esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach
## 4 Results and Discussions
### Random Forest Classifier  
The RFCs, implemented with the help of the scikit-learn predefined class, were trained with the prepared data. The complexity of the classifiers was varied on the basis of the number of used estimators. The set of 5, 10, 20, 50, and 100 estimators (decision trees) shaping the random forest was considered in the experiment. Additionally, different proportions of the training and testing sets were used for the fitting of the RFC parameters and in order to estimate model accuracy. The parameters of the RFCs implemented with the highest accuracy are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**D a**} & \multicolumn{6}{c}{**Variables of the Network**} \\ \cline{2-7}  & **Best Dataset** & \multirow{2}{*}{**\# of Nodes c**} & **\# of Epochs/** & **\# of Filters/** & **Pool Size/** & **Model** \\  & **Partition b** & & **Learning Rate** & **Kernel/Stride d** & **Pool Stride e** & **Accuracy (\%)** \\ \hline \multicolumn{7}{c}{MLP} \\ \hline
1600 & 60/40 & 2000/2000 & 30/0.098 & n.a. f  & n.a. & **84.4** \\
3200 & 40/60 & 700/700 & 20/0.098 & n.a. & n.a. & **89.9** \\
4000 & 30/70 & 350/350 & 15/0.224 & n.a. & n.a. & **91.8** \\ \hline \multicolumn{7}{c}{**CNN** (with same convolutional and pooling layers of Table 2)} \\ \hline
1600 & 60/40 & 2000/2000 & 30/0.098 & 10/30/5 & 10/5 & **83.3** \\
3200 & 40/60 & 700/700 & 20/0.098 & 10/30/5 & 10/5 & **72.7** \\
4000 & 30/70 & 350/350 & 15/0.224 & 10/30/5 & 10/5 & **91.1** \\ \hline \multicolumn{7}{c}{**CNN** (number of optimization filters)} \\ \hline
1600 & 60/40 & 2000/2000 & 30/0.098 & 100/30/5 & 10/5 & **87.2** \\
3200 & 40/60 & 700/700 & 20/0.098 & 100/30/5 & 10/5 & **90.5** \\
4000 & 30/70 & 350/350 & 15/0.224 & 150/30/5 & 10/5 & **95.6** \\ \hline \hline \end{tabular}  
* Data set size, i.e., the number of signals used to feed the network.
* Percentage of signals used as training and testing samples (e.g., 60/40 = 60% for training and 40% for testing).
* Number of nodes used for each hidden layers (e.g., 50/50 = 50 nodes for the hidden layer #1, and 50 nodes for the hidden layer #2).
* # of filters = number of filters used in the convolution layer; kernel = length of the convolution window;stride = stride length of the convolution.
* pool size = size of the window that makes the pooling; pool stride = stride of the pooling window f n.a. = not available, because is not required by the MLP.  
\end{table}
Table 3: Comparison of MLP–CNN.
# Qualitative Assessment of Hazardous Gas Emissions from Sewage Sludge-Derived Biochar
## 2 Materials and Methods
### Chemical Analysis of Gases Emitted from Biochar  
Gaseous emissions from each reactor were routed to glass bulbs, from where air samples were collected for chemical analyses. Solid-phase microextraction (SPME) holders, equipped with DVB/Carboxen/PDMS fibers, were inserted into each gas sampling bulb through a septum [5; 6]. The fiber was then ejected from the housing needle and exposed to the gaseous emissions for 25 min. Gas samples were then delivered for analysis on a gas chromatograph--mass spectrometer (GC-MS), Agilent 5975C GC coupled to a 5983 MS equipped with a quadrupole mass detector (Agilent Technologies Inc., Palo Alto, CA, USA). Separation was carried out in an HP-5 Agilent (Phenomenex, Torrance, CA, USA) (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) column [12]. Scanning (1 scan/s) was carried out in the range of 35-400 \(m/z\) using electron-impact ionization at 70 eV.  
Helium was used as the carrier gas with a flow rate of 1.0 mL-min\({}^{-1}\) in a splitless mode for SPME. The oven temperature program was as follows: the initial temperature was set at 50 \({}^{\circ}\)C increasing by 4 \({}^{\circ}\)C-min\({}^{-1}\) until reaching 130 \({}^{\circ}\)C, then by 10 \({}^{\circ}\)C-min\({}^{-1}\) until 180 \({}^{\circ}\)C, and finally by 20 \({}^{\circ}\)C-min\({}^{-1}\) until 240 \({}^{\circ}\)C, with a hold at 240 \({}^{\circ}\)C for 4 min. The injector was maintained at 200 \({}^{\circ}\)C. Chromatographic conditions were carried out according to [PERSON] et al. [15]. A blank GC-MS run with the same temperature program was performed to establish a baseline bleed profile of the HP-5 column. The results of this blank run confirmed that the detected peaks were not artifacts of the column.  
Qualitative analysis was used. Resulting chromatograms were searched for the presence of hazardous organic compounds using Annex 1 of the European Union regulation EC No. 1272/2008 [20]. The European Parliament Regulation (EC) No. 1272/2008 was the foundation for determining these substances [20]. The document defines the hazard class of individual substances to ensure high levels of protection for human health and the environment. Next, the peak area of each hazardous organic compound was compared to the total peak area of the analyzed sample. The hazard class of individual organic compounds was determined based on CAS numbers and publicly available data.
## 2 Materials and Methods
### Model
#### 2.2.2 Decoder with Temporal Attentions  
In LSTM, only the last hidden state is used for prediction. However, the performance of encoder-decoder architecture will degrade rapidly with the encoder length increases. To solve this issue, a temporal attention mechanism is used in the decoder stage to model the dynamic temporal correlation between different time intervals in the input feature sequence. The attention weight of each encoder hidden state at each time \(t\) is defined as follows:  
\[l_{t}^{i}=v_{e}^{T}tanh(W_{e}[d_{t-1};c^{\prime}{}_{t-1}]+Ulehi), \tag{9}\]  
\[\beta_{t}^{i}=\frac{exp(l_{t}^{i})}{\sum_{j=1}^{T}exp(l_{t}^{i})}, \tag{10}\]  
\[v_{t}=\sum\
olimits_{i=1}^{T}\beta_{t}^{i}h_{t}, \tag{11}\]  
where \(W_{e}\in R^{m\times 2p}\), \(U_{e}\in R^{m\times m}\) and \(v_{e}\in R^{m}\) are parameters to learn, \(d_{t-1}\) is the previous decoder hidden state and \(c^{\prime}{}_{t-1}\) is the memory cell state in the decoder LSTM unit. \(\beta_{t}^{i}\) is calculated by Equation (10), it is the importance weight of the \(i\)-th encoder hidden state at time \(t\) for the prediction [16]. \(v_{t}\) is a weighted sum of all the encoder hidden states. These scores are normalized by a SoftMax function to create the attention mask on the encoder hidden states.  
Once the weighted summed context vector \(v_{t}\) at time step \(t\) is obtained, we can combine them with the Chl-a time series and update the decoder hidden state.  
\[\widetilde{y}_{t-1}=\widetilde{w}^{T}[y_{t-1};v_{t-1}]+\overline{b}, \tag{12}\]  
\[d_{t}=\ f_{2}(d_{t-1},\widetilde{y}_{t-1}), \tag{13}\]  
where \(\widetilde{w}\in R^{m+1}\) and \(\overline{b}\in R\) are the parameters to map the concatenation to the size the decoder input, \(y_{t-1}\) is the decoder input, \(v_{t-1}\) is the computed context vector, [:,:] is a concentration operation. \(\widetilde{y}_{t-1}\) is the decoder hidden state at time \(t\).  
We concatenate the context vector \(v_{t}\) with the hidden state \(d_{t}\), which becomes the new hidden state from which we make final predictions as follows:  
\[\widetilde{y}_{T}=v_{y}^{T}(W_{y}[d_{T};v_{T}]+b_{w})+b_{v}, \tag{14}\]  
where the matrix \(W_{y}\in R^{n\times(m+n)}\) and the vector \(b_{w}\in R^{n}\) map the concatenation \([d_{T};v_{T}]\in R^{m+n}\). Finally, we use a linear transformation (\(v_{y}\in R^{n}\)_and_\(b_{v}\in R\)) to generate the final prediction result. Our approach is smooth and differentiable, and we use the Adam optimizer [21] to train the model by minimizing the mean squared error.
# Coordinated Mitigation Control for Wideband Harmonic of the Photovoltaic Grid-Connected Inverter
## 2 The Generation Mechanism of Wideband Harmonics  
### Topology and Control Structure  
Figure 1 shows the typical topology of the PV grid-connected inverter. The DC side comprises photovoltaic panels, boost circuits, and DC bus capacitance. The maximum power point tracking (MPPT) technology ensures that the renewable sources export peak power. The grid-connected inverter usually uses PQ or DC voltage control, turning the DC energy into clean AC signals. The inverter's output port is linked to a common coupling point (PCC) via the line impedance. Since the switching process of IGBT will cause high-frequency harmonic components, an LCL filter is needed to reduce these harmonic components due to the switching characteristics. The power network is represented by a three-phase infinity power whose valid value is 220 V.  
Generally, the inverter controller can be designed by deriving the transfer function of the closed-loop control system, thus the precise control of PV output current can be achieved. Figure 2 shows the equivalent closed-loop control transfer function. The outer voltage control loop uses a proportional-integral (PI) controller to eliminate the static error for accurate tracking of the target bus voltage, and its output serves as the reference signal for the internal current controller. The PI controller is also used in the current loop, generating the IGBT switch signal through a sinusoidal pulse width modulation (SPWM).  
Figure 1: PV grid-connected system topology.  
The dual-loop controller transfer function can be formulated as:  
\[G_{\text{PI}}(s)=\left\{\begin{array}{l}K_{\text{pv}}+\frac{K_{\text{pv}}}{s}\\ K_{\text{pc}}+\frac{K_{\text{ic}}}{s}\end{array}\right. \tag{1}\]  
where \(K_{\text{pv}}\) and \(K_{\text{iv}}\) are the ratio and integral coefficient of the voltage loop controller, and \(K_{\text{pc}}\) and \(K_{\text{ic}}\) are the ratio and integral coefficient of the current loop controller.
# Path Planning Based on NURBS for Hyper-Redundant Manipulator Used in Narrow Space
## 2 Kinematics Modeling and Workspace Analysis of UCR Parallel Mechanism  
Three degrees-of-freedom (DOFs) parallel mechanisms have high accuracy and large loading capacity. This part concentrates on the Kinematics modeling and workspace analysis of UCR parallel mechanism. Firstly, symmetric three-chain with two rotational DOFs and one translation DOF (2R1T) are designed based on constrained screw theory. Secondly, kinematics model of UCR parallel mechanism is constructed. The relative pose of the moving platform and the fixed platform can be adjusted by changing the length of the drive chain. Finally, the workspace analysis is finished based on simulation.
## 3 Results and Discussion

Table 1: Deposition conditions of ZnO and SiO\({}_{2}\) thin films for both SiO\({}_{2}\)/ZnO/SiO\({}_{2}\) micro-energy generators (SZS-MGs) and ZnO/SiO\({}_{2}\)/ZnO micro-energy generators (ZSZ-MGs) shown in Figures 5 and 6, respectively. Second, we also investigated the effects of deposition power on the output voltage of the ZSZ-MG devices. We fabricated four different ZSZ-MG devices with SiO\({}_{2}\) films deposited using various deposition powers of 50, 100, 150, and 200 W. The output voltage variations of those ZSZ-MGs are plotted in Figure 10. To maintain a similar thickness for each deposited oxide film, the deposition time alone was controlled to be inversely proportional to the applied RF power while keeping all other process conditions constant. The device measurements showed that the largest output voltage (35 mV) was generated by the ZSZ-MG device with a silicon dioxide (SiO\({}_{2}\)) thin film deposited using 150 W for 22.5 min, as shown in Figure 10. This may be due to the formation at 150 W of the relatively fine-grained surface morphology shown in Figure 11. The deposition conditions of both ZnO and SiO\({}_{2}\) thin films are summarized in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Film** & **ZnO** & **SiO\({}_{2}\)** \\ \hline Power (W) & 200 & 50, 100, 150, 200 \\ Pressure (Torr) & 1.0 \(\times\) 10\({}^{-2}\) & 1.0 \(\times\) 10\({}^{-2}\) \\ Gas flow (sccm) & N\({}_{2}\)O (12) + Ar (18) & Ar (20) \\ Substrate temperature (\({}^{\circ}\)C) & 29 & 29 \\ Target-substrate distance (cm) & 12 & 12 \\ Deposition time (min) & 30 (upper layer)/30 (lower layer) & 45, 30, 22.5, 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Deposition conditions of both ZnO and SiO\({}_{2}\) thin films used to fabricate the ZSZ-MGs in Figure 10. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Film** & **ZnO** & **SiO\({}_{2}\)** \\ \hline Power (W) & 200 & 200 \\ Pressure (Torr) & 1.0 \(\times\) 10\({}^{-2}\) & 5.0 \(\times\) 10\({}^{-1}\), 1.0 \(\times\) 10\({}^{-1}\), 5.0 \(\times\) 10\({}^{-2}\), \\  & & 1.0 \(\times\) 10\({}^{-2}\), 5.0 \(\times\) 10\({}^{-3}\) \\ Gas Flow (sccm) & N\({}_{2}\)O (12) + Ar (18) & Ar (20) \\ Substrate Temperature (\({}^{\circ}\)C) & 29 & 29 \\ Target-Substrate Distance (cm) & 12 & 12 \\ Deposition Time (min) & 30 (upper layer)/30 (lower layer) & 15 \\ Film Thickness (nm) & 271 & 50, 54, 57, 51, 59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Deposition conditions of both ZnO and SiO\({}_{2}\) thin films used to fabricate the ZSZ-MGs in Figure 8.  
Figure 9: SEM images obtained at 200 k times magnification showing the surface morphologies of the SiO\({}_{2}\) thin films deposited under three different working pressures: (**a**) 5.0 \(\times\) 10\({}^{-1}\), (**b**) 5.0 \(\times\) 10\({}^{-2}\), and (**c**) 5.0 \(\times\) 10\({}^{-3}\) Torr. Deposition power was fixed at 200 W.
# Investigation of Using CAPTCHA Keystroke Dynamics to Enhance the Prevention of Phishing Attacks
## 3 Proposed Work  
To precisely define the proposed work, this section describes a system interpretation approach to the prevention of phishing attacks as a means of protecting the online services. This system deploys keystroke timing data to prevent phishing attacks, as well as providing secure authentication, using log-in credentials (username and password) and a text-based CAPTCHA, which integrates keystroke dynamics into a single system to prevent phishing attacks. Keystroke dynamics capture a user's typing patterns in order to identify that user as it is difficult to reproduce a user's typing pattern. The keystroke dynamics system evaluates a user's typing pattern in milliseconds (ms). In general, the system verifies users who are requesting to access an online service. If a phisher requests access, they will either be denied or the request will be accepted.  
The approach illustrated in Figure 2 provides the basis of the experimental study presented in the next section. In the system model, (1) a text-based CAPTCHA is shown on the website's log-in page. The user then enters their credentials and solves the CAPTCHA; (2) the request, along with the keystroke timing data, is sent to the authentication server; (3) the server checks the user's details and compares them with the user's profile in the database; and (4) the server grants access or rejects the user's request. The following section details the proposed methodology for this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Contribution** & **Results** \\ \hline
[24] & Proposed a multi-factor authentication scheme using voice traits as behavioural biometrics to protect a mobile banking system (Mpesa) in Kenya. & The results demonstrated that voice biometrics have numerous potential advantages and benefits in terms of reducing risk for mobile financial systems. \\ \hline
[33] & Introduced a new approach involving free-text keystroke dynamics authentication to provide a high degree of usability and security. Arabic and English language typing text was used to compare the performance of Arabic input with another input. Keystroke duration, di-graph duration, and latency were combined as a feature to distinguish between samples of authenticated users and impostors. \\ \hline Current work & Investigate the effectiveness of using CAPTCHA keystroke dynamics in enhancing the prevention of phishing attacks. & The results are promising in terms of providing a practical and cost-effective solution to prevent phishing attacks. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the current and relevant previous studies.
# Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach
## 5 Simulation Results and Analysis
### System Average Cost Comparison with Benchmark Algorithms for Different Number of Parks  
In order to evaluate the performance of power scheduling strategies based on different algorithms under different numbers of parks, this section compares the total system average cost of the TD3 algorithm-based strategy proposed in this paper with other benchmark strategies in a scenario with fixed PV generation and park electrical load.  
Figure 6 shows the cost performance of different algorithms w.r.t. the number of parks in the system. From the figure, it is observed that the TD3 algorithm achieves a better result for its lower-cost performance.  
Figure 5: Reward of different algorithms. The shadowed area represents the range of reward fluctuation of three curves in 1500 episodes. The reward in each episode will not exceed the shadowed area.  
Figure 6: Average cost of algorithms for different number of parks.
# Effects of Adding Batteries in Household Installations: Savings, Efficiency and Emissions
## 7 Conclusions  
Three electrical systems for household uses are analyzed and compared considering their efficiencies, savings, greenhouse gas emissions and the possibility of combine them with other recharge systems. The first one is constituted by a hybrid AC/DC system with a battery, the second one is just an AC/AC system with a battery and the last one is the classic AC system without any accumulation device, i.e., direct use of AC power. The comparison is performed without modifying household habits.  
According to the case analyzed, the highest efficiency and the best savings are obtained with the AC/DC system due to the reduction of losses, because the inverse transformation is avoided (efficiency 9.12% higher) and due to the optimization of the consumption (i.e., the battery is charged during the low prices period (savings of 412.75 \(\upxi\))). The AC/AC system keeps this last feature (savings of 283.10 \(\upxi\)), but needs the inverse transformation of power, therefore it misses the main advantage of the AC/DC system (efficiency 10.80% lower). Even though both systems outperform the classic AC one, the AC/DC system is strongly recommended due to its efficiency and reduction of costs. Moreover, the systems based on batteries have the advantage of being independent of tariffs based on prices-by-periods.  
On the other hand, greenhouse gas emissions in the combined AC/DC system are lower (\(-0.18\) t\({}_{\rm CO2}\)) than those of the other two existing systems. In fact, when considering this feature, the AC/AC system is the one with the worst performance (+0.21 t\({}_{\rm CO2}\)). Therefore, again the AC/DC system outperform the other two systems.  
Finally, when considering alternative or complementary options for recharge, the use of workout machines reduce the costs and the external consumption of energy and, its combination with a battery can help adjust the full process. Moreover, electric vehicles can also help supplying power to the system during peak periods as a back-up battery. In both cases, the use of systems with batteries is essential to obtain the highest efficiency.  
Data curation, M.C.; Formal analysis, D.V. and M.C.; Investigation, M.C.; Methodology, D.V. and M.C.; Project administration, D.V. and A.F. ([PERSON]); Resources, D.V., E.M and A.F. ([PERSON]); Software, M.C.; Supervision, D.V., A.F. ([PERSON]) and E.M.; Validation, D.V., A.F. ([PERSON]) and E.M.; Visualization, A.F. ([PERSON]); Writing--original draft, M.C.; Writing--review & editing, D.V. and A.F. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding,  
The authors declare no conflict of interest
## 4 Methodology
### Maximum Entropy Method  
In this study, the MaxEnt method [54] was applied for modelling the potential and future distribution of gully erosion-prone areas based on a set of geo-environmental factors. MaxEnt is a machine learning method based on the entropy maximisation principle [55], which can handle data from various measurement scales without relying on parametric statistical assumptions. This method is based on information theory and statistics with the concept of the best approximation of an unknown probability distribution [56]. The MaxEnt approach was widely applied in research related to environmental and ecological modelling [54; 55; 57], landslide susceptibility [58; 59; 60; 52], as well as gully erosion susceptibility [61; 22; 34; 62].  
Applying the MaxEnt method, based on the presence-only features, the gully erosion susceptibility is defined as the probability distribution within the study area. The probability of gully occurrences is calculated with the following [59]:  
\[\mathrm{P}(\mathrm{y}\ =\ 1|\mathrm{x})\ =\ \frac{\mathrm{P}(\mathrm{y}\ =\ 1) \mathrm{Px}|\mathrm{y}\ =\ 1)}{\mathrm{P}(\mathrm{x})}\ =\ \frac{\mathrm{P}(\mathrm{y}\ =\ 1) \mathrm{\pi}(\mathrm{x})}{1/|\mathrm{x}|} \tag{1}\]  
where P (y = 1) is the probability of occurrence of gullies and \(|\mathrm{x}|\) is the number of gullies occurring in the study area; x denotes a random site over the study area. The \(\mathrm{\pi}\left(\mathrm{x}\right)\) required by the MaxEnt method is similar to the estimate of the Gibbs probability distribution function [63]. If n variables are assumed in the model (f\({}_{i}\), i = 1, 2,.., n), the Gibbs probability distribution can be written as follows:  
\[\mathrm{q}_{\lambda}(\mathrm{x})\ =\ \frac{\mathrm{exp}(\sum_{i\ =\ 1}^{n} \lambda_{i}f_{i}(\mathrm{x}))}{\mathrm{Z}_{\lambda}} \tag{2}\]  
where \(\mathrm{Z}_{\lambda}\) is the normalisation constant and \(\lambda\) is the sum of the vector weights assigned to the features. The MaxEnt method searches for the Gibbs distribution function that could maximise the log-likelihood of probability and minimise the estimation error. Supposing that m gully phenomena occurred in the study area, the difference between the log-likelihood calculation and regularisation is maximised using the formula of [PERSON] and [PERSON] [63]:  
\[\frac{1}{\mathrm{m}}{\sum_{i\ =\ 1}^{m}\mathrm{ln}(\mathrm{q}\lambda( \mathrm{X}_{1}))-\sum_{j\ =\ i}^{n}\beta_{j}|\lambda_{i}|} \tag{3}\]  
where \(\beta_{j}\) is the normalised variable. In Equation (3), the first term is used for the maximum degree of fitting model value and the second is used for normalising the value. TheMaxEnt method was applied using Maxent software, version 3.4.4. The probability of gully occurrence was set to 0.5, while the maximum number of iterations before interrupting the model building was set to 500. However, a convergence threshold was set to stop the training phase when the drop in log loss per iteration fell under a value of 0.00001. More details on the MaxEnt algorithm are given in [PERSON] et al. [54].