esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 3 Machine Learning for Time Series Prediction
### NARX Neural Network  
NARX represents a nonlinear autoregressive network with external inputs. NARX are dynamical RNN, have return paths surrounding various network connections. NARX networks are based on the auto-regressive with exogenous input (ARX) time series models, commonly used for time series operations, and are considered a nonlinear form of the ARX model. NARX models can simulate various nonlinear dynamic methods; they have been used for multiple problems, including time series simulation. The NARX network uses prior measures of the existing historical time series to make predictions and the previous values of other inputs to make predictions for the target series. NARX is a robust tool suitable for nonlinear modeling systems. Moreover, NARX learns more efficiently than other neural network time series, using a gradient descent learning algorithm. NARX networks have been successfully used in many applications to predict future values of the input signal.  
NARX networks perform better on predictions, where the desired output depends on inputs that exist at absolutely past the time points. NARX is also used as a nonlinear filter whose training data are trained with a noiseless form of input information. When applied to the historical time sequences [20], potential values for the time sequence \(y(n+1)\) are predicted from past values for that historical time sequence and the final measure for the historical time sequence \(u(n)\). The model is mathematically represented as follows:  
\[y(n+1)=f\big{(}y(n),y(n-1),\ldots y\big{(}n-d_{y}\big{)},u(n),u(n-1),\ldots u( n-d_{u})\big{)}+e(n) \tag{1}\]  
In the compact form, we can rewrite Equation (1) as follows.  
\[y(n+1)=f[y(n);u(n)] \tag{2}\]  
Here, \(y(n)\in R\) and \(u(n)\in R\) define the input and output parameters of the network at the time \((n)\), respectively. Moreover, \(y(n)\) and \(y(n+1)\) are the desired and predicted output elements, respectively. Accordingly, \(d_{y}\) and \(d_{u}\) are the time delays of the output and input variables, respectively (\(z^{-1}\) = time delay unit), and \(e(n)\) is the model error between the desired and predicted output [22; 23; 29; 30].  
Figure 2 illustrates NARX neural network architecture [22; 23; 30].
## 2 Reflection Power Estimation Using Eigenbeam Spatial Correlation Model  
In this section, we present the formulation and synthesis of reflection power as a function of time, frequency, and space in the spherical harmonics domain.
# Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity
## 3 Emerging Technologies in Smart Healthcare
### Digital Twins  
Digital Twin technology is a combination of virtual reality technology, big data processing, and 3D graphics to build virtual models of processes, systems, services, products, or physical objects [40]. Through digital twin technology, users can experience virtual effects, identify problems before they happen, or predict future outcomes.  
In the healthcare domain, digital twin technology is considered to be revolutionary and is applied in the prediction and early diagnosis of diseases that may occur in patients by examining organs or symptoms in the body. In the case of atherosclerosis, it can be used to perform vascular surgery to restore blood flow, replace the lenses of cataract patients, transplant organs, etc. [41]. A patient's digital twin is created as a result of transferring the physical characteristics and changes in the patient's body to a digital environment. This technology enables accurate diagnosis and delivers tailored treatment protocols in real time to patients. Figure 5 presents an illustration of digital twins in the healthcare domain.  
In our opinion, digital twins can be extensively applied in the smart healthcare domain in the future. For example, before physical interventions such as drug treatment, radiation  
Figure 5: An illustration of digital twin technology.  
therapy, or surgical operations, paramedics can perform processes on virtual digital twins to determine the optimal treatment for the patient. By creating a digital twin of the patient's body, doctors can perform a diagnosis by examining the vital areas of the patient's digital twin without a direct impact on the patient's body. With the creation of digital twins of medical devices, the prediction and coordination of medical resources for medical examination and treatment have high accuracy and optimization of system resources.
## 4 Conclusions  
When combined with the multivariate data analysis, the application of NIR and MIR spectroscopy finds extensive use in the food industry to assess chemical composition in many raw materials and foods (e.g., protein, moisture, oil). In recent years, the most important development of these technologies has been as tools or methods for authenticity, discrimination, or traceability of foods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Issue or Topic** & **Advantages** & **Limitations** \\ \hline \multirow{2}{*}{_Measurement_} & Single or multiple images & - \\  & Spatial, spectral, and multi-constituent & - \\  & - & Cost of instrument \\  & - & Availability of easy to use instruments/routine \\  & Different algorithms available & Issues related to handling large amounts of data \\  & - & Issues related to handling large amounts of data \\  & - & No generally available for the industry \\  & Traceability, origin, on-line and in-line, & - \\  & chemical and physical properties & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the advantages and limitations of hyperspectral and multispectral imagine systems for the analysis of foods.  
Integrating these technologies with either hyperspectral or multispectral image analyse shows considerable potential for the efficient and reliable monitoring and control of important steps in the food process. These techniques will add to our set of tools to improve our understanding about the chemistry and biochemical characteristics that are associated with raw material origin, to ensure sustainable food production as well as to assure the consumers of the origin of the food that they consume.  
From the literature published in the last 10 years, the strong development of HSI and MSI applications in the food industry steadily emerges with considerably wider applications in the future.  
[PERSON] conceived, designed and wrote the paper; [PERSON], [PERSON], [PERSON] and [PERSON] edited and revised the papers, as well as wrote some sections.  
The authors declare no conflict of interest.
# Tabular Data Models for Predicting Art Auction Results
## 3 Results
### Saint  
Self-Attention and Intersample Attention Transformer (SAINT) is a relatively new approach that utilizes transformers for tabular data. The model applies two main attention mechanisms: self-attention, which focuses on feature interactions within a single sample, and intersample attention, which focuses on interactions between different samples. Despite its innovative architecture, SAINT delivered only moderate performance across the auction datasets, with sMAPE scores ranging from 60.57% \(\pm\) 0.95 in the NoImg dataset to 62.41% \(\pm\) 1.19 in the SVD dataset, as presented in Figure 10.  
Figure 10: Bar chart comparing the sMAPE metric of SAINT model across the datasets.  
Figure 9: Bar chart comparing the sMAPE metric of DeepFM model across the datasets.  
The self-attention mechanism in SAINT allows the model to capture relationships between features within a single sample. It operates by computing attention weights that dictate how much each feature should contribute to the prediction. The self-attention mechanism is particularly useful for handling high-dimensional and structured data, as it can model complex dependencies between features that other models (e.g., traditional decision trees) might miss. However, auction datasets contain nonlinear relationships driven by subjective factors like market volatility, which are difficult to model using feature-based dependencies alone. This likely explains why SAINT, despite capturing feature interactions, did not outperform simpler models like XGBoost and RandomForest.
## 2 Design and Methodology
### Mesh and Study Validation
Figure 5: Sensors’ voltage reading in terms of load position for a 40 kg load magnitude moving with 1 m/s speed. It is necessary to point out the similarity of the voltage readings between L1 and L3, and between R1 and R3, as they are defined symmetrically in the structure (Figures 1 and 2). This is well seen in Figure 6, which presents a perfect fit of the voltage readings obtained by the sensors L1 and L3 for a 40 kg load. The same results are obtained for R1 and R3. Sensors L2 and R2 are more affected by the load mass and position as they are aligned with the structure's central x-axis where the load is displacing. Thus, in the following parts, data analysis of only L2 and R2 sensor readings will be presented. The voltages provided by the sensors L2 and R2 for all simulated load positions are given in Figure 7 for a 40 kg load mass. To determine the speed representation and identify the speed effect fingerprint, the whole voltage spectrum reading is used. Figures 8-10 are extracted from Figure 7 to show the sensors' voltage readings for different load speeds. Because the speed of the load is determined by the load's position on the structure's surface, the number of contact points between the load and the structure varies for each speed. The faster the load is, the fewer contact points are obtained. A load speed of 0.5 m/s corresponds to 11 contact points with a 5 cm distance step (Figure 8). A load speed of 0.75 m/s corresponds to 8 contact points with a 7.5 cm distance step (Figure 9). Figure 6: L1 and L3 sensors’ voltage reading in terms of load position. Figure 7: L2 and R2 sensors’ voltage reading in term of load position.
## 3 Resolution Approaches
### Approximate Approaches
#### 3.2.2 Simulated Annealing  
We consider a third heuristic for solving this problem: the simulated annealing procedure [35].  
This approach starts from an initial solution and then moves to close neighbors across multiple iterations. During each iteration, the neighboring solution replaces the current one if it improves the objective function. Non-improving solutions may also replace the previous solution with a probability that is proportional to the performance difference and taking into account a temperature parameter. This parameters is initially set to a high value and decreases at each iteration. High values increase the probability of accepting non-improving solutions, and low values do not. Therefore, the approach starts by exploring the search space and ends by intensifying its search.  
The performance of the simulated annealing approach is heavily dependent on the choice of the initial temperature and the rate at which it decreases, also called an annealing schedule. Usually, the annealing schedule requires a significantly time-consuming tuning phase in order for the approach to perform well on a new problem.  
Variants of this approach, where the annealing schedule adapts and requires no tuning, have been proposed, and we therefore consider one such approach here. Namely, we focus on the adaptive annealing schedule known as modified LAM [36].  
This adaptive simulated annealing (ASA) using the modified LAM annealing schedule is outlined in Algorithm 2.  
```
1\(S\leftarrow\mathit{GenerateInitialSolution}()\)
2\(S^{\prime}\gets S\)
3\(T\gets 0.5\)
4\(\mathit{AcceptRate}\gets 0.5\)
5 forit\(\in 1\dots\mathit{iterations}\)do
6\(S^{\prime\prime}\leftarrow\mathit{PickNeighbor}(S^{\prime})\)
7 if\(\mathit{Cost}(S^{\prime\prime})>\mathit{Cost}(S^{\prime})\) or \(\mathit{Random}(0,1)<e^{\frac{\mathit{Cost}(S^{\prime\prime})\cdot\mathit{Cost }(S^{\prime})}{t}}\)then
8\(S^{\prime}\gets S^{\prime\prime}\)
9\(\mathit{AcceptRate}\leftarrow\frac{1}{500}(499\cdot\mathit{AcceptRate}+1)\)
10 else
11\(\mathit{AcceptRate}\leftarrow\frac{1}{500}(499\cdot\mathit{AcceptRate})\)
12 if\(\frac{it}{\mathit{iterations}}<0.15\)then
13\(\mathit{LamRate}\leftarrow 0.44+0.56\cdot 560^{-it/iterations}/0.15\)
14 elseif\(\frac{it}{\mathit{iterations}}<0.65\)then
15\(\mathit{LamRate}\leftarrow 0.44\)
16 else
17\(\mathit{LamRate}\leftarrow 0.44\cdot 440^{-(it/iterations}\cdot 0.65)/0.35\)
18 if\(\mathit{AcceptRate}>\mathit{LamRate}\)then
19\(T\leftarrow 0.999\cdot T\)
20 else
21\(\mathit{T}\gets T/0.999\)
22 ift\(\mathit{Cost}(S^{\prime})>\mathit{Cost}(S)\)then
23\(S\gets S^{\prime}\)
24 return\(S\)
```  
**Algorithm 2**Adaptive simulated annealing using the modified LAM annealing schedule
## 2 Background and Previous Research  
### Reference Frameworks or Similar Case Studies  
During the last few years, interest in investigating how to exploit textual data has been increasing. This is due to the emergence of new sources of information such as social networks, where business users can express themselves and generate, for example, recommendations based on their experiences, as reported in [6].  
One of the approaches related to leveraging the voice of the customer corresponds to the development of reference frameworks to improve understanding of the subject and the creation of baseline methodologies for processing comments or reviews by involving techniques such as data mining, text mining, or sentiment analysis, as presented in [8], where the research offers a focus on Twitter applications.  
Some examples can be found in the scientific community. In [9], a model is presented containing four basic stages: data collection, management, classification, and visualization to lay a foundation in sentiment analysis and have certain basic elements of graphical representation on the perception of services in the public and private sectors in Rwanda. Furthermore, in [10], the article provides a comprehensive overview of the various technical approaches employed to classify tweets into positive, negative, or neutral sentiments. They not only describe each stage of the processing but also outline the overall benefits and drawbacks of this type of analysis. Additionally, that research meticulously outlines the methodology, providing a detailed account of each step.  
In Colombia, there has been some research that uses Twitter as a data source to perform analysis; for example [11], proposes a neural network and machine learning model that personalizes the insurance service for each customer according to their needs. In this study, tools such as Tweet Archiver, OpenRefine, and Biome-textSE are applied, and metrics such as precision, recall, and F1 are used to measure the performance of the model. Results are also deployed by using visualization tools such as dashboards.
## 2 Materials and Methods
### Data Contraction Detection Methods
#### 2.2.5 Hilbert Envelope  
The Hilbert transform [38] returns a complex helical sequence, sometimes called the analytic signal, from a real data sequence. The Hilbert transform is useful in calculating the instantaneous attributes of a time series, especially the amplitude and the instantaneous amplitude and frequency. The instantaneous amplitude is the absolute value of the complex Hilbert transform.  
The Hilbert transform is a useful method for performing time-frequency analysis of nonlinear and nonstationary data. Given the Fourier transform of \(f(t)\) as \(S\left(\omega\right)\):  
\[S\left(\omega\right)=\int_{-\infty}^{\infty}f(t)e^{-j  
\(z(t)\) is called the analytic signal of \(f(t)\). The real part of the second term of the right side of (10) is the Hilbert transform of the signal \(f(t),\ H[f(t)]\):  
\[H[f(t)]=\ \frac{1}{\pi}\ \int\frac{f(t^{\prime})}{t-t^{\prime}}dt^{\prime} \tag{11}\]  
Being the analytical signal given by:  
\[AS\left[f(t)\right]=z(t)=f(t)+jH[f(t)] \tag{12}\]  
The Hilbert envelope (HE) is the absolute value \(AS\left[f(t)\right]\).
# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 6 Conclusions  
In this paper, we studied the real-time scheduling problem in the CMfg environment. After presenting the premises and antecedents, several research contents and the results can be pointed out:  
Figure 15: Logical flow chart of application design of ANN-based scheduling in the CMfg environment.  
* We constructed a mathematical model for real-time scheduling in the CMfg environment with optimization objectives as minimizing the cost, minimizing the make-span, and maximizing the service satisfaction constrained by task precedence, resource occupation, and logistics duration;
* An ANN-based approach for real-time scheduling has been designed, it uses the MT attributes and process pending queue of MR as inputs to predict the completion status of MT if it will be allocated to any of the candidate MRs;
* We conducted the comparison experiments and modified the NSGA-II as the referred scheduling method, the results show that: 1. The proposed ANN-based approach performs better than the NSGA-II in terms of objective values; 2. The response time of the ANN is only about 4.4% of the NSGA-II on average; 3. Using ANN, the average decision time for MR allocation is under 50 ms, which indicates that the proposed ANN-based approach is suitable for the real-time scheduling.
* We designed the application of ANN-based real-time scheduling to show how to implement this method to help coordinate providers of MR in the CMfg environment.  
In addition, the proposed method has good performances on different MT scale datasets, which indicates its generalization ability. Since the proposed ANN-based approach solves the common problem for the platform based manufacturing environment, it can also be used in related web based manufacturing environments.  
It should be noticed that the research in this article is mainly based on the library instances rather than the data from the real-world. Since there is no exactly the same real-time scheduling problem as described in this paper, the proposed ANN-based approach is not compared with other articles which also use ANN model to prove we have a better result. How to improve the ANN of the proposed real-time scheduling approach to perform better and apply this approach to the real world is our on-going work.  
Conceptualization, S.C. and S.F.; formal analysis, S.C.; funding acquisition, S.F.; methodology, S.C.; project administration, S.F. and R.T.; validation, S.C.; writing--original draft, S.C.; writing--review and editing, S.F. and R.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by 1. National Natural Science Foundation of China (grant number 71571161); 2. National High-tech Research and Development Program (grant number 2015 AA042101); 3. Science Fund for Creative Research Groups of National Natural Science Foundation of China (grant number 51821093).  
The authors declare no conflict of interest.
## 2 Study Area and Data
### Model Input Data  
The TSEB-SM model can be driven by land surface temperature (LST), leaf area index (LAI), soil moisture (SM), land cover, and meteorological forcing data.  
One of the most important model factors is LST, which will be divided into two categories here: soil temperature and vegetation. The model's simulation performance is greatly impacted by the precision of LST. However, cloud and fog contamination cause the satellite's thermal infrared (TIR) LST product to be discontinuous. [PERSON] et al. [17] introduced a practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for generating all-weather LST data at 1 km spatial resolution by merging reanalysis data and TIR LST output. The above LST data at 1 km spatial resolution across the Heihe River Basin were downloaded from the Central Tibetan Plateau Data Center ([[https://www.tpdc.ac.cn/en/](https://www.tpdc.ac.cn/en/)]([https://www.tpdc.ac.cn/en/](https://www.tpdc.ac.cn/en/)), accessed on 18 January 2022).  
Another important parameter is LAI, which is mainly used to separate the surface fluxes and LST between the soil and canopy. The daily LAI maps over the Heihe River Basin were derived from the 8-day global 500 m GLASS LAI product from 2000 to 2020 [18]. The Heihe River Basin crosses a wide region and often includes three MODIS remote sensing images, namely, h25v04, h25v05, and h26v05. After stitching these three images and clipping them according to the scope of the study area, the treated LAI was then resampled and temporally interpolated to ensure consistency with other data in terms of spatial and temporal resolution.  
In order to identify different vegetation types to assign height empirically, the Terra and Aqua combined Moderate-Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6 data product at 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) spatial resolution was used. Based on the nearest-neighbor resampling method, we downscaled it to 1 km spatial resolution.  
The meteorological data input to the model, including air temperature, relative humidity, atmospheric pressure, shortwave radiation, and wind speed, were simulated by the weather research and forecasting (WRF) model [19]. The WRF model makes up for the deficiency of general circulation models (GCMs) at the watershed scale, especially the coarse spatial resolution. Verified by the hourly ground-based observations, the 5 km hourly WRF meteorological forcing data show a great agreement with them. These datasets were downloaded from the Central Tibetan Plateau Data Center ([[https://www.tpdc.ac.cn/en/](https://www.tpdc.ac.cn/en/)]([https://www.tpdc.ac.cn/en/](https://www.tpdc.ac.cn/en/)), accessed on 25 December 2021).
## 2 Theoretical Model and Hypotheses
### Information Security Marketing, and Response Cost and Information Security Intention  
Response cost (RSC) refers to the perceived personal efforts and/or the external or inherent individual's cost of performing the proposed expected behavioral intention [33]. This paper will argue that RSC is a critical factor in assessing an organization's potential expenses when organizations use cost to obtain employees' information security intention. RSC is the extent of the cost that can be incurred in terms of time, effort, and convenience in complying with ISI [34]. In the resource-based view (RBV) theory, when organizations exhibit strong marketing management, employees acquire more significant support, training, and rewards and expend less RSC in obtaining ISI [28; 29]. It is reasonable to infer that more effective marketing management helps reduce individuals' RSC in realizing an information security plan.  
RSC is related to the cost of implementing the proposed protective information security [16]. In the information security intention context, several studies [16; 23; 35; 36] have reported RSC and ISI's effects. [PERSON] and [PERSON] [16] surveyed 229 students in a business college at a U.S. university, and the results indicated that RSC harmed ISI as student effort related to information security intention incurred a high response cost (e.g., the time required to implement information security measures) to realize their ISI. Reference [35] examined 274 employees working in various industries in Finland to understand the relationship between RSC and ISI and found that complying with information security intention was time-consuming, took up more work time, and made the work more difficult and inconvenient. From these results, the current study hypothesized the following:  
**Hypothesis 2.**_ISM is negatively related to RSC in the container shipping industry._  
**Hypothesis 3.**_RSC is negatively related to ISI in the container shipping industry._