esa-sceva/satcom-chunk-collection huggingface dataset:
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 4 Experimental Results and Analysis  
To test the validity of the MbLRDPL method, extensive comparison experiments were performed on two challenging visual tasks, i.e., set-based video face recognition and set-based object classification. The comparison methods include static modeling methods: CDL, PML, LEML, MMFML and DML; dynamic modeling methods: ISCR, RNP, DLRC, PLRC, DRA and JMLC; dictionary learning methods: D-KSVD, ADDL, DDL, SLatDPL, CEBSR. Among them, DML, JMLC, and CEBSR are the state-of-the-art methods. Moreover, to further reduce the computing time, we first transformed the modeling matrix \(\log(C_{i}^{k})\) or \(Y_{i}^{k}(Y_{i}^{k})^{T}\) into a vector and then performed Principal Component Analysis (PCA) [25] to reduce its dimensions. Our experimental environment was as follows: MATLAB (R2016b), Intel(R) Core(TM) i5 (3.0 GHz) with 24 GB of RAM. The codes of this method will be available on 10 January 2023 via [[https://github.com/xzgao/MbLRDPL](https://github.com/xzgao/MbLRDPL)]([https://github.com/xzgao/MbLRDPL](https://github.com/xzgao/MbLRDPL)).
## 5 Implementation Details
### Mobile Technologies  
Our e-health system considered the use of smartphones and mobile applications developed following the layering model of the Android Architecture Platform [56]. The system considers two types of smartphones: those used by elderly peoples and those used by paramedics. The smartphone used by an elderly individual contains two apps. The first app supports the collection of data on the patient's vital signs and room temperature. The second app is used to record the intake of meals. The apps were implemented with the lightweight MQTT protocol to send the data to the IoT messaging system. The first mobile app is capable of collecting data from the elderly person's wearable subsystem via Bluetooth and collects room temperature data through one of its integrated sensors. Additionally, the logic of a lightweight alert manager was added to produce alert messages for paramedics. The second mobile app enables the housekeeping to record the intake of meals. This app can read the QR code of the foods found in the GUI of the diet preparation services and the QR code of the identification of the elderly person on a wristwatch. Figure 11 shows some examples of the QR codes sent by the mobile app and received by the EMQ cluster.  
Figure 11: **(a) Messages from food intake monitoring and sent to the system. (b) Example of QR codes related to the elderly person’s identification. (c) Example of QR codes related to food identification. (d) Smartphone interface for capturing food intake.**Finally, each parameter uses a smartphone that contains an app to collect medical alerts from the system. This app uses the lightweight MQTT protocol to collect alert messages from the EMQ cluster of the IoT messaging system.
## 1 Introduction and Initial Considerations
### Motivation and Argumentation  
The motivation for proposing a novel structure in this paper is threefold. First of all, the idea is to offer an intelligent solution capable of treating numerous environmental stimuli, noises and disturbances, and use the acquired insights to improve the control performances of dynamical systems. The second goal is to make further progress in the domain of artificial glands and their integration within neural networks. Specifically, the aim is to propose a new type of artificial gland characterized by a novel mechanism for treating input signals and generating optimal output values. The final objective is to consider and discuss potential principles of networking of proposed artificial glands with the point of providing extensive stimulation capabilities of underlying neural networks and making a proper framework for merging all environmental stimuli in a single network, enabling each of them to create influence on another stimulus (as in the nature). The concluding task is to test the aforementioned research goals in a real-world environment, by controlling a complex nonlinear dynamical system in a laboratory environment. One of the most popular benchmark systems for trying new control algorithms is the magnetic levitation system (MLS). This system aims to keep a ferromagnetic ball levitating in a place (or to follow a desired trajectory) by applying an adequate voltage to an electromagnet. MLS is a highly nonlinear system with open-loop instability and time-varying dynamics, making it ideal for testing complex control algorithms, and will also be used as a case study for testing the performance of our control algorithm in this paper. The system was not time-varying at the time of operation and individual performance recording during one experimental session. Nevertheless, the system shows a time-varying nature in our long-term work with it. In any experimental attempt, when stable levitation of a ferromagnetic ball is not established and the equilibrium state is not reached, the levitation object moves uncontrollably. It hits the electromagnet, causing the ball to fall out of the system environment and suffer potential damage. Since it is a laboratory model, it does not include protective equipment that would prevent damage to electromagnets and levitation balls. As a result, there are long-term changes in performance due to such damages to the mentioned mechanical elements. Comparing the performance of this specific ML system from a few years ago with the moment of writing this paper, the difference of when the identical experimental setup was made is significant. Described time-varying properties of the system strictly enforce this performance difference. Several exciting approaches emerged over time based on applying different neural networks in the control of MLS. In [20], hybrid control using a recurrent neural network (RNN) was proposed. The controller itself was based on feedback linearization, while RNN functions were used for uncertainty observers. Study [21] considered a neural adaptive method for position control
## 2 Related Work
Afterward, ST-MetaNet [37] utilizes sequence-to-sequence structure and combines the graph attention network (GAT) with the recurrent neural network (RNN) for capturing the spatial-temporal correlations. [PERSON] et al. [14] integrated Wavenet [15] into the GCN to extract the dynamic temporal dependencies of traffic data, while using an adaptive adjacency matrix to obtain hidden spatial dependencies in the road network. This self-adaptive adjacency matrix is constructed by the similarity of different node embeddings on the road network. However, the learned spatial dependencies between nodes lack the guidance of domain knowledge, and it is prone to overfit during the training phase [18]. In addition, most existing traffic speed prediction methods ignore global spatial correlations between different nodes in the road network, and they hardly utilize multiple temporal correlations and external factors.
## 3 Results
### Data Acquisition and Understanding  
Data from January 2019 to July 2023 are used to create the models, initially comprising 91 variables. The datasets contain 20,725 data records for building the compressive strength prediction model; 607 data records for building the flexural strength prediction model; and 21,335 data records for building the slump prediction model. Figure 3 shows the distributions of the output variables in each dataset.  
To apply machine learning methods, it is necessary for the data to be of good quality, which is why outlier and null cleaning are performed. Outlier cleaning was carried out by evaluating quality rules defined by concrete production experts, as presented in Table 4. Records with values outside the established ranges were removed, as well as records that did not have a value for compressive strength, flexural strength, or slump.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Units** & **Min** & **Max** \\ \hline Coarse & kg/m\({}^{3}\) & 0 & 2500 \\ aggregate—quantity & \% & 0 & 6 \\ \hline Coarse & & & \\ aggregate—moisture & \% & 0 & 6 \\ content & & & \\ \hline Fine & & & \\ aggregate—quantity & kg/m\({}^{3}\) & 0 & 2000 \\ \hline Fine & & & \\ aggregate—moisture & \% & \textgreater{}0 & 15 \\ content & & & \\ \hline Free Water & L/m\({}^{3}\) & \textgreater{}0 & 200 \\ \hline Cement 1 & kg/m\({}^{3}\) & 200 & 1000 \\ \hline Cement 2 & kg/m\({}^{3}\) & 0 & 1000 \\ \hline Admixture\_1 Retarding & g/m\({}^{3}\) & \textgreater{}0 & 5000 \\ \hline Admixture\_2 & & & \\ superplasticizing & g/m\({}^{3}\) & \textgreater{}0 & 5000 \\ \hline Admixture\_3 & & & \\ Set-controlling & g/m\({}^{3}\) & 0 & 2000 \\ \hline SCMs & kg/m\({}^{3}\) & 0 & 100 \\ \hline
28 d Compressive & & & \\ Strength & MPa & 10 & 100 \\ \hline
28 d Flexural Strength & MPa & 0 & 10 \\ \hline Slump & mm & 0 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Definition of maximum and minimum values for concrete components.  
Figure 3: (**a**) Compressive Strength Data Distribution. (**b**) Flexural Strength Data Distribution. (**c**) Slump Data Distribution.  
At the end of the data cleaning process, a dataset with 20,108 rows was obtained for the compressive strength model, a dataset with 551 rows for the flexural strength model, and a dataset with 20,662 rows for the slump model.
## 2 Materials and Methods
### Standard Modal Analysis  
Modal analysis is a method commonly used in practice to study the dynamic properties of various objects. As a result of the analysis, eigenfrequencies, vibration modes, and damping coefficients are obtained. It enables the identification of dynamic properties of building objects. Thus, it allows you to effectively solve construction problems related to vibrations at the design stage or during operation. Its main application is to compare the frequency of excitations in the system with the frequencies of natural vibrations. If these values are close to each other or overlap, the phenomenon of resonance is created. During the resonance, the vibrations are repeatedly strengthened, which can lead to failure  
Figure 1: Sign convention and degrees of freedom of frame element.  
or complete destruction of the building. Eigenvalues and eigenvectors are obtained from the following formula:  
\[(\mathbf{K_{L}}-\omega_{\mathrm{i}}^{2}\mathbf{M})\cdot\mathbf{q_{i}}=0 \tag{7}\]  
where: \(\mathbf{K_{L}}\)--linear stiffness matrix of the structure, \(\mathbf{M}\)--mass matrix of the structure, \(\omega_{\mathrm{i}}\)--natural pulsation (natural circular frequency) of i-th mode [rad/sec], \(\mathrm{f_{i}}=\frac{\omega_{\mathrm{i}}}{2\pi}\)--frequency [Hz], \(\mathbf{q_{i}}\)--eigenmode vector of i-th mode.  
The eigenvectors are normalized such that the maximum term of each vector is 1.0. In Autodesk Robot Structure Professional 2022, the precision value displayed in the eigenvalue dialog box is considered the achieved convergence accuracy. This value is a tolerable error that determines how exactly the eigenvalue \(\lambda_{\mathrm{i}}=\omega_{\mathrm{i}}^{2}\) and the eigenvector \(\mathbf{q_{i}}\) obtained during iteration for the i-th mode satisfy the eigenproblem equation. The participation factor is defined as:  
\[\gamma_{\mathrm{i}}=\mathbf{V_{\mathrm{i}}^{T}MD} \tag{8}\]  
where: \(\mathbf{D}\)--vector of direction cosines is defined as follows:  
\[\left\{\begin{matrix}\mathrm{D(j)}=1.0\ \mathrm{if}\ \mathrm{j}\ \mathrm{is}\ \mathrm{equal}\ \mathrm{to}\ \mathrm{the}\ \mathrm{i}\mathrm{-th}\ \mathrm{degree}\ \mathrm{of}\ \mathrm{freedom}\\ \mathrm{D(j)}=0.0\ \mathrm{if}\ \mathrm{j}\ \
eq\ \mathrm{i}\end{matrix}\right. \tag{9}\]  
\(\mathbf{V_{i}}\)--i-th normalized eigenvector with:  
\[\mathbf{V_{\mathrm{i}}^{T}MV_{i}}=1.0 \tag{10}\]  
Participation masses are dynamic, participating in the motion of the structure for every mode and degree of freedom. They are regarded as the current masses for the current mode and as the relative masses which are the sum of the current masses from the first mode to the current one. Values are shown as a percentage of the total dynamic masses. Current participation masses are defined as follows:  
\[\mathrm{M_{ci}}=\gamma_{\mathrm{i}}^{2} \tag{11}\]  
Currently, the default method for finding a solution to an eigenproblem is the Lanczos algorithm [31; 32], which is a powerful approach to solve large-scale eigenvalue problems. It finds the required n eigenvalues and eigenvectors with arbitrary precision.
## 2 The Processes and Scheduling Strategies
### The Operation Processes
When the robot comes to the PM for unloading \(W_{i,12}\), the processing activity at the PM may not be completed. Thus, the robot has to wait here for some time, and such a robot-waiting task is denoted by \(A_{i1}\). After \(W_{i,12}\) is removed from the PM, the internal chamber should rotate such that the spaces holding \(W_{i,34}\) rotate to the door position of the PM. Also, when the robot comes to the PM again for unloading \(W_{i,34}\), the rotation operation of the PM may not be completed. At this time, the robot should wait, and the robot-waiting activity is denoted by \(A_{i2}\). Furthermore, when the robot loads \(W_{i,12}\) into a PM at Step \(i\), it implies that the PM is empty. Thus, the robot can load \(W_{i,12}\) immediately without waiting. After \(W_{i,12}\) are loaded into the PM, the PM should rotate such that other two empty spaces rotate to the door position of the PM. When the robot comes to the PM again and is ready to load \(W_{i,34}\) into the PM, the internal chamber of the PM may not complete the rotation operation. This means that the robot should wait at the PM. Such a robot-waiting activity is denoted by \(A_{i3}\). In addition, the times for \(A_{i1}\), \(A_{i2}\), and \(A_{i3}\) are represented by \(\omega_{i1}\), \(\omega_{i2}\), and \(\omega_{i3}\), respectively. To meet RTCs, after the wafers are processed in a PM, they should leave the PM within a limited time window. Let \(\delta_{i}\) denote such a limited time at Step \(i\), \(i\in\mathbf{N}_{n}\backslash[0]\), and \(d_{i}\) denote the wafer delay time of a wafer in a PM at Step \(i\), i.e., the time duration starting from the time point at which the processing activity is completed to the time point at which the robot grasps the wafer for unloading. We use \(\tau_{i,12}\) and \(\tau_{i,34}\) to denote the wafer sojourn time of \(W_{i,12}\) and \(W_{i,34}\), respectively. Then, if a schedule for an SF\({}^{3}\)-CT is feasible, the following constraints should be met:  
\[\alpha_{i}\leq\tau_{i,12}\leq\alpha_{i}+\delta_{i},i\in\mathbf{N}_{n}\backslash [0] \tag{1}\]  
\[\alpha_{i}\leq\tau_{i,34}\leq\alpha_{i}+\delta_{i},i\in\mathbf{N}_{n}\backslash [0] \tag{2}\]
## 1 Introduction
[18] used a continuous electrolytic free jet to obtain a high localization of the removal area and electrochemically machined a cavity with a depth of approximately 180 \(\upmu\)m and a width of approximately 190 \(\upmu\)m. [PERSON] [19] presented the flow channel of a spherical electrode for electrochemical milling of stainless steel in which the electrolyte was ejected from seven holes at the bottom of this rotating electrode. When the feed rate was 50 mm min\(-1\), the machining depth was about 0.2 mm for a single pass. [PERSON] [20] machined a simple slot with length of 30 mm and depth of 1.2 mm, fabricated from Nimonic-263 alloy. The MRR were 0.23 g/min when the feed rates were 8 mm/min. [PERSON] and [PERSON] et al. [21; 22] proposed a flow channel structure for a rotating electrode with a dead-end tube used for the ECM of nickel-based alloy GH4169/Inconel 718. A sample with thin-walled structures was produced stably at a feed rate of 2.1 mm/min and a machining depth of 3 mm. The MRR was 282.9 mg/min at the maximum feed rate. The above research results show that ECM can be used as a feasible process method for machining TiB\({}_{2}\)/7050 aluminum matrix composites. However, the above-mentioned studies used either tube electrodes for electrochemical machining of planes or large-diameter hollow tools for electrochemical machining of grooves, cavities, etc. The smaller diameter tool results in a smaller conductive area. Therefore, the MRR was not high and the depth of the structure was also shallow. For large thin-walled parts, more materials must be removed during processing. If the tube electrode is used for processing, it will result in a long processing time and significant processing problems. Thus, the purpose of this work was to study the machining of large planes with hollow tools on a macroscopic scale. The present research proposed the design and development of a tool cathode suitable for large material removal, and a scheme optimization study was carried out through numerical simulation of the flow field. Subsequently, the MRR and surface roughness associated with various machining parameters were studied. In addition, the influence of different transverse movements on the machining surface topography was studied. Finally, with the appropriate transverse movement and processing route, a complete plane was processed.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 2 Related Work
### Space-Based Time Offset Measurement Methods  
Satellite-Ground Two-Way Time Comparison Method  
The TWTT method adopted the working mode of mutual transmission and reception. The ground station \(A\) transmitted the ranging signal to satellites at the ground time \(T_{0A}\). The signal was received by the satellite at the clock reading time \(T_{s}\), and the observation data were sent to the ground station. At the same time, the satellite transmitted the ranging signal at \(T_{0}\)S time and was received by the ground station at the ground clock time \(T_{A}\). At this time, the uplink and downlink pseudo-ranges measured by the satellite and the ground [17] were  
\[\begin{array}{l}\rho_{S}^{{}^{\prime}}=C\tau_{S}^{{}^{\prime}}=C(T_{S}-T_{0}^ {A})=C\Delta T_{SA}+\rho_{S}\\ \rho_{A}=C\tau_{A}^{{}^{\prime}}=C(T_{A}-T_{0}^{S})=C\Delta T_{SA}+\rho_{A}. \end{array} \tag{1}\]  
In this formula, \(C\) is the speed of light; \(\Delta T_{SA}\) is the difference between the satellite clock and the ground clock; \(\rho_{S}^{{}^{\prime}}\) is the L-band uplink pseudo-range measured by the receiving equipment on the satellite; \(\rho_{S}\) is the distance from the satellite to the ground station at \(T_{s}\) time; \(\rho_{A}^{{}^{\prime}}\) is the L-band downlink pseudo-range measured by the ground receiving equipment; \(\rho_{A}\) is the distance from the satellite to the ground station at time \(T_{A}\). The upper and lower formulas can be subtracted to yield  
\[\Delta T_{SA}=\frac{1}{2C}(\rho_{A}^{{}^{\prime}}-\rho_{S}^{{}^{\prime}})+ \frac{1}{2C}(\rho_{S}-\rho_{A}). \tag{2}\]  
The difference between the satellite clock and the ground clock can therefore be obtained.
## 4 Single-Droplet Ignition Prediction Models
### Droplet Ignition Prediction Model Under Normal Gravity
The relative sensitivity coefficient \(S_{E}\) of \(d_{w}\) with respect to \(E\) is calculated by  
\[S_{E}=\frac{\partial d_{w}}{\partial E}\cdot\frac{E}{d_{w}}=\frac{E}{2 RT_{\infty}} \tag{18}\]  
At \(T_{\infty}\ =800\) K, the relative sensitivity coefficients for the three fuels are as shown in Table 6.  
The above results indicate that, under the experimental conditions of this study, the minimum ignition diameter of the droplets is minimally influenced by the activation energy. The aforementioned droplet ignition model was fitted against the experimental data for the three fuels, with the comparative results presented in Figures 11-13. The residuals of the fitting are shown in Figures 14-16. The standard errors of the empirical parameters \(A\) and \(n\) are shown in Table 7. The maximum error remains below 6.5%, and the goodness of fit is greater than 0.96, indicating that the mathematical model fits the experimental data well. In microgravity environments, natural convection driven by buoyancy is nearly eliminated. The movement of fuel vapor occurs primarily through diffusion driven by molecular thermal motion. Because the flow pattern remains the spherically symmetric radial one-dimensional flow caused by the Stefan flow, the previously assumed boundary conditions are still applicable. Due to the absence of natural convection, the diffusion rate of fuel vapor significantly decreases compared to the normal-gravity environment. Consequently, the scale of the mixed vapor field surrounding the droplet decreases markedly, and the reaction \begin{table}
\begin{tabular}{c c c} \hline Ethanol & N-Heptane & N-Dodecane \\ \hline
0.977\% & 0.902\% & 1.128\% \\ \hline \end{tabular}
\end{table} 
Table 6: Relative sensitivity coefficients for the three fuels. Figure 11: Comparison between droplet ignition model predictions and experimental results of ethanol (\(A\) = 6.78909 \(\times\) 10\({}^{-13}\); \(n\) = 5.89138). zone becomes closer to the droplet's surface [12; 13]. In the Stefan flow, the temperature field can be analyzed using the generalized Reynolds analogy and concentration distribution. Figure 12: Comparison between droplet ignition model predictions and experimental results of n-heptane (\(A=1.74129\times 10^{-16}\); \(n=7.06683\)). Figure 13: Comparison between droplet ignition model predictions and experimental results of n-dodecane (\(A=5.12171\times 10^{-17}\); \(n=7.22623\)).
# Prediction of Intraday Electricity Supply Curves
## 3 Methodology
### Models and Predictions
#### 3.4.6 Multilayer Perceptron Model  
The multilayer perceptron [28] (MLP) is the simplest neural network. For an application of MLP in energy forecasting, see [29]. It consists of several layers of neurons (computational unit that processes inputs to produce an output, contributing to the network's overall task), with each neuron being fully connected to all neurons in the previous and subsequent layers. By adjusting the weights and biases (parameters) using a back-propagation algorithm and a cost function, the network learns the patterns in the training data. The considered hyperparameters were the following:  
* epoch. The number of complete passes of the training dataset (epochs). The assessed values were 5, 10, and 20;
* n. The number of layers besides the input and output layers. We considered one, two, three, and four layers;
* neuron. The number of neurons in each layer. The tested values were 5, 10, 20, 50, 100, 200, and 400;
* act. The activation function, which is a mathematical function applied to each node, introduces nonlinearity into the network and allows it to learn complex patterns. We tried two different functions: ReLU and tanh;
* batch. The number of training examples used in one forward and backward pass of the training process. The tested values were 3, 10, and 30.  
The selected hyperparameters for the MLP model are available in Table A1 in Appendix A.