluation of video classification performance using NNs can be measured using metrics such as accuracy, precision, and recall. The choice of evaluation metric depends on the specific application and the relative importance of different types of errors. Overall, video classification using NNs is an actively evolving field with numerous promising applications and associated challenges.
## 4 Results
### Agreement between Landsat and MODIS ET  
As RF outperformed SVR and it produced similar or slightly better accuracy than Cubist, it was used to generate the downscaled 30 m ET and Landsat modeled 1 km ET. In order to evaluate the overall agreement of Landsat downscaled ET against MODIS ET, the 30 m product was aggregated to 1 km grids (Landsat-aggregated 1 km ET), and RMSE and rRMSE were calculated for each site. As shown in Figure 6, RMSE of Landsat-aggregated 1 km ET was within 2 mm/8 days and rRMSE within 23% at Site 1. Site 2 produced slightly higher RMSE (0.8-2.9 mm/8 days) and rRMSE (15%-26%). Korean sites have higher RMSE and similar rRMSE as US sites. The average rRMSE from the four sites is 22%. Sites 1 and 2 showed obviously similar temporal trends, as the aggregated ET had a decreasing agreement with MODIS ET during the growing season compared to leaf-off season. The agreement was slightly lower than, if not similar as, that between the Landsat modeled 1 km ET and MODIS ET shown in Figure 4. This means that the model performance could be used as an indicator to explain the resultant accuracy of downscaled ET with MODIS ET as reference data.  
Figures 7 and 8 show time series of the ET maps at the northeast part of Site 2 (US-Twt) and the southwest part of Site 4 (Cheongmicheon) through 2013 and the corresponding land cover types. Both Landsat modeled 1 km and Landsat downscaled 30 m ET showed very similar spatiotemporal patterns as MODIS ET. At cropland, which is mainly irrigated in Site 2, ET increased from April to July, and then decreased until the end of the year. ET in grassland showed a decreasing trend since April. As grass growth in this area is mainly controlled by the amount of precipitation, it is expected that ET is lower during the hot and dry summer. Spatial variation of ET at cropland was higher than the other vegetated land covers for all three products, and at cropland the Landsat downscaled product showed even more detailed spatial distribution of ET than MODIS and the Landsat modeled 1 km ETs. Since only three dates were available for Site 4, the temporal patterns of ET were not captured in details. However, ET at cropland was a bit lower than that in forests in the middle of the growing season (_i.e.,_ June) while ET was very low during the off-growing season (_i.e.,_ November) regardless of land cover type. The good agreement between Landsat and MODIS ET in terms of spatial and temporal patterns proved the potential of our downscaling approach.  
Figure 6: RMSE and rRMSE (%) of Landsat-aggregated 1 km ET against MODIS ET: (**a**) RMSE and (**b**) rRMSE (%) for Sites 1, 2, 3, and 4.  
Figure 7: MODIS 8-day ET, Landsat 1 km ET and downscaled Landsat 30 m ET in the northeast part of Site 2 (refer to Figure 2b blue outline for the area boundary) during the year of 2013.
## 3 Results
### RQ6: What Methods Are Used to Evaluate the Outcomes?  
The outcome measurement method is used to record the results or outcomes in the mobile AR intervention. According to [PERSON]. [PERSON], there are four main types of measurement: survey research, scaling, qualitative research and unobtrusive measures [84]. Among the primary studies, four main outcome measurement methods have been used, and these methods include an interview, questionnaire, observation and scaling. In total, 36.1% of the papers utilized a combination of different methods for measurement, while 16.7% of the studies did not report it, and the others used a single measurement method. More specifically, 17 studies used survey research (8 for questionnaires and 9 for interviews) along or with other measurement methods, 11 studies applied qualitative observation research, and 14 applied scaling. Survey research includes the design and implementation of interviews and questionnaires, and scaling involves consideration of the main methods of formulating and implementing scales. Qualitative research outlines a wide range of non-numeric measurement methods.
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 3 Identification of CSC
### Side Population  
Another distinguishing characteristic of CSCs is their capability to actively expel chemotherapeutic drugs from within the cell, in a process mediated by drug-efflux proteins of the ATP-binding cassette (ABC) family. The term \"side population\" (SP) refers to the fraction of cells that eliminate the Hoechst 33342 dye (DNA binding dye) via the ABC transporter and therefore appears as a distinct tail or side branch in flow cytometry analysis [90]. Compared to the main non-SP population, the SP subset is characterized by tumor-initiating capacity, expression of stem cell-related markers, and resistance to chemotherapeutic. For all these reasons, this method is commonly used to identify and isolate CSCs [91]. The side population subset of HNSCC cells was found to be characterized by increased in vitro clonogenic potential, invasiveness, and resistance to chemotherapeutic, as well as enhanced tumorigenicity in vivo [92; 93; 94].  
Among ABC transporters, ABCG2 has been considered a universal marker of CSCs, and its high expression has been observed in different types of malignant tumors, including HNSCC [43; 95]. [PERSON] et al. have reported that ABCG2 levels vary among the different HNSCC cell lines as well as the tissues from different types of HNSCC carcinoma, including laryngeal, hypopharyngeal, and nasopharyngeal cancers [95]. Furthermore, ABCG2 expression strongly correlated with the multidrug resistance against various chemotherapeutics in HNSCC cell lines, while a high positivity of ABCG2 was significantly associated with the TNM stage and lymph node metastasis in laryngeal, nasopharyngeal, and hypopharyngeal cancers, suggesting that ABCG2 expression may be a clinically important mechanism of drug resistance [43; 95].
# Deep Learning and Face Recognition: Face Recognition Approach Based on the DS-CDCN Algorithm
## 1 Introduction  
Face recognition (FR) technology has grown in importance and is now a commonly used biometric identification tool across many industries thanks to the ongoing advancements in science and technology [1]. The goal of FR technology is to analyze and process face images (FIs), extract facial feature information, and compare it with faces in the database to confirm or verify identity [2]. However, in practical applications, FR techniques face a number of challenges, such as lighting changes, posture changes, occlusion, and expression changes [3]. To address these challenges, researchers have proposed various methods for FI. Significant advancements in the field of deep learning (DL) have been made in recent years due to the rapid growth of DL technology. DL is a learning method that simulates the neural network of the human brain, enabling it to automatically learn feature representations from large amounts of data through a multilevel neural network model [4]. Compared to traditional FR methods, DL algorithms have better robustness and accuracy. However, due to the specificity of the FR domain, traditional DL models cannot be directly applied to FR tasks [5]. Therefore, this study proposes an improved algorithm called deep separable center difference convolutional networks (DS-CDCN), which enhances the accuracy and robustness of FR by introducing techniques such as variable convolution and depth-separable convolution for modeling local features in FIs. The primary contribution of this thesis is the combination of the DL technique with FR to propose a new FR method. The effectiveness of this method has been experimentallyproven, which is crucial for improving the accuracy and robustness of FR. The study is divided into four parts: a summary of related studies, the design of the FR method, the validation of the FR method, and the conclusion of the study.
## 1 Introduction
The search-based algorithm has a good performance in the case of fewer constraints and a small-size problem. However, once the problem constraints increase or the problem size becomes larger, the search efficiency will drop sharply, which is not conducive to the practical application. Although the artificial potential field method can quickly solve the optimization problem under multi-constraint conditions, it is easy to fall into the local optimal solution and sacrifice the stability. AI-based algorithm solves the path planning problem, multiple constraints can be considered in the optimization model, and the better feasible solutions satisfying the constraints are obtained in an acceptable time. However, real-time performance and robustness of such intelligent algorithms still seriously restrict the extension and utilization of AI-based algorithms. In this paper, a path planning model was proposed to implement the application in the large-scale complex marine environment, and the online learning swarm hyper-heuristic algorithm was proposed to meet the requirements of the real-time performance and robustness. There are two main contributions in this paper. Firstly, the proposed path planning model which combines the global path planning with the local path planning is suitable for the large-scale complex marine environment. After considering the various constraints, such as the energy/time consumption, the turning radius limitation, the marine environment, and the ocean current, the path planning was abstractly modeled as a multi-objective optimization model with the time cost, the curvature cost, the map cost, and the ocean current cost. Secondly, the swarm hyper-heuristic algorithm (SHH) with the online learning ability was proposed to solve the proposed path planning model with real-time performance and stability. The structure of this article is as follows. The autonomous decision-making system is described in Section 2. Then, in the following Section 3, the modeling method of AUV path planning is introduced. Following this, the swarm hyper-heuristic algorithm is proposed to solve this problem in Section 4. Finally, the results of different algorithms are compared and analyzed to verify the correctness and effectiveness of the model.
## 2 Theoretical Modeling  
### Nonlinear Forces of the SFD  
A recent experimental investigation [5] has demonstrated that the short bearing approximation of the SFD shows some consistency with experimental data. Therefore, the short bearing assumption is adopted in this work to incorporate the SFD with the rotor system. The nonlinear forces of the SFD are given in [20,21]:  
\[\begin{cases}f_{x}^{sfd}=-\frac{\mu_{x}Rk^{3}}{c^{2}(x^{2}+y^{2})^{1/2}}\left[x \left(\dot{e}I_{2}+\dot{e}\dot{y}I_{1}\right)-y\left(\dot{e}I_{1}+\dot{e}\dot{y }I_{3}\right)\right]\\ f_{y}^{sfd}=-\frac{\mu_{x}Rk^{3}}{c^{2}(x^{2}+y^{2})^{1/2}}\left[y\left(\dot{e}I_ {2}+\dot{e}\dot{y}I_{1}\right)+x\left(\dot{e}I_{1}+\dot{e}\dot{y}I_{3}\right) \right]\end{cases} \tag{1a}\] \[\dot{e}=\sqrt{x^{2}+y^{2}}/c\] (1b) \[\dot{e}=\left(x\dot{x}+y\dot{y}\right)/\left(c\sqrt{x^{2}+y^{2}}\right)\] (1c) \[\dot{\psi}=\left(y\dot{x}-x\dot{y}\right)/\left(x^{2}+y^{2}\right)\] (1d) \[\tan\psi=y/x. \tag{1e}\]  
\(x\) is the horizontal displacement of the journal, and \(y\) denotes the vertical displacements. \(I_{j}\) (\(j=1,2,3\)) denotes the Sommerfeld integrals. \(c\) is the radial clearance. \(\mu_{s}\) denotes the dynamic viscosity of the film of the SFD. \(L\) and \(R\) are the length and radius of the SFD, respectively.
## 2 Materials and Methods  
The schematic of the laser fusion bonding (or welding) of CFRP and zinc-coated steel is shown in Figure 1. Commercially available 1.6-mm thick tri-axial (0\({}^{\circ}\)/\(-\)60\({}^{\circ}\)/\(+\)60\({}^{\circ}\)) non-crimp fabric with tricot counter-stitch and a polyetheretherketone (PEEK) matrix was used for the CFRP sample (manufacturer: SAERTEX(r) GmbH & Co., Saerbeck, Germany, KG), while 2.6-mm thick commercially available zinc-coated DP 590 steel sheets with 43.8 g/m\({}^{2}\) of zinc coating applied on each side were used as the joining counterpart. The roughness was measured using a surface profiler (Mitutoyo SJ-400), and the values for the CFRP and the steel sheets were Ra = 0.29 \(\upmu\)m, Ry = 2.53 \(\upmu\)m and Ra = 1.21 \(\upmu\)m, Ry = 8.83 \(\upmu\)m, respectively. A continuous mode Yb:YAG disk laser HLD 4002 (Trumpf, Ditzingen, Germany) was used for the laser welding process. The laser beam was delivered through an optical fiber with a diameter of 200 \(\upmu\)m and optic system PFO33 (Trumpf, Ditzingen, Germany). The delivered beam was collimated (length of collimation: 150 mm) and focused (focal length: 450 mm), which resulted in a focal diameter of 0.6 mm with a beam quality (beam parameter product) of 8.5 mm-rad. The welding was done on a custom-made jig (shown in the bottom of Figure 1) with a clamping pressure of \(P_{c}\). No shielding gas was used in this study.  
The rectangular laser pattern on the zinc-coated steel surface was produced using a nanosecond pulsed fiber laser (IPG YLPN-1-20 \(\times\) 120-100) operating at 1064 nm. The laser was set to produce \(\sim\)120-ns pulses at a frequency of 50 kHz. The average output power was varied between 8 and 45 W. The beam shape was near-Gaussian, and at focus, the beam spot size of full width 1/e2 maximum (FW1/e2M) was approximately 125 \(\upmu\)m in diameter. A two-mirrored galvanometric scanner and an X-Y-Z motion stage were used to control the position and the motion of the laser beam spots for the production of the rectangular grid pattern shown in Figure 2.  
The tensile shear tests, to quantify the joining strength of the laser-welded samples, were performed using a Shimadzu AG-300 KNX Universal Testing Machine (maximum load capacity of 30 tons). The dimensions of the specimen and the test procedure conform to the ASTM D 5868 standard, and the schematic of the tensile test specimen is shown in Figure 1. Three samples for each condition were prepared, and the results were averaged. The crosshead speed was 5 mm/min for all the tensile shear tests.
## 1 Introduction
Consequently, the goal of this research is to develop a robust control strategy for robotic manipulators based on an adaptive neural non-singular fast-terminal sliding-mode control (ANNFTSMC) scheme. The main advantages of the suggested control strategy include:  
* The inheritance of NFTSMC advantages in terms of non-singularity, finite-time convergence, fast transient response, low steady-state errors, and high position tracking accuracy. * The achievement of smooth control inputs with chattering behavior elimination. * The removal of demand for an exact dynamic model by applying an adaptive radial basis function neural network to approximate an unknown robot function. * Better tracking performance and less impact by disturbances and uncertainties compared to classic SMC and other control methods based on TSMC. * Improved robustness and stability of the robot system, as demonstrated by Lyapunov theory. The remainder of the report is structured as follows. Following the introduction, the problem statements are presented, succeeded by the design approach for the proposed control strategy, where the proposed strategy is utilized to allow joint position tracking control simulation for a 3-degree of freedom (3-DOF) robot manipulator. Here, its tracking performance is compared with SMC and TSMC to analyze the effectiveness of the proposed control strategy. Finally, conclusions are presented.
###### Abstract
The output stage utilizes a dual closed-loop control strategy modulated by the SVPWM algorithm based on a 120\({}^{\circ}\) coordinate system. Although the three-level circuit is more complex in topology, it exhibits significant advantages over traditional two-level inverter circuits, such as each switch bearing only half of the DC bus voltage, allowing the use of devices with lower voltage ratings and offering the benefits of low output harmonics, low voltage stress, and high efficiency [28; 29; 30; 31; 32]. The rest of this article is as follows. The second section of this paper presents the topology of high-frequency isolated NPC three-level inverter frequency conversion and speed regulation and analyzes the working principles of the system. The third section analyzes the control strategy of the system. The input stage employs a three-phase uncontrolled rectification control strategy, the intermediate stage adopts EPWM control, and the output stage is controlled using a dual closed-loop control SVPWM modulation strategy. The fourth section conducts simulation analysis of the system. The fifth section describes the experimental platform for the high-frequency isolated NPC three-level inverter frequency conversion and speed regulation, the experiments, and the experimental results. Finally, the sixth section provides a summary. Figure 2: The traditional basic topology of three-phase dual PWM voltage-source inverter-based frequency conversion for speed regulation.