esa-sceva/satcom-chunk-collection huggingface dataset:
# Wet-Deposited TADF-Based OLED Active Layers: New Approaches towards Further Optimization
## 2 Materials and Methods
### OLEDs Fabrication and Single Carrier Devices  
ITO-glass substrates were cleaned under sonication, using 2% Hellmanex(r) solution for 30 min. Substrates were washed subsequently with hot water, acetone, and IPA for 15 min for each step in a heated ultrasonic bath. Then, substrates were treated with NaOH 10%vol under sonication for 5 min, rigorously washed with deionized hot water, and dried with N\({}_{2}\)(g) jet. PEDOT:PSS (30 nm) were dynamically deposited by spin-coating technique (5000 rpm; 30 s), and films were annealed in a controlled oven at 120 \({}^{\circ}\)C for 10 min. Then, PEDOT:PSS deposited films were transferred to an N\({}_{2}\)(g) controlled atmosphere MBraun Glovebox ([O\({}_{2}\)]! 10 ppm; [H\({}_{2}\)O]! 0.1 ppm), where the solutions of 2 PXZ-OXD (10%wt) into PVK:mCP (3:2; 10 mg mL\({}^{-1}\); -50 nm) of each solvent were spin-casted as EML. Finally, TPBi (40 nm; ETL), LiF (0.5 nm; EIL), and Al (80 nm) electrical contact were thermally evaporated under ultra-vacuum conditions (\(p\)! 5 \(\times\) 10\({}^{-6}\) mbar), obtaining OLEDs with the following configuration: ITO! PEDOT:PSS! PVK:mCP:2 PXZ-OXD! TPBi! LiF! Al. It should be noted that the ITO! PEDOT:PSS \"layer\" is considered an optimized anode (work function near 5.2 eV, as widely reported in the literature [47], and due to the \"metal-like\" behavior of PEDOT:PSS we will not take it into account for the total \"pure\" organic layers) and, oppositely, the LiF! Al \"layer\" is an optimized cathode (accepted work function near 3 eV [47]), mainly due to the well-known quantum tunneling effect through dielectric LiF material. In such a framework, we will consider (as usual) that our devices are made with  
Figure 1: Molecular structures of TPBi, PEDOT:PSS, PVK, mCP and 2 PXZ-OXD.  
only two \"pure\" organic layers (from an electrical point of view). The final active area of each OLED was 4.5 mm\({}^{2}\).  
Single-carrier diodes were assembled with the following architecture: ITO | PEDOT: PSS | PVK:mCP:2 PXZ-OXD | PEDOT:PSS | Al and ITO | LiF | PVK:mCP:2 PXZ-OXD | LiF | Al for the only-hole and -electron transport, respectively. Conditions of the film deposition, thermal evaporation of LiF and Al, and the J-V measurements were the same as the ones used for the OLEDs. The simple energy band diagram (with optimized anode/cathode work functions and pure organic HOMO/LUMO levels) is shown in Figure 2.
## 3 Effect of Damping Layers in ABH Array on Energy Dissipation and Sound Radiation of Plate
### Energy Dissipation in Plate Embedded with ABH Array with Damping Layers
Therefore, the energy focalization characteristics of the ABH structure can be used to achieve effective energy dissipation by using relatively small damping layers, and the dissipation effect of uniform damping layers is better than power-law profile thickness damping layers in this case. Besides, damping layers attached to ABH areas and boundary condition of the plate can change the energy distribution in the frequency domain. At the same time, the mass of plate with the ABH array is less than that of the uniform plate, so the contradiction between the plate lightweight and vibration control can be harmonized to some extent.
## 3 Results and Discussion
### X-Ray Photoelectron Spectroscopy (XPS)
One reason can be the presence of platinum on the surface of La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) crystals that fulfill the unsaturated Ti in TiO\({}_{5}\) motifs on the Ti-terminated facets. The O 1s region of the LTA-Pt sample (Figure 6b) can be deconvoluted into three peaks, corresponding to La-O/Ti-O bonds at 529.34 eV, C=O and/or OH groups at 531.94 eV, and C-O bonds at 533.48 eV, respectively. The peak at around 531 eV is no longer detectable in the doped sample, which would suggest that this peak is actually given by the presence of a further Ti-O bond in the unmodified case (not an unsaturated Ti termination). The XPS spectrum of the La 3d region exhibits a typical profile for lanthanum-based compounds, with a similar number of components appearing at comparable binding energies, but in different relative proportions compared to the unmodified sample. This indicates that platinum doping also affects the lanthanum bonding environment. Components 1 and 3 (blue and gray lines in Figure 6c), which correspond to multiplet splitting, are separated by approximately 4.5 eV--significantly less than the splitting observed in the unmodified LTA sample, but closer to the typical values reported for La\({}_{2}\)O\({}_{3}\) and La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\), as noted above. The binding energies of 70.36 eV and 71.55 eV are attributed to Pt 4f\({}_{7/2}\). The binding energy at 70.36 eV is attributed to the platinum metal, while the binding energy at 71.55 eV can be attributed to platinum oxide. Figure 5: XPS spectra and the deconvolutions for the LTA sample for (**a**) Ti 2p, (**b**) O 1s, (**c**) La 3d, and (**d**) C 1s. The C 1s region has the same behavior as the unmodified sample, with small shifting of the peak values. The spectrum is deconvoluted into three distinct peaks (Figure 6e). The peak at 284.6 eV is assigned to C-C bonds. The peak at 285.84 eV corresponds to C=O functional groups, while the peak at 288.34 eV is attributed to O-C=O bonds, indicative of carbonate species. Table 3 lists binding energy (BE) and atomic percentage obtained from the analysis of these spectra. The \"deconvolutions\" were performed using Voigt profiles as detailed by [PERSON] et al. [70].
## 1 Introduction
This technique is advantageous because when flow discharge is present, flow separation occurs at the lower surface of the rear spoiler; the downforce and aerodynamic drag are reduced, consequently increasing the power performance in terms of the top speed and other parameters. Most studies on active aerodynamic control via blowing have been focused on drag reduction by preventing the flow separation through blowing or suction. Although a reduction of approximately 7-10% in the aerodynamic drag can be realized by blowing, the main drawback is the large amount of electric energy consumed to inject a high momentum air flow by the fan [12; 15]. In contrast, in the ALA, the ram air with a high total pressure is guided to the discharge position through the duct. Consequently, the momentum of the discharged flow is relatively small; however, a high efficiency can be expected because this operation does not consume any additional energy. To this end, the purpose of this study is to understand the physics underlying the aerodynamic drag and downforce reduction of this flow discharging technology by using computational fluid dynamics (CFD) techniques. High fidelity methods such as LES (Large Eddy Simulation) or DNS (Direct Numerical Method) would provide more reliable results for complex unsteady flow fields such as flow separation and so on. However, due to the request of huge amount of computational resources, limited application is currently available. In contrast, the method using the RANS equation has been widely used thanks to its advantages of low computational cost, acceptable accuracy, and fast turnaround time. [PERSON] et al. [17] investigated the aerodynamic performance of a detailed full scale sedan vehicle using various turbulence models such as unsteady Reynolds-averaged Navier-Stokes (URANS) and LES and compared it with wind tunnel test values. The study concluded that LES is useful for resolving detail flow structures, but RANS is still useful for predicting aerodynamic performance. [PERSON] et al. [18] conducted flow analysis on NACA0015 airfoil using RANS with a Spalart-Allmaras turbulence model in the range of high angle of attack of 18-20 degrees where the flow separation occurred. In addition, [PERSON] et al. [19] conducted a study on active aerodynamic control through blowing and suction using the RANS with the \(k\)-\(w\) SST model for NACA0012 airfoil, and showed good agreement with wind tunnel test values. Based on the results of the literature survey, this study also intends to perform flow analysis using the URANS with \(k\)-\(w\) SST model. The F-Duct and the ALA system are excessively complicated to be studied as complete systems, and the associated technical data are not available in the public domain. Therefore, to understand the concept of flow control using the flow discharge, a simplified two-dimensional vehicle and spoiler model is used.
# Analytical Model of an Energy Detector for Ultra-Wideband Chaotic Communications
###### Abstract  
For the ultra-wideband chaotic radio pulses an analytical model of an energy detector is designed and closed-form expressions for the optimal threshold and the detection probabilities are derived. The analytical solution is compared with the results of the numerical simulation. The model proves to be well-suited for the chaotic radio pulses with large base \(B\) (or processing gain, or the number of freedom degrees), namely, \(B\geq 100\). Potential applications areas of the proposed model are ultra-wideband communications with chaotic pulse carriers, e.g., ultra-wideband systems of 802.15.x standards, and multi-element chaotic communication systems.  
ultra-wideband communications; energy detector; dynamic chaos; chaotic radio pulse; direct chaotic communications; envelope detector; central limit theorem +
Footnote †: journal: Electronics Letters
# Rapid Detection of Key Phenotypic Parameters in Wheat Grains Using Linear Array Camera
## 1 Introduction
[26] used image processing to extract features from corn, building a mathematical model that accurately measured seedling height and germination rate, demonstrating the versatility of these techniques across crops. Furthermore, [PERSON] et al. [27] employed morphological image processing to extract parameters, such as spike length, awn length, number of awns, and spike type, enabling non-destructive variety identification based on wheat morphology. [PERSON] et al. [28] extracted spike area and length through basic image preprocessing, combined with vertical rachis rotation and stem segmentation techniques, establishing predictive models for phenotypic trait analysis. The ongoing development and application of machine vision across diverse agricultural domains offer valuable approaches for wheat grain detection and related industrial uses [29; 30]. [PERSON] [31] developed a machine vision-based germination device to assess seed viability, integrating morphology and machine learning methods to accurately detect wheat seed viability. Collectively, these studies validate the feasibility of using image processing and machine vision technologies for the detection of grain phenotypic traits and viability, providing a solid foundation for further research in this area. Image quality is of great importance for accurate grain identification and segmentation and for acquiring phenotypic parameters. Area-array camera-based image acquisition has been widely adopted in applications such as wheat quality assessment and grain counting due to its ability to capture detailed and reliable images [32]. Furthermore, line-scan cameras offer significant advantages, including a wide dynamic range, high resolution, rapid scanning speeds, and the ability to produce clear images of moving objects. When paired with lenses characterized by low distortion and high optical resolution, line-scan cameras have exhibited great potential in advancing grain detection accuracy and efficiency [33; 34]. This study focuses on five experimental wheat varieties provided by the Jiangsu Academy of Agricultural Sciences. The main research contents are as follows: (1) the design and construction of a wheat grain image acquisition system that integrates a high-precision linear array camera, complemented by custom upper-computer software to optimize the device's operational performance and ensure smooth data capture; (2) the use of Halcon to develop effective methods for extracting image feature regions and quantifying wheat grain phenotypic traits. Critical parameters such as grain length, grain width, and grain weight are modeled through fitting relationships, with their accuracy validated against manual measurement data. Moreover, the precision of bud-point determination was investigated to ensure a comprehensive phenotypic characterization.
# A Technological Framework to Support Asthma Patient Adherence Using Pictograms
## 2 Materials and Methods
### SIMAP: System for Automatic Association of Medical Indications with Pictograms
#### 2.2.3 Assessment Process for Patients' Comprehension of Pictograms  
After the sets of medical indication-pictogram pairs were validated by the experts in the ARU rooms, the validation of pictogram comprehension by patients attending the participating ARU rooms began. This process consisted of two stages. The first stage aimed to understand what patients perceived when confronted with each pictogram. In the second stage, a group of kinesologists with expertise in managing bronchial asthma determined whether the patients understood the pictograms by analyzing their responses. Since the results for some of the pictograms were inconclusive, the process was repeated only for those pictograms that did not achieve an adequate level of comprehension.  
Questionnaire to Assess the Comprehension of Pictograms among Patients with Bronchial Asthma and Users of Family Health Centers  
In this stage, we developed an instrument to collect what patients perceived when confronted with each pictogram adhering to ISO 9186 guidelines. The questionnaire encompassed the following sections:  
* Informed consent.
* Presentation of the study.
* Section for sociodemographic information: gender (male, female, other), age (years), educational level (elementary school incomplete, elementary school complete, high school incomplete, high school complete, technical complete, technical incomplete, university incomplete, university complete, postgraduate), current activity (full-time worker, part-time worker), community of residence, name of the health center, and region.
* Section for the participant's health history: name of the disease, approximate date of diagnosis, diagnosis of other chronic diseases, and presence of asthma attacks in the last 6 months.
* General instructions for answering the questionnaire.
* Presentation of each pictogram with a text box for the answer (Figure 2).
* Acknowledgments  
In relation to the use of your inhaler for asthma treatment, please indicate what the following pictogram means to you.  
This version of the questionnaire underwent a cognitive interview with bronchial asthma patients selected for convenience. The cognitive interview is a valuable tool for analyzing the cognitive mechanisms underlying the questionnaire response process. It involves a series of questions administered in a controlled environment, utilizing a small sample from the target population. The primary objective is to assess comprehension of instructions, memory retention, appropriateness of response options, response time, social desirability, sensitivity, and various other cognitive and motivational factors involved in comprehending and responding to questionnaires.  
This process led to the development of a questionnaire designed to evaluate the comprehension of pictograms in patients with bronchial asthma who use Family Health Centers (Version 2).  
Figure 2: Example of a question designed to assess comprehension of a pictogram.
## 4 Results
### Feature Importance
#### 4.3.1 Answering Q1: Features That Distinguish AI and Human Texts  
The first question we aim to address is as follows: what features distinguish the language style of different AI models compared to human texts? To answer this, we extracted the top features from each human-AI prediction for the four models and used human-human features as a baseline. ChatGPT and GPT3 were the most distinguishable from human texts, as indicated by their AV scores.  
Based on the top fifteen features for each model, we found that ChatGPT and GPT3 shared nine common features. Table 3 lists these nine features and their respective SHAP scores for each model. This table highlights the features that contributed the most to the AV model's ability to distinguish AI-generated texts from human-written texts.  
Figure 5: SHAP summary plot for human-human predictions.  
Figure 6: Top 15 features with the highest average SHAP values for each of the human–AI comparisons.  
Interestingly, only two of the nine features appeared in the human baseline feature analysis. Therefore, the remaining seven features uniquely distinguish the language style of AI and human texts: char_distr_xci,masked_stop_words_distr__t help but, masked_stop_words_distr__you__, NNP, char_distr__\", char_distr__\", char_distr__twi, and masked_stop_words_distr__,VBD, his.  
The most common features distinguishing ChatGPT/GPT3 texts included the occurrence of character tri-grams, such as xci (common in words like _excitement_, _excited_, _excited_) and twi (common in words like _Twilight_, _twisted_, _intertwined_). The stop-phrase _\"t help but\"_, commonly found in phrases like _\"I can't help but wonder\"_, _\"I can't help but think\"_, and _\"I can't help but feel\"_, appeared at significantly different rates in texts generated by these two models compared to human texts. Similarly, phrases such as _\"you_, [singular proper noun]\" and \"[past tense verb]_, _his\"_ occurred at markedly different rates in ChatGPT/GPT3 texts compared to human texts.  
Regarding GPT2, when observing Figure 6a, we see that the top features are primarily based on punctuation characters. When observed together with the top features that were common in human texts (Figure 5), we found several features such as the placement of comma and the quotation mark (char_distr__, \"), the use of Oxford comma (indicated by the masked stop words, and), and a vocabulary richness measure.  
Regarding LLaMA, observing Figures 5 and 6d together, features such as the characters [xci], [..\"], phrases of the form, [singular proper noun], and phrases of the form _\"said_, [present participial verb]\"_ were top features in both human-human and human-LLaMA predictions.
# A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks +
## 2 Methodology
### Algorithm for Detecting Coverage Holes Based on Simplified Rips Complex  
A reliable approach for identifying coverage holes was developed by [PERSON] et al. [17] and is based on the fundamental Rips complex. The notion of the whole outline then states that recurring edges must be eliminated by excess edge cancellation requirements. Based on the previous two levels, the Rips complex has been masterfully streamlined. The development of boundary loop filtering and boundary rules from the viewpoint of the loop is the last step in making it possible to identify coverage holes in WSNs. The pseudocode for coverage hole detection is as follows.  
function detectCoverageHoles():  
initializeGrid()//Create a grid over the network area  
markGridCellsAsCovered()//Mark grid cells with sensor nodes  
for each grid cell:  
if isEmpty(grid cell) or isLowDensity(grid cell):  
markAsPotentialHole(grid cell)  
identifyConnectedComponents()//Analyze connectivity among grid cells  
for each potential hole:  
if isConnectedToBoundary(potential hole):  
markAsCoverageHole(potential hole)  
function initializeGrid():  
//Divide the network area into grid cells  
function markGridCellsAsCovered():  
for each sensor node:  
calculateGridCell(node position)  
markGridCellAsOccupied(grid cell)  
function isEmpty(grid cell):  
//Check if a grid cell has no sensors  
function isLowDensity(grid cell):  
//Check if the sensor density in a grid cell is below a threshold  
function markAsPotentialHole(grid cell):  
//Mark a grid cell as a potential coverage hole  
function identifyConnectedComponents():  
//Use graph traversal algorithms (e.g., Depth-First Search or Breadth-First Search)  
//to find connected components of grid cells  
function isConnectedToBoundary(potential hole):  
//Check if a potential hole is connected to the network boundary  
function markAsCoverageHole(potential hole):  
//Mark a potential hole as a coverage hole
## 3 Experimental Results and Validation
### Effect of Charging Strategies of Fleet of EVs on PAR and Cost Reduction
 \\ \hline ML Prediction & & 581.17 & 1.45 & - & - \\ \hline \multirow{3}{*}{Overnight} & CS1 & 413.63 & 1.06 & 27 & 28 \\  & CS2 & 455.51 & 1.15 & 21 & 21 \\  & CS3 & 497.40 & 1.23 & 15 & 14 \\ \hline \multirow{3}{*}{Workplace/Other Charging Sites} & CS4 & 437.44 & 1.09 & 25 & 25 \\  & CS5 & 461.74 & 1.16 & 20 & 21 \\  & CS6 & 500.58 & 1.26 & 13 & 14 \\ \hline \multirow{3}{*}{Overallp \(\times\) Workplace/Other Charging Sites} & CS1 \(\times\) CS4 & 301.42 & 0.73 & 50 & 54 \\  & CS1 \(\times\) CS5 & 329.47 & 0.79 & 45 & 50 \\ \cline{1-1}  & CS1 \(\times\) CS6 & 357.52 & 0.86 & 41 & 47 \\ \cline{1-1}  & CS2 \(\times\) CS4 & 335.42 & 0.80 & 45 & 50 \\ \cline{1-1}  & CS2 \(\times\) CS5 & 365.45 & 0.87 & 40 & 46 \\ \cline{1-1}  & CS2 \(\times\) CS6 & 395.47 & 0.95 & 35 & 42 \\ \cline{1-1}  & CS3 \(\times\) CS4 & 369.43 & 0.88 & 39 & 45 \\ \cline{1-1}  & CS3 \(\times\) CS5 & 401.42 & 0.96 & 34 & 41 \\ \cline{1-1}  & CS3 \mechanism for EV charge-discharge scheduling to manage the peak demand in low-voltage residential buildings. They found that EV owners experience a reduction in electricity costs, while grid operators benefit from better management of peak demand. [PERSON] et al. (2014) [30], evaluated the impact of a fleet of EVs charging and discharging on the power grid. The findings revealed that optimal charging significantly reduces electricity costs and lowers the PAR of electricity usage. [PERSON] et al. (2014) [20], proposed a demand-side management technique for multi-residential end-users connected to a single energy source. They found that PAR and total energy prices could be substantially minimized. Table 2 represents Ontario's TOU rates.
# Positioning Information Privacy in Intelligent Transportation Systems:
## 2 Solutions for Spotting of Vehicles on the Road
### 5G Communications as an Improvement for Positioning  
The evolution of wireless communications brought us to the next frontier of positioning as well [30]. Moving towards higher millimeter wave (mmWave) frequencies (>6 GHz) as part of 5G NR systems networks pushes towards the utilization of highly directional antennas on both transmitter and receiver sides [56]. In mmWave frequencies higher than 30 GHz, the lower signal wavelengthwould allow for packing hundreds of antenna elements in a small area, enabling the implementation of highly directional beamforming capabilities, thus providing better spatial reuse and, consequently, better positioning [57]. In particular, it is achieved by utilizing massive Multiple Input Multiple Output (MIMO) technique [58]. 5G positioning for vehicular networks represents its separate niche or interest [59]. 5G NR is generally expected to provide an accurate positioning from a single reference base station (BS) also form multipath components of up to 0.5 m [60], which is especially beneficial for V2I scenarios.
## 3 State-of-the-Art Review
### Specific Networks, Routing Mechanisms and Algorithms
Their experimental results showed that C-HARQ is superior to C-ARQ in energy consumption of relay nodes.