esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Extraction and Analytical Methods
### Extraction Techniques
This method can be optimised by modifying some of the parameters that affect extraction efficiency, such as extraction solvent type and volume, sample quantity, pH, salt selection, etc. [54]. Another simple and fast technique of PAEs' extraction is accelerated solvent extraction (ASE); this method is based on the use of high temperatures (above the solvents' boiling points) and pressures and allows a greater efficiency of the extraction of analytes from the matrix [52]. An alternative extraction technique for solid matrices is thermodesorption (TD); this green technique does not require sample preparation treatments and allows a direct analysis of the sample that is heated vaporised and analysed with high reproducibility and quality of results [52]. A summary of the most important techniques used for PAEs from environmental matrices is reported in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Phthalates Extraction in Different Environmental Matrices** & **Extraction Methods** & **Type of Extraction Procedures** \\ \hline \multirow{6}{*}{Water} & Liquid–liquid extraction (LLE) & Separation funnel \\  & & Ultrasound/vortex to assist LLE \\ \cline{2-3}  & & Solid-phase extraction (SPE) \\  & Liquid–solid extraction (LSE) & Dispersive solid-phase extraction (d-SPE) \\  & & Solid-phase microextraction (SPME) \\  & & Stir bar sorptive extraction (SBSE) \\ \hline \multirow{6}{*}{Water} & \multirow{6}{*}{Hybrid method} & Solid–supported LLE (SLE) \\  & & Liquid-phase microextraction (LPME) \\ \cline{1-1}  & & Single-drop microextraction (SDME), \\ \cline{1-1}  & & Hollow-fibre LPME (HF-LPME) \\ \cline{1-1}  & & Dispersive liquid–liquid microextraction (DLLME) \\ \cline{1-1}  & & Microporous membrane LLE (MMLLE) \\ \hline \multirow{6}{*}{Sediments and Biota} & \multirow{2}{*}{Solid–liquid extraction (SLE)} & Ultrasound/vortex to assist SLE \\  & & Soxhlet extraction \\ \cline{1-1}  & & Accelerated solvent extraction (ASE) \\ \cline{1-1} \cline{2-3}  & & Solid extraction (SE) \\ \hline Water, Sediments and & Quick, easy, cheap, effective, & \\ Biota & rugged, and safe (QuECHERS) & Combination of LLE and d-SPE \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the most used extraction method for PAEs from environmental matrix.
## 3 Improved Multiobjective Particle Swarm Optimization Algorithm  
### Multiobjective Particle Swarm Optimization Algorithm  
The MOPSO algorithm is the name of the PSO algorithm used to solve multiobjective optimization problems. Both update the velocity and position of the particle in the same way. The PSO algorithm updates the velocity and position of a particle through two \"guides\": the optimal solution found by the particle (i.e., the individual guide \(p_{best}\)) and the optimal solution found by the entire population thus far (i.e., the global guide \(g_{best}\)). In the PSO algorithm, the velocity and position update with the following conditions:  
\[v_{id}(t+1)=wv_{id}(t)+r_{1}c_{1}(p_{best}-x_{id}(t))+r_{2}c_{2}(g_{best}-x_ {id}(t)) \tag{11}\]  
\[x_{id}(t+1)=x_{id}(t)+v_{id}(t+1) \tag{12}\]  
In the \(d\)-dimension direction of search space, \(v_{id}(t)\) and \(x_{id}(t)\) mean the velocity and position of the \(i\)-th particle in the \(t\)-th iteration respectively, \(c_{1}\) and \(c_{2}\) are acceleration factors of individual and global optimal particles, respectively, \(r_{1}\) and \(r_{2}\) are random numbersbetween [0, 1], \(p_{ibestd}\) is the individual optimal position found by the \(i\)-th particle until the \(t\)-th iteration, \(g_{bestd}\) is the global optimal position found by the entire population until the \(t\)-th iteration, \(v_{id}(t+1)\) and \(x_{id}(t+1)\) indicate the updated velocity and updated position of the \(i\)-th particle obtained after the \(t+1\) iteration, respectively.  
Although there is no difference between MOPSO and PSO in formula expression, MOPSO is different from PSO in the following ways. Firstly, MOPSO is suitable for solving MOPs, so the updating and selection methods guided by individuals and globally are quite different from PSO. In addition, the solution obtained by MOPSO is a non-inferior solution. Moreover, MOPSO algorithm has an archive dedicated to storing the global optimal solution currently found by particles. Researchers have studied and improved MOPSO from many aspects and made some meaningful achievements [32; 33; 34]. Even though MOPSO has been widely used in practical problems, there are still problems such as premature convergence and poor accuracy in searching global optimal solution.
###### Abstract  
Liver diseases are one of the most detrimental conditions that may cause inflammation, leading to tissue damage and perturbations in functions. Several drugs are conventionally available for the treatment of such diseases, but the emergence of resistance and drug-induced liver injury remains pervasive. Hence, alternative therapeutic strategies have to be looked upon. Epigallocatechin-3-gallate (EGCG) is a naturally occurring polyphenol in green tea that has been known for its disease-curing properties. In this study, we aimed to evaluate its anti-oxidative potential and protective role against diethylnitrosamine (DEN)-induced liver injury. Four different groups of rats were used for this study. The first group received normal saline and served as the control group. The second group received DEN (50 mg/kg body wt) alone and third group received DEN plus EGCG (40 mg/kg body wt) only. The fourth group were treated with EGCG only. The liver protective effect of EGCG against DEN toxicity through monitoring the alterations in aspartate transaminase (AST), and alanine transaminase (ALT) and alkaline phosphatase (ALP) activities, serum level of pro-inflammatory mediators and anti-oxidant enzymes, histopathological alterations, measurement of cellular apoptosis, and cell cycle analysis was examined. The rats that were given DEN only had a highly significantly elevated levels of liver enzymes and pro-inflammatory cytokines, highly decreased anti-oxidative enzymes, and histological changes. In addition, a significant elevation in the percentage of apoptotic nuclei and cell cycle arrest in the sub- G1 phase was detected. EGCG acts as a hepatoprotectant on DENs by reducing the serum levels of liver functional enzymes, increasing total anti-oxidative capacity, reducing pathological changes and apoptosis, as well as causing the movement of cells from the sub G1 to 5 or G2/M phase of the cell cycle. In conclusion, EGCG displayed a powerful hepatoprotective additive as it considerably mitigates the liver toxicity and apoptosis induced by DEN.  
DEN; liver; inflammation; ultra-structural changes; oxidative stress; EGCG
# Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach
## 5 Conclusions and Future Work  
### Conclusions  
This study demonstrated the efficacy of temporal convolutional networks in projecting carbon tax prices, achieving significantly improved accuracy compared to traditional forecasting methods. By leveraging the power of deep learning, our approach offers a valuable tool for policymakers and stakeholders navigating the complex landscape of carbon taxation.  
The key findings of our study include: (1) The TCN model consistently outperformed the ARIMA baseline, with a 31.4% reduction in MAE and a 32.7% reduction in RMSE across all countries in our dataset. (2) The TCN model maintained superior performance across different time horizons, from 1-year to 5-year projections, indicating its versatility for both short-term and long-term forecasting. (3) Our model demonstrated better adaptability to sudden policy changes compared to traditional approaches, with an average error increase of 39.8% after major policy shifts, compared to 95.6% for ARIMA. (4) Feature importance analysis revealed the complex interplay of factors influencing carbon tax prices, with historical prices, GDP, and energy consumption emerging as the most significant predictors.  
The superior performance of the TCN model underscores the potential of advanced machine learning techniques in enhancing climate policy decisions. As carbon pricing continues to play a crucial role in global efforts to mitigate climate change, accurate price projections will become increasingly important for effective policy design and implementation.
## 3 PSS Signal Detection at UE
### GLRT Performance  
The performance of the GLRT detector is directly related to the test values such as the probability of false alarm \(P_{FA}\) (i.e., the signal is deemed present when it is not), probability of detection \(P_{D}\) (i.e., the signal is deemed present when it is), and probability of misdetection \(P_{M}\) (i.e., the signal is deemed absent when it is present). These test values are calculated as follows [30; 31]:  
\[P_{FA}=\int\limits_{t^{\prime}}^{\infty}f_{H_{0}}(\Lambda)d\Lambda \tag{9}\]  
\[P_{D}=\int\limits_{t^{\prime}}^{\infty}f_{H_{1}}(\Lambda)d\Lambda \tag{10}\]\[P_{M}=1-\int\limits_{t^{\prime}}^{\infty}f_{H_{1}}(\Lambda)d\Lambda=1-P_{D} \tag{11}\]  
where \(f_{H_{0}}(\Lambda)\), \(f_{H_{1}}(\Lambda)\) are the probability density functions of the test statistics \(\Lambda\) computed in Equation (6) under hypotheses \(H_{0}\) and \(H_{1}\), respectively. It should be noted that there are no analytical expressions for \(f_{H_{0}}(\Lambda)\) and \(f_{H_{1}}(\Lambda)\) because the test statistics \(\Lambda\) are calculated numerically based on Equation (6). Therefore, \(f_{H_{0}}(\Lambda)\) and \(f_{H_{1}}(\Lambda)\) are estimated numerically based on a large number of Monte Carlo simulations [40]. Since the detection of the PSS signal in the initial cell search is categorized as CFAR detection, the value of \(P_{FA}\) will be constant throughout the test and is independent of parameters such as the SNR level or received signal sampling rate. The value of threshold level \(t^{\prime}\) is then calculated numerically using Equation (9). Since \(\Lambda\) depends on the total energy received and matched-filter output, \(t^{\prime}\) is dependent on the SNR level.
# Coordinate Descent for Variance-Component Models
## 3 Parameter-Expanded CD
### Linear Mixed Model Implementation  
We now show that, for Linear Mixed Models (LMM), we can reduce the computational complexity of each sub-problem to \(O(jd^{2})\), for some constants \(j\ll n\) and \(d<n\). As shown in Section 3.1, solving each univariate sub-problem can be simplified to implementing [PERSON]'s method, where at each Newton's update, we solve two \(n\)-dimensional linear systems.  
In settings where \(\operatorname{rank}(\mathbf{\mathrm{V}}i)<n\), for \(i=1,\ldots,m\), we are able to reduce the dimensions of the linear system that is required to be solved. To see this, let us first specify the general variance-component model (also known as the general mixed ANOVA model) [19]. Suppose,  
\[\mathbf{y}=\mathbf{X}\beta+\mathbf{Z}_{1}\mathbf{b}_{1}+\cdots+\mathbf{Z}_{m}\mathbf{b}_{m}+\mathbf{\epsilon}, \tag{19}\]  
where \(\mathbf{X}\) is an \(n\times p\) matrix of known fixed numbers, \(p\leq n\); \(\beta\) is an \(p\times 1\) vector of unknown constants; \(\mathbf{Z}_{i}\) is a full-rank \(n\times c_{i}\) matrix of known fixed numbers, \(c_{i}\leq n\); \(\mathbf{b}_{i}\) is an \(c_{i}\times 1\) vector of independent variables from \(\mathcal{N}(0,\gamma_{i})\), which are unknown and \(\mathbf{\epsilon}\) is an \(n\times 1\) vector of independent errors from \(\mathcal{N}(0,\gamma_{0})\) that are unknown. In this setup, \(\gamma_{0},\dots,\gamma_{m}\), are the variance component parameters that are to be estimated, \(\mathbf{V}_{i}=\mathbf{Z}_{i}\mathbf{Z}_{i}^{\top}\) and \(\mathbf{\Omega}=\gamma_{0}\mathbf{\Omega}+\mathbf{Z}\mathbf{\Sigma}\mathbf{Z}^{\top}\), where  
\[\mathbf{Z}=\begin{bmatrix}|&|&&|\\ \mathbf{Z}_{1}&\mathbf{Z}_{2}&\cdots&\mathbf{Z}_{m}\\ |&|&&|\end{bmatrix},\quad\mathbf{\Sigma}=\textbf{block diag}(\gamma_{1}\mathbf{I}_{c_{1} },\dots,\gamma_{m}\mathbf{I}_{c_{m}}).\]  
We now provide two methods that take advantage of \(\mathbf{V}_{i}\) being low-rank. Let,  
\[c:=\sum_{i=1}^{m}\text{rank}(\mathbf{V}_{i})=\sum_{i=1}^{m}c_{i}.\]  
In the first method, we utilize a QR factorization that reduces the computational complexity when \(c<n\), i.e., the column rank of \(\mathbf{Z}\) is less than \(n\). In the second method, we use the Woodbury matrix identity to reduce the complexity when \(c_{i}<n\) for \(i=1,\dots,m\), i.e., the column rank of each of the matrices \(\mathbf{Z}_{i}\) is less than \(n\).
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 4 Modeling
### Multi-Type Ground Support Vehicle Scheduling Model
#### 4.3.2 Objectives of the Second-Stage Model  
1. Minimize the driving distance of ground support vehicles. The objective function is shown in Equation (12). Minimizing the driving distance aims to reduce vehicle operating costs and avoid extensive movement of vehicles within the operational area, thereby enhancing safety. Since vehicles such as water trucks and baggage tractors need to travel to resource replenishment points or baggage handling centers, the driving distance includes the distances traveled by vehicles to other service locations between their assignments. \[minZ_{1}=\sum_{v\in V}\sum_{n\in N_{v}}\sum_{i,j\in F}s_{ij}^{v}\cdot\varphi_{ ijn}^{v}\] (12)
2. Minimize the number of ground support vehicles used. The objective function is shown in Equation (13). Minimizing the number of vehicles used not only aims to enhance service efficiency and explore the minimum resource requirements for completing support tasks but also serves to reserve vehicles within limited resources to respond to unforeseen circumstances. \[minZ_{2}=\sum_{v\in V}\sum_{n\in N_{v}}\phi_{n}^{v}\] (13)
3. Minimize the waiting time of ground support vehicles.  
The objective function is shown in Equation (14). Minimizing waiting times helps prevent vehicles from staying in the same location for extended periods, thereby increasing their effective usage time.  
\[minZ_{3}=\sum_{v\in V}\sum_{n\in N_{v}}\sum_{i\in F}\left(ST_{in}^{v}-AT_{in}^{v}\right) \tag{14}\]
## 5 Discussion  
In this paper, we propose a smart service robot for elderly and disabled people, presented in multiple subsystems synchronized together. The system is implemented on a shopping cart with a robotic arm equipped with a lifting mechanism to reach high shelves. Then, a scanner with a camera is used to perform automatic scanning based on barcode detection. A validation system with a camera and load cells viewing the cart is used to validate scanned items and alert of non-scanned items based on a computer vision pipeline. The manual control system consists of a robotic arm, a linear actuator, and a controller that controls both. The user grabs items from the shelves through this system, then initiates the automated scanning system. The barcode recognition network is developed based on YOLOv2 architecture trained over 31,500 augmented samples. Three feature extraction layers are tested: pre-trained VGG19, pre-trained DarkNet19, and TinyYOLO network trained from scratch. The results showed a maximum mAP of 92.4%, 86.4%, and 78.4%, with a frame rate of 6.2, 11, and 27 FPS for each network architecture. A validation system is implemented to increase the reliability and robustness of the system. GMM and Kalman filter are used to detect newly inserted items in the cart. SURF features are combined with the hue value of the HSI color space together with the weight value measured by the load cells to validate that the new item is indeed the one has been scanned. The system shows robustness in different scenarios, including detecting unscanned items, items of the same appearance but different weight and items with the same weight but different appearance. The system shows mechanical limitations with the lifting mechanism; the linear actuator can take up to 20 s to lift the arm to the highest level, 500 mm, and bring it back.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 4 Proposed Model
### Detection of Anomalies
#### 4.4.3 Anomaly Detection Score  
The anomaly detection is determined by the two losses above. Calculate the anomaly detection loss based on \(D_{\mathrm{loss}}\) and \(G_{\mathrm{loss}}\), AD-Score:  
\[ADS=\alpha G_{\mathrm{loss}}\,+(1-\alpha)D_{\mathrm{loss}} \tag{7}\]  
Moreover, \(\alpha\) represents the weight of reconstruction loss and discrimination loss, and \(\alpha\) can be set empirically. Then mark the \(X^{\mathrm{test}}\) timestamp according to the obtained \(ADS\): if \(ADS>\tau\), Label \({}^{\mathrm{test}}\,=1\) which represents abnormal data, otherwise Label \({}^{\mathrm{test}}\,=0\) which represents normal data.  
The overall algorithm is summarized in Algorithm 1.  
```
epoch \(\Leftarrow\) 0, \(G_{Trans}\), \(D_{Trans}\Leftarrow\) initialize network parameters ifepoch within number of training iterations then for the \(k^{th}\) epoch do  Generate samples from the random space: \(X=\{x_{i},i=1,2,...,n\}\) \(Z=\{z_{i},i=1,2,...,n\}\) \(\Rightarrow\)\(G_{Trans}(X,Z)\)  discrimination: \(X=\{x_{i},i=1,2,...,n\}\Rightarrow\)\(D_{Trans}(X)\) \(G_{Trans}(X,Z)\Rightarrow\)\(D_{Trans}(G_{Trans}(X,Z))\)  Update Transformer discriminator parameters: \(\min\frac{1}{n}\sum_{i=1}^{n}[-\log D_{Trans}(x_{i})-\log(1-D_{Trans}(G_{Trans} (x_{i},z_{i})))]\)  Update Transformer generator parameters: \(\min\sum_{i=1}^{n}\log(-D_{Trans}(G_{Trans}(x_{i},z_{i})))\)  Record parameters in the current iteration endfor endif fornumber of iterations do  Find the best generated sample: \(Z^{k}=\min\limits_{z}\mathrm{E}\left(X^{\mathrm{test}},G_{Trans}\left(x^{text {test}},z^{i}\right)\right)\) endfor  Reconstruction loss: \(G_{loss}=\left|X^{test}-G_{Trans}\left(X^{test},Z^{k}\right)\right|\)  Discriminate loss: \(D_{loss}=D_{Trans}\left(X^{test}\right)\)  Anomaly Detection Score: \(ADS=\alpha G_{loss}+(1-\alpha)D_{loss}\)
```  
**Algorithm 1** Transformer-GAN Anomaly Detection Algorithm
# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
###### Abstract  
To address the challenges of low-light images, such as low brightness, poor contrast, and high noise, a network model based on deep learning and Retinex theory is proposed. The model consists of three modules: image decomposition, illumination enhancement, and color restoration. In the image decomposition module, dilated convolutions and residual connections are employed to mitigate the issue of detail loss during the decomposition process. The illumination enhancement module utilizes a set of mapping curves to enhance the illumination map. The color restoration module employs a weighted fusion of a 3D lookup table (3 DLLUT) to mitigate color distortion in the images. The experimental results demonstrate that the proposed algorithm effectively improves the brightness and contrast of low-light images while addressing the issues of detail loss and color distortion. Compared to other algorithms, it achieves better subjective and objective evaluations.  
low-light images; image enhancement; deep learning; Retinex theory +
Footnote †: journal: Applied Sciences  
1
## 2 Materials and Methods
### Input Data for System Loss Calculations  
Calculating angular and temperature losses requires input data of irradiance, ambient temperature, and, eventually, wind speed. These data can be obtained from online databases and resources, ideally from experimental measurements conducted under operating conditions as close as possible to those of the intended BIPV system.  
If experimental data are unavailable, which is generally the case, the necessary meteorological information must be obtained from alternative sources. The PVGIS and Copernicus Atmosphere Monitoring Service (CAMS) are monitoring services that provide time data series of the required variables. CAMS solar radiation services obtain the solar irradiance components--global horizontal irradiance (\(GHI\)), direct normal irradiance (\(DNI\)) and diffuse horizontal irradiance (\(DHI\))--from Meteosat satellite imagery [47; 48; 49; 50; 51; 52; 53]. They are based on the Heliosat model for deriving solar irradiance from satellite imagery from Meteosat platform satellites (covering Europe, Africa, the Atlantic Ocean, and the Middle East). CAMS assessment studies using Baseline Surface Radiation Network (BSRN) ground stations have provided high quality and low uncertainty in solar radiation components [54]. POA irradiance calculations for all possible tilt and azimuth angles were performed usingthe pvlib python library v0.11.0 [55], which includes specific functions for applying the Perez transposition model [56; 57] and retrieving CAMS radiation data.
# Photonic Stopband Filters Based on Graphene-Pair Arrays
## 4 Conclusions  
In conclusion, multiple photonic bandgaps and their regulations were investigated in a complex system composed of a bulk dielectric and graphene pairs. Graphene pairs arrange in the substrate material to form periodic graphene arrays. Series of bandgaps are induced in transmission spectra and the central bandgap frequencies and widths can be tuned by the graphene chemical potential. The interval between to adjacent bandgaps is inversely proportional to the spatial period of the graphene pair. The transmittance of passbands between the bandgaps is a function of the phenomenological relaxation time as well. Furthermore, by scanning the incident angle and light frequency, two full passbands can be achieved around the first and second bandgaps. The study could provide a feasible scheme for photonic stopband filters.  
Software, D.Z. ([PERSON]) and L.W.; formal analysis, D.Z. ([PERSON]) and M.W.; investigation, L.W. and E.L.; resources, L.W.; data curation, M.W.; writing--original draft preparation, D.Z. ([PERSON]); writing--review and editing, F.L., D.Z. ([PERSON]) and M.W.; visualization, L.W.; supervision, [PERSON] ([PERSON]) and M.W.; project administration, M.W.; funding acquisition, [PERSON] ([PERSON]), L.W., F.L., [PERSON] ([PERSON]) and M.W. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (NSFC) (51975542); the Scientific Research Project of Hubei University of Science and Technology (BK202017, 2021-23 GP01, 2022-23X11, 2021-22X21), the Science and Technology Plan Research Project of Hubei Education Department (B2020155).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
Currently, neural network algorithms based on time-domain features are used for change-point detection problems, and they have proven to be effective. However, due to the instability of human biosignals, establishing a training dataset with labels is difficult. For supervised learning methods, wherein parameters are updated on a small sample set through a feed-forward mechanism, it is difficult to ascertain the degree to which the performance of the trained neural network corresponds to the overfitting of the dataset upon which the network was trained. To this end, this paper attempted to directly replace the parameters in the convolutional neural network that need to be updated by training. A method based on the combination of the Teager-Kaiser energy operator (TKEO) and the convolutional neural network is proposed. We tested the proposed method on simulated EMG data with different signal-to-noise ratios and real data with labels, respectively. Compared with multiple detection methods, the proposed method had significant advantages in terms of reliability, accuracy, and computational speed. Furthermore, the proposed method does not require any prior knowledge about the signal, lending itself to be flexible and adaptable to any application. It may be a promising alternative to solving change-point detection problems.  
convolutional neural network; energy operator; change-point detection; EMG Article