esa-sceva/satcom-chunk-collection huggingface dataset:
# Bioremediation Methods for the Recovery of Lead-Contaminated Soils: A Review
## 4 Bioremediation via Fungi and Bacteria
### Bioremediation by Fungi
biliaie_ with a corresponding pH of 3.5-4. The production by _Penicillium_ sp. gave concentrations below 1 mM and pH generally above 6.5 regardless of the presence of nutrients. The production of LMWOAs led to the mobilization of lead from the soil, especially in the presence of nutrients, up to a maximum release of 12% of Pb after 5 days. The production of chelating agents, especially citric acid, combined with the consequent decrease in pH, produced an enhanced release of lead from the soil. The biosorption capacity of _A. niger_ for lead present in high concentrations in a heavy-metals-contaminated soil in an Indian peri-urban agricultural area was investigated by varying pH and temperature. The fungal strain showed the maximum absorption efficiency toward Pb (21.5%) in the pH range 4 to 5.4; the absorption capability was lowered at higher pH values, probably due to an accumulation of metal ions inside intrafibrillar capillaries of the cell walls or inside cells following a microprecipitation mechanism. In regard to the temperature effect, _A. niger_ showed a considerable absorption efficiency in a wide range of temperature, with the maximum biosorption efficiency (45.5%) recorded at 37 \({}^{\circ}\)C [73]. Another fungal strain, _Phanerochaete chrysosporium_, showed a good capacity to reduce the concentration of soluble-exchangeable lead thanks to the following:  
1. the chelation between the fungus and lead by means of active functional groups, such as carboxylic or hydroxyl groups present on the surface of the fungus cell walls,
2. an enhanced decomposition of organic matter with the consequent augmentation of the humic substances content and capability to immobilize Pb,
3. an increase in pH with the resulting reduced solubility of the metal [74].
## 4 Numerical Simulation and Analysis
### Model Reliability Verification  
Comparing the numerical simulation results with the field-measured data is an important means to verify the feasibility and accuracy of the numerical model. This method has been widely verified and recognized. To verify the reliability of the model, the simulation results of each phase are compared with the deep horizontal displacement monitoring data of the foundation pit, and the RMS Error (Root Mean Square Error) of each monitoring point is calculated. The simulation results for the horizontal displacement show overall agreement with the measured data in terms of both the change trend and the numerical value; some of the comparison results are shown in Figure 11, and the corresponding RMS Error is shown in Table 3. There is a certain difference between the simulated value and the measured value of monitoring point CX1, and its RMS Error is significantly larger than that of other monitoring points, which is closely related to the quality problems of the supporting structure near the location. The simulated values of the horizontal cumulative  
\begin{table}
\begin{tabular}{c c c} \hline
**Phase** & **Construction Project** & **Time (Days)** \\ \hline
1 & Construction of the SMW pile & 30 \\
2 & Dewatering, excavating the soil to \(-1.7\) m. & 7 \\
3 & Dewatering, excavating the soil to \(-3.4\) m. & 7 \\
4 & Construction of the grouting steel flower tube. & 3 \\
5 & Pile foundation structure in the construction pit. & 5 \\
6 & Construction of the ring enclosure structure. & 5 \\
7 & Dewatering, excavating the soil to \(-5.1\) m. & 7 \\
8 & Dewatering, excavating the soil to \(-6.8\) m. & 7 \\
9 & Dewatering, excavating the soil to \(-8.5\) m. & 7 \\  & Dewatering, excavating the soil to the bottom of & \\
10 & the foundation pit (\(-9.2\) m), and constructing the & 10 \\
11 & Construction of the Larsen steel sheet pile. & 1 \\  & Local precipitation, excavating the soil to the & \\
12 & bottom of the elevator shaft (\(-11.85\) m), and & 3 \\  & constructing the sidewalls and cushions. & 13 \\
13 & Pull out the Larsen steel sheet pile. & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Construction process.  
displacement of monitoring points CX3 and CX4 are slightly larger than the measured values because the foundation pit earthwork is not transported away from the site in time, which also shows that earthwork accumulation plays a role in back pressure. The parameters of the model are derived from an actual project, and the simulation results are verified by comparison with the measured results. Thus, the numerical model is reliable.
# Remote Sensing Analysis of Geologic Hazards
## 6 Glaciers and Sand Dunes  
[PERSON] and [PERSON] [38] conducted a methodological study that compared the performances of fifteen digital image correlation functions and proposed a new similarity index (DOT). They analyzed the outcomes considering four template sizes and thirteen types and levels of noise applied on a shaded relief of a digital elevation. They conducted the same comparison on optical images of a glacier (acquired by a terrestrial camera) and the dunes of the Bodele Depression (Chad) (acquired by Sentinel-2). Overall, they observed that using orientation images provided the best performances in the presence of shadows or snow patches, adopting either frequency-based or DOT correlations.
###### Abstract  
In this paper, a high-performance 0.34 THz sub-harmonic mixer combining two-stage reduced matching technology with an improved active circuit model is established and analyzed. The mixer's improved active circuit model is realized by decomposing passive functional models into basic transmission line units and their impedance matching and filtering are realized through automatic optimization. The improved active circuit model takes out the RF (radio frequency) transition model separately to set an optimization goal instead of operating directly in the mixing circuit. Compared with traditional active circuit models in the SDM (subdivision design method) and GDM (global design method), it provides a massive optimization space, larger working bandwidth, and better results. In the RF frequency range of 320-360 GHz, the SSB (single sideband) conversion loss of the 0.34 THz sub-harmonic mixer is below 9.5 dB and the RF return loss is less than 12 dB.  
Schottky diodes; terahertz technology; terahertz sub-harmonic mixer +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Loss Functions  
For sparse-view LDCT reconstruction, designing the loss function in the sinogram domain is the most natural choice, which is calculated as:  
\[l_{sine}=\left\|\hat{\mathbf{Y}}_{S}-\mathbf{Y}_{S}\right\|_{F}^{2} \tag{4}\]  
where \(\hat{\mathbf{Y}}_{S}\) is the output from the network. \(\mathbf{Y}_{S}\) is the expected output extracted from the full-dose sinogram.  
Since the purpose of LDCT reconstruction is to produce precise CT images, it is logical to design the loss function in the image domain as well. Assuming that \(\mathbf{y}\) is the full-dose sinogram projected from the CT image \(\mathbf{x}\) with the imaging model in (6), the FBP reconstruction defined in (2) will inevitably introduce reconstruction error even if its input is the full-view sinogram \(\mathbf{y}\), which means that a zero-deviation sinogram for the loss \(l_{sine}\) does not necessarily result in a CT reconstruction image with zero deviation from the original CT image, i.e., \(\mathbf{x}\
eq\mathbf{x}\). To harmonize the sinogram network and the FBP module, we push back the loss calculation to the image domain,  
\[l_{image}=\left\|\hat{\mathbf{x}}-\mathbf{x}\right\|^{2} \tag{5}\]  
where \(\hat{\mathbf{x}}\) is the reconstructed CT image output by the FBP module and \(\mathbf{x}\) denotes the original full-dose CT image used to generate the sinogram domain training data. Since the FBP module is linear (see (2)), the errors in the image domain can be effectively propagated to the network layers in the sinogram domain using the back propagation algorithm.  
Supervised training of the sinogram interpolation network with the image domain loss \(l_{image}\) can compensate to some extent for the errors generated by FBP. In the subsequent experimental section, we quantitatively compare and evaluate the two loss functions defined in (3) and (4), testing their performances under different sparse levels. To the best of the authors' knowledge, this study represents the first systematic investigation into the combined impact of sinogram interpolation and FBP reconstruction on LDCT reconstruction and into the ability of the sinogram domain network to reconcile FBP reconstruction errors at different sparse levels.
## 2 Materials and Methods
### Antioxidant Activity Assay on Cell Culture
#### 2.5.1 SH-SYSY Cell Viability  
Human neuroblastoma cells (SH-SYSY, ATCC(r) CRL-2266) were cultured in DMEM F-12 supplemented with 10% (\(v/v\)) fetal bovine serum (FBS), 1% of L-glutamine, 1% of penicillin/streptomycin, and maintained at 37 \({}^{\circ}\)C in a humidified incubator with 5% CO\({}_{2}\). Once the cells reached their logarithmic growth phase, differentiation into a neuronal phenotype was induced with 10 uM retinoic acid (RA) for 7 days. Differentiated SH-SYSY cells were plated in a 96-well culture plate and incubated at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\) for 24 h to facilitate attachment of the cells to the bottom of the wells. Then, the cells were exposed to ten two-fold serial dilutions (ranging from 0.5 to 250 ug/mL) of stem and SFH methanolic extracts of three different globe artichoke genotypes: Ote 1 F4 P10, Ote 2 F7 P2 and Grato 1. The highest DMSO treatment (0.1% DMSO solution) was used as a solvent control. The media containing the extracts was removed, and 0.5 mg/mL of MTT in culture medium was added. Following 3 h of incubation, the MTT solution was discarded, and DMSO was introduced to solubilize the purple-colored formazan product. Absorbance was measured at 595 nm using a microplate reader (Tecan SunriseTM UV-Vis spectrophotometer). The half-maximal effective concentration (EC\({}_{50}\)) for each biological replicate was determined using a log-response curve, with the EC\({}_{50}\) values calculated from three independent biological experiments [11].  
To evaluate the ability of stem and SFH extracts from the three different genotypes to protect against oxidative stress, five no-cytotoxic concentrations diluted one to two were used (0.39 to 6.25 ug/mL for stem extracts and 12.5 to 200 ug/mL for SFH extracts). After 24 h, oxidative stress was induced by exposing the cells to 700 uM H\({}_{2}\)O\({}_{2}\) for 1 h. Cell viability was then assessed by measuring MTT reduction, as previously described.
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 2 Problem Formulation and Proposed Algorithm Description
### Improved Square Sine Wave Interpolation Algorithm
#### 2.3.1 SSWI Function and Spectral Characteristic Analysis  
The discrete signal converted by the TOA sequence is \(x[n]\), \(n\in[1,N]\), where \(N\) is the number of pulses. \(x[i](1\leq i\leq N)\) is TOA of the \(i\)-th pulse. The difference interval of adjacent pulses is  
\[\Delta t_{i}=x[i+1]-x[i],i=1,2,\cdots,N-1 \tag{4}\]  
The SSWI function is a sine wave with equal period and amplitude. The result of the SSWI function is as follows.  
\[\begin{cases}s_{i}(t)=(\Delta t_{i})sin(\frac{2\pi}{\Delta t_{i}}(t-x[i]))\\ x[i]<t\leq x[i+1],i=1,2,\cdots,N-1\\ s(t)=\sum_{i=1}^{N-1}s_{i}(t)\end{cases} \tag{5}\]  
The following formula derivation sets \(x[1]=0\). For a discrete signal with the fixed PRI, the difference interval of the discrete signal is fixed, that is, \(\Delta t=\Delta t_{i},i=1,2,\cdots,N-1\). The SSWI function for the \(i\)-th pulse interval is  
\[\begin{split} s_{i}(t)&=\Delta t_{i}sin(\frac{2\pi}{ \Delta t_{i}}(t-x[i]))\\ &=\Delta t_{i}sin(\frac{2\pi}{\Delta t_{i}}(t-(i-1)\Delta t))\\ &=\Delta t_{i}sin(2\pi\frac{t}{\Delta t_{i}}),x[i]<t\leq x[N]\end{split} \tag{6}\]  
The SSWI function of the discrete signal can be set as  
\[s(t)=\Delta tsin(2\pi\frac{t}{\Delta t}),x[1]\leq t\leq x[N] \tag{7}\]  
It shows that the discrete signal with the fixed PRI is transformed into a sinusoidal signal with a single period by the SSWI, and the FFT can easily extract its period.  
Jittered PRI refers to the random jitter of the PRI value around a fixed value, and the difference interval of the pulse sequence with the jittered PRI is \(\Delta t_{i}=\Delta t_{i}^{\prime}+\overline{\Delta t}\)\(i=1,2,\cdots,N-1\), where \(\overline{\Delta t}=\sum_{i=1}^{N-1}\frac{\Delta t_{i}}{N-1}\). For a discrete signal with the jittered PRI, the SSWI function for the \(i\)-th difference interval is  
\[\begin{split} s_{i}(t)&=(\overline{\Delta t}+ \Delta t_{i}^{\prime})sin(\frac{2\pi}{\overline{\Delta t}+\Delta t_{i}^{\prime }}(t-x[i]))\\ &\approx(\overline{\Delta t}+\Delta t_{i}^{\prime})sin(2\pi\frac {t}{\overline{\Delta t}^{\prime}}-\frac{2\pi}{\overline{\Delta t}}\sum_{j=1}^{ i}\Delta t_{j}^{\prime}),\\ &(x[i]<t\leq x[i+1])\end{split} \tag{8}\]  
When the PRI jitter rate is limited to \(\pm 5\%\), the SSWI function of the discrete signal with PRI jitter is an approximate sine function. Compared with the standard sine function, its spectrum will be broadened, and the spectrum value corresponding to the actual PRI value will decrease. When the PRI jitter rate exceeds \(\pm 5\%\), the PRI value will not be estimated correctly from the mixed signals [21].
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
## 4 Experimental Verification  
The experimental design is rooted in real-world AUV requirements. AUVs typically operate in turbulent underwater environments with lateral/longitudinal offsets up to \(\pm\)200 mm. Thus, we selected a 3 \(\times\) 3 transmitting array (total size: 600 mm \(\times\) 600 mm) to cover this range, with coil side length \(L_{1}\) = 200 mm.  
Figure 13 shows the experimental platform of the array UWPT system. The main circuit consists of a DC power supply, inverter circuit, compensation network, switch group, coupling mechanism, rectifier circuit, DC-DC conversion circuit, and load. The transmitter control system is composed of a drive circuit, sampling circuit, multiplication phase detection circuit, and controller DSP28335. The receiver control system consists of a sampling circuit, drive circuit, and controller DSP28335.  
The system's control subsystem, centered on the DSP28335 microcontroller, enables real-time coil switching. The DSP acquires voltage (\(U_{\text{a}}\)) and current (\(I_{\text{a}}\)) signals from the sampling circuit, computes reflection impedance \(Z_{\text{(x, 3)}}\), and selects the optimal transmitting coil based on \(Z_{\text{max}}\). The drive circuit amplifies the DSP's control signals to activate the inverter and coil relays. The multiplication phase detection circuit measures the phase difference \(\theta\) between \(U_{\text{a}}\) and \(I_{\text{a}}\), enabling calculation of \(Z_{\text{(x, 3)}}\) for receiver position estimation and coil switching.  
Figure 13: Experimental platform.  
The experimental platform was configured in a seawater tank with controlled conditions (salinity: 35%, temperature: 10 \(\pm\) 1 \({}^{\circ}\)C). The transmitting array (3 \(\times\) 3 square coils, \(L_{1}\) = 200 mm) and receiving coil (\(L_{2}\) = 350 mm) were precisely controlled lateral (\(dx\)) and longitudinal (\(dy\)) offsets. Voltage and current measurements were acquired using an oscilloscope and Hall-effect sensors.  
The transmitting and receiving coils are made of Litz wire and are designed to be waterproof to reduce corrosion from seawater. Coil temperatures were monitored via surface thermocouples during experiments, with maximum rises limited to 10 \({}^{\circ}\)C under rated power, confirming stable operation.  
Efficiency measurements were conducted under a rated power of 300 W with a fixed load resistance \(R_{\text{L}}\) = 20 \(\Omega\). The main parameters of the experimental platform are shown in Table 1.
## 6 Results and Discussion
### Comparative Experiments
#### 6.3.4 Available AGV Quantity Factor Analysis  
We calculated the average value of the experimental index according to the value of AAQ for the experimental data in Table 5; the results are shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**WQ**} & **MADPSO** & **TPPM** & **RMFSOM** & \multirow{2}{*}{**MADPSO/TPPM**} & \multirow{2}{*}{**MADPSO/RMFSOM**} \\ \cline{2-2} \cline{6-6}  & **AROS** & **AROS** & & & \\ \hline
2 & 416.16 & 1143.1 & 478.23 & 63.59\% & 12.98\% \\
3 & 384.75 & 1202.72 & 426.09 & 68.01\% & 9.70\% \\
4 & 367.43 & 1221.95 & 410.55 & 69.93\% & 10.50\% \\ \hline \end{tabular}
\end{table}
Table 8: Calculate the average value of AROS in Table 5 according to WQ.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**PQ\(\times\)WQ**} & **MADPSO** & **TPPM** & **RMFSOM** & \multirow{2}{*}{**MADPSO/TPPM**} & \multirow{2}{*}{**MADPSO/RMFSOM**} \\ \cline{2-2} \cline{6-6}  & **AROS** & **AROS** & & & \\ \hline
4 \(\times\) 4 & 338.42 & 1390.66 & 374.09 & 75.66\% & 10.85\% \\
4 \(\times\) 3 & 350.74 & 1388.5 & 393.55 & 74.74\% & 10.88\% \\
4 \(\times\) 2 & 353.41 & 1362.96 & 396.43 & 74.07\% & 9.54\% \\
8 \(\times\) 4 & 364.24 & 1164.37 & 397.313 & 68.72\% & 14.01\% \\
8 \(\times\) 3 & 377.51 & 1164.93 & 418.16 & 67.59\% & 8.32\% \\
12 \(\times\) 4 & 399.64 & 1110.83 & 460.24 & 64.02\% & 9.72\% \\
8 \(\times\) 2 & 415.53 & 1066.95 & 483.24 & 61.05\% & 13.60\% \\
12 \(\times\) 3 & 425.99 & 1054.75 & 466.56 & 59.61\% & 8.70\% \\
12 \(\times\) 2 & 479.53 & 999.41 & 555.03 & 52.02\% & 13.17\% \\ \hline \end{tabular}
\end{table}
Table 9: Calculate the average value of AROS in Table 5 according to PQ/WQ.  
The correlation between algorithm performance and AAQ cannot be confirmed from the statistical data in the above table. To further verify the impact of AAQ on the performance of the algorithm, fix PQ equals 12 and WQ equals 2, we conducted supplementary experiments on different AAQ values. The results are shown in Table 11, where UAQ represents the number of called AGVs in the solution.  
From the experimental results shown in the table above, it can be seen that the number of AGVs used rather than the number of available AGVs really affects AROS, and too-large AAQ will lead to a slight decline in algorithm performance. For different SACOP problems, we need to reasonably set different AAQs, so as to avoid the algorithm performance degradation caused by excessive AAQ, while ensuring sufficient AGV resources.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### The Mamdani-Type Fuzzy Inference Process
#### 4.4.3 Apply Implication Method  
In a solar power system, the consequent of an If-Then rule is represented by a fuzzy linguistic set, characterized by a corresponding membership function. While the antecedent produces a single numerical value, the Then-part modifies the shape of the consequent's fuzzy set according to this result, a process known as the implication method.  
In this scenario, the AND operation is used to truncate the fuzzy set of the consequent, with the degree of modification determined by the numerical value obtained from the antecedent. The extent of this adjustment varies based on the specific membership value derived from the antecedent. For instance, if the antecedent yields a membership value of 0.5, the fuzzy set for the consequent, such as \"power output is moderate\", will be adjusted accordingly to represent this level of truth. Figure 7 illustrates a fuzzy logic system  
Figure 5: Fuzzifying input variable solar irradiance.  
Figure 6: Applying fuzzy operator.  
where low solar irradiance and a high battery charge level result in maximum solar energy absorption, depicting the relationships between antecedents, consequents, and the resulting implication in the fuzzy inference process.
# Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
## 2 Related Study
### Graph Convolutional Networks and Their Application in ABSA  
Graph convolutional networks (GCNs) were introduced by [PERSON] [4]. GCNs are a type of neural network specifically designed to operate on graph data. They take as input an adjacency matrix and a feature node matrix. They are built on the concept of convolutional networks. GCNs apply convolutional operations on graph data, which allows for graph representation learning. GCNs learn node representations that capture both local and global graph structures. This is achieved by iteratively propagating and updating node features using information from neighboring nodes. GCNs have proven to be a very powerful tool for ABSA. They enable the modeling of relationships between different aspects and their associated sentiments.  
[PERSON] et al. [16] proposed Convolutional Attention Neural Network over Graph Structures (CANGS) for ABSA. Their model combines graph convolutional networks with attention mechanisms. It allows the model to focus only on relevant aspects and words for sentiment classification. Their experimental results show that CANGS outperforms several existing methods.  
GCNs require a predefined graph structure, which can be challenging to construct. [PERSON] et al. [17] introduced a method to construct a graph from a dependency tree for each sentence using a dependency parser. In this graph, the nodes represent the words of the sentence, and the edges reflect the syntactic dependencies between these words. Each node includes a feature vector with word embeddings, part-of-speech tags, and dependency labels. Aspect nodes are also added to the graph to represent aspects distinctly. These aspect nodes are connected to the sentiment words in the sentence that express the corresponding aspects. The authors use a predefined aspect dictionary to identify the words associated with each aspect.  
SenticGCN [10] incorporates this dependency graph construction and proposes a GCN model to predict the sentiment of an aspect. This model integrates affective knowledge and emotions to improve the sentiment capture of each aspect. This is achieved by integrating emotional information into the GCN through an external resource called SenticNet [18]. This approach effectively captures emotional correlations between aspects and words. The model outperformed 25 existing approaches, making a significant contribution to the field.  
Another recent work [19] proposed a novel weight-oriented GCN model. This model assigns different weights to aspects based on their importance in the graph. It uses both aspect and context embeddings to make aspect-level sentiment predictions.
# Architectural Design for Digital Twin Networks
## 4 Functional DTN Implementation
### DTN Description
#### 4.1.1 Selective Simulation Facet  
This simulation approach uses predetermined parameter states instead of random values, reducing the need for extensive runs to observe trends efficiently. In our adapted Click Router architecture, selective simulation supports simulated time, extending the native real-time capabilities of Click [30]. An alternative approach could have been to build upon an existing network simulator and adapt it to meet the required DTN capabilities. However, no Bluetooth Mesh simulator was available at the time. Click is a flexible software architecture that allows network devices like routers to be built by combining modular packet-processing components. It lets users customize how packets are handled by defining their own processing flows, making it highly adaptable for different network needs. Thus, with some minor modifications and the addition of real-time capabilities, Click serves as a robust and viable simulator for network systems. Each component, representing a Click element, simulates Bluetooth Mesh functionalities within the DTN. Click packets, or agents, have three headers: properties (Bluetooth Mesh details), monitoring (data monitoring), and instructions (special behaviors). Each network node is represented by a composite node element with interconnected components.