esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Verification  
The experimental test setup is shown in Figure 16, and the LC filter is connected between the inverter and the HPMSM. The experiment is powered by a single-phase regulator with an output voltage of up to 250 V and a rated power of 3 kVA. The torque sensor is coaxially connected with the HPMSM, whose maximum measured torque is 1 N/m, and the measurement accuracy is 0.2%. The measured speed and torque are displayed in real-time on the torque and speed displayer. The hysteresis brake is used as the load; its rated torque is 2 N/m; and the load torque can be changed by the load controller. In order to demonstrate the experimental platform clearly, the schematic diagram of the system is given in Figure 17. Figure 17 shows the connection of the above measurement systems and, in addition, how the control system works. The sampled current and speed position information is processed by TMS320F28335, and the corresponding switching signal is generated in real-time to control the inverter bridge.  
Figure 16: Experimental setup.  
The traditional vector control model using double closed loops of speed and current with \(i_{d}=0\) mode is built and implemented in the TMS320F28335 digital signal controller. The system parameters are given in Table 1, where the choice of the ANF step size weighs the convergence speed and accuracy.
# Factorial Design Analysis for Localization Algorithms
## 4 Results
### Node Density  
Node density describes the number of nodes distributed over an area of 100 m \(\times\) 100 m considering the evaluation scenario proposed in [19; 22]. Based on our references, in our experiment, the node densities of 1 and 9 nodes were used within the NOI's coverage area of 100 m \(\times\) 100 m; these numbers represent the critical node density values (minimum and maximum) over the total network area of 1000 m \(\times\) 1000 m, which correspond to low and high node density, respectively.
# Uncertainty Analysis of Spherical Joint Three-Dimensional Rotation Angle Measurement
## 2 Principle and Experiment  
### Principle  
The team proposed a 3D angle detection theory and method based on spherical stereo coding and an eddy current sensor array [22]. In order to ensure that the rotation angle has a one-to-one mapping relationship with the output of the eddy current, a pseudo-random code method is mapped onto the sphere using the planar pseudo-random code. The pseudo-random sequence is a periodic sequence composed of 0 and 1, which is a kind of predetermined structure. The sequence can be copied or repeated, and exhibits the random character of the encoding.  
Based on the theory of pseudorandom coding, we designed a pseudorandom 3D spherical coding and then identify spherical coding through a sensor array to detect the comprehensive changes caused by the morphologies of spherical head rotation. The key thinking behind measuring the rotary angle of a spherical joint using an eddy current sensor is to use the sensor probe to sweep the comprehensive change in the sensor output signal when different position codes, contours, and groove depths are scanned. As the eddy current meets the groove, according to the eddy current effect, when the spherical joint rotates, the sensor output will change, so as to reflect the surface characteristics of the spherical joint surface changes. By identifying and reading the code of the spherical joint, the network measurement model of the rotary angle and eddy current output of the spherical joint are established. We obtained the output value of the eddy current sensor array through external acquisition as the input and used the rotation angle value provided by a three-dimensional rotary table as the output. The artificial neural network was used to train the measurement model. The position and pose of the smart spherical joint are shown in Figure 1. Based on the right-hand principle, the reference coordinate system O-XYZ is established, with the center O of the ball as the origin. When the ball head rotates within the ball and socket for three degrees of freedom, its rotation position can be broken down into \(\alpha\), the rotation angle at the X axis; \(\beta\), the rotation angle at the Y axis; and \(\gamma\), the rotation angle at the club axis.
# Key Distribution Scheme for Optical Fiber Channel Based on SNR Feature Measurement
## 3 Key Distribution Simulation Platform Setup  
As shown in Figure 4, the data transmission rate of the scheme is 10 Gbps, the optic fiber link is 200 km, the laser transmitting power is 1 mW, and the wavelength is 1550 nm. The sending process of the scheme is as follows: Firstly, [PERSON] obtains the orthogonal frequency division multiplexing signal encrypted by quantum noise stream through DSP processing. Next, the signal is converted from digital to analogue through the arbitrary waveform generator (AWG), and the electrical signal is modulated onto an optical carrier using an I/Q modulator. The signal amplified by erbium-doped fiber amplifier (EDFA) enters the optic fiber link to [PERSON]'s terminal. It is demodulated by the coherent receiver and sampled by a 20 GSa/s oscilloscope (OSC). [PERSON] converts the signal to analogue through AWG and then modulates it to the optical carrier using the I/Q modulator. After passing through the amplifier and optic fiber link, [PERSON]'s coherent receiver carries out coherent demodulation on the signal. The demodulated data are sent to an OSC for sampling (sampling rate = 20 GSa/s). Finally, [PERSON] performs DSP processing on the sampled signal. There are simulation-specific parameters, as listed in Table 1.  
Figure 4: System simulation platform. MZM: Mach–Zehnder modulator. SSMF: standard single mode fiber.  
Figure 3: Optimized key generation factors \(\varepsilon\) and \(a_{q}\).
## 2 Principles and Methods  
### Convolutional Neural Networks for Bayesian Optimization  
Convolutional Neural Networks are an important component of deep learning, originating from the research of [PERSON] and [PERSON] in 1962 and evolving on that basis to become the benchmark for deep learning today [44]. From a theoretical perspective, a convolutional neural network can be a good description of complex nonlinear relationships, and it is a feedforward neural network with better fault tolerance. Convolutional neural networks primarily comprise an input layer, a convolutional layer, a pooling layer, an activation layer,and a fully connected layer [43]. Based on the convolutional layer, a simple linear expression is formed by stacking the operational layers of convolution, pooling, and upsampling, and the fully connected layer is activated by employing the Softmax function to realize the binary classification problem [45; 46].  
Bayesian optimization (BO) is capable of quickly and accurately finding the optimal solution of the model hyperparameters through multiple iterations and further optimizing the model, such that BO takes on critical significance in parameter combination optimization. It is adopted to deal with the complex mapping relationship between the model fitting parameters and the objective function [41], which satisfies Equation (1):  
\[Z^{*}=\underset{z\in Z}{\text{argmax}}f(z) \tag{1}\]  
where \(Z^{*}\) denotes the global optimum of \(f(z)\).  
Landslide susceptibility is used to predict the possibility of a landslide occurring in a certain place based on multiple environmental factors. Thus, the landslide susceptibility model should be constructed with powerful feature extraction, probability prediction, and a linear regression fitting function. In this study, we adopted the deep learning method for model construction, in which the CNN possesses a better function in model construction, which can be subjected to the highly nonlinear landslide prediction to reduce the overfitting phenomenon of the model. Moreover, there are many types of factors for the occurrence of landslides, and the direct processing of the original factor data will bring considerable inconvenience to the model operation, which will waste more computing power and lead to a reduction in the accuracy of the prediction model. For this reason, in this study, by grading the factors and using the Pearson correlation coefficient for the initial screening of landslide factors, we obtained the dataset after screening, normalized the dataset to obtain the feature vectors, adopted the convolution kernel to extract the feature vectors, and then employed the BO algorithm to search for optimization of the hyperparameters of the CNN. Lastly, we obtained the prediction results by computing the fully connected layer. As shown in Figure 1
# Multi-Attention Recurrent Neural Network for Multi-Step Prediction of ChlorophyllI Concentration
###### Abstract  
Chlorophyll prediction facilitates the comprehension of red tide characteristics and enables early warning. In practice, it is formulated as a multivariate time series forecasting problem aimed at forecasting future chlorophyllI concentrations by considering both exogenous factors and chlorophyll. However, the multi-step prediction of chlorophyll concentration poses a formidable challenge due to the intricate interaction between factors and the long temporal dependence between input sequences. In this work, we propose a Multi-attention Recurrent Neural Network (MaRNN) for the multi-step prediction of chlorophyllI concentration. The MaRNN comprises an encoder incorporating two-stage spatial attention and a decoder employing temporal attention. The encoder first learns the significance of exogenous factors for prediction in the first phase, and subsequently captures the spatial correlation between the exogenous sequence and chlorophyllI sequence in the second phase. The decoder further excavates input sequences that exhibit a strong correlation with the task through temporal attention module, thereby enhancing the prediction accuracy of the model. Experiments conducted on two real-world datasets reveal that MaRNN not only surpasses state-of-the-art methods in performance, but also offers interpretability for chlorophyllI prediction.  
ch chlorophyll concentration prediction; multi-step forecasting; red tide; interpretability Article
## 4 Solvents for CO\({}_{2}\) Capture
### Chemical Solvents
#### 4.2.5 Other Solvents
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Class** & **Type** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{**Physical**} & \multirow{3}{*}{Methanol} & Solvent cost \(\downarrow\) & Vapor pressure \(\uparrow\) \\  & & Toxicity \(\downarrow\) & Process complexity \(\uparrow\) \\  & & Vapor pressure \(\downarrow\) & \\  & & Selexol DPEG & Temperature range \(\uparrow\) & Viscosity \(\uparrow\) \\  & & Selectivity for H\({}_{2}\)S \(\uparrow\) & \\  & & Rectisol/NH\({}_{3}\) & Temperature \(\downarrow\) & Selectivity for CO\({}_{2}\)\(\downarrow\) \\  & & & Vapor pressure \(\uparrow\) \\  & & Solvent cost \(\downarrow\) & Stability \(\downarrow\) \\
**Amines** & Primary & Reaction rate \(\uparrow\) & Corrosiveness \(\uparrow\) \\  & & & Regeneration energy \(\uparrow\) \\  & & & Reaction rate \(\downarrow\) \\  & & & Regeneration energy \(\downarrow\) \\  & & Vapor pressure \(\downarrow\) & Reaction rate \(\downarrow\) \\  & & & Regeneration energy \(\downarrow\) \\  & & & Vapor pressure \(\downarrow\) \\  & & & Regeneration energy \(\downarrow\) \\  & & & Stability \(\uparrow\) \\  & & & CO\({}_{2}\) loading \(\uparrow\) \\  & & Stability \(\uparrow\) \\  & & & \\ \multirow{3}{*}{Sterically hindered} & \multirow{3}{*}{CO\({}_{2}\) loading \(\uparrow\)} & \multirow{3}{*}{Reaction rate \(\downarrow\)} \\  & & & Corrosiveness \(\downarrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of different solvents for CO\({}_{2}\) capture.
# Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
## 1 Introduction
Compared to the linear optimization method, which maximizes maintenance benefits with limited costs, the multi-objective optimization method can both maximize maintenance benefits and minimize costs [20]. [PERSON] et al. [21] developed and implemented a multi-objective decision-aid tool that considers the minimization of costs and maximization of the residual value of pavements. Long-term preventive maintenance for multiple pavements is an even more complicated multi-objective optimization problem, which requires a multi-year maintenance plan and a combination of various maintenance strategies that consider the total maintenance benefits and costs for all pavements [22; 23]. [PERSON] et al. [24] proposed an airport pavement maintenance decision-making strategy based on multi-facility and multi-year network optimization models and analyzed the effect of initial budget, maintenance methods, costs, and thresholds on decision outcomes. To solve the multi-objective optimization problem of multi-pavement and multi-year maintenance, the dynamic programming (DP) method has drawn many scholars' interests [25]. [PERSON] et al. [26] proposed a hybrid DP procedure to determine the most cost-effective maintenance and rehabilitation activities for each section in a highway pavement network. [PERSON] [27] used the approximate DP method to reduce the complexity of maintenance optimization problems. [PERSON] et al. [28] developed a multi-stage DP model to achieve an optimal pavement maintenance strategy in a 30-year life cycle. [PERSON] et al. [29] developed an optimization algorithm based on DP to select the optimal treatment maintenance for road networks. Despite plentiful studies on pavement maintenance planning, to the best of the authors' knowledge, few studies have been conducted on composite pavements due to a lack of performance deterioration and maintenance improvement data. This study first aimed to predict the pavement performance deterioration tendencies of different regions in airport runways with composite pavements. Then, this study investigated the benefits of typical preventive maintenance technologies according to data from the Long-term Pavement Performance (LTPP) program. Finally, a decision-making optimization method based on DP for long-term preventive maintenance was proposed, applied, and verified in a five-year maintenance plan.
# Electrocoagulation Based Chromium Removal Efficiency Classification Using Logistic Regression
## 5 Conclusions  
The objective of this work is to build a predictive model using logistic regression that will be used as an Electrocoagulation efficiency classifier. This model should be trained with collected data from laboratory experiments. The main application of the proposed work is predicting either an Electrocoagulation operation will be efficient or not before testing in real world. That allows avoiding trials and errors to optimize the Chromium removal process cost. Training and validation process gives 88% as classification precision, recall and F-Score metrics values while the use of the 10-Folds cross-validation method gave a minimal area under curve value of 97% while the best values attempts 100%. Classification report states that the model performs well comparing to similar experimentation efficiencies. As perspectives, the next works will broaden and generalize this approach. First, we will do our laboratory experiments with these and other parameters. Second, we apply this method to other heavy metals such as nickel, zinc, lead, etc. Then, we apply logistic regression to other chemical treatment methods such as membranes. We hope that the application of Statistics and Machine Learning for Chemistry helps to improve the field.  
Conceptualization, M.A., S.T., R.S. and A.T.; Methodology, M.A., S.T.; Software, S.T., Validation, M.A., S.T., R.S., and A.T.; Formal analysis, M.A. and S.T.; Investigation, M.A.; Resources, M.A., S.T. and R.S.; Data curation, M.A. and S.T.; Writing original draft preparation, M.A.; Writing review and editing, M.A.; Visualization, S.T.; Supervision, R.S. and A.T.; Project administration, S.T., R.S. and A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
I would like to thank the anonymous referees for their valuable comments and helpful suggestions. Special thanks goes to [PERSON] that improved the quality of the language and made this paper more readable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
EC Electrocoagulation  
Cr Chromium  
LR Logistic Regression  
SVC Classification of Carrier Vectors  
TAN Total Ammonia Nitrogen  
TIN Total Inorganic Nitrogen  
XWT Cross-Wave Transformation  
Figure 1: Logistics Regression ROC Curve for every 10-Folds Cross-Validation Training Step.
## 2 Antennas with a Single Reconfigure Feature
### Frequency-Reconfigurable Antennas  
Due to limited space available in modern wireless devices such as smartphones and tablets, the design of frequency-reconfigurable antennas requires much attention in terms of fundamental characteristics. These antennas can move dynamically from one frequency band to another or vary continuously in a range of frequencies [11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. They are mostly used in multi-band/multi-service systems requiring efficient reallocation of the dynamic spectrum: different types of switches are used. The most common method is to tune the electrical length of the antenna radiator using PIN diodes [11; 12; 13]. Varactor diode switches can tune the operation band in a wideband or ultra-wideband (UWB) mode and choose a desired narrowband mode [14; 15]. Alternatively, the matching network has been used to set the resonance frequency [16; 17]. Some other methods such as adding a shorting post on the antenna configuration or changing the input-impedance have been also proposed [18; 19]. Hence, the antenna operational spectrum can be switched to the desired frequency in a variety of ways.  
To help explain the theory of the frequency reconfigurable antenna, the configuration and performance of the antenna in [20] are represented in Figure 1. As illustrated, the antenna configuration is based on a composite right/left-handed transmission line (CRLH-TL), containing a shunt capacitance/inductance and series capacitance/inductance. The operation band of the antenna can be tuned by loading the varactor on the meander-line slot. The design exhibits frequency scanning in the range of 4.13-4.50 GHz at 0-36 V. The antenna provides very good frequency-reconfigurable characteristics and might be suitable for use in a phased array form for fifth generation (5G) base station applications.
# A Three-Dimensional Parametric Biomechanical
## 2 Multibody Model
### Frames and Bodies  
Whole-body inertial properties are usually given with respect to a reference frame centerd on the pelvis. Three planes are defined through such point for a standing body: the sagittal plane (body symmetry plane), the transverse plane (parallel to the floor), and the frontal or coronal plane (normal to the other two planes); see Figure 1a. The \(x\)-axis (sagittal axis) is the intersection between the sagittal and transverse plane and points forward, the \(z\)-axis (longitudinal axis) is the intersection between the sagittal and frontal plane and points upward, and the \(y\)-axis (transverse axis) is defined such that \(x\times y=z\).  
The multibody model of a human body consists of 14 body segments, namely trunk, forearms (2\(\times\)), arms (2\(\times\)), thighs (2\(\times\)), shanks (2\(\times\)), hands (2\(\times\)), feet (2\(\times\)), and head (including neck). Some authors have employed 16 segments by dividing the trunk into upper and lower trunk and not including the neck as part of the head. The location and orientation of each segment is defined using a reference frame centerd on the proximal point of the segment, as reported in Table 1. The reference frame of each body segment is parallel to that centerd in the pelvis, as shown in Figure 1a, when the human body is in the standard anatomical position (i.e., standing with the arms and legs parallel to the trunk and the palms pointing forward).  
When the hands are placed on the handgrips, the \(x\)-axis (palm direction) points downward (vertically), the \(z\)-axis (fingers direction) points backward (horizontally), and the \(y\)-axis completes the frame. When the feet are on the footpegs, the \(x\)-axis points forward (horizontally), the \(z\)-axis upward (vertically), and the \(y\)-axis completes the frame.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Segment** & **Proximal Point** & **Distal Point** \\ \hline Head & \multicolumn{1}{c}{MIDS *} & / \\ Trunk & \multicolumn{1}{c}{MIDB *} & \multicolumn{1}{c}{MIDS} \\ Arm & \multicolumn{1}{c}{RSJC/LSJC *} & \multicolumn{1}{c}{REJC/LEJC} \\ Forearm & \multicolumn{1}{c}{REJC/LEJC *} & \multicolumn{1}{c}{RWJC/LWJC} \\ Hand & \multicolumn{1}{c}{RWJC/LWJC *} & \multicolumn{1}{c}{RWJC/LWJC *} \\ Thigh & \multicolumn{1}{c}{RHJC/LHJC *} & \multicolumn{1}{c}{RKJC/LKJC} \\ Shank & \multicolumn{1}{c}{RKJC/LKJC *} & \multicolumn{1}{c}{RAJC/LAJC} \\ Foot & \multicolumn{1}{c}{RAJC/LAJC *} & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proximal and distal points for each body segment. * indicates the origin of the reference frames attached to each body segment, while / indicates that the distal point is not defined, as it is not necessary for the model construction.  
Figure 1: Rider multibody model: reference frame for the whole body [51], with the sagittal (\(x\)), transverse (\(y\)), and longitudinal (\(z\)) axes and planes (**a**); and main points of the multibody model (**b**).