esa-sceva/satcom-chunk-collection huggingface dataset:
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 2 PFAS Applications and Related Concerns
### Reasons for Concer  
So far, a substantial number of studies have proved many adverse effects of PFASs on human health. PFOS and PFOA, at concentrations of the order of few micrograms per liter in blood, may cause hypercholesterolemia, ulcerative colitis, thyroid disease, cancer, pregnancy-induced hypertension, pre-eclampsia, and liver damage. For each of these outcomes, a molecular mechanism has been tentatively proposed. In this regard, one of the most comprehensive collections of epidemiological studies linking adverse human health effects and many other problems to PFASs (particularly PFOA) is still under investigation and is authored by the C8 Science Panel [4; 16]. Figure 1 summarizes the diseases already confirmed to be associated with PFAS substances.  
the presence of these substances in food [24]. However, in contaminated drinking water, PFASs are sufficiently \"abundant\" that the TWI is easily overpassed just by drinking 2 L of water per day since the monitoring system of many PWSs (Public Water Systems) already report concentrations far above the actual limit of 0.5 \(\mu\)g/L of PFAS [3; 25].
## 1 Introduction
Ions generated from plasma can directly affect WIP temperature. When ions fall from the plasma to the wafer surface, ion bombardment occurs in which the energy of the ions is transferred by converting them into thermal energy [20,21]. Heat flux refers to the transfer rate of thermal energy in the unit area per unit time with a physical metric unit of W/m\({}^{2}\). The heat flux directly affecting WIP temperature from the plasma side surface can be calculated by analyzing the ions. The heat flux from the plasma to the wafer consist of parameters such as the energy of ions and the density of ions [22]. It is essential to determine the effect of changes in process parameters on the ion characteristics in plasma so as to accurately calculate heat with the increase or decrease in the collision of ions in the sheath according to the changes in the mean free path; the source power affects ion density as it controls plasma density, the bias power influences ion energy, and the pressure affects etch rate and etch profile [23]. Thus, the etch profile can be altered depending on the ion energy distribution based on plasma properties or the ion angle distribution applied to the wafer, as shown above [24]. The WIP temperature must be analyzed and controlled to enhance the etching rate and improve uniformity [25]. Different methods such as optical thermometer, thermal flux sensor, and wafer-type monitoring apparatus have been proposed for analyzing WIP temperature [20,26, 27, 28]. In the present study, heat flux was calculated using inductive coupled plasma-reactive ion etch (ICP-RIE) equipment in an invasive way to predict wafer surface temperature. In addition, the parameters required for calculation were compared using single Langmuir probe (SLP) and optical emission spectroscopy (OES) equipment, and the calculation was verified through an etch-temp-sensor-wafer (ETSW). The remaining sections of this study are detailed as follows: Section 2 describes theory related to heat flux, Section 3 describes the experimental apparatus, Section 4 explains the results, and Section 5 presents the conclusions of this study.
# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 4 Experiments
### Longer Clipping Synthetic Policy
\begin{table}
\begin{tabular}{c l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Pooling** \\ **Method** \\ \end{tabular} } & \multicolumn{2}{c}{**AT Task**} & \multicolumn{3}{c}{**SED Task**} & \multirow{2}{*}{**\#Step/s**} \\  & & **mPrc** & **mRcl** & & & **mPrc** & **mRcl** & & **mF1** & **mPrc** & **mRcl** & **mF1** \\ \hline \multirow{4}{*}{CNN} & GWRP & 48.0 & 71.1 & 54.2 & 46.1 & 34.4 & 36.7 & 3.57 \\ \cline{2-9}  & GTAP\({}_{\alpha=0.2}\) & 35.7 & 74.0 & 46.8 & 77.2 & 7.2 & 12.5 & \\  & GTAP\({}_{\alpha=0}\) & 34.4 & 77.7 & 46.6 & 76.3 & 9.2 & 15.7 & 4.37 \\  & GTAP\({}_{\alpha=-0.4}\) & 52.4 & 43.1 & 42.5 & 36.7 & 34.4 & 31.3 & \\ \hline \multirow{4}{*}{U-Net} & GWRP & 54.2 & 80.8 & 63.7 & 48.0 & 50.1 & 47.4 & 8.64 \\ \cline{2-9}  & GTAP\({}_{\alpha=0.2}\) & 50.2 & 78.2 & 59.7 & 62.5 & 34.6 & 42.8 & \\ \cline{2-9}  & GTAP\({}_{\alpha=0}\) & 48.2 & 80.4 & 59.2 & 60.4 & 37.5 & 44.4 & 12.54 \\ \cline{2-9}  & GTAP\({}_{\alpha=-0.4}\) & 67.6 & 69.0 & 66.9 & 47.2 & 51.4 & 47.3 & \\ \hline \multirow{4}{*}{LUU-Net} & AlphaMEX & 60.0 & 73.7 & 64.9 & 67.6 & 25.9 & 35.8 & 21.13 \\  & MEX & 57.0 & 77.0 & 64.3 & 56.6 & 40.0 & 45.4 & 32.33 \\ \cline{2-9}  & GWRP & 56.8 & 78.0 & 64.4 & 53.0 & 44.6 & 46.8 & 28.38 \\ \cline{2-9}  & GTAP\({}_{\alpha=0.2}\) & 57.2 & 76.8 & 64.4 & 62.3 & 36.9 & 44.8 & \\ \cline{2-9}  & GTAP\({}_{\alpha=0}\) & 55.8 & 77.8 & 64.1 & 60.1 & 40.1 & 46.6 & 35.12 \\ \cline{2-9}  & GTAP\({}_{\alpha=-0.4}\) & 66.0 & 71.2 & 67.4 & 50.5 & 48.6 & 48.0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: AT and SED results on the dataset generated by the longer clipping policy. The CNN, U-Net, and proposed LUU-Net are shown in Tables 4–6, respectively. Pooling methods AlphaMEX, MX, GWRP, and the proposed GTAP with varying \(\alpha\) values. Figure 9: Sound event detection examples with various models and global pooling methods. The vertical axis in (**b–h**) represents the class number. (**a**) Spectrogram of the input mixture sample generated by the longer clipping policy. (**b**) Ground truth labels. (**c–h**) display sound event labels predicted by (**c**) CNN with GWRP, (**d**) U-Net with GWRP, LUU-Net with (**e**) GWRP, (**f**) AlphaMEX, (**g**) MEX, and (**h**) GTAP\({}_{\alpha=-0.4}\). The \(x\)-axis is the time in seconds, and the \(y\)-axis represents the frequency (**a**) or event labels (**b–h**). Sound event detection examples with CNN, U-Net, and LUU-Net with GWRP.
# Three-Dimensional Imaging via Time-Correlated Single-Photon Counting
## 2 Theory Analysis
### Imaging Scheme Based on TCSPC  
In our scheme, the dead time of SPD is longer than the pulse width of laser. Therefore, the number \(N_{x,y}\) of photons received by the SPD after each point scanned by the scanning mirror is  
\[N_{x,y}=\frac{1}{\omega T_{r}}\left(1-e^{-\lambda_{x,y}}\right), \tag{5}\]  
where \(T_{r}\) denotes the pulse laser period and \(\omega\) denotes the scanning frequency of scanning mirror. The pixel value of target point \((x,y)\) is represented by \(N_{x,y}\). When the whole object is scanned by the scanning mirror, the two-dimensional image of the target can be obtained by rearranging the pixel values. Besides, because the flight time of each photon and the number of photons can be obtained accurately by the TCSPC module, the depth information of the object can be calculated by the TOF algorithm. Lastly, multiple SPDs are used to detect objects from different angles. By combining the photometric stereo algorithm, we can also get the 3D model of the object.
# Near-Field-Based 5G Sub-6 GHz Array Antenna Diagnosis Using Transfer Learning
## 5 Conclusions  
In this study, near-field-based array antenna diagnosis was performed using transfer learning. For a 5G sub-6 GHz band \(4\times 4\) array antenna, a network capable of normal/abnormal and specific failure port classifications was implemented, and 99.6% and 95.24% accuracies were achieved, respectively. To improve the accuracy of the array antenna diagnosis, a regression network that predicts the amplitude and phase of the \(4\times 4\) array antenna excitation signal was implemented, and the obtained accuracies of the amplitude and phase were 96.2% and 98.65%, respectively. In addition, the array antenna diagnosis by transfer learning was verified by measurements. When the measured data were discriminated using the trained network, accurate discrimination was achieved in 29 out of 30 cases, validating that the array antenna could be diagnosed by transfer learning.  
Conceptualization, H.J.L. and K.C.H.; methodology, H.J.L.; software, H.J.L.; validation, H.J.L., H.B.P. and K.C.H.; formal analysis, H.J.L.; investigation, H.J.L. and D.H.L.; resources, H.J.L. and D.H.L.; data curation, H.J.L. and H.B.P.; writing--original draft preparation, H.J.L.; writing--review and editing, H.J.L., D.H.L. and K.C.H.; visualization, H.J.L; supervision, K.C.H.; project administration, K.C.H. All authors have read and agreed to the published version of the manuscript.  
Data Availability Statement: Data sharing not applicable.  
Conflicts of Interest: The authors declare no conflict of interest.
## 3 Proposed Structure  
In this work, we used an electromagnetic wave solver, Lumerical FDTD solutions software, to design and analyze the proposed structure. A unit cell of the proposed structure has been simulated using the FDTD method. The overall dimensions of the unit cell are 500 \(\times\) 500 \(\times\) 700 nm\({}^{3}\) in the three dimensions \(x\), \(y\), and \(z\), respectively. A silicon substrate with height \(h_{2}\) = 3 \(\upmu\)m is placed on the top of a back oxide material with height \(h_{1}\) = 1 \(\upmu\)m, as illustrated in Figure 1. Silicon nanowires are grown on the top of the silicon substrate with height \(H\) = 5 \(\upmu\)m and period \(P\) = 550 nm between each NW. In addition to that, a graphene layer can be used on the top of substrate material and on the NWs as well.  
The boundary conditions are considered as periodic structures in \(x\)-direction and \(y\)-direction, as the structure is extended in both \(x\)- and \(y\)-dimensions, and perfect matching layer in \(z\)-direction, as the structure is not repeated in \(z\)-direction, where the minimum mesh size is 20 nm in all directions. A plane wave source with wavelength band 400-1100 nm and offset time 7.5 fs is used as a light source. In addition, the solar generation calculation region is given in the active layer to calculate the short-circuit current density.  
The refractive index of back oxide is 1.4; however, the value for silicon is a function of the wavelength and follows the [PERSON] and Studna model [25], and the refractive index of graphene follows the [PERSON] and Taft model [26]. On the other hand, the refractive index of plasmonic NPs is summarized using Equation (6) in Table 1[27].
## 3 Results
### Effects of KF and LP on Aerobic Stability  
The aerobic stability of forage rape silages is shown in Figure 1 and Table 3. After an aerobic exposure period of 28.0 h, silages in group CK deteriorated. During the continuous monitoring time of 120 h, CK reached its maximum temperature of 27.5 \({}^{\circ}\)C after an aerobic exposure period of 100 h. Compared with CK, group KF and LP did not affect aerobic stability (\(p>0.05\)), while the maximum temperature caused by secondary fermentation was even higher in group KF (\(p<0.05\)), although the time to reach maximum temperature was longer (\(p<0.05\)). On the contrary, group KFLP showed significantly improved aerobic stability (\(p<0.05\)). Meanwhile, silages treated with KFLP took a longer time to reach their maximum temperature (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **pH** & **Lactic Acid** & **Acetic Acid** & **Isobutyric Acid** & **Butyric Acid** \\  & **(g/kg DM)** & **(g/kg DM)** & **(g/kg DM)** & **(g/kg DM)** & **(g/kg DM)** \\ \hline CK & 4.84 \({}^{\text{a}}\) & 8.06 \({}^{\text{b}}\) & 1.76 & 0.08 \({}^{\text{a}}\) & 1.87 \({}^{\text{a}}\) \\ KF & 4.17 \({}^{\text{b}}\) & 15.83 \({}^{\text{a}}\) & 1.66 & 0.04 \({}^{\text{ab}}\) & ND \\ LP & 4.23 \({}^{\text{b}}\) & 16.13 \({}^{\text{a}}\) & 1.69 & 0.03 \({}^{\text{bc}}\) & 0.03 \({}^{\text{b}}\) \\ KFLP & 4.22 \({}^{\text{b}}\) & 18.29 \({}^{\text{a}}\) & 1.63 & ND & ND \\ SEM & 0.08 & 1.21 & 0.03 & 0.01 & 0.24 \\ \(p\)-value & \textless{}0.001 & \textless{}0.001 & 0.329 & 0.001 & \textless{}0.001 \\ \hline DM: dry matter; CK: the control; KF: konia flour; LP: _Lactylanthiocillus plantarum_; KFLP: konia flour + _Lactylanthiocillus plantarum_; SEM: pooled standard error; ND: not detected; Means with different superscripts in the same column differ significantly (\(p<0.05\)). & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The pH and organic acid contents of forage rape silages were fermented with KF and LP for 60 days.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Crude Ash** & **CP** & **NDF** & **ADF** \\ \hline CK & 96.55 & 146.29 \({}^{\text{a}}\) & 501.15 \({}^{\text{ab}}\) & 341.64 \\ KF & 102.13 & 130.16 \({}^{\text{b}}\) & 506.49 \({}^{\text{a}}\) & 345.92 \\ LP & 99.17 & 150.03 \({}^{\text{a}}\) & 494.74 \({}^{\text{b}}\) & 347.06 \\ KFLP & 95.57 & 129.04 \({}^{\text{b}}\) & 498.53 \({}^{\text{ab}}\) & 345.39 \\ SEM & 1.04 & 2.96 & 1.63 & 1.80 \\ \(p\)-value & 0.084 & \textless{}0.001 & 0.041 & 0.791 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The chemical composition of forage rape silages fermented with KF and LP for 60 days (g/kg DM).
## 3 Design Solution for FBG Inclinometer for Cantilever-Beam-Type Hydraulic Support  
The cantilever-beam-type hydraulic support FBG inclinometer primarily operates on the principles of gravity and sensitivity enhancement. By leveraging these principles, it effectively augments the sensitivity of the grating. Additionally, a well-considered design incorporating a calibration-zero device has been implemented. This not only circumvents the inherent drawbacks of the cantilever-beam-type hydraulic support FBG inclinometer but also compensates for its deficiencies. This design approach ensures that the inclinometer can accurately and reliably measure the inclination of the hydraulic support, thereby enhancing the overall safety and efficiency of coal mining operations in underground environments.  
The cantilever-beam-type FBG inclinometer on the hydraulic support of seismicity has been greatly improved, and made safer and more reliable.
# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 4 Experimental Results
### Analysis of Experimental Results
#### Ablation experiments  
Ablation experiments were used to compare the errors of the proposed improved algorithm employing background restoration or not, and the results are shown in Table 9. RPE (T) represents the relative positional translation error. This table shows that the overall cases using background repair are all smaller than the errors without background repair.  
The ATE accuracy is only improved by 1.49% in the \"sitting_static\" sequence, while the ATE accuracy is improved by 98.52% in the \"walking_halfsphere\" sequence. By observing the output images of the improved DeepLabv3+ semantic segmentation, as well as the feature extraction and background restoration effects of the images during the system operation, it is found that in the \"sitting_static\" sequence, most of the image frames are occupied by the dynamic target \"human\", so the algorithm is unable to extract enough static feature points for feature matching, and the background obscured by the person cannot be repaired because the person has been sitting on the chair for a long time without moving. In the \"walking_halfsphere\" sequence, the camera's trajectory is a hemispherical arc with a diameter of 1 m, and the character is constantly moving, which allows the camera to maximize the acquisition of static scenes from various angles and repair the background effectively. After the algorithm fails to track the feature points, the repaired background can provide more accurate feature matching during relocation. The use of background repair makes loop closing and mapping more realistic, resulting in the best accuracy improvement in the \"walking_halfsphere\" sequence.
## 4 Conclusions  
This study utilizes salt sludge (SAS) as an alkali activator, steel slag (SS) and ground granulated blast-furnace slag (GGBS) as pozzolanic materials, and light magnesium oxide (MgO) as an external admixture to prepare SSGM binder for solidifying artificially configured soil. The research investigates the influence of different clay contents on the freeze-thaw characteristics and mechanisms of SSGM-solidified low-liquid-limit clay under freeze-thaw-cycling conditions. The main conclusions obtained are as follows:  
(1) Under the action of freeze-thaw cycles, the mass loss rate of solidified soil increases with the number of freeze-thaw cycles, and at the same number of cycles, the mass loss rate increases with the increase in clay content.  
(2) As the number of freeze-thaw cycles increases, the unconfined compressive strength of the solidified soil gradually decreases, following a pattern consistent with an exponential function model. When the clay content is 0%, 20%, 33%, and 100%, the unconfined compressive strength loss rates of the solidified soil after 10 cycles are 18.37%, 22.70%, 23.49%, and 29.09%, respectively. The higher the clay content, the greater the strength loss rate.  
(3) Microscopic test results indicate that the hydration products generated by the binder mainly include C-S-H, C-A-H, C-A-S-H, and AFt. These products cement soil particles and fill internal pores while also forming an interconnected mesh-like structure, thereby enhancing the internal stability of the solidified soil. However, freeze-thaw cycles cause the migration and redistribution of moisture within the sample, leading to structural damage in the solidified soil. This results in an increase in the number of large pores and the formation of interconnected cracks and voids, ultimately reducing its strength.  
Conceptualization, R.L.; methodology, J.Y.; formal analysis, Y.W.; investigation, R.L.; resources, J.Y.; data curation, R.L.; writing--original draft preparation, R.L.; writing--review and editing, R.L., J.Y. and Y.W.; supervision, J.Y. and R.L.; project administration, Y.W.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Natural Science Foundation of China (Grant Nos.52378380, 52078474, and 51779235).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to the confidentiality of the research subject.  
The authors declare no conflicts of interest.
# Dual Enhancement Network for Infrared Small Target Detection
## 3 The Proposed Dual Enhancement Network  
For the infrared small target detection task, we propose a residual sparse enhancement (RSE) module and a spatial attention enhancement (SAE) module to cope with the complex background interference and adaptive enhancement of weak small targets. As shown in Figure 2, we named our approach dual enhancement network (DENet), since these two enhancement modules are placed on either side of the network. Specifically, the RSE module processes multi-level features as input, sparsely propagates semantic information, and outputs finely processed features, significantly reducing the effect of background noise. The SAE module, on the other hand, focuses on solving the problem of enhancing weak and small targets in infrared images and adaptively enhances the relevant features. Notably, the SAE module focuses on the weak and small targets while reducing the focus on the complex background, which greatly improves the overall segmentation performance. This dual enhancement strategy effectively solves the key problem in infrared small target detection and provides reliable technical support for improving the accuracy and robustness of target detection.
## 2 Intelligent Inversion Method of the In Situ Stress Field  
### Determination of Boundary Conditions  
In the analysis of the in situ stress field in the underground caverns, it is widely acknowledged that the gravity stress and the geotectonic movement are the primary contributing factors, while the influence of temperature and groundwater effects is generally considered to be less significant and, hence, often ignored [22; 23]. This paper adopts the fast stress boundary method to conduct the in situ stress inversion by using the FLAC\({}^{\rm 3D}\) 7.0 software. This method assumes that the in situ stress field results from a combination of self-weight and tectonic stresses. By adjusting the boundary conditions, a stress field that best matches the test data can be determined, which is known as the inverse in situ stress.  
In the fast Lagrangian method, the model's horizontal stress state can be adjusted by specifying the coefficient \(k\) (the ratio of horizontal stress to vertical stress), and the shear stress state is simulated by applying the corresponding shear stress \(\tau\). In this paper, six boundary coefficients are used to correspond to six different stress boundary conditions, as shown in Figure 1. These coefficients include the self-weight coefficient (\(k_{\rm g}\)), the horizontal stress coefficients (\(k_{\rm x}\) and \(k_{\rm y}\)), and the shear stress coefficients in three directions (\(\tau_{\rm xz}\),\(\tau_{\rm yz}\)and \(\tau_{xy}\)). In this way, the challenging problem of in situ stress inversion can be transformed into finding an optimal solution for the six boundary conditions.
## 5 Conclusions  
In summary, this study considered the issue of PAPR reduction for OvTDM systems in the context of clipping. We demonstrated that, while deliberate clipping effectively addresses concerns related to high PAPR, it simultaneously introduces considerable nonlinear distortion. Therefore, this contribution proposed a class of receiver-side iterative recovery schemes for mitigating the impact of clipping. Specifically, we focus on achieving accurate clipping noise estimation through iterative techniques, ultimately enhancing the decoding outcomes. We exhibited that the proposed technique can significantly improve the reliability and efficiency of OvTDM, particularly when dealing with distortions induced by clipping. Since such a receiver-side strategy provides practical approaches to counteract nonlinear distortion, the effectiveness of OvTDM is further improved in high-capacity channel sharing. In future works, we will focus on quantifying the effect of clipping on OvTDM with coding and iterative processing in different system configurations. We look forward to further exploring these topics and contributing to the advancement of the field.  
Methodology, C.H. and Y.X.; Software, C.H. and Q.Z.; Formal analysis, X.L.; Data curation, X.L.; Writing--original draft, C.H. and Q.Z.; Writing--review & editing, Y.X.; Supervision, Y.X. All authors have read and agreed to the published version of the manuscript.  
This works is supported by Dongguan Key Research & Development Program, China (No.20201200300072) and the Fundamental Research Funds for the Central Universities under Grant 2242022k60006.  
Not applicable.  
Data Availability StatementData are contained within the article.  
Not applicable.