esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we proposed five methods for the classification of four-class motor imagery EEG signals using DL. The results obtained by the application of the proposed CNN1 model gave better results compared to our proposed and state-of-the-art methods.  
The proposed fusion models show an acceptable classification rate of the EEG MI signals; therefore, it would be very interesting to apply the multi-layer CNN fusion models  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **FBCSP** & **KNN** & **LDA** & **NB [34]** & **Ensemble** & **SVM** & **FLS** & **Proposed** \\  & [32] & [32] & [33] & & [34] & [34] & [34] & **CNN1** \\ \hline Subject 1 & 73.50 & 71.60 & 64.58 & 63.90 & 59.40 & 68.80 & 71.90 & 68.97 \\ Subject 2 & 59.40 & 51.10 & 58.08 & 49.30 & 46.50 & 48.30 & 53.10 & 68.97 \\ Subject 3 & 61.90 & 52.80 & 61.11 & 75.70 & 72.60 & 76.00 & 76.40 & 86.21 \\ Subject 4 & 71.50 & 91.00 & 67.34 & 63.20 & 59.00 & 61.80 & 66.70 & 53.45 \\ Subject 5 & 61.40 & 65.20 & 66.55 & 39.90 & 39.90 & 41.30 & 39.20 & 43.10 \\ Subject 6 & 70.10 & 61.30 & 59.72 & 43.80 & 35.10 & 41.00 & 42.40 & 36.21 \\ Subject 7 & 69.60 & 71.10 & 69.79 & 70.50 & 65.60 & 77.80 & 73.30 & 62.07 \\ Subject 8 & 62.00 & 67.10 & 67.10 & 80.20 & 70.80 & 81.60 & 80.20 & 75.86 \\ Subject 9 & 75.50 & 68.60 & 60.06 & 78.50 & 75.00 & 79.20 & 81.60 & 48.28 \\
**Average** & **67.21** & **66.64** & **63.81** & **62.80** & **58.20** & **64.00** & **65.00** & **68.77** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the classification accuracy (\(\%\)).  
on other EEG datasets. We wish to study other feature fusion methods in order to improve the performance of our proposed method.  
Conceptualization, methodology, simulation and writing original draft, A.E.; methodology, A.M. Supervision, validation and writing--review & editing, W.Z.; Visualization, supervision, M.G. Validation, C.M. Project administration, H.H. All authors have read and agreed to the published version of the manuscript.  
This research was carried out under the MOBIDOC scheme, funded by the European Union (EU) through the EMORI program and managed by the National Agency for the Promotion of Scientific Research (ANPR), grant number 739.  
Not applicable.  
The dataset used in this study is public and can be found at the following links: BCI Competition IV dataset 2a [[http://www.bbci.de/competition/iv/](http://www.bbci.de/competition/iv/)]([http://www.bbci.de/competition/iv/](http://www.bbci.de/competition/iv/)), accessed on 30 January 2020.  
The authors declare no conflict of interest.
## 3 Results
### Influence of Lactation Stage on Milk Quality
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Components 1**}} & \multicolumn{4}{c}{**Stage of Lactation 2**} & \multirow{2}{*}{**SEM**} & \multirow{2}{*}{**\(p\) Value 3**} \\ \cline{2-2} \cline{5-6}  & **First** & **Mid** & **Late** & & \\ \hline Fat \% & 4.46 & 4.81 & 4.63 & 0.32 & 0.656 \\ Protein \% & 3.97 3  & 4.09 4 & 4.69 4  & 0.12 & \(<\)0.0001 \\ Lactose \% & 4.51 & 4.43 & 4.24 & 0.16 & 0.45 \\ Total soel & 13.53 & 13.52 & 13.75 & 0.37 & 0.88 \\ SNF & 9.14 & 9.11 & 9.52 & 0.18 & 0.17 \\ FPD & 0.57 & 0.55 & 0.55 & 0.02 & 0.59 \\ SCC & 531.39 & 686.49 & 533.57 & 86.29 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Influence of lactation stage on the milk components of Najdi ewes fed traditional and TMR diets. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Fatty Acid Profile 1**}} & \multicolumn{4}{c}{**Stage of Lactation 2**} & \multirow{2}{*}{**SEM**} & \multirow{2}{*}{**\(p\) Value 3**} \\ \cline{2-2} \cline{5-6}  & **First** & **Mid** & **Late** & \\ \hline C6:0 & 1.34 & 1.37 & 1.29 & 0.06 & 0.71 \\ C8:0 & 1.66 & 1.72 & 1.54 & 0.12 & 0.49 \\ C10:0 & 5.47 & 6.06 & 6.43 & 0.39 & 0.24 \\ C11:0 & 0.15 & 0.17 & 0.18 & 0.02 & 0.68 \\ C12:0 & 5.16 & 5.84 & 6.05 & 0.45 & 0.22 \\ C14:0 & 11.30 3  & 13.26 4  & 13.25  & 0.62 & 0.04 \\ C14:1 & 0.10 3  & 0.22  & 0.01 & 0.01 \\ C15:0 & 0.71 & 0.70 & 0.81 & 0.07 & 0.44 \\ C16:0 & 28.65 3 40.49 4  & 31.35  & 0.79 & 0.05 \\ C17:0 & 0.28 & 0.24 & 0.27 & 0.02 & 0.16 \\ C18:0 & 10.34  & 0.25  & 0.33  & 0.04 & 0.38 \\ C18:1 & 0.40 4 & 0.30 4  & 0.04 \\ C18:1 & 11.89 & 1.64 & 1.74 & 0.21 & 0.74 \\ C18:1 & 0.30 3  & 0.03 & 0.003 \\ C18:1 & 16 & 0.55 & 0.46 & 0.55 & 0.05 & 0.20 \\ C18:1 & cis9 (OA) & 25.23 4  & 19.81 3  & 0.81 [FOOTNOTE:3]Footnote 3: \(<\) within a row, the means with
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 1 Introduction
Therefore, we use the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method [15] for all attack nodes constructed by Sybil clients that fall within the detection threshold and within the RSSI threshold. The clustering method distinguishes the attacking nodes from the normal nodes simultaneously. The algorithm enhances the overall detection accuracy. At the same time, we propose a Sybil attack detection system. The system was able to passively detect WiFi attacks on users or AP, both static and dynamic, in real time. Finally, we experimentally test Sybil attacks at different positions and angles of the algorithm. Experiments show that physical layer-based detection can ignore any attacks on the protocol layer and the MAC layer. It can accurately detect dynamic and static attacks. Our main contributions are summarized as follows. * We propose a novel Sybil attack detection based on WLAN physical layer information. It can detect the static and dynamic attackers without any dedicated infrastructure. * We develop a novel self-adaptive MUSIC algorithm to calculate the phase offset between the antennas. It can estimate the angle of wireless devices more accurately than traditional MUSIC algorithms. * We realize a Sybil attack detection system on a common commercial platform. Extensive experiments prove that the performance of the Sybil detection is good. The rest of paper will be carried out in the following sections: Related work is discussed in Section 2. Preliminary channel state information and Sybil attack models are given in Section 3. Section 4 is the Sybil attack detection system. Based on this, we propose a static attack detection algorithm in Section 5 and a dynamic attack detection algorithm in Section 6. Section 7 contains the experiment simulation. We will summarize in Section 8.
# A Study of Discriminatory Speech Classification Based on Improved Smote and SVM-RF
## 3 Data and Methods
### Data
#### 3.1.2 Data Preprocessing  
Data preprocessing mainly includes data cleaning, text cleaning, word splitting, and stopwords removal.  
Data cleaning is the processing of duplicate and abnormal data. Too long microblogs need to separately request the full text, which will lead to the acquisition of incomplete text; in addition, too long microblogs will have an impact on the subsequent analysis. These incomplete and too long data are deleted and processed. For duplicate microblogging content such as repeated posts by the user, only one microblog will be retained, and the rest are deleted.  
Text cleaning is the processing of microblog text content. This includes removing special symbols, special characters, emoji, HTML tags, punctuation, URLs, etc. from microblog text by regular expression matching.  
Word splitting is an operation performed on the cleaned plain text data, using the Jieba Chinese segmentation tool to separate words one by one, which is convenient for subsequent word frequency statistics, vectorization, and model training.  
Figure 1: Overall framework.  
Stopwords removal is the removal of meaningless words [29], such as prepositions, auxiliaries, conjunctions, etc., by matching them using a stopwords list.
## 3 Results and Discussion  
### Electrical Characteristics of Hydrogeological Background  
According to the data collected by ERT, we plotted profile resistivity distribution maps. All of the inversion models are presented with a uniform color scale in each figure for comparison. The inversion result of the ERT profile is shown in Figure 4.  
Table 1 shows the resistivity ranges of common stratum. According to the hydrogeological data of the site, the site formation was relatively flat and layered and the resistivity distribution should have been consistent with the formation distribution. The 0-2 m layer under the site was dominated by miscellaneous fill, consisting of construction waste, cohesive soil, cinder, etc. The resistivity fluctuated greatly; there were areas as high as 200 \(\Omega\)-m and some were as low as 30 \(\Omega\)-m. The 2-3.5 m layer was dominated by silty clay. The resistivity of the dry clay or silty clay was between 1 and 100 \(\Omega\)-m, which accorded with the detection results. The 3.5-9.2 m layer was composed of medium and coarse sand. The resistivity of the dry rock formation was between 10 and 9.6 \(\times\) 10\({}^{4}\)\(\Omega\)-m and the water-bearing rock formation was between 10 and 90 \(\Omega\)-m, which was consistent with the detection results. The 9.2-10.5 m formation was dominated by silty clay; the resistivity of the dry formation was between 10 and 200 \(\Omega\)-m and the water-bearing rock formation was less than 30 \(\Omega\)-m. The in situ detection results showed that the thickness of the aquifer was 15.0-20.0 m and that the depth of the water table was 6.0-7.0 m.  
Due to the limitation of the terrain, the ML-I survey line was short and the detection depth was shallow. The overall resistivity of the ML-I profile was relatively low. According to the inversion profile of the ERT data, the background resistivity value of the site was mainly between 20 and 40 \(\Omega\)-m, which was more consistent with the sandstone formation. There was a relatively high-resistance anomaly area at a distance of 25-30 m and at a depth of 2 m on the profile. This resistivity exceeded 65 \(\Omega\)-m, which could be caused by contaminants. Near the bottom area, at about 6 m depth, it can be seen that the resistivity dropped significantly, relative to the water table, as shown in Figure 4 (ML-I).
# From Iris Image to Embedded Code: System of Methods
## 8 Error Correction Cascade  
We describe the applied methods in the sequence of their execution by the decoder, which is also the method \"from simple to complex\". In the beginning data unit is a single bit, at the end it is the whole message. The problem is to devise an error correction method, which encodes a message into a block of data with redundancy, and then is able to reconstruct the message if no more than \(p\leqslant 0.35\) share of these bits is altered. Popular Walsh-Hadamard and Reed-Muller (1976) methods can be used only for \(p<0.25\), thus they are not directly applicable. Furthermore, the errors of neighboring elements of the template are strongly correlated, whereas almost all ECC methods show the best performance against uncorrelated errors.
## Materials and Methods
### Flow Experiments  
To evaluate the device occlusion performance, two types of flow experiments were conducted: an occlusion experiment and a digital particle image velocimetry (DPIV) experiment. A plastic bifurcate hepatic artery model, similar to the one mentioned in Section 2.3, was manufactured and connected to  
Figure 4: Cutting the design pattern of the spherical occlusion device onto a hypotube by laser.  
Figure 5: (**a**) Steel ball expanding the spherical occlusion device; (**b**) Special fixture for shaping the device into a perfect sphere from the outside.  
a tubing system. The spherical occlusion device was deployed at the upstream of outlet 2 to evaluate its occlusion performance, as shown in Figure 6. A blood simulating fluid, phosphate buffered saline (PBS) buffer solution (Biokit Biotechnology Inc., Miaoli County, Taiwan), was used as the operating fluid.  
In the occlusion experiment, 500 mL PBS buffer solution from a beaker was supplied to the inlet of the hepatic artery model through a communicating pipe and a vertical burette (Figure 6). Two more beakers were placed at the downstream of this open-loop tubing system to collect and measure the fluid volumes from outlet 1 and outlet 2, respectively. The spherical occlusion device with the metal density of 27% was tested and the occlusion rate was calculated as:  
\[\text{Occlusion rate}=\frac{V_{2i}-V_{2o}}{V_{2i}} \tag{4}\]  
where \(V_{2i}\) is the fluid volume collected from outlet 2 without occlusion and \(V_{2o}\) is the fluid volume collected from outlet 2 with occlusion.  
Digital particle image velocimetry (DPIV) experiments were also conducted to evaluate the occlusion performance with the same hepatic artery model connected to a closed-loop tubing system. A peristaltic pump (Gamma X, ProMinent Corp., Heidelberg, Germany) was used to generate a pulsatile flow of 72 beats per minute to simulate the blood flow driven by the cardiac impulse. Flow-field visualizing particles (Long Win Science and Technology Corp, Miaoli County, Taiwan) of 30 \(\upmu\)m in diameter were added into the solution. In order to illuminate the particles and visualize the flow, a 300 W green laser was refracted to create a sheet of light. A 25-frame per second digital camera was placed perpendicular to the light sheet to capture images of illuminated particles. MATLAB PIVlab (developed by Dr. [PERSON] and Prof. Dr. [PERSON]) was used to analyze the visualized flow field [24; 25; 26; 27]. Contrast limited adaptive histogram equalization (CLAHE) was applied to increase the contrast of the images. Direct Fourier Transform Correlation with Multiple Passes and Deforming Windows (FFT window deformation) was chosen as the DPIV algorithm. Two passes with interrogation areas of 64 pixels and 32 pixels were used.
## 5 LoW Mob Mobity Support Scheme  
LoW Mob defines the interface for multi-hop communication between the GW and the MN. It thus enables MN's to utilize short range IEEE802.15.4 links to send their data packets to the GW, even though it is far away from the GW. In this effort, our scheme proposes to utilize SNs to route MNs packets to or from the GW. However, the proposed scheme takes into consideration that SNs themselves are resource constrained devices and periodically switch off to sleep state in order to conserve energy.  
Like PMIPv6, LoWMob is a network based mobility scheme where the SNs and the GW are responsible for providing mobility support to the MN. However, in PMIPv6 the mobility-related features are provided at the network layer. In other words, mobility-related signaling packets are carried by IP traffic [6]. LoWMob proposes the mobility support in an adaptation layer of 6 LoWPAN.  
The LoWMob mobility support scheme can be divided into two parts. The first part deals with the case when a new MN enters the PAN for the first time. The second part discuses the handoff support for MNs when they move within the PAN. The aim of the handoff support is to prevent packet losses. Note that, for our LoWMob scheme, we have only discussed a single MN's communication scenario with the GW, or with the static CN - that can be within or outside the PAN. Because the purpose of introducing the LoWMob scheme is to introduce the concepts and techniques related to the mobility in 6 LoWPAN. Furthermore, we have improved the scheme by proposing a distributed version of it. The distributed version considers route optimization between multiple corresponding MNs in a PAN.
## 3 Results and Discussion
### Thermodynamic Parameters  
Table 4 shows thermodynamic parameters calculated at various temperatures. \(\Delta H^{0}\) and \(\Delta S^{0}\) were calculated from the slope and intercept of van't Hoff plots of ln\(K_{C}\) against 1/\(T\) and the \(\Delta G^{0}\) from Equation (4). The results showed that the process of removal of all heavy metals and biogenic substances on zeolite, halloysite, diatomic, limestone sand and activated carbon was endothermic, as evidenced by the increase of contaminant removal degree with temperature and positive values of \(\Delta H^{0}\)[80]. Further, negative values of \(\Delta G^{0}\) indicated that the removal processes of the analyzed contaminants on 5 reactive materials had a spontaneous nature and was thermodynamically favorable in the given experiment conditions [81]. In turn, positive values of \(\Delta S^{0}\) indicated that contaminant removal was spontaneous at high temperatures, reflected the affinity of reactive materials to the analyzed contaminants, and confirmed the increased randomness at the solid-liquid interface during adsorption [82]. Based on the obtained calculation results, it may be assumed that the processes of Cu removal on diatomic and zeolite, NH\({}_{4}\)-N removal on activated carbon, and PO\({}_{4}\)-P removal on diatomic represented physisorption. Removal of zinc on all five materials, copper on limestone sand and halloysite, ammonium ions on zeolite, limestone sand and halloysite and phosphates on all tested materials except diatomic resulted from electrostatic interaction. The mechanism of chemical sorption played a role only in copper and ammonium ion removal with the application of activated carbon and diatomic. These conclusions were drawn based on an overview by [PERSON] et al. [83], according to which the value of \(\Delta H^{0}\) < 20 kJ/mol is characteristic for physisorption, \(\Delta H^{0}\) in the range of 20 to 80 kJ/mol is typical for electrostatic interaction, a \(\Delta H^{0}\) in the range of 80 to 450 kJ/mol is characteristic for chemical adsorption.
## 3 Results
### Photosynthetic Parameters  
The photosynthetic rate (A) in the NT plants was not affected (\(p\geq 0.05\)) for the first four days of the drought stress, and it had its lowest (\(p\leq 0.05\)) value on day six of drought (Figure 2a). Photosynthesis recovered (\(p\leq 0.05\)) in the NT plants on day eight of stress (Figure 2a). Transgenic plants from the 35S line had a decrease (\(p\leq 0.05\)) in their photosynthetic activity on day six of drought, which remained unchanged by the end of the treatment (Figure 2a). Transgenic wheat transformed with the stress-inducible rd29A promoter maintained a stable photosynthesis during the drought treatment (Figure 2a). In the rd-1B plants, despite some variations in the mean value, the photosynthetic rate did not decrease when drought stress was applied (Figure 2a), whereas in the rd-1C plants, photosynthesis had no significant changes during drought stress (Figure 2a).  
The intercellular CO\({}_{2}\) (_Ci_) in NT wheat plants had no significant changes during drought stress. In the 35S line, the _Ci_ varied from day zero to day six, and it had its lowest (\(p\leq 0.05\)) value by the end of the drought treatment (Figure 2b). The transgenic rd-1B plants had a decrease (\(p\leq 0.05\)) in their _Ci_ at day four of drought and remained unchanged (\(p\geq 0.05\)) by the end of the treatment (Figure 2b). In the rd-1C plants, the mean _Ci_ value had some variations throughout the drought experiment, but the differences were not significant (Figure 2b).  
No effect (\(p\geq 0.05\)) was detected in the transpiration rate (E) of the NT wheat plants for the first four days of drought, but it decreased (\(p\leq 0.05\)) on day six (Figure 2c). On the eighth day of drought, the E in the NT plants was more than 50% lower (\(p\leq 0.05\)) than in non-stressed plants (Figure 2c). The transgenic 35S line had no changes (\(p\geq 0.05\)) in  
Figure 2: Drought stress effect on leaf gas exchange parameters of wheat plants. Panel (**a**) photosynthetic rate (A); Panel (**b**) intercellular CO\({}_{2}\) concentration (_Ci_); Panel (**c**) transpiration rate (E). Values are mean \(\pm\) SE (\(n=5\)). Letters indicate significant differences during days of drought (\(p\leq 0.05\)).  
the E from day zero to day six of drought (Figure 2c). However, the E in the 35S plants was significantly decreased by day eight (Figure 2c). In rd-1B wheat plants, an increased (\(p\leq 0.05\)) E was detected on days two and four of drought (Figure 2c). When drought stress increased on days six and eight, the E in the rd-1B plants was not different (\(p\geq 0.05\)), as in non-stressed plants (Figure 2c). Transpiration in the transgenic line rd-1C varied throughout the drought stress, but no significant differences (\(p\geq 0.05\)) were found between non-stressed and stressed plants (Figure 2c).
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 4 Evaluation
### Experiment II: Outdoor Navigation  
The two main purposes of the second experiment were to evaluate the performance of the navigation system in outdoor environments and to determine whether blind pedestrians could successfully arrive at a destination with the assistance provided by the prototype.
## 4 Experimentation and Results
### Discussions  
Detecting winding faults in transformers, mainly at an early stage (i.e., low severity), from vibration signals represents a significant challenge due to their nonstationary properties and high-level noise inherent in vibrations. This challenge is more difficult when the transformer that generates them operates in transient regimens. Therefore, there is a pressing need to introduce innovative methods or approaches, such as the one outlined in this study, which can accurately evaluate the transformer's condition when faced with these scenarios. In this regard, Table 5 presents a comparative analysis between the proposed approach and the most recent methodologies documented in the literature for detecting the presence of failures in the winding of a transformer from vibration signals. Specifically, it details the techniques or methods employed in each approach, the assessed transformer operating state, the severity levels, and the pattern recognition algorithm used for automatic diagnosis.  
As shown in Table 5, unlike other works that have focused on only one operating state [3; 19; 20], this proposal presents a solution for two operating regimes (i.e., transient and steady), which is desirable in order to provide a solution for a wider range of transformer applications. It is also important to mention that the proposed method provides a complete solution for condition monitoring since an automatic pattern recognition for automatic classification is included, eliminating the need for an expert to interpret the monitored data, which could be the case for the works presented in [3; 13]. Finally, while similar results are found in [21] and in this work, it is crucial to emphasize that the NMD method involves much more complex and time-consuming signal processing compared to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SCT Class** & **Accuracy** & **Recall** & **Specificity** & **Precision** & **F1-Score** \\ \hline
0 & 0.9333 & 0.9333 & 0.9944 & 0.9655 & 0.9492 \\ \hline
5 & 0.8667 & 0.8667 & 0.9833 & 0.8966 & 0.8814 \\ \hline
10 & 0.9000 & 0.9000 & 0.9778 & 0.8710 & 0.8852 \\ \hline
15 & 0.9333 & 0.9333 & 0.9778 & 0.8750 & 0.9032 \\the methods used in this work. This condition makes the proposal a more promising solution for software/hardware-based online monitoring implementations, mainly if low-end processors are used.  
Although the proposed research has yielded good results, it is important to describe diverse limitations, which should be investigated in future research: (1) the proposal must be tested during diverse types of linear and nonlinear loads, as the present study is focused exclusively on an unloaded transformer and (2) the methodology robustness must be explored under other fault conditions, i.e., winding deformations and core faults, among others, including their combination, since in real practice, the appearance of multiple combined faults is normal.