esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 2 Sample Preparation
### Extraction
#### 2.2.5 Energy-Assisted Extraction  
Energy-assisted liquid extraction techniques, such as Soxhlet extraction, accelerated solvent extraction and microwave-assisted extraction facilitate sample extraction by applying energy (heat and microwave). As a mass transfer of aflat between the sample and extraction solvent increases with applied energy, temperature and/or pressure, these extraction techniques can provide excellent extraction efficiency, significantly reducing extraction time and consumption of extraction solvents. Energy-assisted extraction for aflatoxin analysis started with Soxhlet extraction, a semiautomated extraction process that could circulate the extraction solvent to repetitively extract the sample using specialized glassware. The exhaustive extraction durations were cumbersome and co-extracted matrix components need to be removed afterwards [160, 161].  
Microwave-assisted extraction and accelerated solvent extraction [162] have also been evaluated for aflatoxin analysis. Unlike Soxhlet, these energy-assisted extraction techniques facilitate aflatoxin transfer from samples to extraction solvents by applying high temperature and pressure in a more controlled and efficient manner, improving extractability and accessibility of aflat with a much shorter extraction time. However, using these technologies additional matrix components are prone to co-extraction and additional steps are required to clean-up the resulting extracts due to the non-selective and aggressive nature of these extraction techniques. It is not surprising that only a handful of applications displaying microwave-assisted extraction and accelerated solvent extraction have been developed for aflatoxin analysis [163; 164; 165; 166]. Compared to conventional liquid extraction, these technologies demonstrate limited advantages other than efficiency. The cost and maintenance of the instruments for accelerated solvent extractions and microwave-assisted extractions are significantly higher than conventional lab glassware used for liquid extraction. Unless better selectivity or online sample clean-up features could be incorporated with these energy-assisted extraction devices, they would remain as research-oriented devices but adopted for the routine aflatoxin analysis.
## 2 Landmark Identification Using Annotator Precision
Result: Ground truth labels \(X_{G}\in\Re^{D}\) while\(A>1\)do
2 Initialise the current ground truth estimate \(x_{0}=\overline{\textbf{X}}\);
3 for\(j:0\to M\)-\(1\)do
4 Compute each annotator's precision \(p_{n}(a)\);
5 Compute each annotator's weights \(w_{n}(a)\);
6 Compute the annotator weighted average ground truth estimate \(x_{j+1}\) using the individual annotator predictions and the corresponding annotator weights;
7 if\(min(x_{j+1}-x_{j})<T\)then
8 break;
9
10 end if
11
12 Sum the precision of each annotator in all dimensions \(p_{t}(a)=\Sigma_{d=0}^{D}p_{d}(a)\);
13 Remove the annotator with the lowest cumulative precision \(p_{t}\) from the dataset, such that \(A\mapsto A-1\). 14
15 end for
```  
**Algorithm 1**Ground truth label derivation for a single label \(n\). Refer to [14] for an implementation in Python. The pseudocode of Algorithm 1 describes the process of deriving ground truth labels for regression problems. Provided with this paper is a Python implementation with usage examples [14]. This Python code is used in the following sections to first demonstrate the usage of the method with a toy problem as well as a case study of how it can improve the identification of ground truth facial landmarks in 2D images.
## 2 Materials and Methods
### Patient-Reported Outcomes (PROs) and Compliance  
Table 2 reports the results from the PROs and compliance.  
For PROs, statistically significant differences were found in questions 5 and 8 of the questionnaire. For question 5 (\"Do you notice a drier mouth after using the mouth rinse?\"), the mean value in the test group was 3.1 (standard deviation, SD = 2.6), while the control group had a mean value of 1.3 (SD = 1.0), with statistically significant differences at 6 weeks  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**6 Weeks**} \\ \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Group**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**Diff.**} & \multirow{2}{*}{**Lower**} & \multirow{2}{*}{**Upper**} & \multirow{2}{*}{**Value**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**95\% CI**} & \multirow{2}{*}{**Upper**} & \multirow{2}{*}{**Value**} \\ \cline{3-3} \cline{8-12} \cline{8-12} \cline{8-12} \multirow{2}{*}{**Light**} & Control & 15 & 877.2 & 2540.0 & \multirow{2}{*}{\(-\)20.2} & \multirow{2}{*}{\(-\)1916.8} & \multirow{2}{*}{1876.5} & \multirow{2}{*}{0.603} & 15 & 778.8 & 2557.4 & \multirow{2}{*}{567.5} & \multirow{2}{*}{\(-\)789.6} & \multirow{2}{*}{1924.5} & \multirow{2}{*}{0.256} \\ \cline{3-3} \cline{8-12}  
(\(p\) = 0.016) that disappeared at 12 weeks (\(p\) = 0.122). For question 8 (\"Which is your general opinion after using the mouth rinse in this study?\"), after 6 weeks, the mean value in the test group was 6.5 (SD = 2.3), while the control group had a mean value of 8.3 (SD = 1.5), with statistically significant differences (\(p\) = 0.022); after 12 weeks, the mean score in the test group increased up to 7.2 (SD = 2.1) and it was maintained in the control group at 8.2 (SD = 1.2), with no statistically significant differences between the groups (\(p\) = 0.165).  
Regarding compliance, no statistically significant differences were observed between the groups at 6 and 12 weeks and a similar amount of liquid was left in the bottles in both groups.
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 3 Research Methodology
### Study Selection and Data Extraction  
AEB systems comprise multiple components that incorporate various technologies for detection, connection, and data processing. A rigorous study selection phase was essential to focus exclusively on the most pertinent and comprehensive works that met our criteria. This research was carried out using electronic databases such as Scopus and Google Scholar, considering documents from 2020 to 2024. Initially, 25 papers were chosen for full-text review. Out of these, only 11 papers were deemed appropriate for the data extraction process. During the data extraction phase, we concentrated on the proposed research questions (RQs) and identified a set of features from each document after a thorough review. These features, relevant to our study, were categorized as follows:  
Detection layer--radar, LiDAR, camera, and ultrasonic sensors. Communication layer--vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) GPS. Processing layer--onboard computer, machine learning algorithms, and electronic control units (ECUs).  
These features were crucial for addressing the proposed RQs and for identifying the optimal AEB architecture.
# Tunneling Current Variations in Small-Sized Devices Based on a Compact Threshold Voltage Model
## 2 Theoretical Model Derivation
### The Influence of Narrow-Channel Effect on Threshold Voltage  
As shown in Figure 3, in the strained Si NMOSFET device process, there exists a conical oxide transition region between the thin gate oxide layer and the thick field oxide layer, which is referred to as a bird's beak. When the gate voltage is applied, a gate-controlleddepletion region is formed at the edge transition region of the device. The total additional depletion charge caused by the gate voltage at the device edge is \(\Delta Q_{W}\) (\(0.5\Delta Q_{W}\) for each edge). If the channel width W of the device is much larger than the depletion layer width, \(X_{dep}\) caused by the gate and the additional depletion charge \(\Delta Q_{W}\) caused by the gate voltage can be neglected compared to the total depletion charge \(Q_{B}\). However, if W is comparable to \(X_{dep}\), the extra charge \(\Delta Q_{W}\) cannot be ignored, resulting in an increase in the threshold voltage. The increment of threshold voltage is given by \(\Delta V_{th,W}=\Delta Q_{W}/C_{ox}\), which means that as W decreases, while L remains constant, \(V_{th}\) increases. To facilitate circuit simulation, different approximations are made for the shape of the depletion region. It is found that \(\Delta V_{th,W}\) is inversely proportional to the device width W and directly proportional to the thickness of the gate oxide layer and the surface potential under strong inversion conditions. Further considerations are given to the substrate bias \(V_{bs}\) and the specific process effects, resulting in the development of a semi-empirical \(\Delta V_{th,W}\) model:  
\[\Delta V_{th,W}=(K_{3}+K_{4}V_{bs})\frac{T_{ox}}{W_{eff}}\phi_{s}, \tag{31}\]  
where \(K_{3}\) and \(K_{4}\) are process-fitting parameters, which are obtained through parameter extraction.
Four modulation formats were adopted in this paper: (1) On-Off Keying (OOK), (2) Binary Phase Shift Keying (BPSK), (3) Quadrature Phase Shift Keying (QPSK), and (4) 16-Quadrature Amplitude Modulation (16-QAM). The rest of this paper is organized as follows: Section 2 indicates an overview of CNN's theoretical background. Channel statistics with a gamma-gamma model is dissected in Section 3. The experimental process and the structure of network we designed are introduced in detail in Section 4. Finally, some results and discussions are maintained in Section 5, and conclusions are in Section 6.
###### Abstract  
The damage to structures caused by the velocity pulse effect of near-fault earthquake waves cannot be ignored, yet there are few studies on the risk assessment of seismic performance for precast concrete frame under near-fault earthquake waves. A novel self-centering precast concrete (SCPC) frame with hysteretic dampers is proposed to obtain great self-recovering and energy consumption characteristics. To accurately assess the seismic behaviors of the novel SCPC frame under the near-fault earthquake waves, a prototype structure is modelled and elastoplastic dynamic analysis is conducted at the design basis earthquake (DBE) and the maximum considered earthquake (MCE) seismic levels. Incremental dynamic analysis and the vulnerability analysis are performed. Annual and 50-year exceeding probabilities of the novel SCPC frame are calculated afterwards. In addition, the reinforced concrete (RC)frame and the traditional SCPC frame are also modelled, whose section sizes, reinforcements arrangement and seismic intensity are consistent with the novel SCPC frame. The dynamic time-history analysis at the two seismic levels are also carried out for two types of frames. The analysis results demonstrate that the novel SCPC frame has great seismic performance and low seismic risk possibility under the near-fault earthquakes loading.  
risk assessment; self-centering; hysteretic dampers; DBE and MCE levels; incremental dynamic analysis; vulnerability analysis +
Footnote †: journal: Applied
# Permeability and Stiffness Assessment of Paved and Unpaved Roads with Geocomposite Drainage Layers
###### Abstract  
Poor subsurface drainage is frequently identified as a factor leading to the accelerated damage of roadway systems. Geocomposite drainage layers offer an alternative to traditional methods but have not been widely evaluated, especially in terms of the impact of changes on both drainage capacity and stiffness. In this study, both paved and unpaved test sections with and without an embedded geocomposite drainage layer were constructed and tested. The geocomposite layers were installed directly beneath the roadway surface layers to help the rapid drainage of any infiltrated water and thus prevent water entering the underlying foundation materials. The laboratory, field, and numerical analysis results showed that the geocomposite layers increased the permeability of roadway systems by two to three orders of magnitude and that it can effectively prevent the surface and foundation materials from becoming saturated during heavy rainfall events. For the stiffness of the sections, the paved sections with and without a geocomposite layer showed that the composite modulus values measured at the surface were more reflective of the foundation layer support conditions beneath the geocomposite layer than the geocomposite layer itself. The unpaved road section with the geocomposite layer yielded lower composite modulus values than the control section but showed overall better road surface conditions after a rain event due to the improved subsurface drainage condition.  
geocomposite; permeability; drainage; stiffness; elastic modulus; concrete pavement; asphalt pavement; unpaved road; water infiltration model +
Footnote †: journal: Applied Sciences
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 4 Complexity Analysis  
Table 4 shows the complexity analysis for traditional FD-TAS and distinct supervised TAS systems. \(\mathbf{H}_{T}\big{(}\mathbf{\chi}_{i}-\mathbf{\chi}_{j}\big{)}\) requires \(N_{rx}\times N_{T}\) computations. \(\big{(}\mathbf{H}_{T}\big{(}\mathbf{\chi}_{i}-\mathbf{\chi}_{j}\big{)}\big{)}^{H}\big{(} \mathbf{H}_{T}\big{(}\mathbf{\chi}_{i}-\mathbf{\chi}_{j}\big{)}\big{)}\) needs \((N_{rx}\times N_{T})^{2}\) computations. These computations are carried out only for one value of FD. This process is repeated \((N_{g}2^{\mathbf{n}_{FGSM}}(2^{\mathbf{n}_{FGSM}}-1))\) times for all possible antenna subsets. It is observed that the number of computations required for TAS using the traditional method is tedious. For ML techniques, it depends on the attribute vector length \(N_{Q}\). The number of computations required is much less compared to the traditional FD-TAS for a higher-order configuration, i.e., for a larger number of \(N_{T}\), \(N_{rx}\), and \(\mathbf{\alpha}_{FGSM}\). KNN is the least computationally efficient of all ML approaches because it stores the training data. This analysis is conducted for \(B=2\times 10^{5}\) bits. By decreasing the length of the attribute vector, we can reduce the complexity of ML-based TAS practices. Traditional FD-TAS becomes increasingly complex as these components rise, necessitating the use of ML techniques for TAS to simplify the system.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 4 System Optimization Scheme Based on A Multi-Constraint Multi-Objective Genetic Algorithm
### Problem Formulation
#### Function utilization  
There exists a set of functions that satisfy the event requirements \(F^{\prime}=\left\{f_{1},f_{2},\ldots,f_{j}^{S},\ldots f_{j}^{S}\right\}\) in the domain G, where each function corresponds to the set of function utilization values \(\left\{futl_{1},futl_{2},\ldots,futl_{j},\ldots futl_{j}\right\}\). Then, the system function utilization can be expressed as  
\[\text{utl}F^{\prime}=\sum_{j=1}^{J}\frac{1}{j}\cdot\text{level}f_{j}^{S}\cdot futl _{j}, \tag{15}\]  
Since each QoS has a different effect on the function utilization, each function has a weight of \(w_{ij}\). Then, the function utilization \(futl_{j}\) can be expressed as  
\[futl_{j}=\sum_{i=1}^{3}w_{ij}\cdot\frac{QoS_{i}-QoS_{j}}{QoS_{i}}, \tag{16}\]  
where \(QoS_{j}=\left\{\text{cap}f_{j},\text{mem}f_{j},\text{bank}f_{j},\text{cost}f_{ j}^{S}\right\}\). The \(QoS_{i}\) denotes the resources and the cost assigned to the function \(f_{j}\), and the \(QoS_{j}\) denotes the resources and the cost consumed by the function \(f_{j}\).
## 3 Results and Discussion
### Rheological Analysis of CNC/Alg/PEGDA Formulations  
#### 3.2.1 Shear-Thinning Properties of Pre-Gel Formulations  
As seen in Table 2, the power-law index (\(\eta\)) and consistency index (K) shows the prediction of the extrusion behaviour of the pre-gel formulations. When the power-law index is \(\eta<1\), the fluid displays shear-thinning properties whereas when \(\eta=1\), the fluid displays Newtonian behaviour. The results revealed that pre-gel formulation without CNCs displayed near Newtonian behaviour, showing very limited to no shear-thinning behaviour over the range of shear rates. However, it was observed that the addition of CNCs in the Alg/PEGDA pre-gel formulations reduced the power-law index, \(\eta\), indicating shear-thinning property. In Figures 5 and 6, the pre-gel formulations with the presence of CNC [F2, F4, F6, F8, and F9] mostly showed shear-thinning behaviours where the viscosity decreased with increasing shear rate. Shear-thinning property is required in extrusion bioprinting as it improves the printability of the bioink by reducing flow resistance through the disturbance of the crosslinked systems [13]. Additionally, the liquid-like state of the bioink generated during the extrusion process at a high shear rate could shield the cells from any shear stress [13,35]. Interestingly, at low Alg concentrations [\(1\%\) (\(w/v\))], the shear-thinning behaviour of the CNC/Alg/PEGDA pre-gel formulations is more pronounced than at high Alg concentrations [2.5 and \(4\%\) (\(w/v\))]. This observation suggests that the increase in the Alg concentration might hinder the effect of CNCs within the hydrogel network. Hence, this finding highlights the importance of CNC in the formulation, as it could improve the shear-thinning property. This result is also in agreement with other studies; Refs. [18,20,36] reported that the addition of nanocellulose in the bioink increased shear-thinning behaviour.  
On the other hand, the flow curve for F7 [4% Alg: 40% PEGDA] displayed shear-thinning property even without the addition of CNCs. This may be due to the unique interaction and entanglement between Alg and PEGDA at these particular concentrations which makes them easier to be aligned under high shear rates [22].
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Energy Consumption Factors in the IoT
#### 2.1.3 Technological Evolution  
The rapid development of IoT technology makes device renewal and iteration inevitable. As technology continues to advance, replacing old devices with new ones results in excessive e-waste. In addition to the potential environmental threat posed by the e-waste generated by equipment obsolescence, the disposal process also induces energy resource demands [14]. Meanwhile, producing, configuring, and deploying new-generation equipment also leads to  
Figure 3: Energy-consumption factors in IoT systems.  
transitional energy waste. Though it is an inevitable part when embracing new technologies, we need to provide energy-efficient strategies during device-renewal processes.  
A variety of strategies can be utilized to effectively reduce energy consumption in the IoT. For instance, optimizing data-processing algorithms can reduce the burden on data centers, thereby reducing energy consumption. Energy-efficient communication technologies can help slash the amount of power support required for communications. The use of renewable energy in the manufacturing and operation of IoT devices can decrease the reliance on non-renewable energy sources, thereby enhancing overall energy sustainability. In addition, sustainable update strategies for devices are conductive to mitigating energy demands caused by device renewal. Moreover, energy-efficient hardware and software technologies can be introduced to further enhance the energy efficiency of IoT devices and thus reduce energy costs. Lastly, holistic approaches can be leveraged to enhance IoT energy efficiency and achieve the goal of sustainable development.
# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach
## 4 Model Application and Results
### Comparison with Previous Studies  
DO modeling holds significant ecological importance, and numerous ML studies have been conducted to predict DO levels. As shown in Table 5, this section provides a comparative analysis between the present study and previous research. These studies employed different ML models for DO forecasting, with detailed descriptions in the original references. [PERSON] et al. (2016) established AR-RBF (additive regression-radial basis function neural network) and MLP-RF (multilayer perceptron-random forest) models to predict the DO levels in the Mississippi River, at the same station used in the present study. Their models produced an RMSE < 0.8 mg/L, indicating higher prediction errors than our model results. Similarly, [PERSON] et al. (2016) developed an integrated DL approach for estimating DO concentration at coastal inlets, yielding a CD = 0.83, an RMSE = 0.58 mg/L, and a MAE = 0.074 mg/L. The present study demonstrates slightly improved predictive performance relative to the previous approach. A comparison with other studies supports a similar conclusion. In the study by [PERSON] et al. (2017), the RMSE values for FH = 1 d, 3 d, and 7 d are 0.43 mg/L, 0.682 mg/L, and 0.817 mg/L. In comparison, the present study produces an RMSE = 0.463 mg/L for FH = 5 d and RMSE = 0.545 mg/L for FH = 10 d, demonstrating accurate results over extended prediction horizons.  
Although the comparisons demonstrate the superiority of the developed model, we recognize a key limitation: the datasets and environmental conditions vary between studies, making direct comparisons difficult (2016). The models in each study have been trained and validated on distinct datasets with different characteristics, which can affect their performance and generalizability. To overcome this, future research should focus on creating standardized datasets for benchmarking. These datasets would allow for a more accurate comparison of model performance across different studies and watersheds, leading to a better understanding of the strengths and weaknesses of various modeling approaches.  
Figure 9: Boxplot comparison of measured and predicted DO at varying forecast horizons. Gray color = measurement, and pink color = ML prediction.
## 1 Introduction
### Efficiency  
Because of the severe energy limitations of sensor networks and also because of their time-critical application scenaria, protocols for sensor networks should be efficient, with respect to both energy and time.  
Fault-tolerance  
Sensor particles are prone to several types of faults and unavailabilities, and may become inoperative (permanently or temporarily). Various reasons for such faults include physical damage during either the deployment or the operation phase, permanent (or temporary) cease of operation in the case of power exhaustion (or energy saving schemes, respectively). The sensor network should be able to continue its proper operation for as long as possible despite the fact that certain nodes in it may fail.