esa-sceva/satcom-chunk-collection huggingface dataset:
# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 3 Methodologies
### Data Processing and Evaluation Indicators  
Before training the models, because of the different prediction factors with different dimensions and size ranges, it is very necessary to normalize the data to make the typhoon intensity forecast models easy to train and to achieve reasonable prediction results. The data normalization method used is as follows:  
\[X(i)=(0.1*(max-x(i))+0.9*(x(i)-min))/(max-min) \tag{3}\]  
where \(X(i)\) is the value before normalization, \(x(i)\) is the value after normalization, \(min\) is the minimum value in the group, including \(x(i)\), and \(max\) is the maximum value in the group, including \(x(i)\).  
In addition, typhoons change during their life cycle, and considering the limited samples of one typhoon case, typhoon intensity can be forecasted as a time series problem based on historical typhoon cases and LSTM. For example, the time series of one typhoon case is X(1), X(2), \(\cdots\), X(n), and the time series of another typhoon case is Y(1), Y(2), \(\cdots\), Y(m); thus, the processed time series of these typhoon cases is X(1), X(2), \(\cdots\), X(n), Y(1), Y(2), \(\cdots\), Y(m).  
After training the models, the data need inverse normalization as follows:  
\[x(i)=(X(i)*(max-min)+0.9*min-0.1*max)/0.8 \tag{4}\]  
where \(X(i)\) is the value before normalization, \(x(i)\) is the value after normalization, \(min\) is the minimum value in the group, including \(x(i)\), and \(max\) is the maximum value in the group, including \(x(i)\).  
After typhoon intensity forecasting based on the LSTM models with lead times of 6, 12, 24, 48, 72, 96, and 120 h, the prediction results of the LSTM models measured the mean absolute error (MAE).  
The MAE between the observation and prediction values of typhoon intensity forecast models is defined as follows:  
\[MAE=\frac{\sum_{1}^{n}\lvert y(i)-x(i)\rvert}{n} \tag{5}\]  
where \(y(i)\) is the observation value, \(x(i)\) is the prediction value, and \(n\) is the length of \(x\). The results will be better with smaller MAE values.  
The root mean squared error (RMSE) between the observation and prediction values of typhoon intensity forecast models is defined as follows:  
\[RMSE=\sqrt{\frac{\sum_{1}^{n}\left(y(i)-x(i)\right)^{2}}{n}} \tag{6}\]  
where \(y(i)\) is the observation value, \(x(i)\) is the prediction value, and \(n\) is the length of \(x\). The results will be better with smaller RMSE values.  
As regards the above processes, the typhoon intensity forecasting models based on LSTM and an FNN can be seen in Figure 3.  
Figure 3: The typhoon intensity forecasting models based on LSTM and the feed-forward neural network (FNN).
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 4 Forecasting of Chaotic Time Series in Various Applications
### Hydrological Systems  
The RBFNN model is developed to estimate the Mekong River's nonlinear hydrological time series in Thailand and Laos, the Chao Phraya River in Thailand, and sea-surface temperature anomaly data [43]. In addition, the presented approach can also be applied to other geological time series. In [67], an adaptive fuzzy inference-based neural network model is developed to predict the medium- and long-term hydrological residual time series. The data are collected from the Guantai hydrological station, Zhang River, China. An empirical, statistical, and chaotic nonlinear dynamic model in [19] was applied to forecast the stream water temperature from the available solar radiation and air temperature in the Lake Tahoe basin, California, Nevada, USA. The chaotic FNN for predicting the hydraulic pump's vibration signal was presented in [75]. It is to be reported that the proposed approach can be extended further to improve prediction accuracy by readjusting the minimal embedding dimension optimally. The coupled quantity-pattern similarity model reported in [18] predicts the monthly precipitation of hydrological systems in the Danjiangkou reservoir basin, China. The proposed approach can also be applied to time series with various lead time scales.
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 2 Related Works
### The Multicast Repair Mechanism (M-REP)  
There are not many IP FRR solutions with reactive behavior. This is because a key feature of existing proactive FRR solutions, namely the fast reroute capabilities, is the fact that alternative backup paths are calculated in advance. However, we have addressed this issue in some of our previous works, culminating in a functional proposal of our Multicast Repair (M-REP) mechanism, the first reactive FRR solution [57; 58]. The M-REP FRR mechanism uses the well-known Protocol Independent Multicast--Dense Mode (PIM-DM) version of the multicast routing protocol.  
The PIM-DM is characterized by its specific flooding behavior, which is performed at the beginning of the multicast transmission and subsequent periodic intervals. M-REP uses this specific behavior to flood FRR traffic around the failed element in the network. The M-REP mechanism of the source router S, upon failure detection, encapsulates the unicast protected traffic to the multicast traffic characterized by the specific (S, G) multicast pair and floods it around the failed segment toward the router D (Figure 3).  
To make it work properly and to be able to build a multicast distribution tree, the M-REP proposes the modification of the PIM-DM Reverse Path Forwarding (RPF) check. Using the modification, each M-REP enabled router creates RPF interfaces of the first arrival and the M-REP backup path is created as follows: S-R1-D. Destination router D removes the added M-REP multicast header and restores communication to the original state.  
A further development is the optimized version called the Enhanced Multicast Repair (EM-REP) [58], which extends the use of multicast even for multiple failures (Figure 3) and supports the optimization of multicast flooding behavior in networks with multiple areas (OSPF, IS-IS).
## 3 Methodology
### Technical Condition Assessment
#### 3.1.2 Inspection Index Evaluation  
Subsequently, in the process of on-site inspection, inspectors are also required to evaluate the inspection indices of each member. Therefore, a standardized inspection index evaluation approach of BECIs with both specific qualitative and quantitative regulations are proposed for the determination of inspection index scale. Corresponding to various types of BECIs, the regulations of inspection index evaluation are shown in Tables 2-6, respectively. In particular, the scale will be defined as 1 when the technical condition of inspection index remains excellent. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inspection Object** & **Inspection Index** & **Scale** & **Qualitative Description** & **Quantitative Description** \\ \hline \multirow{8}{*}{Side beam} & & 2 & Slightly uneven & Elevation difference \(\leq\) 10 mm \\  & Evenness & 3 & Obviously uneven, local fractures occur & 10 mm \(<\) elevation difference \(\leq\) 30 mm \\  & & 4 & Excessively uneven, vehicle bumping, and side sinking occur & Elevation difference \(>\) 30 mm \\  & & 2 & Slightly narrow & 50 mm \(<\) spacing \(\leq\) design value \\  & Spacing & 3 & Obviously narrow & 10 mm \(<\) spacing \(\leq\) 50 mm \\  & & 4 & Excessively narrow or wide & Spacing \(>\) design value, or spacing \(\leq\) 10 mm \\ \hline \multirow{8}{*}{Anchorage concrete} & & 2 & Local fractures occur & Fractures \(\leq\) 3, fracture width \(\leq\) 2 mm, damaged area \(\leq\) 10\% anchorage \\  & & & & concrete area \\  & & & 3 \(<\) fractures \(\leq\) 5, & 3 \(<\) fractures \(\leq\) 5, \\  & & 3 & Multiple fractures occur & 2 mm \(<\) fracture width \(\leq\) 10 mm, \\  & & & 10\% \(<\) damaged area \(\leq\) 20\% anchorage \\  & & & & concrete area \\  & & & & Fractures \(>\) 5, \\  & & 4 & Serious fractures occur, anchorage function & fracture width \(>\) 10 mm, \\  & & & fails & damaged area \(>\) 20\% anchorage \\  & & & & concrete area \\ \hline \multirow{2}{*}{Rubber sealing tape} & & 2 & Slightly aging, local fractures occur & Fractures \(\leq\) 3, damaged area \(\leq\) 20\% \\  & Aging & 3 & Obviously aging, multiple fractures occur & 3 \(<\) fractures \(\leq\) 5, 20\% \(<\) damaged area \\  & & 4 & Excessively aging, serious fractures occur & Fractures \(>\) 5, damaged area \(>\) 50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Inspection index evaluation of the BECI of MA modular type.
## 4 Discussion
### Performance on Different Sub-Datasets  
In proposing the improvement approach of CS-DINEOF, we assumed that data within subregions stratified based on annual average concentration would have similar spatiotemporal patterns. Based on this assumption, stratified reconstruction was performed, resulting in improved accuracy and efficiency compared to the ordinary DINEOF method. To assess the performance of the proposed algorithm across different sub-datasets, we carried out a stratified comparison of reconstruction results, calculating the RMSE for each sub-dataset reconstructed by both DINEOF and CS-DINEOF methods. The results, as shown in Figure 13, demonstrate that the stratified reconstruction strategy of CS-DINEOF improved the accuracy in each sub-dataset. Notably, the most substantial improvement was observed in subregion 10, suggesting that the CS-DINEOF method is more effective than DINEOF in reconstructing missing chl-a data in the coastal areas of the Bohai Sea. The reason is that the spatiotemporal patterns of chl-a in coastal areas are different from those in open-sea regions, and the CS-DINEOF method can enhance the stability of spatiotemporal patterns within stratified clusters by independently reconstructing data within each subregion, thereby significantly improving reconstruction accuracy in these regions.  
Further analysis was conducted to explore how the proposed CS-DINEOF algorithm achieves the enhancement of reconstruction accuracy for each sub-dataset. Table 4 presents the sub-dataset reconstruction results from the CS-DINEOF experiments. In contrast to the ordinary DINEOF, which relies on fixed optimal EOF modes to reconstruct the whole dataset, the CS-DINEOF method determines the best EOF modes for each sub-dataset.  
Figure 12: RMSEs obtained from CS-DINEOF and ordinary DINEOF methods of different data SMRs of all images in the chl-a dataset.  
Figure 13: RMSEs obtained from CS-DINEOF and DINEOF methods of different sub-datasets.  
This allows every subregion to be reconstructed with the most appropriate spatiotemporal modes, thereby achieving better reconstruction accuracy. Moreover, most sub-datasets have a relatively small number of optimal modes with high variance contribution rates (VCRs). This suggests that the internal spatiotemporal correlations in these sub-datasets are robust, so that most of the original information in the dataset can be effectively captured with fewer modes. Therefore, stratification based on annual average concentration can be recognized as a straightforward and effective strategy that makes full use of the inherent spatiotemporal distribution characteristics of the chl-a data.  
Finally, the total calculation time for stratified reconstruction was greatly reduced, thereby markedly improving the computational efficiency. In practical applications, given the independence of each sub-dataset, parallel reconstruction using multi-threading can further save the data processing time, therefore making the CS-DINEOF method particularly well-suited for the reconstruction of high-resolution and large-scale remote sensing datasets.
## 6 Localization Systems
### Indoor Localization Systems
#### 6.1.3 Ultra-Wideband Localization--UWB  
UWB localization systems are the most recent indoor localization technologies. They consist of a radio-based communication technology for short-range use that makes use of very short radio frequency pulses (less than a nanosecond) [186], transmitted over a large bandwidth (>500 MHz) [179], to detect objects' location devices, as well as people, with excellent spatial resolution [187; 188]. The main advantageous properties of UWB are good multipath resolution, a large channel capacity, and low energy consumption [186]. This feature makes it possible to obtain a centimeter-level accuracy. Therefore, it has become an essential solution for various applications. It is quickly becoming the leading technology in the indoor positioning market as measuring the variables consisting of velocity and acceleration, displacement estimation, and change of direction has recently been used in many studies to measure such parameters [117; 118]. The UWB systems in recent years operated in many articles to measure motion tracking, especially in an indoor positioning system since there are errors and limitations to using other devices, including GNSS in indoor salons, and because UWB is immune to interference from other signals (due to the different electrical signal features) [179].  
An exciting application of UWB technology occurred in hockey to determine the position of players on the court during a game or practice [186]. By exploiting this information, valuable characteristics have been derived, such as the covered distance and maximum reached speeds. In this case, the location system was combined with an IMU attached to the stick. Exploiting a sensor fusion process merging the data obtained by these two systems has created a broader and more detailed picture of what each player is doing at a specific time (Figure 8).  
Another exciting study (conducted on American football players) compared the accuracy of the data obtained by UWB and GNSS systems. Such a study shows that the UWB localization system is a valuable solution to assess the players' position on the pitch with high precision, as well as in outdoor sports [189].
# Superior Mechanical Behavior and Fretting Wear Resistance of 3D-Printed Inconel 625 Superalloy
## 3 Results and Discussion
### Tensile Tests
where \(M=3.06\) is the mean orientation factor for the fcc polycrystalline matrix [34]; \(G\) is the shear modulus, 81.4 GPa; \(\upsilon\) = 0.28 is Poisson's ratio of the Inconel 625 matrix; \(b\) is the magnitude of the Burgers vector, 0.179 nm. For Ni-based superalloys, the dislocations in \(\gamma\) phase have a Burgers vector of \(a/2\) since precipitates reside within a coherent face-centered cubic (fcc) matrix [35]; \(\overline{r}=\sqrt{2/3}r\) is the mean radius of a circular cross section in a random plane for a spherical precipitate, \(r\) is the radius of a precipitate, 600 nm. The distribution of spherical precipitates in a cubic grid is applied to simplify a small volume fraction of precipitates, \(\lambda\) can be expressed as follows:  
\[\lambda=2\overline{r}\bigg{(}\sqrt{\frac{\overline{\pi}}{4f}}-1\bigg{)} \tag{3}\]  
where \(f\) is the volume fraction of precipitates, 0.8-1.1%. Thus, the strengthening contribution from the precipitates \(\Delta\sigma_{p}\) is 27.9 MPa. Since the grain size of the AM-Inconel 625 is significantly reduced, if compared to that of initial powders, the grain boundary (GB) strengthening mechanism is next considered as a possible cause of the YS improvement. It has been established that GB strengthening obeys the Hall-Petch relationship [34]:  
\[\Delta\sigma_{GB}=K^{HP}d^{-1/2} \tag{4}\]  
where \(K^{HP}\) and \(d\) are the Hall-Petch coefficient (0.158 MPa m\({}^{1/2}\), [36] and the average grain diameter (5.61 um), respectively. The strength increment due to GB, \(\Delta\sigma_{GB}\), is 66.7 MPa. The contributions from different strengthening mechanisms can be added linearly [37; 38]. Therefore, the yield strength of the AM-Inconel 625 alloy \(\sigma_{y}\) can be expressed as:  
\[\sigma_{y}=\sigma_{0}+\Delta\sigma_{GB}+\Delta\sigma_{p} \tag{5}\]  
where \(\sigma_{0}\) is 687.6 MPa, calculated by the linear relation in Figure 4b. Using the results from Equation, \(\sigma_{y}\) is 782.2 MPa, which is in good agreement with the experimental result of the yield strength (793 MPa). It is worth noting that the full density (100% relative density) is not achieved during the AM process and the presence of a small amount of porosity in the printed samples is not taken into the consideration in strength calculations. Micro-pores may lead to premature failure through the coalescence of micro-pores and the subsequent propagation of cracks, especially for tensile tests. From the good agreement between the results of the calculation and the experiment, it is reasonable to conclude that the strength of AM-processed Inconel 625 alloys is predictable by quantifying the contributions from different active strengthening mechanisms. The quantification of different strengthening mechanisms can be determined from characterizing the microstructure of Inconel 625 (e.g., distribution and size of spherical precipitates, grain size, etc. ), providing a guideline for designing advanced high performance bulk metallic materials by additive manufacturing.
# SmartX Multi-View Visibility Framework
## 2 Background and Requirements of Flow-Centric Visibility
### Flow-Centric Visibility Requirement for Unified Monitoring
#### 2.2.2 Visibility Data Aggregation and Tagging  
Data aggregation is a process in which information is expressed in summary form for purposes such as statistical analysis and data reduction [15]. Visibility data aggregation goal is to gather particular information about individual groups based on specific variables and cater missing data issues when data is coming from distributed geographical locations. Visibility data should be aggregated for all data instances over a specified time period and filter out unnecessary data to overcome any additional metrics burden. Raw visibility data should be kept at DataLake [16] for analysis and aggregated data should be sent to the next component for further processing.  
SmartX MVF is designed to offer unified monitoring by integrating multiple layers of visibilities together. As of now, due to the diverse nature of workloads, workload-layer visibility is missing from the SmartX MVF. As a workaround, visibility data tagging should be supported to incorporate additional information from both playground operators and developers into collected data for catering missing information issues. Data tagging allows playground users to organize information efficiently by associating pieces of information with tags. Examples of this data are playground user tenant and application information.
# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
## 7 Conclusions  
This work described the design and implementation of a finger-gesture recognition system for visible light communication systems. The system employs a single low-cost LED at the Tx and a single photo-diode at the Rx and operates on the patterns of blocking of direct light by the finger motion. The LSTM algorithm can correctly categorize the finger gestures with an average accuracy of 88%, and the optimized number of hidden units was 50. A good performance-to-complexity state could be achieved by sampling the light at 250 kHz. The system has many applications in human-computer interaction, including health-care, commerce, and in the home. Our further work will focus on increasing the number of gestures and tasking the system with recognizing individuals from their gesture signatures.  
All authors contributed to the paper. Conceptualization & methodology. J.W., A.M. and R.T.; software, J.W.; validation, investigation, formal analysis, all authors; writing--original draft preparation, J.W., A.M. and R.T.; writing--review and editing, all authors; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Kuwait Foundation for Advancement of Sciences (KFAS) under Grant PR19-13 NH-04.  
Not applicable.  
The authors would like to thank anonymous reviewers for their constructive comments, which helped in improving this manuscript.  
The authors declare that there are no conflict of interest regarding the publication of this paper.
## 3 Case Study
### Analysis Results
#### 3.3.2 Determination of Monitoring Index  
The method in Section 2.2.2 of this paper is used to determine the monitoring index of horizontal displacement at monitoring point B3. Based on the probability density distribution of horizontal displacement at monitoring point B3 of the dam obtained from SFEM at each water level, the upper and lower limits of the water pressure component at the significance level of \(\alpha=5\%\) are determined (shown in Figure 13) and fitted with the expressions shown in Equation (14). Subsequently, the expressions for the upper and lower limits of the water pressure component are substituted into the trained SFEMM model in Section 3.3.1 to obtain the displacement monitoring index of the rockfill dam.  
\[\left\{\begin{array}{l}\delta_{H_{\text{up}}}\!=\!6\times 10^{-5}H^{3}+3.3 \times 10^{-3}H^{2}+5.79\times 10^{-2}H+3.6831\\ \delta_{H_{\text{down}}}\!=\!1\times 10^{-4}H^{3}-2.1\times 10^{-3}H^{2}+1.236 \times 10^{-1}H+2.0257\end{array}\right. \tag{14}\]  
Figure 14 shows the comparison of SFEM-based monitoring index with the monitoring indexes of the traditional confidence interval method and small probability method (significance level of \(\alpha=5\%\)). (1) Because of the accurate prediction result of SFEMM model, the measured displacements are located in the interval of the hybrid monitoring index, and the measured displacements are also in the range of the monitoring index of confidence interval method, which indicates that the dam displacement changes are normal, and the conclusion is consistent with the actual operation pattern of the project. (2) Compared with the traditional confidence interval method, the range of SFEM-based monitoring index is slightly larger than the range of monitoring index of the confidence interval method. This is because the SFEM-based monitoring index takes into account the physical and mechanical relationship between rockfill dam materials and displacement, which can better reflect the probability distribution of dam displacement under different loads, reducing excessive early warning situations in displacement monitoring in practical applications. (3) Comparing the SFEM-based monitoring index with the traditional small probability method-based monitoring index, due to the obvious trend change in the displacement of the B3 measuring point (shown in Figure 4 or Figure 5), reveals that the monitoring index of the traditional small probability method based on mathematical statistics has poor applicability and less rationality to the displacement of this measuring point. For example, in the first 20 sets of displacement measurements, there is a part of the measured value close to or even smaller than the lower limit of the monitoring index.  
Figure 13: Upper and lower limits of water pressure components and their fitting curves.