esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Mode-I Interlaminar Fracture Toughness Measurements  
Mode I interlaminar fracture toughness was accessed utilizing the double cantilever beam (DCB) method technique following ASTM D5528-13 guidelines. The geometry and dimensions of a DCB specimen are illustrated in Figure 2.  
A non- sticky vacuum bag membrane, which was 70 mm long and 0.1 mm thick, was introduced at the sample's mid-plane to initiate a delamination. It should be noted that the initial pre-crack (a\({}_{0}\)) should be around 50 mm with the length of the piano hinges included. A Universal Instron loading machine (Model 5500R) was used to apply a tensile force at a constant displacement rate of 1 mm/min to piano hinges attached to the specimen. The applied force (P) and crosshead displacement (\(\delta\)) were recorded and the crack length (a) was measured using a travelling USB camera. The crack on the laminate was calculated with the help of ImageJ software. A small metallic ruler (15 cm total length) was previously placed on the one clamp of the Instron system in order to act as a scale, as shown in Figure 3.  
Figure 1: Photo of the vacuum assisted resin transfer molding process (VARTM).  
For clearer observation of the crack growth, the samples were painted with white color. The recorded values were then used to calculate the \(G_{\text{IC}}\) values along the DCB specimen using the modified beam theory (MBT) method. The beam theory expression for the strain energy release rate of a perfectly built-in (that is, clamped at the delamination front) double cantilever beam is as follows:  
\[G_{\text{IC}}=\frac{3\text{ P }\delta}{2\text{ b a}} \tag{4}\]  
where P = load (N), \(\delta\) = load point displacement (mm), b = specimen width (mm), and a = delamination length (mm).  
Nevertheless, this approach undervalues the compliance (C = \(\delta\)/P) as the beam is not perfectly constructed. A way to correct this consequence, is to apply somewhat longer delamination length of  
Figure 3: Representative snapshot during the Mode-I test.  
Figure 2: Schematic of the specimen for the Mode I test. L represents the length of the specimen, w the width, h the thickness and a\({}_{0}\) the initial 50 mm pre-crack. The inset photo shows specimens ready to be measured.  
a \(+\)\(|\Delta|\) and a correction factor (F) for values of compliance over 0.4. This method of calculating G\({}_{\text{Ic}}\) is known as corrected beam theory (CBT). All propagation values were calculated according to  
\[\text{G}_{\text{Ic}}=\frac{3\text{ P }\delta}{2\text{ b }(\text{a}+\)\(|\Delta|)}\text{F} \tag{5}\]  
where G\({}_{\text{Ic}}\) = Mode-I interlaminar fracture toughness (J m\({}^{-2}\)), P = load (N), \(\delta\) = load point displacement (mm), b = specimen width (mm), a = delamination length (mm), \(|\Delta|\) = increase in delamination length without underestimating the compliance (mm), and F = correction factor.
## 2 Related Works
### Characteristics of Color Space
#### 2.2.3 CIE L*a*b* Color Space  
The CIE 1976 (L*, a*, b*) color space was introduced by Commission Internationale de I'Eclairage (CIE) in 1976. The CIE L*a*b* color space is based on the compressed CIE XYZ color space, which can predict spectral-power distributions that will be perceived as an identical color, coordinates nonlinearly. The CIE L*a*b* color space is perceptually uniform, whereas the CIE XYZ color space is not. The CIE L*a*b* color space is considered the most complete color space [22]. It is derived from CIE XYZ tristimulus values. In addition, the CIE XYZ color space can be derived from the RGB color space. The CIE XYZ color space was introduced in 1931 by CIE to match the perceived color of any given spectral energy distribution. The system of imaginary non-physical primaries is known as X, Y, and Z. Only Y contributes to the luminance, and X and Z have the value of chromaticity. All the colors can be matched on the basis of the positive amounts of X, Y, and Z, unlike the RGB color space [27].  
The CIE L*a*b* color space is transformed from CIE XYZ tristimulus values as follows [28]:  
\[\begin{split} X&=k\sum_{i=0}^{N-1}\bar{x}\left( \lambda_{i}\right)l\left(\lambda_{i}\right)r\left(\lambda_{i}\right)\\ Y&=k\sum_{i=0}^{N-1}\bar{y}\left(\lambda_{i} \right)l\left(\lambda_{i}\right)r\left(\lambda_{i}\right)\\ Z&=k\sum_{i=0}^{N-1}\bar{z}\left(\lambda_{i} \right)l\left(\lambda_{i}\right)r\left(\lambda_{i}\right)\\ \begin{bmatrix}X\\ Y\\ Z\end{bmatrix}&=\begin{bmatrix}0.412453&0.357580&0.180423\\ 0.212671&0.715160&0.072169\\ 0.019334&0.119193&0.950227\end{bmatrix}\begin{bmatrix}R\\ G\\ B\end{bmatrix}\\ L^{*}&=116\left(\frac{Y}{Y_{n}}\right)^{\frac{1}{3}}-16\\ a^{*}&=500\left(\left(\frac{X}{X_{n}}\right)^{\frac{1}{3}}-\left(\frac{Y}{Y_{n }}\right)^{\frac{1}{3}}\right)\\ b^{*}&=200\left(\left(\frac{Y}{Y_{n}}\right)^{\frac{1}{3}}-\left(\frac{Z}{Z_{n }}\right)^{\frac{1}{3}}\right)\end{split} \tag{11}\]  
The CIE L*a*b* color space comprises L, a, and b components. The L component represents the luminosity layer, and the a and b components denote the chromaticity layers. As previously mentioned, the CIE L*a*b* color space is cognitively uniform because the L component is linearly related to the brightness recognized by the human eyes. Among the chromaticity layers, the values of a component are distributed from the red axis to the green axis. The green and red colors indicate negative and positive values of a component. The value of the b component is distributed following the blue to yellow axis. The blue color has negative values of the b component, and the yellow color has positive values of the b component. Conversely, the a component is related to red color, while the R component of the RGB color space is related to red color. In addition, the a component is related to green color, while the G component of the RGB color space is related to green color and not the R component of the RGB color space, unlike the case of red color [22].
# Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
## 2 Materials and Methods  
### Dataset  
A total of 388 VFSS video files were collected randomly from among those stored by the Department of Rehabilitation Medicine, Dankook University Hospital. The files were acquired from 106 patients who complained of symptoms related to dysphagia between March 2015 and January 2018. All video files were reviewed and confirmed for the presence or absence of airway invasion by two physiatrists experienced in VFSS analysis. Only video files for which the two physiatrists agreed on the presence or absence of airway invasion were selected. Among these files, sixty-nine were excluded due to poor image quality; hence, three-hundred-nineteen files were ultimately included in the VFSS dataset. Airway invasion was evident in 127 of these files, and no evidence of airway invasion was found in the remaining 192 files. Airway invasion was defined as the occurrence of a bolus passing beyond the entrance of the laryngeal vestibule. By separating the video files into individual image frames, two-hundred sixty-seven thousand seven-hundred forty-eight image frames (15,335 with airway invasion and 252,413 without airway invasion) were obtained. Each image frame was labeled for the presence of airway invasion in the same way as the video files were reviewed (agreement between two physiatrists). The study protocol was approved by the Institutional Review Board of Dankook University Hospital (IRB No. 2018-10-028).
## 3 Results and Discussion
### Real Sample Analysis  
The thus-fabricated sensor was examined for its applicability to the detection of hydroxylamine spiked in real specimens, including river (Halil, Jiroft, Iran) and well water (Graduate University of Advanced Technology, Kerman, Iran), by measuring via the standard addition method (Table 2). The spike recovery rate ranged from 96.7% to 104.3%, underlining the capacity of the modified electrodes for sensor applications in hydrodynamic determination in real specimens.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Electrochemical Sensor** & **Method** & **Linear Range** &
\begin{tabular}{c} **Limit of** \\ **Detection** \\ \end{tabular} & **Refs.** \\ \hline Graphene oxide/TiO\({}_{2}\)/ & Differential pulse & 0.1–300.0 \(\upmu\)M & 0.065 \(\upmu\)M & [56] \\ screen-printed electrode & voltammetry & & & \\ \hline Nickel(I)-morin complex modified & & & & \\ multi-wall carbon nanotube & & & & \\ paste electrode & & & & \\ \hline Gold nanoparticle-polypyrrole & Differential pulse & & & \\ nanowire/glassy carbon electrode & voltammetry & & & \\ \hline Gold nanoparticle-metal- & & & & \\ metallopopynyrin frameworks/ & & & & \\ glassy carbon electrode & & & & \\ \hline Cobalt(II) bis (benzoylacetone) & & & & \\ ethylenediimino multi-wall carbon & & & & \\ nanotube/carbon paste electrode & & & & \\ \hline La\({}_{2}\)O\({}_{3}\)/Co\({}_{3}\)O\({}_{4}\) nanocomposite/onic & Differential pulse & & & \\ liquid/carbon paste electrode & voltammetry & & & \\ \hline Polypyrrole nanotubes/graphite & Differential pulse & & & \\ screen-printed electrode & voltammetry & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparing the analytical performance of the PPy NTs/GSPE with other modified electrodes used for hydroxylamine determination.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \
\begin{tabular}{c} **Spiked** \\ **Concentration** \\ \end{tabular} &
\begin{tabular}{c} **Found** \\ **Concentration** \\ \end{tabular} & **Recovery (\%)** & **R.S.D. (\%)** \\ \hline River water & 0 & - & - & - \\ \cline{2-5}  & 5.0 & 5.1 & 102.0 & 2.2 \\ \cline{2-5}  & 7.5 & 7.3 & 104.3 & 3.0 \\ \hline Well water & 0 & - & - & - \\ \cline{2-5}  & 6.0 & 5.8 & 96.7 & 3.1 \\ \cline{2-5}  & 7.0 & 7.1 & 101.4 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Electrochemical determination of hydroxylamine in various water samples with the PPy NTs/GSPE. Concentrations are reported as \(\upmu\)M (\(n=3\)).
## 3 Methodology
### Pixel-Wise Spectral Features
#### 3.2.2 Vegetation Indices  
Typically, the vegetation index is derived from optical red and near-infrared (NIR) reflectance via linear or non-linear combination operations. They are simple but effective parameters for characterizing vegetation cover and growth status in agricultural remote sensing applications. Additionally, compared to other multi-spectral images (such as Landsat images), Sentinel-2 images contain three additional red-edge bands that are sensitive to vegetation growth [36]. To expand spectral features for Sentinel-2 images, eight vegetation indices, including NDVI, EVI, MTCI (terrestrial chlorophyll index), NDRE (normalized difference red edge index), MCARI2 modified chlorophyll absorption ratio index), REP (red edge position), IRECI (novel inverted red-edge chlorophyll index), and CI_ral-edge_ (red-edge chlorophyll index) were calculated [37; 38]. Also, to maintain an exact spatial resolution of 10 m, the 20 m spectral bands were resampled into 10 m using the nearest neighbor sampling algorithm when calculating vegetation indices.
## 4 Results
### Notification Perception
#### 4.1.1 Labeling  
We were able to obtain a total of 423 labels that were derived from the open-ended diary statements provided by the participants. We applied inductive reasoning in which we created and applied certain labels based on the descriptions of these statements. Once all statements were attended to, we then proceeded to organize them to obtain consistency throughout the entire set of labels. During this process, we eliminated duplicates, merged similar labels, and renamed a portion of them to ensure consistency. After sorting and organizing, we identified 15 unique labels that were distinct from each other. These labels represented specific aspects regarding the participants' day, their approach to receiving notifications, and their characteristics.  
Table 2 represents the entire set of unique labels that emerged from the diary entries. The labels and their corresponding descriptions are displayed to elaborate in detail the characterization of each individual label.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Label** & **Description** \\ \hline Personal & Notifications related to participant’s personal life \\ \hline Work & Notifications related to participant’s work-life \\ \hline Important & Notifications that are considered important \\ \hline Irrelevant & Notifications that are considered irrelevant \\ \hline \multirow{2}{*}{Stressful} & A stressful day may involve many notifications being received or a busy schedule at work/personal life \\ \hline Calm & A calm and quiet day \\ \hline \multirow{2}{*}{Surprising} & A surprising or unexpected day, where participants indicate unusual or extraordinary events occurring \\ \hline Expected & Represents a common or typical day, with nothing extraordinary \\ \hline Distracted & The participant states notifications caused a distraction \\ \hline Not distracted & The participant states notifications were not distracting \\ \hline Urgent & The participant states notifications required urgent attention \\ \hline Not-urgent & The participant states notifications did not require immediate attention \\ \hline Tends to act quickly & The participant stated the urge to react quickly toward push notifications \\ \hline Opts out of notifications & Participant mentions their desire to opt out of apps that send out notifications \\ \hline Has ringer off & The participant mentions that their smartphone finger feature is off, or on silent \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table consisting of labels and their corresponding descriptions.  
In Figure 1, the entire set of labels that emerged from the diary entries is represented. We proceeded by associating them into three main groups: (1) notification-related labels, which includes notification type and its importance, (2) day characteristics-related labels, and (3) user-related labels which include behavior and attitude-related aspects.
# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 3 Atmospheric Light Estimation  
### Influence of Atmospheric Light Intensity  
According to Equations (7) and (8), it is evident that the dehazing effect of the image relies on the accurate estimation of the global atmospheric light \(A\). The intensity of the atmospheric light significantly influences the quality of the dehazed image. While it is challenging to precisely measure the theoretical intensity of the atmospheric light, according to the atmospheric degradation model, we can infer that when the estimated atmospheric light intensity is lower than the theoretical value, the dehazing effect weakens. Conversely, if the estimated atmospheric light intensity is higher than the theoretical value, it can lead to excessive amplification of high-frequency information in the scene or cause some parts of the scene to become too dark or exhibit color abnormalities.  
Figure 2 illustrates the impact of the atmospheric light estimation on the dehazing effect. As shown in Figure 2b,c, when we weaken the intensity of \(A\), the dehazing effect is also weakened (green rectangle region). On the other hand, as demonstrated in Figure 2e,f,  
when we enhance the intensity of \(A\), the dehazing effect becomes more pronounced. However, using an excessively large value for \(A\) can lead to the overall darkening of the image, localized dark regions, increased noise, noticeable layering, and overall distortion of the image (red rectangle region).  
In [PERSON]'s method, researchers recorded the coordinates of the brightest 0.1% of points in the dark channel of the hazy image and selected the intensity value of the brightest point at the corresponding position in the hazy image as the estimation for \(A\) (atmospheric light). However, this approach has limitations, particularly when the image contains regions with colors similar to the atmospheric light. In such cases, errors in atmospheric light estimation may occur, leading to inaccurate results in transmission rate and restored image. In the next section, we analyze the differences in energy between atmospheric light and scene light polarization. By considering the polarimetric differences, we can separate the global atmospheric light component from the image and use it as a more accurate basis for estimating \(A\). This allows us to overcome the limitations of the previous method and improve the accuracy of the dehazing results.
## 3 Results and Preliminary Analysis
### Preliminary Analysis
#### 3.2.1 Theoretical Analysis  
During the axial tension of the specimen, the axial deformation increases, the transverse and longitudinal (thickness direction) deformation decreases, and the cross-sectional area changes continuously. The relationship between cross-sectional area and axial strain is required to determine the section stress, and the axial strain is defined as:  
\[\varepsilon_{a}=\int_{L_{0}}^{L_{f}}\frac{dL}{L}=\ln\biggl{(}\frac{L_{f}}{L_{0 }}\biggr{)}=\ln\biggl{(}\frac{L_{0}+\Delta L}{L_{0}}\biggr{)}=\ln(1+\varepsilon _{aE}) \tag{1}\]  
where  
\[\varepsilon_{aE}=\frac{\Delta L}{L_{0}}=\frac{L_{f}-L_{0}}{L_{0}} \tag{2}\]  
The cross-sectional area is given as:  
\[A_{u}=W_{\varepsilon_{a}}T_{\varepsilon_{a}} \tag{3}\]  
where  
\[W_{\varepsilon_{a}} =W_{\varepsilon_{a}=0}\bigl{(}1-\mu_{y}\varepsilon_{a}\bigr{)} \tag{4}\] \[T_{\varepsilon_{a}} =T_{\varepsilon_{a}=0}\bigl{(}1-\mu_{z}\varepsilon_{a}\bigr{)} \tag{5}\]  
Figure 4: Relationship between axial tension and displacement of transverse specimen at (**a**) 0–60 \({}^{\circ}\)C; (**b**) \(-40\)–0 \({}^{\circ}\)C.  
Hence, the axial stress may be calculated by the following formula:  
\[\sigma=\frac{F}{A_{u}}=\frac{F}{W_{\epsilon_{a}=0}T_{xa=0}\big{(}1-\mu_{y} \epsilon_{a}\big{)}(1-\mu_{z}\epsilon_{a})} \tag{6}\]
# Decentralized and Secure Blockchain Solution for Tamper-Proof Logging Events
## 3 Materials and Methods
### Components of the System  
This section describes the components used in the system and their relationship, which are illustrated in Figure 2. The process is broken down into four steps from left to right: (1) the managed equipment (e.g., router, switch or PC) sends a log event to the Syslog server located in the DRACSC system; (2) the events are then queued in the message broker to be further processed by the log module; (3) depending on the blockchain used, the log module will perform specific tasks such as obtaining the hash of the event information, connecting to a blockchain node and sending the corresponding transaction to the network; and (4) when the hash obtained in the previous step is available and signed, it is sent to the log management server for storage and further exploitation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Blockchain** & **Kind of** & **Consensus** & **TPS** & **Transaction** & **Deterministic** & \
\begin{tabular}{c} **Smart** \\ **Contract** \\ **Support** \\ \end{tabular} &
\begin{tabular}{c} **Year** \\ **Launched** \\ \end{tabular} \\ \hline Bitcoin & Public & PoW & 7 & High & No & Limited & 2009 \\ Ethereum & Public & PoS & 15 & High & No & 2015 \\ Multichain & Private & Several & * & * & Limited & 2015 \\ Exonum & Private & BFT & * & * & **✓** & 2016 \\ Hyperledger & Private & Several & * & * & **✓** & 2016 \\ Fabric & Public & PoS & 250 & Low & **✓** & 2017 \\ IBM Blockchain & Private & Several & * & * & **✓** & 2017 \\ Solana & Public & PoH + PoS & 65,000 & Low & No & 2020 \\ \hline \hline \end{tabular}  
* These features on private blockchain depend on the configuration.  
\end{table}
Table 2: Comparison between the several blockchains studied in the related work.
# Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning
## 3 Smoothing Optimisation A*-Guided DWA
### DWA Algorithm
#### 3.1.2 Speed Sampling  
In practice, the speed of a mobile robot is affected by the following three factors:  
(1) Maximum speed and minimum speed  
The speed limit of the mobile robot originates from its power system, where the maximum speed represents the fastest speed at which the vehicle can operate stably, and the minimum speed denotes the slowest speed at which the vehicle can still maintain control at low speeds. The set of the vehicle velocity and the angular velocity of the pendulum \(v_{s}\) is:  
\[v_{s}=\{(v,\omega)|v\in[v_{min},v_{max}],\omega\in[\omega_{min},\omega_{max}]\} \tag{10}\]  
(2) Motor performance  
The motor of the mobile robot directly determines the power output and motion capability of the vehicle, and its speed constraint formula is shown in Equation (11):  
\[v_{d}=\left\{(v,\omega)\middle|v\in\left[v_{0}-\dot{v}_{n}*\Delta t,v_{0}+\dot {v}_{m}*\Delta t\right],\omega\in\left[\omega_{0}-\dot{\omega}_{n}*\Delta t, \omega_{0}+\dot{\omega}_{m}*\Delta t\right]\right\} \tag{11}\]  
Figure 5: Mobile robot motion modelling.  
Obstacles  
The mobile robot needs to stop before hitting an obstacle with a maximum deceleration that satisfies the following constraints:  
\[v_{a}=\left\{(v,\omega)\middle|v\leq\sqrt{2*dist(v,\omega)*\dot{v}_{n}},\omega \leq\sqrt{2*dist(v,\omega)*\dot{\omega}_{n}}\right\} \tag{12}\]  
The final velocity is taken as the intersection of \(v_{s}\), \(v_{d}\) and \(v_{a}\), and the space of desirable velocities for the mobile robot is denoted by \(v_{f}\), which takes the value:  
\[v_{f}=v_{s}\cap v_{d}\cap v_{a} \tag{13}\]  
The parameters in Equations (10)-(13) are shown in Table 2: