esa-sceva/satcom-chunk-collection huggingface dataset:
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
## 6 Performance Evaluations
### Simulation Setup
#### 6.1.2 Metrics  
The metrics used to evaluate the performance of the proposed algorithms and compare the methods are as follows:  
* Mean relative error (MRE): The ratio of absolute difference and observed value, in which absolute difference is between the predicted value and the corresponding observed value. MRE evaluates the accuracy of the traffic flow singular point detection and prediction.
* Normalized computing time: This metric is used to evaluate the algorithms' computation cost. To balance the magnitudes of different algorithms, we normalize the computing time that can reflect the computation cost and computing increment directly.
* Sum of squares error (SSE) and coefficient of determination (R-square): The lower the SSE is, the closer are the predicted traffic flow and the observed value. The performance is better when the value of R-square closes to 1. These two metrics reflect the prediction robustness during the different processes.
## 5 Concluding Remarks
### Practical and Theoretical Contributions  
To compare the differences between the preventive process and the CBM/PdM process, two diagrams were drawn in the BPMN format (Appendix A). When analyzing the two different approaches, it was possible to verify that their actors were different. Unlike preventive maintenance, where operators and industrial engineers play a role in collecting and analyzing data in the physical space, predictive and condition-based maintenance has replaced their actions with a monitoring system and an information system, the \"Smart Observer\". These changes led to the automation of the data collection process. Thus, the question that arises is no longer \"What has failed or what can fail?\", but \"What will fail and when?\". Renault can now better anticipate the breakdown of its machines before failures occur and also schedule the replacement of components in the most convenient way. This, in turn, has allowed the company to minimize periodic inspections, emergency stops, and shutdowns. Ultimately, this led to the fulfillment of the company's goal, which was to increase productivity and, at the same time, successfully test this concept in Renault facilities.  
Although the integration of this concept has already been a success in M1 and other Renault Cacia manufacturing lines, the complexity of this new approach cannot be overlooked. Therefore, before thinking about moving towards a global application of this maintenance program, the entire team of the maintenance group and the other actors in the company's production processes must follow the same path and contribute to the advancement of the project.  
The elaborated BPMNs, as well as others that may arise in the process, can play an essential role in this journey, helping to integrate all employees without having to attend a large number of training and meetings. In addition, these diagrams are intended to assist in the definition of a standard of action for maintenance, which every worker related to the subject must know and follow. In the future, it is also expected that a process model cannot only illustrate the links between different activities but can also assist in the execution of experiments that cannot be performed on a real object, namely through long-term simulations and analyzes.  
Figure 7: Renault Cacia differential box asset: (**a**) Differential box; (**b**) HESSAP machine status before the intervention.
## 5 Perceptual Audio Features Extraction Techniques
### Frequency Domain Perceptual Features
#### 5.2.4 Loudness-Related Perceptual Frequency Features  
This section summarizes those features that are related to the loudness of the audio signal, a notion that is defined as the subjective impression of the intensity of a sound (see [PERSON] _et al._[34]).  
* **Loudness:** in the original work by [PERSON] [178] the loudness measurement procedure of a complex sound (e.g., speech, music, noise) is described as the sum of the loudness index (using equal loudness contours) for each of the several subbands in which the audio us previously divided. In the work by [PERSON] and [PERSON] [179] the authors compute loudness by firstly computing the power spectrum of the input frame and then normalizing by subtracting (in dB) an approximation of the absolute threshold of hearing, and then filtering by a bank of gammatone filters and summing across frequency to yield the power in each auditory filter, which corresponds to the internal excitation as a function of frequency. These excitations are then compressed, scaled and summed across filters to arrive at the loudness estimate.
* **Specific loudness sensation:** this is a measure of loudness (in Sone units, a perceptual scale for loudness measurement (see [PERSON] _et al._[34]) in a specific frequency range. It incorporates both Bark-scale frequency analysis and the spectral masking effect that emulates the human auditory system ([PERSON] _et al._[162]). This feature has been applied to audio retrieval ([PERSON] _et al._[42]).
* **Integral loudness:** this feature closely measures the human sensation of loudness by spectral integration of loudness over several frequency groups ([PERSON] [180]). This feature has been used for discrimination between foreground and background sounds (see [PERSON] _et al._[181], [PERSON] _et al._[182]).
## 5 Conclusions  
The ground-based radio navigation system is a local navigation system that uses synchronized pseudolites. It has been studied as an alternative to satellite navigation systems, such as GPS or Galileo. One of the major problems to be resolved in relation to a pseudolite-based navigation sensor is the near-far problem. The transmission of pulsed signals is known to be the most effective means of mitigating the interference caused by the near-far problem. Previous studies suggested pseudolite transmitters using a randomized pulsed sequence to overcome the aliasing issue caused by the additional frequency term of the periodic pulse. However, a randomized pulse sequence may not be adequate in some applications, due to the dwell time required to use all of the sequence chips.  
This paper introduced a mathematical model for the post-correlation of the periodic pulsed signal and proposed algorithms to resolve the false lock issue due to the aliasing process. A flight test using an unmanned helicopter was conducted to verify the implemented algorithm. The results showed that there were a few outliers caused by an extremely poor DOP condition at a low elevation, as well as power fluctuations and signal blockages due to the rotation of the heading while hovering. However, the overall navigation performance was quite good, _i.e._, nearly equivalent to that of a satellite navigation system. Most importantly, there were no side effects caused by a false lock, such as a velocity bias or a frequent loss of the lock.
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## Methodology
### Landsat 8 Data  
Landsat provides the longest continuous archive of optical satellite data for land applications. Since its first launch in 1972 with the Multi-spectral Scanner (MSS), Landsat data have significantly improved over the years both in terms of spectral and spatial resolution. The launch of the Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) in February 2013 in particular, has brought new opportunities and a platform for continuity in optical remote sensing. Landsat data are the most widely used in global land-cover mapping and are also freely available to the user community, and can be downloaded at [[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/)) or [[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)).  
The Landsat 8 carries two instruments: the OLI sensor comprises refined heritage bands from the Enhanced Thematic Mapper plus (ETM+) or Landsat 7, a new deep blue band for coastal/aerosol studies, a shortwave infrared (SWIR) band for cirrus detection, and a quality assessment band. The Thermal Infrared Sensor (TIRS) provides two thermal bands. These sensors onboard Landsat 8, have brought marked improvement in signal-to-noise ratio (SNR) and radiometry is quantized over a 12-bit dynamic range. There are 4096 potential grey levels in Landsat 8 images compared with the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **AT** & **RON** & **PD** & **ST (GMT)** & **Polarization** & **Resolution** \\ \hline
9 June 2015 & Normal & 105 & Descending & 21:49 & VV VH & High \\
8 July 2015 & Normal & 171 & Ascending & 09:54 & VV VH & High \\
1 August 2015 & Normal & 171 & Ascending & 09:54 & VV VH & High \\
25 August 2015 & Normal & 171 & Ascending & 09:54 & VV VH & High \\
18 September 2015 & Normal & 171 & Ascending & 09:54 & VV VH & High \\ \hline \hline \end{tabular} AT, RON, PD and ST are acquisition type, relative orbit number, pass direction and sensing time, respectively.  
\end{table}
Table 1: Characteristics of Sentinel-1A level 1 GRD IW products used in this study.  
256 in previous 8-bit instruments (Landsat 4, 5 and 7). The improved SNR performance in Landsat 8 enables better land-cover classification, making it suitable for many environmental applications.  
Due to persistent cloudy weather conditions in the study area during the rice growing season, only a single cloud-free Landsat 8 image acquired on 3 August 2015 was used in this study. This image is a standard Level-1 terrain corrected (LT1) product on path/row 118/038, projected to the Universal Transverse Mercator (UTM) zone 51 N and World Geodetic System 84 (WGS84) datum.
# Phtyromediation of Toxic Metals: A Sustainable Green Solution for Clean Environment
## 9 Enhancement Technique of Phyto-Extraction Efficiency
### Common Approaches to Increase Toxic Metal Bioavailability
#### 9.1.3 Through the Application of Microbial Augmented Acidified Cow Dung  
According to [PERSON] et al., 2018, the application of the acidified product in Pb- and Cd-polluted soil improved the bioavailability of those metals. The concentrations of Pb and Cd in ryegrass shoots were found to be 114 percent and 126 percent, respectively. To begin, isolated toxic metal resistant sulfur-oxidizing bacteria (SOB) were used to bio-augment cow dung, and acidity was achieved by adding elemental sulfur (S\({}^{\circ}\)) and molasses solution. The bioavailability of Pb and Cd from soil to grasses employed for phyto-extraction was then increased in tub trials by introducing bio-augmented acidified cow dung. The mechanism of acidity in cow dung is based on the equation below [238].  
\[\text{S}^{\text{o}}\left(\text{Sulfur}\right)\;+\;3\text{O}_{2}\left(\text{ Oxygen}\right)\;+\;2\text{H}_{2}\text{O}\quad\overline{\text{SOB},\;\text{Molasses}}\quad 2\text{H}_{2}\text{SO}_{4} \left(\text{Sulfuric Acid}\right)\]  
According to [PERSON] et al., 2018, the MIC of added SOB for Pb and Cd was 1000 mg/L and 180 mg/L, respectively, and SOB oxidize elemental sulfur (S\({}^{\circ}\)) and create sulfuric acid (H2 SO4). As a result, by lowering the pH of the soil, acidified cow dung increased the bioavailability of Pb and Cd. According to [PERSON] 2017, diluted acidified cow dung reduced the soil pH by 0.92 points, which increased the content of Pb and Cd in the ryegrass sprout. Pb increments ranged from 44 to 94.32 mg/kg, while Cd increments ranged from 34 to 77 mg/kg. The key ingredients in phyto-extraction are high plant biomass and acidified cow dung, rich in nutrients and microorganisms with a low pH. As a result, plant growth is aided by both nutrients and microbes. For a short time, acidified cow dung lowered soil pH and increased Cd and Pb bioavailability. Sulfate ions in acidified cow manure react with water to generate sulfuric acid (H\({}_{2}\)SO\({}_{4}\)). H\({}_{2}\)SO\({}_{4}\) reacts with \(CaCO_{3}\) and dissolves it to generate the fertilizer CaSO\({}_{4}\) when bioaugmented cow dung is applied, as illustrated in the equation below.  
\[\text{H}_{2}\text{SO}_{4}+CaCO_{3}\ \ \rightarrow\ \text{CaCO}_{4}+\ \text{H}_{2}\text{O}\ +\text{CO}_{2}\]
## 3 Materials and Methods  
The key to compensating systematic error in the CoM method lies in predicting the corresponding systematic error \(\sigma_{\bar{x}_{g}}\) of the actual star centroid position \(\bar{x}_{g}\) accurately and rapidly. Researchers have proposed several compensation methods in recent years, such as the BP method [16], analytical compensation (AC) method [17], bivariate polynomial method [18], and LSSVR compensation method [19], and they can reduce the systematic error to some extent, but there are some shortcomings in the compensation model of these methods. For example, the poor performance indices and low learning rate of the BP algorithm, along with how easily it becomes trapped in a local optimum, limit the compensation accuracy of this method; the simplified approximation and iteration estimation in the analytical compensation method lead to a reduction in the prediction accuracy and high on-line computational complexity, which is not suitable for the on-orbit embedded system; the bivariate polynomial compensation template is valid only for some specific Gaussian width cases, and its application range is thus limited; and the problem of scientifically setting the penalty factor and kernel parameter in LSSVR still remains unsettled, meaning that model training is more difficult because of a time-consuming parameter selection process.  
Figure 4: Signal intensity profiles and pixel detected signals under different Gaussian widths \(\sigma_{PSF}\) and centroid positions \(x_{0}\) (line: signal intensity profile, bar: pixel detected signal in three-pixel window, \(y\) axis: relative intensity). (**a**) \(\sigma_{PSF}=0.3\), \(x_{0}=2.0\); (**b**) \(\sigma_{PSF}=0.3\), \(x_{0}=2.25\); (**c**) \(\sigma_{PSF}=0.3\), \(x_{0}=2.5\); (**d**) \(\sigma_{PSF}=0.671\), \(x_{0}=2.0\); (**e**) \(\sigma_{PSF}=0.671\), \(x_{0}=2.25\); (**f**) \(\sigma_{PSF}=0.671\), \(x_{0}=2.5\).  
Figure 5: The relationship curve between \(\sigma_{\bar{x}_{g}}\) and \(x_{0}\). (**a**) Gaussian width \(\sigma_{PSF}=0.3\). (**b**) Gaussian width \(\sigma_{PSF}=0.671\).  
ELM [24; 25] has attracted attention in robot control [26], human face recognition [27], medical diagnosis [28], sales forecasting [29], and protein structure prediction [30] fields, among others, due to its simple training process and excellent generalization ability when compared to other traditional algorithms [31]. However, the randomness of input weights and hidden layer biases become a bottleneck restricting the stability and prediction accuracy of the ELM network. In order to overcome the adverse effects of extreme learning parameters and improve the performance of compensation methods, a systematic error compensation method based on BA-ELM is given herein to eliminate the systematic error of the star centroiding method considering both compensation accuracy and on-line computational load; the BA-ELM model is utilized to predict the systematic error and then improve the accuracy of the CoM method.
## 3 System Development
### Results Analysis  
The prototype of our system described above was tested in order to assess its correct operation and to perform a preliminary performance evaluation. In over 100 tests, the actuation commands on the UV-C lamp (activation and deactivation) were never lost; we report in Table 3 the execution time of the two main lambda functions of sensor data processing and video processing.  
It can be immediately noticed that, in both cases, a relevant but bearable delay is introduced by the notification system (in the cases in which we need to notify the administrator) while, specifically for the image processing, calling the AWS Recognition API introduces between 0.5 and 1 s of delay. The latter can be significantly reduced in the case of local execution, where only the label analysis on the video frame is needed and it takes between 280 ms and 400 ms (but with the possibility of exclusively identifying people), with almost identical values for the SNS notification time. Obviously, advanced (and more expensive) devices provided with specific AI-components such as Google Coral Board and Nvidia Jetson Nano are able to support powerful ML models, and they can more than halve the image processing time with respect to a Raspberri4, which is intended for general-purpose computing tasks. However, in our case, a response time of approximately 1 s is acceptable and not critical for the system's operation. Conversely, it is worth noting that, regardless of the specific edge device, the bandwidth consumption in the case of local execution is limited to a few KBs for the notification, thus avoiding GBs of video to be forwarded on the cloud for processing, with subsequent money saving. For the sensor data processing, instead, the PostgreSQL operations are also practically instantaneous, and the anomaly checks between 10 and 15 ms. Finally, it should be considered that the Raspberry Pi 4 uses about 3.1 watts while reading sensor data and up to 5.5 watts at maximum load during video analysis: in similar conditions, the power consumption of a cloud server is in the order of kWatts.
# Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman
## 2 Materials and Methods  
### Dune Sand  
Dune sand (DS) was sampled from the northern part of Wahiba which is about 200 km to the south-east of the Capital Muscat (Figure 1a) of Oman and transported to the experimental site in plastic bags. The Northern Wahiba is predominantly a large mega-ridge system, whereas the Southern Wahiba mostly comprises of linear dunes, sand sheets, and sabkha fields. The collected sand sample is classified as poorly graded fine sand (SP) according to the Unified Soil Classification System (USCS) [30]. The sand has a specific gravity (Gs) of 2.62 with fine contents of 3.5%. The standard proctor compaction test gave a maximum dry density (\(\gamma_{\text{MAX}}\)) of 1.69 g/cm\({}^{3}\) at an optimum water content of 17%. The photomicrograph of DS indicates the rounded and semi-angular particle shapes with smooth surfaces (Figure 1c) which are typical particulate characteristics of quartz sand weathered and transported via aeolian processes in deserts [31]. Carbonate sands are present throughout the Wahiba sand area with some quartz-rich sands in the Southern region [32]. The elemental analysis (Table 1) confirmed the presence of elements, calcium, carbon, and oxygen in the sand sampled from the northern ridge.  
Figure 1: (**a**) Location of Wadi Jezzi dam and Northern Wahiba Sands sampling sites, (**b**) example sampled reservoir sediments, and example photomicrograph of (**c**) dune sand (DS) and (**d**) reservoir sediments (RS). Note the different magnification between images (**c**,**d**).
## 5 Evaluation
### Running Time Comparison  
In Table 7, we provide the averaged running time and standard deviation (std) values in ms, for both LSM mappings, DR and SVM-Acc. Note that the values are obtained by repeating the detection algorithm, which is depicted in Figure 3, for ten iterations (including the entire data set, respectively). For the experiments, we used Matlab, version R2019b, in combination with an old Intel Core i7-6700K @ 4 GHz, with the operating system Windows7, 64 bit. From Table 7, we can make the following observations.  
Applying the DR measure leads to a _much_ faster check for (FS-)ordinal structures, in comparison to applying the SVM-Acc mapping. The difference is st  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Type** & **Iris** & **Seeds** & **Forests** & **Vehicles** & **Segment** & **Mfeat** \\ \hline DR & ✓ & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) \\ SVM-Acc & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ordinal Structure Detection (Traditionally Non-Ordinal Data Sets). DR: Detection based on the discriminant ratio. SVM-Acc: Detection based on the linear SVM resubstitution accuracy. ✓: Ordinal class structure found. \(\times\): No ordinal class structure found.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Type** & **CMC** & **LEV-4** & **SWD** & **Cars** & **Nursery** & **ESL-5** & **LEV** & **BVDB** & **ERA-7** & **ESL** & **ERA** \\ \hline DR & ✓ & ✓ & ✓ & \(\times\) & \(\times\) & ✓ & ✓ & ✓ & ✓ & \(\times\) & ✓ \\ SVM-Acc & ✓ & ✓ & ✓ & ✓ & \(\times\) & ✓ & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ordinal Structure Detection (Traditionally Ordinal Data Sets). DR: Detection based on the discriminant ratio. SVM-Acc: Detection based on the linear SVM resubstitution accuracy. ✓: Ordinal class structure found. \(\times\): No ordinal class structure found.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Data Set** & **Cl** & **Fea** & **Sam** & **Class Distribution** \\ \hline Iris & 3 & 4 & 150 & 50 per class \\ Seeds & 3 & 7 & 210 & 70 per class \\ Forests & 4 & 27 & 523 & 83 – 86 –159 –195 \\ Vehicles & 4 & 18 & 846 & 199 –212 –217 –218 \\ Segment & 7 & 19 & 2310 & 330 per class \\ Mfeat & 10 & 649 & 2000 & 200 per class \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data Set Properties (Traditionally Non-Ordinal Data Sets). Cl: Number of Classes. Fea: Number of Features. Sam: Number of Samples.  
according to a two-sided Wilcoxon signed-rank test [28], with a \(p\)-value of \(2.93\cdot 10^{-4}\). The largest difference can be observed for the BVDB. Using the DR mapping leads to an averaged detection time of approximately 44 ms, whereas applying the SVM-Acc measure leads to an averaged detection time of approximately 469,839 ms, which is approximately 7.8 min, with a standard deviation of 2.6 s.
# Social-Aware-Based Resource Allocation for NOMA-Enhanced D2D Communications
## 3 Social-Aware-Based Resource Allocation
### Optimization Model
Constraint (14) indicates \(\mu_{n,k}\) should be either 1 or 0, and it shows the members of the D2D cluster. Constraint (15) indicates \(\rho_{n,n^{\prime}}\) should be either 1 or 0, indicating whether two D2D clusters reuse the same cellular channel. Constraint (16) limits D2D content providers' transmit power. Considering that the optimization problem is a nonconvex mixed integer nonlinear programming (MINLP) problem which is usually NP-hard and there is no systematic and effective method to directly solve such problems, we decompose the optimization problem into two subproblems to solve the D2D resource allocation problem. We solve the channel allocation problem by many-to-one matching game and power control problem by SCA and GA.