esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
This means that, in comparing the polarizing 3D and 2D image brightness values, the 3D image brightness decreases by approximately 60.8%. For a 95% confidence interval, degradation values of 52.4%-69.2% are within the reasonable range of consideration, as shown in Table 9. Because the polarized 3D image brightness is approximately 60% less than that of the corresponding 2D image, in order to achieve the same image brightness as the 2D image, the 3D image brightness must be increased. \begin{table}
\begin{tabular}{c c c c} \hline
**Shutter** & **2D Mode** & **RGB Values** & **3D Mode** \\
**Speed (s)** & **Brightness Value (lx)** & **Brightness Value (lx)** \\ \hline
1/125 & 6.6 & R:125, G:131, B:126 & 2.2 \\
1/250 & 2.2 & R:74, G:79, B:74 & 0.7 \\ \hline \end{tabular}
\end{table} 
Table 7: F5.6 aperture experimental results. \begin{table}
\begin{tabular}{c c c} \hline
**Shutter** & **2D Mode** & **3D Mode** \\
**Speed (s)** & **Brightness Value (lx)** & **RGB Values** & **Brightness Value (lx)** \\ \hline
1/20 & 19.8 & R:193, G:198, B:194 & 6.6 \\
1/40 & 9.4 & R:138, G:144, B:139 & 3.2 \\
1/50 & 6.6 & R:117, G:123, B:117 & 2.2 \\
1/80 & 3.2 & R:81, G:87, B:82 & 1.1 \\ \hline \end{tabular}
\end{table} 
Table 8: F10 aperture experimental results. \begin{table}
\begin{tabular}{c c c} \hline
**Experimental Item** & **Values** \\ \hline
3D Image Brightness Degradation & 60.8\% \\
3D Image Brightness Degradation within 95\% Confidence Level & 52.4\%–69.2\% \\ \hline \end{tabular}
\end{table} 
Table 9: Comparison of 2D and 3D image experimental results. The above reference data is applicable to the production of 3D stereoscopic displays, and shows that the 3D image brightness must be increased in order to achieve the same image brightness as a 2D image on a screen. The use of this data can reduce the time period required for setting adjustments. Complementary adjustments taking the various environmental factors into consideration are recommended for practical imaging designs, so that the gap in display brightness between 2D and 3D images can be reduced. As a result, the main idea of this study is trying to measure the brightness loss (or difference) between filming 3D movie and watching 3D movie. The results show 60.8% brightness loss, which means we have to increase 2.5 times lighting intensity while filming to reach the standard brightness. This modification elevates the watching 3D movie to proper brightness.
## 3 Results
### Analysis of the Artistic Domain of the Applications  
Most of the apps (71%) (Figure 1) offer the function of customizing the stroke, of which 53% have brushes and pencils of various shapes and styles, 46% allow the use of brushes and pencils varying the size of the stroke, and a smaller number (12%) allows for varying the opacity of the stroke. Two other functions examined concerning the stroke were the possibility of blurring and controlling dispersion and dithering, and it was found that only 21% of the apps can blur; likewise, only 12% can control the dispersion and dithering of the stroke.  
The erase action is a function appearing in 23 apps (71%), of which 62% offer the possibility of erasing in parts, and 6% can erase in parts or as a whole; however, some apps offer the possibility of modifying the size of the eraser (62%), its opacity (25%), erasing as a line (46%) and a smudge (6%), whereas we found that only two apps (6%) allow erasing with effects, and only one App (3%) offers various shapes of erasers: iconic, hard iconic, soft, square, smooth, or paisley, among other options.  
Color is a tool present in 24 apps (75%), but only 7 of them (21%) were found to have good diversity of color range; likewise, only four apps (12%) offer the option of selecting and working with a variety of color profiles, whereas most of the apps (75%) do not have a color selector, but more than half of them (56%) have a paint bucket function.  
Another aspect examined was the degree of experimentation. It was observed that only 18% of the apps had highly developed functions for this, and in 42% of them, the functions for experimentation are scarce or deficient. Concerning this aspect, the possibility of free drawing was examined, finding that the function is integrated into 20 apps (62%). The function for creating canvases (34%) and the ability to restore the canvas at any stage of the creation process (68%) was examined. Additionally, nine apps (28%) support changing the canvas background with color and adding photos from the gallery. In addition, two apps (6%) allow playing with background transparency, one App (3%) allows the use of gradient backgrounds, and two apps (6%) had different types of frames. Fourteen apps (43%) allow to resetting the canvas completely and recovering what has been done at any stage, while nine apps (25%) only offer a limited number of recovery actions.
# Correlation between Coda Wave and Stresses in Uni-Axial Compression Concrete
## 2 Correlation Model of Sound Velocity and Stress in Concrete  
Sound velocity in concrete is mainly determined by the elastic parameters of concrete. The development of microcracks in concrete under stresses would change the elastic parameters of the concrete medium, resulting in sound velocity changes. In order to simulate the changes of sound velocity in concrete under uni-axial stress, two basic assumptions are made: (a) concrete is considered as a kind of two-phase material with cracks distributed randomly in the elastic matrix; (b) all cracks are in elliptical shape with the same size.
# Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images
## 7 Conclusions  
In this paper, we investigated the impact of integrating pixel-level analysis in the threat detection pipeline of a large-scale and imbalanced X-ray security image dataset. We reintroduced the object separation as a unique task domain for analyzing X-ray security images and address the first part of the task, which aimed to accurately delineate target object instances from X-ray images, including pixels that were shared by overlapping objects. We developed a straightforward and effective object separation pipeline composed of a detection model for localizing possible threat regions and a segmentation model for verifying the existence of threats in the predicted regions. We chose the appropriate detection and segmentation models by extensively evaluating current state-of-the-art deep learning models.  
Our empirical results show that object-level and pixel-level approaches significantly outperformed image-level approaches for threat detection in X-ray security images. Our approach outperformed the baseline classification and end-to-end instance segmentation methods by up to 26.86% and 4.85%, respectively, on the subset that closely mirrored a practical scenario. Furthermore, our approach consistently outperformed the previous works across all the subsets with increasing scales of imbalance. These preliminary results support the claim that our intuitive approach works effectively without the need to build an entirely new framework or device a complicated data processing technique. Object separation has the potential to advance research in automated threat detection in X-ray security images as well as other X-ray imaging applications. However, exploration in this field is limited due to the lack of pixel-level annotations of X-ray security image datasets. We believe that this study would encourage researchers to create more high-quality labeled datasets and develop more sophisticated approaches to fully solve the object separation task, which can help in the generalization of the detection of all prohibited items such as weapons, explosives, and drugs. In our future research, we intend to create an end-to-end object separation framework.  
Conceptualization, J.K.D. and Y.-J.J.; methodology, J.K.D. and Y.-J.J.; software, J.K.D.; validation, Y.-J.J.; formal analysis, J.K.D. and Y.-J.J.; investigation, J.K.D. and Y.-J.J.; resources, J.K.D.; data curation, J.K.D.; writing--original draft preparation, J.K.D.; writing--review and editing, J.K.D. and Y.-J.J.; visualization, J.K.D.; supervision, Y.-J.J.; project administration, Y.-J.J.; funding acquisition, Y.-J.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Kwangwoon University and by the MISP Korea, under the National Program for Excellence in SW (2017-0-00096) supervised by IITP.  
Not applicable.  
The authors declare no conflict of interest.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
###### Abstract  
Historical districts face pressing disaster preparedness challenges due to their special spatial properties--risks compounded by static approaches that overlook dynamic fire-pedestrian interactions. This study employs an agent-based model (ABM) for fire simulations and AnyLogic pedestrian dynamics to address these gaps in Dukezong Ancient Town, Yunnan Province, China, considering diverse ignition points, seasonal temperatures, and wind conditions. Dynamic simulations of 16 scenarios reveal critical spatial impacts: within 30 min, \(\geq\)28% of streets became impassable, with central ignition points causing faster obstructions. Static models underestimate evacuation durations by up to 135%, neglecting early stage congestions and detours caused by high-temperature zones. Congestions are concentrated along main east-west arterial roads, worsening with longer warning distances. A mismatch between evacuation flows and shelter capacity is found. Thus, a three-stage interaction simplification is derived: localized detours (0-10 min), congestion-driven delays on critical roads (11-30 min), and prolonged structural damage afterward. This study challenges static approaches by highlighting the \"fast alert-fast congestion\" paradox, where rapid alerts overwhelm narrow pathways. Solutions prioritize multi-route guidance systems, optimized shelter access points, and real-time information dissemination to reduce bottlenecks without costly infrastructure changes. This study advances disaster modeling by bridging disaster development with dynamic evacuation, offering a replicable framework for similar environments.  
historical district; emergency evacuation evaluation; fire simulation; evacuation interaction; accessibility; ABM-SFM integration Article
# Review of Recent Advances in QEPAS-Based Trace Gas Sensing
## 3 QEPAS Sensor with High-Power Excitation Source
### Intra-Cavity QEPAS Sensor  
An intra-cavity QEPAS (I-QEPAS) sensor was first reported by [PERSON] [55] and was further developed for sensitive carbon dioxide (CO\({}_{2}\)) and nitric oxide (NO) detection [56; 57; 58]. Due to the output coupling loss, the laser power intensity in the intra-cavity is much higher than outside. In [55], a bow-tie type optical resonator with a high finesse (>1500) was developed, which is shown in Figure 5. A distributed feedback quantum cascade laser (QCL) emitting at 4.33 \(\upmu\)m was used as the excitation source for carbon dioxide (CO\({}_{2}\)) detection. Compare to the incident laser power, the intra-cavity optical power was boosted by a factor of \(\sim\)250. Finally, a ppt (parts per trillion) level detection limit and a normalized noise equivalent absorption (NNEA) of 3.2 \(\times\) 10\({}^{-10}\) cm\({}^{-1}\) W/\(\surd\)Hz were achieved.  
An I-QEPAS sensor based on fiber-ring laser was reported by [PERSON] [59]. The schematic of the experimental setup is shown in Figure 6. An erbium-doped fiber laser with a ring cavity structure was adopted. A fiber coupler #1 with a ratio of 1:9 was used to ensure that the laser power intensity within the cavity was much higher than outside. Hence, it would make maximum use of the laser power for the I-QEPAS. A fiber Bragg grating (FBG) was used to modulate the laser wavelength. C\({}_{2}\)H\({}_{2}\) detection at the wavelength of 1531.6 nm was performed to evaluate the fiber-ring laser based I-QEPAS sensor performance. A minimum detection limit of C\({}_{2}\)H\({}_{2}\) of 29 ppb at 300 s integration time was achieved when the optimized laser modulation depth and gas pressure were selected. The linear dynamic range of the fiber-ring laser-based I-QEPAS sensor was on the order of 10\({}^{5}\).  
Figure 5: Schematic of the optical resonator.  
Figure 6: Schematic of the I-QEPAS sensor based on fiber-ring laser.
## 4 Simulation Results and Discussion
When the frequency offset lies between (\(-1\) GHz, 1 GHz), the proposed algorithm achieves an NMSE to the order of \(10^{-8}\), whereas the traditional QPSK partitioning algorithm only reaches the \(10^{-7}\) range. This indicates the superior estimation accuracy of the proposed algorithm, showcasing its exceptional performance. Figure 12b further presents the results under strong turbulence, where the proposed algorithm continues to maintain higher estimation accuracy, consistent with the findings under weak turbulence. Overall, Figure 12 clearly demonstrates that the proposed algorithm consistently delivers better estimation accuracy across different turbulence conditions, highlighting its robustness and wide applicability in diverse environmental scenarios.
## 3 Linearized System
### No-Slip Boundary Condition: Modal Structure  
It is now convenient to express separation of variables for the no-slip problem for each normal mode in terms of exponential factors appropriate for growing standing waves (\(\omega\) real):  
\[\psi\left(x,z,t\right) = \Psi\left(z\right)\exp\left(ikx\right)\exp\left(\omega t\right),\] \[T\left(x,z,t\right) = \Theta\left(z\right)\exp\left(ikx\right)\exp\left(\omega t\right),\] \[p\left(x,z,t\right) = P\left(z\right)\exp\left(ikx\right)\exp\left(\omega t\right). \tag{30}\]  
The general solution can be found as:  
\[\Psi = \sum_{j=1}^{4}\alpha_{j}\exp\left(\lambda_{j}z\right),\] \[T = -ik\sum_{j=1}^{4}\frac{\alpha_{j}}{\lambda_{j}^{2}-\lambda_{5}^{2 }}\exp\left(\lambda_{j}z\right)+\sum_{j=5}^{6}\alpha_{j}\exp\left(\lambda_{j} z\right),\] \[P = i\left(k+\frac{\omega}{\text{Pr}k}\right)\frac{d\Psi}{dz}-\frac{ i}{k}\frac{d^{3}\Psi}{dz^{3}}, \tag{31}\]where \(\lambda_{1}=k\), \(\lambda_{2}=-k\), \(\lambda_{3}=\left(k^{2}+\frac{\omega}{\Pr}\right)^{1/2}\), \(\lambda_{4}=-\lambda_{3}\), \(\lambda_{3}=\left(k^{2}+\omega\right)^{1/2}\) and \(\lambda_{6}=-\lambda_{5}\) are the characteristic values of the general solution.  
The modal structure can be examined by considering the long wave limit  
\[\lim_{k\to 0}\Delta\left(\omega,k;\Pr,M,K,G\right)=0 \tag{32}\]  
implicitly defines \(\omega\left(k\to 0;\Pr,M,K,G\right)\). This limit can be determined symbolically from:  
\[\lim_{k\to 0}\Delta\left(\omega,k;\Pr,M,K,G\right)=\frac{-2\,e^{-\sqrt{\omega }-\sqrt{\frac{\omega}{\Pr}}}\,\left(1+e^{2\,\sqrt{\omega}}\right)\,\left(1+e^ {2\,\sqrt{\frac{\omega}{\Pr}}}\right)\,\omega^{\frac{13}{2}}\,\sqrt{\frac{ \omega}{\Pr}}}{\Pr^{2}}. \tag{33}\]  
Three finite roots exist: \(\{\{\omega\to 0\},\{\omega\to\frac{-\pi^{2}}{4}\},\{\omega\to\frac{-\left(\pi^{2} \Pr\right)}{4}\}\}\). We find that the neutrally stable root \(\omega\to 0\) has multiplicity of at least seven. The root \(\omega\to\frac{-\pi^{2}}{4}\) is associated with exponential decay. Since the scaling for time is diffusive, the exponential decay constant is a factor of the thermal time scale. Thus, this mode is termed the thermal mode. As the root \(\omega\to\frac{-\pi^{2}\Pr}{4}\) decays according to the viscous time scale, we label this the viscous mode.  
A key property of all of these modes is their stationarity. The long wave slip modes comprise a pair of propagating simple waves. The no-slip bottom boundary condition has the effect of rendering all long waves stationary. Thus, if any propagating modes exist, they must occur at finite wavelengths. This suggests investigating the \(k\)-dependence for long waves branching from the infinitely long wave modes identified above.
## 3 Experimental Results
### The Collisional Shift Evaluation
As the clock regularly operates, the average excitation fraction \(P_{\rm e-nor}\) is about 0.4 which corresponds to the point of half the maximum excitation. Thus, we carefully evaluate the collisional shift when the excitation fraction is \(P_{\rm e-nor}\) as shown in Figure 3b. The weighted mean of the ten measurements is 4.31(27) mHz per unit fluorescence intensity, where the measurement uncertainty has been multiplied by the square root of the reduced-chi-square \(\chi^{2}_{\rm red}=2.06\). Thus, as the clock regularly runs, the corresponding fractional collisional shift is 4.54 \(\times\) 10\({}^{-16}\) with an uncertainty of \(2.8\times 10^{-17}\). The residual SCME, caused by fast changed drift, will lead random frequency offset to each measurement, which eventually increases the uncertainty of the measurements. However, the experimental result shows that with the DISC method, the collisional shift is obtained with a precision of \(2.8\times 10^{-17}\), though we have not compensated the frequency drift.
## 4 Numerical Examples
### Comparative Analysis Settings  
The proposed NSPSO is designed to deal with RMOPs such as the finding of LSF which can also be seen as a special multimodal optimization problem. Therefore four--NMMSO [31], RSCMSAESII [25], Multi_AMO [32] and DP-MSCC-ES [33]--are chosen as competitive algorithms for the comparative study. Notably, the RS-CMSA-ESII won the championship in the GECCO 2020 Competition on Niching Methods for Multimodal Optimization. NMMSO won the CEC'2015 competitions on multimodal optimization. Parameter settings of each comparison algorithm uses their corresponding recommendations. The main parameters settings of NSPSO are as follows:  
* \(ns=d+4\);
* \(k=25\);
* \(d_{np}=\frac{\left|x_{1}^{\text{max}}\right|+\left|x_{1}^{\text{min}}\right|+ \left|x_{2}^{\text{max}}\right|+\left|x_{2}^{\text{min}}\right|+\cdots+\left|x _{d}^{\text{max}}\right|+\left|x_{d}^{\text{min}}\right|+\cdots+\left|x_{d}^{ \text{min}}\right|+\left|x_{d}^{\text{min}}\right|}{\frac{N}{N}\text{rend}_{d}}\), where \(x_{i}^{\text{min}}\) and \(x_{i}^{\text{max}}\) are the lower and upper limit of the \(i\)-th variable, respectively.
* \(B_{p}=50\%\).
* \(n_{b}=1\) (two dimensions), and \(2\) for (three dimensions).  
To make the comparison fair, \(Np_{\text{end}}\), maximum function evaluation (\(MFE\)), and the acceptable error (\(e\)) were set to be the same for each method, as shown in Table 1. For different dimensions, the optimization difficulties were different, so the parameter settings were set differently. All algorithms were conducted fifty times for each example.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 6 Discussion of Approach Planning Results under Severe Weather Conditions
### Results of Diversion Calculations and Sequence Planning
It can happen that routes approach each other for up to a few miles but are continued differently due to a small spatial and temporal distance (Figure 21). Figure 21: Two different route calculations for aircraft approaching Malpensa from the northeast through a loose group of convective areas. Even a small variance in temporally different initial conditions can lead to different optimal routing. Malpensa traffic mix from 11 May 2019 with weather from 6 August 2019 and the RaNDeVIL nowcast model. Figure 20: Route calculation for the aircraft EJU36 LR approaching MXP from the northeast through a loose group of convective areas. Currently, this route is still blocked by a convective cell; however, based on the forecast, the AMAN knows that the route will be clear by the time the aircraft arrives. MXP traffic mix from 11 May 2019 with weather from 6 August 2019 and the RaNDeVIL nowcast model. All routes calculated and displayed in this paper are 4D trajectories that include altitude, speed, and time in addition to position. They are always calculated on the basis of EUROCONTROL's BADA 3.13 [36] and are therefore realistic within the scope of technical possibilities. They should also therefore be able to be safely flown by the respective types of aircraft. BADA data for the respective aircraft types are also used for sequence calculation on the finals and the runways so that the distances between the aircraft follow the separations corresponding to their weight classes. This is also valid for the 4D trajectories calculated as diversion routes around severe weather areas. As an example, an aircraft approaching from the north and its speed and altitude profile are presented in Figure 22. It can be seen from the altitude profile in the upper diagram that it is not possible for an aircraft to fly over or under a thunderstorm cell due to the descent that is usually already initiated at the distance of a few dozen miles from the airport. Technically, 3D polyhedrons could be used for weather areas to specify the upper and lower boundary of convective cells; however, this does not bring any operational advantages in the context of approach planning. These visualizations indicate the strict dependence of AMAN-calculated 4D trajectories under the influence of local weather on the applied weather model and its assessment of meteorological severity for aviation.
## 4 IoMT Authentication Taxonomy
### Authentication Schemes
#### 4.6.3 Certificate-Based Authentication  
The authentication schema in IoMT can depend on using a certificate to identify legitimate entities. Accordingly, authentication schemes may require a hard certificate, soft certificate, implicit certificate, or no certificate for identifying entities, see Figure 16. Most of the authentication literature in the IoMT environment suggests a hard certificate to accomplish the authentication process. A smart card is a hard certificate used to authenticate users in the IoMT system [73,92]. Similarly, an RFID chip is used to authenticate IoMT devices uniquely by providing it as a device identifier [25,44,45,46]. A hard certificate also requires to be validated and signed by a reliable party. The second type of certificate is a soft certificate, or a digital certificate that refers to a token that requires to be validated by a reliable party [111,112]. The reliable party is called a certificate authority (CA) or a delegated entity.  
Implicit certificate-based authentication schemes are more lightweight than soft and hard certificates. The advantages of implicit certificates over explicit certificates (i.e., soft  
Figure 16: Certificate-based authentication.  
Figure 15: (**a,b**) Key agreement scheme.  
and hard certificates) include fast processing, small size, and no need for verification from a trusted party [113]. The Digital Signature Algorithm (DSA), and the Elliptic Curve Digital Signature Algorithm (ECDSA) operate as examples of the implicit certificate [32; 43; 91]. Elliptic Curve Qu-Vanstone (ECQV) is also an implicit certificate that offers faster processing and less certificate storage for generating certificates. [PERSON] et al. [55] adopted a suitable IP security solution for IoMT since it relies on a certificate-enabled DTLS scheme. [PERSON] [56] designed an authentication technique to identify IoMT devices to users through the blockchain-based Lamport-Merkle scheme for generating and verifying the implicit certificate.  
For enhancing lightweight authentication in the IoMT environment, there is a recent focus on adopting certificateless schemes. The researchers in [114] proposed a lightweight certificateless authentication method that depends on an aggregated-signature and pairing-free scheme in the IoMT system. They exploited an aggregation mechanism that does not use pairings, thus reducing the communication and computation transmission overhead. [PERSON] et al. [115] developed a certificateless-based authenticated key agreement (CALKA). CALKA releases the IoMT system from the burden of certificate storage and management.