esa-sceva/satcom-chunk-collection huggingface dataset:
# The Challenges of Advanced CMOS Process from 2D to 3D
## 5 The Etching Challenges and Solution of 3D
### Gate Etching Control  
As new 3D structures are introduced, selective and residue-free etch processes will be even more challenging. There are a few new process and materials challenges in processing FinFETs as summarized below. Firstly, the Si surface of fins appears different than in bulk since an excessive Si loss was observed after the usual pre-gate-oxide clean as shown in Figure 14a. Therefore, wet cleaning was optimized with dilute concentration and lower temperatures. Similarly, the oxidation of fin is also faster at corner and tip of fins. Furthermore, the dry etching on fins is more stringent due to the 3D structures (Figure 14b) and a bias plasma pulsing scheme may be viable for minimizing Si loss [124; 125].  
Self-Aligned Contact (SAC) Etching Selectivity Control  
In general, SAC is a necessary process for 3D transistor with gate length smaller than 14 nm. It is required to etch the slit pattern of the fine SiO\({}_{2}\) layer with ultra-high selectivity to SiN. In the conventional etching methods which fluorocarbon (FC) gas is usually used, the selectivity is enhanced by utilizing the etch rate difference of the protective film, which is caused by the composition difference of SiO\({}_{2}\) and SiN [126; 127]. However, if the FC film thickness is increased in attempt to improve the SiN selectivity, an excessive FC film is deposited on SiO\({}_{2}\) and the slit opening is blocked by excess deposition where finally it causes an etch-stop to occur. Therefore, a better trade-off between the fine process control of the SiO\({}_{2}\) layer and SiN selectivity can be reached by applying a new Quasi-Atomic Layer Etch (ALE) technology [128].  
Figure 15 shows the concept of Quasi-ALE. In the conventional method, radicals and ions in the plasma are transported to the surface of wafer at the same time. In contrast, in Quasi-ALE, they are separated as the radical flux and ion flux are performed independently in each step time. In this scheme, it is possible to control the etching reaction more precisely.  
Figure 16 shows the etching result of SAC structure by the conventional etch and optimized Quasi-ALE. In the conventional etch, etch stop occurs at the fine slit while the amount of SiN loss is large. In Quasi-ALE, the fine slit can be successfully etched while the SiN selectivity is highly preserved. SEM image of Quasi-ALE shows when FC on the bottom of fine slit has been removed but the SiN surface is still protected by FC layer. From the state of the FC layer formation, precise control ability of the etching surface by Quasi-ALE is obtained. Thus, Quasi-ALE enables very sophisticated fabrication.  
Figure 14: (**a**) The Si-loss of fins appears more severe than bulk and (**b**) the plasma etching is more difficult on 3D structures.
## 3 Governing Equations
### Free Vibrations Analysis  
There exist two possible configurations for the patches, namely the short-circuit and open-circuit configurations. In the open-circuit configuration, which is often denoted as the sensor configuration, the two patch electrodes are disconnected. In the case where the piezoelectric layers are shorted, the difference of the electric potentials between their electrodes vanishes (\(v_{1}=v_{2}=0\)). Therefore, the Equation (20) becomes  
\[M\ddot{\mathbf{d}}+\mathbf{K}\mathbf{d}=\mathbf{F}_{m} \tag{23}\]  
On the other hand, if the piezoelectric layers are open-circuited, then the electric potentials between their electrodes can be evaluated using the Equations (21) and (22), such that  
\[v_{i}=C_{p_{i}}^{-1}\mathbf{\Theta}_{i}^{T}\mathbf{d} \tag{24}\]Substituting Equation (24) into (20) the following condensed equation of motion is obtained  
\[M\ddot{\mathbf{d}}+\mathbf{K_{o}}\mathbf{d}=\mathbf{F}_{m} \tag{25}\]  
where  
\[\mathbf{K_{o}}=\mathbf{K}+C_{p_{1}}^{-1}\mathbf{\Theta_{1}}\mathbf{\Theta_{1}}^{T}+C_{p_{2}}^{-1} \mathbf{\Theta_{2}}\mathbf{\Theta_{2}}^{T} \tag{26}\]  
Once the global mass matrix \(\mathbf{M}\) and stiffness matrix \(\mathbf{K}\) have been generated, the natural frequencies and mode shape for the short-circuit and open-circuit configurations can be calculated by solving the following eigenvalue equations:  
\[\left(\mathbf{K}-\omega_{s}^{2}\mathbf{M}\right)\mathbf{u}_{s} =\mathbf{0} \tag{27}\] \[\left(\mathbf{K_{o}}-\omega_{o}^{2}\mathbf{M}\right)\mathbf{u}_{o} =\mathbf{0} \tag{28}\]  
where \(\omega_{s},\mathbf{u}_{s}\) and \(\omega_{o},\mathbf{u}_{o}\) are the eigenfrequencies and eigenvectors for the short-circuit and open circuit configuration, respectively.
## 3 Results and Discussion
### Detection of CSA in the VBA Fermentation Process  
Both GC-MS and an electronic nose can qualitatively and quantitatively identify the volatile gases to distinguish and determine the pickled-vegetable fermentation process, but their operation is very tedious. CSA imitates the olfactory system of mammals through the cross-reaction of a variety of dyes, which can quickly and intuitively distinguish different samples according to color changes. It can also quantify the measurement results combined with digital image-processing technology [36]. According to the results of GC-MS, the acid content in VBA increased with the fermentation, accompanied by the transformation of sulfur, aldehydes, and other substances. In this study, a simple CSA was prepared to react with VBA samples at different fermentation stages. Compared with CSA prepared by [PERSON] et al. [14], We added aldehyde-sensitive indicator points to improve the detection sensitivity of CSA. The CSA color-difference maps of VBA during fermentation are shown in Figure 7A. The acid content increased during the VBA fermentation process, and the brightness of the CSA color-difference maps of indicator points 2 and 3 sensitive to pH change gradually increased, while the color of the color-difference maps of points 1, 5, and 8 sensitive to aldehydes changed at different fermentation times. Compared with the different images obtained by [PERSON] et al. [14], the visual difference between the difference image obtained in different fermentation states in this study is more obvious.  
Studies have shown that olfactory visual sensor technology combined with appropriate chemometric methods can provide qualitative and quantitative analysis [37]. LDA data of CSA grouped by fermentation days are shown in Figure 7B; the sample classification accuracy of cross-validation reached 93.8% (Table S5). If the CSA data is grouped into three stages according to the information in Figure 7B (the three stages are 0 d (early fermentation), 5 d and 10 d (middle fermentation), and 15-35 d (late fermentation), and are then analyzed by LDA (Figure 7C), then the accuracy of sample classification of cross-validation can reach 100% (Table S5), indicating that it is feasible to distinguish VBA in different fermentation stages using simple CSA.  
Figure 6: Electronic nose analysis of VBA. (**A**) PCA; (**B**) DFA. V represented VBA; Numbers (0, 5, 10, 15, 20, 25, 30, 35) represent fermentation time.
## 2 System Model
### Vlc-Ofdm  
In VLC-OFDM, the IM-DD scheme has been predominantly considered due to its system simplicity. A block diagram of the IM/DD-based VLC-OFDM communication system is illustrated in Figure 1[19]. In Figure 1, GI stands for Guard Interval, which refersto the insertion of a portion of the latter part of each OFDM symbol at the beginning of the symbol to mitigate inter-symbol interference caused by multipath delay. A/D denotes Analog-to-Digital Conversion, and P/S represents Parallel-to-Serial Conversion.  
Unlike conventional RF-OFDM systems, IM/DD systems are unable to transmit complex-valued signals [8], and consequently, they cannot convey phase information. To address this limitation, the frequency-domain signal before IFFT is constrained to satisfy Hermitian symmetry. This is because when Hermitian conjugation is applied to the signal, the IFFT output consists exclusively of real-valued components.  
Let \(X_{m}\)\((m\in 0,1,\ldots,\dfrac{N_{c}}{2}-1)\) denote the complex-valued symbols to be transmitted. The Hermitian symmetry condition can be expressed as follows:  
\[X_{k}=\begin{cases}X_{m}&(0<k<\dfrac{N_{c}}{2}-1)\\ X_{N_{c}-m}^{*}&(\dfrac{N_{c}}{2}<k)\\ 0&(k=0,\dfrac{N_{c}}{2})\end{cases}, \tag{1}\]  
where \(X_{m}^{*}\) represents the complex conjugate of \(X_{m}\) and \(N_{c}\) is the total number of functionally active subcarriers. Under this constraint, the IFFT output yields a real-valued time-domain signal, which is expressed by the following equation.  
\[x_{n}=\sum_{k=1}^{N_{c}-1}X_{k}\exp{\left(j\dfrac{2\pi n}{N_{c}}\right)}. \tag{2}\]  
Furthermore, owing to LEDs, which cannot emit negative optical power, a DC bias component is introduced to ensure non-negative transmitted signals. In this work, the DC bias \(I_{DC}\) is superimposed onto the time-domain OFDM signal, as defined by the following equation:  
\[I(t)=\sum_{k=1}^{\frac{N_{c}}{2}-1}I_{k}\sin{(2\pi f_{k}t+\phi_{k})}+I_{DC}. \tag{3}\]  
where, \(I_{k}\) represents the current amplitude of the k-th subcarrier (the frequency component), \(f_{k}\) is the frequency of the k-th subcarrier, and \(\phi_{k}\) denotes the initial phase of the k-th subcarrier.
## 4 Experiments and Results
### Implementation Details  
In an experiment to evaluate the performance of the proposed methods, we used the \"U-Net\"-based architecture proposed in Pix2 Pix [34]. The differences between the original U-Net architecture [35] and the U-Net-based architecture are that several skip connections between layers were added, batch normalization was used, and dropout layers were added to the decoding part. We used the [PERSON] [36] optimizer with a linearly decaying learning rate from \(1\times 10^{-4}\) to 0. The dropout probability of all dropout layers was set to 0.3, and the Bernoulli sampling probability \(k\) was set to 0.3.  
To improve the performance of the neural network, we also used data augmentation, including horizontal flipping, vertical flipping, and diagonal flipping, and we used \(N=50\) in Equation (19) for the denoising scheme. To evaluate the performance of the proposed method, we used recent deep learning-based self-supervised denoising methods and conventional denoising methods as counterpart methods: _N2V_[24], _N2S_[25], nonlocal mean filtering, total variation, and bilateral filtering. These methods were applied in the projection image domain. All of the counterpart denoising neural networks were trained using published training code.
## 5 Field Tests  
The field tests were conducted at a test site in Hefei, Anhui Province, China. The presence of a large number of mechanical devices operating continuously in the monitoring environment produced a significant increase in background noise and power frequency interference. When the energy of interference exceeds the pre-set threshold of the system, it is recorded as an event and seriously affects the quality of effective microseismic signals collected by the microseismic monitoring system. In this case, the automatic identification of effective microseismic signals from a huge microseismic data set becomes very difficult. Therefore, it is especially necessary to use the method for recognizing effective microseismic signals with a low signal-to-noise ratio proposed in this paper to accurately identify effective signals.  
The algorithm was applied to the microseismic signals collected on-site in the above experimental field to verify the ability of the algorithm to process actual signals. Typical signals and processing results are shown in Figures 6 and 7.  
According to Figures 6 and 7, the decomposition of seismic signal maps by VMD can efficiently extract IMFs, regardless of effective signals or ineffective signals. After processing, the trained GA-SVM model was able to adaptively extract the components of effective microseismic signals from IMFs and form microseismic signals with a high signal-to-noise ratio by reconstruction. In contrast, no effective components were recognized in the noise signals during the decomposition process, thus showing an empty set after signal reconstruction.  
In order to evaluate the effect of recognition and processing by the algorithm here on effective microseismic signals, we calculated the signal-to-noise ratio (SNR) and root mean square error (RMSE) of the processed effective microseismic signals. The signal-to-noise ratios and root mean square errors of some typical signals are presented in Figures 8 and 9.  
Figure 6: VMD results of effective signals collected in the field: (**a**) original waveforms, (**b**–**f**) IMF1–5, (**g**) residuals, (**h**) reconstructed signals.  
As shown in Figures 8 and 9, after processing by the algorithm, the SNR is greatly enhanced, from 23.9\(\sim\)34.55 to 51.31\(\sim\)59.74, and the RMSE is significantly reduced, from 35 \(\times\) 10\({}^{-4}\)-114 \(\times\) 10\({}^{-4}\) to 1.9 \(\times\) 10\({}^{-4}\)-7.6 \(\times\) 10\({}^{-4}\), which proves the good recognition and processing of effective signals by the algorithm and verifies the effectiveness of the algorithm.  
Figure 8: The signal-to-noise ratios of typical signals collected in the field before and after processing by the algorithm.  
Figure 7: VMD results of noise signals collected in the field: (**a**) original waveforms, (**b–f**) IMF1–5, (**g**) residuals, (**h**) reconstructed signals.
## 2 Related Work  
### Encoder-Decoder Architecture  
Encoder-decoder frameworks have emerged as the prevailing architectural paradigm in semantic segmentation tasks, owing to their capacity to simultaneously capture semantic abstraction and spatial precision. The encoder component systematically reduces the spatial resolution of the input through a series of downsampling operations, while hierarchically extracting deep semantic features. In contrast, the decoder is responsible for the gradual restoration of spatial dimensions via upsampling mechanisms such as transposed convolutions, thereby producing dense, pixel-wise predictions.  
Recent lightweight segmentation models commonly adopt such encoder-decoder designs. However, a notable limitation in these models is the uniform allocation of channel dimensions across all decoder stages. This static design leads to disproportionate computational overhead in the early decoder layers, where spatial resolution remains high, thereby introducing unnecessary redundancy and inefficiencies in resource utilization.  
In order to address this disparity, PPLiteSeg integrates a Flexible Lightweight Decoder (FLD) that dynamically modulates channel density throughout the entire decoding stage. This mechanism is illustrated in Figure 1:  
Figure 1a presents the conventional decoder architecture, where a fixed number of channels (C6) is maintained across all decoding levels. Although straightforward, this approach causes a substantial computational burden, particularly in shallow layers with larger spatial dimensions.  
Figure 1b, by contrast, depicts the architecture of the proposed FLD, where the number of feature channels is progressively reduced from C6 at higher-level decoder stages to C8 at lower-level stages as spatial resolution is gradually restored. This design allows for an efficient reallocation of computational resources, concentrating more channels where semantic information is rich, while reducing redundancy in shallow layers with higher spatial dimensions. Consequently, the FLD improves overall decoding efficiency while maintaining accurate spatial detail reconstruction.  
By coupling the expansion of spatial resolution with a corresponding reduction in channel density, FLD introduces a deliberate trade-off between spatial granularity and computational efficiency, enabling a more resource-aware decoding process. This design paradigm not only minimizes redundant computations in shallow layers but also preserves the model's ability to recover spatial details effectively. Consequently, the proposed FLD  
Figure 1: (**a**) Traditional Encoder–Decoder, where the decoder maintains the same number of feature channels. (**b**) The PPLiteSeg encoder and the proposed Flexible Lightweight Decoder (FLD) are employed, wherein the FLD progressively reduces the number of feature channels from higher-level to lower-level features.  
enhances inference efficiency and model deployability, all while maintaining competitive segmentation performance.
# An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data
## 3 Methodology  
### Problem Statement  
There are three key phases during landing of a flight, i.e., final approach phase, flare phase, and landing phase. In detail, when the aircraft descends to an altitude of about 50 feet above the ground, the pilot will apply back stick inputs to increase the aircraft pitch angle and reduce the vertical speed, to ensure a steady touchdown. After this back stick operation is completed, the aircraft enters the flare phase. Usually, at the beginning of the flare, the aircraft speed is still very high, and it does not touch ground immediately. Therefore, the pilot has to maintain the pitch attitude to make the speed continue to decrease in this phase, so that the aircraft will touch the ground at a relatively small vertical speed, as shown in Figure 2. The phase when the aircraft touches the ground and the subsequent movement on the runway until the aircraft ground speed reduces to specific level is called the landing phase.  
_Adverse event_: In this paper, the adverse event is specifically referred to as the events in which the pilot applies forward stick inputs during the flare phase. In this phase, both airspeed and vertical speed of the aircraft gradually decrease, and the lift continues to decrease. In order to make the lift of the aircraft approximately equal to gravity and allow the aircraft to slowly descend close to the ground, the pilot should maintain the pitch attitude of aircraft, help increase the lift to reduce the aircraft's vertical speed, and finally touch the ground smoothly [39]. It is generally required by airlines that pilots should not apply any forward stick inputs during the flare. However, our statistical results from a large number of flights show that this unsafe pilot stick operation occasionally happens. Specifically, from the QAR data of 86,504 flights covering A320 and A321 models at domestic airports in China from May 2017 to December 2018, we observe a total number of 11,385 flight samples with the adverse event. The above unsafe pilot operation may eventually result in an excessive vertical overload at touchdown and cause serious damage to the aircraft's landing gears.  
In order to uncover the typical reasons for the adverse event, an innovative approach based on unsupervised K-means clustering algorithm is established in this paper. Our approach is divided into three parts. Specifically, we first preprocess the flight data and select key features based on expert experience. Then, the processed data are input into K-means algorithm to automatically classify them into different clusters. Finally, we investigate the classification results to reveal different reasons for the adverse events. We also analyze the performance of different types of adverse events from different airlines, airports, and pilots.  
Figure 2: Normal pitch control during flare phase.
## 3 Methodology
### Multivariate Linear Regression (MLR) Model  
MLR aims to build an appropriate mathematical equation that relates a set of independent variables (i.e., predictors or causal variables) to the dependent variable (i.e., response variable). Specifically, the dust effect (represented in terms of exposure days (\(D\))) and the average daily ambient temperature (\(T\)) are considered as independent variables, whereas the daily system conversion efficiency (\(\eta\)) is considered as the dependent variable. Thus, the mathematical equation provided by the MLR can be written as follows [54]:  
\[\eta_{i}=a_{o}+a_{1}D_{i}+a_{2}T_{i}+\epsilon_{i}, \tag{2}\]  
where \(\eta_{i}\) is the \(i\)-th daily system conversion efficiency, \(D_{i}\) is the \(i\)-th exposure day, \(T_{i}\) is the \(i\)-th average daily ambient temperature, \(a_{o}\) is the regression model intercept, \(a_{1}\) and \(a_{2}\) are the regression coefficients, and \(\epsilon_{i}\) is the \(i\)-th error of the daily system conversion efficiency (i.e., the difference between the true and predicted daily system conversion efficiencies of the \(i\)-th day), \(i=1,\ldots,n\).
# Innovative Approaches for Minimizing Disinfection
## 6 Recent Research Trends  
Table 2 presents the recent research trends via selected publications outlined in the relevant scientific literature during the last five years. During scientific literature search, it can be observed that for all sectors described during the above sections, there has been a significant increase in the number of publications and reported progress/advances as well as in the variety of applied/tested techniques.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**DBPs Minimization** & **Techniques** & **Reference** \\ \hline \multicolumn{3}{c}{**Precursors and/or DBPs removal**} \\ \hline Coagulation & Enhanced coagulation & [60] \\ Coagulation & Low-level ferrous iron & [61] \\ Coagulation & Tri-protonated ferrate as preoxidant/coagulant & [62] \\ Various techniques & Pre-ozonation, coagulation–sedimentation, sand filtration, and ozone combined with & [63] \\  & biological activated carbon (O\({}_{3}\)-BAC) & \\ Various techniques & Adsorption, boiling, membrane filtration & [64] \\ Adsorption & Anion-exchange resin adsorption followed by electrolysis & [65] \\ Adsorption & Carbon nanomaterial-based adsorbents & [66] \\ Adsorption & Graphene oxide/ferrithydrite & [67] \\ Adsorption & Granular activated carbon & [68] \\ Adsorption & Nanoscale silver supported on activated carbon & [69] \\ Adsorption & GAC & [70] \\ Adsorption & Hydrolyzed polyacrylamide UF membrane & [71] \\ Adsorption & Biological activated carbon & [72] \\ Adsorption & Biological biochar and activated carbon filters & [73] \\ Adsorption & Nanofliltration & [74] \\ AOPs & Heterogenous photocatalysis followed by granular activated carbon & [68] \\ AOPs & Pre-oxidation by ozone, permanganate, and ferrate & [75] \\ AOPs & Pre-oxidation by ozone, permanganate, and ferrate & [76] \\ AOPs & Solar heterogeneous photocatalysis & [30] \\ AOPs & Pre-oxidation by ozone, chlorine dioxide, permanganate, and ferrate & [64] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Current research trends in DBPs minimization research.  
DBPs continue to be a crucial issue related to human health and environmental protection; the technological achievements since their discovery 50 years ago are impressive and, currently, there are evolving novel methods that, when combined/optimized, can be of high value towards achieving actual DBP removal/minimization in treated waters. Major developments have been performed in recent years, from DBP precursors and formation examination and interpretation to the detection of different compounds at trace levels and toxicity testing. Adsorption techniques, AOPs, and alternative disinfectants have been applied, and they have shown very promising results for water quality improvement. Currently, machine learning approaches that are developed and applied can also contribute to the confrontation of DBP issues, through valorizing datasets obtained to estimate/model DBPs concentrations, therefore assisting in the selection and application of the most effective minimization techniques.