esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Description  
To demonstrate the application of the finite-difference scheme, we use an example of laser irradiation on silicon. Below, we present the full set of nonlinear differential equations for the continuum description of electron density and electron/phonon energy density dynamics in silicon under ultrashort laser irradiation. For the derivation of the following expressions we refer to the works in [17] and [25]. Due to laser pulse irradiation (in this example Ti:Sapphire laser at 800 nm wavelength), free carriers are generated in the material, electrons in the conduction band, and holes in the valence band. Both types of carriers are assumed to quickly equilibrate in the corresponding bands and move together due to the Dember field preventing charge separation [27]. To each of them, we apply separate Fermi-Dirac distributions with different chemical potentials, \(\phi_{e}\) and \(\phi_{h}\), for the electrons and holes, respectively, but with shared carrier density \(n\) and temperature \(T_{e}\) (two-chemical potentials model). The reduced chemical potentials are defined as follows,  
\[\eta_{e}=\frac{\phi_{e}-E_{C}}{k_{B}T_{e}}\text{ and }\eta_{h}=\frac{E_{V}- \phi_{h}}{k_{B}T_{e}}, \tag{1}\]  
where \(E_{C}\) and \(E_{V}\) are the conduction and valence band energy levels, respectively, so the energy gap is \(E_{g}=E_{C}-E_{V}\). The integration of the carrier distribution functions over the energy leads to the expressions for the carrier density (parabolic bands are assumed):  
\[n=2\left(\frac{m_{c}^{*}k_{B}T_{e}}{2\pi\hbar^{2}}\right)^{\frac{3}{2}}F_{ \frac{1}{2}}\left(\eta_{c}\right), \tag{2}\]where subscript \(c\) stands as \(e\) for electrons and \(h\) for holes. The Fermi-Dirac integral is defined as  
\[F_{\xi}\left(\eta_{c}\right)=\frac{1}{\Gamma\left(\xi+1\right)}\int_{0}^{\infty} \frac{x^{\xi}}{1+\exp\left(x-\eta_{c}\right)}\,\mathrm{d}x. \tag{3}\]  
The carrier current is the sum of contributions from the electrons and the holes:  
\[ \begin{split}\vec{J}=-D\Bigg{[}&\
abla n+\frac{n}{ k_{B}T_{e}}\left[H_{-\frac{1}{2}}^{\frac{1}{2}}\left(\eta_{e}\right)+H_{-\frac{1}{2}}^{ \frac{1}{2}}\left(\eta_{h}\right)\right]^{-1}\
abla E_{g}\\ &+\frac{n}{T_{e}}\left[2\frac{H_{0}^{1}\left(\eta_{e}\right)+H_{0 }^{1}\left(\eta_{h}\right)}{H_{-\frac{1}{2}}^{\frac{1}{2}}\left(\eta_{e} \right)+H_{-\frac{1}{2}}^{\frac{1}{2}}\left(\eta_{h}\right)}-\frac{3}{2} \right]\
abla T_{e}\Bigg{]},\end{split}  \tag{4}\]  
where \(H_{\xi}^{\xi}\left(\eta_{c}\right)\equiv F_{\xi}\left(\eta_{c}\right)/F_{\xi} \left(\eta_{c}\right)\) and the ambipolar diffusion coefficient is  
\[D=\frac{k_{B}T_{e}}{q_{e}}\frac{\mu_{e}\mu_{h}H_{\frac{1}{2}}^{0}\left(\eta_{e }\right)H_{\frac{1}{2}}^{0}\left(\eta_{h}\right)}{\mu_{e}H_{\frac{1}{2}}^{0} \left(\eta_{e}\right)+\mu_{h}H_{\frac{1}{2}}^{0}\left(\eta_{h}\right)}\left[H _{-\frac{1}{2}}^{\frac{1}{2}}\left(\eta_{e}\right)+H_{-\frac{1}{2}}^{\frac{1 }{2}}\left(\eta_{h}\right)\right] \tag{5}\]  
with \(q_{e}\) the elementary charge.
## 1 Introduction  
Chronic lung disease (CLD) is a major morbidity in preterm neonates that is largely driven by the imbalance between pro- and anti-inflammatory mediators and is influenced by several factors, including sepsis, ventilation-induced trauma, free radical production, and pulmonary edema in an immature lung [1]. The use of the steroid dexamethasone in the prevention and treatment of CLD has been extensively explored [2; 3; 4; 5; 6]. Dexamethasoneinduces lung maturational changes, promotes antioxidant activities, and helps with surfactant synthesis in the neonatal population [7]. Steroids were liberally used in the past in preterm neonates to treat and prevent CLD. However, due to concerns about adverse effects mainly on neuro-developmental outcomes, its use is now restricted for newborns at high risk for developing CLD or those who are ventilator dependent for prolonged periods in order to facilitate extubation. The current recommendation by the Canadian Pediatric Society (CPS) is that clinicians can consider a short course of low-dose dexamethasone in neonates at high risk for CLD or those with severe CLD [8].  
Dexamethasone is known to have a broad range of effects on multiple organ systems, in addition to the lungs. The heart, in particular, is one such organ that has been postulated to be both directly impacted by steroid use as well as indirectly through possible changes in lung compliance. The effects of dexamethasone on left heart function are well documented and include myocardial thickening, hypertrophic cardiomyopathy involving the inter-ventricular septum and the left ventricle, and left ventricular outflow tract obstruction [9; 10; 11; 12]. Some authors have described these effects as transient [10; 13; 14]. An increase in blood pressure has also been reported after dexamethasone use, with speculation on its ability to increase systemic vascular resistance and enhance responsiveness to catecholamines [15]. However, not much is known about its effects on the right ventricle and pulmonary vascular resistance (PVR). Pulmonary vascular remodeling with a resultant increase in PVR is an integral factor in the pathogenesis of CLD. Pulmonary hypertension (with resultant right ventricular dilatation and dysfunction) is now increasingly being recognized as a complication of CLD, significantly contributing to the morbidity and mortality in children with CLD [16; 17; 18].  
As our understanding of CLD evolves, the interplay between lungs, PVR, right heart function, and steroids warrants careful attention. Echocardiography provides a safe and easy method to investigate cardiac function in preterm neonates. With the evolution of advanced echocardiographic techniques, such as tissue Doppler imaging, cardiac performance can be assessed in greater detail, which allows us to gain novel insights into the cardiopulmonary physiology of the preterm population. Hence, we designed this study to determine the longitudinal effects of low-dose dexamethasone therapy on echocardiographic parameters that measure PVR and cardiac performance in preterm neonates at risk of CLD.
###### Abstract  
The utilization of Internet of Things (IoT) devices in various smart city and industrial applications is growing rapidly. Within a trusted authority (TA), such as an industry or smart city, all IoT devices are closely monitored in a controlled infrastructure. However, in cases where an IoT device from one TA needs to communicate with another IoT device from a different TA, the trust establishment between these devices becomes extremely important. Obtaining a digital certificate from a certificate authority for each IoT device can be expensive. To solve this issue, a group authentication framework is proposed that can establish trust between group IoT devices owned by different entities. The Chebyshev polynomial has many important properties, _semigroup_ is one of the most important. These properties make the Chebyshev polynomial a good candidate for the proposed group authentication mechanism. The secure exchange of information between trusted authorities is supported by Blockchain technology. The proposed framework was implemented and tested using Python and deployed on Blockchain using Ethereum's Goerli's testnet. The results show that the proposed framework can reasonably use Chebyshev polynomials with degrees up to four digits in length. The values of various parameters related to Blockchain are also discussed to understand the usability of the proposed framework.  
public-key cryptosystem; authentication; blockchain technology; Internet of Things; Chebyshev polynomial +
Footnote †: journal:  
1
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 5 Damage Degree Detection  
### Damage Degree Dataset  
Sections 3 and 4 both verify the effect of the structural damage location detection. This section verifies that the method still has a good recognition effect on the structural damage degree. Only column 1 was selected as the damaged column. In order to simulate different degrees of damage, the stiffness of the column 1 was reduced by 0, 10%, 20%, 30%, and 40%, respectively. The damage degree dataset includes five categories: \"0\", \"10%\", \"20%\", \"30%\", and \"40%\". The number of samples is 399 in each category, and the dataset contains a total of 1995 samples. After these samples are randomly sorted, 1500 samples were used for training and verification, 495 samples were used as test set.
# Cyber-Storms Cone from Clouds: Security of Cloud Computing in the IoT Era
## 5 Cloud-Specific Security Issues
### Integrity
#### 5.2.1 Virtualization Level Issues  
In the following paragraphs, security issues rooted in the virtualization layer and with the potential to impact the integrity of data are presented.  
VM Isolation Issues  
At this level, virtual machine escaping is the way in which data and software integrity can be attacked. Indeed, a compromised VMM can threaten the integrity of data [74]. More specifically, if a virtual machine is able to escape from isolation and compromise the VMM, it can access memory locations belonging to other users while having the required privileges to write or delete their content [47; 49], in such a way to perform a VM hopping attack [86; 87]. The VMM can possibly be attacked through several attack vector: device drivers, VM exit events or hypercalls [88]; a throughout list of vulnerabilities typical of common VMMs used to deploy Cloud systems, can be found in [89]. For this reason, in order to protect users' data integrity, it is essential to protect the isolation capabilities and integrity of virtual machine monitors. A list of possible mechanisms to guarantee VMM integrity and enhance isolation is reported in [48; 49].  
VM Management Issues  
Bad management of VM images has negative repercussion on the integrity of the Cloud environment. Indeed, vulnerabilities in the Cloud environment can be introduced by injecting malware into VM images repositories [67]. Thereafter, with lacks of proper VM image management and controls, sporadically running images are in the perfect position to carry worms and compromise the integrity of other images while avoiding detection thanks to low activity level [62]. Therefore, integrity checks and scans of VM images are required as a consequence of VM cloning and sharing. Moreover, such controls are also paramount in relation to the necessity to protect Cloud repositories against the increasing trend of \"bad repositories\", i.e., the use of Cloud repositories as containers of services for illicit activities [90].  
VM Migration Issues  
Live virtual machine migration is paramount for Cloud environments, however, it needs to be properly implemented from a security perspective (see also Section 5.1). As for integrity, the attack surface of the migration protocol is potentially quite vast [65]: common vulnerabilities may be used to inject malicious code in the programs implementing the migration process; if no encryption is used to secure the exchange of messages controlling the transfer, then, messages might be manipulated to impair integrity of the process; moreover, even compromised hosts might be exploited for affecting integrity of the migrated VM once it is moved to a controlled malicious host.
# A High Capacity Watermarking Technique for the Printed Document
## 2 Related Theory
### Quadratic Quantization Function  
Currently there exist some schemes based on character as a carrier to embed watermarks. In [21], each Chinese character or English letter can be embedded with a one bit watermark information. In [22], each English letter can be embedded with a one bit watermark. According to the complexity of its partial structure, some Chinese characters can be divided into multiple character components to embed information of multi-bits. The quantization process of these two methods is relatively simple and not suitable for large-capacity watermark embedding. Furthermore, according to the resolution step size they selected, the maximum number of pixels flipped by a single character is \(\pm 0.15*M\) (\(M\) is the average of the pixel points). In this paper, based on the error rate before and after the print-scan, a quadratic quantization function is proposed to embed \(n\)\((n=1,2\cdots)\) bits watermark information into a single character by flipping the pixels.  
According to the analysis of the print-scan invariant in Section 2.1, the error rate of the print-scan equipment used in this paper is less than 0.015. The experimental results also show that the value of the print-scan invariant ranges from 0.4 to 2.0. Therefore, in the case where the resolution and the flipping ratio are appropriate, it is conceivable to divide \(2^{n}\)\((n=1,2\cdots)\) gradients within a certain pixel inversion range \(\delta\), and then select an appropriate quantization function for watermark coding.  
In this paper, the quantization function is proposed under the principle of resolution, invariant, coding scheme and simplicity, and it is shown in Equation (6), where \(x\) indicates the print-scan invariant of the character; \(\rho\) is a constant indicating the quantization step size, where \(\rho=300\) is available; \(t\) can be obtained according to the selected pixel flipping range \(\delta\), here assume \(\delta=0.1\).  
\[f(x)=((x-t)\cdot\rho)^{2},\;\;t=\left\{\begin{array}{ll}0.5&(0.4\leq x<0.6)\\ 0.7&(0.6\leq x<0.8)\\ 0.9&(0.8\leq x<1.0)\\ 1.1&(1.0\leq x<1.2)\\ 1.3&(1.2\leq x<1.4)\\ 1.5&(1.4\leq x<1.6)\\ 1.7&(1.6\leq x<1.8)\\ 1.9&(1.8\leq x<2.0)\\ \end{array}\right. \tag{6}\]  
Taking the error rate of the print-scan process and the precision of the pixel flipping into account, the quantization function can be combined with the Gray code for watermarking coding, then the error-rate will be reduced during watermark extraction. Therefore, Equation (7) is used to quantize \(n\) bits watermark information, and different quantized values \(G_{i}\) represent different watermark codes. Then the inverse function of the Equation (7) can be used to obtain the print-scan invariant according to the watermark information. Thus, the number of black pixels that need to be flipped by different watermark information is shown in Equation (8).  
\[f(\hat{x}_{i})=G_{i},\;\;(i\in[0,2^{n}-1]) \tag{7}\]  
\[\Delta x_{i}=(\hat{x}_{i}-x)\cdot M\;;\;\;\left(\hat{x}_{i}=f^{-1}(\hat{x}_{i })\right) \tag{8}\]
## 3 Energy-Saving Optimization Model
### Power Demand Estimation  
After status recognition, it is also necessary to estimate the power demand, which includes the time demand of time-type appliances and the temperature demand of temperature-type appliances.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Appliances** & **Operating Status** & **Clustering Center/W** \\ \hline Light & On & 58.28 \\ \hline Water Dispenser & \begin{tabular}{c} Insulation \\ Heating \\ \end{tabular} & \begin{tabular}{c} 0.46928 \\ 426.26 \\ \end{tabular} \\ \hline Air Conditioning & \
\begin{tabular}{c} Cooling \\ Heating \\ Ventilation \\ \end{tabular} &
\begin{tabular}{c} 1139.77 \\ 1407.65 \\ 56.25 \\ 20.56 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reference values of operating status and characterized power.  
Figure 1: The operating status recognition results. (**a**) The operation status recognition of the air conditioning unit. (**b**) The operation status recognition of the water dispenser.  
For the time demand of the time-type appliances, the power demand can be excavated by statistical analysis of the historical operation of the electric appliance, or by using the environmental sensors to monitor user activities, as shown in Equation (8), where \(P_{i}^{on}(t)\) represents the operating probability of appliance \(i\) at the moment of \(t\), \(n_{total}\) denotes the total number of tests, and \(n_{0}\) denotes the number of electricity-consumption occurrences. For the temperature demand of the temperature-type appliances, real-time monitoring can be performed through the temperature sensor.  
\[P_{i}^{on}(t)=\frac{n_{0}}{n_{total}} \tag{8}\]  
After clarifying the power demand, the time/temperature utility is evaluated in conjunction with the identified operating status, along with the corresponding utility functions which have been previously defined.
###### Abstract  
This study aimed to evaluate the effect of try-in paste contamination on the bond strength of lithium disilicate glass-ceramic to dentin treated with immediate (IDS) or delayed (DDS) dentin sealing techniques. Occlusal halves of 33 molars were decapitated and divided into three groups (n = 10). Lithium disilicate discs (3 \(\times\) 5 mm) were prepared. For Group A, the provisional crown was applied over dentin and was soaked in distilled water. Lithium disilicate discs were cemented following dentin conditioning with a three-step etch and rinse adhesive. In Group B (IDS), the three-step adhesive was applied to dentin. The dentin surfaces were conditioned only in the final cementation for Group C (DDS). The intaglio surfaces of test groups were contaminated with try-in paste. All specimens were thermally cycled 3000 times at 5-55 \({}^{\circ}\)C and were subjected to shear tests. An additional three specimens for each group were contaminated with try-in paste and subjected to the same surface cleaning as the test specimens were examined with SEM/EDS. The adhesive surfaces were also examined with SEM/EDS for try-in paste remnants. Group C showed a significant decrease in bond strength values compared to Group B and Group A (5.84 \(\pm\)1.4 MPa, 11.45 \(\pm\)24 MPa, and 10.29 \(\pm\)2.5 MPa, respectively). No statistically significant difference was detected between Group B and Group A (\(p\geq\) 0.05). The SEM-EDS analyses revealed obstructions of the dentinal tubules in the try-in-paste-contaminated specimens. Immediate dentin sealing application enhanced the bonding strength of lithium disilicate to the try-in-paste-contaminated dentin. Try-in paste contamination over dentin negatively influenced the bonding process.  
bond strength; delayed dentin sealing; immediate dentin sealing; try-in paste; lithium disilicate
## 4 Numerical Evaluations
### Numerical Results  
#### 4.3.1 Direct Experiment  
Distance attenuation characteristics are investigated where the upward-facing antenna is fixed to the ground and the UAV, which has a downward-facing antenna installed as an access point, is gradually raised to a higher altitude directly above the ground. Similar to our experimental results, the achievable throughput is used as a metric to indirectly evaluate radio propagation characteristics. In this case, it is assumed that the antenna on the ground does not receive ground-reflected waves since it is at an altitude of 0 m, and only the Line of Sight (LOS) path from the access UAV is received. In addition, both the access UAV and the ground terminal's antenna gains are assumed to be the same. The numerical results are shown in Figure 16. It can be seen that a saturated throughput can be achieved up to a certain height (distance between the transceivers) and then decays gradually due to propagation attenuation. It can also be found that the higher the antenna gain is, the higher the quality of communication can be ensured.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 3 Definition of the User Requirements
### Observation Requirements  
In this section we describe the critical requirements necessary to observe the target area and report data necessary to achieve the FMFs described in the previous section. Observation requirements described here include those which describe the required coverage and data latency.
# Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
## 2 Methods
### Solve Transformation Matrix  
After obtaining several sets of optimal correspondences, a rigid transformation is computed from the source point cloud data to the target point cloud data. This rigid transformation allows for the registration of the source point cloud data into the coordinate system of the target point cloud data [26].  
Initially, the centroids of both the source point cloud data (P) and the target point cloud data (Q) are calculated. The centroid calculation is shown in Equation (21):  
\[\begin{split} C_{P}=\frac{1}{N_{P}}\sum\limits_{i=1}^{N_{P}}p_{i}\\ C_{Q}=\frac{1}{N_{Q}}\sum\limits_{i=1}^{N_{Q}}q_{i}\end{split} \tag{21}\]  
where \(N_{P}\) and \(N_{Q}\) represent the number of points in the source point cloud data and the target point cloud data, respectively.  
The corresponding pairs in both datasets are then translated to the origin, as shown in Equation (22):  
\[\begin{split} p_{i}^{\prime}=p_{i}-C_{P}\\ q_{i}^{\prime}=q_{i}-C_{q}\end{split} \tag{22}\]  
The covariance matrix \(H\) is then obtained by multiplying the translated matching pairs, as shown in Equation (23):  
\[H=\sum\limits_{i=1}^{N^{\prime}}p_{i}^{\prime}\cdot q_{i}^{\prime T} \tag{23}\]  
where \(N^{\prime}\) is the number of matching pairs.  
Singular value decomposition is performed on the covariance matrix \(H\), as shown in Equation (24):  
\[H=U\cdot\Sigma\cdot V^{T} \tag{24}\]  
where \(U\) and \(V\) are orthogonal matrices and \(\Sigma\) is a diagonal matrix. The rotation matrix \(R\) can be calculated using \(U\) and \(V\), as shown in Equation (25):  
\[R=V\cdot U^{T} \tag{25}\]  
The translation vector \(t\) can be calculated from the centroid of the point cloud data and the rotation matrix \(R\), as shown in Equation (26):  
\[t=C_{Q}-R\cdot C_{P} \tag{26}\]  
After calculating the rotation matrix \(R\) and the translation vector \(t\), the source point cloud data can be transformed into the target point cloud data using Equation (27):  
\[q_{i}=p_{i}\cdot R+t \tag{27}\]  
where \(p_{i}\) and \(q_{i}\) represent the point clouds in the source and target point cloud data, respectively.  
In this way, the registration of point cloud data is completed.