esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 5 Computational Results
### Evaluation of Balancing Methods: A Focus on HOUM Variants  
Table 4 summarizes the accuracy and G-mean results of the algorithms. Five balancing methods (HOUM-R, HOUM-L, SMOTE, SMOTE-ENN, and SMOT-Tomek) were applied (to balance the datasets) to each imbalanced dataset (Climate, Diabetes, Liver, Haberman, Transfusion, Ionosphere, Column_2c, and TexYarn). The original forms of the datasets were also studied, as shown in the last columns of Table 4. After balancing the datasets using the five balancing methods, the KNN, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) algorithms were applied to compare the accuracy and G-mean parameter values. The best accuracy and G-mean results are written in bold and italics for each line. According to the results, in five of the eight datasets (Liver, Haberman, Transfusion, Ionosphere, and TexYarn), the highest G-mean results were obtained after applying one of the proposed methods (either HOUM-R or HOUM-L). The values are underlined in Table 4. The Wilcoxon signed rank test was performed on five datasets (Liver, Haberman, Transfusion, Ionosphere, TexYarn), and the \(p\)-value was found to be 0.0625 when the variants of the HOUM were compared to the algorithm that gave the best G-mean value (considering same classification technique). Therefore, the difference between the values of compared algorithms is large enough to be statistically significant, with a 93% confidence interval in the analyses. When the results of the proposed method were thoroughly examined, three of the five best G-mean results (Liver, Haberman, Transfusion) were recorded for the ANN classifier, and two of them (Ionosphere, TexYarn) were found for the RF classifier. This could demonstrate the suitability of the proposed method for these two classifiers. Please also note that, according to the TexYarn dataset, almost all the  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **R Package** & **Parameters** \\ \hline KNN & class & k = 1:20; preProc = “center”, “scale” \\ RF & randomForest & mtry = 1:10; method = “rf”; metric = “Accuracy’ \\ SVM & e1071 & kernel = Radial/Linear; sigma = 0.01, 0.015; C = 0.75, 1, 1.25 \\ ANN & nnet & decay = 0.001, 0.01, 0.1; size = 1:10 \\ SLS & smotefamily & K = 5; C = 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Algorithms and parameters used.  
yarn lots can be detected, which might suggest the use of the proposed methodology as a decision support system (DSS) to increase efficiency.  
Hybrid Oversampling and Undersampling Method (HOUM), via SLS and SVM.
## 2 Wettability Measurement Methods
### Methods That Consider the Characteristics of Liquid Absorption
#### 2.3.2 Nuclear Magnetic Resonance (NMR)  
The method for determining rock wettability using nuclear magnetic resonance is based on the fact that water has different relaxation times in the volume and surface of the sample. Water exists in the rock as separately as in the oil. At least three parameters are determined by NMR: T1, T2, and diffusion. Wettability affects all these parameters, which means there is no universal method to extract wettability data using NMR [30]. For example, the authors of [31] proposed a model to determine wettability based on T2. This model works well in sandstones under intermediate and water-wettable conditions. In a subsequent study, the NMR wettability of quartz-rich sandstone was investigated for CO  
Figure 8: Infrared spectra of fresh and treated calcite and dolomite particles [25].  
Figure 7: Infrared spectra of the dense sandstone Chang 7 [24].  
displacement by water in this rock, and it was found that CO\({}_{2}\) significantly reduces the hydrophilicity of sandstone [32].  
The NMR test can be performed under different temperature and pressure conditions; the method itself is fast, but it is difficult and expensive to perform.
# Investigation of the Opposite-Electrode Effect on the Planar Solid-State Pulse-Forming Line
## 2 Theoretical Analysis of the Solid-State PFL  
The classic planar solid-state PFL, referred to as the full-electrode PFL in this article, as shown in Figure 1, is mainly composed of a dielectric plate, a high-voltage electrode, a ground electrode, wiring terminals, and an insulating package. The planar solid-state PFL presented in this paper is based on the PbO-SrCO\({}_{3}\)-Na\({}_{2}\)CO\({}_{3}\)-Nb\({}_{2}\)O\({}_{5}\)-SiO\({}_{2}\) glass-ceramic material, with a relative dielectric constant of roughly 320. The high-voltage electrode is usually in the form of a curved structure, while the ground electrode is usually spread over the entire plane to achieve a certain electromagnetic shielding effect. The wiring terminals are located at the ends of the curved electrode and the relative position of the ground electrode. The insulating package wraps around the entire planar solid-state PFL to reduce surface flashover.  
The authors of previous studies have found that high-voltage ceramic capacitors may exhibit surface discharge during withstand voltage tests. During withstand voltage tests of high-voltage ceramic capacitors with different electrode margins, it was found that as the margin increases, the starting voltage of surface discharge of the capacitor shows a downward trend and approaches a fixed value as the margin increases [29; 30].  
The full-electrode PFL's high-voltage electrode and ground electrode have unequal widths, which may result in additional electric field enhancement effects. To study the relationship between the width of the ground electrode and the edge electric field of the high-voltage electrode, a simplified cross-sectional model of the forming line as shown in Figure 2 was established. In this model, the width of the high-voltage electrode is \(w\), the width of the ground electrode is \(w+d\), the thickness of the electrodes is neglected, the thickness of the dielectric substrate is \(h\), the relative dielectric constant of the dielectric material is \(\varepsilon_{r}\), and the external environment is vacuum.
## 3 Results and Discussion
### Analysis of the CFD Calculation Results
It can be observed that the increase in the kinetic energy of the fluid at the bottom of the boundary layer corresponded to a decrease in the kinetic energy of the fluid above the boundary layer. The S-type velocity profile took the center of the vortex core as the symmetrical point, which is the main principle of using VGs to control flow separation. In particular, due to the rotation of the concentrated vortices, the high-energy fluid outside of the boundary layer was transferred into the bottom of the boundary layer, inhibiting the boundary layer separation. By comparing the velocity profiles developed under the four VG spacings, it can be seen that the kinetic energy of the fluid at the bottom of the boundary layer increased with the decrease of the VG spacing. However, when \(\lambda/H=3\), downstream of \(x/H=15\), the center of the vortex core was higher than the wall, and the high-energy fluid in the boundary layer was also higher than the wall height. When \(\lambda/H=5\), the kinetic energy at the bottom of the boundary layer was the largest. \[Q=\frac{1}{2}(\left\|\Omega\right\|^{2}-\left\|\mathrm{E}\right\|^{2}) \tag{6}\]2 Correlation coefficient distribution of fluctuating velocity \(v^{\prime}\). Figure 16 shows the relationship between the pressure loss coefficient \(C_{\Delta P}\) and the VG spacing. The percentage decrease of \(C_{\Delta P}\) with VGs compared to that without VGs (clean) can be observed. When VGs were installed, the \(C_{\Delta P}\) in the calculation domain decreased compared to that without VGs. Among them, the \(C_{\Delta P}\) was the smallest when \(\lambda/H=5\), and the maximum \(C_{\Delta P}\) was reduced by 30.95%. When \(\lambda/H=9\), the \(C_{\Delta P}\) was the largest, which was reduced by 2.37% compared to that without VGs. Therefore, \(\lambda/H=5\) was more suitable for flow control.
## 1 Introduction
At present, SIF data based on satellite inversion has been adopted to monitor and evaluate large-scale drought events in Russia [13], Texas, the central great plains of the United States [14; 15; 16], Europe [17], Yunnan province [8], the North China plain [6] of China, Australia [1; 18], etc. As one of the main abiotic stress factors affecting plant growth and production, drought can directly or indirectly inhibit photosynthesis [19]. The inhibition of photosynthesis by drought stress can be divided into stomatal and non- stomatal restrictions. Stomatal restriction mainly refers to the decline of leaf water potential and the stomatal closure in mild drought conditions, which inhibits photosynthesis by limiting the entry of photosynthetic substances (carbon dioxide, CO\({}_{2}\)) into plants. The non-stomatal restriction generally refers to the damage to the protein metabolic process of the photosynthetic system II reaction center and the changes in chloroplast structure of crops caused by severe drought, which inhibits the activity of Rubisco activate, thus reducing plant photosynthesis [20; 21]. Gross primary productivity (GPP) reflects the ability of green plants to produce organic matter through photosynthesis. It measures the photosynthetic rate and is a crucial component of the global carbon cycle. Studies have shown that SIF is directly related to the actual photosynthesis of plants, and the good linear relationship between SIF and GPP provides a direct method for remote sensing estimation of GPP [22]; however, when plants are suffering from environmental stresses (e.g., drought and heatwave), the linear relationship between SIF and GPP will be uncertain, which will lead to a decrease in the accuracy of vegetation GPP monitoring by remote sensing under stress [23]; therefore, further exploration of the effects of drought stress on the SIF-GPP relationship is of paramount significance for assessing the ability of SIF to capture and quantify drought-induced GPP losses. To the best of the author's knowledge, few studies have been undertaken to explore the recent 2019 drought impacts on vegetation in the middle and lower reaches of the Yangtze River (MLRYR). Moreover, the performance of SIF in drought monitoring and in assessing drought-induced GPP losses during vegetation growth from lush to senescence still needs to be further investigated. Thus, the specific objectives of this study are (1) to investigate the spatiotemporal variations of GPP, GOSIF, and VIs during 2000-2020; (2) to analyze the spatiotemporal evolution characteristics of the continuous drought in summer and autumn from July to December in 2019; (3) to evaluate the impacts of drought on vegetation physiological and ecological indicators and reveal the performance of SIF in vegetation drought monitoring; (4) to explore the effects of drought on vegetation productivity and clarify the potential of SIF in capturing and quantifying drought-induced GPP losses.
## 4 Simulation and Test Results
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Glossary** & **Amin (V)** & **Amax (V)** & **Fmin (Hz)** & **Fmax (Hz)** & **\(\Phi_{\text{min}}\) (°)** & **\(\Phi_{\text{max}}\) (°)** \\ \hline G\({}_{7}\) (1-bit) & 0.75 & 1.00 & 7995 & 8005 & \(-\)5 & \(+\)5 \\ G\({}_{8}\) (1-bit) & 0.50 & 1.00 & 7998 & 8002 & \(-\)3 & \(+\)3 \\ G\({}_{9}\) (1-bit) & 0.01 & 1.00 & 7990 & 8010 & \(-\)10 & \(+\)10 \\ \hline \end{tabular}
\end{table} 
Table 2: Glossary interval boundaries for SPEC space _@_ 8 kHz central frequency. Figure 11: Block diagram of PBCS test platform. \begin{table}
\begin{tabular}{l l l l l} \hline  & **Input** & **Output** & \\ \hline
**Glossary** & **Bandwidth (Hz)** & **BER** & **SNR (dB)** & **LSE (bps/Hz)** \\ \hline G\({}_{7}\) & 5.75 & 10\({}^{-5}\) & 20.25 & 10.869 \\ G\({}_{8}\) & 7.21 & 10\({}^{-5}\) & 15.75 & 8.668 \\ G\({}_{9}\) & 381.56 & 10\({}^{-5}\) & \(-\)3.10 & 0.163 \\ \hline \end{tabular}
\end{table} 
Table 3: PBCS performance analysis _@_ 8 kHz test platform. making some improvements to the ANN structure against multi-path effect problems, they have not been taken into consideration here in order not to increase complexity at this level. Solutions and performance for medium property related problems within specified frequency range will be published as joint work of this study. As shown by the simulation results, PBCS provides the advantage of selecting the most suitable point among different possible combinations in the SNR _vs._ LSE space. Practically, there are two significant problems of PBCS a) high sampling rate and processing power requirement, if direct RF sampling is preferred after RF signal receiver b) on-line training of ANN with the received signal, if it is down converted to baseband. The minimum sampling rate should be twice the central frequency according to the Nyquist Theorem. Moreover, the recognition layer, namely ANN, can be realized either by a floating point Digital Signal processor (DSP) or a Field Programmable Gate Array (FPGA) with sufficient amount of multiplier and adder elements inside. A critical consideration is the recognition time performance of the neural receivers in hardware design. We preferred using FPGAs instead of DSPs especially for the recognition mechanism since the progress can totally be mathematically modeled and requires higher speed processing as the base frequency increases. Although the decoding process takes around 0.00014 s/bits during the given simulations on a standard PC platform (Intel T2400 _@_ 1.83 GHz for MATLAB on a Windows XP system), this changes dramatically when we directly program on an embedded system. FPGA based hardware implementation also provides real time performance assurance. For example the FPGA EP4 SE230 from Altera STRATIX IV E family has 18-bit 1,288 multipliers in one package and this allows the construction of neural networks with 120 by 5 by 2 neurons in each layer respectively. These numbers indicate the quantity of nodes in the input, hidden and output layers of any used neural network in simulation results for 1-bit glossary.
## 5 Conclusions  
A mini plot field study was conducted to test the long-term effects of amending a heavily contaminated mine Technosol with a single application of MWS, with emphasis on soil reaction, chemical partitioning, and PHE retention. Soil acidity (pH = 3.1-3.5) and release of PHE were effectively reduced by amendment after its incorporation and these improvements were still observed after 12 years of its application. The addition of MWS provided a high pH-buffering capacity (pH = 5.8-6.4) to soil, making it less susceptible to eventual re-acidification over time, and therefore to PHE mobilization. Soil acidity neutralization affected the status and occurrence mode of PHE, rendering them less potentially phytotoxic and available for plant absorption, as indicated by the presence of plant species spontaneously growing in the amended site. Decreased mobility of Cu, Zn, Ni, and Cd most likely occurred by retention in reducible phases, mainly poorly crystallized Fe oxides, while Pb, As, Sb, Tl, and Cr were mostly found accumulated in the residual fraction, linked to insoluble secondary oxidation minerals and undissolved phases.  
Consistently, application of MWS amendment is a promising option for assisting sustainable natural remediation of degraded mining landscapes with long-term efficacy on PHE immobilization, as well as an attractive way of industrial waste recycling. However, it is important to note that, in the event of re-acidification, a considerable proportion of Zn and Cd retained in topsoil as exchangeable and weak acid soluble forms could be released by ion exchange and dissolution reactions. It has also been shown that the competitive effect of Ca\({}^{2+}\) ions for sorption sites could displace PHE from soil binding sites into soil solution enhancing the mobility of weakly adsorbed metallic cations. Therefore, further monitoring would be required to assess the chemical stability of PHE over a longer time span, the ageing process of the amendment, and the potential risk of transfer from soil to growing plants.  
Conceptualization, J.C.F.-C.; methodology, J.C.F.-C. and I.G.; validation, J.C.F.-C. and I.G.; formal analysis, I.G. and E.M.; investigation, I.G., S.F.-L., C.B.-B. and E.M.; data curation, S.F.-L. and C.B.-B.; writing--review and editing, J.C.F.-C.; visualization, I.G., S.F.-L., and C.B.-B.; supervision, J.C.F.-C.; project administration and funding acquisition, J.C.F.-C. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the Regional Government of Andalusia (Spain) and the European Regional Development Fund Analusia 2014-2020, through the Project P-18-TP-3503.  
Not applicable.  
The authors declare no conflict of interest.
# Towards Lightweight URL-Based Phishing Detection
## 3 Methodology  
In order to build the phishing detection engine our work explored the use of supervised machine learning algorithms that have been frequently used in relevant literature [11], namely Naive Bayes [40], Decision Tree [41], Random Forest [42], Support Vector Machine [43], and Multi-Layer Perceptron [44]. These algorithms are trained using Python 3 and the SciKitLearn library [45]. The comparison of the machine learning models performance utilizes a set of metrics which are commonly used in this domain, i.e., (i) precision, (ii) sensitivity, (iii) F- Measure, (iv) Accuracy, (v) Receiver Operating Characteristic (ROC) curve, and (vi) Confusion matrix.  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{2}\]\[F-Measure=2\times\frac{Precision\ \times\ Sensitivity}{Precision+Sensitivity} \tag{3}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FN+FP} \tag{4}\]  
The metrics above are using True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN) for their calculations. TP classify URLs pointing to phishing websites, correctly, as phishing and TN classify benign URLs, correctly, as benign. FP misclassify benign URLs as phishing and FN misclassify phishing URLs as benign URLs. A system is said to be precise (1) when it delivers a high level of correct prediction values. Sensitivity (2), also known as recall, outlines the system's predisposition in classifying a legitimate URL as being malicious. As the system increases in sensitivity, it reduces the number of false-negatives it produces. For clarification, precision is the ratio between all adequate classification errors and all classification errors. In contrast, recall is the ratio of all classification errors and all existing errors. The F-Measure (3) is the harmonic mean of precision and sensitivity. It is a composed metric which penalises the extreme values and is meant to provide a single measurement for a system which illustrates the level of optimisation. The accuracy (4) measures the ratio of true-positives and true-negatives. Finally, the Receiver Operating Characteristic (ROC) curve illustrates how well a model is capable of distinguishing between a phishing and a benign URL and the Confusion matrix offers a view of the classification performance details of the model.  
The best-performing models were selected for both hyperparameter tuning and further experimentation with feature selection. Model optimisation is performed using GridSearchCV which performs an exhaustive search over specified parameters and selects the best performing model.  
The dataset used for training the classifiers is based on the datasets from [46] and PhishTank. It includes 100,000 URLs that were collected in April 2020 and fed into the training phase with a 80/20 split between training and testing data respectively. Specifically, it consists of the first 40,000 benign URLs from [PERSON] [46] and 60,315 phishing URLs from PhishTank.
## 1 Introduction  
### Fire Protection in Industrial Safety  
Fire protection systems help mitigate the effects of a fire in industrial facilities. Fire protection was declared an \"underpinning of good process safety\" by [1] and was separated into two distinct branches: passive and active systems. This simple classification of fire safety systems is the base of most of the fire safety regulations, not only in the process industry but also in the whole of civil engineering [2]. In some aspects, the classification of systems into passive and active solution also reassembles the segregation used for explosion and detonation prevention and suppression [3]. In fire safety, passive systems act by limiting the growth of the fire, confining the fire within a compartment and improving the structural resistance to fire. The active systems typically act by early detection or suppression of the fire. In the case of suppression, [1] focused mainly on traditional water-based fire extinguishing systems such as sprinkler systems. However, it must be noted that besides sprinkler systems, modern fire safety engineering includes a vast array of water-based suppression technologies, such as high-pressure water mist suppression systems [4; 5], high expansion foam systems [6] or water spray curtains used to mitigate hazards related to toxic gases [7; 8]. Despite their well-known performance, in some areas of the modern industry, the release of water from water-based fire extinguishing systems can cause unacceptable losses. Examples of such areas are trafo-stations and power supply rooms, turbine halls, server rooms, laboratories, 3D printing and CNC facilities, etc. In some cases, water-based systems may also have a negative impact on the structure [9; 10]. In these spaces, different suppression techniques must be employed, among them: fixed gaseous extinguishing systems (further referred to as FES-gaseous) [11], powder extinguishing systems [12] or inertisation [13; 14]. An emerging area where gaseous systems may find suitable use is in energy storage facilities, primarily based on Lithium batteries that pose significant fire hazard [15; 16; 17]. Also, the use of gaseous systems is promoted in some compartments, where the release of water-based medium may cause problems with visibility in smoke. Besides limiting losses related to the occurrence of the fire, some remote facilities require an additional layer of protection by prolonged suppression effect of the used medium. In such a case, the FES-gaseous systems optimised for prolonged retention time are highly feasible [11].
## 3 Methodology
### Extending to Local Explainability  
While the frameworks described above provide global explanations that capture the overall behavior of the black-box model, they may not adequately explain individual predictions. To address this limitation, we extend our pre hoc and co hoc frameworks to incorporate local explainability, enabling the generation of instance-specific explanations. To avoid confusion, we distinguish between two related but distinct concepts:  
**Local Explainability:**: Refers to explanations generated for a _single instance_\(x_{i}\). These explanations describe why the model made a specific prediction for that particular input.  
**Neighboroid Fidelity:**: Measures how well the explanation remains accurate for a set of instances \(\mathcal{N}(x_{i})\) in the vicinity of \(x_{i}\). This metric evaluates the _stability_ of local explanations but is not itself a form of explanation.  
Throughout this paper, \"local explanations\" exclusively refers to instance-specific interpretations, while neighborhood-based metrics assess the quality and consistency of these explanations.
## 3 Results and Discussion
### Effect of Stirring Speed  
In BIA with amperometric detection, the rate of convective analyte transport (phenolic compounds and Trolox) to the biorecognition layer is affected dramatically by the stirring speed, therefore it has an effect on sensitivity of the TYR biosensor. The stirring speed was varied from 100 to 500 rpm. Finally, the stirring rate of 400 rpm was selected as the optimum value, because no significant increase was observed by further increasing the stirring rate.