esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results and Comparisons
The values of the obtained kappa index are a proof of high reliability (the reliability can be considered as high when \(\kappa\geq 0.70\)) [35; 36]. The superiority of the introduced, novel model focuses on its robustness, accuracy and generalization ability. The overall behavior of the model is comparable to a corresponding supervised one. Specifically, it reduces the possibility of overfitting, it decreases variance or bias and it can fit unseen patterns without reducing its precision. This is a remarkable innovation which significantly improves the overall reliability of the modeling effort. This is the result of the data processing methodology which allows the retention of the more relevant data for upcoming forecasts. The following Table 8 presents the results obtained by the analysis of the GPD. The above, Table 8, also provides information on the results of the McNemar test, to assess the importance of the difference between the classification accuracy of the proposed network and the other approaches examined. The improvement of the overall accuracy values obtained by the novel algorithm (compared to the other existing methods) is statistically significant. Finally, the use of the ensemble approach in this work is related to the fact that very often in multi-factor problems of high complexity such as the one under consideration, the prediction results show multiple variability [43]. This can be attributed to the sensitivity of the correlation models to the data. The main imperative advantage of the proposed ensemble model is the improvement of the overall predictions and the generalization ability (adaptation in new previously unseen data). The ensemble method definitely decreases the overall risk of a particularly poorer choice. The employed bagging technique offers better prediction and stability, as the overall behavior of the model becomes less noisy and the overall risk of a particularly bad choice that may be caused from under-sampling is significantly reduced. The above assumption is also supported by the dispersion of the expected error, which is close to the mean error value, something which strongly indicates the reliability of the system and the generalization capability that it presents. As it can be seen in Tables 5-8, the ensemble method appears to have the same or a slightly lower performance across all datasets, compared to the winning (most accurate) algorithm. The highly overall accuracy shows the rate of positive predictions, whereas k reliability index specifies the rate of positive events which were correctly predicted. In all cases, the proposed model has high average accuracy and very high reliability, which means that the ensemble method is a robust and stable approach which returns substantial results. Tables 7 and 8 show clearly that the ensemble model is very promising.
## Materials and Methods
### Model Validation and Uncertainty Estimation  
The derived models were applied to the validation datasets and the root-mean-square error (RMSE) between NDVI-based ANPP estimates and cLDMC-based ANPP was calculated in each case. The derived RMSE value was then used as an estimate of the image-specific uncertainty in ANPP measurements.
## 2 Materials and Methods
### Optical Properties  
Turbidity (T), absorption coefficient (\(\alpha\)), and water depth penetration (\(\lambda\)) were evaluated as optical properties. Turbidity, expressed in nephelometric turbidity units (NTU), wasdetermined using a Micro 100 HF Scientific, Inc. turbidimeter (Fort Myers, FL, USA). The absorption coefficient, expressed in 1/cm or 1/mm, was measured according to [PERSON] et al. [20], using FireflySci detachable quartz cells of different depths (0.1, 0.2, 0.5, and 1 mm) at 254 nm and a UV/Vis Lambda 25 spectrophotometer (Perkin-Elmer, Waltham, MA, USA). The coefficient was calculated by determining the slope of the absorbance graph. The penetration depth parameter (\(\lambda\)) was measured using Equation (2) [20].  
\[\lambda=\frac{1}{\alpha} \tag{2}\]
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 4 Analysis of PDIV and Modelling Tool Performance for Variable Temperature
### Performance of PDIV Models for Variable Temperature
#### 4.2.4 FEM and Paschen's Law  
The combination of FEM and Paschen's law was widely used in the literature to estimate the PDIV [38; 60; 62; 63; 69; 70]. However, there was no consensus on the definition of \(d_{\text{air}}\) in Paschen's law (Equation (2)) for twisted pairs. Therefore, the use of both \(d_{\text{air}}\) definition methods identified in the literature (see Section 2.2) was evaluated. When considering the straight line, the model was defined as in [38; 60; 62; 69]. In the case of the constant electric field, the lines were considered and the model was defined as in [70].  
The experimental and model results for different temperatures are depicted in Figure 14. Sample groups B and C were used and the specifications for the models are defined in  
Figure 13: Comparison of experimental measurements and Lusuardi model: (**a**) sample group B (higher dependence of the permittivity on temperature); (**b**) sample group C (lower dependence of the permittivity on temperature); and (**c**) error of the model.  
As observed in Figure 14, the results for the PDIV estimation using straight lines or constant electric field lines do not vary from each other. Indeed, as mentioned by [69], the difference between the length of the straight lines and constant electric field lines is not higher than 1%. With both methods, the maximum error for the analysed range of temperature is lower than 15%. In the case of sample group C, the estimation is into the uncertainty limits of the measurements, whereas the estimation for sample group B is not. This difference could be due to the effect of moisture (not considered in the models) or the uncertainty in sample thickness and geometry due to temperature [73]. Based on the lack of difference in the PDIV estimation, considering the straight distance between conductors might be more convenient for twisted pairs. The computational load necessary to develop constant electric field lines analysis in comparison with straight lines is higher.  
Overall, the FEM and Paschen's law model can provide accurate results for the estimation of the PDIV at high temperatures. Additionally, the model takes into account the changes in permittivity. Nevertheless, it requires a high computational load, especially if constant electric field lines are used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group Name** & \(d_{\text{in}}\) **(mm)** & \(t_{\text{ins}}\) **(mm)** & \(d_{\text{0}}\) **(mm)** & \(\gamma\) \\ \hline B & 0.8 & 0.28 & 0.001 & 0.0002 \\ C & 0.9 & 0.2975 & 0.001 & 0.00065 \\ \hline \hline \end{tabular}
\end{table}
Table 4: FEM and Paschen’s law model specifications.  
Figure 14: Comparison of experimental measurements and FEM and Paschen’s law models: (a) sample group B; (b) sample group C; and (c) error of the model.
## 6 Anomalous Nernst Effect in Topological Semimetals  
Apart from the AHE, there is also the corresponding thermoelectric effect, the anomalous Nernst effect (ANE), which can be utilized for measurements of Berry phase effects in band structures. In the ANE, a transverse electrical current in magnetic materials is generated by an applied longitudinal temperature gradient, instead of an electric field [76; 77; 78], as presented in Figure 5a. Because also the ANE is dependent on the Berry curvature, a strong ANE is expected in magnetic topological materials. Very recently, large ANEs were observed in the magnetic WSMs Co\({}_{2}\)MnGa [79; 80] and Co\({}_{3}\)Sn\({}_{2}\)S\({}_{2}\)[81], which are believed to originate from the Weyl points and the nodal line band structures. Owing to the contribution of the topological band structure, the anomalous Nernst conductivity (ANC) in Co\({}_{2}\)MnGa and Co\({}_{3}\)Sn\({}_{2}\)S\({}_{2}\) can reach values one order of magnitude larger than in traditional magnetic materials (see Figure 5d).  
Although AHE and ANE share the same symmetry and both can be understood from the Berry curvature, their dependence on the Berry curvature is different [78]. The ANC is generated by the combination of Berry curvature (\(\Omega\)), distribution function \(f(n)\), and temperature \(T\) in the form of  
\[\alpha^{i}=\frac{1}{T}\frac{e}{\hbar}\sum_{k}\sum_{n}\Omega^{i}[(E_{n}-E_{F}) f_{n}+k_{B}T\ln\bigg{(}1+exp\frac{E_{n}-E_{F}}{-k_{B}T}\bigg{)}]. \tag{9}\]Similar to other thermoelectric signals, the ANE is more sensitive to the first derivative of the electrical characteristics with respect to energy, rather than to the value itself. As discussed above, the AHC shows a peak value at the energy of the Weyl points in high symmetric WSMs due to the big contribution from the Weyl points. In contrast to that, the ANC is zero at the energy of the Weyl points (see Figure 5b,c), and two peak values appear away from that energy. Owing to the different mechanism in the ANE, it can be used to detect topological band structure features away from the Fermi level by the means of thermoelectric transport measurements, even if the electronic transport signal is weak [90].
# Review of Recent Advances in QEPAS-Based Trace Gas Sensing
## 2 Standard QEPAS-Based Gas Sensor System  
A standard QEPAS-based gas sensor system is shown in Figure 2. A diode laser or quantum cascade laser (QCL) is often used as a gas sample excitation source. For a diode laser, the emission wavelength is less than 3 \(\mu\)m. Usually it is a fiber-coupled output. The gas analytes flow into an acoustic detection module (ADM) with a slow flow rate to avoid flow noise. Usually the rate is smaller than 200 mL/min. In order to increase the Q factor of QTF and narrow the gas absorption line width, a pressure controller is adopted to reduce the gas pressure. The laser wavelength is modulated through employing a sinusoidal dither at frequency \(f\) (\(f\) = \(f_{0}\)/2, where \(f_{0}\) is the resonance frequency of QTF). The QTF in QEPAS sensor converts the acoustic wave into an electrical signal due to the piezoelectric effect, and the resonant property of QTF enhances the signal amplitude. Commercially available QTFs with a \(f_{0}\) of \(\sim\)32.76 kHz are typically employed. An obvious enhancement of the QTF current signal can be obtained after adding two metallic tubes (mRs) to the QTF sensor architecture. The mRs act as micro-resonators [45]. To obtain a strong acoustic wave coupling, the length \(L\) of mR should be optimized. Typically, the optimum \(L\) is in the range of \(\lambda_{s}\)/4 \(<\)\(L\)\(<\)\(\lambda_{s}\)/2, where \(\lambda_{s}\) is the acoustic wave wavelength [46]. The weak current signal of QTF (in the scale of nanoampere) is amplified and converted into voltage by a transimpedance amplifier (TA) with a resistance of \(\sim\)M\(\Omega\)  
Figure 1: The principle of the PAS technique.  
The lock-in amplifier is used to demodulate the second harmonic component (2_f_) generated by the QTF. To improve the slow vibrational-translational (V-T) relaxation processes of the target analyte, H\({}_{2}\)O vapor is usually added to the target trace gas. The presence of H\({}_{2}\)O vapor in the analyzed gas increases the V-T relaxation process, which results in a higher detected amplitude of the QEPAS signal. Furthermore, the excitation laser wavelengths and absorption lines of the target analyte should avoid overlap with the metastable vibrational transitions of atmospheric nitrogen (N\({}_{2}\)) and oxygen (O\({}_{2}\)) to ensure efficient V-T relaxation of the target molecule, thus contributing to the QEPAS signal.
## 4 Performance Evaluation
### Generic TCN versus TCN-TS-SW  
To evaluate the performance of the proposed TCN-TS-SW with that of a normal TCN model [39] comparative study is presented. The effectiveness of introducing the two-stage sliding window strategy to the TCN model is observed to minimize the training loss as shown in Figure 5a and also increases the accuracy of prediction implicated by the results shown in Figure 5b. The figures both showed the effectiveness of two stage sliding window strategy implementation to that of a traditional TCN model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Machine Learning Models** & **RMSE** & **MAE** & \(R^{2}\) \\ \hline MLB-LSTM & 0.171 & 0.143 & 0.987 \\ GRU & 1.004 & 0.989 & 0.914 \\ LSTM & 0.782 & 0.635 & 0.896 \\ TCN-TS-SW & **0.169** & **0.135** & **0.991** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison Results for RMSE, MAE, and \(R^{2}\).  
Figure 4: The performance of TCN-TS-SW against the multi look-back LSTM, GRU, and LSTM. (**a**) Time-series training loss over epoch. (**b**) Time-series training accuracy over epoch.
## 2 Related Works  
### Emotional Speech Database  
The Berlin Emotional Speech database (EmoDB) is a database of emotional speech recorded with German utterances [19]. The database was constructed using everyday sentences, so that priority was given to the naturalness of speech. Ten actors, five female and five male, recorded speech data according to specified emotions, producing 10 different sentences for seven kinds of emotion: anger, boredom, disgust, fear, happiness, sadness, and neutral. The special feature of EmoDB is that all the utterances in it are composed with emotionless words, which means that EmoDB is helpful for finding out the proper features of emotion due to its exclusion of the influence of the emotional information of words.  
The Interactive Emotional Dyadic Motion Capture (IEMOCAP) database consists of about 12 h of recorded data [21]. Speech, facial, and landmark data were captured during each recording session. Each session was a sequence of conversations involving men and women. A total of 10 actors split into five pairs took part in the recording. All data were recorded in a professional cinema studio, to maximize the sound quality. Actors were seated across from each other at a \"social\" distance of three meters. This setup enables realistic communication. An evaluation test was carried out with seven subjects, to label each utterance based on both audio and video streams. The data was categorized using 10 labels: neutral, happiness, sadness, anger, surprise, fear, disgust, frustration, excited, or other. Speech data not recognized by the subjects as exhibiting the appropriate emotion were not included in the database. Data were only included in the database when there were more than half consistent answers. This process provided evidence for the credibility of human recognition.  
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) is a video database of emotional speech and songs in North American English, classified into eight emotions, including neutral, calm, happy, sad, angry, fearful, disgusted, and surprised [20]. The database comprises information from 24 professional actors, and each actor has 60 audio-visual (AV) items and 44 song items, for a total of 104 data points. Each recorded production of an actor was available in three modality formats: AV, video only, and audio only.
## 4 Blending Ensemble Learning-Based Landslide Susceptibility Mapping and Risk Assessment
### Optimization Method Based on Blending Ensemble Learning
#### 4.1.4 Blending Ensemble Model  
The basic idea of blending ensemble learning is to analyze and process the prediction results of different models to achieve better outcomes [37]. In this study, three heterogeneous algorithms--Random Forest, XGBoost, and CatBoost--were selected as the first-level base learners, while logistic regression served as the second-level meta-learner. The specific implementation process is as follows: First, the original training set was divided into a subtraining set (80%) and a validation set (20%) at a 4:1 ratio. The three base models were then trained on the subtraining set. Next, these base models were used to generate probabilistic predictions for the validation set, forming a meta-feature dataset that served as new training samples. A logistic regression model was trained as the meta-classifier on this meta-feature dataset. The predictions of the base models on the test set (acting as new test data) were then fed into the trained meta-classifier. Finally, the ensemble model output the landslide prediction results.
# Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes
## 7 Conclusions and Future Work  
Ensuring the authenticity of a user beyond the point-of-entry to access a service has become a crucial issue, hence, the need for continuous authentication. This paper introduces a continuous authentication framework that utilizes contextual information. This framework has the ability to protect home devices against unauthorized access from anonymous and known users, whether locally or remotely, by routing all communication to said devices through the secure home Gateway. The users, devices and policies can all be configured to create a system which authenticates users and grants them access to services based solely on contextual information, without necessarily needing to provide any credentials for continuously authenticating users, unless a specific situation requires this. It also monitors all access-related activities, such as attempted logins, service requests, and access durations, storing them in a database for future analysis by establishing usage patterns and detecting brute force attempts and other anomalies. The implementation and evaluation show that the proposed framework provides continuous authentication in a flexible manner, without requiring additional intervention by users during the access session. It is also concluded that the security measures do not impose significant connection overhead, which amounts to the system acting almost entirely transparently.  
Ultimately, results show that considerable contextual information can be retrieved in a reasonable time, and that such contextual information can be used in providing a seamless, usable, and secure authentication for the IoT. Finally, including contextual information in the authentication process should increase the level of security beyond point-of-entry by providing non-intrusive, convenient, and continuous authentication throughout the duration of the access session. This is possible by enhancing the standard knowledge-based static authentication with contextual information.  
For future work, measuring the quality of the retrieved contextual information will be considered according to the techniques discussed. In addition, we plan to explore the possibility of further analyzing the historical data (long-term contextual information) retrieved from end-user devices and stored in the Gateway to generate higher fidelity user profiles using machine learning and other techniques. Being able to accurately identify a user based on usage patterns with the use of contextual information would greatly improve the system's ability to counter various attack scenarios, such as insider unauthorized access, as well as reduce the number of false positives and requests for user intervention.  
Writing-Original Draft Preparation, Y.A.; Writing-Review & Editing, D.K.; Supervision and Writing-Review & Editing, Q.H.M.  
This research received no external funding.  
The authors declare no conflict of interest.
# Fine-Grained High-Utility Dynamic Fingerprinting Extraction for Network Traffic Analysis
## 3 Data Processing
### SSI/TLS Encryption Protocol and Communication Process
#### 3.1.2 Exchange Keys and Certificate  
The two parties need to exchange keys and certificates. There are four types of messages related to keys: Server Certificates, Server Key Exchanges, Client Certificates, and Client Secret Exchanges. In addition, there is a \"Change Password Specification\" message and a \"Certificate Request\" message to complete some functions. Figure 2 describes the process in detail.
# Lactic Acid Bacteria as Biocontrol Agents against Potato (_Solanum tuberosum_ L.) Pathogens
## 1 Introduction
The simplicity of the multiplication of biocontrol agents on various available organic media is also important [17]. Potential candidates for biocontrol can be lactic acid bacteria (LAB), especially facultative heterofermentative lactic acid bacteria belonging to genera _Lactiplantibacillus_, _Lactiobacillus_, _Lactiaseibacillus_, _Leuconstoc_ and _Lactococcus_ that is often found in dairy products, meat, vegetables, wine, silage and vaginal, ureogenital and gastrointestinal tracts. The omnipresence of the species suggests an imminent potential for adaptation to a wide variety of environments and a metabolic pathway diversity [18; 19]. The LAB has the Generally Recognised As Safe status (GRAS) and the Qualified Presumption of Safety status (QPS). For this reason, great attention has been given to the species in the case of probiotics and biological control agents [19]. LAB strains are able to produce multiple antimicrobial compounds, such as organic acids (including lactic and phenyllactic acids), diacetyl, hydrogen peroxide and bacterioxins which create a relatively harsh environment for any pathogens. However, acids play the most significant role in biocontrol abilities [20; 21]. The study aimed to investigate the possibility of using a biological preparation containing LAB as a biocontrol agent against seed potato pathogens. The selection of an appropriate bacterial strain was developed, and the chemical composition of the culture medium based on a waste product-acid whey, was optimised. After the selection in laboratory tests, the biocontrol ability of the selected LAB was assessed in situ on seed potatoes. Our hypothesis is that some strains of lactic acid bacteria can inhibit the growth of potato pathogens, and that the selection of the optimal growth medium increases antimicrobial activity.
## 5 Binary Tree Structure Compression  
In the original compression method (EEDC), the binary tree structure is not compressed in any way. This structure, a stream of 1s and 0s, is stored in groups of 8 bits--_bytes_. In this section, the compression of the binary tree structure is analyzed using the compression methods and data transformation algorithms described earlier. An example of a binary tree structure can be seen in Figure 12.  
Figure 11: Comparative overview of reconstructed images ranging from 0.8 bpp (**top row**) to 0.05 bpp (**bottom row**). Compression methods: (**a**) JPEG, (**b**) JPEG 2000, (**c**) EEDC i, and (**d**) EEDC-RANGE (BWT).  
Figure 12: A segment of the binary tree structure obtained using triangular coding.  
When looking at Figure 12, certain blocks/sequences can be seen, but they have a size of 2 or 3 bits, which cannot be effectively used for increasing the compression ratio. The compression method _EEDC-RANGE (BWT)_ is the basis of this research and, in this binary tree structure, compression is implemented in it. After applying data transformation and compression methods, the results can be seen in Table 8.  
The data reported in Table 8 proves that not a single value was below the reference value of 3.61 obtained by _EEDC-RANGE (BWT)_ at 0.02 bpp. The best compression was achieved with dictionary coders, especially LZ77, due to the repetition of subsets in the data stream. By using delta coding, a new character, \(-1\), appeared, which caused compression to be more difficult, as shown by the results in Table 6. Rearranging the bits, as BWT does, does not provide better results. The best result is obtained by combining DMC with arithmetic coding, but it is still above the value of 3.61.