esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the last decade, predictive maintenance has attracted a lot of attention in industrial factories because of its wide use of the Internet of Things and artificial intelligence algorithms for data management. However, in the early phases where the abnormal and faulty machines rarely appeared in factories, there were limited sets of machine fault samples. With limited fault samples, it is difficult to perform a training process for fault classification due to the imbalance of input data. Therefore, data augmentation was required to increase the accuracy of the learning model. However, there were limited methods to generate and evaluate the data applied for data analysis. In this paper, we introduce a method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset. The enhanced data set could increase the accuracy of the machine fault detection model in the training process. We also performed fault detection using a variety of preprocessing approaches and classified the models to evaluate the similarities between the generated data and authentic data. The generated fault data has high similarity with the original data and it significantly improves the accuracy of the model. The accuracy of fault machine detection reaches 99.41% with 20% original fault machine data set and 93.1% with 0% original fault machine data set (only use generate data only). Based on this, we concluded that the generated data could be used to mix with original data and improve the model performance.  
generative adversarial network; data augmentation; machine fault detection +
Footnote †: journal: Applied Sciences
## 4 Methodology  
In this section, we describe our approach to surveying the literature so as to conduct an effective and meaningful survey of the literature.  
**Eligibility Criteria** We determined our search terms leading to the most relevant articles. We chose the search terms detailed in Table 4.  
We expect these to result in good coverage of the relevant literature. We searched each database using the identified search terms. The literature search was conducted up to September 2021. Generally, we have chosen to exclude works that have not yet been peer-reviewed, such as those appearing on arXiv, unless deemed by the authors as a significant paper that makes a clear contribution to the subject domain. We collated the searches and any subsequent duplicates were removed. Each paper was screened by reading the title and abstract to determine the relevance. Inclusion criteria were: the article is related to functionality preservation in adversarial machine learning for cybersecurity or intrusion detection with insight into robust classification.  
From this large list, we specifically focused on adversarial machine learning attacks and defenses, narrowing the literature down to relevant papers. Our selection process was roughly based on the preferred reporting items for systematic meta-analysis (PRISMA) framework [76]. Figure 4 details our selection process.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Topic** & **Search Query** \\ \hline Cyber security/intrusion detection & (“cyber security” OR “intrusion detection” OR “IDS”) \\ Adversarial machine learning attacks and defenses & (“adversarial machine learning” OR “machine learning” OR “adversarial example”) and (“attack” OR “deference”) \\ Robustness/Functionality Preservation & (“robustness” OR “generalization error” OR “accuracy” OR “f1 score” OR “f-score” OR “TPR” OR “FRR”) \\  & (“functionality” OR “payload”) AND “preservation”)) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Topics and associated search terms used in this survey.  
**Information Sources** We selected the following databases with a sizeable computer science element to search and retrieve literature: **IEEE Xplore**, **ACM Digital Library**, **ScienceDirect**, **Scopus**, **SpringerLink**, and **Google Scholar**.
## 3 Simulation and Analyses
### Transient Analyses
Due to the higher resonance frequency and symmetrical structure of the ILUM-D, the asymmetrical vibration is more significant under the same excitation conditions, which leads to higher output velocity. It is worth mentioning here that the comparison of the output parameters between the ILUM-S and the ILUM-D in the simulation analyses was performed to provide data support for the subsequent experimental measurements to facilitate a more comprehensive comparison of the output performance of the motors. Furthermore, the simulation results indicate that the output performance of the motor, e.g., the velocity at a fixed preload force, can be determined from the results of the stator assembly transient analyses. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **ILUM-S** & **ILUM-D** \\ \hline \(\delta_{\text{F}}\) (\(\upmu\)m) & 0.33623 & 0.40420 \\ \(\delta_{\text{B}}\) (\(\upmu\)m) & \(-0.19448\) & \(-0.14609\) \\ \(\Delta\delta\) (\(\upmu\)m) & 0.14175 & 0.25811 \\ \(\varphi_{F}/T\) & 0.57612 & 0.58158 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data calculated in MATLAB. Figure 5: Transient analyses results of the prototype: (**a**) contact model of the ILUM in ADINA; (**b**) output displacement of the moving element under the 500 V\({}_{\text{P-P}}\) sawtooth wave voltage. Figure 6: Transient response results of the velocity of the moving element and the driving shaft under the 500 V\({}_{\text{P-P}}\) sawtooth wave voltage: (**a**) ILUM-S; (**b**) ILUM-D.
## 1 Introduction
In addition, the proposed Asymmetric Programming scheme could supplement both ECC and redundancy methods to further improve the reliability of the metadata in MLC NAND flash storage systems. We have conducted a set of experiments on a real hardware platform. We have designed the MLC NAND flash memory array and implemented the proposed Asymmetric Programming inside the processing system of the Xilinx Zynq on-chip programmable SoC architecture [21]. Asymmetric Programming is compared with a representative design [17] and the default error correction scheme in the Linux kernel [22]. The experimental results show that, Asymmetric Programming can reduce uncorrectable page errors by 88.11% and 72.32% on average compared to [22] and [17], respectively. In terms of I/O performance, Asymmetric Programming only incurs a time overhead of less than 0.07% compared to the baseline error correction scheme.
## 3 The Supporting Communication Protocols
### The Virtualized Routing Protocols: VNIBR  
The layout of virtual nodes defined by the VaNetLayer provides convenient grounds to develop a combination of topological and geographical routing, with road-based paths connecting successive intersections that lead from sources to destinations. Specifically, VNIBR (Intersection-Based Routing on Virtual Nodes) differentiates three types of routing entities (hence the coloring of Figure 2):  
* The level 1 entities (L1 VNs, red in Figure 2) cover the regions located at the intersections. L1 VNs are the place in which the routing decisions are made, and where the relevant information (e.g., routing tables or lists of vehicle encounters) is kept as PSI.
* The level 2 entities (L2 VNs, orange in Figure 2) cover the regions neighboring an intersection. L2 VNs forward packets onto a road segment as mandated by the neighboring L1 VN, regardless of which vehicle actually does the transmission. Additionally, L2 VNs act as backing entities that trying to continue relaying packets onto other road segments during downtimes of the neighboring L1 VNs.
* The level 3 entities (L3 VNs, yellow in Figure 2) cover the regions in intermediate positions of road segments. The basic task of L3 VNs is to relay packets from one end to the other, again regardless of the specific PNs involved.  
L1 VNs on opposite ends of a road segment exchange _HELLO_ packets steadily, to keep track of the connectivity conditions between them. The messages are transmitted hop-by-hop across the L2 VNs and L3 VNs, which do no other processing than adding the average number of PNs in the corresponding regions. Upon receipt of a _HELLO_ packet, an L1 VN assigns a QoS value to the road segment, estimating the L1 VN-to-L1 VN transmission delay, the average number of vehicles in the intermediate virtual nodes, and the number of messages delivered and lost.  
On top of these foundations, three different flavors of VNIBR were developed, which we use in the experiments of Section 5: the reactive version (VNIBR-R) builds the communication routes on request, looking for the destination by broadcasting requests omnidirectionally; the proactive version (VNIBR-P) manages trees of routes between intersections, aiming to provide faster responses at the expense of increased overhead; finally, the encounter-based version (VNIBR-E) builds the routes on demand, but aided by lists that keep track of the last time any vehicle traversed any intersection. The details of the operation of the three protocols can be found in [13].
## 1 Introduction
Spectral preprocessing methods aim to reduce noise and variation in the spectra and enhance relevant information for prediction, for example, Savitzky-Golay (SG) smoothing, standard normal variate (SNV), and continuous wavelet transform (CWT) [27; 32]. While these methods do not require any information about the sample distribution to be transferred, they may sometimes produce unfavorable results. Global modeling methods, on the other hand, try to build a single model that can cover different fruits by using a large and diverse dataset. This approach commonly produces acceptable results for all fruits, but may degrade the already established model. Moreover, global modeling necessitates some spectral and reference values to be modeled for each fruit. Calibration transfer without standard methods attempts to correct the prediction bias when the model built for one fruit is applied to another, such as semi-supervised parameter-free calibration enhancement (SS-PFCE) [33] and PLS correction [34]. Similar to global modeling, these methods have comparable data requirements, but provide a more advanced modeling approach to update the model for each type of fruit with a few new corresponding measurements. It is important to note, however, that these techniques may not be as accurate or precise as using reference standards. Calibration transfer without standards should only be used when it is not possible to use reference standards, and the resulting data should be carefully evaluated to ensure that the accuracy and precision are acceptable for the intended use. In this paper, the feasibility of transferring NIR spectral models from one kind of fruit to another is investigated through two case studies: strawberry-to-grape transfer and strawberry-to-apple transfer. PLS regression is used as a baseline method to build NIR models for predicting the SSC of strawberries from their NIR spectra. The performance of the strawberry model on original datasets with grape and apple datasets is compared using root mean square error of prediction (RMSEP) as an evaluation metric. Some methods that can improve the transferability of NIR models between fruits, such as spectral preprocessing methods (including CWT, SG smoothing, and SNV), global model, and calibration transfer without standards (PLS correction, SS-PFCE), are also explored. The advantages and disadvantages of these methods are discussed, and some suggestions for future research directions are provided.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.5 Transmission Opportunity (TXOP)  
* _For IEEE 802.11-2007:_ TXOP defines a period of time for which a station accessing the channel is allowed to transmit multiple frames without using channel access procedure for all the frames.
* _For IEEE 802.11n/ac/ah:_ In these amendments, the TXOP procedure is enhanced, where the reverse mechanism allows the holder of TXOP to allocate the unused TXOP time to its receiver to enhance the channel utilization and perform reverse direction traffic flows. This mechanism is known as Reverse Direction (RD) protocol.
* _For IEEE 802.11 ah:_ IEEE 802.11 ah has introduced bi-directional TXOP (BDT) that can help non-AP station (i.e., sensors etc.) to minimize energy consumption. This technique allows the combination of transmission and reception of frames within a single TXOP, where the reduction in the required frame exchange enables stations to extend their battery life time. In addition, this mechanism assists in efficient use of contention based channel accesses.
## 3 Results
### Joint Angle Analysis
#### 3.2.2 Pull Task  
For the pull task, joint angular displacements were analyzed at the pose where the object began to move toward the participant and are presented in Figure 5a-f. All joint displacements were significantly different between the Real_HM and Predicted_HM (\(p\)! 0.0001 for the four joints: Knee_F/E (F = 26.7), Elbow_F/E (F = 33.89), Sh_F/E (F = 35.81), and Sh_Abd/Add (F = 102.81); \(p\) = 0.001, F = 12.78 for the Trunk_F/E; \(p\) = 0.03, F = 5.11 for Hip_F/E). The average values in Predicted_HM were greater than the Real_HM. Particularly, the joint Sh_Abd/Add was adducting in the Real_HM, but it was abducting in the Predicted_HM; the joint Elbow_F/E was extending in the Real_HM but flexing in the Predicted_HM.  
For the joint comparison between genders, the Hip_F/E (\(p\) = 0.03) and Sh_F/E (\(p\)! 0.0001) were significantly different in the Real_HM. Only the Knee_F/E was significantly different in the Predicted_HM, but the difference was only 1.0\({}^{\circ}\). Female participants had greater hip and shoulder flexion than males in the Real_HM.  
When comparing the cross-correlation between angular displacements, there was a weak negative correlation between the Trunk_F/E and Hip_F/E for female participants with R = \(-\)0.2 and a moderate positive correlation between trunk and hip for males with R = 0.66 in the Real_HM. This could indicate that males tended to use more of the lumbar spine to pull the weight, ending in extending the trunk. Female participants tended, instead, to use their hammstrings by pushing their backsides back, as in a standard deadlift movement. In the Predicted_HM, there was a strong relationship between these two variables for both males and females with Rs of 0.99 and 0.99, hence failing to characterize the two different strategies.
# Dynamic Characteristic Master Curve and Parameters of Different Asphalt Mixtures
## 3 Characteristic Parameters of Master Curves
### Phase Angle Master Curve Parameters  
#### 3.2.1 \(\delta_{0}\), A, and \(\omega\)  
In the same mixture, different asphalt will create a difference in the viscoelasticity of the mixture, which is mainly reflected in the change of the master curve parameters of the phase angle. Table 1 and Figure 2B show that the phase angle master curve parameter \(\delta_{0}\) increases significantly with the increase of asphalt penetration grade, and the height parameter A of the master curve decreases with the increase of asphalt penetration grade. The half-width parameter \(\omega\) decreases with the increase of asphalt penetration grade. This shows that different asphalt brings different viscoelastic characteristics to the same mixture; the viscous component of high-grade asphalt is large, so the viscous component of asphalt mixture is also high, resulting in a higher initial phase angle (\(\delta_{0}\)), which brings a narrower phase angle change range (A value) and a narrow viscoelastic response frequency range, which is reflected in the reduction of FWHM value (\(\omega\)).  
Figure 6 shows the correlation analysis between the phase angle curve parameters and the asphalt indexes. The master curve of the phase angle of the mixture is closely related to the asphalt properties. The initial phase angle (\(\delta_{0}\)) is positively correlated with penetration and negatively correlated with softening point and viscosity; phase angle height A is negatively correlated with penetration; softening point viscosity is positively correlated; and the viscoelastic interval, represented by FWHM (\(\omega\)), is negatively correlated with penetration, but positively correlated with softening point and viscosity. It can be considered that asphalt is the source of the viscoelasticity of the asphalt mixture. Although the influence of particle gradation of the mixture will bring about the change of the modulus, the phase angle of mixture depends on the viscoelastic properties of asphalt, which is consistent with the previous studies; that is, there is a good sinusoidal relationship between the phase angle of asphalt and the asphalt mixture [11; 22; 23; 24]. Moreover, the phase angles of different asphalt mixtures from previous studies, stone mastic asphalt (SMA-13), large-stone porous asphalt mixture (LSPM-25), F fine asphalt mixture AC-13F, and asphalt mixture AC-25, are shown in Figure 7.  
Figure 5: Relationship between asphalt modulus and shape parameter \(\beta\).
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 4 Current State of the Art of C-ITS Systems
### C-Roads Pilot Sites
#### 4.1.2 Germany  
C-Roads Germany comprises two pilot sites located, respectively, in Lower Saxony and Hessen, whose test activities are harmonised by the Federal Highway Research Institute (BASt). Specifically, the companies NORDSYS and OECON P&S run the Lower Saxony pilot site, while the local public road operator Hessen Mobil is responsible for the second area. The tested services are the Day-1 type, and both ITS-G5 and cellular communications are under study. According to the planned timeline, by the end of 2019, the entire ITS system in Lower Saxony with three C-ITS services, namely SSV, VSGN, and PVD, will be operational. On the other hand, in Hessen, PVD services are already operational and will be followed by RWW, SSV, and EVA applications throughout 2019. Finally, SWD, TJM, and GLOSA services will be operational in 2020. As stated above, Germany is one of the Member States involved in the European project C-ITS Corridor.
# Physico-Chemical Parameters and Health Risk Analysis of Groundwater Quality
## 2 Materials and Methods
### Groundwater Analysis
This once again demonstrates the increased degree of pollution in the groundwater of Bangladesh, perfectly in contrast to that of groundwater in the Dobrudja area. Another area where trace metals were monitored was in the Bazman Basin, located in southeastern Iran [35]. From metals analyzed in this area, it was shown that only Cr, Cu, Fe, Mn, Ni, and Zn were within the maximum allowed limits. **Determination of the health risk index. **  
The health risk index (HRI) was estimated as the ratio of daily metal intake (MRI) to mean reference metal (RfD) values, according to the report [36]:  
\[\text{HIR}=\frac{\text{DIM}}{\text{RfD}}=\frac{\text{C}_{\text{water}}\text{-I }}{\text{BM}\text{-RfD}} \tag{1}\]  
where:  
Cwater--metal content in water samples;  
I--daily water intake;  
BM--average body mass of an adult;  
RfD--the value for the average reference dose of metals. From specialized data [36], it is known that I = 2 L and BM = 70 kg. The results obtained after determining the health risk index of metals (Cd, Pb, Cu, Cr, Zn, Mn, Ni) are represented in Table 7. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Cd** & **Pb** & **Cu** & **Cr** & **Zn** & **Mn** & **Ni** & **Fe** \\ \hline pH & NaN & \(-\)0.61 & 0.13 & \(-\)0.68 & 0.11 & \(-\)0.20 & \(-\)0.47 & NaN \\ C & NaN & 0.29 & \(-\)0.62 & 0.21 & \(-\)0.22 & \(-\)0.48 & 0.28 & NaN \\ TA & NaN & 0.84 * & 0.23 & 0.79 & 0.48 & 0.63 & 0.73 & NaN \\ NO\({}_{3}^{-}\) & NaN & 0.53 & \(-\)0.50 & 0.45 & 0.36 & \(-\)0.14 & 0.54 & NaN \\ NO\({}_{2}^{-}\) & NaN & \(-\)0.23 & \(-\)0.28 & \(-\)0.29 & \(-\)0.08 & \(-\)0.45 & \(-\)0.19 & NaN \\ d\({}_{\text{T}}\) & NaN & 0.08 & \(-\)0.37 & 0.05 & \(-\)0.03 & \(-\)0.36 & 0.03 & NaN \\ TU & NaN & \(-\)0.24 & 0.96 ** & \(-\)0.29 & 0.83 * & 0.78 & \(-\)0.17 & NaN \\ NH\({}_{4}^{+}\) & NaN & \(-\)0.46 & \(-\)0.28 & \(-\)0.59 & \(-\)0.44 & \(-\)0.65 & \(-\)0.32 & NaN \\ Cl\({}^{-}\) & NaN & \(-\)0.04 & \(-\)0.36 & \(-\)0.14 & \(-\)0.07 & \(-\)0.43 & 0.01 & NaN \\ \hline \hline \end{tabular} pH units, C—conductivity, TA—total alkalinity, NO\({}_{3}^{-}\) nitrates, NO\({}_{2}^{-}\) nitrites, d\({}_{\text{T}}\) hardness, TU turbidity, NH\({}_{4}^{+}\) ammonium, and Cl\({}^{-}\) chloride. NaN (not a number) means there is a result, but it cannot be represented in the computer. ** Correlation is significant at the 0.01 level. * Correlation is significant at the 0.05 level.  
\end{table} 
Table 6: [PERSON]’s correlation coefficient of metals with physico-chemical parameters in groundwater samples. The order of the toxicity of heavy metals according to the average HRI values, calculated for each groundwater sample, was as follows:  
* for the groundwater sample from Constanta Zn> Pb> Cr> Mn> Cd, Cu, Ni;
* for the groundwater sample from Palazu Mare Pb> Ni> Zn> Mn> Cr> Cd, Cu;
* for the groundwater sample from Palazu Mare for lead, zinc, and nickel. This exceedance is also observed in the case of the waters from Lumina and Casimcea for zinc, thus demonstrating that their use presents a danger for human health.
# Treatment of Water Contaminated with Diesel Using Carbon Nanotubes
## 2 Materials and Methods
### Adsorption Tests  
To evaluate the adsorbent properties of the CNTs, in order to purify the water contaminated by diesel, a two-phase system was initially prepared consisting of known quantities of water and diesel.  
Subsequently, the adsorption tests were carried out by introducing variable quantities of CNTs into the two-phase system.  
The whole was stirred at a constant speed, for a precise time and in a well-sealed container to avoid evaporation phenomena. At the end of the programmed adsorption time, the CNTs were separated from the biphasic system under examination by means of filtration. In particular, the filtration was performed using a paper filter (Whatman) and a flask for collecting the filtrate, the latter connected to a vacuum pump.  
The final filtrate obtained was collected in a graduated cylinder to measure the volume reduction in the diesel phase. The nanotubes obtained after filtration were subsequently characterized (Figure 1).  
During the tests, the quantities of carbon nanotubes and adsorption times were varied, while the diesel/water ratio and stirring speed remained constant. Table 1 shows, in detail, the experimental parameters used in the experimentation.
## 6 Failure Analysis  
Failure analysis is common in electronic industry; however, the depth of analysis needs to be further to uncover the underlying science of the failure mode. Physics of failure is needed to ensure product reliability as only then root cause can be identified. [PERSON] et al. demonstrated a step-by-step failure analysis methodology for multilayer printed circuit boards that led an observed failure mode to the root cause with verification of the root cause and the effectiveness of the corresponding corrective action. This printed circuit board (PCB) is used in public transport systems. With the identified root cause, a modified processing method was developed and the observed failure mode was no longer observed with this modified method, which verified the root cause. The PCBs with the modified processing method also underwent appropriate reliability tests and the corrective action of this modified processing method is confirmed.  
[PERSON] et al. performed detail failure analysis on failure obtained from low temperature and shock test that mimic deep space exploration. They found that the failure was in the Pb-free (Sn-3.0 Ag-0.5 Cu) solder joint, and the low temperature changes the fracture characteristic of Sn-3.0 Ag-0.5 Cu (SAC305) from ductileness to brittleness. The crack occurred at solder joints from the stress loaded by shock test. When the crack reached a specific length, the failure occurred. A verification test was conducted to verify the failure mechanism. The transition temperature range of SAC305 was also confirmed as \(-\)70--80 \({}^{\circ}\)C.