esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance Robustness of the Considered Approaches  
The aim of this last subsection is to evaluate the performance robustness of the proposed histogram-based approach, compared to the considered state-of-the-art DNN architectures.  
In order to provide a visual interpretation of the results, shown in Table 9, and check how robust these solutions are regarding the testing images, Figure 6 shows, for both the considered datasets, the confusion matrices obtained by the state-of-the-art (supervised) DNNs.  
The confusion matrices in Figure 6 clearly show the poor robustness of the DNN approaches. They illustrate that the main confusion is for the Non-COVID class, where, especially, in the case of CT scans, many instances are erroneously classified.
# A Physicist's View on Partial 3D Shape Matching
## 2 A Physics Approach to Shape Comparison
### WKS: Spectral Signatures for Meshes  
The wave kernel signature (WKS) was described in detail in [29]. Here, we present its main concept and describe its implementation for surfaces represented as triangulated meshes.  
The WKS is based on solving the time-dependent Shrodinger wave equation in the absence of an external potential for a nonrelativistic particle on the surface of the shape of interest. The evolution of such a particle is described by its wave function \(\psi(x,t)\), which is the solution of  
\[\frac{\partial\psi}{\partial t}(x,t)=i\Delta\psi(x,t), \tag{2}\]  
where \(\Delta\) is the Laplace-Beltrami (LB) operator. Note that this equation is complex due to the presence of \(i\). We will write \(E_{0}\leq E_{1}\leq\ldots\leq E_{k}\ldots\) the eigenvalues of the LB operator,and \(\phi_{k}\) the eigenvector associated to the eigenvalue \(E_{k}\). For a given energy probability distribution \(f_{E}^{2}\) with expectation value \(E\), the wave function of the particle is given by  
\[\psi_{E}(x,t)=\sum_{k}e^{iE_{k}t}\phi_{k}(x)f_{E}(E_{k}). \tag{3}\]  
The probability to measure the particle at position \(x\) and time \(t\) on the surface is then \(|\psi_{E}(x,t)|^{2}\). As the time parameter has no straightforward relevance to the geometry of the shape, the WKS is defined as the average probability over time to measure the particle at position \(s\):  
\[WKS(E,x)=\sum_{k}\phi_{k}^{2}(x)f_{E}^{2}(E_{k}). \tag{4}\]  
Assuming that deformation of shapes are independently distributed, [PERSON] et al. assumed a log-normal distribution for \(f_{E}^{2}\), leading to the following definition for the WKS [29].  
**Definition 1**.: _The wave kernel signature at a point \(x\) of the surface considered is a real-valued function in the logarithmic energy scale \(e=\log(E)\) defined as_  
\[WKS(x,e)=C_{e}\sum_{k}\phi_{k}^{2}(x)e^{-\frac{e-\log(E_{k})}{2 \sigma^{2}}}, \tag{5}\]  
_where \(C_{e}=\left(\sum_{k}e^{-\frac{e-\log(E_{k})}{2\sigma^{2}}}\right)^{-1}\) is a normalization constant and \(\sigma\) is a parameter corresponding to the width of the normal distribution._  
It is possible to compute the WKS \(p(x)=(WKS(x,e_{1}),\ldots,WKS(x,e_{n}))\) for each point of the shape, where \(e_{min},\ldots,e_{max}\) are some fixed energy values.
Sensors_**2015**, _15_, 29036-29055; doi:10.3390/s151129036  
\begin{tabular}{c} _Series of_ \\
**ISSN 1424-8220** \\ www.mdpi.com/journal/sensors \\ \end{tabular}  
Capacity Model and Constraints Analysis for Integrated Remote Wireless Sensor and Satellite Network in Emergency Scenarios  
**[PERSON], [PERSON] *, [PERSON], [PERSON] and [PERSON]**  
College of Communication Engineering, PLA University of Science and Technology,  
88 Houbiaoying Rd. Nanjing 210007, China; E-Mails: [EMAIL_ADDRESS] (W.Z.);  
[EMAIL_ADDRESS] (F.D.); [EMAIL_ADDRESS] (Z.X.); [EMAIL_ADDRESS] (D.B.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-139-5181-8291.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 9 September 2015 / Accepted: 11 November 2015 / Published: 17 November 2015_  
This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN.  
capacity; remote wireless sensor networks; satellite network; emergency communications; integrated remote wireless sensor and satellite network
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 5 Simulation Model, Setup and Results
### Energy Model  
Whenever nodes transmit and receive messages, the energy consumption of these nodes are calculated using Equations (11) and (12), as presented below.  
Energy consumptions to transmit a packet of size \(n\) over distance \(d\) is given by:  
\[E_{TX}=n\star\varepsilon_{dec}+n\star\varepsilon_{fs}\star d^{\alpha}. \tag{11}\]  
Energy required receiving a packet is:  
\[E_{RX}=n\star\varepsilon_{dec} \tag{12}\]  
where \(\varepsilon_{dec}\) and \(\varepsilon_{fs}\) in Equations (11) and (12) represent energy spent in transmitter electronics circuitry and energy spent in RF amplifiers for propagation loss, respectively. The constant (propagation loss exponent) \(\alpha\) is dependent on the surrounding environment. For free space without any obstruction in the line of sight, \(\alpha=2\).  
In the proposed ODCR protocol and existing LEACH, TEEN and LORA protocols, each node transmits and receives data packets during cluster formation, routing and cluster restoration phases. Based on this energy model, the energy consumptions of a node are directly proportional to the size of the data and the distance a data packet travels.
## 4 The Transverse Isotropic Stress Distribution Law
### The Effect of the Formation Dip Angle on Stress Distribution  
In fact, isotropic planes often incline due to long-term geological tectonics, as shown in Figure 12. The coordinate plane OX'Y' is consistent with isotropic planes, while plane OXY is the horizontal plane, \(\theta\) is the inclination angle of the isotropic plane, and \(\alpha\) is the angle between the y\({}^{\prime}\) axis and the x axis clockwise. Equation (28) shows the constitutive relation of the model in OX'Y'Z' [47; 48; 49].  
\[\left\{\epsilon^{\prime}\right\}=\left[A^{\prime}\right]\left\{\sigma^{\prime }\right\} \tag{26}\]  
Figure 11: Analytical results for the different values of \(\mu_{1}/\mu_{2}\). (**a**) vertical stress, (**b**) horizontal stress, (**c**) shear stress.  
When the isotropic plane is tilted, the elastic matrix of the isotropic foundation in the axial coordinate system is:  
\[A^{\prime}=Q^{T}AQ \tag{27}\]  
where [_A_] is the elastic matrix in the positive axis coordinate system, and [_Q_] is the transformation matrix of elastic parameters  
\[Q=\left[\begin{array}{cccccc}\sin^{2}\alpha&\cos^{2}\alpha&0&0&0&-\sin 2 \alpha\\ \cos^{2}\theta\cos^{2}\alpha&\cos^{2}\theta\sin^{2}\alpha&\sin^{2}\theta&- \sin 2\theta\sin\alpha&-\sin 2\theta\cos\alpha&\sin 2\alpha\cos^{2}\theta\\ \sin^{2}\theta\cos^{2}\alpha&\sin^{2}\theta\sin^{2}\alpha&\cos^{2}\theta&\sin 2 \theta\sin\alpha&\sin 2\theta\cos\alpha&\sin 2\alpha\sin^{2}\theta\\ -\frac{1}{2}\sin 2\theta\cos^{2}\alpha&-\frac{1}{2}\sin 2\theta\sin^{2}\alpha& \frac{1}{2}\sin 2\theta&-\cos 2\theta\sin\alpha&-\cos 2\theta\cos\alpha&- \frac{1}{2}\sin 2\alpha\sin 2\theta\\ -\frac{1}{2}\sin\theta\sin 2\alpha&\frac{1}{2}\sin\theta\sin 2\alpha&0&\cos \theta\cos\alpha&-\cos\theta\sin\alpha&\cos 2\alpha\sin\theta\\ \frac{1}{2}\cos\theta\sin 2\alpha&-\frac{1}{2}\cos\theta\sin 2\alpha&0&\sin \theta\cos\alpha&-\sin\theta\sin\alpha&-\cos 2\alpha\cos\theta\\ \end{array}\right] \tag{28}\]  
In Figure 13a, the value of the vertical stress increases when the formation dip angle increases from 0\({}^{\circ}\) to 90\({}^{\circ}\), while the opposite trend can be observed in Figure 13b,c. The greater the formation dip angle is, the lower the value of horizontal stress and shear stress. It can be seen that the formation dip angle has prominent effects on the distribution of the stress. The relationship is due to the changes of the formation dip, and the direction of stresses deflects and forms some range of stress redistribution.  
Figure 12: Transversely isotropic constitutive model with isotropic plane inclines.  
Figure 13: _Cont._
# A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities
## 5 Results  
### Optimization Results  
Solving the model presented in Section 2, the optimal energy community configuration has been calculated. It is based on 2 PV power plants and 1 hydroelectric plant. In Figure 10, the optimal power is compared with the theoretically available one. This production portfolio includes 14.4 kW of photovoltaic generators and 20 kW of hydroelectric. With this configuration, the energy community is expected to produce on average 102.2 MWh/year, sharing 95.2% of it amongst the members (97.4 MWh/year) and selling the other 4.8% to the market (4.9 MWh/year). Thanks to the incentive on shared energy, and considering the actualization of these values, shared energy provides an average income equal to 7230.5 EUR/year, while producing savings of 3397 EUR/year. The energy sold to the market contributes with 173.7 EUR/year to the yearly income. The result is that the incentive of shared energy contributes to 66.9% of the total revenues over the lifetime of the investment (Figure 11), the second driver for the investment is the saving due to the not bought energy (31.5%). The smallest part of the income is due to the energy not shared within the community but sold to the market. Considering that this is not the main goal of the energy community, it contributes only for 1.61% of the total. The net present value of the investment is equal to 85,866 EUR. The profitability index is equal to 1.84 and the payback time is less than 9 years. It is interesting to notice that the energy storage system does not result in the optimal solution, due to the high cost of investment.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Name** & **Value** & **Unit** & **Description** \\ \hline \(Cost_{ESS}\) & 400 & [€/kWh] & Storage investment \\ \(Rep_{ESS}\) & 200 & [€/kWh] & Storage replacement cost \\ \(O\&M_{ESS}\) & 10 & [€/kWh] & Operation and maintenance \\ \(ESES^{max}_{cap}\) & 200 & [kWh] & Max storage size \\ \(EPR\) & 2 & [h] & Energy to power ratio \\ \(\eta_{CH}\) & 0.95 & [-] & Storage eta CH \\ \(\eta_{DIS}\) & 0.95 & [-] & Storage eta DIS \\ \(SOC_{min}\) & 0.2 & [-] & Minimum state of charge \\ \(Cycle_{max}\) & 5000 & [-] & Max cycles \\ \hline \hline \end{tabular}
\end{table}
Table 4: Storage data.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Name** & **Value** & **Unit** & **Description** \\ \hline \(Cost_{adm}\) & 5 & [€/user/year] & Administrative cost of the REC \\ \(SE_{inc}\) & 110 & [€/MWh] & Incentive on shared energy \\ \(SCsi\) & 80 & [€/MWh] & Self-Consumption saving index \\ \(SEsi\) & 8.22 & [€/MWh] & Shared Energy saving index \\ \(load_{trend}\) & +2\% & [\%/year] & Yearly growth of the load profiles \\ \(price_{trend}\) & \(-2\%\) & [\%/year] & Yearly growth of the price profiles \\ \hline \hline \end{tabular}
\end{table}
Table 3: Economical data.
## 2 Fault Analysis of Load Mutation Caused by Electrical Interference
### Pulsing Load-Mutation Fault  
Pulsing load mutation is a fault with a sudden load change (increase or decrease) and returns to normal values within a short period [18]. Figure 1 shows the actual load historical data of a 300 MW coal-fired unit. Pulsing load mutations frequently occur in units owing to electrical interference. This abnormal state causes the unit to change flow demand energy management (FDEM), a comprehensive steam flow demand for steam turbines, to ensure that the actual load output matches its AGC command.  
The characteristics of pulsing load mutation can be obtained by comparing many sudden load change faults. As shown in Figure 1, the period of the pulsing load fault is maintained at 1-2 s and the magnitude of the fault is variable; that is, the fault magnitude at 1770 s is approximately 19 MW. Because of the slow regulation rate of the coal-fired unit, its regulation command is still not transmitted to the actual load after the pulsing load fault returns to a normal value. Therefore, the pulsing fault data for the actual load during normal operation can be considered as data for the sudden change in this load during the open-loop regulation of coal-fired unit.  
The above analysis makes it possible to extract a specific sudden load change fault characteristic. When a pulsing load mutation occurs, the load corresponds to a pulse signal with a pulse width of 2 s and a pulse amplitude of 6.3% Pe (rated load), superimposed on the stable output of the unit's thermal system. The fault characteristics of the pulsing  
Figure 1: Pulsing load mutation.  
load mutation of a coal-fired unit with the same capacity are mapped onto the open-loop regulation process of single-shaft CCPs as the fault characteristics of the pulsing fault of a single-shaft CCPP.
## 2 Samples and Analytical Procedure
### Chemical Analysis  
Filter samples (PM\({}_{1.0}\)) were analysed for water-soluble diacids, oxoacids, and fatty acids by the method reported previously [7; 50]. Briefly, the known area of the filter was extracted with organic free pure water, and then organic acids (diacids, oxoacids, benzoic acid, and fatty acids) were derivatized with 14% BF\({}_{3}\)/n-butanol to derive dibutyl esters, dibutoxy acetal/butyl esters, and butyl esters. These derivatives were determined using a capillary GC (HP 6890) equipped with a split/splitless injector, a HP-5 fused silica capillary column (0.2 mm i.d. \(\times\) 25 m long \(\times\) 0.50 \(\upmu\)m film thickness), and a flame ionization detector (FID). The GC peak areas for the derivatives were calculated with a Shimazdu C-R7 Aplus data system. The GC peaks were identified by comparing GC retention times with authentic standards and confirmed by GC/mass spectrometry (GC/MS). The concentrations of all the species reported here are corrected for field blanks. We also performed the recovery test by spiking authentic diacids to the quartz filter and analyzing it as we would a real sample. The recoveries were 90% for oxalic acid (C\({}_{2}\)) and more than 95% for malonic acid (C\({}_{3}\)) to azelaic acid (C\({}_{9}\)). The analytical error of C\({}_{2}\) is less than 2% and those of other species are less than 5%.
## 1 Introduction
The poor conductivity of quartz and the absence of cooling water channels in the quartz parts as well as oil lubrication on the piston contribute to changing the characteristics of heat transfer in optical engines. These two factors lead to low heat losses and high wall temperatures that lead to shorter ignition delays [11]. Optical engines tend to present smaller temperature gradients between the surface and the gas inside the cylinder. [PERSON] et al. [18] developed a study that compared the heat release of an optical engine with a metal engine. Results showed that the lower heat losses from the optical engines directly impacted the combustion phasing, and consequently the combustion process and emissions. This effect can be compensated or minimized by adjusting different intake temperatures and pressures for each kind of engine (optical and metal). Furthermore, the presence of crevices due to changes in the position of piston rings impact directly on the compression ratio and in-cylinder flow. Thus, blow-by also needs to be taken into account in the thermodynamic analysis to consider an accurate in-cylinder trapped mass. The main objective of this work was to develop a specific methodology to reduce the uncertainties in the thermodynamic analysis in a single cylinder optical engine. These uncertainties are much higher than those in metal engines due to the particular characteristics of optical engines. The methodology is based on the combination of experimental measurements and 0D thermodynamic modeling. A high speed camera was used to measure the piston position at firing and valve overlap TDC in order to determine the effective compression ratio considering the mechanical deformation. It also measured the in-cylinder pressure to be used as input data in the 0D model. 0D thermodynamic analysis was used to calibrate the heat transfer model and to determine the rest of the uncertainties based on the minimization of the heat release rate residual at motored conditions.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Mathematical Foundations of DDPM
#### 3.2.7 Architectural Components  
DDPM often incorporates architectural components such as invertible neural networks and reversible \(1\times 1\) convolutions. These components contribute to the model's ability to perform the denoising diffusion process effectively and efficiently [52].  
In summary, the mathematical foundation of DDPM lies in the formulation of the denoising diffusion process, probabilistic modeling, reversibility, and a carefully designed training objective. These components collectively enable DDPM to model complex data distributions, denoise images, and generate realistic samples. The connection to Markov Chain Monte Carlo further enriches the theoretical understanding of DDPM. Understanding the mathematical principles of DDPM is crucial for both implementing and interpreting the model's behavior in various applications, including biomedical image segmentation.
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 3 System Calibration and Image Reconstruction Methods
### System Calibration Method  
A system calibration method capable of getting the accurate system parameter vector is studied here. In this method, a specific calibration plate with a group of line-like scratches and distance-marked dot pairs on its surface is employed as the test sample. The distance-marked dot pair refers to a pair of point-like defects, such as digs, between which the distance is known. As can be found in Figure 5, this method can be divided into three major steps. The first step is to extract the feature sequence number sets, which consist of the sequence numbers of intensity signals that come from the defects. In the second step, a constraint function for constraining the image coordinates mapped from the feature sequence number sets is defined, and the third step is to perform a nonlinear optimization to minimize the constraint function, so that the optimal system parameter vector can be obtained. More details about these three steps will be given in the following.
## 2 Methods
### Boundary Value Problem of Elastostatics  
This article considers the quasi-static boundary value problem of elastostatics:  
\[\
abla\cdot\mathbf{\sigma}-\mathbf{f}_{b} =\mathbf{0} \text{in}\ \Omega,\] \[\mathbf{u} =\mathbf{u}_{D} \text{on}\ \xi_{D}, \tag{1}\] \[\mathbf{\sigma}\mathbf{n} =\mathbf{f}_{t} \text{on}\ \xi_{N}.\]  
\(\mathbf{u}\) is the displacement field to be identified, and \(\mathbf{\sigma}\) is the stress field of the domain \(\Omega\). For the Neumann boundary \(\xi_{n}\) with normal vector \(\mathbf{n}\), the force \(\mathbf{f}_{t}\) is specified as a boundary condition. In addition, in this article, the volume forces are assumed to be \(\mathbf{f}_{b}=\mathbf{0}\) and the displacements \(\mathbf{u}_{D}=\mathbf{0}\) on the Dirichlet boundary \(\xi_{D}\).  
To solve the boundary value problem (1), an additional constitutive law is required. For this purpose, [PERSON]'s linear-elastic law is used:  
\[\sigma=\tilde{\mathbb{C}}[\varepsilon(\mathbf{u})]. \tag{2}\]  
The strain tensor \(\varepsilon(\mathbf{u})\) is given by \(\varepsilon(\mathbf{u})=\frac{1}{2}(\
abla\mathbf{u}+(\
abla\mathbf{u})^{T})\). In Equation (2), the elasticity tensor \(\tilde{\mathbb{C}}\) is a function that can be described in the single-material case by \(\tilde{\mathbb{C}}=\chi_{1}(\mathbf{x})\mathbb{C}_{1}\) and, in the multi-material case, by \(\tilde{\mathbb{C}}=\sum_{i}\chi_{i}(\mathbf{x})\mathbb{C}_{i}\). The indicator functions \(\chi_{i}(\mathbf{x})\) of the i-th material each have the value of 1 if the i-th material is present and 0 otherwise.  
Using the constitutive law and the above simplification, the weak form of the boundary value problem can be derived:  
\[\int_{\Omega}\varepsilon(\mathbf{u})\cdot\tilde{\mathbb{C}}[\varepsilon(\mathbf{\psi} )]d\Omega=\int_{\xi_{N}}\mathbf{f_{t}}\cdot\mathbf{\psi}d\xi\quad\forall\mathbf{\psi}\in U _{ad}. \tag{3}\]  
Here, \(U_{ad}\) denotes the space of the kinematically admissible displacement fields.  
If one also introduces the two abbreviations  
\[\begin{split}\mathbf{a}(\mathbf{u},\mathbf{\psi})&=\int_{ \Omega}\varepsilon(\mathbf{u})\cdot\tilde{\mathbb{C}}[\varepsilon(\mathbf{\psi})]d \Omega,\\ I(\mathbf{u},\mathbf{\psi})&=\int_{\xi_{N}}\mathbf{f_{t}}\cdot \mathbf{\psi}d\xi,\end{split} \tag{4}\]  
Equation (3) can be written as \(\mathbf{a}(\mathbf{u},\mathbf{\psi})=I(\mathbf{\psi})\).