esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
## 4 Discussion
### Recovery Comparison with Machine Learning-Based Diffusion Models  
To provide a comprehensive understanding of ATTD in TSVD-based MMF-SPI, we compared its performance with that of the machine learning-based diffusion model. For this comparison, we used the measurement matrix (\(\mathbf{A}_{(0,0)}\)) and the practically disturbed \(\mathbf{y}_{(m,n)}\) as inputs for the diffusion model. Detailed information about the trained model can be found in [43]. In evaluating performance, we used the Pearson correlation between the recovered image and the ground truth as opposed to the clairvoyant benchmark used in earlier experiments. This approach was necessary because ATTD and the diffusion model employ different methodologies to solve the inverse problem. The comparison results are illustrated in Figure 6.  
Figure 6 compares the diffusion model and ATTD under the following noise conditions: (0, 0), (8, 0), (0, 8), (0, 16), and (8, 8). The numbers in the images indicate the Pearson  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{-70}\) \(\mathbf{dB}\) & \(\mathbf{-35}\) \(\mathbf{dB}\) & \(\mathbf{-30}\) \(\mathbf{dB}\) & \(\mathbf{-20}\) \(\mathbf{dB}\) \\ \hline \(\mathbf{A}_{(0,24)}\) & 0.98 & 0.77 & 0.63 & 0.48 \\ \(\mathbf{A}_{(24,0)}\) & 0.97 & 0.78 & 0.65 & 0.48 \\ \(\mathbf{A}_{(24,24)}\) & 0.98 & 0.77 & 0.63 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average Pearson correlations relative to USC-SIP (different \(\mathbf{A}\) and TOL = 0.4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{L}\)**-Curve** & \(\mathbf{GCV}\) & \(\mathbf{SURE}\) & \(\mathbf{ATTD}\) & \(\mathbf{Fixed}\) \\ \hline computational time (s) & 76 & 75 & 0.04 & 0.013 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average computational time for a single recovery.  
correlation coefficients between the reconstructions and the ground truth. At the (0, 0) noise level, both methods exhibited a high correlation of 0.79. As noise levels increased, the diffusion model maintained a slight edge, with correlations of 0.56 and 0.66 at (8, 0) and (0, 8) compared to ATTD's 0.52 and 0.63, respectively. At (0, 16), the diffusion model performed better (0.40 vs. 0.24). However, at the highest noise level (8, 8), ATTD showed improved resilience, with a correlation of 0.41, surpassing the diffusion model's 0.36. These results indicate that while the diffusion model performed slightly better at moderate noise levels, ATTD was more robust under high and mixed noise conditions. Additionally, generating a single recovery result with the diffusion model takes approximately 86 s, highlighting ATTD's significant time advantage. We believe that if more disturbed data can be used for training, better performance may be realized under high noise levels.
# CQFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
## 5 Experiments
### Results and Discussion
#### 5.4.2 Results of Traffic Assignment Simulation  
As mentioned previously, two parallel simulations are used in the experiment, where one simulation represents the real scenario and the other is used to establish a simulation-based dynamic traffic assignment model and generate pairwise data for neural network training. There are three types of path selection as mentioned in the last chapter: fixed, efficient, and random, and the ratio of these three for the \"real traffic\" simulation is \([0.35,0.53,0.12]\). The calibrated simulation-based DTA model obtains the ratio of these three as \([0.4,0.5,0.1]\). Figure 6 illustrates the relationship between the average 5-min flow per hour between these two simulations.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **c-SPSA** & **EKF** & **MLP** & **CydeGAN** & **Proposed** \\ \hline RMSE & 9.0643 & 22.2006 & 7.8831 & 11.1776 & 4.1796 \\ MAE & 6.9656 & 13.5181 & 5.5624 & 6.9010 & 3.0373 \\ MAPE & 24.78\% & 59.44\% & 14.38\% & 18.99\% & 10.10\% \\ \(R^{2}\) & 0.5778 & -0.0341 & 0.6536 & 0.4793 & 0.8053 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison.  
Figure 6: **Comparison of real and simulated traffic counts**. The 45-degree line represents an ideal estimation. The nearer the state points are to this line, the more accurate the estimates they represent. A total of 10,000 state points are randomly sampled.
###### Abstract  
Background: Aim of the study was to test the accuracy of AI-based software for detection of large vessel occlusion (LVO) with computed tomography angiography (CTA) in stroke patients using an experienced neuroradiologist's evaluation as the reference. Methods: Consecutive patients who underwent multimodal brain CT for suspected acute ischemic stroke were retrospectively identified. The presence and site (classified as proximal and distal) of LVO were assessed in CTA by an experienced neuroradiologist as a reference and compared to readings of three medical students and AI-based software, the e-CTA. Results: One-hundred-eight participants with a mean age of 70 years (\(\pm\)12.6); 55 (50.9%) females were included. Neuroradiologist found LVO in 70 (64.8%) cases: 45 (41.7%) proximal, and 25 (23.1%) distal. The overall sensitivity for e-CTA was 0.67 (95%CI 0.55-0.78); 0.84 (95%CI 0.71-0.94) for proximal, and 0.36 (95%CI 0.18-0.57) for distal LVOs. Overall specificity and accuracy for e-CTA were 0.95 (95%CI 0.82-0.99) and 0.77 (95%CI 0.68-0.84), respectively. The student's performance was similar to e-CTA. Conclusions: The tested software's performance is acceptable for the detection of proximal LVOs, while it appears to be not accurate enough for distal LVOs.  
artificial intelligence; cerebral angiography; spiral computed tomography; acute stroke; cerebrovascular occlusion 2021  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] 2021 11, 1007. [[https://doi.org/10.3390/app11211007](https://doi.org/10.3390/app11211007)]([https://doi.org/10.3390/app11211007](https://doi.org/10.3390/app11211007))
# A Review of Recent Literature on Audio-Based Pseudo-Haptics
## 3 Discussion
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt}} \hline \hline
**Article** & **Brief Summary** \\ \hline [PERSON] et al. [50] & \(\bullet\) Contextual sound led to higher perceived haptic fidelity. Accuracy was higher with sound (contextual or non-contextual) than without sound. \\ \hline [PERSON] et al. [51] & \(\bullet\) Sound alone influenced judgments of texture roughness and hardness. Sound produced during interaction with the texture most effectively captured surface roughness and hardness. Haptic cues alone were less effective for roughness and hardness but primarily determined slipperiness. \\ \hline [PERSON] et al. [52] & \(\bullet\) Participants identified textures most accurately with both auditory and haptic cues. Realistic feedback from virtual objects enhances virtual environments through combined visual, auditory, and haptic interactions. \\ \hline [PERSON] et al. [53] & \(\bullet\) Accompanying auditory descriptions improve the understanding and interpretation of tactile pictures. Optimal duration for these descriptions is 5 to 10 s. Descriptions longer than 10 s become incomprehensible. \\ \hline [PERSON] et al. [54] & \(\bullet\) Believable, real-world-like auditory and visual feedback are necessary for inducing pseudo-haptic sensations. A consistent and immersive virtual reality (VR) environment is also required for this effect. \\ \hline [PERSON] et al. [42] & \(\bullet\) Lower frequencies were perceived as heavier. Increased delay times led to stronger heaviness sensations. Louder sounds were perceived as heavier. Sound offset (end of the sound) was more critical in heaviness perception than sound onset. Haptic feedback can be simulated through auditory feedback without expensive haptic devices \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of articles considered in this review.
# Study on Burr Formation and Tool Wear in Drilling CFRP and Its Hybrid Composites
## 4 Conclusions  
In this paper, the drilling characteristics of carbon fiber-reinforced polymer (CFRP) and aramid fiber-reinforced polymer (AFRP) hybrid composites (C-AFRP) have been experimentally investigated. This work investigated the impact of a number of consecutive holes drilled on burr formation and tool wear in the drilling of CFRP and C-AFRP composites by using an 8 mm diameter carbide drill with a point angle of 90\({}^{\circ}\), a relief angle of 10\({}^{\circ}\), a helix angle of 30\({}^{\circ}\) and a total length of 63 mm. The following conclusions can be drawn:  
Figure 10: Tool wear in drilling of CFRP and C-AFRP according to hole number.  
Figure 9: SEM images of carbide drill after drilling 30 and 300 holes for CFRP (**a,b**) and C-AFRP (**c,d**).  
* The C-AFRP hybrid composites were successfully synthesized for the first time, and their drilling characteristics, including burr formation and tool wear, were investigated, compared with CFRP.
* The tensile properties of the CFRP were improved with the mixing of carbon and aramid fibers. The average ultimate tensile strength of C-AFRP hybrid composites was increased by about 70%, compared to CFRP.
* The thrust force and tool wear caused by drilling C-AFRP hybrid composites were significantly less than those by drilling CFRP. However, the only disadvantage is that drilling C-AFRP composites produced more burrs than drilling CFRP. Therefore, it is necessary to focus on reducing burr formation during the drilling of C-AFRP hybrid composites in the future.  
Conceptualization, J.H.L.; methodology, J.H.L.; validation, J.H.S.; formal analysis, J.H.L.; investigation, J.H.L., J.C.G. and J.H.S.; resources, J.H.L.; data curation, J.H.L., J.C.G. and J.H.S.; writing--original draft preparation, J.H.L.; writing--review and editing, J.H.L., J.C.G. and J.H.S.; funding acquisition, J.H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2019R1I1A1A01043653). This research was supported by \"Research Base Construction Fund Support Program\", funded by Jeonbuk National University in 2018.  
Not applicable.  
The data presented in this study are available on request from the first author and corresponding authors.  
The authors declare no conflict of interest.
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Algorithms for Transmission Scheduling of Physiological Signals
#### 3.4.5 Method 5: WFQ with Timestamp, Priority, and Empty Buffer  
(WFQ_Timestamp_Priority_Empty)  
On the basis of method 4, the timestamp consideration can provide a possibility for lower priority signals to be allocated resources to reduce latency, higher priority signals still have the necessity to be sent first. For that reason, after obtaining the first timestamp in each queue, the first arriving queue will be served first, and the remaining queues will be sent in order of priority. The transmission bandwidth is allocated according to Equation (2), and the queue will only be polled to the next queue when the queue is empty. As shown in Figure 15, the timestamp from the first packet in each queue is obtained (e.g., the timestamp of ECG = 1,662,219,425.096878 (s), EEG = 1,662,219,425.099889 (s), and EMG = 1,662,219,425.072183 (s), respectively). The EMG with the smallest timestamp will be served first, then the priority of the rest queues will be evaluated. The higher-priority queue ECG will be sent first, and the EEG will be sent only after the queue is cleared. Each queue still follows the FIFO principle that the packets in each queue are transmitted in order.
## 5 Surface Roughness Estimation
### Texture Skew Correction and DTCWT Filtering  
One of the captured images is shown in Figure 13a. It can be observed that the phenomenon of texture skew obviously occur in the original image. Texture skew influences not only the roughness measurement but also the classification accuracy. As presented in Section 2, an improved Sobel operator enhanced by Hough transform is proposed for the surface texture direction adjustment. The detected edge information is shown in Figure 13b. The adjusted image is shown in Figure 13c. The skewed image results show that the proposed method has good skew correction ability.  
With 2D-DTCWT performed on the adjusted image, the decomposition images representing wavelet sub-spaces are shown in Figure 14. Compared with interfering noises, the low frequency periodic component (Figure 14d) contains more useful information of the surface topology. The first two sub-bands located in higher frequency range contain much more irregular contents (Figure 14a,b). \(w_{3}(t)\) sub-band (Figure 14c) can be seen as generated random noises of the boundary  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Roughness** & **Original Sample Number** & **Adjusted Sample Number** & **Label** \\ \hline \([-\)Inf, 0.957) & 308 & 370 & C1 \\ \([0.957,\,1.67)\) & 152 & 370 & C2 \\ \([1.67,\,2.38)\) & 100 & 370 & C3 \\ \([2.38,\,3.1)\) & 135 & 370 & C4 \\ \([3.1,\,3.81)\) & 120 & 370 & C5 \\ \([3.81,\,4.52)\) & 200 & 370 & C6 \\ \([4.52,\,5.24)\) & 365 & 370 & C7 \\ \([5.24,\,5.95)\) & 370 & 370 & C8 \\ \([5.95,\,6.66)\) & 190 & 370 & C9 \\ \([6.66,\,\infty)\) & 100 & 370 & C10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of the surface roughness condition.  
Figure 13: Image skew correction (**a**) original image; (**b**) detected edge and (**c**) adjusted image.  
noise. Therefore, low frequency periodic component (Figure 14d) will be used as the surface topology in the following research.  
A step of normalization is applied to the filtered image such that the image gray value ranges from 0 to 255 in the 8-bit grayscale. In this paper, an additional step is also employed to scaling all values into the range of [0,1]. The feature scaling formula is defined as  
\[X^{\prime}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}}=\frac{X}{255}, \tag{24}\]  
where \(X\) is the original signal, \(X^{\prime}\) is the new signal after normalization.
## 4 Discussion
From the obtained results, the highest evaluation metric values (\(\overline{R^{2}}\pm\sigma(R^{2})\), \(\overline{MAE}\pm\sigma(MAE)\), \(\overline{RMSE}\pm\sigma(RMSE)\)) on the testing dataset were achieved in Case 1 (\(0.9876\pm 0.0048\), \(1.0096\pm 0.1215\), \(1.5506\pm 0.3050\).) The combined set of the five best SEs selected from Cases 1, 2, and 3 achieved the highest estimation accuracy when evaluated on the entire dataset. The results of the evaluation metrics (\(\overline{R^{2}}\pm\sigma(R^{2})\) and \(\overline{MAE}\pm\sigma(MAE)\), \(\overline{RMSE}\pm\sigma(RMSE)\)) in this case were equal to \(0.991087\pm 0.001291\), \(0.878362\pm 0.063662\), and \(1.326449\pm 0.100901\). Therefore, the results showed that the custom combination of the best SEs in terms of the evaluation metric values outperformed Cases 1-3. The analysis of the number of variables required to compute the output if a custom solution (five SEs) is used showed that all input variables were required. Even if only Equation (5) was used, all input variables were required. However, the high estimation accuracy can be achieved only using Equation (7), and this equation required only seven input variables. However, 4 (PCstart, PFstart, PArise, and PFrise) out of those 7 variables had a high correlation with the output variable, as seen from Figure 3.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 7 Limitations and Challenges
### Satire vs. Fake  
Besides being the highest propagator of false news, social media platforms are the biggest platform for sharing satirical or sarcastic views about any topic. Everyone is eager to share their perspective regarding any issue, resulting in images of satire, sarcasm, and jokes. Thus, any image manipulated or tampered with this non-malicious intention will also be marked as fake. The models and proposed methods do not discriminate between fake images posted for misinformation and tampered images posted as jokes. Both are treated as fake. [PERSON] et al. [137] have created models to weed sarcastic tweets out. Research work can be extended to learn the intrinsic feature differences between satire and fake. More work is needed in detecting sarcastic images from fake images.
# Photovoltaic Cell and Module I-V Characteristic Approximation Using Bezier Curves
## 6 Conclusions  
A novel method for modeling a PV cell or a PV module I-V characteristic was introduced. To the best of our knowledge, Bezier curves have not been used to model the I-V characteristic of PV devices before. The method proved high accuracy and was validated both in the case of a single PV cell and in the case of a whole PV module, for different technologies and manufacturers. The method was also used in the case of varying irradiance and temperature. The proposed method can be used for implementing hardware solar array simulators, for teaching or remote study. It is far easier to use the proposed method to find the I-V characteristic of a PV cell or module when compared with solving the exponential equations associated with the single or double diode model largely used today. A common microcontroller can compute the points on the I-V curve with a minimum of resources, inherently increasing the computing speed and the response of the system.  
The advantage of our method relies on the ease of I-V characteristic generation: if we exclude \(V_{oc}\) and \(I_{sc}\), only 10 different values (\(P_{23x}=V_{oc}\), \(P_{00y}=I_{sc}\), \(P_{01y}\), \(P_{02y}\), \(P_{03y}\), \(P_{11y}\), \(P_{12y}\), \(P_{13y}\), \(P_{21y}\)) have to be stored--SAS manufacturers usually use 1024 or more double points to accurately define  
Figure 17: Relative error (**a,b**) and curvature plots (**c,d**) for Shell ST40 Thin film PV module (a,c) and 6.5 Wp L amorphous Silicon PV Cell (b,d).  
the I-V characteristic. Alternate use is for any graphical plot of the I-V (and subsequently P-V) curves. Furthermore, little knowledge of the device itself is required, as only common data from the datasheet is needed.  
As future work, fitting spiral segments will be investigated and compared with our current method.  
Both authors contributed to this research. [PERSON] conceived and designed the study and carried out the simulations. [PERSON] analyzed the data. [PERSON] wrote the paper and reviewed the manuscript. Both authors read and approved the manuscript.  
This work was supported by both the Romanian National Authority for Scientific Research and Innovation, CNCS/CCCDI-UEFISCDI within PNCDI III, project number PN-III-P2-2.1-PED-2016-0074 and by the Politehnica University Timisoara, according to the Administration Board research policy.  
The authors declare no conflict of interest.
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 3 Experiment
### Experimental Design and Result Analysis
#### 3.2.5 Time Complexity Analysis of Clustering and Association Rule Mining Algorithms  
To validate the time complexity of the clustering and association rule mining algorithms used in our framework, we incorporated additional analyses, including closure-based association rule mining and attribute sensitivity updating. The corresponding results are shown in Table 13.  
Field \"Cluster\" indicates that association rule mining was disabled in this configuration. Field \"Cluster-rules\" represents the results of experiments that incorporate association rule mining and attribute sensitivity updating. Field \"\(\bigtriangleup\)\" indicates the degree of improvement in system performance, measured in terms of execution time and accuracy. Field \"Time\" denotes the total execution time spanning from the calculation of initial attribute sensitivity based on information entropy to the final system-level classification.  
The inclusion of association rule mining increases the average system execution time by 2.084 s. Classification accuracy declined only in System 3, while the remaining three system experienced some improvement in accuracy. The average accuracy improved by 4.81%. Therefore, although the introduction of association rule mining increases the processing time, it remains within an acceptable range. Moreover, association rule mining contributes to improvements in accuracy.  
Among all systems, System 3 incurs the highest additional time cost due to mining the largest number of association rules, totaling 22. In comparison, Systems 1, 2, and 4 mine 10, 14, and 14 rules, respectively. The slightly decrease in accuracy for System 3 may be attributed to the fact that most of the attributes labeled by experts are insensitive. The introduction of association rules increases the number of attributes identified as sensitive, which consequently results in a reduction in accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**System** & \multicolumn{2}{c}{**Cluster**} & \multicolumn{2}{c}{**Cluster-Rules**} & \multicolumn{2}{c}{\(\bigtriangleup\)} \\
**Name** & **Time** & **ACC** & **Time** & **ACC** & **Time** & **ACC** \\ \hline System1 & 2.7596 s & 57.40\% & 3.5124 s & 58.33\% & 0.7528 s & 0.93\% \\ System2 & 3.4349 s & 58.16\% & 4.2741 s & 73.47\% & 0.8392 s & 15.31\% \\ System3 & 2.6426 s & 59.57\% & 8.1265 s & 58.51\% & 5.4839 s & \(-\)1.06\% \\ System4 & 4.1840 s & 53.75\% & 5.4448 s & 57.80\% & 1.2608 s & 4.05\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Time complexity analysis of cluster analysis, association rule mining, and mutual information computation.  
Figure 7: The experimental results of GPT\(-\)3.5 generating regular expressions under different prompts.
## 4 The Optimization Model
### The Optimization Algorithm  
The purpose of this paper is to investigate the cost of energy for a low wind speed turbine considering rotor diameter and hub height as variables. The optimization progress aims to find the ideal blade shape parameters such as chord, twist, thickness distributions and the blade length, and to find the structural performance like webs locations, layer-up distributions and the tower parameters (height, taper and wall thickness distributions). The design process interfaces the particle swarm optimization (PSO) algorithm, BEM code, PreComp code and the COE model. There are three modulesintegrated in the PSO algorithm by using MATLAB: (1) the aerodynamic performance analysis based on BEM theory; (2) the structural performance analysis by PreComp code; and (3) the design constraints by beam theory and strength theory. The basic parameters of the PSO algorithm are defined as: the population size (\(p=80\)), maximum number of generations (\(g=200\)), learning factors (\(c_{1}=c_{2}=0.5\)), variable dimensions (\(n=n_{aerodynamic}+n_{structural}=36\)), and the inertia factor (0.85).  
Figure 9 shows the flowchart of the optimization process. The aerodynamic and structural variables for blades and the tower are randomly generated as the initial population. The aerodynamic variables are used to control the blade shape, the structural variables are used to control the webs locations and number of layers along the blade, and the variables for the tower are used to control the tower height and structural parameters. BEM theory is applied to calculate the annual energy production and the aerodynamic loads which will cause the blade deflection and tower deformation. Moreover, the PreComp code is adopted to evaluate the mass and stiffness of the blade sections. The requisite parameters are airfoil profiles, chord length and twist angle, materials characteristics, internal configurations of blade sections and the lay-up distributions. When a wind turbine is acquired during the optimization process, the blade and tower constraints are validated. By analyzing the results, a new wind turbine design with minimum COE will be outputted.