esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Dataset Description  
The present research has considered the three datasets such CICIDS-2017, NSL-KDD and UNSW-Bot.  
UNSW-Bot: This dataset encompasses various categories of IoT, namely, normal traffic, IoT traffic, and several types of botnet attacks. The IoT-Bot training dataset encompasses 3,037,933 IoT traffic archives, with 1018 normal traffic archives and 3,036,915 attack traffic archives. On the contrary, the test dataset encompasses 3,668,552 IoT traffic archives, consisting of 477 normal traffic archives and 3,668,552 attack traffic archives. Hence, these IoT traffic archives are generated using IoT composed of smart home-based devices, like smart refrigerators, smart lights, remote garage doors, etc. Overall, this dataset comprises three main groups, namely, time-based, byte-based and packet-based. This dataset can be consulted at [[https://research.unsw.edu.au/projects/bot-iot-dataset](https://research.unsw.edu.au/projects/bot-iot-dataset)]([https://research.unsw.edu.au/projects/bot-iot-dataset](https://research.unsw.edu.au/projects/bot-iot-dataset)) (accessed on 1 February 2022).  
NSL-KDD: The NSL (Network Security Laboratory) dataset is the advanced form of the DARPA-98 dataset. It is composed of specific records given by the complete KDD dataset. Currently, NSL-KDD is used for the analysis of the efficacy of several classification approaches used in finding abnormalities in the structures of network-traffic. Four kinds of attacks can be found in NSL-KDD, including R2L (Remote to Local), U2R (User to Root), Probe attacks and DoS (Denial of Service). This dataset can be consulted at [[https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)]([https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)) (accessed on 15 February 2022).  
CICIDS-2017: The Canadian Institute of Cyber-security has developed a conventional dataset termed CICIDS-2017, comprising recent threats and features. This dataset encompasses non-threatening and recent commonly occurring attacks, using actual real-time data. It also includes the results of network traffic enquiries made with a flow meter consisting of destination ports, attacks, protocols and time-stamps. The dataset covers 5 days of data collection, with 2, 25 and 745 sets containing almost 80 features. In this dataset, attacks are divided into seven categories, known as infiltration attack, DoS attack, brute-force attack, webattack, Botnet attack, DDoS attack and heart bleed attack. This dataset can be consulted at [[https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)]([https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)) (accessed on 1 April 2022).
# The Evaluation of the Detection of Cr(VI) in Leather
## 2 Materials and Methods  
The colorimetric method was conducted in accordance with BS EN ISO 17075-1:2017. Test solutions contained variations of 280 mg L\({}^{-1}\) Cr(III) (Chromasol B, 26% Cr2O3, Lanzess GmbH, Germany), 10 mg L\({}^{-1}\) Cr(VI) (KCr2O7, 99%, British Drug House, UK), and 0.5% (_w/v_) gelatine (Bovine Gelatine, British Drug House). All UV spectra were recorded using a Shimadzu UV-2501 equipped with either 10 mm or 40 mm QX rated quartz cells with UV-Prove (version 2.43) software. Spectra were recorded within the 200-800 nm range with a 2 nm slit width and a 1 nm measurement step. The reaction speed between Cr(VI) and 1,5-diphenylcarbazide (99%, Fisher Scientific, Cumbria, UK) was monitored with the above apparatus at 540 nm at a 1 count/second data collection rate.  
The chromatographic method was conducted in accordance with BS EN ISO 17075-2:2017. All test solutions contained a variation on 280 mg L\({}^{-1}\) Cr(III), 0.05-1 mg L\({}^{-1}\) Cr(VI) and 0.5% (_w/v_) gelatine. All chromatograms and UV spectra were collected using Thermo Fisher Scientific Ultimate 3000 equipped with LPG-3400 SD gradient pump, WPS-3000 SLautosampler, TCC-3000 SD column compartment. and DAD-3000 UV-visible detector array. The buffer loop, needle seat capillary, and needle were constructed from biocompatible materials. 0.005\" ID PEEK tubing was used pre-column, whereas 0.010\" ID PEEK tubing was used post-column. The column compartment contained a Thermo Fisher Scientific AS11 (4 mm \(\times\) 250 mm) anion exchange column (quaternary ammonium functionality, 13 um particle size) coupled with a 50 mm AG11 guard column. A PEEK zero-dead-volume tee was connected to a syringe pump (KdS Scientific, Model 200, Holliston, MA, USA), and a 750 \(\mu\)L reaction coil. The wash solvent was 10% methanol (HPLC grade, Fisher Scientific, Cumbria, UK) in ultrapure water. Direct Cr(VI) detection (BS EN ISO 17075-2:2017 annex B) method conditions: * Fluent system: 25 mmol (NH4)2 SO4 (99%, Fisher Scientific, Cumbria, UK), 1 mmol NaOH (99%, Sigma Aldrich, Cumbria, UK); * Column temperature: 30 \({}^{\circ}\)C; * UV spectrum range: 200-500 nm; * Extracted chromatogram wavelength: 372 nm; * Flow rate: 1 mL min\({}^{-1}\); * Injection volume: 50 \(\mu\)L; * Run time: 15 min. Chromatographic condition with post-column DPC reaction (BS EN ISO 17075-2:2017 annex C): * Fluent system: 125 mmol (NH4)2 SO4, 5 mmol NaOH; * Post-column reagent: 2 mmol 1,5-diphenylcarbazide in acidified 10% methanol solution; * Column temperature: 30 \({}^{\circ}\)C; * UV spectrum range: 200-700 nm; * Extracted chromatogram wavelength: 540 nm; * Flow rate: 1 mL min\({}^{-1}\); * Post-column reagent flow rate: 0.33 mL min\({}^{-1}\); * Injection volume: 50 or 100 \(\mu\)L; * Run time: 7 min.
## 2 Materials and Methods
### Reduced-Order Model Establishment  
The finite element simulations for various thicknesses and friction coefficients were limited due to calculation times. A short calculation time with acceptable prediction accuracy would benefit the parametric studies. A predictive model which provides output response in a much shorter time is preferable. In this study, a predictive model was established based on a model reduction technique proposed by [PERSON] [44]. The reduced-order modelling consists of a learning step and a predictive step as in the case of a supervised learning algorithm. For the learning step, the proper orthogonal decomposition (POD) algorithm was employed to decompose the original dataset from the physical reference frame onto a new set of basis with special and useful properties. The result of decomposition and projection was a decouple version of the original dataset in another vector basis. Predictions of new space-time responses were reconstructed via multiplication of two interpolated uncouple fields. The Kriging method was selected for the spatial interpolation. The algorithm was implemented using the ODYSSE software [45] for ROM modelling. Figure 6 shows the process of ROM establishment. The upper block was the process of obtaining the training data which were provided by the finite element simulations. Thirty-six training data were the finite element simulation results with 3 thickness and 3 friction coefficients with 4 impact configurations for each type of drop test. Two ROMs were established. One for the DOT drop test to predict the head linear acceleration. The other was for the 45\({}^{\circ}\) inclined drop test to predict the rotational acceleration. The inputs were the impact configuration, the liner thickness and the head-helmet friction coefficient. Comparisons of the prediction with the finite element calculation were performed to validate the ROM. Finite element simulation results with the conditions illustrated in Table 3 were utilized for validation purposes.  
The validated ROM model was then used to generate more data as illustrated in the lower block of Figure 5. The ROM was used to predict the output responses based on the liner thickness ranging from 10 to 20 mm with an increment of 0.5 mm and the head-helmet coefficient ranging from 0.05 to 0.65 with an increment of 0.05. A total of 5124 cases were generated for each type of impact test.
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
###### Abstract  
We proposed a novel technique for detecting optical filter shift in ROADMs in optical transmission lines by applying machine learning on DP-16 QAM constellation data captured just after Analogue-to-Digital Converters (ADCs) in a digital coherent receiver. For this purpose, we implemented Docker container applications in WhiteBox Cassini to acquire the real-time raw digital data. By using the captured data, we generated CNN models for the detections in off-line processing and used them for real-time detections. As preliminary results, we confirmed the successful detection of optical filter shift in real-time with an accuracy of 51 GHz. To enhance the detection accuracy, we challenged ourselves to reproduce digital coherent constellation data by using a Generative Adversarial Network (GAN) for real-time optical filter shift detection for the first time. By utilizing a GAN, we successfully generated clone data based on actual constellation data. By adding the cloned data onto the actually captured data, we successfully enhanced detection sensitivity to as high as 26 GHz. As a result, we reduced the amount of required data for the high detection accuracy by 68% with the help of GAN-supported data augmentation. Furthermore, we confirmed our augmentation method enables the prediction of faults before they occur by enabling high enough detection sensitivity to detect an optical filter shift before degradation of Bit Error Rates (BERs) appears. This demonstrates the potential of GAN-based data augmentation in optimizing the efficiency and precision of optical network impairment sensing by using captured digital coherent optical signal.  
optic communications; digital coherent transmission; optical performance monitoring; filter shift detection; optical fiber bending; optical cross talk; artificial intelligence; machine learning; generative adversarial network; fault prediction
# An Overview of Vehicular Communications
## 2 Vehicle-To-Vehicle
Nevertheless, the implementation of V2V communications and an intelligent transport system involves three main obstacles: the need for car manufacturers to agree with rules of security and operation; the guarantee of privacy and confidentiality of data sent in broadcast and multicast; the financing necessary for the development and dissemination of all technology. At the moment, it is not clear whether public or private subjects will finance the creation and maintenance of the network infrastructure. However, among the leading car manufacturers who are working on ITS systems and V2V communications indeed it is useful to mention General Motors, BMW, Audi, Daimler, and Volvo. In the last five years, the evaluation of prototypes concerning V2V applications has been the focus of the research and development of many vehicles all over the world, especially, in the USA [44]. As a result, of these works, some experimental prototypes have been developed to estimate the environmental impacts, challenging to observe today due to the lack of a standard, and to the potential future impacts due to a broad hypothetical adhesion of applications in the field of vehicular communications, through a series of analytical methods. The USDOT (Department of Transportation of the United States of America) has documented the implementation and development of the most advanced applications, including the requirements of systems, design documents, algorithms and source codes associated with prototypes [45]. Table 1 presents some of the applications for connected vehicles, arranged by USDOT.
## 4 Results and Analysis
### Effect of Network Parameters on Welfare of P2P Platform
#### 4.5.1 Effect of Contribution Factor  
The contribution factor \(\eta_{c}\) determines the percentage of the network fee that the participants need to pay to the market operator for providing the services. In the suggested network utilization fee model (refer to Equation (20)), the network fee paid by the prosumers depends on the factor (\(1-\eta_{c}\)). Contrarily, the customers pay the \(\eta_{c}\) portion of the total network fee. Hence, by increasing the value of the \(\eta_{c}\), the portion of the network fee that the customers need to pay for each energy transaction increases. Contrarily, the network utilization fee decreases by increasing the value of the \(\eta_{c}\) for the prosumers. Therefore, the prosumers' welfare is directly proportional to the value of \(\eta_{c}\). On the other hand, the customers' welfare decreases with the increase in the \(\eta_{c}\) due to the fact that it increases the amount of the utilization fee that customers need to pay for the market services. Figure 15 shows the effect of the contribution factor for the suggested platform. At the intersection point of Figure 15, the \(\eta_{c}\) found the best compromise between the welfare of both the customers and prosumers. Note that, for the both ZI and GT strategies, the general trend remained same, with the only difference being the lower welfare for the GT method, as is explained above in the Section 4.
## 4 Results Analysis
### BIM Integration with Laser Scanning and Sensors  
The interviewer asked, 'In high-rise building projects, scaffolding and ladder failure (scaffolds are not fastened/tightened properly) due to negligence of construction workers while tightening the screws and bolts of scaffoldings, what is your opinion how with the laser scanning techniques integrating with BIM to detect the scaffolds which are not fastened/tightened in order to avoid accidents, in addition if there is (no warning signs), (no erection of signs as required) are present?'.  
In this case, two types of opinions were gathered from interviewees. One group was in favor and showed a positive attitude towards integrating BIM with a laser scanning approach, but the other group was opposed and showed a negative attitude towards integrating BIM with a laser scanning approach. They assumed that the use of low-quality tools and materials (scaffolding and ladder) for any form of construction could lead to serious misfortunes, thus avoiding any mishap. Responses included 'According to my experience yes, it can be done with the help of sensors' and 'I do not think it is possible'. When the interviewer asked 'Why cannot it be possible?', responses included 'I think to use and place sensors in entire scaffolding areas is costly and need more worker force, therefore should be avoided', 'I think it would raise the cost of project, not feasible', and 'I do not think it is a good idea to use sensors'.  
They believed that in the pre-construction phase, it was important to place the sensors in the scaffolding and ladder area, so that in the situation that the screws or bolts of the scaffolding and ladder would become shaky and loose, the sensors would automatically detect the area and in this way the warning alarm would be sent to the construction manager for early intervention in order to prevent an accident. In addition, the laser scanning technique will reliably capture point clouds of damaged bolts and screws. To acquire point cloud data for the target area, scanning is conducted at various points and as a result, point cloud data acquired from each scan point are inserted into a file. This will allow the construction manager to quickly imagine the faulty area in order to prevent accidents. While the other group of interviewees who had a negative attitude towards using BIM with a laser scanning approach assumed that installing sensors in every location where there is scaffolding or a ladder would be time consuming and expensive, this should be avoided. As a result, for the purpose of thoroughly inspecting the scaffolding and ladder materials in high-rise buildings, laser scanning technology refers to a group of technologies capable of acquiring and altering point clouds, such as light detection and ranging (LiDAR). With great precision and resolution, laser scanning collects data on building projects in a direct manner in order to boost effective and safer construction.
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 4 k-NN, SVM, and U-Net CNN Algorithms Evaluation
### Classifiers Comparative Study  
The results of the three examined methods were presented in the previous subsections. In this subsection, using the common metric of accuracy, the results for these methods five and ten days before the swarming events are presented in Table 13.  
As shown in Table 13, SVM has the highest accuracy (90%) for late swarming detection events, while k-NN fails to detect swarming events accurately, with an overall accuracy score of 85%. U-Net CNN is slightly outperformed by SVM, but outperforms k-NN, for late detection events, providing a score close to the SVM's 89% accuracy score. U-Net CNN's 89% accuracy for early detection seems to demonstrate great potential if appropriate data input filtering in the 0-2 kHz band is enforced in its training process. The authors set further experimentation with the U-Net CNN towards that direction as future work.  
As shown in Table 13, k-NN has the highest accuracy results (98%) for late swarming detection, closely followed by SVM with 97%. U-Net CNN follows with 95% accuracy, which is close to the SVM and k-NN classifier results.  
Additionally, when it comes to using images (such as the ones used by the U-Net CNN) for the swarming detection process, the need for huge amounts of communication data can be avoided, reducing the energy expenditure of IoT devices. For example, periodic data uploads in raw sound form (2.69-3.5 Mbytes per minute snapshots), required by k-NN and SVM, need 10-20 times more than the U-Net CNN with the IoT device CPU set in an idle state. In contrast, fast processing and uploading of single image Mel spectrograms, as required by the U-Net CNN classifier, is much more device energy efficient. This problem can be alleviated for SVM and k-NN if part of the processing is performed at the IoT device, providing dimensionality reduction of discrete frequency vectors in a CSV file sent to the cloud.
## 5 Sensor Faults and Outlier Detection Models for IoTs
### Comparison of Fault and Outlier Detection Techniques for IoTs
#### 5.6.5 Classification Techniques  
**Pros:**  
* This model is not dependant on either a statistical model or on the estimated data parameters.
* Provides optimal and sometimes maximum identification of sensor faults and outliers.
* Can be used on multidimensional data to detect sensor outliers and faults.
**Cons:**  
* This model is computationally complex when compared to clustering and statistical techniques.
* The model needs to train itself for new data points.
## 2 Theoretical Background
### Feature Selection  
Today, practitioners and researchers deal with large datasets comprised of hundreds, if not thousands, of features. In machine learning, variations in the target variable are primarily attributed to the initial input variables; however, their influence on the target variable may differ. Conversely, features that are either unrelated or highly correlated to the target variable can significantly compromise model performance. To address these challenges, practitioners utilize feature selection methods during the data pre-processing stage. Feature selection involves distinguishing relevant features from less important ones through dimensionality reduction techniques, ultimately enhancing model performance. Feature selection methods can be categorized into four groups for evaluating feature subsets: filter, wrapper, embedded, and hybrid methods. Filter methods assess feature relevance based on the dataset's nature to produce a ranked list of all features, regardless of the estimator used. Technical abbreviations will be explained at their initial usage throughout the text. Filtering techniques include mutual information, PC, correlation coefficient, information gain, gain ratio, Laplacian score, Fisher score, chi-squared, correlation-based feature selection, fast correlation-based filter, constraint score, relief, minimal-redundancy-maximal-relevance. Meanwhile, wrappers determine the relevance of feature subsets based on their performance in model prediction using ML-DL. Any greedy search strategies, including branch-and-bound, simulated annealing, genetic algorithms, and any classifier algorithms, can be utilized to produce a wrapper method. Recursive feature elimination (RFE) and Boruta algorithm are both examples of wrapper methods. RFE is a well-known and effective feature selection algorithm as a wrapper, which is easy to set up and utilize, and capable of identifying more relevant features in a training dataset. The random forest classification model is used to rank features based on their importance using a recursive method [43]. Built-in components of certain ML algorithms, embedded feature selection methods, rank features during the model's training phase. Tree-based algorithms such as decision trees, random forests, and gradient boosting are some examples of these methods [44]. On the other hand, nearly any filter, wrapper, or embedded technique can be integrated into a multi-step operation to create a hybrid approach that utilizes the strengths of each method. By applying the filter step in a hybrid approach, the feature search space is reduced, facilitating the use of computationally expensive wrapper or embedded methods for high-dimensional datasets [45].
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 3 Sources and Detectors for Lidar Imaging Systems in Autonomous Vehicles
### Multipixel photon counters
Such a photoelectron is accelerated to impact onto a cascaded series of electrodes named dynodes, where more electrons are generated by ionization at each impact creating a cascaded secondary emission. Each photoelectron generated is multiplied in cascade enabling again single-photon detection. It is possible to obtain gains up to \(10^{8}\) at MHz rate [136]. They have dominated the single-photon detection scene for many years, especially in scientific and medical applications. They are still the only photodetectors with a decent response and gain in the UV region, present unrivaled gain at all wavelengths and their rise times are in the ns scale, so their bandwidth is very large (\(>\)1 GHz). However, PMTs are bulky, fragile devices which are not solid-state, and which are affected by magnetic fields, which strongly limits their applicability in autonomous vehicles. Other disadvantages include the requirement for a high-voltage supply, the high cost and, in some cases, their low quantum efficiency. Table 4 presents a brief summary of the main features of photodetectors we have just described.
## 3 Purification Routes Applied to Lignocellulosic Acid Hydrolysates
### Combination of Different Techniques  
The technologies mentioned previously have sometimes been combined in integrated purification processes of lignocellulosic acid extracts.  
For instance, a monomeric sugar acid hydrolysate obtained from bamboo was treated by AC to remove color compounds (e.g., phenolic compounds, furanic compounds), then by SMB, to separate the sulfuric acid from the sugars. Under the best conditions, AC led to the removal of 93% of the color with no adsorption of sugars, and SMB led to the recovery of 90.5% of the sulfuric acid and the acetic acid on one side, and of 99.9% of xylose and 97.4% of glucose on the other side [67].  
Another combination of different purification techniques involved the use of ultrafiltration to remove macromolecules such as lignin or proteins from a wheat bran acid extract, then ED to recover sulfuric acid, and finally, ion exchange to complete the demineralization (conductivity < 10 \(\upmu\)S/cm) [71; 92]. Overall, the sugars' recovery was 90% and their purity close to 100%.