esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications and Limits of ATPs in Water and Wastewater Treatment
### Applications of ATPs in Water and Wastewater Treatment
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Contaminant** & **Type of Process** & **Remarks/Results** \\ \hline \hline \multicolumn{3}{c}{_Aromatic compounds_} \\ \hline \multirow{2}{*}{Phenol} & Ultrasound + O\({}_{3}\) & Complete elimination of TOC \\  & UltraSound + Fe\({}_{2}\)O\({}_{3}\)/SBA\(-\)15 + H\({}_{2}\)O\({}_{2}\) & 30\% TOC degradation @584 kHz, with \\  & & 0.6 g/L SBA\(-\)15 catalyst \\ \hline \(p\)-Chlorophenol & O\({}_{3}\) & 95\% \(p\)-chlorophenol, 26\% DOC removed after 9 min \\ \hline \(4\)-Chloro\(-\)3-methyl phenol & Anodic oxidation (Ti/SnO\({}_{2}\)–Sb/PbO\({}_{2}\) & Complete 4-chloro\(-\)3-methyl phenol removal after 90 min, 49\% TOC removal after 8 h \\ \hline \multirow{2}{*}{Nitrobenzene} & Heterogeneous Catalytic ozonation (Cu-cordierite + O\({}_{3}\)) & 77.9\% nitrobenzene, 62.3\% TOC removed in 20 min \\  & Anodic oxidation (Ti/B-doped diamond & TOC removal 98\% in Ti/BDD anode cell with applied charge of 3.2 Ah/L \\  & BDD, Ti/SnO\({}_{2}\)–Sb/PbO\({}_{2}\), Ti/SnO\({}_{2}\)–Sb & \\  & anodes) & 66.4\% degradation observed for 0.5\% \\  & Sono-Fenton & \(p\)-nitropon concentration (\(w\)/\(v\)) \\ \hline \multirow{2}{*}{Nitrophenols} & Anodic oxidation (Ti/Bi–PbO\({}_{2}\) anode) & Degradation efficiency in the order: 2,6-dinitrophenol \(>\) 2,5-dinitrophenol \(>\) 2,4-dinitrophenol \(>\) 2,4-dinitrophenol \(>\) 2,4,6-trinitrophenol \\ \hline \multirow{2}{*}{2,6-dimethylaniline} & Homogeneous Fenton & Nearly complete degradation was after 10 min \\  & & 10 min \\ \hline \multirow{2}{*}{2,4-Dinitrotoluene} & Homogeneous catalytic ozonation (Mn\({}^{2+}\) + O\({}_{3}\) + oxalic acid) & 65\% degradation in 15 min \\  & & (Mn\({}^{2+}\) + O\({}_{3}\) + oxalic acid) & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Application of AOPs for the removal of different types of compounds (data from [18]).
## 4 Simulation
### Discussion of Results
#### 4.2.1 Parameter Discussion  
In the VG-SBL algorithm, there is an uncertain parameter, the decay factor \(\delta\), which significantly impacts the sound source identification results. Different choices of this parameter directly determine the number of times the algorithm jumps out of the iteration loop, which in turn affects the identification accuracy of the final results. Simulation comparison experiments are carried out to analyze the influence of the decay factor \(\delta\) on the sound source identification results.  
In the simulation, the frequency of 500 Hz is taken as an example, and the SNR is taken as 10 to 40 dB, with an interval of 10 dB. The decay factor \(\delta\) is adjusted from 0.1 to 0.9, with an interval of 0.1, and the higher the value, the slower the decay speed is. The resultsof RMSEL and RMSEM are calculated for different values of \(\delta\), as shown in Figure 4a, representing the average of all SNRs. Figure 4b shows the corresponding calculation time and total number of iterations under different values of \(\delta\).  
As shown in Figure 4, when \(\delta\) is gradually increased from 0.1, the source identification error of VG-SBL goes through a process of decreasing and then increasing. This is because VG-SBL updates the grid with denser spacing and a smaller aperture centered on the sound source location in the previous iteration result, moving towards the minimum value of the objective function. If the mesh decay rate is too slow or fast, it is easy to fail to converge to the actual sound source location, leading to significant source identification errors. At the same time, too large a \(\delta\) also leads to slower iteration speed and longer computation time. Therefore, the above results show that a reasonable choice of \(\delta\) can improve the accuracy of localization and strength estimation at the same time. When \(\delta\) is set at 0.5, accurate identification results with high probability can be obtained, so \(\delta\) is selected as 0.5 in this paper.  
When the adjacent grid spacing \(d\) reaches \(\sigma\), the stopping condition of jumping out of the iteration is reached and the loop is ended. For the actual test situation, when the spacing between adjacent grid points reaches about 0.001 m, the grid division is already detailed enough, and the accuracy of source identification is already high enough, so there is no need to continue the subdivision. \(\sigma\) is set to 0.001 m in this paper.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ASE & Amplified Spontaneous Emission \\ BER & Bit Error Rate \\ OC1 & Optical Circulator 1 \\ OC2 & Optical Circulator 2 \\ EDFA & Erbium-Doped Fiber Amplifier \\ EVM & Error Vector Magnitude \\ HPLS & High Power Laser Source \\ ISO & Isolator \\ IM/DD & Intensity Modulation and Direct Detection \\ MMF & Multimode Fibers \\ MZM & Mach-Zehnder Modulator \\ NF & Noise Figure \\ PD & Photodetector \\ PPC & Photovoltaic Power Converter \\ PoF & Power over Fiber \\ PON & Passive Optical Networks \\ PSD & Power Spectral Density \\ RIN & Relative Intensity Noise \\ RoF & Radio over Fiber \\ SMF & Single-mode fiber \\ SFDR & Spurious-Free Dynamic Range \\ SRS & Stimulate Raman Scattering \\ TLS & Tunable Laser Source \\ TOF1 & Tunable Optical Filter 1 \\ TOF2 & Tunable Optical Filters 2 \\ WDM1 & Wavelength Division Multiplexer 1 \\ WDM2 & Wavelength Division Multiplexer 2 \\ \end{tabular}
# A Novel Optical-Based Methodology for Improving Nonlinear Fourier Transform
## 2 Scattering Coefficient for Nonlinear Fourier Transform
The equation \(\mathcal{L}\Phi=\lambda\Phi\) should have only two linear independent solutions, and so \(\phi\) and \(\overline{\phi}\) should be linearly dependent with respect to the pair \(\varphi\) and \(\overline{\varphi}\), as follows:  
\[\phi(t,\lambda) =b(\lambda)\varphi(t,\lambda)+a(\lambda)\overline{\varphi}(t,\lambda) \tag{12}\] \[\overline{\phi}(t,\lambda) =\overline{a}(\lambda)\varphi(t,\lambda)+\overline{b}(\lambda) \overline{\varphi}(t,\lambda) \tag{13}\]  
This is true when \(\lambda\in\mathbb{R}\), as the four functions are limited, and in that case, these equations will define the relative four scattering coefficients data denoted by \(a(\lambda)\), \(b(\lambda)\), \(\overline{a}(\lambda)\), \(\overline{b}(\lambda)\). We note here that these scattering coefficients are time invariant, a property that will be used for transmitting data over the optical fiber. To find the spatial evolution of the scattering data, under the hypothesis that: \(\mathcal{L}\Phi=\lambda\Phi\) holds for every \(z\), the operator \(A\) is such that:  
\[\Phi_{z}=A\Phi= \begin{pmatrix}-2i\lambda^{2}+i\sigma|u|^{2}2u\lambda+iu_{t}\\ -2i\sigma u^{*}\lambda+i\sigma u_{t}^{*}2i\lambda^{2}-i\sigma|u|^{2}\end{pmatrix} \Phi \tag{14}\]  
which is the matrix representation that we will use to find the spatial evolution of the scattering data. The new Jost functions will be defined now as:  
\[\Phi(t,z;\lambda) =e^{-2i\lambda z}\phi(t,z;\lambda),\overline{\Phi}(t,z;\lambda)=e ^{+2i\lambda z}\overline{\phi}(t,z;\lambda) \tag{15}\] \[\Psi(t,z;\lambda) =e^{+2i\lambda z}\varphi(x,t;\lambda),\overline{\Psi}(t,z;\lambda) =e^{-2i\lambda z}\overline{\varphi}(t,z;\lambda) \tag{16}\]  
They are valid for every \(z\), they will solve equation Equation (8), and they will satisfy both Equations (9) and (10). Let's take the spatial derivative of Equation (12) with the new Jost function, and after some manipulation, we obtain the following relations:  
\[a_{z}(\lambda,z)=0,b_{z}(\lambda,z)=4i\lambda^{2}b(\lambda,z) \tag{17}\]  
\[\overline{a_{z}}(\lambda,z)=0,\overline{b_{z}}(\lambda,z)=-4i\lambda^{2} \overline{b}(\lambda,z) \tag{18}\]  
We can write the spatial evolution of the continuous scattering data, starting from the initial ones as follows:  
\[a(\lambda;z)=a(\lambda;0),\overline{a}(\lambda;z)=\overline{a}(\lambda;0) \tag{19}\]  
\[b(\lambda;z)=b(\lambda;0)e^{+4i\lambda^{2}z},\overline{b}(\lambda;z)= \overline{b}(\lambda;0)e^{-4i\lambda^{2}z} \tag{20}\]  
When the condition \(a(\lambda)=0\) is true, then we can define the discrete spectrum of the Lax operator, which consists of a finite number of discrete eigenvalues. The discrete scattering coefficient corresponding to these discrete eigenvalues can be computed by using Equations (12) and (13). In this work, we will focus only on the continuous parameter \(\lambda\) and its associated continuous scattering coefficient. Altogether, they will define our nonlinear spectrum of the signal \(u(t,z)\).
# Vibration Reduction of an Existing Glass Window through a Viscoelastic Material-Based Retrofit
###### Abstract  
The damping properties of glass windows have a great influence on the comfort of the occupants, especially for buildings that are close to a vibration source, such as rail tracks. With the increasing popularity of light rails in cities, there is a strong need to retrofit existing glass windows to improve their damping properties, and increase the occupants' comfort. In this paper, a new method of retrofitting existing glass windows for vibration reduction was developed using viscoelastic treatment. The key component of the retrofit was a self-adhesive glass constrained viscoelastic strip (SaGCVS), consisting of a thin glass constraining layer, a viscoelastic layer, and an adhesive layer. The SaGCVSs can be easily bonded onto an existing glass panel near its edge in an effort to improve its inherent damping properties, and to reduce its vibration when subjected to external excitations. The method is simple, and the proposed viscoelastic strip is easy to install. Experiments were carried out to demonstrate the effectiveness of the proposed method. For the vibration measurements, lead zirconate titanate (PZT) patches were mounted onto the four corners and the center of the window glass panel. Comparative studies were performed, and the results clearly showed that the vibration of the window glass panel was reduced, revealing the effectiveness of the proposed retrofitting method for improvement in the damping properties of existing window structures.  
Keywords:window glass; retrofit for damping improvement; vibration reduction; viscoelastic materials; lead zirconate titanate (PZT); constrained viscoelastic layer +
Footnote †: journal: Applied Sciences
# An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
## 3 Methods  
In this part, we state the detailed information of our network. Figure 2 shows the full architecture of our method, which only predicts the missing parts from incomplete inputs. It consists of an encoder with Attention Combined Multi-Layer Perceptron (ACMLP) and a decoder with a Bidirectional Point Pyramid Attention Network (BiPPAN). The attention module was applied to the encoder and decoder as detailed in Section 3.1. The encoder shows its talent not only with excellent feature extraction, but also in the presentation of feature aggregation. We detail the point feature encoder in Section 3.2. Between the input and the decoder, skip connections connect the geometric information of the point cloud with the point features in the decoder. BiPPAN performs the prediction of missing point clouds with different resolutions. We describe the decoder in detail in Section 3.3. To optimize the network parameters, multistage completion loss was applied. The multistage completion loss was set as the Chamfer Distance (CD) error between the predicted point cloud and ground truth, which was composed of three items. We detail the loss function in Section 3.4.  
Figure 2: Illustration of our framework. The encoder (yellow) adopts ACMLP to extract features from input point clouds. The decoder (green) adopts BiPPAN with skip connections to predict the multiscale missing parts. Then, the Chamfer Distance errors of the prediction point cloud and ground truth are calculated.
## 3 Results  
The experimental results are shown on two timescales, as explained below:  
* Seasonal results from the complete dataset for energy consumption, thermal comfort, and visual comfort are presented as boxplots in terms of daily energy consumption in kWh, PMV, PMV*, and DGP (Figures 6-8, respectively).
* Daily results for energy consumption, thermal comfort, and visual comfort are presented as hourly profiles for representative days (Figures 9-12). These figures plot daily outdoor temperature and global horizontal irradiance (GHI), daily global vertical radiation on the building facade, transmitted radiation through the window, HVAC power, HVAC energy consumption and energy savings, indoor air temperature and mean radiant temperature (MRT), PMV, PMV*, and DGP.  
These results are grouped by season to better understand the seasonal behavior and the impacts of PCM glazing. The seasons analyzed are summer, mid-season warm, mid-season cold, and winter. Mid-season warm and mid-season cold were selected depending on the cooling and heating needs. Mid-season cold is characterized by heating-dominance and comprises autumn from the end of April to June. In contrast, mid-season warm is dominated by cooling energy consumption and occurs in spring between the end of September and December.  
Figure 6: Seasonal result of daily energy consumption (EC). Winter is not included because of insufficient days with valid data.  
Figure 8: Seasonal results of DGP.  
Figure 7: Seasonal result of PMV and PMV\({}^{\star}\).  
Figure 9: Results for representative days of summer. (**a**) Outdoor temperature and global horizontal radiation (GHI); (**b**) global vertical radiation; (**c**) HVAC power and energy consumption; (**d**) indoor air temperature & mean radiant temperature (MRT); (**e**) heat flux; (**f**) PMV; (**g**) PMV; (**h**) DGP.  
Figure 10: Results for representative days of mid-season warm. **(a)** Outdoor temperature and global horizontal radiation (GHI); **(b)** global vertical radiation; **(c)** HVAC power and energy consumption; **(d)** indoor air temperature & mean radiant temperature (MRT); **(e)** heat flux; **(f)** PMV; **(g)** PMV; **(h)** DGP.
# A Study on the Development of Tourist Support System Using ICT and Psychological Effects
## 4 History of the Development of a Sightseeing Support Application for Nikko
### Phase3: Optimization of Delivered Information  
As explained in Section 3, one critical issue in using a smartphone for sightseeing is that there is too much information. Too much information not only makes selection difficult but also denies or complicates access to the information. This is a phenomenon called overload of clue [51]. In the information delivery using the BLE beacon that we are developing, one beacon provides one information. However, it is necessary to link various information to the beacon for practical use. For example, around a BLE beacon, there are not only shrines and temples but also souvenir shops and restaurants. However, of all that information, what is the most relevant for tourists? For example, it is conceivable to obtain the user's profile and send the most appropriate information in consideration of triggers such as position and time. However, it is difficult to formulate a strategy for providing information that also assumes the user's psychological and physical conditions. As a result, it is expected that similar information will be provided to tourists in the same pattern. It is essential to select and provide the most appropriate and in-demand information from the uncountable bits of information to increase tourists' satisfaction. As shown in Figure 11, sightseeing information is to be provided following the change of user requirements. To implement a function to realize such a procedure, we first consider the value of information for each tourist and situation. Moreover, this function represents the \"intra-cognitive informatics\" as described in Figure 2.  
Prospect Theory [52; 53] is one of the important theories in behavioral economics. The theory has the following three features.  
* Reference Point is used for evaluation
* Diminishing Sensitivity
* Loss Aversion  
Figure 12 shows a graph to explain the Prospect Theory [54]. This graph explains that the loss gives larger mental damage rather than gain. We tried to apply this idea to our sightseeing support application. It means deciding the information to be delivered based on the psychological value. According to Figure 12, if the value of the information is left as the reference point (the origin of the graph), the psychological value should be considered lower. The reference point is also changed by many reasons such as the knowledge and experiences, history of the daily behavior of a day of a tourist. If we can follow the change of the reference point, in other words, the mental state of a tourist, we think that it is possible to provide sightseeing information to increase travel expectation of the travel and satisfaction of the tourist.  
Figure 11: A model to provide information.
The Support Vector Machine (SVM) is also an excellent classifier, but its application is more challenging since it needs the definition of essential input parameters as the kernel type [30]. RF is the most extensively used ML method in the GEE context [31] and, in general, for remote sensing data categorization [32; 33]. This is due to its non-parametric nature, ability to manage dimensionality and overfitting, and excellent performance compared to other classifiers. RF is based on numerous CARTs, with their average used to make the final classification. One of its most significant inherent weaknesses is the unknown splitting rules [34]. Integrating satellite data from different sensors is a common approach in RS science [35; 36]. Sentinel-2 (S2) and Sentinel-1 (S1) data have been often combined to improve the accuracy of LULC classification [37; 38; 39], and also integrated with Landsat 8 data [40; 41]. PL imagery has previously been combined with S2 and S1 in PB land cover classification approaches for crop mapping of smallholder farms in eastern India using SVM [42], and crop and LULC mapping in Northern Malawi, using RF within GEE [43]. The former achieved the highest accuracies when using the model that included all three sensors (85%). The overall accuracy was 3% higher than that obtained with the best two-sensor model and 13% higher than that obtained with the best one-sensor model. Similarly, in the latter, the combination of the three sensors outperformed all other image combinations, producing higher overall accuracies (>85%) with 13.53% and 11.7% improvement in the Sentinel-2-only experiments. In all these applications, the higher spatial resolution of PL imagery improved the classification accuracy of smaller features. To the best of our knowledge, only one study has partially quantified the improvement from applying an OB approach to PL data within GEE [21]. Differently, no study has assessed the advantages of integrating the PL imagery with Sentinel 2 and Sentinel 1 data for an OB LULC classification within the same cloud environment. Adopting an OB approach, which combines the object identification and the textural analysis steps typical of GEOBIA processes, considering the PL spatial resolution may be beneficial to improve the thematic and geometric accuracy. At the same time, the data integration of PL with the S2 and the S1 datasets may further improve the classification results due to the broader spectral information available, keeping the native PL resolution. In this direction, in this research, we compared ten LULC classification approaches in GEE (PB and OB, all based on the RF algorithm), where the three satellite datasets were used according to different levels of integration and combination. The objective was to assess the advantages of (a) adopting an OB approach with PL data; and (b) integrating the PL datasets with Sentinel 2 and Sentinel 1 data in PB or OB land cover classifications.
## 3 Experimental Validation
### Dataset  
First, to apply the proposed methodology, an experimental dataset was collected and processed. The datasets used in this research were taken from the nine PZT sensors and the eleven load steps referenced in the third column of Table 1. The test codes included in this last column will be used to make reference to the analyzed load steps. The EMI signatures were acquired by the nine sensors using an Agilent 4294A impedance analyzer with a 1-volt (V) voltage excitation within a range of 10-100 kiloHertz (kHz) and with a step interval of 12.5 Hertz (Hz). Therefore, an impedance spectrum of 7201 points was fed to the deep AE and VAE models for each sensor and load step.  
Regarding data preprocessing, the dataset was normalized by means of min-max scaling with the range [0, 1] (Equation (2)).
## 3 Results
### Physico-Chemical Assay Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{**Parameters of Linear Roughness**} \\ \hline \multicolumn{3}{c}{Anterior surface} & \multicolumn{2}{c}{Posterior surface} \\ \hline Ra [nm] & 50.66 \(\pm\) 7.48 & Ra [nm] & 115.19 \(\pm\) 20.31 \\ Rq [nm] & 68.23 \(\pm\) 11.17 & Rq [nm] & 150.19 \(\pm\) 27.79 \\ Rz [nm] & 366.73 \(\pm\) 83.59 & Rz [nm] & 779.89 \(\pm\) 146.97 \\ \hline \multicolumn{3}{c}{**Parameters of Surface Roughness**} \\ \hline \multicolumn{3}{c}{Anterior surface} & \multicolumn{2}{c}{Posterior surface} \\ \hline Sa [nm] & 29.18 \(\pm\) 5.95 & Sa [nm] & 66.05 \(\pm\) 5.05 \\ Sq [nm] & 41.47 \(\pm\) 8.45 & Sq [nm] & 86.23 \(\pm\) 6.12 \\ Sz [nm] & 1407.65 \(\pm\) 917.73 & Sz [nm] & 1319.87 \(\pm\) 161.49 \\ Sp [nm] & 698.25 \(\pm\) 540.04 & Sp [nm] & 568.91 \(\pm\) 95.04 \\ Sv [nm] & 709.19 \(\pm\) 390.53 & Sv [nm] & 750.96 \(\pm\) 93.62 \\ \hline \multicolumn{3}{c}{**Water Contact Angle**} \\ \hline \(\theta_{\text{C}}\) [\({}^{\circ}\)] & 66.575 \(\pm\) 2.65 & 75.95 \(\pm\) 3.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the surface roughness values, the linear roughness (PN-EN ISO values and the contact angle values for zirconium crowns. Figure 3: Comparison of anterior and posterior coronal surfaces (\(\times\)4500) and results of energy-dispersive X-ray diffraction analysis conducted with the Phenom ProX microscope for (**A**), the anterior side, and (**B**), the posterior side. Figure 4 shows an XRD diagram of a zirconia crown measured in a 5-70\({}^{\circ}\) 2\(\theta\) range with marked positions of ZrO\({}_{2}\), HfO\({}_{2}\), and Y\({}_{2}\)O\({}_{3}\). It is clearly visible that the crown is composed mostly of ZrO\({}_{2}\). There is also a HfO\({}_{2}\) phase visible (peak at 2\(\theta\) = 28.2\({}^{\circ}\)). In the case of Y\({}_{2}\)O\({}_{3}\), there are bumps in the position of the most intense peaks (2\(\theta\) = 29.3\({}^{\circ}\), 34\({}^{\circ}\), and 48.8\({}^{\circ}\)). According to the Match! Software, the composition of the crown is ZrO\({}_{2}\)--91.2%; HfO\({}_{2}\)--6.7%; and Y\({}_{2}\)O\({}_{3}\)--2.1%.