esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Wireless Local Area Networks Based on Compressed Sensing
## 1 Introduction
At the transmitter side of each wireless node, an analog CS framework is applied at the sensing step before analog to digital converter, in order to generate the compressed version of the input signal. At the receiver side of wireless node, a reconstruction algorithm is applied in order to reconstruct the original signals from the compressed signals with high probability and enough accuracy. In this work, a new architecture based on the following algorithms is presented for improving the QoS feature in the WLANs: (1) a real-time network monitoring algorithm based on CS theory; (2) a new real-time network classification based on CS theory; (3) a new quantitative quality measure method to identify the cause of QoS degradation. The proposed architecture allows DDP to 15%, BER to 14% at each wireless node, FDR to 25%, and PD to 15%, which are good records for WLANs. The proposed architecture increased DT to 22% and S/N ratio to 17%, and 10% accuracy of wireless transmission. The proposed algorithm outperforms existing algorithms by achieving a good level of QoS, which provides a good background for establishing high quality wireless local area networks. This paper is organized as follows. Section 2 reviews related work. Section 3 proposes two new algorithms, RTNTM and RTNTC, based on CS. Section 4 explains the results. Finally, the paper concludes in Section 5.
## 4 Proposed Deep Learning-Based Patient Load Prediction Model
### Centralized Patient Load Prediction System
#### 4.1.1 Data Collection Phase  
The Cc is responsible for collecting all information of the health facilities. Let \(PS\) be the patient load sequence of every facility in the last \(N\) time slots (or time intervals) recorded by the Cc. Let \(T=\{t_{1},t_{2},\cdots,t_{n}\}\) be the collection of time intervals over which a hospital's traffic load can be predicted. We can assume \(t_{i}\) to be hours over which the hospital is operational, say 7:00-17:00. Then let \(\triangle\) be the length of each time interval. Let \(ps^{i}_{k}\) be the recorded patient load of a facility \(i\) in the last time interval \(k\). Then the past patient load \(PS^{i}\) of facility \(i\) is given by:  
\[PS^{i}=\{ps^{i}_{k},ps^{i}_{k-1},\cdots,ps^{i}_{k-N+1}\} \tag{7}\]  
which is just a length-\(N\) vector. Where \(N\) is the number of considered past time intervals. Note that \(N\) depends on the complexity of the input data and is decided according to the training performance. The controller collects all patient load series of every facility and formats them as a patient load matrix:  
\[PS=\{PS^{1},PS^{2},\cdots,PS^{M}\}. \tag{8}\]  
This can also be expressed as:  
\[PS=\{ps_{k},ps_{k-1},\cdots,ps_{k-N+1}\}. \tag{9}\]  
Which is just a time series expression of the patient loads of all health facilities in the past \(N\) time intervals.  
The patient load matrix \(PS\) is the main output of the data collection phase and is taken as the input of training data. In the next time slot, the Cc records the patient load as real future patient load as:  
\[ps_{k+1}=\{ps^{1}_{k+1},ps^{2}_{k+1},\cdots,ps^{M}_{k+1}\}. \tag{10}\]  
This record is taken as the output of the training data. The process is repeated several times and the Cc gathers all such labeled data for training the deep NN in the training phase.
# Word Sense Disambiguation Using Clustered Sense Labels
## 6 Conclusions  
This paper proposes a clustering method to develop compressed sense vocabularies from sense definition vectors. The sense definition vectors are generated from the sense definition sentences in a dictionary, and they are clustered using affinity propagation clustering, \(k\)-means clustering, and modified hierarchical agglomerative clustering programs. The performance of the word sense disambiguation program based on the BERT sequence labeling program greatly increased from 62.9% to 69.1% in the English experiment, and 95.9% to 97.3% in the Korean experiment regarding the F1-score when compressed sense vocabularies were used. This is comparable to the performance of the thesaurus-based compression model, i.e., 75.6%, considering that only sense definitions were used without knowing the sense hierarchy in the thesaurus. Our proposed method will be useful for finding the compressed sense vocabulary without using a manually constructed thesaurus.  
**Author Contributions:** Conceptualization, J.Y.P. and J.S.L.; Methodology, J.Y.P. and H.J.S.; Software, J.Y.P. and H.J.S.; Validation, J.Y.P.; Investigation, J.Y.P.; Writing-original draft, J.Y.P. and J.S.L.; Writing-review and editing, J.S.L.; Supervision, J.S.L.; funding acquisition, J.S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1 IIA3059545).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 6 Simulation Results
Specifically, \(\epsilon\) indicates the probability that the actual channel state is not equal to the estimated channel state. In this case, the estimated channel state is randomly selected among all the possible values with equal probabilities. As shown in this figure, the user-perceived capacity will decrease with the increment of \(\epsilon\). Figure 3: The power–period tradeoff in a fading channel. Additionally, Table 1 displays the average running times of Algorithm 2 and the Algorithm 3 under different values of \(B\) and \(T\). Notably, Algorithm 3 exhibits a significant reduction in running time compared to the dynamic programming in Algorithm 2. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(B\) & \multicolumn{4}{c}{50} & \multicolumn{4}{c}{100} \\ \hline \(T\) & 25 & 30 & 35 & 40 & 50 & 60 & 70 & 80 \\ \hline Algorithm 2 & 0.0106 & 0.0119 & 0.0120 & 0.0119 & 0.0331 & 0.0338 & 0.0462 & 0.0519 \\ \hline Algorithm 3 & 0.0033 & 0.00089 & 0.00091 & 0.0011 & 0.0036 & 0.0021 & 0.0030 & 0.0034 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Running times of Algorithms 2 and 3. Figure 4: User-perceived capacity in fading channel. Figure 5: _Cont._  
Figure 7 illustrates the threshold structure of the optimal policy. In this simulation, we set the number of periodically arriving data packets to \(B=10\) and the number of time-slots in a period to \(T=24\). Subplots 1 through 3 represent the optimal scheduling policy under three different channel states, respectively. We observe that within the feasible space \((q,m)\in\{(q,m)|qT+mB\leq TB,B-q\leq mS\}\), the optimal policy exhibits a threshold structure, validating the queue length-based optimal policy structure proposed earlier. Additionally, we note that as the channel condition improves, the transmission rate increases, confirming the channel-based optimal policy structure we proposed. Figure 5: Comparisons of the optimal scheme with a fixed rate transmission scheme. Figure 6: The influence of imperfect CSI for user-perceived capacity.
## 2 Related Research Analysis
Particular attention should be paid to solutions combining optimization and artificial intelligence [23; 24]. Moreover, one of the features of implementing load balancing based on TE is its use in networks with edge/fog computing. Consequently, the proposed approach in this study can be effectively applied to managing routing and load limitation at the network's edge in different types of networks, mainly based on Software-Defined Networking architectures.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 3 Transport Measurement Methods
### Hall Bar Devices  
The first transport measurements were performed using Hall bar structures patterned on 6H-SiC(0001) by [PERSON] et al. [4]. In this case, low mobility values were obtained in most of the samples, which may have been limited by the substrate step edges. Yet, the samples clearly demonstrated 2D electron gas properties and most of the important transport features of epitaxial graphene [4].  
At the step edges, a second graphene layer is often formed, which is associated with step bunching and results in non-uniform graphene thickness [17] and unreliable transport measurement results [51]. [PERSON] et al. demonstrated that a strong impact of substrate steps degrades the transport properties of epitaxial graphene on SiC using scanning tunneling microscopy-based localized transport measurements [51]. [PERSON] et al. reported somewhat similar results noting that conductivity anisotropy in graphene was associated with substrate steps. The observed results were explained using a model where a charge build-up at the substrate steps leads to scattering [52]. For these reasons, Hall bar structures are often patterned to be parallel or perpendicular to steps (see Figure 5) to mitigate the effects of steps, and this approach was used for quantum Hall effect [53] devices. We note that [PERSON]  
Figure 4: Band structure of EG on (000\(\overline{\mathrm{l}}\)) 6H-SiC with 11 epi-layers obtained from ARPES. Two linear Dirac cones are observed. Reprinted with permission from Reference [35]. Copyright (2009) by American Physical Society.  
and co-workers developed an approach to form EG on SiC(0001) that is essentially free of steps [54] and demonstrated excellent quantum Hall effect results.
# Advancing Biological Research: New Automated Analysis of Immunofluorescence Signals
## 3 Results
### Validation Systems
#### 3.3.1 8-OHdG Quantification in Human Fibroblasts under Oxidative Stress  
Control fibroblasts and fibroblasts stimulated with H2O2 for 24 h are subjected to IF for 8-OHdG as described in Section 2.1. 8-OHdG is a valid biological marker for evaluating the oxidative modifications on the nitrogenous bases of nucleic acids (DNA and RNA), which occur due to oxidative stress [18]. As expected, the acquisition of the images under the confocal microscope highlights a remarkable increase in the immunopositivity of 8-OHdG in H2O2-treated fibroblasts as shown in Figure 6. The images are analysed both manually by the operator using ImageJ software for Windows 10 (Figure 6A) as described in Section 2.3 and subjected to automatic analysis using the Q-IF tool (Figure 6B).  
Considering the exceptionally low IF signal of the oxidative stress marker in the control group, the analysis is launched with the option designed for dealing with a low IF signal on this experimental set. This option allows a more precise calculation of the cell area in the context of low fluorescence intensity. We claim this due to the empirical result obtained in the experimental process. As shown in Figure 6, the analysis of the 8-OHdG signal intensity shows a statistically significant increase between the two experimental groups. In particular, both the manual analysis and the automatic quantification obtained via the Q-IF tool report a four-fold increase in fluorescence intensity between the control- and the H2O2-treated cells, indicating that the quantification obtained by the automatic image analysis provides reliable results. This finding is reinforced by the evidence that statistical indices, such as SD and \(p\) value (calculated by the same statistical test), also appear to be similar when comparing the manual analysis method and Q-IF.  
Figure 5: Statistical analysis report comparing experimental groups with ANOVA and [PERSON]’s post hoc test. The table appears on the display at the end of the analysis and provides the information deriving from the mathematical analysis of the data: the name of the groups being compared, the mean difference between the average values of the groups, the adjusted \(p\)-value, the lower and upper limits of the confidence interval for the mean difference between two groups and the decision to reject the null hypothesis (“True” if the groups are significantly different, “False” if no statistically significant differences are detected by the comparison).
# Flying Watchdog-Based Guard Patrol with Check Point Data Verification
## 4 Results and Discussion
### Light Color and Pattern Detection Based on OpenCV  
The computer vision processing in this study is segmented into two distinct phases: object detection and classification. Object detection, in turn, is further subdivided into three different scenarios: low light, darkness, and standard lighting conditions. These divisions assess the reliability and performance of the developed detection software under varying environmental conditions, including the impact of ambient light on detection range. We utilized a 0.06 A 5 V LED to create a distinct point pattern, as depicted in Figure 4. Each LED emitted light in a manner that produced a circular scattering pattern. Our research incorporated LEDs in five colors, red, purple, yellow, blue, and white, to investigate potential variations in detection distances. For precision in our measurements and experimental setup, the experiments were conducted horizontally, aligning the camera with the target object. The results of these experiments are presented in Figure 9, shedding light on the outcomes of our investigations.  
The primary objective of the airborne watchdog system is to operate effectively during nocturnal hours or in environments with limited ambient light. Experimental trials were conducted under two distinct light intensity conditions, precisely 20 lux (considered meager light) and 35 lux (classified as low light). The findings from these experiments are presented in Figure 10. All four LEDs were subjected to identical power settings; however, it was observed that only the yellow LED remained undetectable, while the white LED exhibited the most extended detection range in low-light conditions, spanning approximately 18 to 20 m.  
Notably, with a 15 lux difference between meager light conditions, only the blue LEDs demonstrated a substantial difference in detection distance, extending up to 5 m. In comparison, the red and purple LEDs exhibited a more modest difference of only 2 m. The results indicate that detection distances increase as ambient lighting conditions become darker. Consequently, white light outperforms other LED colors in terms of detection range, making it the preferred choice for the LED pattern in the upcoming stages of development. Furthermore, these findings underscore the potential of single LEDs, or combinations of single LEDs arranged in different designs, to substitute traditional markers effectively. In conclusion, when the data readings align with the pre-established database, the system transmits a message to the serial monitor. It activates the data transfer process through a laser.  
Figure 10: Color detection range for different colors.  
Figure 9: Color detection with a dot square pattern.
## 3 Experimental Setup and Results  
In order to validate the reliability of the present two-dimensional OSB PD composite model, a CFRP double cantilever beam (DCB) test is conducted. The CFRP composite DCB test setup is shown in Figure 3a. The experiment was conducted in accordance with ASTM standard D5528-13 [33]. The DCB test specimens are bonded with two piano hinges at the initial delamination end. White paint marking is sprayed on the side of the specimen for visualizing the delamination front position. As shown in Figure 4, the length of the specimen is 167 mm, with 25 mm width and 4 mm thickness. The layup of the specimen is \([0]_{32}\), with 0.125 mm/ply. Initial delamination \(a_{0}=50\) mm is preset on the mid-plane of the specimen, between Layer 16 and 17. In this experiment, five DCB specimens are tested. The loading velocity is 1 mm/min. The resulting load-displacement curves of the specimens are presented in Figure 12. The delamination growth of each specimen is also recorded in Table 1. Modified beam theory (MBT) method from ASTM standard D5528-13 was used to analyze the experimental data, and the resulting average \(\mathrm{G_{IC}}\) of the CFRP composite is shown in Table 2.  
Figure 3: Carbon fiber-reinforced polymer (CFRP) double cantilever beam (DCB) test and two-dimensional PD modeling. (**a**) Double cantilever composite beam (DCB) test setup, (**b**) peridynamic (PD) modeling deformed displacement field in thickness direction (mm).
## 2 Numerical Simulation Method  
### Fuel Injection and Turbulent Combustion Models  
Prior to this simulation, we have established and verified the supercritical fuel injection model and chose the modified standard k-\(\varepsilon\) two equation model as the turbulence model to study the supercritical injection process; and to validate the model accuracy of the spray model, the supercritical nitrogen environment was studied by numerical simulation, the results shows relative error of the jet length is less than 5%, whereas that of the divergence angle is less than 8%. Both values meet the requirements for the accuracy of the calculation simulation [27].  
The combustion of fuel in the operation of an internal combustion engine is turbulent. Turbulent combustion involves not only the complex flow of fuel and air but also the chemical reaction of fuel and air in the flow process. This complex process occurs when fuel combustion interacts with turbulence. Combustion can affect the turbulent flow in the cylinder through various means. For example, the heat released by combustion causes a change in fluid temperature in the flow field, which in turn results in partial fluid expansion and a change in the density gradient. This change in the cylinder leads to a highly complex turbulent flow and increases the difficulty of performing a numerical simulation. Meanwhile, the released heat changes the temperature, physical properties, and transport characteristics of the fluid, resulting in flow randomness. Turbulent flow also affects combustion, which in turn influences the chemical reaction rate, mixed condition of fuel and air, heat and mass transfer, and the combustion rate. Notably, the turbulence combustion reaction is primarily affected by turbulence disturbances, molecular diffusion, and chemical kinetics. The interaction of these influencing factors must be considered when selecting a combustion model.  
In this study, the extended coherent flamelet model (ECFM) was used. ECFM is an extension of the coherent flamelet model. To determine the flame surface density, the ECFM uses the fuel/air equivalence ratio in fresh gases, the composition (including residual gases), and the temperature near the flame. The resulting flame surface density is used to describe large scale burned/unburned stratification. This model is fully coupled to the spray model and enables stratified combustion modeling including EGR effects and NO formation.
# Artificial Intelligence for Student Assessment:
## 2 Educational Applications of AI
### AI for Tutoring  
We can find interesting studies about intelligent tutoring systems [13; 18], which report that its development goes hand in hand with human natural language processing systems and learning analytics. All these systems are based on the idea of efficient mechanisms to provide feedback with enough quality to complement the teacher's action and substitute it in some cases. Considering the diversity of complex tasks that students can develop, it is a relevant advance to understand that these systems provide personalized answers. [PERSON] et al. [13] remark that AI will mainly impact the process of personalized education because of the automated assistance, especially in the context of virtual interaction, and they include some very well-known examples such as Duolingo, which is the visible demonstration of the accessibility of applications based on the interaction between machines and humans, relying on AI principles.  
[PERSON] et al. [19] remark that personalized tutoring feedback is one of the fields with more educational applicability, related to computer-based technologies. These authors studied a web-based intelligent learning environment used in mathematics to correct tasks and trace students' progress. They concluded that are some differences in feedback efficiency related to gender, because firstly, females take more advantage from tutoring feedback conditions (especially when feedback is based on conceptual hints). Secondly, girls improved their perceived competence more than boys, and finally, boys showed an increase in intrinsic motivation.  
[PERSON] et al. [20] demonstrate the usefulness of IA in both feedback and assessment as well as for formative evaluation, by using machine learning and checklists. Its research showed that automatic answers were good strategies to track students' progress and to identify areas to improve clinical practices.  
The experience of [PERSON] and [PERSON] [21] is of special interest because it utilizes AI to support collaborative learning experiences. They propose a Collaborative Logical Framework (CLF) based on AI to promote interaction, discussion, and collaboration as educational learning strategies. They also use this intelligent support to monitor students' behavior, reducing the workload in teachers. It is an adaptive guidance system developed for an e-learning platform (dotLRN) that can help the control and management of students' collaboration.  
[PERSON] et al. [13] studied AI applications in higher education, and they conclude that intelligent tutors \"have offered supportive assistance on several topics from their modest origins; topics such as training in geography, circuits, medical diagnosis, computing and programing, genetics and chemistry\" (p. 563) and that they can be basic tools to improve the presumably ubiquitous online learning in the future. Some of the applications for providing feedback and tutoring are also thought for assessment, for instance [PERSON] et al. [22] or [PERSON] et al. [23].