esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Methods and Algorithms
### Path Planning and Trajectory-Pre-Processing  
Having ensured that the necessary joint angles of the path are reachable by the robot, the question appears if a sequence of joint angles can be executed at a given printing speed. This question is referred to manipulability and is treated within this section. The corners in a perfect zigzag pattern require the robot's joint angles to change in an infinitesimally short period of time. Since that is physically impossible, the quasi unsmoothed zigzag path is defined as a zigzag path with a 0.1 mm corner radius and is hereinafter used for benchmarking. Furthermore, the index \(k_{\omega,i}\) is defined as the maximum occurring angular velocity of a joint divided by the critical angular velocity limit of said joint. A reasonable printing speed for WAAM processes is \(14\,\mathrm{mm}\,\mathrm{s}^{-1}\). Using this speed, the \(k_{\omega,i}\) values for the quasi unsmoothed path are indicated in Table 3 when filling a 120 mm by 80 mm rectangle with a 10 mm path width. This path is not manipulable because several \(k_{\omega,i}>1\). To ensure  
Figure 16: Weights calculation of: (**a**) \(g_{reachability}\); and (**b**) \(g_{geometry}\).  
manipulability, the corners need to be smoothed although the slice coverage should not suffer as a result. Therefore, a smoothing algorithm (cf. Figure 17) is introduced which is based on Bezier curves [36] and includes two parameters: a bump factor \(\Phi\) and a smoothing factor \(\Psi\).  
Before creating the actual curve, the Bezier supporting points (green in Figure 17) need to be determined. For each set of three consecutive path points (blue in Figure 17), three supporting points are calculated: do this, an isosceles triangle is created first. The first Bezier supporting point is determined by extending the center line of this triangle by \(\Phi\cdot d_{path}\). The second and third supporting points lie on the path lines, \(\Psi\cdot d_{path}\) from the second path point, although they can never be further away than half of the path segment length. With these supporting points, a Bezier curve is created. Using this algorithm, the limit cases shown in Figure 18 appear.
## 2 Materials and Methods
### Testing Equipment and Measurement Parameters  
The emission of non-volatile PN is monitored with an on-board PEMS (MAHA-AIP GmbH & Co. KG, Haldenwang, Germany). Before each test drive, the obligatory leak check of the condensation particle counter (CPC) is performed in the form of a particle zero check with the use of a high-efficiency particulate air (HEPA) filter. The zero check of CPC systems should be at least nearly 0 #/cm\({}^{3}\)[17]. Regarding the measurement accuracy, the study from [10] includes a PEMS device from the same manufacturer and reveals that the accuracy for measuring sub-23 nm PN is within \(\pm\)20-40%. This is a promising level of accuracy as the differences of the reference laboratory systems themselves can reach up to 30% (ibid.).  
Additionally, an exhaust flow meter (EFM) is mounted to the vehicle hitch to measure the exhaust mass flow, so that the emission concentrations can be converted to absolute mass emissions. The built-in CPC of the PEMS features a cut-off diameter at 23 nm.  
In addition, an EEPS (TSI Inc., Shoreview, MN, USA) is operated onboard. The EEPS measures the particle size distribution of particles in the range 5.6-560 nm in 16 channels at 10 Hz. The aerosol is dried with a diffusion dryer before entering the instrument. Pretests show that a dilution of the aerosol is not necessary. The EEPS is calibrated by the manufacturer before the campaign using an emery oil standard. Before each drive, the electrometer currents are zeroed to assure low-noise conditions.  
A CLD700 NOx analyser (range 0-1000 ppm) from Eco Physics AG (Duernten, Switzerland) is used for NOx measurements. The NOx analyzer is calibrated before each drive using a NOx calibration gas (single point calibration) and nitrogen gas as zero air.  
The EEPS, the NOx analyser and the extra battery for the energy demand of the devices account for an additional weight of approx. 100 kg. Further, the vehicle is equipped with a 12 V measurement system that records multiple vehicle internal, electrical, thermal fuel-side and GPS parameters, including 17 temperatures, 8 electric currents, 5 voltages as well as humidity, air pressure and fuel mass flow measurements. Fuel consumption is measured using a flowtronic sensor series S8005 (Gregory Technology GmbH, Mainz, Germany), which is an external sensor. The measuring range is between 0.1 L/h and 250 L/h and the measuring accuracy is \(\pm\)0.5%. Figure 2 gives an overview on the measurement arrangement and the measured values.  
Table 2 summarizes the used measuring systems and the respective measuring parameters, which are relevant for the subsequent data analysis and results presented in this article.
# NURBS-Enhanced Meshfree Method with an Integration Subtraction Technique for Complex Topology
## 1 Introduction
The scaled boundary finite element method (SBEM), introduced by [PERSON] and [PERSON] [30], shares the advantages of FEM and the BEM. Like in the FEM, no fundamental solution is required and like in the BEM, the spatial dimension is reduced by one, which would greatly decrease the freedom of degree. Furthermore, with the combination of IGA and SBEM, applications to problems in complex geometry have been made by [PERSON] [31] and [PERSON] [32]. Moreover, some three dimension problems with singular [33] or nonsingular [34] boundary element method have also been investigated. The SBEM still requires a subdomain (having the same role as an element in the finite element method) to satisfy the scaling requirement (i.e., the whole boundary is directly visible from a point) in the discretization process [35], which is still a challenge work in the adaptive problems for complex two dimension domain and even three dimension domain. The method we present here is the application of an integration subtraction technique to the meshfree method. The meshfree radial point interpolation method (RPIM) is chosen for its quick implementation. In the integration subtraction technique, the predesigned domain can be meshed as an initial integration background cell using one of many methods, while the awaiting designed hole is meshed using a homotopic method (such as the discretization idea in the scaled boundary method [32]) and subtracted from the initial integration background (short for RSUB), as illustrated in Figure 1. Unlike the scaling requirement in SBEM, there are no limitations for the shape of hole, which means convex and concave holes are all available, as the concave part would be cancelled out in the circle subtract integration process of the hole. Therefore, when the problems are expanded from two dimension to three dimension, the mesh for holes can still be easily generated, and no local refinement is needed with this homotopic meshing strategy. With this subtraction technique, the trimming curve used in CAD models to describe arbitrary topology can be directly applied in analysis procedures. As the integration of the new hole is calculated, the relevant part of the stiffness matrix can be directly subtracted from the global one to construct the new stiffness matrix with no change in size. Then, if the hole is changing, a more efficient moving subtraction technique (short for RMOV) is developed in which only the changing integration part between the old and new boundaries of the hole needs to be recalculated and subtracted. This will be discussed in detail later in Section 3.2.5. This paper is organized as follows. Section 2 provides the basic theory about the NURBS and RPIM meshfree methods. In Section 3, the NURBS-enhanced meshfree method and the integration subtraction technique are introduced. Section 4 provides numerical examples. In Section 5, final conclusions are presented.
## 3 Performance Analysis of iPyroxy Block
### S Parameter Using Vector Network Analyser  
The iPyrovy block within a specific frequency range may be thoroughly understood by examining its S-parameters (Scattering Parameters) [47]. Beginning at 1.000 GHz, the study is conducted up to 15.000 GHz, which is the breaking point. The amplitudes and phases of incident and reflected signals at various network ports are related by complex coefficients called S-parameters. In this instance, reference plane 1 is set 3.276 cm from the origin, whereas reference plane 2 is 3.215 cm away. To correctly depict the electrical properties of the iPyrovy block throughout the designated frequency spectrum, these  
Figure 4: Absorption value of iPyrovy block.  
reference planes, which identify the locations at which the S-parameters are measured, are necessary. The block's signal integrity, transmission, and reflection characteristics will be better understood by examining the S-parameter data collected at the designated reference planes and throughout this frequency range.
# Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models
## 4 Spatiotemporal Prediction Models
### Deep Learning Models for Spatiotemporal Prediction  
We compare the temporal prediction algorithms with the spatiotemporal prediction algorithm to analyze how the prediction performance changes with and without spatial information. In this paper, we use convLSTM as a spatiotemporal prediction algorithm.  
ConvLSTM is a network structure that can be employed to predict spatiotemporal data by applying convolution to a fully-connected LSTM structure. The LSTM cell structure itself does not change much. However, the most significant difference is that the input datum is not a vector but an image, and the convolution is added to the LSTM internal operation. The convLSTM is presented as follows [8].  
\[i_{t}=\sigma(W_{xi}*x_{t}+W_{hi}*h_{t-1}+W_{ci}\bigotimes c_{t-1}+b_{i}) \tag{11}\]  
\[f_{t}=\sigma(W_{xf}*x_{t}+W_{hf}*h_{t-1}+W_{cf}\bigotimes c_{t-1}+b_{f}) \tag{12}\]  
\[o_{t}=\sigma(W_{xo}*x_{t}+W_{ho}*h_{t-1}+W_{co}\bigotimes c_{t-1}+b_{o}) \tag{13}\]  
\[g_{t}=tanh(W_{xg}*x_{t}+W_{hg}*h_{t-1}+b_{g}) \tag{14}\]  
\[c_{t}=f_{t}\bigotimes c_{t-1}+i_{t}\bigotimes g_{t} \tag{15}\]  
\[h_{t}=o_{t}\bigotimes tanh(c_{t}), \tag{16}\]  
where \(Ws\) are the weight matrices for the layers, and \(b_{f}\), \(b_{i}\), \(b_{o}\), \(b_{g}\) are the biases of the layers. The \(\bigotimes\) is element-wise matrix multiplication, and \(*\) represents a convolution operation. The input datum is convoluted in image form. In this model, the spatial information is incorporated in the convolution operation, and the recurrent structure of the LSTM incorporates the temporal information.
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 2 Methodology
### Model Development  
As shown in Figure 6, the proposed artificial intelligence model is intended to seek the best fit that yields the best results for deployment purposes. Input data for the model consist of historical rainfall data from 11 telemetry stations at the upper catchment of the Klang River basin taken from 1 January 2008 to August 2021 with an interval of 30 min. Moreover, the target data consist of flow data at the confluence between the Ampang-Klang rivers with equal intervals and similar time ranges. The confluence is considered the point of interest in this study as the current flow will determine the mode of operation, as mentioned earlier. Three steps of model development are introduced to pursue the best relationship between historical data and predictors.  
Step 1 employs the LSTM model as the deep learning framework for streamflow prediction, and ANN is the benchmark model. Several performance indices are performed to compare the models.  
Step 2 introduces the novel rates of change method and implements multi-step ahead forecasting to analyze the results better. The models' performance on fitness and errors are checked.  
Step 3 develops the novel optimization method for deep learning using meta-heuristics to find the near-optimum weights and biases. Three optimization algorithms were picked for this study: bat algorithm, firefly algorithm and Java algorithm. After going through the optimization algorithm, the data are fed into the LSTM model. Performances on fitness and errors are checked. The best model is deployed after the three steps.
# LSRM: A New Method for Turkish Text Classification
## 4 Application of the Model
### Experimental Study, Analysis, and Results
Hence, these findings were similar to the results from the ML and DL algorithms, indicating that the proposed LSRM method improved the average classification success rate. The tests performed in the study also revealed that the specialized BERT version for the Turkish language achieved an average of 12% higher success rate than the regular BERT algorithm. Table 9 presents the results of the TL algorithms. Figure 2 displays all the test results, indicating that the DL and TL algorithms generated the best outcomes. The indicate (*) is an algorithm performance in Figure 2.  
Figure 3 displays the comparison table of the dataset success rates. Accordingly, assessing the dataset results in terms of average ACC values indicated that the additional use of the LSRM method increased the average success rate instead of using the other algorithms alone. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Alg Name**} & \multicolumn{3}{c}{**Original**} & \multicolumn{3}{c}{**LSRM**} & \multirow{2}{*}{**Precision**} \\  & & **ACC** & **FM** & **Precision** & **Recall** & **ACC** & **FM** & **Precision** & **Recall** \\ \hline \multirow{3}{*}{TTC-3600} & BERT & 0.844 & 0.846 & 0.845 & 0.850 & 0.840 & 0.841 & 0.841 & 0.842 \\  & RoBERTa & 0.823 & 0.824 & 0.824 & 0.830 & 0.855 & 0.857 & 0.857 & 0.859 \\  & BERTurk & **0.947** & **0.947** & 0.946 & 0.950 & 0.940 & 0.941 & 0.940 & 0.942 \\ \hline \multirow{3}{*}{Ulakbim-v2} & BERT & 0.888 & 0.889 & 0.883 & 0.891 & 0.900 & 0.900 & 0.900 & 0.901 \\  & RoBERTa & 0.875 & 0.876 & 0.877 & 0.876 & 0.883 & 0.884 & 0.883 & 0.885 \\  & BERTurk & **0.968** & **0.968** & 0.968 & 0.969 & 0.958 & 0.958 & 0.958 & 0.958 \\ \hline \multirow{3}{*}{TbTT} & BERT & 0.635 & 0.632 & 0.641 & 0.630 & 0.730 & 0.726 & 0.731 & 0.726 \\  & RoBERTa & 0.602 & 0.597 & 0.595 & 0.597 & 0.722 & 0.715 & 0.729 & 0.717 \\  & BERTurk & 0.895 & 0.891 & 0.891 & 0.892 & **0.917** & **0.914** & 0.913 & 0.919 \\ \hline Average & & 0.831 & 0.830 & 0.830 & 0.831 & 0.860 & 0.860 & 0.861 & 0.861 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Experimental results for the TL algorithms. Figure 2: Boxplot of ACC.
## 3 Research about Impact of AI on Education
### Applications for Academic Libraries  
Libraries are traditionally the heart of academic institutions, providing a wealth of resources and a space for learning. With the advent of AI, academic libraries are beginning to undergo a transformation.  
Current AI applications in education have the potential to transform academic libraries significantly. [PERSON] et al. [46] suggest that AI could impact various aspects of libraries, including search and recommendation systems, personalization, text and data mining, and analytics. Such technologies could enhance user experiences and the overall functionality of library spaces.  
Although there is some hesitancy in academia regarding AI adoption, research by [PERSON] et al. [47] indicates that academic librarians are generally receptive to integrating AI into their operations. They are often early adopters of new information and communication technologies. The study by [PERSON] et al. [47] also reveals that librarians are interested in incorporating AI in reference services, cataloging, and improved library searches.  
[PERSON] et al. [48] note that AI has become a reliable substitute for some human services, assisting in various user services. [PERSON] et al. [49] discuss specific roles and applications of AI in academic libraries, including data curation for collection management and digital preservation and navigating new information environments to better understand the scholarly communication landscape. By automating certain tasks, AI can improve librarians' productivity and efficiency.  
One common example of AI in libraries is chatbots. [PERSON] et al. [46] highlight the benefits of chatbots, such as 24/7 availability, consistency, and patience in answering queries. AI also has the potential to offer personalized assistance and targeted services to students, faculty, and staff. It could help users find relevant resources quickly and efficiently while simultaneously analyzing user behavior to retrieve the most useful materials.  
AI's potential impact on academic libraries extends beyond patron assistance; it could also optimize the library's daily operations. By automating routine tasks, such as cataloging, shelving, and book sorting, librarians could devote more time to research assistance, outreach, and programming. AI could also aid in digitizing library collections, improving accuracy and speed in retrieval methods. This would save resources and time by reducing the need for physical storage space and manual labor for organizing and maintaining collections.
# Online Sparse DOA Estimation Based on Sub-Aperture Recursive LASSO for TDM-MIMO Radar
## 2 Materials and Methods  
A colocated MIMO radar that contains isotropic uniform linear receiving and transmitting arrays (ULA) is considered. The receiving array element spacing is \(d_{r}=\lambda/2\), where \(\lambda\) denotes the carrier wavelength. The location distributions of \(M_{r}\) receiving antennas and \(M_{t}\) transmitting antennas are shown in Figure 1 (with four transmitters and four receivers as an example). The transmitting array element spacing is \(d_{t}=M_{t}\cdot d_{r}\). According to the MIMO theory, the equivalent virtual array of this MIMO can be considered a single-input, multiple-output (SIMO) antenna with 16 elements.
## 1 Introduction
In [27], the low-frequency band component of the circulating current between stator branches is extracted to determine whether a rotor windings' internal short-circuit fault occurs; but, the sensitivity and maloperation prevention ability of the proposed protection are not analyzed. However, the capacity and winding structure of variable-speed pumped storage units are quite different from those of the doubly fed wind generator. It is necessary to carry out further research based on the characteristics of variable-speed pumped storage units. In Japan and Germany, a small number of variable-speed pumped storage units have been put into commercial operation. These units do not specially configure the rotor winding short-circuit fault protection, but use the instantaneous over-current protection of the converter (power electronic excitation device) as the rotor winding short-circuit fault protection. This lacks pertinence and cannot reflect some rotor winding short-circuit faults with weak fault characteristics. A rotor windings' internal short-circuit fault protection method for variable-speed pumped storage units is proposed in this paper. Through the derivation of the magneto-motive force generated by the short-circuited coil, it is found that the circulating current between stator branches will produce characteristic harmonic components related to the slip ratio and pole-pairs number when a rotor winding is short-circuited. On this basis, a protection method for the rotor windings' internal short-circuit fault is proposed by using the harmonic characteristics. A practical variable-speed pumped storage unit is taken as an example to verify the proposed protection method. Simulation results show that the method has strong practicability at different rotor speeds and has strong maloperation prevention ability under other kinds of faults.
# Facile Synthesis of Highly Conductive Vanadium-Doped NiO Film for Transparent Conductive Oxide
## Materials and Methods
### Characterization  
The properties of both pristine and vanadium-doped NiO (V:NiO) nanoparticles were characterized by field emission scanning electron microscopy (FESEM, Hitachi S-4700, Tokyo, Japan), transmission electron microscopy (TEM, JEM-2010-JEOL, Tokyo, Japan), X-ray diffractometry (XRD, PANalytical X'Pert PRO, Almelo, The Netherlands), Raman spectroscopy (Renishaw, UK), ultraviolet-visible spectroscopy (UV-vis, Jasco V-670, Easton, MD, USA), and X-ray photoelectron spectroscopy (XPS, AXIS-NIVA CJ 109, Kratos, Manchester, UK). The film properties were characterized by tapping-mode atomic force microscopy ([PERSON], Multimode-8, Billerica, MA, USA) and a source meter (Keithley-2450, Beaverton, OR, USA).
## 3 Results and Discussion
### Kinetic Modelling
#### 3.5.3 Kinetic Model Fitting at Several Temperature Runs  
As a further proof of the bounty of the proposed model, a third fitting was performed using datasets from four runs performed at temperatures from 80 to 110 \({}^{\circ}\)C, to observe if the kinetic constant is an exponential function of temperature, following the Arrhenius trend-Equation (2). To further validate previous findings, the K\({}_{\text{cat}}\) value was again set at 2.72. Fit results are displayed in Figure 16, while Table 5 compiles key parameters of the kinetic constant together, and the most important goodness-of-fit statistical parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **RSS** & **S\({}_{\text{e}}\)** & **VE \%** & **F** \\ \hline Catalyst concentration & 0.001631 & 0.62\% & 99.85\% & 44,206 \\ \hline Reagent molar ratio & 0.03712 & 2.90\% & 98.71\% & 16,400 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Goodness-of-fit parameters belonging to the fit of the proposed kinetic model to the relevant experimental results: residual sum of squares (RSS), standard error of estimate (S\({}_{\text{e}}\)), percentage of variation explained (VE %) and the Fisher parameter (F).  
Figure 15: Glycerol conversion (X\({}_{\text{Gly}}\)) in the transesterification reaction of glycerol with ethylene carbonate catalyzed by Tunisian smectite at diverse experimental conditions. Dots represent experimental data, while lines represent the trend of the model for each experimental data. (**A**) Effect of the catalyst loading at 80 \({}^{\circ}\)C with CE/Gly ratio of 2. Gly conversion as a function of reaction time with different catalyst loadings, where \(\blacksquare\) represent 2% catalyst loading, \(\bullet\) 4% catalyst loading and \(\blacktriangle\) 6% catalyst loading. (**B**) Effect of the initial molar ratio of reactants at 80 \({}^{\circ}\)C with catalyst loading 2%. Gly conversion as a function of reaction time with CE/Gly ratio, where \(\blacksquare\) represent 1.5 M; \(\bullet\) 2 M; and \(\blacktriangle\) for 3 M.  
The model fits relevant data at all temperatures in an almost perfect way, and the activation energy optimal value retrieved is 75.42 kJ/mol, a value included in the bibliographic interval of values for this reaction. In consequence, the kinetic model adequate for this chemical reaction under the tested conditions is:  
\[\frac{dX_{Gly}}{dt}=exp\bigg{(}18.09-9077\frac{1}{T}\bigg{)}\cdot\frac{C_{cat}} {2.72+C_{cat}}\times\Big{(}1-X_{Gly}\Big{)}\cdot\Big{(}M-X_{Gly}\Big{)} \tag{9}\]