esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implementations and Results
### Comparison and Evaluation of the Proposed FLR-FSWGARCH Model with Benchmark Models for Two Datasets  
For contrast, the performance of the proposed model was compared to the SWGARCH, GARCH, ARIMA-GARCH, and EWMA models. Two datasets were involved; the first was the daily S&P index, as shown in Section 3.1, and the second was the yearly data for vanilla production covering 1961 to 2017 obtained from knoema.com. The mean absolute percentage errors produced by the models for both the monthly S&P and yearly vanilla datasets were compared and the results are summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Day** & **S\&P** & **Forecast Value** & **[Percentage Error]** \\ \hline
243 & 2041.89 & 2073.34 & 1.5403 \\
244 & 2005.55 & 2042.22 & 1.8284 \\
245 & 2021.15 & 2005.94 & 0.7526 \\
246 & 2038.97 & 2021.58 & 0.8528 \\
247 & 2064.29 & 2039.30 & 1.2104 \\
248 & 2060.99 & 2064.58 & 0.1741 \\
249 & 2056.50 & 2061.25 & 0.2309 \\
250 & 2078.36 & 2056.66 & 1.0440 \\
251 & 2063.36 & 2078.49 & 0.7332 \\
252 & 2043.94 & 2063.51 & 0.9573 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Forecast value and error percentage of the S&P data.  
Figure 5: Plot of the actual data and the forecast values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Day** & **S\&P** & **Return** & **Fuzzy Window Variance** & **Variance** \\ \hline
1 & 2058.20 & & \\
2 & 2020.58 & \(3.40\times 10^{-4}\) & & \\
3 & 2002.61 & \(7.98\times 10^{-5}\) & & \\
4 & 2025.90 & \(1.34\times 10^{-4}\) & & \\
5 & 2062.14 & \(3.14\times 10^{-4}\) & & \\
6 & 2044.81 & \(7.12\times 10^{-5}\) & \(1.67636\times 10^{-4}\) & \(1.6764\times 10^{-4}\) \\
7 & 2028.26 & \(6.60\times 10^{-5}\) & \(1.27025\times 10^{-4}\) & \(1.3585\times 10^{-4}\) \\
8 & 2023.03 & \(6.67\times 10^{-6}\) & \(8.49053\times 10^{-5}\) & \(1.0901\times 10^{-4}\) \\
9 & 2011.27 & \(3.40\times 10^{-5}\) & \(5.67693\times 10^{-5}\) & \(7.3588\times 10^{-5}\) \\
10 & 1992.67 & \(8.63\times 10^{-5}\) & \(5.27244\times 10^{-5}\) & \(5.9790\times 10^{-5}\) \\ � ⋮ & ⋮ & ⋮ & ⋮ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fuzzy window variance and variance of the FLR-FSWGARCH model.  
As shown in Table 5, the FLR-FSWGARCH model produced a lower MAPE compared to the SWGARCH, GARCH, ARIMA-GARCH, and EWMA models for both datasets. It is clear that the FLR-FSWGARCH model outperformed the other models at forecasting the time-series data. The proposed model also underwent a goodness-of-fit test using regression analysis to test its efficiency. The results of the analysis are tabulated in Tables 6 and 7.  
From Tables 6 and 7, the values of R\({}^{2}\) were 0.9981 for S&P and 0.9999 for vanilla production; thus, a strong relationship existed between the variables of the model. By taking the significance level to be
## 4 Spatio-Temporal Big Data Visualization Framework
### Distributed Storage of the MAP Pyramid in Hbase  
In order to improve the storage scalability of MAP-Vis, we chose HBase (a distributed, column-oriented non-relational database) as the storage database. Unlike traditional relational databases, data in HBase are organized in a sorted list of key-value pairs. Each row stores one object, represented by the designed unique _rowkey_, while each column belongs to a particular column family. At the physical level, the data are physically sorted by _rowkey_, column name and timestamp; thus, the design of the _rowkey_ and column family in HBase are vital to the efficiency of the multidimensional query.  
The height of the pyramid in the spatial dimension is usually very high (e.g., 12 levels in GeoHash), resulting in the explosive increase of decomposed spatial cells. For the time series of data, moreover, the record number along the temporal dimension is also very large; on the contrary, the attached attributes are usually of limited sizes and the _fua_tree_ length is short. Accordingly, the spatial and temporal information are put together in the Row dimension, while the _fua_tree_ information is placed in the Column Family.  
The spatial coordinates of each record are firstly encoded into _sfc_code_ using the Z curve, which is one of the most widely used space filling curves. As shown in Table 1, the level \(l\), _sfc_code_ and time interval \(t\) are then joined in the form of a _rowkey_ for each row, and two column families (_Heatmap_ and _Sum_) are then defined. Each column of the _Heatmap_ column family is set according to the _fua_tree_ node and used to store the _st-tiles_; in addition, each column of the _Sum_ column family is used to store the total value of all _st-pixels_ at time interval \(t\).  
A _rowkey_ design of this kind exhibits high spatial-temporal aggregation and ensures that objects close in spatial-temporal distance can be stored closely on disks. This storage schema can also fully leverage the column-oriented advantages of HBase, as well as scaling well with the dataset size.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Rowkey**} & \multicolumn{3}{c}{**CF:Heatmap**} & \multicolumn{3}{c}{**CF:Sum**} \\ \cline{2-9}  & **F1** & **F2** &... & **Fn** & **F1** & **F2** &... & **Fn** \\ \hline L\_T\_Z2 & A\({}_{\text{all}}\) & A\({}_{1}\) &... & A\({}_{\text{n}}\) & S\({}_{\text{all}}\) & S\({}_{1}\) &... & S\({}_{\text{n}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The HBase storage schema for MAP-Vis.
# Addressing Chemophobia: Bridging Misconceptions in Food Chemistry
## 2 Definitions and Origins of Chemophobia
### The Role of Media and Marketing  
Media sensationalism has played a significant role in perpetuating chemophobia. News coverage often emphasizes chemical-related health risks without providing context about dosage, exposure, or regulatory safeguards. For example, headlines about pesticide residues in food rarely clarify that the detected levels are well below safety thresholds [15]. Similarly, food advertising exploits chemophobia by promoting \"chemical-free\" or \"100% natural\" products, creating an unrealistic standard that stigmatizes synthetic additives and preservatives.
# Context-Based Energy Disaggregation in Smart Homes
## 4 Disaggregation Algorithm
### Context-Based Disaggregation  
The contextual conditioning has been realized by adopting and extending the Conditional FHMM [35] solution, which allows integrating context information to the classical FHMM in order to obtain dynamical, rather than static, state transition matrices.  
Among the various approaches made available in literature, including in particular the Conditional Random Fields [41], our choice fell on Conditional FHMM as it allowed us to easily extending the approach by [PERSON] and [PERSON] [9], while maintaining the use of the above-mentioned AFAMAP inference algorithm.  
We selected the following types of context information:  
* timing-usage statistics, which has been generated through a statistical analysis over the Tracebase dataset.
* user presence information, which has been synthetically generated for the purpose of this work. In real world cases, these data could be collected through presence sensors located in the private home rooms.  
Figure 1: (**a**) the single Hidden Markov Model; (**b**) the additive Factorial Hidden Markov Model. Image adapted from [8]. Copyright 1997, Kluwer Academic Publishers.  
The selection of these features has been performed taking into account cost of real-life deployments. Therefore, we preferred to use a very small number of presence sensors, typically one for each room in the house, instead of a huge number of different sensors (e.g., pressure, ignition switches, movement, _etc._).
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### Types of Accelerometers
#### 1.3.1. Machined Piezoelectric Resistive  
When a piezoelectric material experiences stress, it generates an electrical charge. When paired with a seismic mass, it can produce an electric charge signal that is proportional to the acceleration of vibrations. While they tend to be pricier than other sensor types, piezoelectric accelerometers offer significant benefits [3; 4]:  
* This system has a very wide dynamic range and produces almost no noise. It is suitable for measuring shocks and can also detect vibrations that are barely noticeable;
* This system has the best linearity in the dynamic range;
* This system has a side frequency range--very high frequencies can be measured;
* This system is compact and sensitive;
* This system has no moving parts (reliable);
* This system has no need for an external supply;
* This system has different models available;
* This system has speed and displacement that can be computed using output data.
# Feasibility of a Helium Closed-Cycle Gas Turbine for UAV Propulsion
## 2 The Closed-Cycle UAV Propulsion System
### The Stirling Closed-Cycle Engines  
The Stirling cycle engine is a closed-cycle heat engine that undergoes an isothermal compression and expansion process while rejecting heat at constant volume. This engine has the potential to operate at high efficiency and capable of utilising a variety of heat sources and fuels. Other advantages of the engine include low noise and vibration, especially for the free-piston Stirling engine, clean-burning and low emission, working fluid flexibility and high reliability. In 1986, [PERSON] demonstrated the capability of using Stirling engines for a UAV mission. His 0.350 kg Stirling engine operating on helium was able to produce 0.025 kW of power to fly small UAV for 8 min [15]. Besides the [PERSON] UAV test, the US Navy has also implemented several designs for UAV underwater operations [18].  
Designing a Stirling engine could be overly complicated because of the dynamic behaviour of the mechanism and heat exchangers, as it is not easy to build a heat exchanger to operate continuously at high temperature. Increasing the working temperature and pressure increases the power density. However, the maximum temperature is constrained by material technology, which is currently within the range of 800-900 \({}^{\circ}\)C. Good Stirling engine performance requires that the total gas volume be minimised. Maximum performance is also achieved with gases that have low specific heat, such that the maximum possible pressure increase is obtained for a given amount of heat input. Ideally, helium and hydrogen would be well suited for this application; however, the challenge will be how to manage seal leakage effectively [19; 20]. One major difference between Stirling and the internal combustion engine is the rapid variation of power required and the instantaneous control of fuel supply to meet this demand. For the Stirling engines, there is usually a substantial time lag between fuel input and power output which make less competitive to the Brayton closed cycle. Another disadvantage would be the turbomachinery design, as its TRL is far below its Brayton closed-cycle counterpart. The Stirling engine, similar to any other reciprocating engine, can never be as light as a Brayton closed-cycle gas turbine but can compete only by achieving higher efficiency.
## 4 Experiment  
### Experimental Setup  
An absolute ranging experiment based on a pair of free-running femtosecond lasers is conducted by using the basic SSA filter for ranging precision improvement in practice. The experimental setup is shown in Figure 4. The ranging system uses a pair of free running carbon nanotube (CNT) mode-locked Er-fiber lasers working at 1550 nm, serving as the signal laser and LO, respectively. The average output powers after the Er-doped fiber amplifier (EDFA) are 10 mW and 13 mW, respectively. Their repetition rates are \(\sim\)74 MHz, having a differential repetition rate of \(\sim\)2 kHz. The laser pulses emitted by the signal laser are separated by a polarization beam splitter (PBS) and are sent to the reference mirror and target retro-reflector, respectively. The target is a retro-reflector mounted on a motorized linear translator that allows one-dimensional movement at various speed. The retro-reflected pulses from the target and the reference are beam-combined and are nonlinearly sampled by the local oscillator with an \(\sim\)2-kHz repetition rate offset in a periodically-poled potassium titanyl phosphate (PPKTP)-based optical cross-correlator, thus accomplishing nonlinear ASOPS. The cross-correlation traces are detected by a high speed avalanche photo-detector, and the output voltage signals are digitized and stored by a 14-bit 100-MHz digitizer (PXIe-5122, National Instrument, Austin, TX, USA). The repetition rate of the signal laser is measured by a commercial frequency counter (53220A, Agilent, Santa Clara, CA, USA) referenced to a rubidium atomic clock. Detailed laser ranger design, as well as data gathering and processing can be found in our recent publications [16; 18].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & **i** & **ii** & **iii** \\ \hline Velocity (mm\(\cdot\)s\({}^{-1}\)) & 0 & 0 (initial velocity) & 0.5 \\ Acceleration (mm\(\cdot\)s\({}^{-2}\)) & 0 & 2 & 0 \\ Duration (s) & 0.374 & 0.25 & 0.871 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulated distance signal states of motion, corresponding to Figure 3.  
Figure 3: Simulated distance signal generated by Table 1. (**a**) Simulated distance in time-domain. Black line: noisy simulated distance, red dashed line: real value; blue dotted line: filtered result. State i: stationary; State ii: uniformly-accelerated motion; State iii: uniform motion. (**b**) Correlation coefficient (black line) and RMS of residual (red line) between real value and filtered result.
# Online Deformation Measurement of Laser Repair Substrate Based on Orthogonal Sampling Moire
## 2 Principle of Deformation Measurement with Orthogonal Sampling Moire  
In two-dimensional deformation measurement, orthogonal grid lines instead of unidirectional grid lines are needed. As shown in Figure 1, orthogonal grid lines can be obtained by superposing two mutually perpendicular one-directional gratings, which have two perpendicular main directions. In this case, the gray scale of orthogonal grating can be expressed as follows [15]:  
\[\begin{array}{ll}I&=\left[I_{b1}+I_{a1}\cos\left(2\pi\frac{x}{p_{1}}\right) \right]\times\left[I_{b2}+I_{a2}\cos\left(2\pi\frac{y}{p_{2}}\right)\right]\\ &=I_{b1}I_{b2}+I_{a1}I_{b2}\cos\left(2\pi\frac{x}{p_{1}}\right)+I_{a2}I_{b1} \cos\left(2\pi\frac{y}{p_{2}}\right)\\ &+\frac{I_{a1}I_{a2}}{2}\cos\left[2\pi\left(\frac{x}{p_{1}}+\frac{y}{p_{2}} \right)\right]+\frac{I_{a1}I_{a2}}{2}\cos\left[2\pi\left(\frac{x}{p_{1}}-\frac {y}{p_{2}}\right)\right]\end{array}, \tag{1}\]  
where \(I_{b1}\) and \(I_{b2}\) are the background light intensity of the two grid lines, respectively, and \(I_{a1}\) and \(I_{a2}\) are the modulation amplitude of the two grating images.  
Figure 1: Orthogonal grid lines composed of two groups of vertical grid lines.  
directions. Compared with the traditional filtering method, orthogonal sampling directly processes the orthogonal grating image without filtering, which has higher efficiency.  
For the gratings before and after deformation, phase-shifted orthogonal sampling is carried out to compute the phase of moire pattern. Suppose that \(\varphi_{m1}\) and \(\varphi_{m2}\) are the phases in \(x\) and \(y\) directions before deformation, \(\varphi^{\prime}_{m1}\) and \(\varphi^{\prime}_{m2}\) are the phases in \(x\) and \(y\) directions after deformation, and \(p_{S}\) is the grating period; then, the displacement field can be obtained through Equation (4) [16]:  
\[\begin{cases}u_{x}(x,\,y)=\frac{p_{S}(\varphi_{m1}(x,\,y)-\varphi^{\prime}_{m1 }(x,y))}{2\pi}\\ u_{y}(x,\,y)=\frac{p_{S}(\varphi_{m2}(x,\,y)-\varphi^{\prime}_{m2}(x,y))}{2\pi} \end{cases} \tag{4}\]
# Effective Implementation of Edge-Preserving Filtering on CPU Microarchitectures
## 4 CPU Microarchitectures and SIMD Instruction Sets
_gather_ has been implemented in the AVX2, and _scatter_ has been implemented in the AVX512. Before _gather_ was implemented, the _set_ instruction was used. The _set_ instruction stores data in the SIMD register from scalar registers (see Figure 2). Thus, the instruction incurs a high computational cost. Third, even with the same instruction, instruction latency depends on CPU microarchitecture (for example, the latency of the add instruction indicated at [[https://software.intel.com/sites/landingpage/IntrinsGuide/#text=_mm256_add_ps](https://software.intel.com/sites/landingpage/IntrinsGuide/#text=_mm256_add_ps)]([https://software.intel.com/sites/landingpage/IntrinsGuide/#text=_mm256_add_ps](https://software.intel.com/sites/landingpage/IntrinsGuide/#text=_mm256_add_ps))). Therefore, the effective vectorization is different for each CPU microarchitecture. The expansion of SIMD instructions influences vectorization for the edge-preserving filtering. We can accelerate the filters by the increased number of vectorizing elements. Furthermore, the _gather_ instruction is useful for referencing lookup tables (LUTs). Using LUTs is a typical acceleration technique for arithmetic computation [47]. Weights are stored in the LUTs, and then, the weights are used by loading them from the LUTs. The loading process is accelerated by _gather_. Moreover, FMA3 is beneficial for FIR filtering. The summation term of Equation (1) can be realized by using FMA3. Therefore, we can accelerate the edge-preserving filtering with proper usage of SIMD.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 5 Aviation Big Data Multi-Layer Security Sharing Framework
### Privacy-Preserving and Evaluable Model-Sharing Scheme for ATM  
With the advancement of ATM technology, ATM systems generate vast amounts of flight operation-related data, including flight data, flight plans, meteorological data, airport operation data, and intelligence data. Sharing and analyzing such data can facilitate activities like flight delay prediction and service assurance, thereby improving flight operation efficiency and service quality. However, concerns over data security and privacy may hinder necessary data-sharing activities.  
To maximize the value of ATM data while ensuring security and privacy, it is essential to incentivize data sharing. By integrating blockchain and federated learning technologies, this scheme leverages blockchain's decentralized nature to eliminate reliance on third-party central servers, providing a secure, trustworthy, and reliable data storage environment for federated learning.  
The Shapley value contribution assessment method is employed to fairly evaluate data-sharing participants, identifying low-quality data and equitably distributing rewards to incentivize high-quality data sharing. Furthermore, a reweighting strategy is applied to update local models, enhancing the performance of the global model even when some participants engage in passive training.  
The specific process of model training and contribution assessment is illustrated in Figure 5.  
Figure 4: The specific process of the data-sharing scheme.
# FA-RCNet: A Fused Feature Attention Network for Relationship Classification
## 5 Conclusions and Future Work  
In this paper, a new FA-RCNet entity relationship classification model is proposed. The study found that the shallow information of the BERT model also has a positive effect on the relationship classification task. Therefore, the FA-RCNet model fuses the feature outputs of different levels of the BERT pre-training model. A fusion feature containing shallow semantic information and deep semantic information is formed. Improving the accuracy of subsequent relation classification tasks by fusing rich features in features. At the same time, a feature amplification attention module is designed to amplify the positive features and suppress the negative features in the semantic features. Through this operation, the effect of highlighting positive features is achieved. The formed semantic features can further improve the accuracy of relation classification tasks. At the same time, the experiments of FA-RCNet on the SemEval-2010 Task 8 and KBP37 data sets show that the performance of the FA-RCNet model is better than those of the existing methods, and the F1 values reach 90.33% and 69.95%, respectively. Ablation experiments on two datasets show that different modules in the FA-RCNet model have positive effects on relation classification tasks.  
In addition, we believe that the FA-RCNet model is still insufficient in dealing with multi-entity relationship problems. The model can only recognize two entities in a sentence, which is determined by the mask matrix that extracts entity features. When there are multiple entities in a sentence, there may be a problem that the relationship between a certain pair of entities cannot be identified. In addition, when there are multiple relationships between two entities, the model in this paper can usually only identify the relationship with a higher probability, and cannot accurately identify all of them. This is the problem with our model.  
In future work, we will continue to conduct research on how to dynamically generate the mask matrix according to the data format in the dataset to realize the relationship extraction between multiple entities. At the same time, we will look into how to introduce external information in the feature fusion stage to accurately identify the various relationships between entities.  
Software, resources, methodology G.L.; methodology, validation, formal analysis, data curation, writing--original draft preparation J.T.; software, visualization M.Z.; project administration, funding acquisition M.L.; writing--review and editing D.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding from the National Key R&D Plan of China (2022 YFF0608000).  
Not applicable.  
The authors declare no conflict of interest.