esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Charge Injection and Transport of Light-Emitting Diodes Based on Two-Dimensional Materials
## 2 Two-Dimensional (2D) Materials in Organic Light Emitting Diodes (OLEDs)
### 2D Materials as Interlayers  
#### 2.1.1 2D Materials as Hole Transport Layer (HTL)  
Lee presented polymer LEDs (PLEDs) with a solution-processed GO/rGO HTL, as shown in Figure 3a [35]. The GO and rGO films with different thicknesses (0, 2.0 nm, 2.6 nm, 4.3 nm, 5.2 nm) were studied to substitute poly(styrenesulfonate)-doped poly(3,4-ethylenedioxythiophene) (PEDOT:PSS) as HTL. Optimal PLEDs with a 4.3 nm GO HTL showed maximum luminance (L\({}_{\text{max}}\)) of 39,000 cd/m\({}^{2}\), maximum current efficiency (CE\({}_{\text{max}}\)) of 19.1 cd/A and maximum external quantum efficiency (EQE\({}_{\text{max}}\)) of 6.7%, exhibiting an enhancement of 15%, 120% and 91% compared with the reference PLED with PEDOT:PSS HTL, respectively. The improved performance can be attributed to two factors. Firstly, as shown in Figure 3b, the GO layer with a wide band gap blocks electrons moving from EML to indium tin oxide (ITO) and thus restricts more electrons in the EML region, suggesting the increased recombination of electrons and holes in EML and correspondingly the enhanced performance of PLEDs. Besides, as shown in Figure 3c, exciton quenching, existing at PEDOT:PSS/EML interface, can be suppressed by replacing PEDOT:PSS with the GO HTL. These results indicate that GO can be an excellent HTL to substitute PEDOT:PSS.
## 1 Introduction
The time change of the Rayleigh scattered light intensity obtained at the incident end by sending the coherent laser light pulse is a waveform (referred to as a time-intensity waveform) in which the interference effect between the scattered lights is conspicuous. Rayleigh scattered light changes its time-intensity waveform as the environment (strain or temperature) around the optical fiber changes. The resulting shift in the frequency of the incident light, which reflects the magnitude of the environmental change, may be determined by recovering the same time-intensity  
Figure 1: Brillouin time domain analysis (BOTDA) sensing scheme. waveform prior to the environmental change. Meanwhile, the location at which the scattered light is generated can be found from the arrival time of the scattered wave. In this way, the magnitude of environmental change at any location may be evaluated. The optical frequency domain reflectometry (OFDR) is the first attempt to utilize the principle of Rayleigh backscattering in optical fibers for strain measurement [6]. The time-domain approach proposed by [PERSON] [13] has the important advantages of long distance and high accuracy. Table 1 summarizes and compares the key parameters of Brillouin and Rayleigh-based sensing technologies. It is noted that Rayleigh-based sensing has a much higher resolution than Brillouin-based sensing does. Rayleigh and Brillouin backscattering techniques have been successfully applied in measuring metallic plate strains [14]. In this study, they are applied to measure frequency shift along the interval of a single optical fiber which is attached to and circles around the peripheral of two different sandstone cylindrical core samples under controlled confining/pore pressure conditions in the laboratory. The frequency shift measurements are then converted to strains using conversion coefficients. The strains thus obtained are compared with those measured by strain gauges attached to the same core samples.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 4 Case Analysis
### Case Verification  
In order to verify the validity of the established MWP-DLP model, 70 groups of data from November 15 of the second year to March 15 of the third year (excluding weekends and holidays) were selected for verification, and the prediction results were analyzed. The known basic parameters included the average temperature for 70 days; wind value for 70 days; and 70 days of solar radiation. Partial data of 70 days are shown in Table 4. The average temperature of 70 groups of data, the average temperature of the previous day, the solar radiation value, and the wind value were, respectively, brought into Equation (7), and the predicted value of the daily gas load of 70 groups could be obtained. The comparison between the predicted value of daily gas load obtained by MWP-DLP model and the actual value is shown in Figure 10. The analysis shows that, as can be seen from Figure 10, the overall change trend between the predicted daily gas load value obtained by MWP-DLP model and the actual value was basically consistent, which was consistent with the actual value. When the temperature fluctuation was large, the difference between the predicted value and the actual value was not large, and the prediction effect of the model was good, indicating that the established MWP-DLP model is effective, feasible, and has certain practicality. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{\begin{tabular}{c} **Daily Load** \\ **(10\({}^{4}\) m\({}^{3}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cumulative** \\ **Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Wind** \\ **(Section)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Solar** \\ **Radiation** \\ **(J/cm\({}^{3}\))** \\ \end{tabular} } \\ \hline
33 & Second year-12-29 & 290 & 8.4 & 8.7 & 8.55 & 0.2 & 680 \\
34 & Third year-01-03 & 301 & 3.3 & 7.2 & 5.25 & 13 & 592 \\
35 & Third year-01-04 & 321 & \(-\)0.1 & 3.3 & 1.6 & 0.2 & 265 \\
36 & Third year-01-05 & 330 & \(-\)0.7 & \(-\)0.1 & \(-\)0.4 & 0.2 & 274 \\
37 & Third year-01-08 & 421 & \(-\)0.4 & \(-\)0.7 & 0.2 & 13 & 264 \\
65 & Third year-03-08 & 248 & 5.8 & 5.6 & 5.7 & 0.2 & 1299 \\
66 & Third year-03-09 & 248 & 6.7 & 5.8 & 6.25 & 4.9 & 1415 \\
67 & Third year-03-12 & 134 & 14.2 & 13.8 & 14 & 4.9 & 1858 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Basic data of a city.
## 3 Methods
### Feature Extraction Techniques
#### 3.4.4 Fast Correlation Based Filter  
FCBF is a multivariate type of feature selection technique that uses Symmetrical Uncertainty (SU) for calculating the dependencies of the features and identifying the best subset while using backward selection technique along with sequential search strategy. It contains internal conditions, where the process stops when the necessary criteria is satisfied. It is based on correlation that generally runs faster than the other subset selecting techniques. Entropy and conditional entropy values are used for calculating the feature dependencies [25]. The entropy is calculated by the following expression:  
\[H(X)=\sum p(Xi)+\log_{2}(p(Xi)) \tag{7}\]  
where, \(x\) is a random variable and \(p(x)\) is its probability.
## 4 Conclusions  
The performance of the planar solid phase microextraction (PSPME) non-contact sampler/extraction device coupled to COTS ion mobility spectrometers (IMSs) to detect the presence of explosives was evaluated through the development of receiver operating characteristic (ROC) curves. A total of 360 replicate measurements were collected for different scenarios varying container volume (0.94-45 L) and amount of smokeless powders concealed within the container (10-500 mg). True positive rate (TPR) analysis suggested the optimum alarm threshold and detection limits for each individual compound.  
ROC curves are found useful to illustrate the detector system performance in terms of true and false positive probabilities. The portable IMS resulted in slightly reduced performance; however, the  
Figure 4: ROC curves for the portable PSPME-IMS (**a**), benchtop PSPME-IMS (**b**) and laboratory based SPME-GC-MS (**c**). These ROC curves were constructed using JMP software from 360 samples (140 samples for SPME-GC-MS) including all defined scenarios.  
instrument performed well with high sensitivity for NG (AUC = 1.0) and 2,4-DNT (AUC = 0.87). DPA resulted with a lower AUC of 0.85 due to large amount of false negatives. The benchtop IMS resulted in improved sensitivity resulting with an area under the curve (AUC) of 1.0 for NG and 2,4-DNT and similar performance for DPA with an AUC of 0.81. Even though poor detection was observed for DPA for both IMS instruments, the presence of NG from the same smokeless powders was sufficient for a positive alarm, suggesting the presence of an explosive. Moreover, although SPME-GC-MS resulted with better performance with an AUC greater than 0.9 under the defined scenarios, SPME requires a minimum of 10 min static headspace sampling and the laboratory-based GC-MS requires long analysis time which is impractical in high throughput locations. This study illustrates how well the preconcentration power of the PSPME is able to perform in brief (~1 min.) static and dynamic extractions with high sensitivity and high specificity and can be coupled to the 15,000 IMS instruments currently deployed at security checkpoints throughout the US without further modification. However, detection of explosives with low vapor pressure by non-contact sampling was not successful, resulting in little to no detection for ETN, PETN, RDX, TNT and C4. Further optimization studies will be investigated in order to construct ROC curve studies based on the volatile organic compounds associated with these low vapor explosives. Overall, the PSPME-IMS technique provides less false positive results for non-contact vapor sampling, cutting the cost and providing an effective sampling and detection needed in high-throughput scenarios with excellent potential to be a used as a sensor system for the detection of volatile chemicals associated with explosives.
# An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks
## 3 System Model and Design of the Proposed Protocol
### Problem Formulation  
Despite their advantages, OppNets face significant challenges, particularly in optimizing resource utilization and reducing delays while maintaining high delivery probabilities [4; 6; 39; 40]. Routing protocols must strike a balance between exploiting contacts and minimizing redundant transmissions [11]. The reliance on node mobility, buffer management, and contextual information underscores the need for adaptive and intelligent solutions, particularly in environments like vehicular networks, where contact opportunities are brief and unpredictable [41; 42]. The unique requirements and constraints of OppNets drive ongoing research into innovative routing strategies that can harness their potential in real-world applications.  
In the light of these challenges, this work proposes a novel fuzzy-based routing protocol to enhance decision-making in OppNets. By leveraging fuzzy logic to process contextual information such as buffer occupancy, angle to destination, and the number of unique connections, the proposed protocol aims to optimize resource usage while ensuring high delivery probability.
## 3 Machine Learning Algorithms
### Fairness Mechanisms  
Next we turn to discuss the three fairness mechanisms clustered on the timing of the application of debiasing mechanism into preprocessing, in-processing, and postprocessing. * Preprocessing. Preprocessing methods deal with removing the protected features or their covariates before training the model. Similar to anticlassification, this method come with severe disadvantages as the protected feature might be correlated with many other unprotected features, and it is practically infeasible to identify all such covariates and exclude them without losing a lot on predictive accuracy. [PERSON] and [PERSON] (2017) suggest a set of data processing techniques aimed at ensuring fairness for classification tasks. These include suppression, massaging the dataset, reweighting, and sampling. * In this process, exactly like anticlassification, all the features that correlate with the protected set of features \(X_{p}\) are first identified which are then removed from the classification model. * In this process, labels of some data points are manipulated in order to remove existing discrimination from the training data. In order to find a good set of labels to change, [PERSON] and [PERSON] (2017) proposed a combination of ranking and learning. * Instead of changing the labels, in this method the tuples in the training dataset are assigned asymmetric weights in order to overcome the bias
* [PERSON] and [PERSON] (2017) introduced \"uniform sampling\" and \"preferential sampling\", where the training data is sampled with the help of a ranker as a debiasing method. * [PERSON] and [PERSON] (2017) found that suppression of the protected attributes does not always result in the removal of bias and massaging and preferential sampling techniques performed best for debiasing with a minimal loss in accuracy. * Another idea developed in preprocessing is to learn a new representation of the data such that it removes the information correlated to the sensitive attribute ([PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017). The central algorithm such as classification then use the cleaned data. An advantage of this method is that the analyst can avoid the need to modify the classifier or access sensitive attributes during test time. * In-processing. In this method, the optimization procedure is modified to incorporate cost of unfairness. This is typically done by addition of a constraint to the optimizing problem or
# Analysis and Design of a Transient-State Resonant Converter Used as a Frequency Multiplier
## 2 Resonant Converter Transient
### Transient Operation of the RLC Equivalent Circuit in Series of the Resonant Converter  
Considering the operating condition of (7) and the value of \(\mathrm{R_{L}}\) given by (8), the equivalent circuit of the resonant converter is behaving as a series RLC circuit as shown in Figure 5b.  
In the circuit of Figure 5b, the total series inductance value is the sum of the inductances \(\mathrm{L_{r}}\), \(\mathrm{L_{dp}}\), and \(\mathrm{L_{eq1}}\). This total series inductance value \(\mathrm{Ls}\) in the series equivalent circuit must maintain the current oscillations in the resonant tank at the operating frequency determined by the multiplication between the inverter frequency and the multiplier \(f_{o}=\eta f_{sw}\), where the switching frequency \(f_{sw}\) and the multiplier \"\(\eta\)\" are values set in the operating parameters of the inverter. Equation (9) determines the value of \(\mathrm{L_{s}}\).  
\[L_{s}=L_{r}+L_{dp}+L_{eq1}=\frac{DR_{g}a}{2 nf_{sw}\pi} \tag{9}\]  
Figure 5: Circuit conditioning to satisfy the series operation of the equivalent RLC circuit, (**a**) Series behavior of the circuit affected by the reactance of the magnetizing inductance \(\mathrm{L_{mp}}\), (**b**) Reactance of the magnetizing inductance acting as an open circuit.  
where D is the inverter duty cycle, R\({}_{\rm s}\) is the total series resistance in the equivalent circuit, \(n\) is the frequency multiplier, \"\(a\)\" is the underdamping constant determined.  
\[a=\frac{\pi n}{-\ln\left(d_{f}\right)} \tag{10}\]  
where \(d_{f}\) is the decay factor and defines the decay in the amplitude of the current up to the half-period of the inverter switching signal. The decay factor \(d_{f}\) is a parameter that is set in the operating specifications of the inverter and can have values from \(d_{f}\) = 0.1 \(-\) 0.9. Figure 6 shows the effect of the decay factor on the current waveform, where the higher the value, the more sinusoidal waveforms can be obtained, as shown in the blue graph in Figure 6. The decay factor is an important parameter in the transient operation of the circuit; [20] explains in detail the most important characteristics and implications.  
Considering (9), it is possible to determine the value of the inductance L\({}_{\rm r}\) by means of (11), where L\({}_{\rm r}\) is the inductance in the resonant tank of the underdamped converter. The value of the capacitor that is in the resonant tank is determined by (12), where the total series inductance L\({}_{\rm s}\) of the equivalent RLC circuit is considered. The capacitor Cr that is in the converter tank is the same series capacitor C\({}_{\rm s}\) = C\({}_{\rm r}\) shown in the equivalent RLC circuit in Figure 2.  
\[L_{r}=L_{s}-L_{dp}-L_{eq1} \tag{11}\]  
\[C_{r}=\frac{1}{\omega_{o}L_{s}} \tag{12}\]
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 2 Related Work
### Knowledge Graphs for Computer Vision  
Knowledge graphs have emerged as powerful tools for incorporating structured domain knowledge into computer vision tasks ([PERSON] et al., 2016; [PERSON] et al., 2017). A knowledge graph represents concepts (entities) as nodes and their relationships as edges, providing a structured representation of domain knowledge that can guide visual recognition systems.  
In object recognition, [PERSON] et al. [PERSON] et al. (2017) leveraged knowledge graphs to improve zero-shot learning by capturing the semantic relationships between object categories. Similarly, [PERSON] et al. [PERSON] et al. (2017) proposed a hierarchical embedding approach that uses knowledge graphs to exploit class hierarchies in zero-shot scenarios. These approaches demonstrate that structured knowledge can significantly enhance generalization to unseen categories.  
In the food domain, knowledge graphs have been developed to represent various aspects of food, including ingredients, nutritional content, and cultural contexts ([PERSON] et al., 2016; [PERSON] et al., 2017). FoodKG ([PERSON] et al., 2016) is a comprehensive food knowledge graph that integrates data from multiple sources, including recipes, nutritional information, and food-health relationships. While these resources provide valuable domain knowledge, they primarily focus on nutritional and culinary aspects rather than food safety.  
Some recent works have begun to explore the integration of knowledge graphs with computer vision for food analysis. [PERSON] et al. [PERSON] et al. (2017) introduced a knowledge-enhanced framework for zero-shot food detection that leverages correlations between ingredients from food knowledge graphs. Their approach, known as the Knowledge-Enhanced Feature Synthesizer (KEFS), uses multi-source graph fusion to combine different types of food knowledge (inggradients, hyperclasses, etc.) to generate discriminative features for unseen food categories.  
Our work extends this line of research by developing a specialized FSKG that encodes the relationships between food categories and visual safety attributes. Unlike previous food knowledge graphs that focus on nutritional or culinary aspects, our FSKG is specifically designed to capture visual characteristics of food safety hazards, enabling the more effective zero-shot detection of safety issues across different food categories.
## 3 Results and Discussion
### Chemical Make-Up via X-ray Fluorescence (XRF)
The commercial dolomite in its pristine stage was not examined in the Automated SEM-EDX Mineralogy measurements, but we have evaluated its partial carbonated version under STF/N\({}_{2}\) environment [CO-Dolo-STFN2]. This sample exhibits the majority of phases from the Fe-rich dolomite and magnesite and a few minority phases from Fe-Co-Ni minerals. Therefore, the CO-Dolo-STFN2, Automated SEM-EDX Mineralogy results are agreement with the higher concentration of Fe\({}_{2}\)O\({}_{3}\) as previously demonstrated with the XRF results. Another interesting feature noticed based on the Table 2 and Figure 2 results is that the Fe-containing dolomite or Ankerite do occur in low concentration for both the commercial and handpicked samples. However, the Ankerite concentration increases, while heat treatment of these Fe containing dolomites which are commonly referred as Ferrovan dolomite/Ankerite phases [44]. It is interesting to note that the quartz phase (SiO\({}_{2}\)) is below 0.5%, because of its removal while processing at the industrial scale. The Fe-free dolomite phase, up to 50%, and calcite phase, up to 20%, are predominantly seen in the HP-Dolo samples, which are handpicked at the phosphate mining site in the Central Florida region. At least 22% arises from the quartz, which is considered to be an inert material that does not contribute to the thermochemical reactions. The presence of such high concentrations of SiO\({}_{2}\) in the earth-abundant dolomites, may lead for the de-agglomeration of particles resident during long-term thermal cycling; however, we have not validated this aspect experimentally. The handpicked dolomite samples are now exposed to the ball milling in the presence of DI water [HP-Dolo-BM] at room temperature. This is carried out to enhance the dolomite reactive species, and to reduce the inert deadweight SiO\({}_{2}\). To our surprise, these HP-Dolo-BM processed materials showed up to 75% of the pure dolomite phase and reduction of calcite to less than 1%. It is unknown how the ball milling enhances the original sample wt% of HP-Dolo from 50% to HP-Dolo-BM, 75% since it is theoretically not plausible. We hypothesize that the ball milling is a highly energy intensive mechanochemical process, with which the calcite phase from 17.5% (HP-Dolo) is fully consumed and that increases the concentration of pure dolomite phase. Another possible reason is the reduction of SiO\({}_{2}\) from 22% to 6% after ball milling which increases the initial concentration of dolomite phase. The handpicked dolomite samples were calcined in a single-zone tube furnace under nitrogen [HP-Dolo-STFN2] and oxygen (or air) [HP-Dolo-STFO2] atmospheres from 25 \({}^{\circ}\)C to 800 \({}^{\circ}\)C. This was