esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Conventional wastewater treatment is often insufficient in the case of mature landfill leachate. Among novel methods, internal microelectrolysis (IME) has been recently recognized as an effective method for refractory wastewater treatment. The aim of this study was the evaluation of the impact of various parameters (pH, time, types, ratios of Fe/GAC, H\({}_{2}\)O\({}_{2}\) dose, the time of application and the soaking of GAC in LL) on the efficiency of the IME process applied to mature landfill leachate. As microelectrodes, waste cast-iron chips and granulated activated carbon (GAC) were used. The application of a multi-step treatment of leachates, PC \(>\) m-IME \(>\) m-IME/H\({}_{2}\)O\({}_{2}\), resulted in 82.1% COD removal and a significant increase in biodegradability (BOD\({}_{5}\)/COD rose up to 0.39). The optimal parameters were established as follows: pH 3, 120 min, ratio Fe/GAC 40/10 g/g/dm\({}^{3}\) and ratio COD/H\({}_{2}\)O\({}_{2}\) = 1/2. The results showed that the PC is a reasonable pretreatment process before IME--the improvement in total COD removal was up to 12.5%. COD removal in the IME process increased with the increase in GAC dose only up to a certain level. Also, too high doses of Fe negatively affected the efficiency of the IME process, which shows that the efficiency depends not only on the dose but also on the mass ratio of Fe/GAC. The addition of H\({}_{2}\)O\({}_{2}\) significantly improved the COD removal effectiveness, especially when it was applied after 120 min of the m-IME process. The results showed also that the soaking of GAC in LL before the process did not eliminate the sorption process.  
internal microelectrolysis; landfill leachate; hydroxyl radicals +
Footnote †: journal: Applied sciences  
0000-0002-2300-3300][PERSON]  
1 Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology,  
Nowoweigska Street 20, 00-653 Warsaw, Poland; [PERSON] (J.L.)
## 3 Multidimensional Taylor Network Adaptive Robust Controller
### Robust Control
\begin{array}{l}g^{*}(u(t))-\Psi\cdot[\widehat{m},\widehat{a}, \widehat{b}]^{\text{T}}+d(t)\leq\varepsilon_{1}\\ r(t)(g^{*}(u(t))-\Psi\cdot[\widehat{m},\widehat{a},\widehat{b}]^{\text{T}}+d( t))\leq\zeta\\ r(t)g^{*}(u(t))\leq 0\end{array} \right. \tag{15}\]  
where \(\varepsilon_{1}\) and \(\zeta\) are normal numbers small enough. As shown in Equations (14) and (15), \(g_{\text{MTN}}(u(t))\) is used to comprehensively deal with the disturbance \(d(t)\) of the uncertain system and the parameter adaptive modeling error \(\Psi\cdot[\widehat{m},\widehat{a},\widehat{b}]^{\text{T}}\). **Theorem 1**. : _Provided that the following adaptive function is selected_  
\[\Omega(x_{2},h,t)=\Psi^{\text{T}}\cdot r(t) \tag{16}\]  
_then the adaptive control law is as shown in Equation (11), which can make all signals of the system bounded. And if after a finite time \(t_{0}\) the disturbance error is 0, only the uncertain parameters are left, that is, \(\forall t\geq t_{0}\), \(d(t)=0\). Further, the tracking error approaches zero. In addition, there are positive definite functions expressed as_  
\[V_{s}(t)=\frac{1}{2}m\tau^{2}(t) \tag{17}\]  
_that are bounded._  
_The upper bound is \(\frac{1}{2}[e^{-\frac{2k_{2}}{m_{\text{max}}}t}m\tau^{2}(0)+\frac{\zeta m_{ \text{max}}}{k_{2}}(1-e^{-\frac{2k_{2}}{m_{\text{max}}}t})]\), i.e.,_  
\[V_{s}(t)\leq\frac{1}{2}[e^{-\frac{2k_{2}}{m_{\text{max}}}t}m\tau^{2}(0)+\frac {\zeta m_{\text{max}}}{k_{2}}(1-e^{-\frac{2k_{2}}{m_{\text{max}}}t})] \tag{18}\]  
**Proof of Theorem 1. ** According to Equation (14), the ramification of \(V_{s}(t)\) can be obtained as  
\[\dot{V}_{s}(t)=r(t)\cdot m\dot{r}(t)=r(t)\cdot[-k_{2}r(t)+g_{\text{MTN}}(u(t)) -\Psi\cdot[\widehat{m},\widehat{a},\widehat{b}]^{\text{T}}+d(t)] \tag{19}\]  
From Equation (15), we can obtain  
\[\dot{V}_{s}(t)\leq-k_{2}r^{2}(t)+\zeta\leq-\frac{2k_{2}}{m_{\text{max}}}\cdot \frac{1}{2}m\tau^{2}(t)+\zeta=-\frac{2k_{2}}{m_{\text{max}}}\cdot V_{s}(t)+\zeta \tag{20}\]Equation (18) can be derived by substituting Equations (17) and (19) into Equation (20). When \(\forall t\geq t_{0}\) and \(d(t)=0\), define the new positive definite function \(V_{new}(t)\) as follows:  
\[V_{new}(t)=V_{s}(t)+\frac{1}{2}[\widetilde{m},\widetilde{a},\widetilde{b}] \cdot\Psi^{-1}(t)\cdot[\widetilde{m},\widetilde{a},\widetilde{b}]^{\mathsf{T}} \tag{21}\]  
According to Equations (15) and (19), the derivative of \(V_{new}(t)\) is  
\[ \begin{array}{l}\dot{V}_{new}(t)=\dot{V}_{s}(t)+[\widetilde{m},\widetilde{a },\widetilde{b}]\cdot\Psi^{-1}(t)\cdot[\widetilde{m},\dot{\widetilde{a}}, \dot{\widetilde{b}}]^{\mathsf{T}}\\ \leq-k_{2}r^{2}(t)+[\widetilde{m},\widetilde{a},\widetilde{b}]\cdot\Psi^{-1}(t )\cdot[\widetilde{m},\widetilde{a},\dot{\widetilde{b}}]^{\mathsf{T}}-[ \widetilde{m},\widetilde{a},\widetilde{b}]\Omega(x_{2},h,t)\\ \leq-k_{2}r^{2}(t)\end{array}  \tag{22}\]  
Since \(\dot{r}(t)\) is finite, \(r(t)\) is uniformly continuous. According to Barbalat lemma, when \(t\) tends to \(\infty\), \(e(t)\) and \(r(t)\) both tend to zero. Proof complete. \(\square\)
# Enhancing the Photocatalytic Activity of Immobilized TiO2 Using Laser-Micropatterned Surfaces
## 3 Results and Discussion  
In the frame of this study, five different surfaces were evaluated in terms of their photocatalytic efficiency: (a) Flat Si-TiO\({}_{2}\) solgel (F-Si-solgel), (b) Flat Si-Degussa P25 + TiO\({}_{2}\) solgel (F-Si-P25-solgel), (c) Microconical Si-TiO\({}_{2}\) solgel (M-Si-solgel), (d) Microconical Si-Degussa P25 + TiO\({}_{2}\) solgel (M-Si-P25-solgel), and (e) Rippled Si-TiO\({}_{2}\) solgel (R-Si-solgel). The first two surfaces consist of flat silicon substrates coated either with the pure TiO\({}_{2}\) film or with the composite TiO\({}_{2}\) + Degussa P25 film. The latter is used as a reference coating, against which the photocatalytic activity of the materials developed in this work is assessed. Surfaces c and d consist of laser-patterned, microconical silicon substrates coated either with the pure TiO\({}_{2}\) film or with the composite TiO\({}_{2}\) + Degussa P25 film. The R-Si-solgel surface consists of laser-patterned, rippled silicon substrates, coated with the pure TiO\({}_{2}\) film. The purpose of the two different patterned silicon substrates (microconical and rippled) is to evaluate the effect of the substrate morphology on the photocatalytic activity of the surface.
# Towards Explainable Augmented Intelligence (AI) for Crack Characterization
## 3 The AutoNDE Code for Semi-Automatic Crack Characterization
### Signal Processing
If the measurements are taken only when the crack is located more or less underneath the array center the region of interest is reduced to the central region underneath the probe. Any reduction of the region of interest speeds up the crack characterization process. 2. _RayTracing_ starts by issuing a fan of rays from each array element. The central angle of each fan is \(-90^{\circ}\) to the \(x\)-axis, the optimal vertex angle has been found to be \(60^{\circ}\) and the optimal difference between the angles of neighboring rays, \(0.057^{\circ}\). These values effect a trade-off between the code accuracy and speed. For each ray the _RayTracing_ submodule locates the point where it hits the upper surface, finds the refracted ray issuing from this point (in the current version no shadowing is accounted for) and calculates the time it takes the ray to reach each row in the region of interest. In the present version of AutoNDE mode conversion is allowed as well as one reflection from the backwall. Figure 4: An algorithm for automatic identification of the interpolating polynomial. 4. _IntensityFunctionGenerating_ utilizes the matrix \(A\) of A-scans to generate the intensity function  
\[I(x)=\sum_{k,l}h(A_{kl})(t_{k,x,l})| \tag{4}\]  
where time \(t_{k,x,l}\) is the moment of time the corresponding pulse is at its peak. In the standard TFM (Total Focusing Method) the summation in (4) is carried out over the whole probe. In addition to TFM we use an MTFM (a Modified TFM), a signal processing approach developed by trial and error to produce not just one image for one position of the probe as in TFM but a series of images \(m\): Inside each such image, each vertical segment \(x\) is scanned with a \"partial probe\" \([k+D_{\text{m}}k+D_{\text{m}}+L]\), \(k<K-D_{\text{m}}-L\) when \(D_{\text{m}}>0\) (a blue colored portion of the transducer array in Figure 5 or \([k+D_{\text{m}}-L,k+D_{\text{m}}]\), \(k>L-D_{\text{m}}\) when \(D_{\text{m}}<0\), where the transducer element \(k=\lfloor x/D_{e}\rfloor\) or \(k=\lceil x/D_{e}\rfloor\), respectively. Here \(\lceil x/D_{e}\rceil=ceil(x/D_{e})\). This allows us to use the same amount of information to image each vertical segment of the specimen, except for the segments close to the array ends. However, as a rule, the end portions of the array lie outside the region of interest. The approach often filters out the \"blinding\" surface reflections and enhances images of diffraction spots. Both the TFM and MTFM images are produced using the normalized version of intensity function, \(I_{\text{l}}(x)=256\)\(I(\mathbf{x})/\underset{x}{max}\)\(I(x)\). Each image is stored in the standard way, using 256 different intensities, the highest indicated by red color and the lowest, by blue.
## 4 Results and Analysis
### Testing the Robustness of the Proposed Model with Dataset 2  
In this section, we tested the model performance with Bangladesh's unseen data. The model performed extremely well even though it was not trained with data on Bangladesh water bodies. The ensemble model had an accuracy of 99.72% with a precision of 0.9, recall of 0.98, and an F1 score of 0.99. The confusion matrix and the ROC AUC are shown in Figure 6. Every instance from classes 4 and 5 was accurately classified, as we can see from the confusion matrix. Only one instance was misclassified for both classes 1 and 3. On the other hand, for class 2, four instances were misclassified.  
Figure 5: Performance analysis of the proposed model using the confusion matrix, ROC, and AUC on dataset 1.  
Figure 6: _Cont._For all classes, the ROC is close to 1, and the AUC score is 1 for classes 4 and 5. Classes 1 and 3 have the same AUC score of 0.99, and class 2 has the lowest AUC score of 0.98.
## 2 Materials and Methods  
### Participants  
This work expands and extends the one presented in [6] by encompassing groups with a broader range of ages, varied training states, and different health conditions and employing a graduated test protocol. We then analyzed 123 cardiopulmonary tests (CPET) from four distinct groups: 27 athletes (Group A [6]), 20 obese individuals (Group O, [7]), 19 cardiac patients (Group C, [8]), and 57 young athletes (Group Y, [17]). The participants (25.35 \(\pm\) 16.62 years) were between 11 and 70 years old and were divided based on age, fitness level, and health conditions. The athletes and the young athletes' participants were free from serious health conditions like neuropathy, autonomic dysfunction, and cardiovascular disorders, while the obese individuals and cardiac patients met specific health criteria. The cardiac patients underwent testing before and after rehabilitation sessions. The study was conducted in accordance with the Declaration of Helsinki and received approval from various ethical committees: the CAR-IRB--University of Rome 'Foro Italico' Committee (Approval No. CAR 37/2020), the Sapienza University of Rome Ethical Committee (Approval No. 70/11, 2011) for groups A and O, the Ethical Committee of Tours (France) for group C, and the Institutional Review Board at Appalachian State University (IRB no. 18-0147) for group Y.  
Participants provided informed consent, and clinical examinations were conducted to ensure safety and eligibility for physical activity. The combined analysis offers a comprehensive view of the physiological responses across a wide age range and varying health and fitness levels, employing time series data collected during incremental exercise tests. The methodologies and protocols were carefully designed to ensure data reliability and ethical compliance, providing valuable insights into cardiopulmonary responses under different physical conditions.  
Groups underwent different cardiopulmonary tests (CPET), which are described in Appendix A. During these tests, time-series data related to gas exchange (VO\({}^{2}\), VCO\({}^{2}\), oxygen saturation SpO\({}^{2}\), etc.) and heart rate (HR) were collected. In this study, we have focused exclusively on analyzing the HR and VO\({}^{2}\) time series, comparing them against the gold standard for validation purposes.
## 2 Theoretical Basis and Related Work
### The Framework of CS  
#### 2.1.1 Sparse Representation of Signals  
Consider a sparse signal \(\mathbf{X}\in\mathbf{R}^{N\times 1}\) of sparsity level \(K\) (\(K<<N\)) and an orthogonal basis matrix \(\mathbf{\
abla}\in\mathbf{R}^{N\times N}\). \(\mathbf{X}\) may not be sparse itself, but can be represented sparsely in this certain domain, where the \(i\)-th column vector is denoted by \(\mathbf{\
abla}_{i}\in\mathbf{R}^{N\times 1}\). Therefore, \(\mathbf{X}\) can be expressed as:  
\[\mathbf{X}=\sum_{i=1}^{N}\mathbf{\theta}_{i}\mathbf{\psi}_{i}=\mathbf{\psi}\mathbf{\theta}, \tag{1}\]where \(\theta\in\mathbf{R}^{N\times 1}\) is a sparse vector. A signal is called \(K\)-sparse in case that the number of non-zero elements in \(\theta\) is \(K\).
## 4 Forecasting Results and Discussion
### Multistage Models with Hybrid Algorithms  
In this subsection, we present the results of the hybrid models where we used two different forecasting algorithms to enhance the accuracy. Table 6 and Figure 10 show a performance comparison between the two multistage hybrid models constructed from the SVR, FBP, and LSTM. The results show a slight advantage to the FBP-LSTM (without consideration of temperature as a feature) compared to all other models, including the FBP-SVR, and SVR-LSTM, with and without consideration of temperature. The recorded metrics are an \(R^{2}\) value of 68.22%, an MAE of 2.54, and an RMSE of 3.28.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model** & & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** \\ \hline \multirow{3}{*}{Without Temperature} & \multirow{3}{*}{Multistage Hybrid} & FBP-SVR & 67.86\% & 2.56 & 3.29 \\ \cline{3-6}  & & FBP-LSTM & 68.22\% & 2.54 & 3.28 \\ \cline{3-6}  & & SVR-LSTM & 42.70\% & 3.32 & 4.40 \\ \hline \multirow{3}{*}{With Temperature as a Feature} & \multirow{3}{*}{Multistage Hybrid} & FBP-SVR & 67.97\% & 2.55 & 3.29 \\ \cline{3-6}  & & FBP-LSTM & 67.13\% & 2.60 & 3.33 \\ \cline{3-6}  & & SVR-LSTM & 39.62\% & 3.35 & 4.52 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Forecasting performance results for all multistage hybrid models: \(R^{2}\), MAE, and RMSE values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model** & & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** \\ \hline \multirow{3}{*}{Without Temperature as a Feature} & Multistage Similar & SVR-SVR & 43.57\% & 3.17 & 4.37 \\ \cline{3-6}  & & FBP-FBP & 68.06\% & 2.52 & 3.28 \\ \hline \multirow{3}{*}{With Temperature as a Feature} & Multistage Similar & SVR-SVR & 44.14\% & 3.13 & 4.34 \\ \cline{3-6}  & & FBP-FBP & 68.81\% & 2.51 & 3.25 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Forecasting performance results for all multistage similar models: \(R^{2}\), MAE, and RMSE values.  
Figure 9: Testing data vs forecasted data from Multistage Similar model with FBP of the hospital’s electricity demand.  
The output from the FBP-LSTM multistage model is plotted to visualise its performance. As shown in Figure 11, the actual/testing data can be visually identified, especially for values that are less than the mean value and outliers.
# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## Appendix B  
Confidence interval: The confidence interval is the range in which the population parameter is most likely to be found. The degree of certainty for which it is likely to be within that range is called the confidence level. When gathering sample data, the precise value of the parameter is unknown.  
Confidence level: The confidence level is the required degree of certainty that the population parameter will be in the confidence interval. This is the probability that the calculated confidence interval contains the population parameter. Researchers frequently employ a confidence level of 0.95. The 95% confidence interval postulates that if one were to compute the confidence interval for an infinite number of samples, then 95% of the calculated ranges would encompass the population parameter.  
When the population's standard deviation (\(\sigma\)) is known, the normal distribution is utilized. The distribution of the sample mean (\(\overline{X}\)) is normal with a mean of Mean and a standard deviation of \(\sigma/\surd n\). If the population's standard deviation is unknown, the t distribution with \(n-1\) degrees of freedom is used, employing the sample standard deviation. The distribution of (\(\overline{X}-\text{Mean}\))/(\(S/\surd n\)) is T.  
The mean confidence interval formula:  
* The population standard deviation is known: \[\overline{X}\pm Z_{\alpha/2}\times\frac{\sigma}{\surd n}\]
* The population standard deviation is unknown: \[\overline{X}\pm\text{T}_{\frac{\pi}{2}}(df)*\frac{S}{\surd n}\] The standard deviation confidence interval formula: \[\frac{(n-1)S^{2}}{X_{(1-\frac{\pi}{2})}(df)}\leq\sigma^{2}\leq\frac{(n-1)S^{ 2}}{X_{(\frac{\pi}{2})}(df)}\] where: \(\overline{X}\): The sample average. \(\sigma\): The population standard deviation; typically, the population standard deviation is unknown and may be obtained from other research as a sample standard deviation with a larger sample size. In this scenario, it is permissible to assume it as the population standard deviation. \(S\): The sample standard deviation. \(n\): The sample size (the number of observations). CL: confidence level. \(\alpha=1-\text{CL}\). \(Z_{\alpha/2}\)--the z-score based on the standard normal distribution, p(z \(<Z_{\alpha/2}\)) = \(\alpha/2\). T\({}_{\alpha/2}\)--the t-score based on the t distribution, p(t \(<\text{T}_{\alpha/2}\)) = \(\alpha/2\). \(df\)--degrees of freedom. \(df=n-1\).
# Facial Paralysis Detection on Images Using Key Point Analysis
## 2 Methodology
### Facial Landmarks Extraction  
The process to extract facial landmarks from a face within an input image, preferably a frontal face photograph, is based on the method and model proposed by [PERSON] et al. in [12]. The full facial landmarks extraction process follows as:  
1. Convert the color input image to gray scale.
2. Resize the gray image using an scale factor (\(sf\)) of \(sf=\frac{W}{nW}\), where \(nW=200\) and \(nH=\frac{H}{sf}\). \(W\) and \(H\) refer to the size of the input image (width and height, respectively).
3. Detect the face on the resized image using the publicly available \(dlib\) libraries.
4. Re-scale the detected face area using \(sf\).
5. Predict the facial landmarks on the re-scaled face area.
6. Store the extracted data for future data processing.  
The MEE shape predictor is trained to detect 68 points, but only 51 of them are of interest in this work. As depicted on Figure 2, the 51 points are reorganized to facilitate the computation of measures.  
Figure 1: Framework of the proposed facial paralysis classification system.  
Since the accuracy of the facial symmetry quantification can be affected by the tilt angle of the head [16], before computing any symmetry measures tilt correction was performed using the facial landmarks previously extracted. In this work, the first and last jaw points, points 48 and 49 in Figure 2b, were used to correct the possible tilt of the head. Here, point 48 is set as the origin of the coordinate plane and the angle between it and point 49 is the tilt angle to be corrected, with a geometry rotation of the key points. Notice that the angle between the two points was obtained according to Equation (1):  
\[\angle(Pa,Pb)=\arctan 2(\triangle x,\triangle y)\times 180/\pi \tag{1}\]  
where \(\triangle x=Pa_{x}-Pb_{x}\) and \(\triangle y=Pa_{y}-Pb_{y}\).  
The facial landmark rotation process goes as follows:  
1. Compute a rotation matrix \(M\). The function _getRotationMatrix2D_ from OpenCV [13] can be employed using \(P48\) and the angle \(\angle(P48,P49)\) between the two jaw points.
2. Rotate the 51 key points using \(M\): \[\begin{pmatrix}\mathit{Pir}_{x}\\ \mathit{Piry}\end{pmatrix}=\begin{bmatrix}M_{1,1}&M_{1,2}&M_{1,3}\\ M_{2,1}&M_{2,2}&M_{2,3}\end{bmatrix}\begin{bmatrix}\mathit{Pi}_{x}\\ \mathit{Pi}_{y}\\ 1\end{bmatrix}\] (2)
3. Store the rotated data for future data processing.
## 1 Introduction  
Highway infrastructure stability faces critical threats from ground subsidence (vertical displacement) processes, including subsidence, landslides, and structural failures. These challenges, often triggered by groundwater over-extraction, climatic variability, and geological complexities, result in substantial economic losses, environmental degradation, and public safety risks. Advanced geophysical techniques, such as Interferometric Synthetic Aperture Radar (InSAR), Ground Penetrating Radar (GPR), and Electrical Resistivity Tomography (ERT), have emerged as state-of-the-art methods to provide high-resolution insights into subsurface dynamics, such as detecting structural voids, soil compaction, and groundwater fluctuations. Although each method has strengths and limitations, their integration creates a robust, multi-scale framework for diagnosing and addressing subsurface instability.