esa-sceva/satcom-chunk-collection huggingface dataset:
# Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain
## 4 Results and Discussion  
The results of the different models under analysis were compared with the real data from the facilities. The comparison includes results obtained from all climate models available in SAM (isotropic, Perez, HDKR) and all climate databases included in PVGIS and PVSYST. PV*SOL was set with the Perez radiation model, since it is optimal in this study. In total, 11 studies were performed for each PV installation.  
One of the most widely used values when assessing the suitability of a PV project, is the amount of electrical energy that can be generated throughout a year. Table 4 shows the total annual results obtained from the different models, in addition to the percentage deviation from the actual value for each system.  
The results showed large differences depending on the type of sun-tracking technology. In general, the deviation obtained for System 1 (fixed) was low. The simplicity of this installation, in addition to the low operating uncertainty, meant that the simulations could be adjusted with deviations of below 5%. Except for PV*SOL and RETScreen, the results obtained were slightly conservative, estimating lower energy productions.  
In System 2 (1 axis), the results showed greater deviation. This installation incorporates a manual tilt adjustment that increases uncertainty due to system operation errors. However, despite this factor, the deviation was still lower than 10%.  
Finally, the most complex system had the 2-axis solar tracker (System 3). This type of facility is highly conditioned by the wind velocity, which, together with the greater probability of failures in these devices, reduces the availability of the facility. Therefore, the results obtained by the models considerably overestimated the energy generated, even above 20%.  
When the program results were individually analyzed, the results of the free software were generally better. In general, these models are simpler, benefiting from the type of  
Figure 5: Monthly average energy supplied to the grid for the three systems under analysis. Values shown for each nominal kW installed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **kWh/Year** & **(kWh/Year)/kWp** \\ \hline
**System 1** & 55,445 & 1426 \\
**System 2** & 144,962 & 1435 \\
**System 3** & 24,726 & 1732 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Yearly energy production of each system.  
preliminary study carried out (with a low number of predefined parameters). Although PVGIS is the simplest software, its results were excellent when using the COSMO database.  
The results provided by SAM showed the best global fit of the three systems under analysis. Specifically, regardless of the irradiation model in use, the deviation was less than 6%. On the contrary, PV*SOL showed very high deviation values, overestimating by around 10% for all installations, but this deviation was consistent for the three systems under analysis.
## 3 Numerical Simulations and Experiment Validation  
### Theoretical Basis for Laser Ultrasonics Generation  
To investigate the interaction between laser-generated SAWs and the porosity in metal AM components, it is essential to establish the theoretical basis for thermoelastic wave generation under grating laser excitation. In laser ultrasonics, when a pulsed laser irradiates the surface of a solid at an energy density below the material's melting threshold, a rapid and spatially non-uniform temperature field is induced due to the partial absorption of optical energy. The resulting thermoelastic expansion generates elastic waves.  
In this study, a two-dimensional simulation model is developed in the x-z plane, where the laser source is applied along the top surface of the specimen. The transient thermal response induced by laser absorption is governed by the heat conduction equation:  
\[\kappa\
abla^{2}\mathbf{T}-\rho c\dot{\mathbf{T}}=0 \tag{7}\]  
where \(\mathbf{T}\) is the temperature vector, \(\kappa\) is the thermal conductivity, \(\rho\) is the material density, and \(c\) is the specific heat capacity. Since the laser energy is absorbed at the material surface, the heat input is treated as a surface heat flux rather than a volumetric heat source. The boundary condition at the laser-irradiated surface is therefore defined as follows:  
\[\mathbf{n}^{T}\
abla\mathbf{T}=-\frac{\mathbf{q}_{s}(x,t)}{\kappa} \tag{8}\]  
Figure 1: Overall schematic diagram of measurement method.  
where \(n\) denotes the outward surface normal vector and \(\mathbf{q_{s}}(x,\ t)\) represents the pulsed laser heat flux. For a pulsed laser, this surface heat input can be modeled as a separable function in space and time:  
\[\mathbf{q_{s}}(x,t)=A\mathbf{l_{0}}f(x)g(t) \tag{9}\]  
where \(A\) is the optical absorptivity of the material surface, \(\mathbf{l_{0}}\) is the peak laser intensity, and \(f\left(x\right)\), \(g\left(t\right)\) are the spatial and temporal distributions of the laser pulse, respectively.  
For the grating laser source used in this study, the spatial and temporal distributions can be defined by the following:  
\[\begin{split}& f(x)=\exp\left(-\left(x-x_{0}\right)^{2}/2a_{0}^{2 }\right)\cdot\left(1-\cos(2\pi x/w)\right)/2\\ & g(t)=8\frac{t_{0}^{3}}{t_{0}^{3}}\exp\left(-\frac{2t^{2}}{t_{0} ^{2}}\right)\end{split} \tag{10}\]  
here, \(x_{0}\) is the center position of the laser source, \(a_{0}\) is the half-irradiation width of the grating laser source, \(w\) is the grating line spacing, and \(t_{0}\) is the characteristic rise time of the laser pulse.  
As the temperature field evolves, localized thermal expansion induces internal stresses that excite elastic waves in the surrounding medium. According to thermoelastic theory, the displacement field \(\mathbf{u}\ (x,\ z,\ t)\) satisfies the following governing equation:  
\[\rho\tilde{\mathbf{u}}=\mu\
abla^{2}\mathbf{u}+(\lambda+\mu)\
abla(\
abla\cdot\mathbf{u})- \alpha(3\lambda+2\mu)\
abla T \tag{11}\]  
where \(\lambda\) and \(\mu\) are the Lame constants, and the third term on the right-hand side represents the thermal driving force due to temperature gradients.
## 1 Introduction
### Challenges and Contributions  
In this subsection, we will first discuss the technical challenges of existing MPTCP over IEEE \(802.11 ay\) networks. Thereafter, we discuss the impending challenges of existing stochastic and queuing theories used for designing MPTCP protocols. Stochastic theories and fluid modeling have been celebrated in designing and evaluating new TCP and MPTCP algorithms. In general, we follow a waterfall approach for designing these algorithms [7]. To this end, we first develop approximate models using fluid and statistical estimation, evaluate their performance with queuing theory and/or probabilistic reasoning, and then employ optimization for maximizing network utility (commonly referred to as _Network Utility Maximization_ in TCP literature) by adjusting the algorithm parameters.  
Figure 1: _Motivating Example:_ Consider a fast-moving convoy of drones flying over the Darling Harbour of Sydney, Australia. The convoy (head to tail) has direct access to several Internet connection sites via mmWave WiFi \(802.11 ay\) antenna array system. Control information and actions from the server are disseminated within the convoy instantaneously via uplinks. Downlinks are mostly used for HD video streaming for monitoring anomalies around the Harbour area.
# Performance Studies of High-Power Optical Parametric Oscillators Pumped by a Pulsed Fiber Laser
###### Abstract  
High-power optical parametric oscillators (OPOs), as mature radiation sources in mid-infrared (MIR), degenerate gradually with wavelength increase, mainly above 3700 nm. Using a periodically poled magnesium-oxide-doped lithium niobate (MgO:PPLN) as the nonlinear crystal, we build a high-power signal-resonant OPO pumped by ytterbium-doped fiber laser (YDFL). To improve the OPO's output power at \(\sim\)3.8 \(\upmu\)m, the parameters, such as the pump beam's waist diameter and location, the curvature radius of the output coupler and the length of MgO:PPLN, are discussed in detail. When pump power is 79 \(\pm\) 4 W with a repetition rate of 200 kHz, the OPO provides up to 8 \(\pm\) 0.4 W average power in beam quality with M\({}^{2}\) factors of \(\sim\)1.84 and \(\sim\)1.69 in the two axes. Under the highest output power, the center wavelength of the idler beam is 3768.4 nm with a full-width at half-maximum (FWHM) bandwidth of \(\sim\)18.6 nm. When the output power reaches \(\sim\)6.3 W, its power stability is 1.6% root mean square (RMS) over 7 h. Further analysis of the factors affecting OPO's performance and simple structure are critically essential for compact OPO prototypes with a capacity of high output power.  
optical parametric oscillator; high-power; MgO:PPLN; mid-infrared; compact +
Footnote †: journal: Applied Sciences
# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
###### Abstract  
Phishing email assaults have been a prevalent cybercriminal tactic for many decades. Various detectors have been suggested over time that rely on textual information. However, to address the growing prevalence of phishing emails, more sophisticated techniques are required to use all aspects of emails to improve the detection capabilities of machine learning classifiers. This paper presents a novel approach to detecting phishing emails. The proposed methodology combines ensemble learning techniques with various variables, such as word frequency, the presence of specific keywords or phrases, and email length, to improve detection accuracy. We provide two approaches for the planned task; The first technique employs ensemble learning soft voting, while the second employs weighted ensemble learning. Both strategies use distinct machine learning algorithms to concurrently process the characteristics, reducing their complexity and enhancing the model's performance. An extensive assessment and analysis are conducted, considering unique criteria designed to minimize biased and inaccurate findings. Our empirical experiments demonstrates that using ensemble learning to merge attributes in the evolution of phishing emails showcases the competitive performance of ensemble learning over other machine learning algorithms. This superiority is underscored by achieving an F1-score of 0.90 in the weighted ensemble method and 0.85 in the soft voting method, showcasing the effectiveness of this approach.  
phishing email detection; machine learning; ensemble learning; cybersecurity Article
# Variables Affecting Peri-Implant Radiographic Bone Loss-8-23 Years Follow-Up
###### Abstract  
The aim of this study is to evaluate factors associated with long-term peri-implant bone-loss and to create a statistical model explaining bone-loss. The dental records in a private periodontal practice were screened for implant-patients with a minimal follow-up period of 8 years with periapical radiographs at implant-placement (T0) and last follow-up (Tf). Collected data included demographics, general health, medications, periodontal parameters, implant parameters, bone augmentation procedures, restoration and antagonist data, number of supportive periodontal appointments (SPT), and radiographic bone-loss between T0 and Tf. Bivariate and Mixed Logistic Regression analyses were performed. \"Goodness-of-fit\" of the model was elaborated with Receiver Operating Characteristic Curve (ROC) analyses. Thirty-seven patients receiving 142 implants were included. Mean clinical follow-up period was \(11.7\pm 3.7\) years (range 8-23). Most implants 64.4% were SPT-maintained more than twice a year. Patients with osteoporosis and smokers were prone to increased radiographic peri-implant bone-loss. External-hex implants placed without guided bone regeneration (GBR) and implants 10-12 mm long and diameter of 3.7-4 mm showed less peri-implant bone-loss. The model's Area Under the Curve (AUC) was 76.9% (Standard Error 4.6%, CI 67.8-86%).  
long-term follow-up; dental implant; marginal bone loss; periodontal private practice +
Footnote †: journal:  
Cases from \"everyday practices\" may enhance our understanding regarding the effectiveness of implant therapy. The evidence in implant therapy comes from private practices [10; 11; 12; 13] and from patients treated in university clinics [3; 10; 14], yet more private practice data would be valuable.  
We looked for patients in a single private practice who received dental implants and had documentation at least 8 years following implant installation. The main objective of the present study was to evaluate factors that were associated with long term peri-implant bone loss. Additional aim was to create a statistical model that may explain radiographic bone changes around dental implants.
## 4 Analysis of Results
### Studying Detection Results
These results demonstrate the decoy state protocol's ability to detect PNS attacks across a wide set of commonly implemented configurations to include when the decoy state intensity is very weak and the occurrence percentage is very small. Moreover, these results illustrate that the protocol can be further optimized with respect to the signal and decoy state occurrence percentages to maximize quantum throughout on the signal state as identified by the large \"white space\" between the signal and decoy states efficiencies in Figure 5 for even the most stringent configurations (when the occurrence percentages are: 99% signal, 0.5% decoy, and 0.5% vacuum).
###### Abstract  
In the realm of mobile robotics, the capability to navigate and map uncharted territories is paramount, and Simultaneous Localization and Mapping (SLAM) stands as a cornerstone technology enabling this capability. While traditional SLAM methods like Extended Kalman Filter (EKF) and FastSLAM have made strides, they often struggle with the complexities of non-linear dynamics and non-Gaussian noise, particularly in dynamic settings. Moreover, these methods can be computationally intensive, limiting their applicability in real-world scenarios. This paper introduces an innovative enhancement to the FastSLAM framework by integrating Multi-Objective Particle Swarm Optimization (MO-PSO), aiming to bolster the robustness and accuracy of SLAM in mobile robots. We outline the theoretical underpinnings of FastSLAM and underscore its significance in robotic autonomy for mapping and exploration. Our approach innovates by crafting a specialized fitness function within the MO-PSO paradigm, which is instrumental in optimizing the particle distribution and addressing the challenges inherent in traditional particle filtering methods. This strategic fusion of MO-PSO with FastSLAM not only circumvents the pitfalls of particle degeneration, but also enhances the overall robustness and precision of the SLAM process across a spectrum of operational environments. Our empirical evaluation involves testing the proposed method on three distinct simulation benchmarks, comparing its performance against four other algorithms. The results indicate that our MO-PSO-enhanced FastSLAM method outperforms the traditional particle filtering approach by significantly reducing particle degeneration and ensuring more reliable and precise SLAM performance in challenging environments. This research demonstrates that the integration of MO-PSO with FastSLAM is a promising direction for improving SLAM in mobile robots, providing a robust solution for accurate mapping and localization even in complex and unknown settings.  
simultaneous localization and mapping; extended Kalman filter; particle swarm optimization; multi-objective optimization +
Footnote †: journal: applied sciences
## 4 Inverse Problems
### Fluid Mechanics and FSI
#### 4.2.3 Constraints  
A series of constraints can be implemented on the optimization algorithms to restrict solution spaces and parameter values. These constraints can be used to ensure the physiological and physical meaning of the results and to funnel down solution multiplicity. One of the most relevant constraints required for material parameter estimation on tissue mechanics is compliance with the second law of thermodynamics. One of the required conditions for this compliance is that the strain energy function must be positive convex, which restricts the relative value of material parameters [24; 133].  
The assumption of material incompressibility is another common constraint imposed on cardiac and arterial wall mechanics. Full incompressibility introduces singularities to the solution of numerical formulations; therefore, nearly incompressible behavior is enforced by restraining the relative values of material properties. However, experimental and in silico evidence have shown that cardiovascular tissues are compressible to some degree and that myocardial volume varies throughout the cardiac cycle [134]. The most recent in vivo measurements of myocardium compressibility in human and large-mammal animal models agree on estimating peak compressibility between 1% and 20% [134; 135; 136] Moreover, it has been shown that the accuracy of heart-mechanics models is significantly increased if this compressibility effect is considered [137]. Thus, the incompressibility constraint is a reasonable yet rough approximation that must be carefully considered in simulation analyses [134; 138; 139].  
Microstructure-based models allow the introduction of physiologically and structural meaningful constraints to material parameters, e.g., maximum possible fiber stiffness, or maximum cellular volume fraction. Inequality type constraints can restrain material parameters within expected physiological ranges. Inequality relations between model parameters can also be introduced to address structural component differences, e.g., collagen fibers are typically stiffer than elastin fibers. In the study of FSI inverse problems, pulse wave velocity is constrained by the maximum possible speed of sound on the liquid media, and some authors have introduced constraints on the maximum volume change of the fluid-solid domain [140].  
In addition, constraints can also be introduced to promote numerical stability of the solution, or to smooth the solution when the parameters to be fit are temporal or spatial distributions, e.g., the first-order Tikhonov regularization functional has been used in the estimation of heterogeneous material parameter distributions [141].
# An Algorithm of Acoustic Emission Location for Complex Composite Structure
## 3 The Algorithm Testing
### Experimental Verification  
The travel time algorithm and its maximum relative error are compared with theoretical calculation in the 1D wave velocity structure above. However, for complex structures, theoretical calculations are too difficult. Therefore, we carry out an experiment to verify the algorithm.  
The sample is composed of three materials, including copper, aluminum, and steel (Figure 5). The P-wave velocity of copper, aluminum, and steel is 4394 m/s, 6305 m/s and 5918 m/s, respectively. The dimension of the sample is 100 mm \(\times\) 100 mm \(\times\) 100 mm and is divided into 1,000,000 regular cubic cells. By doing this, the side length of each cell is 1 mm. A piezoelectric ceramic can generate an electric signal after receiving the vibration of the sample surface, which can be used as a receiving sensor. On the other hand,  
Figure 4: Schematic diagram showing the structure of the test model. The P-wave velocity from the top to bottom layers is 3000 m/s, 3500 m/s, 4000 m/s, 4500 m/s and 5000 m/s, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grid Spacing** & **Adjacency Radius** & **Path Correction** & **The Maximum Relative Errors** \\ \hline
2 & 5 & No & 1.43\% \\
2 & 5 & Yes & 1.28\% \\
1 & 5 & No & 1.16\% \\
1 & 5 & Yes & 0.64\% \\
0.5 & 5 & No & 1.16\% \\
0.5 & 5 & Yes & 0.61\% \\
0.5 & 7 & No & 0.50\% \\
0.5 & 7 & Yes & 0.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The maximum relative errors obtained by calculating travel time under different options.  
the piezoelectric ceramic can also be used as an excitation source by applying an electric pulse to generate vibration. In testing, we arranged 16 piezoelectric ceramic sensors on the sample surface, one of them was used as the excitation source, whereas the other 15 sensors were used for receiving AE waveforms. The diameter of each piezoelectric ceramic sensor was 8 mm. A total of 25 AE events were excited at the same location during the experiment.  
We first calculate the ray paths and travel times, and then use the time-reversal imaging method to locate the excited signals. Figure 5 shows the ray paths (white lines) obtained by correcting the travel time of ray paths from the excitation source to each sensor, which is straight inside the same block and only deflected at the block interfaces. Among them, several ray paths in the copper block are similar to the primary wave in seismology. Because the P-wave velocity in aluminum is faster than that in copper, and the first wave runs along the interface between copper and aluminum.  
The results of the 3D AE location (red dots in Figure 5) show that 25 AE sources are concentrated near the excitation point with the absolute error less than 4 mm (it is equivalent to the sensor radius). This indicates that an algorithm is effective in complex layered structures. The calculation results also show that the travel time error is acceptable for the experimental sample meshing with the cell size of 1 mm and the adjacency radius of 7 cells.
## 3 DNN-Based Synchronization and the Proposed Receiver
### Internal Structure of the DNN Model  
The DNN consists of one input layer, \(L\), hidden layers, and one output layer. We used the vectors \(\left\{\mathbf{y}_{l}\right\}_{l=0}^{L}\) to represent the value of the layers. The input layer vector is calculated using the biased timing offsets enlarged by a fixed scaler, \(\alpha_{\text{In}}\), written as:  
\[\mathbf{y}_{0}=\alpha_{\text{In}}\Big{[}\ \ \tau_{\text{B}}(0)\ \ \ \ \tau_{\text{B}}(T_{\text{Frm}})\ \ \ \ldots\ \ \ \tau_{\text{B}}(N_{\text{Frm}}T_{\text{Frm}})\ \ \Big{]}^{T}. \tag{10}\]  
Similarly, the output vector is the multiplication of the output scaler, \(\alpha_{\text{Out}}\), and the expected bias of the timing offsets, written as:  
\[\mathbf{y}_{L}=\alpha_{\text{Out}}\Big{[}\ \ \Delta(0)\ \ \ \ \Delta(T_{\text{Frm}})\ \ \ \ldots\ \ \ \Delta(N_{\text{Frm}}T_{\text{Frm}})\ \ \Big{]}^{T}. \tag{11}\]  
The generation of the internal layers can be described as follows:  
\[\mathbf{y}_{l}=f_{l}(\mathbf{w}_{l}\mathbf{y}_{l-1}+\mathbf{c}_{l}),\quad 0 \leq l<L, \tag{12}\]  
Figure 1: Deep neural network (DNN) training diagram for the timing bias estimation. MSE, mean square error.  
where \(\mathbf{w}_{l}\) and \(\mathbf{c}_{l}\) are the multiplication matrix and the additive vector for the linear transformation, respectively, and \(f_{l}(*)\) is the activation function for the nonlinear transformation. The activation function was selected as the rectified linear unit (ReLU) for the internal layers, whose expression is given by:  
\[f_{l}(x)=\max(0,x). \tag{13}\]  
The output layer is directly obtained by linear transformation without the activation function, written as:  
\[\mathbf{y}_{L}=\mathbf{w}_{L}\mathbf{y}_{L-1}+\mathbf{c}_{L}. \tag{14}\]  
The size of the internal layers and the scalers \(\alpha_{\text{In}},\ \alpha_{\text{Out}}\) are the hyper-parameters defined before the DNN's optimization. Under the given hyper-parameters, the parameters \(\left\{\mathbf{w}_{l};\mathbf{c}_{l}\right\}_{l=0}^{L}\) are optimized by minimizing the mean square error (MSE).  
The random displacement \(d(t)\) is generated by the surface wave model in Equation (4). The DNN is trained with simulation data instead of the experimental data to avoid overfitting.