esa-sceva/satcom-chunk-collection huggingface dataset:
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 3 Advanced Oxidation Processes (AOPs) for PFAS
### Heterogeneous Photocatalysis Materials
#### 3.1.1 Metal Oxides
Mixed metal oxides possess multifunctionality qualities owing to the combined effect generated by different metals, where one metal will act as the active site, whereas the other metal modulates the electronic and structural configuration. [PERSON] and coworkers [85] synthesized the nanocrystalline ferrite catalyst Zn\({}_{\rm x}\)Cu\({}_{1-x}\)Fe\({}_{2}\)O\({}_{4}\) using a sol-gel autocombustion method for the degradation of PFOA in the presence of oxalic acid. Under UV light irradiation, Zn\({}_{\rm x}\)Cu\({}_{1-x}\)Fe\({}_{2}\)O\({}_{4}\) produces large quantities of ROS in aid of oxalic acid, as detected from intermediate products using liquid chromatography.
## 5 Simulation and Analysis
### INCM Beamforming Performance
#### 5.3.2 Relationship between Output SINR and Input SNR  
In order to further compare and study the performance of the beamformer proposed in this paper, the output SINR under a different OFDM signal input SNR is simulated, and 100 Monte Carlo experiments are conducted independently for each SNR, and the simulation results are shown in Figure 7. To more intuitively analyze the performance gap of each algorithm compared to the optimal beamformer, Figure 8 makes the deviation graph of the output SINR of each beamformer from that of the optimal beamformer.  
As can be seen from Figures 7 and 8, the algorithm proposed in this paper is the closest to the optimal algorithm under both high and low SNR conditions. In addition to this, the INCM-subspace algorithm also has a better performance, but at higher SNRs, it starts to suppress the desired signal, leading to a performance decline. The INCM-volume algorithm and the INCM-linear algorithm are more stable but with relatively poor performance, and the INCM-projection1 and INCM-projection2 algorithms have a sharp drop in performance  
Figure 8: Deviation of different algorithms from the optimal output SINR.  
Figure 6: Gain comparison of different algorithm beamformers.  
Figure 7: Output SINR versus SNR.  
at SNRs greater than 0 due to the incomplete removal of OFDM signal components. Thus, it can be seen that the algorithm in this paper not only has a better performance but also has stronger robustness.
## 2 Experiment Setup
### Measurement of LLCR  
Daisy chain layout: The pattern was designed by following these strategies: (a) The whole array shall be divided into small chains to get reasonably accurate measurement; (b) the high risk of shorting shall be caught; and (c) the high risk of opening or high resistance shall be caught. There were 119 chains in total. The schematic representation of the daisy chain layout is shown in Figure 5.  
The four-wire measurement technique was adopted: The electrical measurement instrument provided two separate circuits. One was a precision current source to deliver the test current. The other was a precision voltmeter circuit to measure the voltage drop between the desired points. Thus, the total resistance was able to be calculated based on the measured current and voltage from each chain. The total resistance per chain (\(R_{\text{Total}}\)) measured thus included the resistance of package and package shorting bars, contact LLCRs, solder balls, and motherboard and motherboard shorting bars, as shown in Figure 6.  
Figure 4: Load on integrated beat spreader.  
Figure 5: Schematic representation of daisy chain layout.  
Parasitic resistance: LGA TVs were flush-mounted directly to the motherboard fixtures in oreer to measure the average parasitic resistance (\(\overline{R}_{\text{parasitic}}\)), which included the resistance of the package, the motherboard, and solder balls. Thirty samples were used for statistical reasons.  
The value of low-level contact resistance \(R_{\text{LLCR}}\) can be calculated by:  
\[R_{\text{LLCR}}=\frac{R_{\text{Total}}-\overline{R}_{\text{parasitic}}}{N_{\text{Chain}}} \tag{1}\]  
where \(N_{\text{Chain}}\) is the number of contacts within the chain.
## 3 Asphalt Mixture Meso-Structure Modeling Technology  
At present, there are two methods for three-dimensional heterogeneous structure modeling of asphalt mixtures; one is image-based (IB) modeling technology [18; 19], and another is computer-generalized (CG) modeling technology [20; 21].  
The images used in IB technology can be acquired from X-ray CT scanning. A series of image slices [22; 23] can be obtained by industrial CT scanning of asphalt mixture specimens. Then, image enhancement, denoising, threshold division, material separation and three-dimensional reconstruction can be carried out using digital image processing (DIP) technology [24], and the images can be transformed into a finite element model [25]. Thereby, follow-up mechanical or heat transfer simulations can be carried out.  
CG technology generates asphalt mixture numerical specimens randomly according to the parameters of aggregate shape, particle size, gradation, void rate, etc. by adopting commercial software or custom program and then imports them into finite element software to mesh and generate an analysis model [26].  
As far as the convenience and freedom of modeling are concerned, CG technology has obvious advantages. Only the supports of computer hardware and software are needed; the model can be generated directly. However, IB technology has obvious advantages in terms of accuracy and reducibility of the model. IB technology can theoretically completely restore the internal structure of an asphalt mixture based on CT scanned images of actual specimens.  
Therefore, IB technology based on X-ray CT scanned images was selected to model the heterogeneous structure of asphalt mixtures in this paper, so as to restore the internal structure of asphalt mixtures to the greatest extent, which is conducive to improving the accuracy of simulation results.  
At present, there are two main methods to generate finite element meshes from an IB generated model: mapped meshing [25; 26; 27] and free meshing [28; 29; 30; 31]. Among these two methods, the model element established by the mapped meshing method is of a regular shape, the calculation process is more convergent and the analysis results are more reliable. Thus, the mapped meshing method was adopted in this paper, and the detailed modeling process is shown in Figure 1.  
The finite element model building process is mainly divided into three steps: specimen preparation, meso-structure modeling and simulation and verification. There are many sub-steps in each step. The process of model building is described in detail in the following sections.
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 2 Cavitation Mechanism and Flow Dynamics in Cavitating Jets  
### Cavitation Number  
The erosion level achieved using cavitating jets is highly dependent on the jet nozzle geometry, the fluid properties, and the flow regime. In most investigations on cavitating jets, the cavitation number \"\(\sigma\)\" is used to predict the occurrence of cavitation; it is defined as follows:  
\[\sigma=\frac{P_{a}-P_{v}}{(1/2)\rho V_{e}^{2}} \tag{1}\]  
where  
\(P_{a}\) = ambient pressure;  
\(P_{v}\) = vapor pressure of liquid;  
\(\rho\) = liquid density;  
\(V_{e}\) = the mean jet exit velocity.  
Since existing experimental techniques are not able to accurately measure the velocity \(V_{e}\) in such two-phase flow and the numerical prediction of the jet exit velocity should be considered with precaution, an estimation of \(V_{e}\) can be obtained from standard fluid mechanics principles. A theoretical prediction of \(V_{e}\) using a standard pipe flow model [19] can be used, assuming a steady state flow regime. Using the energy balance equation and the conservation of mass, the following equation can be derived [20] for a cavitating jet with a convergent section and a throat:  
\[\frac{1}{2}\rho V_{e}^{2}\left(1-\frac{D_{2}^{4}}{D_{1}^{4}}\right)=P_{B}+\rho gh -\rho gh-Loss \tag{2}\]  
where  
\(V_{e}\) = jet exit velocity;  
\(D_{1}\) and \(D_{2}\) = diameter just upstream of the nozzle and at the nozzle throat exit, respectively;  
\(P_{B}\) = the static pressure measured just upstream of the nozzle;  
\(H\) = liquid height above the jet exit;  
\(h\) = height difference between inlet and exit of the nozzle;_Loss_ = pressure losses across the nozzle, which can be computed using the following formula:  
\[Loss=\ \left(\frac{1}{2}\rho V_{1}^{2}\right)K_{L}+\left(\frac{1}{2}\rho V_{1}^{2} \right)f_{1}\frac{l_{1}}{D_{1}}+\left(\frac{1}{2}\rho V_{2}^{2}\right)f_{2} \frac{l_{2}}{D_{2}} \tag{3}\]  
The loss coefficient for the nozzle convergent section can be taken as: \(K_{L}=0.05\)[21], and the friction factors \(f_{1}\) and \(f_{2}\) can be calculated assuming a smooth inner pipe surface and using the Haaland equation [19].  
In summary, the jet exit velocity (\(V_{e}\)) can be estimated using Equations (2) and (3) once the static pressure is measured just upstream of a cavitating jet. Then, the jet Reynolds number and cavitation number can be calculated. It should be noted, however, that the above model assumes that cavitation is not present inside the nozzle. [PERSON] [22] showed that if cavitation occurs inside the nozzle, there would be a velocity increase in the nozzle (Vena contracta effect).
## 3 Methodologies
### LSB 3-3-2 Embedding Technique  
Assuming both the secret image and the cover image are in the RGB scheme, the LSB 3-3-2 embedding technique embeds the first 3 bits, the next 3 bits, and the last 2 bits of one color channel of the secret image into the last 3 bits of the red channel, the last 3 bits of the green channel, and the last 2 bits of the blue channel of the cover image, respectively. This is  
Figure 6: Keystream generation using logistic map.  
where the LSB 3-3-2 embedding technique gets its name. However, this also means that the LSB 3-3-2 embedding technique must use an RGB scheme cover image rather than another scheme. Figure 7 illustrates the changes in a pixel in the cover image after embedding a red channel from a secret image. The changes are difficult to discern with the naked eye.  
The distribution of secret image bits into cover image bits is made this way because the human eye is most sensitive to changes in the blue channel [27]. Therefore, to minimize visual detection by the human eye, embedding in the blue channel is kept to a minimum while preserving the smallest possible quality changes in the red and green channels.
## 2 Data and Methods
### Ensemble Learning Models  
#### 2.4.1 Random Forest  
RF is a common and widely used homogeneous ensemble learning algorithm. Based on the idea of bagging [22], n training samples are extracted from the training set every time there is a return, a new training set is formed, multiple sub-models are trained, and finally the sub-model with the most votes is selected by a voting method for classification. RF consists of multiple DTs [23]. DT has the advantages of simplicity and strong interpretability and is a supervised learning algorithm. RF reduces the impact of inaccurate classification results caused by outliers in the DT. At the same time, due to the use of partial features from some samples to construct a DT in RF, the possibility of overfitting is reduced. We chose the Gini coefficient as the criterion corresponding to the CART classification tree. The RF model diagram of this experiment is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Hot-Deck Imputation** & **Mean Imputation** \\ \hline IV \(<\) 0.001,VIF \(\geq\) 10 & 10 & 9 \\ IV \(<\) 0.01,VIF \(\geq\) 10 & 7 & 6 \\ AIC Regression & 11 & 11 \\ BIC Regression & 9 & 9 \\ Lasso Regression & 9 & 7 \\ Elastic Net Regression & 11 & 7 \\ PCA & 20 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions of the data after using different feature selection algorithms.
# The Impact of Orthodontic Treatment on Pre-Existing Gingival Recessions: A Retrospective Study
## 3 Results
### Patient-Related Factors  
* Sagitttal relationship: Significant mean GR improvement was found in patients with dental Angle I (0.60 mm, CI: 0.31, 0.89; \(p\) = 0.001) and Angle II (0.47 mm, CI: 0.20; 0.74; \(p\) = 0.003), however, no significant GR changes were observed in Angle class III patients (mean 0.18 mm, CI: \(-\)0.25; 0.61; \(p\) = 0.44). No difference between three groups in GR change was found (\(p\) = 0.21).
* Overbite: Significant median GR improvement after OT was found in patients with normal/deep (\(\geq\)1 mm) pre-treatment overbite (0.59 mm, Q1; Q3: 0.28; 0.93) (\(p\) = 0.001). No median GR improvement in subjects with a reduced overbite (<1 mm), rather a tendency to median recession worsening was observed (0.002 mm, Q1; Q3: \(-\)0.49; 0.36) (\(p\) = 0.75). Significant difference in GR change between patients in three overbite groups was found (\(p\) = 0.03). Median GR improvement was significantly lower in patients with pre-treatment open bite (<1 mm) in comparison to normal (1-3 mm) (\(p\) < 0.05) or deep (>3 mm) (\(p\) < 0.05) overbite.
* Biotype: Mean GR improvement from T0 to T1 was found in subjects with thin (0.29 mm, CI: 0.09, 0.49; \(p\) = 0.01; n = 15) and normal/thick biotype (0.56 mm, CI: 0.30; 0.82; \(p\) = 0.001; n = 22), with no significant differences between groups (\(p\) = 0.13).
* Overjet: No significant differences in patient-level GR changes were found between pre-treatment overjet < 4 mm and \(\geq\) 4 mm subjects (\(p\) = 0.22).
* Crowding: No significant differences in patient-level GR changes were found between patients with (n = 28) or without (n = 9) pre-treatment crowding (\(p\) = 0.57).
* Sex: No significant differences in patient-level GR changes were found between males and females (\(p\) = 0.57).
* Age: No significant differences in patient-level GR changes were observed in adults (\(\geq\)18 years) and young (<18 years) subjects (\(p\) = 0.53).
## 4 Results  
### Overview of the Data  
Table 1 shows an overview of both groups within the data set. The first group of stations, those with a scheduled dwell time of 42 s, account for 94% of observations within our data. Stops at these stations are characterized by a low average number of passengers boarding and alighting, with the median volume of boarding and alighting passengers being five passengers, compared to 27 passengers for stations with a scheduled dwell of 120 s. A similar pattern is found when looking at the frequencies of passenger volumes, as shown in Figure 3. Although low passenger volumes are most common in both cases, higher passenger volumes are found to be more common for stations with a scheduled dwell time of 120 s, as shown in Figure 3b.  
Table 1 also shows the median value for the dwell time deviation, referring to the difference between the scheduled and actual dwell times, which is greater than zero in both cases. This indicates that the actual dwell times exceed the scheduled time on a regular basis. The interquartile range is similar for both types of stations, 19 and 20 s. However, the median, first, and third quartile values are lower for stations with a scheduled dwell time of 120 s, and the first quartile is found to be a negative value here. This indicates that a small number of trains arriving with a delay are sometimes able to make up time while at the station by using less dwell time than scheduled. The time which trains are able to recover is rather small, however. Delay recovery is not common for stations with scheduled dwell times of 42 s, where all values for the dwell time deviation are greater than zero.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Scheduled Dwell Time 42 s** & **Scheduled Dwell Time 120 s** \\ \hline Number of observations * & 632,310 & 42,568 \\ \hline Range of passenger volumes * & 0; 40 & 1; 70 \\ \hline Median passenger volume & 5 & 27 \\ \hline (Q1; Q3) passenger volume & 2; 10 & 7; 43 \\ \hline Median dwell time deviation & 10 s & 5 s \\ \hline (Q1; Q3) dwell time deviation & 3 s; 22 s & \(-\)7 s; 13 s \\ \hline \end{tabular}
* Limited to the observations which meet the criteria for chi-square tests.  
\end{table}
Table 1: Overview of the data.  
Figure 3: Frequency and cumulative percentages for passenger volumes at stations with a scheduled dwell time of 42 s (**a**) and 120 s (**b**).
# Influence of Ozone on the Biochemical Composition of Birch Sap
## 4 Discussion
[62] reported that ozonation of freshly squeezed orange juice--applying ozone at a concentration of 0.6\(-\)10.0% \(w/w\) for 0-10 min--did not result in any significant changes in pH, \({}^{\circ}\)Brix, titratable acidity, or turbidity (\(p<\) 0.05). [PERSON] et al. [31] did not find a negative effect of ozone on the phenol content, antioxidant capacity, or pH of orange juice. [PERSON] et al. [68] used ozone (<0.4 mg L\({}^{-1}\)) with heat in apple juice studies and found that it had no effect on quality. According to [PERSON] et al. [78], ozone had no effect on the pH, soluble solids, acidity, and color parameters of coconut water. However, there are studies in which the changes were larger than expected, but in those cases, the ozone concentrations were significantly higher. Significant changes in apple juice quality parameters (vitamin C, carotenoids, and total antioxidant activity) were observed when using gaseous ozone at a concentration of 7.0 \(\pm\) 2.4 g L\({}^{-1}\) for 30 and 60 min, but the total phenol content was significantly increased in ozonized juice [64]. In summary, this study provides initial evidence that ozone, at low concentrations, is effective at reducing bacterial, yeast, and lactic acid bacteria concentrations in birch sap. This should encourage the industry to further investigate the commercial application of the technology. However, the introduction of this technology into production requires a number of technical issues to be addressed, such as the ozone concentration and treatment interval used, the ozone depletion rate, the dynamics of ozone depletion, possible toxic by-products, and energy consumption and efficiency. These issues will be addressed in further research.
# Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation
## 1 Introduction
suggested accessing a fixed optimal wiener filter for the controller by minimizing an improved cost function, which is defined as the sum of the mean squared error of human ears at different positions [15]. Experimental results show that the proposed active headrest system, combining with the infrared head tracking technique, effectively controls broadband noise when the head is translated or rotated. The system is non-adaptive, does not require any virtual error microphones, and therefore has difficulty tracking time-varying sound fields and acoustic transfer paths. Head rotation is of the same importance as translation. For example, when one rests on a car headrest, the head lateral movement may be naturally confined to a small range due to the limited width of the headrest. In addition, usually only a little back and forth movement takes place. Instead, the head is often rotated in order to find a more comfortable sitting position and stays at a certain rotation angle. Furthermore, some wrap-around headtests further limit the translation of the human head when head rotation becomes an issue. Therefore, discussions on the robustness to head rotation is meaningful and will be the major task of this paper. Most research is based on the measured acoustic transfer responses to optimize the system, lacking a systematic analysis of the variation and optimization of the acoustic transfer responses in practical situations. In this paper, a physical model of an active noise-reducing headrest with a diffractive rigid sphere in the diffuse sound field was developed to analyze the variation of acoustic transfer responses. Then the noise reduction performance of the active headrest system using a virtual sensing technique was analyzed. The system robustness against head rotation was then improved by the design of an optimized plant transfer responses. While most previous studies have been based on single channel systems, there is some research using dual-channel systems to improve system performance [16; 17; 18; 19]. The performance of single channel and dual-channel systems are compared in this paper and the influence of a physical system arrangement is discussed. Moreover, the effects of several parameters on optimization performance are analyzed based on a horizontally arranged dual-channel system. Finally, the simulation results are verified by experiments in an ordinary room.
# Measuring Maritime Paint Thickness under Water Using THz Cross-Correlation Spectroscopy
## 2 Materials and Methods
### Silicone Contact Patch  
The high absorption coefficient of water in the THz frequency range makes measurements through any significant amount of water [24] almost impossible. A rounded silicone patch was made to remove all water in the THz beam path, illustrated in Figure 2D. The design philosophy of the contact patch is to have a parabolic end that can push water away and avoid trapping water under the contact patch. A silicone with hardness \"Shore A15\" was chosen due to it being the softest silicone available, which should be a good candidate to fully make water-tight contact when pressure is applied. At the initial contact between the silicone and the paint, only the center part of the parabolic silicone end touched the paint. When enough pressure was applied, all of the parabolic silicone end touched the paint, and water was pushed away, which resulted in a contact surface large enough to contain the entire THz beam, where full contact was made. Additionally, there was no indication of any loss of contact due to the central part of the contact patch bending away  
Figure 3: Determining the thickness of the given paint sample: (**A**) The sample given by Jotun. The red square indicates the area of the 3D scan. (**B**) Three-dimensional scan of the paint. (**C**) Height along the line indicated in (**B**). An average thickness can be derived from the difference in the average height between the metal and the top of the paint. For the paint sample, the average paint thickness was 230.0 \(\upmu\)m.  
from the paint. Due to the high Young's modulus of maritime paint relative to silicone [32], it could be assumed that the deformation of the paint was negligible.  
The molding of the silicone was done with 3D-printed molds printed with PLA plastic using the Ultimaker 3 3D printer. Due to its low absorption coefficient at THz frequencies, a TOPAS wafer was used to apply pressure to the silicone patch [33].