esa-sceva/satcom-chunk-collection huggingface dataset:
# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
## 5 Experiments and Analysis of Results  
### Experimental Data  
The dataset used in this paper is the traffic generated by the transit link of the Japanese WIDE network (AS2500) since February 2013, generated through a monitoring tool called Agurim [26]. Agurim is a network traffic monitor based on flexible multidimensional traffic aggregation, which allows users to dynamically switch views at different temporal and spatial granularities depending on the number of flows and packets, addresses, or protocol attributes. The views are dynamically switched, and the supported data sources are pcap, sFlow, and netFlow [27], making the dataset real-time and self-similar.  
The main view in Agurim contains two plots, the first based on BPS (bits per second, i.e., how many bits are sent per second) as shown in Figure 5 and the second based on PPS (packet per second, i.e., how many packets are sent per second). By default, each graph shows seven significant aggregated flows, with the legend labels showing the main attributes of each aggregated flow and their proportion of the total traffic, and the sub-attributes of the aggregated flow and their proportion. In this experiment, the total network flow is aggregated using seven aggregated flows, which are located in the last column of the dataset and therefore do not distinguish between the primary and secondary attributes of the address and protocol. The output data format is a text format based on the BPS pivot view.  
In this paper, a total of 52,493 data units were sampled from this data set between 1 January 2020, and 31 December 2020, with a sampling interval of 10 min. For uncontrollable reasons, the sampled data set contained some missing values. As such, we used the zero-fill method to fill in the gaps. The first 70% of the processed data were classified as the training set, 20% as the validation set, and the last 10% as the test set.
## 4 Results and Analysis
### Comparison of Various Classification Model  
Different classification models were tested with the dataset to identify the best classifier. Table 4 displays the accuracy, precision, recall, and f-1 score for each ML technique.  
Figure 6 shows that, for the generated dataset, the decision tree (CART) classifier achieves the best classification results, whereas OPTICS has the worst classification report. Furthermore, it can be seen that logistic regression yields good results for all of the parameters.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**ML Algorithm** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F-1 Score (\%)** \\ \hline Logistic Regression & 70 & 83 & 70 & 70 \\ SVM & 65 & 83 & 65 & 65 \\ K-NN & 56 & 81 & 56 & 52 \\ XGBoost & 69 & 82 & 69 & 69 \\ Decision Tree (CART) & 83 & 83 & 81 & 80 \\ OPTICS & 29 & 60 & 29 & 23 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of various Traffic Classification Model.  
Figure 6: Evaluation of various algorithms on the training dataset.  
After classification, we achieved 70% and 65% accuracy rate for logistic regression and SVM, respectively. The program performed well in regard to accurately classifying voice and data traffic in both cases, but it performed poorly in regard to video traffic.  
Compared to the first two algorithms, K-Nearest Neighbors has a different pattern. It accurately classifies data and video traffic, while voice traffic is only classified with a precision of 43%. K-NN's overall accuracy is likewise quite low, coming in at only 56%. Both data and voice traffic are accurately classified by XGBoost; however, video traffic is not.  
While classifying, CART performs accurately; however, it cannot accurately classify video traffic in the manner that logistic regression and SVM can. However, the decision tree's total f-1 score is higher than any other algorithm's, and its accuracy is higher than average at 83%. Due to its strong classification report and strong performance in terms of accuracy, precision, recall, and f-1 score, CART is the most preferred algorithm for classification. The decision tree classifier is also well suited for real-time classification because of its short execution time.
## 1 Introduction
### Proposed System  
This paper presents a monitoring system to measure dust, noise, ultraviolet radiation, illuminance, temperature, and humidity and to check the presence of flammable gases--liquefied petroleum gas (LPG), propane, hydrogen, butane, methane, and carbon monoxide. The system is composed of the following parts:  
1. Monitoring devices: they must be clipped to the employees' clothes to collect measures and send them via encryption through the Internet to the server.
2. Server: It runs applications to collect, process, and display data received from the monitoring devices. The server hosts a web application that allows companies to register employees and provide their health information.
3. Mobile application: it is intended for workers to check their exposure data. These components are described in detail in Section 3. The system presented in this article is aligned with the concepts of Industry 4.0 and 5.0 because IoT technologies are applied for continuous monitoring, storage, and display of workers' exposure to the risks specified above. Workers' health data are also used to help the company plan preventive actions.  
The proposed system provides continuous monitoring of workers' exposure to the agents mentioned above, addressing privacy issues and creating transparency for companies and workers when accessing that information as its main contributions. Regarding innovative aspects of the system, during employee registration, the health history of the worker in respect to common diseases and symptoms related to the monitored agents must be informed. The set of diseases and symptoms was defined in collaboration with two physicians. By observing alerts coming from the analysis of health data through a set of predefined rules, the company can plan professional activities aiming to minimize exposure to certain agents, decide to monitor health conditions more frequently, and guide internal health awareness campaigns.  
The architecture of the monitoring device and the web interface for recording employees of an early-stage version of the proposed system is described in [PERSON] et al. [23]. At that stage, the monitoring device only had humidity, temperature, and illuminance sensors. The solution was tested to check the functioning of the web application concerning employee registration (still without health information), the full information path, data acquisition from the sensors, communication protocols, data storage in the server, and display graphs in Grafana. Tests regarding the suitability of the hardware resources available on the server (memory, disk, and CPU) were also conducted.
## 2 Figure of Performance for Analysis  
To evaluate GNSS availability in SSV, we assessed receiver performance in terms of satellite visibility, GDOP, \(C/N_{0}\), UERE, and position errors for four types of multi-GNSS constellations and an additional first side-lobe signal. In addition, we set the threshold of \(C/N_{0}\) and analyzed results according to receiver performance through comparison of two cases, which are pessimistic and optimistic receiver parameters such as DLL bandwidth and correlator spacing.
# A Novel Batch Streaming Pipeline for Radar Emitter Classification
## 4 Experiment
### Pattern Recognition Model Setup
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**context\_vec\_dim** & **n\_layers** & **fc\_1** & **fc\_2** & **fc\_3** & **lr** & **Score** \\ \hline
3 & 6 & 807 & 281 & 1000 & \(2.615\times 10^{-5}\) & 50.1031 \\
24 & 6 & 768 & 786 & 668 & \(6.291\times 10^{-6}\) & 53.1601 \\
6 & 6 & 376 & 743 & 936 & \(3.707\times 10^{-5}\) & 62.2538 \\
15 & 3 & 659 & 473 & 695 & \(4.915\times 10^{-5}\) & 83.7921 \\
15 & 2 & 257 & 1022 & 665 & \(1.226\times 10^{-5}\) & 83.8592 \\
15 & 5 & 363 & 480 & 537 & \(1.811\times 10^{-5}\) & 88.1225 \\
13 & 6 & 716 & 376 & 376 & \(4.778\times 10^{-5}\) & 88.6277 \\
12 & 4 & 326 & 268 & 426 & \(3.549\times 10^{-5}\) & 91.1847 \\
3 & 5 & 800 & 272 & 1001 & \(4.398\times 10^{-5}\) & 92.0817 \\
8 & 6 & 804 & 270 & 989 & \(3.103\times 10^{-5}\) & 92.7828 \\
21 & 5 & 510 & 986 & 684 & \(4.416\times 10^{-5}\) & 95.5356 \\
22 & 8 & 399 & 488 & 644 & \(2.112\times 10^{-5}\) & 95.5872 \\
24 & 5 & 556 & 696 & 313 & \(4.062\times 10^{-5}\) & 96.4275 \\
26 & 2 & 397 & 490 & 659 & \(1.456\times 10^{-5}\) & 96.8141 \\
28 & 3 & 979 & 677 & 428 & \(2.006\times 10^{-5}\) & 97.9637 \\
14 & 5 & 826 & 316 & 281 & \(3.764\times 10^{-5}\) & 98.6442 \\ \hline \end{tabular}
\end{table} 
Table 7: Bayesian optimization result for pattern recognition model (LSTM).
## 2 AR/VR Applications
### Virtual Education  
AR and VR have gained popularity by providing students an enhanced authentic classroom experience equipped with instructions from the educator. Utilizing AR/VR technologies in classrooms has been proven to be successful in engaging students with truly unique and quality content with 360-degree views. VR tools are used to supplement the materials of the educators by helping them enhance classroom instruction by overcoming discipline restrictions and offering the students an immersive learning experience. CAVE, an immersive virtual reality environment, is considered a suitable choice for classrooms as it can be easily used inside a room whose walls, ceilings, and floors may have projection screens [54]. CAVE has a significantly lower price compared to the other HMDs and makes it easier for the teachers to assist the students in performing experiments [55]. A novel framework known as ScoolAR [56] was designed as an autonomous content creation tool for AR/VR applications by allowing the teachers to plan structured and tailor-made didactic proposals involving the students and also to avoid content that is misleading in nature. A mobile augmented reality (MAR) game named Alien Contact was designed by [PERSON], [PERSON], and [PERSON] for teaching mathematics, language, arts, and scientific literacy to middle and high school students. An MAR game called Explore! [57] was presented by [PERSON] and others to support middle school students in the exploration of archaeological sites in Italy. Google Expeditions Footnote1 [58] is a virtual application mainly used for educational purposes, which allows the users to be a part of several virtual visits to the most-evocative locations around the world including virtual travel to the depths of the oceans and space. Recently, the Air Force has introduced a virtual-reality-based flight simulator [59] designed to train pilots to have adequate flying skills in a safe environment. This flight simulator provides an immersive and natural experience for the pilot of a real aircraft by replicating the movements of a real-world control joystick in a cockpit.
# Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees
## 5 Results and Discussion
While random tree and C4.5 algorithms perform well with limited data sets, the performance of predictions on a larger dataset could be better. However, the developed models can always be updated to yield better results, as new data becomes available. Moreover, the dataset is unbalanced, owing to the fact that there are more failed cases than stable cases. Therefore, the establishment of a larger and more balanced pillar stability database is important. Other variables could also affect the prediction results. Numerous factors influence the stability of the pillar, such as external environments and the inherent characteristics of the pillar. While the five factors used in the current study, to some extent, may characterize the fundamental conditions for pillar stability, few other factors like underground water, joints, and blasting disturbance may also influence pillar stability. Analyzing the influence of these factors on the results of predictions is important. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{_ACC (\%)_} & \multirow{2}{*}{_MCC_} & \multicolumn{3}{c}{**Failed**} & \multicolumn{3}{c}{**Stable**} \\ \cline{5-10}  & & & & & **Precision** & **Recall** & \(F\)**-Measure** & **Precision** & **Recall** & \(F\)**-Measure** \\ \hline \multirow{3}{*}{RT} & Training & 100 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\  & Testing & 100 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \cline{1-1}  & Training & 97.500 & 0.943 & 1.000 & 0.964 & 0.982 & 0.923 & 1.000 & 0.960 \\ \cline{1-1}  & Testing & 83.333 & 0.632 & 0.800 & 1.000 & 0.889 & 1.000 & 0.500 & 0.667 \\ \cline{1-1}  & Training & 95.000 & 0.882 & 0.933 & 1.000 & 0.966 & 1.000 & 0.833 & 0.909 \\ \cline{1-1}  & Testing & 83.333 & 0.632 & 0.800 & 1.000 & 0.889 & 1.000 & 0.500 & 0.667 \\ \cline{1-1}  & Training & 100 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \cline{1-1}  & Testing & 100 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results of the developed random tree and C4.5 decision tree with available fishery discriminant analysis (FDA) and support vector machine (SVM) models.
# CRCSI: A Generic Block Interleaver for the Next Generation Terrestrial Broadcast Systems
## 3 Designing the Block Interleaver
### CRCSI Design
#### 3.2.1 Row Crossing
Here, we define the cross principle as: when the value of a cycle exceeds the range, the last digit of the starting value of the previous cycle is selected as the initial value of the next cycle. Specifically, to ensure \(d_{min}\) is maximized, two conditions for \(p\) need to be corrected. The first case is that the block is divisible by \(M\) and \(z\) after adding one element. For example, for an \(11\times 5\) data block, according to (11)-(13), we have \(z=3\) and \(\widetilde{p}=3\). Considering only the row sorting, the calculation result is shown in Figure 0(a), where it is indicated that \(d_{min}=p+N=8\). This kind of block structure layout can be improved with a larger \(p\), as shown in Figure 0(b). Another case is that the block is divisible by \(M\) and \(z\) after subtracting one element. For example, for a \(17\times 3\) data block, we have \(z=4\) and \(\widetilde{p}=4\). Similarly, the comparison of increasing the \(p\) value is shown in Figure 2.  
In these two cases, the original rules cannot meet the principle of maximizing the minimum interleaving distance, therefore, the interval value \(p\) ought to be rounded up. Finally, the incremental interval \(p\) is developed as  
\[p=\left\{ \begin{array}{ll}\lceil M/z\rceil,&M\bmod z=z-1\text{ or }M\bmod z=1\\ \\ \lfloor M/z\rfloor,&else.\end{array} \right. \tag{14}\]  
Based on above derivation, the row mapping rule \(\pi_{2}\) is redefined as the row index vector \(R_{i},i=0,1,\cdots,M-1\) following:  
1. \(p\) is a prime number of \(M\), \[R_{i}=(p\cdot i+s_{0})\bmod M,i=0,1,\ldots,M-1\] (15)
2. \(p\) is a common divisor of \(M\), \[R_{i+1}=\left\{ \begin{array}{ll}R_{i}+p,&R_{i}+p<M\\ s_{k}+1,&R_{i}+p\geq M\end{array} \right.,i=0,1,\ldots,M-1\] (16)  
where \(s_{k}=k,k=0,1,\ldots,p-1\). For example, for a \(10\times 6\) block, the group size is \(z=3\), and the incremental interval is \(p=4\), hence, the row index is \(R=\{0,4,8,1,5,9,2,6,3,5\}\). Up to now, we have implemented the row mapping rule \(\pi_{2}\) by the design of the incremental interval \(p\) and the cross value \(s_{i}\). Next, we consider designing the column mapping rule \(\pi_{1}\) in a cyclic shift manner.
## 6 Conclusions  
Due to its vast coverage in spatial and temporal scales, flood is considered to be one of the most complex disasters in the world. A novel inundation mapping approach based on spatiotemporal context learning and Modest AdaBoost is proposed and verified in this paper. The proposed method is implemented and evaluated in two different flooding cases using images from different sensors, HJ-1A CCD and GF-4 PMS. The experimental results show that the proposed approach is effective, and is able to produce more accurate mapping results than other state-of-the-art methods and, more importantly, without any artificial samples and thresholds.  
On one hand, compared with the traditional global-based unsupervised flood mapping methods (such as K-MEANs), the SP-MADB, STP-MADB and the proposed method combine an automatic sample selection strategy with a machine learning classifier, leading to higher accuracies in an automatic way. With the samples extracted using local information, each of these three methods achieves an overall accuracy of more than 90% in both of the first and second case studies. By comparing the results of the SP-MADB and STP-MADB methods, it can be seen that only extending the neighbouring region to the temporal domain cannot significantly improve the performance of the SP-MADB method. With a formulised model of the spatiotemporal context information instead of simple counting, the proposed approach achieves a more accurate and robust result than other methods. As a result of mixed pixels, there are still some inaccuracies in the result. Moreover, the effect of the proposed method on cloudy data needs to be explored. Future work will focus on these aspects and validate the proposed method with more kinds of data.  
This work is supported by the National Natural Science Foundation of China (Grant No. 41401474) and the National Key Research and Development Plan (No. 2016 YFB0502502). The HJ-1A/B CCD data, GF-1 WFV data and GF-4 PMS data were provided by the China Centre for Resources Satellite Data and Application (CRESDA, [[http://www.cresda.com](http://www.cresda.com)]([http://www.cresda.com](http://www.cresda.com))). The MODIS water mask (MOD44W) was obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Centre (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Centre ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov))). We thank the above organisations for providing the data. We thank the University of Chinese Academy of Sciences for scholarship support of [PERSON].  
[PERSON] conceived and designed the experiments, performed the experiments and wrote the paper under the guidance of [PERSON] and [PERSON]. [PERSON] and [PERSON] analysed the data and contributed analysis tools.  
The authors declare no conflict of interest.
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 4 Model Evaluation  
Energy consumption prediction is a time series problem; therefore, error loss metrics were used to measure the differences between the predicted value and the actual value. The metrics used for the evaluation are mean absolute error (MAE), root-mean-square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination \((R^{2})\). The MAE, RSME, and MAPE are metrics that measure the amount of error in the prediction generated by the model, while \(R^{2}\) was used to determine how much of a power consumption variability may be explained by its relationship to other factors. These metrics were selected based on the type of the problem and their usage in related studies, such as [28; 32; 33; 34].  
The MAE measures the absolute deviation between the true values and the predicted values; the lower the MAE, the better the model in fitting the data. The RMSE describes the degree of deviation between the true values and the predicted values. Lower RMSE values indicate a more stable model. The MAPE evaluates the model's error through the ratio reflection of the absolute deviation between the true values and the predicted values to the actual values. The model is the most accurate when the index is closest to 0. \(R^{2}\) represents the proportion of variation in the dependent variable explained by the model. The values of its output range from 0 to 1, and the closer the value is to 1, the better the fitting effect of the model [31]. The formulation of the metrics is shown below. In all cases, N represents the sample size.
## 4 The Impacts of Microalgae/Cyanobacteria and Their Extracts on Crops' Production
### Soil Improvement
#### 4.1.2 Nutrients' Availability in Soils
Finally, microalgal and cyanobacterial biomass present in agricultural fields may be degraded by other organisms and grazers, which also increases the organic carbon content in the soils. The increase of organic carbon contents in soils using microalgae and/or cyanobacteria has already been described in the literature [86; 87; 91]. For example, [PERSON], et al. [86] demonstrated that the use of cyanobacterial suspensions (_Calothrix ghosei_, _Hapalosiphon intricatus_ and _Nostoc_ sp.) in wheat production increased organic carbon concentration in the soil, while promoting higher grain yields. More recently, [PERSON], et al. [91] evaluated the effect of unicellular and filamentous microalgal-cyanobacterial consortia on soil composition and grain quality of a wheat crop. Besides an increase in micronutrient availability in the soil (mainly zinc, iron, copper and manganese), the studied consortia also enhanced its organic carbon content. This improvement in soil quality resulted in an increased grain yield and in a higher quality product, as the obtained grains presented an improved nutritional composition.