esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
###### Abstract
With the recent developments in deep learning, the deep learning models used to study traffic flow prediction are diverse, e.g., the deep belief net (DBN), stacked autoencoder (SAE), convolutional neural network (CNN), and long short-term memory (LSTM) network. Generally, these simple neural network models can be applied to obtain only a single traffic data characteristic--CNN extracts spatial features, whereas the LSTM model extracts temporal features. Therefore, they fail to completely capture the complex characteristics of traffic. Although several studies have developed hybrid models, most of them estimated the predictions from individualistic traffic flow data and independent processing. To address these issues and obtain real-time, accurate traffic predictions, this paper proposes a combined CNN-BILSTM model that integrates flow and speed characteristics for short-term traffic flow forecasts. The major contributions of this paper are stated below:  
1. We propose a CNN-BILSTM hybrid model for predicting traffic flow by extracting the spatial features via CNN and the temporal features via the BILSTM model. 2. As the majority of existing research is limited to predicting traffic flow only, neglecting the influence of other factors, in this study, when predicting traffic flow, the influence of traffic flow and speed on traffic flow were considered at the same time. 3. Due to the unique periodic nature of traffic flow data, this research established two cyclic components--weekly and daily--to predict the traffic flow. The main research purpose of this study was to predict traffic flow by proposing a CNN-BILSTM model. First, through data preprocessing, the processed data were used as input into the CNN model to extract spatial features, then the BILSTM model was used to extract temporal features, and finally, the model was tested by substituting the dataset to predict its performance. The rest of the paper is structured as follows: Section 2 mainly reviews the literature on this research direction, at home and abroad, in recent years; Section 3 describes the algorithm framework and algorithm steps proposed in this paper in detail; in Section 4, the data and experimental parameters are explained in detail, as well as the selection of evaluation indicators; Section 5 is the specific analysis of the experimental results. By setting different components and different time steps, the experimental prediction results are analyzed in detail; Section 6 is the conclusion and outlook.
## 1 Introduction
This could lead to the abnormal operation of instruments mounted on the satellite, especially the GNSS receiver, seriously affecting the observation quality [12; 13; 14]. The POD of spacecrafts and satellites can be seriously affected by charged particles ejected by the sun. This is especially true for LEO satellites because most of them hold a near-polar orbit and the orbit estimation accuracy can be degraded by the interference of the Earth's magnetic field and solar wind particles. In addition, solar storms affect the Earth's upper atmosphere, destroying the structure of the ionosphere. The operation of onboard GNSS receivers can be easily degraded or interrupted by ionospheric scintillations at altitudes of LEO satellites (200-1000 km). During a solar storm, the behavior of the ionosphere differs significantly from its behavior during quiet times and the risk of degradation of the GNSS signal amplitude or a failure to acquire signals in severe cases increases considerably, as has been reported in many studies [15; 16; 17]. Degraded GNSSsignals can reduce the availability and reliability of the GNSS-based POD. In addition, with increasing ionospheric density, the accuracy of a conventional orbit determination is seriously reduced [18]. The effect of spaceborne observational errors on a large LEO-constellation-augmented GNSS POD has not yet been studied. We focus on the effects of actual observational errors on the BDS/LEO combined POD in this study. First, in Section 2, BDS-3, as well as various other LEO constellations, is designed and the approach for ground-based and spaceborne observation simulations based on the BDS-3/LEO orbits is introduced. Then, the LEO-constellation-augmented BDS POD strategies are described in detail in Section 3. In the experiments described and analyzed in Section 4, the actual spaceborne measurement noise is first evaluated and then taken as the calibration value for the subsequent observation simulations. Then, the observational errors extracted from real-observed spaceborne data are analyzed and added to the simulated observations. The effects of these observational errors on the BDS PODs augmented with various LEO constellations are then assessed. The shortcomings of the work and an outlook for future research are summarized in Section 5. Some useful conclusions are given to provide references for future LEO constellation designs in Section 6.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 4 Proposed Methodology
### Defuzzification  
The method of obtaining a numerical result based on a defined fuzzy set and on an output member function is called defuzzification. The output member function is shown in Figure 6. The center of gravity technique is considered to defuzzify the fuzzy result. The following formula describes the defuzzifier technique:  
\[Fuzzy\_Cost=[\sum_{all\ rules}R_{i}*\eta(R_{i})]/[\sum_{all\ rules}\eta(k_{i})] \tag{5}\]  
where \(Fuzzy\_Cost\) represents the degree of deciding factor \(R_{i}\) denotes all fuzzy rules and variables and \(\eta(R_{i})\) its member function. The output of the \(Fuzzy\_Cost\) function is changed to numerical result based on the above described defuzzification method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rule** & **T** & **R** & **B** & **U** & **V** & **N (o/P)** \\ \hline
1 & Good & Medium & Low & Bad & Slow & Bad \\
2 & Medium & Medium & Low & Bad & Slow & Bad \\
3 & Bad & Low & High & Bad & Slow & Bad \\
4 & Medium & Medium & High & Medium & Fast & Fair \\
5 & Medium & Medium & Low & Medium & Fast & Fair \\
6 & Good & Medium & Medium & Medium & Fast & Fair \\
7 & Good & Medium & Medium & Medium & Slow & Fair \\
8 & Medium & Medium & High & Good & Fast & Fair \\
9 & Medium & High & High & Medium & Fast & Fair \\
10 & Medium & High & Low & Medium & Very fast & Fair \\
11 & Medium & Medium & High & Medium & Fast & Fair \\
12 & Good & High & Low & Medium & Fast & Fair \\
13 & Bad & High & Low & Bad & Slow & Bad \\
14 & Bad & Low & Low & Bad & Slow & Bad \\
15 & Bad & Low & Medium & Medium & Fast & Fair \\
16 & Medium & Low & High
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 3 Spectroscopic Techniques
### Measurement of Luminescence Lifetimes
#### 3.3.1 Measurement of Lifetime in the Frequency Domain  
The measurement of lifetimes in the frequency domain involves exciting the luminescent indicator with a sinusoidally modulated light source, typically at frequencies between 0.1 and 10 \(\tau^{-1}\). Once the sensor is excited, its emission follows the excitation frequency, with a phase delay that is a function of the lifetime, as shown in Figure 10.  
The lifetime can be determined from the measurement of the phase delay between the excitation and light emission:  
\[\tau=\frac{1}{2\pi f_{exc}}tan\varphi \tag{9}\]  
where \(f_{exc}\) is the frequency of the modulated excitation source. This method is relatively simple and does not require overly complex instrumentation for lifetimes ranging between \(\upmu\)s and ms. If the indicators have ns lifetimes, higher modulation frequency light sources are required, which are more expensive. The main advantage of performing measurements in the frequency domain is that the bandwidth of the measurement system can be very small, since the measurement is carried out at a single frequency. Noise increases with bandwidth, so working under these conditions significantly improves the signal-to-noise ratio. In practice, the phase delay and the determination of the lifetime can be affected if part of the excitation light reaches the detector. This may also happen if there are other  
Figure 10: Basis of the method used for measuring the lifetime in the frequency domain.  
fluorescent processes in the sensor phase, resulting in a spectral overlap between emissions. In both cases, the use of optical filters is essential. The most popular instruments for this technique are dissolved oxygen sensors [44].
## 3 Finite Element Analysis
### Validation of the Derived Physical Characteristics
While minor discrepancies between the peak values of the reconstructed and simulated results may exist, these are likely to have been due to the simplified consideration of only the first two vibration modes. Despite these minor differences, the overall agreement between the two sets of results supports the inference that the transfer matrices for both high-frequency and low-frequency displacements are indeed identical, as theoretically postulated in Equation (15). Subsequently, we conducted further validation to ascertain the theoretical feature that the transfer matrix for high-frequency responses would be equally applicable to other scenarios characterized by varying temperatures and traffic conditions. In the first scenario, the bridge's material stiffness was adjusted to 0.75 times its original value to simulate the effects of temperature variations. In the second scenario, the vehicles' moving speed was increased to 10 m/s to represent different traffic conditions. The comparisons of the reconstructed versus simulated low-frequency displacements at the 5 m point for these two distinct scenarios are presented in Figures 13a and 13b, respectively. The results clearly demonstrate that the reconstructed displacements were in close alignment with the simulation outcomes. This concurrence confirms that the transfer matrix calibrated for high-frequency responses in one scenario can reliably be employed to reconstruct low-frequency responses in other scenarios, irrespective of differing traffic conditions and ambient temperatures. It should be noted that while structural damping was not included in this numerical simulation, the physical characteristics discussed should still hold true when structural damping is taken into account. This is because these characteristics are founded on the principle that the mode shape functions remain constant. For linear bridge structures, whether or not structural damping is considered, the mode shape functions are invariant. Figure 12: Comparison of low\(-\)frequency displacement at 5 m between reconstruction and FEM simulation. Thus, these characteristics and the proposed method should also be applicable in practical engineering scenarios. In practical engineering, damping reduces the amplitude of higher vibration modes. If measurement noise is significant, it may obscure these higher modes, which should be identified during preliminary data processing. For this simply supported beam model, only the first two vibration modes were considered. However, in real-world engineering, additional vibration modes may contribute to structural responses and should be accounted for. Therefore, installing more observation points with sensors is likely to be necessary to achieve a more accurate reconstruction result.
# End-Users' Augmented Reality Utilization for Architectural Design Review
###### Abstract  
To effectively use augmented reality (AR) technology for end-user involved design collaboration, it is necessary to ensure the effectiveness of the AR system from the end-user's perspective. However, most efforts have mainly focused on technological development, and as such, limited attention has been paid to the end-user's application of the AR system. Therefore, this study investigates how the AR system affects architectural design review based on the user's perspectives. Three different display systems presenting a 3D model including a 2D screen, VR, and AR were tested, and a total of 76 participants evaluated visual presentation quality, perceived acceptability, and user experience according to their usage of the visualization platform types during the design review activities. Compared to other systems, the results indicated that the AR system could be more effective in reviewing the visual elements of a building. Furthermore, AR showed the highest ratings for acceptance level and user experience. The innovation provided by AR created a positive user experience, despite its remaining challenges to be resolved in terms of functionality. Since it is expected that the use of AR can be promoted by overcoming certain technological limitations, this study contributes to guiding AR system applications for end-users involved in the design review process.  
augmented reality; virtual reality; 3D building model; design review +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Phase II: Greenhouse Panel Study
#### 3.3.2 Cu in Surface Runoff and Leachate  
Total Cu concentration (mg/L) in surface runoff in the control panel was significantly higher than in the panel with amelina plants (Figure 3C). In samples taken four months after sowing amelina, Cu concentration in the surface runoff was in excess of twenty-five times higher than that of the amelina panel. The high concentration of total Cu in surface runoff, as compared to dissolved Cu, was due to stamp sand erosion in surface runoff in the control panel. Total Cu concentration (mg/L) in leachates in the control panel was not significantly different from the amelina panel after two and four months of amelina sowing (Figure 3D). However, in the leachate samples taken after six months, the Cu concentration in the control panel was significantly lower than the amelina panel.  
Soil erosion, leaching [26], and deep ploughing [27] are the three main processes that lead to the redistribution of Cu in the soil. Plants influence chemical conditions in the soil, as root growth increases soil OM especially around rhizosphere. This in turn stimulates the growth of microorganisms [28]. Various researchers have found that plants can alter the chemical mobility of metals in the rhizosphere [29; 30; 31]. Roots also release soluble organic compounds (root exudates) into the rhizosphere, which can form complexes with Cu and can increase Cu uptake [32; 33; 34]. Once in the roots, Cu can be stored in the root cell walls [35], or can be transported to the aerial parts of the plant [30]. Root exudates can mobilize metals, which not only leads to increased uptake, but can also result in increased leaching [28; 36; 37]. This is likely to be the reason for the leaching of Cu in the control panel to be similar to that of the panel planted with amelina, although plant cover significantly lowered both TSS and turbidity in the leachate.
# Effects of Nano-TiO\({}_{2}\) Mediated Photocatalysis on _Microystis aeruginosa_ Cells
## 3 Results and Discussion
### Charge Density on the M. aeruginosa Cell Surface  
Figure 5 shows how the charge density of the _M. aeruginosa_ cells varies with the time of nano-TiO\({}_{2}\) mediated photocatalysis. The zeta potential of the control _M. aeruginosa_ cell surface was \(-\)30.20 \(\pm\) 0.32 mV. _M. aeruginosa_ was characterized with negative charges, which resulted from the molecular groups on the surface. Both the cell wall and the cell membrane contain plenty of proteins, which consist of amine (-NH\({}_{2}\)) and carboxylic acid (-COOH) functional groups that are joined orderly through peptide bonds. The carboxylic acid groups (-COOH) can be deprotonated to become the negatively charged carboxylates (-COOH), and the amino groups (-NH\({}_{2}\)) can be protonated to become the positively charge ammonium groups (-NH\({}_{3}\)\({}^{+}\)). When the pH of the cell suspension exceeded the isoelectric point, the cells would carry negative charge. Generally, the pH of the cell suspension is higher than the isoelectric point of bacteria. As a result, the _M. aeruginosa_ cells were characterized with negative charges [23; 24].  
After the _M. aeruginosa_ cells were subject to nano-TiO\({}_{2}\) mediated photocatalysis, their zeta potential decreased; that is, the negative charges on the cell surface decreased. When the time of nano-TiO\({}_{2}\) mediated photocatalysis was increased to 6 h, the zeta potential could no longer be measured. Both the carboxylic acid groups and the ammonium groups in the proteins are hydrophilic. The proteins are inserted into the phospholipids bilayer and extracellular polymeric substances in the cell wall and cell membrane, and the hydrophilic groups (-COOH and -NH\({}_{3}\)\({}^{+}\)) are exposed to the external  
Figure 5: Zeta potential of _M. aeruginosa_ cells after nano-TiO\({}_{2}\) mediated photocatalysis.  
environment and easily damaged by free radicals during nano-TiO\({}_{2}\) mediated photocatalysis [24]. Therefore, the charge density decreased significantly due to the damages of the hydrophilic groups.
## 2 Study Area and Datasets
### Datasets
#### 2.2.1 Satellite RS Data
Since the majority of the available S1 images over the study site were acquired in an ascending orbit, this study only utilized S1 data acquired in an ascending orbit. The study site was entirely covered by two ascending orbits with relative orbit numbers of 72 and 174 (Figure 1b), resulting in multiple S1 scenes capturing the study site.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 4 Results
### Example 2
Lower encoding levels result in higher undetectability of the steganogram but come at the cost of reduced resistance to distortions and noise in the information transmitted between consecutive video frames--iterations of the iterative video steganogram.
## 4 Results and Discussion
### First Design
#### 4.2.1 Changing Magnet Grade  
The magnet grade of the prototype was N48H, although there were several magnet grades of lower than N48H, including N45H, N42H, and N40H. Therefore, the electromagnetic material properties of different magnets and analysis results are shown in Table 2. The force factor and magnetic flux leakage are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Label** & **Magnet Grade** & **Remanence [G]** & **Coercivity [KA/m]** & **Force Factor [N/A]** & **Magnetic Flux Leakage [G]** \\ \hline
1 & N48H & 14,000 & 1046 & 3.12 & 643.7 \\
2 & N45H & 13,500 & 1011 & 3.03 (\(-2.88\%\)) & 619.2 (\(-3.81\%\)) \\
3 & N42H & 13,000 & 979 & 2.94 (\(-5.77\%\)) & 594.4 (\(-7.66\%\)) \\
4 & N40H & 12,700 & 956 & 2.88 (\(-7.69\%\)) & 579.4 (\(-9.99\%\)) \\
5 & N38H & 12,500 & 930 & 2.85 (\(-8.65\%\)) & 569.5 (\(-11.53\%\)) \\
6 & N35H & 12,000 & 895 & 2.75 (\(-11.86\%\)) & 544.6 (\(-15.40\%\)) \\ \hline \end{tabular}
\end{table}
Table 2: Analysis results with different magnet grades.  
Figure 6: Comparison between experiment and analysis results of the prototype: (**a**) displacement; (**b**) impedance.  
With a decrease in the magnet grade, the magnetic flux leakage reduced linearly. However, the force factor also decreased. Compared to the prototype, there was a 15.40% decrease in magnetic flux leakage and an 11.86% decrease in the force factor when using an N35H magnet.
# QoS-Aware Flexible Handover Management in Software-Defined Mobile Networks
## 3 QoS-Based Flexible Handover Scheme in SDN  
In the proposed scheme, it is assumed that the action fields in the forwarding plane nodes include a buffering function in addition to the dropping the packets, forwarding through the specific port and encapsulating and forwarding to the controller as defined in the OpenFlow specification [12]. This can be a practical assumption because hardware or software-based forwarding nodes generally have a buffer to store and process the incoming packets. In addition, there have been works which consider buffers to provide scalable forwarding node designs [42; 43] and analyze the optimum buffer size for QoS provisioning [44]. Except for the buffering function, the proposed scheme exactly follows the operations in the OpenFlow specification [12].
## 4 Discussion
### Common Techniques  
The analysis of approaches for agricultural commodity price prediction reveals the increasingly sophisticated nature of the field in terms of the use of advanced computational techniques. Figures 7 and 8 present the frequency of the techniques and methods used in the price prediction of the selected agricultural commodities. Multi-class classification is usually handled using machine learning techniques, as they are convenient and can work with various datasets. Since feature selection techniques such as Support Vector Machines, Decision Trees, and Random Forests excel at capturing nonlinear relationships as well as performing robust predictions, these methods can be very useful [26; 29; 31]. Ensemble learning combines various models to strengthen their individual algorithms' faults to improve predictive accuracy. Techniques such as GBM and Boosting can work on complex datasets and adjust prediction horizons [26].  
Deep learning methods have become increasingly popular due to their efficiency in learning complex patterns in extensive and unstructured data. These architectures include Long Short-Term Memory (LSTM) networks [53], CNNs, and hybrid models, which are suited to long-term forecasting and temporal dependencies [48]. These models often outperform traditional methods in accuracy but have greater complexity and computational demands. Time series models, including some of their hybrids (ARIMA), remain important for forecasting because of their interpretability and their effectiveness in trend analysis [60].  
Support Vector Machines (SVMs) are popular machine learning approaches because they work well when dealing with high-dimensional data and nonlinear relationships [68]. Decision Trees are often used for predictive modeling because they are simple and easy to interpret [25]. Random Forest is a dominant technique for ensemble learning. It reduces overfitting and improves predictive accuracy through the aggregation of multiple decision trees [29]. GBM is popular for its efficiency and has strong performance in structured  
Figure 8: Common models used in commodity price prediction.  
Figure 7: Frequency of techniques used.  
datasets [26]. Featuring the ability to model sequential data and the ability to capture temporal dependencies, LSTM networks are widely used with deep learning methods for time-series forecasting [53]. Other architectures, including Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), complete the toolkit for complicated patterns and large datasets [43]. Traditional time series methods such as ARIMA provide solid trend analysis and interpretability, and more modern approaches like Autoregressive methods are flexible in working with seasonality and irregularities [64].
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 4 Evaluation
### Design Overheads  
The proposed SARDIMM integrates an NMP module into a buffer device without requiring any additional modification of the DRAM. The NMP module comprises buffers, an NMP controller, and an FPBF module, designed using Verilog HDL and implemented on the Xilinx UltraScale+ ZCU104 FPGA board. Table 2 presents a summary of the hardware resources utilized in the NMP module, which includes 10,493 look-up tables (LUTs), 2224 flip-flops (FFs), 40 digital signal processors (DSPs), and 144 block RAMs (BRAMs). In addition, the power consumption per NMP module was 0.678 W, since two NMP modules were inserted into the SARDIMM, with 1.356 W consumed per SARDIMM. Considering that the power of the DIMM was 13 W [28], the power of the NMP module was negligible.