esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Foundation of Medical Image Analytics with Machine Learning
### Contrast-Enhanced Computed Tomography  
A _CECT scan_ is a medical imaging technique that uses CT imaging in conjunction with a contrast agent to produce detailed images of internal organs, blood vessels, and other tissues.  
A contrast agent is a substance, typically iodine-based or barium sulfate-based, that is administered to the patient either orally, intravenously, or rectally before or during the CT scan. The contrast agent increases the contrast within the images by highlighting specific areas, thereby improving the visibility of organs, tissues, and vessels.  
CECT offers significant advantages over non-contrast CT, particularly for specific diagnostic tasks. A primary benefit is the enhanced visualization of target objects in CT slices, which is particularly significant for several types of anatomical and pathological structures. For example, blood vessels and vascular structures such as arteries and veins are much more clearly defined with the use of contrast agents.  
Another key benefit of using CECT-based diagnosis is the ability to observe the temporal evolution of medical conditions. This includes monitoring changes in the size, shape, and characteristics of lesions, tumors, or other abnormalities over time. This information is crucial for assessing the progression or regression of diseases, evaluating the effectiveness of treatments, and making informed clinical decisions. An example of CT slices showing temporal changes in a lesion is shown in Figure 1.  
The CT image in the figure shows axial slices of the abdomen, specifically highlighting the liver. The arrows indicate lesions of interest within the liver. The figure also reveals the temporal changes in the organ over four phases. The arrows point to a lesion in the liver, showing the progression or response of a hepatic lesion over different phases of contrast enhancement.  
Figure 1: Temporal changes in a lesion with contrast-enhanced CT.  
The CT slice #1 shows the liver in the unenhanced (non-contrast) phase, captured in an axial plane. CT slice #2 depicts the liver in the arterial phase after the administration of contrast material. CT slice #3 represents the liver in the venous phase, and CT slice #4 displays the liver in the delayed phase. This observation of temporal evolution on medical objects is essential in diagnosing some diseases, such as liver cancer [31].
## 4 Discussions
### Thermodynamic and Kinetic Parameters  
Results showed that the corrosion process for C-Mn steel increases more rapidly according to the temperature in the absence of inhibitor, rather than in its presence. This result confirms that the inhibitor acts as an efficient corrosion inhibitor in the range of temperatures studied.  
Enthalpy and entropy of the corrosion process may be evaluated from the effect of temperature by an alternative formulation of transition state, as displayed in the following Equation (8) [35].  
\[W=\frac{RT}{Nh}\exp\left(\frac{\Delta S^{\circ}{}_{a}}{R}\right)\exp\left(- \frac{\Delta H^{\circ}{}_{a}}{RT}\right), \tag{8}\]  
where \(h\) is [PERSON]'s constant, \(N\) is Avogadro number, and \(\Delta S^{\circ}{}_{a}\) and \(\Delta H^{\circ}{}_{a}\) are the entropy and enthalpy of activation, respectively. Table 9 presents the calculated values of \(E_{a}\), \(\Delta S^{\circ}{}_{a}\), and \(\Delta H^{\circ}{}_{a}\) in inhibited and uninhibited corrosive solutions. It is observed that the activation energy value is higher in the presence of the bio-copolymer inhibitor than in the uninhibited solution. The obtained activation energy value of the corrosion process in the inhibitor's presence, compared to its absence, can be attributed to its sorption. It is the result of electrostatic attraction between charged metal surface and charged species in solution and/or chemical interaction between polymer and metal.  
The values of \(\Delta H^{\circ}{}_{a}\) are reported in Table 9. The positive sign of the reflects the endothermic nature of the steel dissolution process and values vary in the same way with inhibitor concentration and acid solutions [36].  
On the other hand, values of are more positive in the uninhibited solutions and decrease by increasing the inhibitor concentration. Large and negative values of entropies imply that the activated complex in the rate-determining step represents an association rather than a dissociation step, meaning that a decrease in disordering takes place on going from reactants to the activated complex. A similar observation has been reported in the literature [37].  
One can notice that \(E_{a}\) and \(\Delta H^{\circ}{}_{a}\) values vary in the same way (Figure 11). This result allows for the verification of the known thermodynamic relationship between the \(E_{a}\) and \(\Delta H^{\circ}{}_{a}\), as shown in Equation (9).  
\[\Delta H^{\circ}{}_{a}=E_{a}-RT \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Inhibitor Concentration (mg L\({}^{-1}\))** & \(E_{a}\) **(kJmol\({}^{-1}\))** & \(\Delta H_{a}\) **(kJmol\({}^{-1}\))** & \(\Delta S_{a}\) **(Jmol\({}^{-1}\))** \\ \hline
0 & 40.9 & 38.5 & \(-\)36.7 \\
5 & 36.6 & 34.1 & \(-\)37.1 \\
10 & 35.7 & 33.3 & \(-\)37.2 \\
50 & 35.3 & 32.9 & \(-\)37.4 \\
100 & 26.5 & 24.0 & \(-\)37.5 \\
200 & 27.5 & 25.0 & \(-\)38.0 \\
300 & 28.8 & 26.3 & \(-\)38.1 \\ \hline \end{tabular} (\(E_{a}\)—activation energy, \(\Delta S^{\circ}{}_{a}\) and \(\Delta H^{\circ}{}_{a}\)—the entropy and enthalpy of activation).  
\end{table}
Table 9: Calculated parameters at different concentrations of the bio-copolymer.
# Semiconductor Laser Linewidth Theory Revisited
## 10 Numerical Results for a DBR Laser  
In the simulations described below we study a simple two-section DBR laser as sketched in Figures 1 and 2. It consists of a 1 mm long active gain section (without Bragg grating) and a 3 mm long passive Bragg reflector section (without active layer) with a  
Figure 3: **(a)** Modal gain (blue) and rate of spontaneous emission (green), left axis, and population inversion factor (red), right axis, versus carrier density obtained from a microscopic simulation (symbols) and the analytic models (122), (123) and \(n_{\rm sp}=r_{\rm sp}/g\) (solid lines) for a fixed wavelength \(\lambda_{0}=1064\) nm. The dashed green line is the spontaneous emission computed using a constant population inversion factor \(n_{\rm sp}=2\). See the Appendix A for the consideration of the gain at a fixed wavelength within the framework of the present model. **(b)** Microscopically computed gain spectra for carrier densities ranging from \(N=1\cdot 10^{18}\) cm\({}^{-3}\) to \(1\cdot 10^{19}\) cm\({}^{-3}\). The position of the fixed wavelength \(\lambda_{0}\) is shown by a blue line, the peak gain position is indicated by a red line. coupling coefficient of \(\kappa=2\) cm\({}^{-1}\). The full set of parameters characterizing the simulated DBR laser is listed in Table 1. The usage of such DBR lasers having long reflector sections with low coupling coefficients was suggested in Ref. [16] for the first time. In fact, in Ref. [57] an intrinsic linewidth of 2 kHz at an output power of 180 mW was reported. This device had a 3 mm long gain section and a 1 mm long reflector section, where the active layer extended over the whole cavity (i.e., including the reflector section). The device studied here resembles the one presented in Ref. [60], where an intrinsic linewidth of 4 kHz at an output power of 73 mW was reported. Experimentally, the intrinsic linewidth is determined from the plateau of the frequency noise spectrum, as in the theory [61]. Within the active section, we assume the logarithmic gain model (122) supplemented with the nonlinear gain saturation factor  
\[g(z,N,\|\Psi\|^{2})=g^{\prime}(z)N_{\text{tr}}(z)\ln\left[\frac{\text{max}(N,N_{ \text{cl}}(z))}{N_{\text{tr}}(z)}\right]\frac{1}{1+\|\Psi\|^{2}/P_{\text{sat}}( z)}, \tag{124}\]
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 3 Experimental Setup
### Evaluation Framework
#### 3.3.3 Speaker Count Estimation  
Accurate speaker count estimation is crucial for downstream applications (such as meeting transcription systems or automated call center analytics) and system reliability. We evaluate this aspect using two complementary metrics:  
* Speaker Counting Error: Quantifies the absolute difference between predicted and actual speaker counts
* Speaker Count Accuracy: Measures the system's precision in exactly matching the true number of speakers  
This dual approach helps understand both the magnitude and frequency of speaker counting errors.
## 2 Materials and Methods
### Characteristics of the Database
#### 2.1.2 Seismic Energy and PPV  
This study aimed to determine whether, given the geological, mining, and technical conditions occurring in a selected point of the opening, a tremor of a certain energy, generating peak particle velocity at this point, leads to a rock burst or not. Rock bursts in coal mines in the USCB are mostly correlated with strong tremors (i.e., energy greater than or equal to 1 \(\times\) 10\({}^{5}\) J). However, there have also been cases of rock burst after medium-energy tremors(i.e., with an energy of 10\({}^{4}\) J). Low-energy tremors, mainly of the upper range of energy around 10\({}^{3}\) J, may also be associated with damage to the openings. Such cases mainly concern drilled headings. Selected excavation points included in the database were associated with tremors of energies from 8 \(\times\) 10\({}^{2}\) J (\(M_{L}\) = 0.58) to 6 \(\times\) 10\({}^{8}\) J (\(M_{L}\) = 3.67). Samples related to strong tremors accounted for approximately 94% of the database. Low- and medium-energy tremors each accounted for approximately 3% of the database.  
Numerous studies have shown the relationship between high values of PPV and the occurrence of rock bursts. Such studies have been conducted in mines in the USCB [52; 53; 54]. The PPV values for rock bursts in USCB coal mines usually range from about 0.05 m/s to 1 m/s [54]. When mining activities are conducted in areas of concentrated stress, excavation damage may occur at lower values of PPV [54]. There is a high risk to the stability of  
Figure 1: Violin plots representing statistics of the (**a**) GEO index, (**b**) tremor energy, and (**c**) PPV of samples included in the database.  
openings if the PPV value exceeds 0.4 m/s [52]. The PPV value can be measured directly in the openings or calculated using the empirical formula [54]:  
\[log(\text{PPV}\cdot R)=0.66\;log(M_{o})-7.4 \tag{1}\]  
where PPV is expressed in m/s, \(R\) is the hypocentral distance in [m], and \(M_{o}\) is the scalar seismic moment in [Nm].  
PPV values, calculated based on Formula (1) for individual samples in the database, ranged from about 0.006 m/s to about 0.77 m/s (mean 0.08 m/s). Considering only rock bursts, the calculated PPV ranged from about 0.043 m/s to about 0.77 m/s (mean 0.15 m/s). The distribution of tremor energies and PPVs, with division into rock bursts (1) and cases without damage and destruction in the openings (0), is illustrated using violin plots and presented in Figure 1b and Figure 1c, respectively. The values of the parameters presented in Figure 1a-c for rock bursts and non-destructive tremors were in the similar range. However, a high value of a given parameter does not mean that a rock burst is certain.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 4 Case Study
### Power Station Structure and Vulnerability
#### 4.3.2 Gas Pressure Regulating and Metering Station (PRMS)  
The Gas Pressure Regulating and Metering Station (PRMS) regulates and measures natural gas flow and pressure. Key components of a PRMS consist of inlet and outlet piping with associated valves, essential electrical and mechanical equipment, power sourced from the external grid complemented by a redundant backup generator, a compressor, and the encompassing structural framework. The parameters of the fragility curves for the PRMS's subcomponents are detailed in Table 4, and the fragility curves are illustrated in Figure 5. Additionally, this table includes the expected probability of damage to these subcomponents. After conducting an FTA (Figure 6), the overall probability of PRMS failure was determined to be 0.082.  
Figure 4: Fault Tree Analysis (FTA) diagram of the water treatment facility.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subcomponent** & **Median (\(\theta\))** & **Std (\(\beta\))** & **Damage Probability \({}^{*}\)** \\ \hline Grid Power Supply & 0.4 & 0.8 & 0.193 \\ Backup Generator & 0.3 & 0.4 & 0.155 \\ Chlorine Tank & 0.7 & 0.7 & 0.037 \\ Settling Basin & 0.6 & 0.5 & 0.014 \\ Chemical Tank & 0.4 & 0.6 & 0.124 \\ Electrical Equipment & 0.6 & 0.6 & 0.034 \\ Pipes and Valves & 1.0 & 0.6 & 0.004 \\ Mechanical Equipment & 2.0 & 1.0 & 0.011 \\ \hline \hline \end{tabular}  
\({}^{*}\)Damage probabilities are calculated based on a PGA of 0.2 g.  
\end{table}
Table 3: Subcomponents of the water treatment facility with corresponding fragility curve parameters and damage probability estimates.
# High-Resolution and Wide-Swath 3D Imaging for Urban Areas Based on Distributed Spaceborne SAR
## 2 Materials and Methods
### Distributed SAR 3D Imaging Geometry and Theory
\[p(g|\theta)=1/(\pi^{m}|\sum_{\epsilon}|\exp[-(g-A_{\gamma})^{H}{\sum_{\epsilon }}^{-1}(g-A\gamma)]) \tag{3}\]  
where \(\sum_{\epsilon}=\delta^{2}I\) represents the covariance matrix of noise which assumes that the noise of each observation is independent of each other and independent of the strong backscattering wave source. The Fisher information matrix of the above Gaussian distribution is [30]:  
\[J(\theta)_{i,j}=2\text{Re}[(\partial A\gamma/\partial\theta_{i})^{H}{\sum_{ \epsilon}}^{-1}(\partial A\gamma/\partial\theta_{j})] \tag{4}\]  
where \(\theta\) represents the parameters involved in the model, including the amplitude \(a_{i}\) the corresponding \(K\) scatters, the phase \(\phi_{i}\), and the elevation \(s_{i}(i=1,2,\ldots,K)\). Finding the inverse of the Fisher information matrix is to obtain the Cramer-Rao lower bound \(CRLB=J^{-1}\), which indicates the estimation accuracy that the algorithm can achieve.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 3 Experiment Results
### Performance Evaluation Metrics  
In segmentation tasks, precision and recall are important accuracy indicators. In most cases, it is difficult to evaluate performance well using only one of them. The F1 score represents the balance between precision and recall and can better reflect the overall performance of the model, especially in the case of unbalanced samples. TP (true-positive) represents the number of pixels that are predicted as the right categories. FP (false-positive) denotes the number of pixels from other categories that are incorrectly predicted as this category. FN (false-negative) indicates the number of pixels belonging to this category that are incorrectly classified. In this paper, the XView2 Challenge metric [48] is used to evaluate the results. The F1 for weighted mean of the building segmentation (\(F1_{loc}\)) and the F1 for harmonic average of class damage classification (\(F1_{cls}\)) are applied. \(F1_{C_{i}}\) refers to the F1 score of each damage class and \(C_{i}\) represents the \(i\)-th damage class, where \(C_{1}\) to \(C_{4}\) denote no damage, minor, major, and destroyed, respectively. \(F1_{loc}\) and \(F1_{cls}\) are defined as  
\[F1_{loc} =\frac{2 TP}{2 TP+FP+FN}, \tag{20}\] \[F1_{cls} =\frac{n}{\sum_{i=1}^{n}\frac{1}{F1_{C_{i}}}}, \tag{21}\]  
The final score [48] of overall evaluation comprehensively reflects the building segmentation and damage classification performance, which is formed based on \(F1_{loc}\) and \(F1_{cls}\).  
\[score=0.3\times F1_{loc}+0.7\times F1_{cls} \tag{22}\]
# Method for Accuracy Assessment of the Length Measurement Unit of Laser Tracking Systems
## 3 Results
The developed standard was calibrated on the reference PMM 12106 coordinate machine with the measurement uncertainty for the longest calibrated distance equaling 1.2 \(\upmu\)m.  
The central sphere in each socket can be replaced by a spherical retroreflector. In addition, thanks to the special design of the socket, it is possible to move the retroreflector between the sockets without interrupting the laser beam. Three distances between the central points of successive sockets were measured, which are: 100 mm, 500 mm and 900 mm. Each distance is determined between socket \"zero\" and the appropriate socket offset from it by an appropriate distance. Only readings from the laser tracker's interferometer were used to calculate measured length. The procedure was repeated ten times then the A type uncertainty was estimated. For each distance measured on the ball-bar standard, a simulation of uncertainty with the Monte Carlo method was applied. The simulation is based on 10,000 Monte Carlo repetitions. The t-distribution was used for all three considered distances. The mean values and standard deviations which are the input data for simulation were calculated on the basis of results obtained during previous parts of the experiment (the results for the inactive motor state were used for both measurement directions). The results obtained experimentally and by means of simulation are presented in Table 3.  
Figure 6: The reconfigurable ball-bar standard: (**a**) the technical drawing; (**b**) real specimen of reference object.
## 4 Results and Discussion
### Uncertainty Analysis
Then, \(\big{(}U_{eff}-\overline{U}\big{)}/\overline{U}\cong 0.56\%\)\((=q_{3})\). This error can be considered as a systematic error when implementing the hot-wire anemometry, and this error also contains the influence of the directional sensitivity. The other systematic errors in the measurement can be neglected because the mean value of the fluctuating component of velocity was 0 and the histogram showed a nearly Gaussian distribution, which also will be presented in a later section. The uncertainty related to the freestream temperature variation and other uncertainties inherent in the freestream were included in the random uncertainty. Thus, the uncertainty generated by the hot-wire anemometry can be considered the governing source for the total uncertainty. The uncertainty in the velocity measurement by the hot-wire anemometry is given in Equation (31). Every error was considered to propagate independently:  
\[q_{123}[\%]=\sqrt{q_{1}^{2}+q_{2}^{2}+q_{3}^{2}}=\sqrt{\bigg{(}\frac{0.26}{1 00}\bigg{)}^{2}+\frac{1}{n}\sum_{i=1}^{n}\bigg{(}\frac{U_{i}-\overline{U_{z=12 -20\ mm}}}{U_{z=12-20\ mm}}\bigg{)}^{2}+\bigg{(}\frac{0.56}{100}\bigg{)}^{2} \times 100=0.62\%. \tag{31}\]  
The uncertainty inherent in the calculation of the integral time scale was also considered. Since the autocorrelation function indicated a monotonically decreasing feature for the duration of interest, the uncertainty associated with the integration when calculating the integral time scale arises from the integration increment. The uncertainty in the integration with an increment of \(\Delta t=5\)\(\upmu\)s was considered as following \(q_{4}\). Here, \(n\) is 5 for the measurement points in the freestream region:  
\[q_{4}\ [\%]=\frac{0.1\Delta t}{\frac{1}{n}\sum_{i=1}^{n}t_{0,\ i}}=\frac{0.005}{ 0.291}=1.72\%. \tag{32}\]  
Each uncertainty was considered to propagate independently, and thus the cumulative error of \(q_{1}\) through to \(q_{4}\) gives the total uncertainty (\(q_{total\_1}\)) for deriving the integral time scale. The total uncertainty for the derivation of the integral time scale can be estimated as 1.8% for \(n=5\) (measurement points in the freestream region) as shown below:  
\[q_{total\_1}=\sqrt{q_{123}^{2}+q_{4}^{2}}=1.83\%. \tag{33}\]  
Note that the proposed method evaluates the reduction of skin friction drag based on the horizontal components of the turbulent scales and \(U\) velocity since the streamwise component of the velocity was focused on in this evaluation. It should also be noted that if the other components of the velocity fluctuations than the streamwise velocity fluctuation are obtained by other highly resolved measurements, such as three-dimensional measurements, the uncertainty may be reduced.
## 3 Results and Discussion
### Effect of ECAP on Erosion-Corrosion at Different Sand Concentrations  
The effect of ECAP passes on E-C of copper at different sand concentrations is depicted in Figure 11. The tests were performed at 0, 10, 20, and 30 wt.% of sand in the slurry. It is observed that the increment of sand concentration resulted in an increase in material weight loss. The increase of weight loss is attributed to the enormous number of solid particles impacting the surface and causing material removal [39]. In the absence of sand, the improvement of corrosion resistance due to four ECAP passes was 17%. On the other hand, with sand concentration of 30%, there is a 28% improvement in E-C resistance of copper. In addition, comparing 0% and 30% sand concentration for copper at annealed condition (0 ECAP pass), it can be seen that the increase in weight loss is about 250% (i.e., corrosion condition). However, after four ECAP passes with similar sand concentration an increase of 200% is achieved.  
Figure 10: Schematic diagrams showing the effect of impact angle on formation of surface plastic deformation. **(a)** 90\({}^{\circ}\); **(b)** oblique angle cutting; and **(c)** shallower impact angle.  
Figure 9: Hardness measurements versus ECAP passes at different impacting angles (\(v\) = 3.8 m/s, 3.5% NaCl, and sand concentration = 20 wt.%).
## 5 Experimental Study
### Comparative Analysis
#### 5.2.3 Impact of Latency  
The latency is defined as the delay associated with the execution of operations involved in analyzing the behavior of driver. The latency is an important metric to be considered in analyzing the driver behavior and generation of personalized assistance in emergency situations. The latency of the proposed MODAL-IoCV approach is compared with the existing approaches with respect to the number of vehicles, as illustrated in Figure 11. The latency increases with an increase in the number of vehicles. The proposed approach possesses reduced latency due to the execution speed of the proposed SqueezeNet, which requires only a fewer number of parameters. The existing approaches implemented more complex methods resulting in increased latency, thereby affecting the performance of those approaches in emergency situations.  
Figure 10: Number of edges vs. accuracy.  
Figure 9: Number of vehicles vs. accuracy.  
Figure 12 illustrates the comparison of latency of the proposed approach and the existing approaches with respect to the number of edge nodes. The latency decreases with an increase in the number of edge nodes. The latency of the proposed approach increases with the increase in the number of edges is found to be low due to the efficacy of the SqueezeNet in the classification of driver behavior. The lightweight nature of the proposed model makes it possible to execute with less consumption of time. The conventional methods possessed by the existing approaches resulted in increased latency even with the increase in the number of edge nodes.