esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Finally, each solution in the outstanding subset is assessed using a precise evaluation. A precise evaluation is one that can yield exact estimates of performance. The one with the best performance in the outstanding subset is the good enough solution. The OO theory has been successfully used in various applications, such as the flow line system [10], assemble-to-order systems [11], and network-type production line [12]. To reduce the computing time of PCSOP, a method that embeds ordinal optimization (OO) into tree-seed algorithm (TSA) [13; 14; 15; 16] (OOTSA) is proposed to determine a near-optimal solution in a short computational time. The OOTSA method contains three modules: surrogate model, exploration and exploitation. Firstly, the regularized minimal-energy tensor-product splines (RMTS) [17] is utilized as a surrogate model to approximately evaluate a solution. Secondly, an improved tree-seed algorithm (ITSA) is proceeded to choose \(N\) outstanding solutions from the large search space. Finally, an incremental optimal computing budget allocation (IOCBA) technique is employed to determine the optimal solution from the \(N\) outstanding solutions. These three modules drastically diminish the computing effort of PCSOP. To test the performance of the proposed OOTSA approach, it is employed to a pull-type production system. The pull-type production system is formulated as a PCSOP that is comprised a huge search space. The goal of a pull-type production system is to determine the optimal work-in-process inventory for minimizing the expected lead time while satisfying a tolerable service level. The first contribution of this paper is to propose an OOTSA approach for a general PCSOP that is lack of structural information to determine a near-optimal solution in a short computational time. The second contribution is to employ the proposed approach to minimize the expected lead time of a pull-type production system such that the work-in-process inventory is optimized. This paper contains five sections. After this Introduction, Section 2 explains the proposed OOTSA to search for an outstanding solution of a general PCSOP. Section 3 formulates the pull-type production system as a PCSOP and discusses the application of the proposed OOTSA for optimization of this PCSOP. Finally, Sections 4 and 5 are the results of this research and the conclusion, respectively.
## 2 Materials and Methods
### Paired Datasets of PlanetScope Imagery and Corresponding Red Tide Maps  
In this study, we used PlanetScope imagery to detect the pixel-wise existence of red tides. The PlanetScope was launched in June 2016, and the satellite constellation consists of approximately 130 satellites and can image the entire land surface of the Earth every day [47]. Each PlanetScope satellite is a CubeSat 3U with dimensions of 10 \(\times\) 10 \(\times\) 30 cm\({}^{3}\) and carries a telescope and a frame charge-coupled device (CCD) camera equipped with a Bayer-mask filter. Table 1 shows a constellation overview of the PlanetScope. The orbit altitude is 475 km, and the equator crossing time is between 9:30 am and 11:30 am at local solar time. Images around the Korean Peninsula were obtained at approximately 10:40 am every day. Each image pixel had four spectral bands and a spatial resolution of 3 m. An imagery product covers an area of approximately 24 \(\times\) 7 km\({}^{2}\), with some variability depending on the satellite altitude.  
Figure 1: The study area covers the southern coast of Korea, including Goeung, Yeosu, Namhae, and Tongyeong. Black dotted boxes show the acquisition area in PlanetScope imagery between 24 July and 2 August in 2018. The frequency map shows the main regions of red tide occurrence in 2018. These data come from the red tide daily reports provided by NIFS.  
Imagery products are available as either individual basic scenes (level 1B), ortho scenes (level 3B), or ortho tile products (level 3A). Nevertheless, we used the orthorectified level 3B surface reflectance (SR) PlanetScope product. Level 3B ortho scenes consist of raw digital number (DN) and SR product suitable for analytic and visual applications. The DN value is converted into Top of Atmosphere (TOA) radiance (\(Wm^{2}sr^{-1}\mu m^{-1}\)), then the SR is calculated from the TOA reflectance through an atmospheric correction [47]. We acquired PlanetScope imagery during the red tide event in 2018. Table 2 summarizes the PlanetScope imagery available during the red tide bloom event in the southern coast of Korea in 2018. Except for 28 July, a total of 36 images were collected consecutively from July 24 to August 2. Most images were cloud-free, with the exception of some images.  
The ground truths of _M. polykrikoides_ red tide maps corresponding to 36 PlanetScope images were obtained from daily red tide reports provided by NIFS [5] (Figure 1). Each pixel value has a discrete binary value that represents the nonexistence and existence of a red tide, respectively. To construct paired red tide map matching with the spatial regions of PlanetScope imagery, the red tide data in polygon format, which were provided by NIFS, were converted into ground-truth maps in raster format. We calculated the areas of red tide blooms on the ground-truth maps corresponding to 36 PlanetScope images. Among the 36 images, three had a red tide area of over 10 km\({}^{2}\), and the others had areas of less than 5 km\({}^{2}\). The widest red tide area of 16.81 km\({}^{2}\) was observed on 29 July 2018.
# Magnetooptics in Cylindrical Structures
## 5 Conclusions  
Magnetooptics in circular cylindrical structures was treated in terms of the Helmholtz vector wave equation for axially magnetized media characterized by electric permittivity and magnetic permeability tensors. In TCP components the Helmholtz vector wave equations splits into three ordinary differential equations. With the restriction to the magnetooptic effects linear in the off-diagonal tensor elements, their solutions were expressed analytically and applied to nonreciprocal guiding in dielectric circular cylindrical waveguides.  
The eigen value equation deduced from the boundary conditions at the core--cladding interface provided the propagation parameters for guided modes. It displays the symmetry imposed by the electric permittivity and magnetic permeability tensors for axially magnetized cylindrical waveguides. There are no first order effects on the TE and TM modes and the weak guidance approximation is included as a limiting case.  
Numerical evaluations of magnetooptic waveguides showed that fiber compatible nonreciprocal devices using yttrium iron garnets with controlled gallium concentration display reasonable magnetooptic characteristics at the propagation distance of \(\sim 10^{2}\)\(\upmu\)m.  
The analysis may be applied to optimization of magnetooptic waveguides, fiber sensors of currents and magnetic fields, and to the evaluation of magnetic field effects in fiber gyroscopes [39; 40; 41; 42; 43]. The approach may be extended to nonreciprocal multilayer and graded circular cylindrical waveguides, nonreciprocal plasmonic waveguides, nonreciprocal cylindrical waveguides of (near) square cross sections [44; 45; 46; 47; 48], circular waveguide structures containing cylindrically anisotropic metamaterials [49], and waveguides displaying optical activity [50]. From the analytical point of view, the problem is similar to that of cylindrical quantum potential well with penetrable walls [51].  
**Funding:** This research received no external funding.  
**Acknowledgments:** Charles University covered the costs to publish in open access.  
**Conflicts of Interest:** The author declare no conflict of interest.
## 7 Discussion  
The preference model can predict the customer's choice and there has not been any research like this before. This model has many benefits, i.e., helping financial institutions (boards or those related to this field) to make decisions or plan strategies for the future, offering real-time decision making.  
The approval model integrates published factors and practical factors which have been used in some commercial and non-commercial banks. This model has many benefits, i.e., offering real-time decision making, giving advice or obtaining reasons for disapproval. Both the preference model and approval model had the same limitations in that the amount of data used to develop the model was insufficient and diverse. So, it makes the performance unsatisfactory because usually an NN needs to use sufficient data for learning and developing.  
From model validation of the hybrid decision model, the result has some errors from sub-models which comprised an expense model, revenue model, appraisal model and profit model. This is because:  
1. The tool for creating the model for forecasting was multiple linear regression but some data were not suitable for using this technique.
2. Data from the revenue model which comprised factors from the preference model have not been used to evaluate revenue before so system dynamics were first used to forecast the revenue. Then, it was used again to create an equation for forecasting. The appraisal model had some errors similar to that of the revenue model. So, in future forecasting, advance statistical tools can be used to give more accuracy.  
In this research study, the results were compared, and can be compared with other research, namely the results of Model 2. Other research [23] has a higher percentage of accuracy (93%) than this research (65%). It is noteworthy that although this research has a lower accuracy than other research, its lower accuracy may be due to a greater number of approval variables being utilized in practical decision-making, and a lack of sufficient data to train the neural network. There is no prior study that matches this research for Models 1 and 3.  
Conceptualization, S.R. and N.J.; methodology, S.R. and N.J.; writing-original draft preparation, N.J.; writing-review and edition, S.R. All authors have read and agreed to the published version of the manuscript. This research work was partially supported by Chiang Mai University, Thailand.  
**Acknowledgments:** This research work was partially supported by the Graduate Program in Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University and the Supply Chain and Engineering Management Research Unit, Chiang Mai University, Chiang Mai, Thailand.  
**Conflicts of Interest:** The authors declare no conflict of interest.
Figure A3: Structure of the NN by using Pareto technique (80:20) model used for predicting.
## 4 Results and Discussion  
The self-written program of the present study is implemented in Python version 3.6.8 and is executed on an Intel CoreTM i7-7700 CPU (Intel Corporation, Santa Clara, CA, USA). All the simulation parameters are listed in Tables 2 and 3. Figure 6 describes the absorption coefficient function within the powder layer, as determined by [PERSON] [14]. To compare these two models, the simulation parameters are selected based on delivering the same amount of heat energy to the powder bed with \(P=45\) W and \(v~{}=~{}120\) mm/s. As shown in Figure 7, the geometry of the system is 150 \(\upmu\)m in the \(y\) direction and 200 \(\upmu\)m in the \(z\) direction, with 150 \(\upmu\)m upstream for \(\xi>0\) and a sufficiently long distance downstream for \(\xi<0\).  
\begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Values** \\ \hline \multicolumn{3}{c}{Material} \\ \hline Type & AISI 316L stainless steel [24] \\ Melting temperature \(T_{m}\) (K) & 1648 \\ Boiling temperature \(T_{b}\) (K) & 3200 \\ Latent heat \(L\) (kJ/kg) & 300 \\ Half of transition temperature \(\Delta T\) & 20 \\ Powder layer thickness \((\upmu\text{m})\) & 50 \\ Packing density \(\overset{\sim}{p}\) & 0.55 \\ Average coordination number \(\overset{\sim}{n}\) & 5.02 \\ Contact size ratio \(\overset{\sim}{x}\) & 0.0119 \\ Powder bed porosity \(\varnothing\) & 0.45 \\ \hline \multicolumn{3}{c}{Conditions} \\ \hline Ambient temperature \(T_{0}\) (K) & 300 \\ Convective coefficient \(h\) (W/m\({}^{2}\)K) & 20 \\ Emissivity \(\varepsilon\) & 0.4 \\ Stefan–Boltzmann constant \(\sigma\) (W/m\({}^{4}\)K) & \(5.67\times 10^{-8}\) \\ \hline \end{tabular}
\end{table}
Table 2: Simulation parameters with material properties, BCs and IC.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Values** \\ \hline \multicolumn{3}{c}{Ray-tracing method heat source (Model 1)} \\ \hline Laser power \(P\) (W) & \(35,45(\text{validation}),55\) \\ Scan speed \(v\) (mm/s) & \(80,120(\text{validation}),160\) \\ Laser beam radius \(r_{0}\) (\(\upmu\text{m}\)) & 50 [14] \\ Absorption coefficient function \(f(z)\) (1/\(\upmu\text{m}\)) & Figure 6 \\ \hline \multicolumn{3}{c}{Double-ellipsoidal shape heat source (Model 2)} \\ \hline Laser power \(P\) (W) & \(35,45(\text{validation}),55\) \\ Scan speed \(v\) (mm/s) & \(80,120(\text{validation}),160\) \\ Semi-axes in \(y\) direction \(b\) (\(\upmu\text{m}\)) & 50 \\ Semi-axes in \(z\) direction \(c\) (\(\upmu\text{m}\)) & 70 \\ Front semi-axes in \(\xi\) direction \(a_{f}\) (\(\upmu\text{m}\)) & 50 \\ Rear semi-axes in \(\xi\) direction \(a_{r}\) (\(\upmu\text{m}\)) & 200 \\ Absorptivity \(\beta\) & 0.59 \\ \hline \end{tabular}
\end{table}
Table 3: Simulation parameters with two different heat source models.
## 2 System Design
As a result of this, the unwanted signal component arising from the Earth's magnetic field in the signal is removed. Thus, a voltage of 1 V for 50 \(\mu\)T is obtained regardless of the position of the sensor. However, this voltage is produced on a  
Figure 2: Appearance of the BED system. reference of 2.5 V. A differential amplification operation is performed in the instrumentation amplifier to remove the effect of this reference voltage. At the OUT (\(-\)) output of the channel sensor, there is a reference voltage of 2.5 V. The difference signal between the signal reaching the (+) input terminal of the instrumentation and 2.5 V obtained from OUT (\(-\)) output of the channel sensor is amplified. This signal is then passed through the first order, passive-low pass filter consisting of R3-C1 elements and directed to the relevant ADC channel [38]. Figure 4: The basic diagram of the instrumentation circuit. Figure 3: Appearance of the TE100 fluxgate sensor. The ADC module is used to digitize the analogue voltages obtained from the instrumentation circuit. This module transfers digitized data to the DAQ and MCS. A 32-channel ADC network was constructed using four ADC modules each having eight channels, and this network is controlled by the DAQ and MCS. The TAMCS is a system designed to obtain data from the magnetic behavior of the BE by moving the designed sensor array over the soil covering measurement area. DAQ and MCS, together are a system that establishes communication between GUIU, SA and IC, and TAMCS. Through DAQ and MCS, the commands from the computer are transferred to TAMCS, and the data obtained from the SA and IC are transferred to the computer during magnetic field scans. A graphical user interface (GUI) was designed for recording the magnetic field data in a certain format in the classification stage, for sending the commands to control TAMCS, for starting/finishing BE scans, and analyzing the graphics. The GUIU was developed using Matlab GUI. A screen appearance of the GUIU is shown in Figure 6. The data collected in the GUIU were recorded in \"txt\" format, and have been used as an example dataset for the classification. Figure 5: The circuit schematic of instrumentation circuit only for one sensor channel. Figure 6: Screen appearance of developed graphical user interface unit (GUIU).
# Predicting Dynamic User-Item Interaction with Meta-Path Guided Recursive RNN
## 5 Experiment
### Results
#### 5.3.2 Ablation Study  
To investigate whether the components in our proposed model are all useful, we further compare MRRNN-S with the following four variants.  
* MRRNN-1 drops the meta-path module which is able to acquire the heterogeneity and semantic information from user-item interaction graph. Only the embedding learned by the word2 vec module is fed to the prediction model.
* MRRNN-2 drops the word2 vec module which can capture the sequential information from the interaction sequence. Only the feature vectors extracted by the meta-path module are fed to the remaining part of MRRNN-S.
* MRRNN-3 drops the GCN module which is designed to catch the structural information from the user-item interaction graph. Only the feature vectors processed by the meta-path module and word2 vec module are input into the model.
* MRRNN-4 drops the meta-path, word2 vec, and GCN modules at the same time, and randomly initializes the embedding by a normal distribution as the model input.  
As shown in Table 4, one can see that the word2 vec module, the meta-path module, and the GCN module are all useful because the performance will decrease when any one of them is removed.  
In order to show the influence of each module on MRRNN-S more intuitively, we draw a histogram of the percentage of each variant's performance to the performance of the full model over the two datasets presented in Figure 6. Through Figure 6, one can clearly observe that the sequential information hidden in the interaction sequence seems to be more important on both two datasets because ignoring such information will result in a remarkable performance decline in terms of MRR and Recall@10. One of the possible reasons is that the data sets are mainly composed of sequence data, while the graph data structure is relatively simple. In particular, there are only two types of nodes that can be utilized. In addition, one can also observe that the performance of MRRNN-2 on the two datasets is quite different, with the performance on the Wikipedia dataset significantly better than the performance on the JingDong dataset. This is probably because of the contradiction between the large amount of data and too few types of nodes in the JingDong dataset, which makes the model more difficult to acquire useful auxiliary information from the sparse heterogeneity. Overall, MRRNN-S achieves the best performance in both Wikipedia and JingDong datasets when the three modules are combined, which verifies that the four proposed modules are all helpful for improving the model performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Metric** & **MRRNN-1** & **MRRNN-2** & **MRRNN-3** & **MRRNN-4** & **MRRNN-S** \\ \hline \multirow{2}{*}{Wikipedia} & MRR & 0.753 & 0.483 & 0.581 & 0.449 & 0.779 \\  & Recall@10 & 0.782 & 0.610 & 0.692 & 0.583 & 0.801 \\ \multirow{2}{*}{JingDong} & MRR & 0.19 & 0.009 & 0.121 & 0.003 & 0.264 \\  & Recall@10 & 0.261 & 0.012 & 0.220 & 0.005 & 0.343 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment result of the ablation study.
## 3 The Structure of the Decision Support Platform
### Production Scheduling Module
#### 3.5.2 The Steps of the Proposed Algorithm  
The algorithm considers the following steps to obtain the proposed objective represented by the flattening of the energy consumption profile.  
* _Step 1. Determine the working characteristics of equipment, installations, and systems_ The records from the measurement database represents the base to determine the operating characteristics associated with the working time multiplied by the hourly energy consumption for each equipment, installation, and system. If two equipment are dependent and work together, the aggregate characteristic cumulates the working times.
* _Step 2. Initialization of the input data matrix_ In the input data matrix (IDM) having the size (\(n_{e}\times 8\)), where the total number of rows indicates all equipment and installations \(e\), \(e=1,\;\ldots,\;n_{e}^{a}\), associated with all activities \(a\), \(a=1,\;\ldots,\;n_{a}\), and the columns refers to the name of the equipment or installation, the characteristics relating to the operation mode (on or off), the working cycles, the working time, type (programmable or nonprogrammable), the dependency to work together with other equipment to obtain the final product \(f\), and the start and stop working hours of the equipment, information are introduced.
* _Step 3. Optimization Process_ Combinatorial optimization was implemented to obtain the optimal solution regarding the production scheduling, which leads to flattening of the energy consumption profile.
# A Stock Market Decision-Making Framework Based on CMR-DQN
## 4 Experiments and Results
### Feature Selection
#### 4.3.2 Volume-Weighted Moving Average Convergence Divergence (VMACD)  
In financial market technical analysis, the moving average convergence divergence (MACD) indicator is commonly used. However, MACD typically relies solely on price data, particularly the closing price, without considering volume data. To address this limitation and incorporate volume data into the analysis, the volume-weighted moving average convergence and divergence (VMACD) indicator is introduced. The calculation formula for VMACD is as shown in Equation (15):  
\[\begin{split}&\text{DIFF}:\text{EMA}(\text{VOL},\text{SHORT})- \text{EMA}(\text{VOL},\text{LONG})\\ &\text{DEA}:\text{EMA}(\text{DIFF},\text{M})\\ &\text{VMACD}:\text{DIFF}-\text{DEA}.\end{split} \tag{15}\]  
Here, DIFF represents the difference between two volume exponential moving averages (EMAs), one with a shorter period (SHORT) and the other with a longer period (LONG). DEA is the EMA of the DIFF values over a specified period (M). Finally, the VMACD line is derived by subtracting DEA from DIFF.  
In practical applications, similar to MACD, VMACD signals potential market trends. However, VMACD may anticipate market movements a few days earlier than MACD. Specifically, when trading volume significantly deviates from its normal level and prices increase, it often signals the beginning of a market reversal. Crossing the zero line is also considered a signal of a market reversal.  
It is essential to note that while VMACD provides valuable insights into market dynamics, it should be used in conjunction with price-based indicators for comprehensive analysis. Additionally, VMACD is commonly utilized alongside other indicators like KD and KDJ for enhanced decision-making in trading strategies.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 4 Security Analysis
### Non-Repudiation  
In the proposed scheme, we used ECDSA to achieve the repudiation issues. Every message transmitted by the sender must sign with their private key, then the receiver can be verified using its public key. We have sorted a list of verification equations that need to be verified by the receiver in every phase, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Phase**} & \multicolumn{2}{c}{**Party**} & \multirow{2}{*}{**Verification Function**} \\ \cline{2-2} \cline{4-4}  & **Sender** & & & \\ \hline \multirow{2}{*}{Authentication Phase} & A & B & \(Verify(h_{A_{i}}{}^{\prime},r_{A_{i}},s_{A_{i}})\) \\  & B & A & \(Verify(h_{B^{\prime}},r_{B_{i}},s_{B_{i}})\) \\ \hline \multirow{4}{*}{Issuing and Manufacture Phase} & MF & IDI & \(Verify(h_{M_{i}}{}^{\prime},r_{M_{i}},s_{M_{i}})\) \\  & IDI & CA & \(Verify(h_{IDI_{i}}{}^{\prime},r_{IDI_{i}},s_{IDI_{i}})\) \\  & CA & IDI & \(Verify(h_{CA_{i}}{}^{\prime},r_{CA_{i}},s_{CA_{i}})\) \\  & IDI & MF & \(Verify(h_{IDI_{i}}{}^{\prime},r_{IDI_{i}},s_{IDI_{i}})\) \\ \hline \multirow{2}{*}{Logistics Phase (Shipping)} & SHP & LG & \(Verify(h_{SHP}{}^{\prime},s_{SHP})\) \\  & LG & SHP & \(Verify(h_{LG_{i}}{}^{\prime},r_{LG_{i}},s_{LG_{i}})\) \\ \hline \multirow{2}{*}{Logistics Phase (Receiving)} & LG & RCP & \(Verify(h_{LG_{i}}{}^{\prime},r_{LG_{i}},s_{LG_{i}})\) \\  & RCP & LG & \(Verify(h_{RCP}{}^{\prime},r_{RCP},s_{RCP_{i}})\) \\ \hline \multirow{2}{*}{Consumption Phase} & RT & CA & \(Verify(h_{RT_{i}}{}^{\prime},r_{RT_{i}},s_{RT_{i}})\) \\  & CA & RT & \(Verify(h_{CLA_{i}}{}^{\prime},r_{CA_{i}},s_{CA_{i}})\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Non-repudiation’s verification of the proposed scheme.  
Figure 15: The mechanism of the blockchain architecture.