esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Stimuli  
Three different auditory conditions were used: (i) the vehicle emitted the noise of a car with a gasoline combustion engine; (ii) the vehicle emitted the noise of a car with an electric engine; and (iii) the vehicle emitted no sound. The third type of stimulus was considered as a control condition to assess the role auditory cues play in crossing decision-making.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Conditions**} & \multicolumn{2}{c}{**Stop**} & \multicolumn{2}{c}{**Slow Down**} \\ \cline{2-5}  & \(\text{D}_{\text{i}}\) & \(\text{D}_{\text{f}}\) & \(\text{D}_{\text{i}}\) & \(\text{D}_{\text{f}}\) \\  & **(m)** & **(m)** & **(m)** & **(m)** \\ \hline m & 15.80 & 5.71 & 16.72 & 7.01 \\ sd & 5.62 & 2.40 & 6.21 & 4.14 \\ max & 27.37 & 14.06 & 28.20 & 22.04 \\ min & 6.58 & 1.26 & 2.94 & 2.69 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean, maximum, minimum, and standard deviation of vehicle distance to the crosswalk for stop and slow down movement patterns.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Conditions**} & \(\text{V}_{\text{i}}\) & \(\text{V}_{\text{f}}\) & \(\text{D}_{\text{i,mov}}\) & \(\text{D}_{\text{i}}\) & \(\text{D}_{\text{f}}\) & **Speed** \\  & **(km/h)** & **(km/h)** & **(m)** & **(m)** & **(m)** & **Pattern** \\ \hline
1 & 30 & 30 & 30 & - & - & Constant \\
2 & 30 & 10 & 30 & 25 & 5 & Slow Down \\
3 & 30 & 0 & 30 & 20 & 5.50 & Stop \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of vehicle movement in the different conditions presented in the experiment.  
Table 3 presents the main characteristics of the stimuli audio component. The measurement time corresponds to the interval between the beginning of the stimulus presentation and the instant when the vehicle stopped or passed by the participant.  
As expected, the noise emitted by gasoline combustion vehicle was in general louder than that emitted by electric vehicle. The difference between the noise indicators for electric and gasoline combustion vehicles is bigger for the stimuli regarding the slow down pattern and lower for the constant speed pattern.  
Each of the three conditions presented in Tables 2 and 3 was repeated five times for every auditory condition (electric engine (electric), gasoline combustion engine (gasoline), and no auditory cue (no sound)). Thus, in total, throughout the experiment, 90 stimuli were presented to each participant in a random order (3 speed patterns \(\times\) 3 auditory conditions \(\times\) 2 scenarios \(\times\) 5 repetitions).  
The virtual models of the approaching vehicle used in the experiment were a Kia Ceed SW (Seul, South Korea, Kia Motors, 2011) for gasoline condition (Figure 2a) and a Renault Zoe ZE (Boulogne, France, Renault S.A., 2018) for electric condition (Figure 2b). For the no sound, the visual aspect of the vehicle varied between both models.
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 1 Introduction
Moreover, real-time container migration is an important technology [7] that has triggered extensive research by technicians. Through real-time container migration, the containers in one device can be migrated to another device without user awareness. Generally, container transfers can be completed within 30 s. These values can reach millions of seconds under various optimization methods and conditions. Therefore, container migration can be effectively applied to solve reliability problems using a similar approach to fault recovery or load balancing. The major contribution of this paper is that we propose a novel two-phase method, the combination of fault prediction and container migration, on the edge computing. The fault prediction is used to decide when to trigger the container migration. The migration is to prevent the service failure. We first forecast a container failure and then migrate the container to another healthy node. We develop several approaches for both steps and select the most suitable approach to address this issue. In Section 3, we describe problems with the system model. In Section 4, we introduce fault prediction and cost optimization for migration. Machine-learning algorithms are applied to historical data to build a model for fault prediction. Based on this fault prediction, the container migration strategy is modeled as an optimization problem, and a migration node selection algorithm based on a genetic algorithm (GA) is proposed to determine the appropriate migration target in healthy edge devices and migrate the container services on the devices. Using the hot-migration technology of containers, services are migrated to other devices before a device failure occurs. The migration cost is reduced as much as possible to balance the load on the edge network, which simultaneously increases its stability. Section 5 describes simulation experiments conducted using the proposed methods, including the prediction of equipment failures based on historical datasets and the effectiveness of the container migration strategy based on failure prediction. The experimental results demonstrate that the proposed method can effectively predict faults, reduce migration costs, and ensure the load balance of the edge network to ensure the reliability of services.
## 2 Materials and Methods
### Sequence Modeling Classifier
#### 2.3.2 Long Short-Term Memory  
The gating mechanism controls the amount of information from the previous time step, which contributes to the current output. Using this gating mechanism, LSTM overcomes vanishing or exploding gradients, wherein a standard RNN there is no gate [29]. The LSTM gate mechanism implements three components; (1) inputs, (2) forget, and (3) output gate [29]. The LSTM input layer must be in 3-dimension vectors (samples, time steps, features). The samples are the amount of train or test set, time steps are 15-lead ECG signals, and features are 4000 samples (4 s) in each window size.  
In the LSTM algorithm, the process consists of a forward and backward pass, as shown in Figure 4. A forward pass is calculated as input \(x\) with a length \(T\) by starting \(t=1\) and recursively by applying an update equation while adding, \(t\). The scripts \(i,f\) and \(o\) refer to the input, forget, and output gates from the block, respectively. The script \(c\) refers to one of the \(C\) memory cells. In time, \(t\), LSTM receives a new input in the form of vector \(x^{t}\) (including bias), and the output of the vector \(h^{t-1}\) in the previous time steps (which \(\otimes\) denotes element-wise product Hadamard).  
Weights from cell \(c\) to input, forget, and output gates are annotated as \(w_{i},w_{f},w_{o}\) respectively. The equations are given by  
\[a^{t}=\tan\hh(W_{c}x^{t}+U_{c}h^{t-1}) \tag{8}\]  
\[i^{t}=\sigma\qty(W_{i}x^{t}+U_{i}h^{t-1})=\sigma\qty(i^{t}) \tag{9}\]  
\[f^{t}=\sigma\qty(W_{f}x^{t}+U_{f}h^{t-1})=\sigma\qty(f^{t}) \tag{10}\]  
\[o^{t}=\sigma\qty(W_{o}x^{t}+U_{o}h^{t-1})=\sigma\qty(o^{t}) \tag{11}\]  
Figure 4: (**a**) Forward, and (**b**) backward passes in the Long Term-Short Term Memory (LSTM).  
Ignoring the non-linearities,  
\[z^{t}=\left[\begin{array}{c}\dot{a}_{t}\\ \dot{\tau}^{t}\\ f^{t}\\ \dot{\sigma}^{t}\end{array}\right]=\left[\begin{array}{cc}W^{c}&U^{c}\\ W^{i}&U^{i}\\ W^{f}&U^{f}\\ W^{\circ}&U^{p}\end{array}\right]\left[\begin{array}{c}x^{t}\\ h^{t-1}\end{array}\right]=W\bullet l^{t} \tag{12}\]  
Then, the memory cell values updated by combining \(a^{t}\) and the contents of the previous cell \(c^{t-1}\). The combination is based on the magnitude of the gate input \(i^{t}\) dan forget gate \(f^{t}\):  
\[c^{t}=i^{t}\bullet d^{t}+f^{t}\bullet c^{t-1} \tag{13}\]  
In the end, the LSTM cell calculates the output value by passing an updated cell value through non-linearity:  
\[h^{t}=o^{t}\bullet f\left(c^{t}\right) \tag{14}\]  
Backward pass computes starting from \(t=T\) and recursively calculating the derivative unit in each time step. As standard RNN, all status and activations are initialized to zero at \(t=0\), and all \(\delta=0\) at \(t=T+1\).
# A Survey of the Applications of Text Mining for the Food Domain
## 8 Challenges and Future Directions  
### Opportunities  
In the food domain, text mining technology offers promising applications, especially in food safety monitoring, business value analysis, and consumer support, with ample room for further development.  
Text mining plays a crucial role in supporting food safety regulations by analyzing data from social media, online forums, and government sources. This enables early detection of foodborne disease outbreaks and helps identify problematic foods, providing decision-making support to regulatory authorities [134; 130; 131; 132; 133]. Additionally, text analysis can monitor dietary patterns and population health, allowing for targeted dietary guidance and health education [1; 69; 138].  
In the business landscape, text mining provides food enterprises with valuable market intelligence. By analyzing consumer feedback on digital platforms, businesses can understand customer preferences, informing product innovation and marketing strategies [33; 58].  
Consumers also benefit from text mining technology. Through the analysis of diverse data such as purchase records and browsing behavior, mobile applications and recommendation systems can offer personalized dietary advice and shopping guidance, facilitating informed food choices and promoting healthy eating habits [111; 139; 140].
# Opportunistically Exploiting Internet of Things for Wireless Sensor Network Routing in Smart Cities
## 5 Simulation Details and Results
### Simulation Environment  
In our simulator, we visualize our approach with realistic wireless channel conditions and radio models. Interference is handled using received signal strength. Due to this realistic channel modeling, there is some packet loss caused by interference or poor value of received signal strength. The wireless channel is defined as a map of path loss and not simply by connection between sensor nodes. Moreover, at radio level, probability of reception depends on SINR (Signal to Interference and Noise Ratio). Multiple TX power levels of Radio can be configured for individual sensor nodes.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Power** \\ \hline Radio Off & 0.04 mW \\ \hline Radio Sleep & 1.4 mW \\ \hline Radio Receiver & 62 mW \\ \hline Radio Transmitter (0 dBm) & 57.42 mW \\ \hline Radio Transmitter (\(-\)5 dBm) & 46.2 mW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power consumption in CC2420 Radio.
## 4 Feature Extraction and Selection  
The objective for feature extraction and selection is to build a set of representative features that can be used by ML models for making various types of predictions, such as the _next offset_ and _hotness_ of a file. Based on the analysis of file access patterns discussed in Section 3, we extracted a set of 37 features, presented in Table 2 and discussed in Section 4.1, that are derived from information extracted from file requests, file metadata and patterns, directory patterns, and file format patterns. The target variables are presented next in Section 4.2, while feature evaluation is presented in Section 4.3.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Feature** & \begin{tabular}{c} **Next File** \\ **Hotness** \\ \end{tabular} & \begin{tabular}{c} **Next Offset** \\ **Hotness** \\ \end{tabular} & \
\begin{tabular}{c} **Next File** \\ **Class** \\ \end{tabular} &
\begin{tabular}{c} **Next Offset** \\ **Class** \\ \end{tabular} \\ \hline
**Request** & & & & & & & & \\ \hline Request Operation & 0.77 & & 2.20 & ✓ & 1.36 & & 1.00 & \\ Request Offset & 2.24 & ✓ & 2.73 & ✓ & 1.71 & ✓ & 2.64 & ✓ \\ Request Length & 0.99 & & 1.35 & ✓ & 1.33 & & 1.72 & ✓ \\ \hline
**File** & & & & & & & & \\ \hline File ID & 1.33 & ✓ & 1.33 & ✓ & 1.73 & ✓ & 1.61 & ✓ \\ File Size & 1.89 & ✓ & 1.40 & ✓ & 2.05 & ✓ & 2.07 & ✓ \\ Time Since Creation & 0.90 & & 1.11 & ✓ & 1.53 & ✓ & 1.21 & \\ Time Since Last Access & 1.03 & & 1.25 & ✓ & 1.11 & & 1.25 & \\ Time Since Last Update & 0.55 & & 0.66 & & 1.07 & & 0.83 & \\ History of Open Times & 1.53 & & 1.10 & & 1.23 & & 1.13 & \\ File Hotness & 1.90 & ✓ & 1.09 & ✓ & 2.13 & ✓ & 1.59 & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Combined score of \(p\)-value, Chi-squared, and Gini Importance for all the features across all traces. ✓ indicates selected features.
## 1 Introduction  
The United Nations developed the concept of the Maritime Internet of Things (MIoT), which aims to provide ubiquitous connectivity for devices used in the maritime environment on a global scale and to enhance services related to safety and security at sea, environmental protection, and research [1].  
Wireless communications are a cornerstone towards this end. They are an essential part of modern electronic systems with sensing capabilities for remote environments. They allow for the collected data to be transmitted to the user without physical contact with the device and without hassle while preserving long-lasting battery life [2] and providing a variety of methods that add robustness and failure protection to the network.  
Establishing communication networks at sea is an especially difficult challenge that must be overcome, since in oceanographic observation it is imperative to monitor physical, chemical, and biological variables [3]. Many events that affect seawater quality occur in narrow time frames [4], hence requiring real-time information collection. With it, it is possible to aid in decision making to manage problems such as man-made impacts, climate change, erosion, and natural disasters [5].  
Furthermore, as the maritime industry develops, robust wireless communication networks with adequate data rates and low costs are necessary [6; 7]. However, due to the lack of optical cables and base stations, maritime communications are highly complex environments. Communication may need to be established near shore or across a large distance. Additionally, weather can greatly influence the quality of the wireless link [8].  
The present paper contains a literature review of the MIoT systems that have been developed and deployed by researchers and engineers over the past few years. The goal of this paper is to serve as a go-to guide for engineers and researchers that need to implement an over-the-air radio-frequency wireless sensor network at sea. The selection criterion for the papers included in this review was that the implemented wireless communication networks were tested in a real-world scenario. Papers with only lab testing were also included when the utilized communication method was particularly interesting. The following sections present numerous applications for which the MIoT is extremely important as well as highlight the architecture of the MIoT and the numerous challenges faced by a communication network at sea. Then, communication protocols that are commonly used in the bibliography are briefly discussed, both for near-shore and long-distance communications. Finally, examples of state-of-the-art wireless communication networks found in the literature are presented.
## 3 Performance Analysis and Simulations
### System Efficiency
#### 3.2.1 Optical Power Ratio  
The optical power ratio of the CPV unit is simply defined as the ratio of the solar radiation received by the solar cells to the solar radiation hit on the Fresnel lens's surface.  
The optical power ratio is calculated by:  
\[\eta_{\text{opt\_CPV}}=\frac{P_{\text{in\_cell}}}{P_{\text{in\_FL}}}, \tag{8}\]  
where \(P_{\text{in\_cell}}\) is the incident solar radiation received at the solar cells and \(P_{\text{in\_FL}}\) is incident solar radiation hit on the Fresnel lens's surface of the CPV unit.  
For the proposed CPV unit, the incident solar radiation received at the solar cells including the MJ solar cell and the Si solar cell is determined by:  
\[P_{\text{in\_cell}}=P_{\text{in\_MJ\_cell}}+P_{\text{in\_Si\_cell}}, \tag{9}\]  
where \(P_{\text{in\_MJ\_cell}}\) and \(P_{\text{in\_Si\_cell}}\) are the incident solar radiation received at the MJ and Si solar cells, respectively.  
To evaluate the efficiency of the designed CPV units, the following CPV units were considered, including a CPV unit without the SOE and the additional low-cost cell, a CPV unit with the SOE, and the designed CPV unit with both the SOE and the additional low-cost cell. The models of these CPV units are shown in Figure 11a-c, respectively.  
Simulations were conducted using LightTools(tm) to evaluate and compare the optical power ratio of the designed CPV unit with that of the other CPV units. The optical power ratio of the CPV units in the simulations was recorded and is shown in Figure 12a,b, respectively. The simulation results showed that the optical power ratio of the designed CPV unit is better than that of the other CPV units.  
Figure 11: CPV units: (**a**) the CPV unit without the SOE and low-cost cell; (**b**) the CPV unit with the SOE; and (**c**) the designed CPV unit with both the SOE and low-cost cell.
## 3 Results and Discussion
### Thorough Proxy Analysis
The significant variation observed in the permeability changes among the different scenarios depicted in Figure 10 reflects the uncertainty quantified in Table 8 when the CO\({}_{2}\) injection is a parameter. Furthermore, the primary changes observed in the model are concentrated at the top of the injection interval and in the shallowest layers. This observation raises concerns regarding the integrity of the caprock, as discussed in [62]. These changes can be attributed to the upward migration of CO\({}_{2}\) within the reservoir, driven by its buoyancy, resulting in a localized concentration of effects near the injection zone. Figure 9: Crossplot with the simulation results of the injectivity index after 5 years of injection and the corresponding calcite dissolution. \begin{table}
\begin{tabular}{l c} \hline \hline
**Numerical Experiment** & **P10/P90 Ratio for II** \\ \hline Sections 3.1 and 3.2: Fixed CO\({}_{2}\) injection rate of 0.5 metric tonne/day & 1.002 \\ Section 3.3: CO\({}_{2}\) injection rate as a parameter, varying from 0.1 to 2.5 metric tonnes/day & 3.670 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The ratio between P10 and P90 estimates for the CO\({}_{2}\) injectivity index after 5 years of injection. The uncertainty highlighted in this analysis emphasizes the range of potential outcomes and the importance of carefully considering the uncertainties associated with alterations in injectivity induced by calcite dissolution. Understanding and monitoring these uncertainties is crucial for ensuring the long-term effectiveness and integrity of CO2 storage projects.
# Decoupling Control of Multi-DOF Supporting System of MLDSB
## 6 Experimental Result of Decoupling
### Analysis of Experimental Results
Similarly, the step signal (Amplitude is 2 \(\upmu\)m and time is 6 ms) is given in the \(x\)-direction and \(y\)-direction respectively, and the subsequent displacement curves before and after decoupling are obtained as shown in Figures 23 and 24. Figure 21: Displacement under sinusoidal signal in channel \(x\). Figure 23: Displacement under step signal in channel \(x\). Figure 22: Displacement under sinusoidal signal in channel \(y\). According to Figures 23 and 24, the rotor displacement before decoupling in the input channel present the step motion law with high-frequency oscillation as shown in the blue curve of Figures 23a and 24b. After decoupling, the decoupling controller effectively eliminates high-frequency oscillation of the rotor and greatly improves the displacement characteristics as shown in the red curve in Figures 23a and 24b. Due to the coupling characteristics of the magnetic circuit, the rotor displacement before decoupling in the non-input channel is also affected by the coupling of the input channel, and it shows a small motion with the same frequency as shown in the blue curve of Figures 23b and 24a. After decoupling, the decoupling controller effectively eliminates the rotor high-frequency oscillation and greatly improves the zero-retaining characteristic of the displacement as shown in the red curve of Figures 23b and 24a. Then, the theoretical correctness of the decoupling controller can be effectively proven.
## 1 Introduction
Since it is highly sensitive to minute differences in composition and is not easily affected by moisture, it was thought to be an effective method for identifying the origin and variety of dried kelp with high accuracy. This perspective has not been sufficiently examined in previous research. Various machine learning methods have been developed and combined with other methods to identify the geographical origin of food such as peaches, Chinese Longjing tea, and Pu'er tea, including VIS-NIR, fluorescence spectroscopy, image-processing technology, \({}^{1}\)H nuclear magnetic resonance spectroscopy, hyperspectral imaging (HIS) technology [16; 17; 18]. [PERSON] et al. [19] have proposed a method for identifying the adulteration of camellia oil and quantifying the level of adulteration using excitation-emission matrix spectroscopy and a CNN. [PERSON] et al. [20] have proposed a method for detecting the adulteration of nine types of vegetable oils using three-dimensional (3D) fluorescence spectroscopy and a CNN. In addition to a CNN, \(k\)-nearest neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares have been used for comparison. [PERSON] et al. [21] have reported that a CNN shows high classification accuracy when using 3D fluorescence spectroscopy, SVM, RF, and CNN to detect counterfeit camellia oil. In this study, fluorescence fingerprint data assessed using fluorescence spectroscopy were used in a machine learning model to evaluate the identification of kelp varieties. Building on the findings of previous studies, this study makes new academic and practical contributions to the literature. First, it is novel as it uses fluorescence fingerprint data obtained by fluorescence spectroscopy to identify the origin and variety of dried kelp. Fluorescence spectroscopy has not been used for dried kelp, and no attempts have been made to analyze the data with high accuracy using machine learning. This study aims to fill this technological gap. Second, it aims to extract information on the origin and variety from fluorescence fingerprint data with high accuracy using a CNN, a type of deep learning. A CNN can automatically learn the features of complex patterns and is expected to have a higher classification accuracy than conventional machine learning algorithms. Therefore, improved accuracy is expected by applying this method to identify the origin and variety of dried kelp. Furthermore, this study aims to provide a highly practical method for identification to prevent food fraud and ensure quality assurance.
# Review of Advanced Implementation of Doppler Backscattering Method in Globus-M
## 2 Doppler Backscattering on Globus-M
### Data Analysis
Cross-bicoherence as a measure of phase coupling between three spectral components of signals is described by the following expressions:  
\[b^{2}(f_{1},f_{2})=\frac{\left|\langle Y_{k}^{*}(f_{3})Y_{i}(f_{1})Y_{j}(f_{2} )\rangle\right|^{2}}{\left\langle\left|Y_{k}(f_{3})\right|^{2}\right\rangle \left\langle\left|Y_{i}(f_{1})Y_{j}(f_{2})\right|^{2}\right\rangle};\ f_{3}=f_{1}\pm f _{2}\]  
where \(Y_{i}(f)\), \(Y_{j}(f)\), \(Y_{k}(f)\) are spectra of amplitude of the complex output signal of the IQ detector or spectra of E \(\times\) B velocity determined from the Doppler frequency shift.