esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Variable Density Incompressible Flow Model  
Let the computational domain \(\Omega\) be a subset of the \(d\)-dimensional space; the model describing an incompressible flow of a Newtonian fluid with variable density is defined by augmenting the set of incompressible Navier-Stokes equations  
\[\begin{split}\frac{\partial u_{j}}{\partial x_{j}}& =0,\\ \rho\frac{\mathrm{D}u_{i}}{\mathrm{D}t}&=-\frac{ \partial p}{\partial x_{i}}+\frac{\partial\tau_{ij}}{\partial x_{j}}+\rho g_{ i},\end{split} \tag{1}\]  
with the continuity equation  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{j}}(\rho u_{j})=0.\]  
Suitable initial and boundary conditions must be introduced to complete the definition of the variable density incompressible flow problem. Above (and in what follows) equations are written in the Einstein notation and the spatial coordinates are described by means of a Cartesian reference frame. The \(\frac{\mathrm{D}(\cdot)}{\mathrm{D}t}\) is the material derivative.  
Written in conservative form, the set of equations for the variable density incompressible (VDI) flow model reads  
\[\begin{split}\frac{\partial u_{j}}{\partial x_{j}}& =0,\\ \frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x _{j}}(\rho u_{i}u_{j})&=-\frac{\partial p}{\partial x_{i}}+ \frac{\partial\tau_{ij}}{\partial x_{j}}+\rho g_{i},\\ \frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{j}}( \rho u_{j})&=0,\end{split} \tag{2}\]  
with \(i,j=1,\ldots,d\) and  
\[\tau_{ij}=2\mu\left[\frac{1}{2}\left(\frac{\partial u_{i}}{\partial x_{j}}+ \frac{\partial u_{j}}{\partial x_{i}}\right)-\frac{1}{3}\frac{\partial u_{k}}{ \partial x_{k}}\delta_{ij}\right].\]  
The body acceleration is typically constant, e.g., gravitational acceleration, while the dynamic viscosity can be defined as a suitable function of the density \(\mu=\mu(\rho)\).  
The peculiar feature of the model in (2) lies in the simultaneous presence of the continuity equation and the divergence-free constraint, which leads to a null material derivative constraint for the density variable. In practice, the advected density variable can be employed as a phase variable. Accordingly, in the context of incompressible flow problems involving immiscible Newtonian fluids of different densities, e.g., water and air, the VDI flow model allows to discriminate different components simply by means of their different density values. Similarly, interfaces between components are identified in a diffuse fashion as smooth (and possibly sharp) variations of the density variable.
## 3 Proposed Methodology  
The workflow of the proposed methodology is depicted in Figure 1. First, in the Data Acquisition block, a comprehensive search for datasets related to botnet and IDS traffic flows is conducted by collecting those that resemble real backgrounds, but with balancing issues. Furthermore, in the Feature Examination and Labeling block, datasets are subjected to an examination process aiming to exploit features that are considered useful by most authors in several state-of-the-art approaches [41; 42; 43; 44]. Once data are normalized into a set of unique features, each sample is labeled as benign or malicious depending on the structure stated from the original data. During the Synthetic Minority Oversampling block, the percentage of minority-class samples from the training set are inspected to serve as a basis to oversample the data via synthetic production, resulting in a fully balanced training set. Subsequently, datasets are merged and split into training and testing sets. Therefore, in the Feature Extraction and Selection block, the balanced training set is preprocessed using Principal Component Analysis (PCA) [45] as the algorithm to extract and select the most informative and relevant features in a new dimensional subspace. In the Supervised Machine-Learning Classification Algorithms block, a portfolio of widely used algorithms is proposed to train the fully balanced set, thus enhancing the classification ratio through grid search, exhaustively tuning preconeceived hyperparameters. Finally, the resulting classification models are evaluated by scoring the classification outcomes (predictions) from a testing set in terms of the following performance metrics: Accuracy, Recall, and F1-Score.  
Figure 1: Workflow of proposed methodology.
###### Abstract  
In this paper, a novel photonic scheme for the generation of square and triangular waveforms is proposed and experimentally demonstrated by using a dual-drive Mach-Zehnder modulator (DDMZM) without any optical signal processing. By properly setting the modulation index of the DDMZM, the square and triangular microwave waveforms could be easily achieved in conjunction with a 90\({}^{\circ}\) hybrid coupler. Furthermore, the modulation index of the DDMZM could be tuned for a low radio frequency (RF) signal power requirement by tuning the phase difference of the RF signals applied to the two arms of the DDMZM. The proposed scheme for the photonic generation of microwave waveforms is theoretically analyzed and experimentally demonstrated. The periodical triangular and square waveforms with repetition rates of 5 GHz, 8 GHz, and 10 GHz are successfully obtained. The root-mean-square errors between the generated and theoretical triangular and square waveforms with a repetition of 5 GHz are 0.042 and 0.053, respectively.  
Keywords:square and triangular waveform generation; microwave photonics; dual-drive MZM (DDMZM); microwave arbitrary waveform generation +
Footnote †: journal: Applied Sciences
# A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces
## 1 Introduction
### Monitoring Data Characteristics
#### 1.2.1 Multi-Systematic  
The composition of a concrete dam is complex, which contains a large number of subsystems. The structure composition of a concrete dam includes the dam body, the dam foundation, and the dam near the reservoir area, and each represents a subsystem. Deformation monitoring can be divided into horizontal displacement monitoring, vertical displacement monitoring, crack opening monitoring, and each monitoring project can also be regarded as a subsystem. Different monitoring targets need different monitoring instruments to locate at different measuring points. Therefore, the monitoring information of the concrete dam deformation is multi-systematic.
## 2 Material and Method
### Descriptive analysis of the data  
The measurement process, especially its temporal distribution, leads us to 5 equally distributed time slots covering the diurnal period from 07:00 h to 22:00 h. For each of these time slots, there are 78 samples available, one per measurement location. Table 1 describes the information gathered by each node.  
Figure 3: Location of the measurement points for the plan of Algemesi (**a**); fixed nodes and moving nodes (**b**).  
Table 2 and Figure 4 show the results of the descriptive analysis of the noise level in these five temporal periods. The values in Table 2 and the box diagrams suggest the invariance of the mean value in the measured data, whose results are shown in Table 3.  
The 78 locations are divided into two sets in order to check the validity of the proposed models: a modelling set, composed of 68 locations and a validation set, which has 10 locations. Both sets are used for all of the time periods and are shown in Figure 5. In order to select the nodes of the validation set, a distribution that bounds the semivariogram error has been chosen, so the nodes are spatially distributed throughout the whole area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Description** & **Values** \\ \hline Zone & Zone Type & Industrial, Urban, Services \\ \hline Day & Week day & _M, T, W, Th, F_ \\ Hour & Hour of measure & - \\ Time period & Time period of measure & \(1=\{7-10\},\ldots,5=\{19-22\}\) \\ Long. & Geographical longitude & - \\ Lat. & Geographical latitude & - \\ \(L_{eq}\) & Equivalent noise level & - \\ Nr. l. veh & Number of light vehicles & - \\ Nr. h. veh & Number of heavy vehicles & - \\ Nr. moto & Number of motorcycles & - \\ \((Q)\) & Total amount of vehicles & - \\ \(\log(Q)\) & Logarithm of number of vehicles & - \\ Avg. speed & Average speed of vehicles & - \\ W street & Width of the street & - \\ Height.b & Height of the building at the back & - \\ Height.f & Height of the building in the front & - \\ Slope & Slope of the road & - \\ \hline \hline Pavement & Type of Pavement & Asphalt, Concrete, Paving Stone, Soil \\ \hline vParked & Number of vehicles parked & - \\ D2 Junct & Distance to next junction & - \\ Circulat. & Number of circulation directions & \(1,2\) \\ Nr. lanes & Number of useful lanes & - \\ Voices & Presence of people speaking & \(Y,N\) \\ Animals & Presence of animal sounds & \(Y,N\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables used.  
Figure 4: Box diagram of the measurements.
## 4 Material and Methods
### LC-MS/MS Analysis of GPP, FPP, and GGPP  
Enzyme products were analyzed using an Agilent 1200 HPLC system (Agilent Technologies, Santa Clara, CA, USA) coupled to an API 3200 triple-quadrupole mass spectrometer (Applied Biosystems, Foster City, CA, USA). For separation, a ZORBAX Extended C-18 column (1.8 \(\upmu\)m, 50 mm \(\times\) 4.6 mm; Agilent Technologies, Santa Clara, CA, USA) was used. The mobile phase consisted of 5 mM ammonium bicarbonate in water as solvent A and acetonitrile as solvent B, with the flow rate set at 0.8 mL/min and the column temperature kept at 20 \({}^{\circ}\)C. Separation was achieved by using a gradient starting at 0% B (_y_/_v_), increasing to 10% B in 2 min, 64% B in 12 min, and 100% B in 2 min (1-min hold), followed by a change to 0% B in 1 min (5-min hold) before the next injection. The injection volume for samples and standards was 3 \(\upmu\)L and the autosampler temperature was 4 \({}^{\circ}\)C. The mass spectrometer was used in  
\begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Sequence** & **Usage** \\ \hline PtGPPS1-fwd & ATGGTAGGTCTCACGCGCATGGCAGATCTG & cloning \\ PtFPPS1-rev & ATGGCTACATCATATTCTGCCTTGMAATTTTAGCC & cloning \\ PtGPPS1.LSU-fwd & ATGGTAGGTCTCCAAATCCATTTGATTCA & cloning \\ PtGPPS1.LSU-lev & ATGGATCTGCAAACCGCCTTTTGCGTATTACAACTTGATT & cloning \\ PtGPPS2.LSU-fwd & ATGGTAACTCTGAAAATCCCTTTTGAGAGTTTAT & cloning \\ PtGPPS2.LSU-lev & ATGGTAACCTGATGGCGTTTTTGCCTGGAACCATTGATT & cloning \\ PtGPPS.SSU1-fwd & ATGGTAGGTCTCAAAATGCGAACTTGCCAACTACTAC & cloning \\ PtGPPS.SSU1-rev & ATGGTAGGTCTCACAACTACCGCGCGAAGTGTGTTCCA & cloning \\ PtGPPS.SSU2-fwd & ATGGTAGGTCTCACAACCACCGAATTGAACC & cloning \\ PtGPPS.SSU2-rev & ATGGGTGTCCAACGCGCGCTTTTTTGC & qRT-PCR \\ PtGPPS1-rev1 & TGAGTAGTAGGAGGCTTCTG & qRT-PCR \\ PtGPPS1.LSU-fwd1 & CGGTCTCCCACATAATG & qRT-PCR \\ PtGPPS1.LSU-rev1 & GGTCTGCAAACAGTGTGTGTGTG & qRT-PCR \\ PtGPPS2.LSU-lev1 & TCCAATAACCCTCCCGGTATC & qRT-PCR \\ PtGPPS.SSU1-fwd1 & TGCCTCAGGCTTGTGTGTGTGTGTAG & qRT-PCR \\ PtGPPS.SSU1-rev1 & ACCCATACCACTCTCCTGTTAG & qRT-PCR \\ PtGPPS.SSU2-fwd1 & GATTGCGTAGCCGGTGTCCAAG & qRT-PCR \\ PtGPPS.SSU2-rev1 & CCTCCGCTACCTCTATAGCC & qRT-PCR \\ \hline \end{tabular}
\end{table}
Table 1: Oligonucleotides used for isolation (cloning) and qRT-PCR analysis (qRT-PCR) of poplar _trans_-IDS genes.  
the negative electrospray ionization (EI) mode. Optimal settings were determined using standards. Levels of ion source gases 1 and 2 were set at 60 and 70 psi, respectively, with a temperature of 700 \({}^{\circ}\)C. Curtain gas was set at 30 psi and collision gas was set at 7 psi, with all gases being nitrogen. Ion spray voltage was maintained at \(-\)4200 V. Multiple-reaction monitoring (MRM) was used to monitor analyte parent ion-to-product ion formation: _m/z_ 312.9/79 for GPP, _m/z_ 380.9/79 for FPP, and _m/z_ 449/79 for GGPP. Data analysis was performed using Analyst Software 1.6 Build 3773 (Applied Biosystems, Foster City, CA, USA).
## 3 Sampling and Methodology
### NMR Methodologies
#### 3.3.2 NMR T\({}_{2}\) Spectral Parameters  
NMR \(T_{2}\) spectra contain essential information on the petrophysical properties, pore size distribution, and fluid occurrence states. Therefore, NMR \(T_{2}\) spectra are vital for pore structure evaluation [50; 51]. In addition to the Free Fluid Index (FFI), Bulk Volume of Immovable Fluid (BVI), NMR porosity, and \(T_{2\text{cutoff}}\), a number of quantitative parameters can be extracted from \(T_{2}\) spectral analysis, including \(T_{2\text{gm}}\), \(T_{2}\) arithmetic mean value (\(T_{2\text{ave}}\)), \(T_{2\text{max}}\), median \(T_{2}\) relaxation time (\(T_{2\text{mid}}\)), sorting coefficient (\(\sigma_{NMR}\)), and skewness (\(S_{\text{kp}}\)) [41; 43]. Among them, \(T_{2\text{max}}\) is associated with the NMR response corresponding to the maximum pore size. \(T_{2\text{mid}}\) is defined as the \(T_{2}\) relaxation time corresponding to 50% of the total NMR porosity under a 100% water-saturated state. It is a vital parameter for pore size evaluation. \(T_{2\text{-peak}}\) is the \(T_{2}\) relaxation time associated with the highest peak of the NMR \(T_{2}\) spectra. \(\varphi_{\text{i-max}}\) is the highest interval porosity, which is indicative of high content of pore fluid and porosity contribution. The graphical representations of various quantitative parameters in an NMR \(T_{2}\) spectrum are shown in Figure A2.  
\(T_{2\text{gm}}\) and \(T_{2\text{ave}}\) can be used to characterize the average \(T_{2}\) relaxation time [52]. The calculation equations are shown in Equations (4) and (5).  
\[T_{2 gm}=\left(\prod_{i=1}^{N}T_{2i}\varphi_{i}\right)^{\frac{1}{\sigma_{mm}}} \tag{4}\]  
\[T_{2\text{ave}}=\frac{\sum\limits_{i=1}^{N}T_{2i}\varphi_{i}}{\varphi_{mmr}} \tag{5}\]  
The sorting coefficient (\(\sigma_{NMR}\)) and skewness (\(S_{\text{kp}}\)) can also be derived from the morphological analysis of NMR \(T_{2}\) spectra. Among them, \(\sigma_{\text{NMR}}\) describes the dispersion degree of the \(T_{2}\) relaxation time centered on \(T_{2\text{ave}}\) (Equation (6)). A small sorting coefficient indicates a uniformity distribution of the \(T_{2}\) relaxation time.  
\[\sigma_{\text{NMR}}=\sqrt{\frac{\sum(T_{2i}-T_{2\text{ave}})^{2}}{\varphi_{ mmr}}} \tag{6}\]  
The asymmetric distribution of the \(T_{2}\) relaxation time can be described by \(S_{\text{kp}}\), and the variation range of \(S_{\text{kp}}\) is [-1,1]. A positive value suggests that the sample is dominated by a short \(T_{2}\) relaxation time, while a negative value implies a high proportion of long \(T_{2}\) relaxation time (Equation (7)).  
\[S_{\text{kp}}=\frac{\varphi_{84}+\varphi_{16}-2\varphi_{50}}{2(\varphi_{84}- \varphi_{16})}+\frac{\varphi_{95}+\varphi_{5}-2\varphi_{50}}{2(\varphi_{95}- \varphi_{5})} \tag{7}\]
# Permeability and Stiffness Assessment of Paved and Unpaved Roads with Geocomposite Drainage Layers
## 5 In Situ Permeability and Stiffness of The Test Sections  
### In Situ Permeability Assessment  
The CHP test was conducted on the PCC and WMA sections to compare the subsurface drainage conditions of the GC-1 geocomposite layer and conventional base materials. The CHP test results showed that the in situ hydraulic conductivity of the GC-1 layer varied between 637 and 695 m/day with a coefficient of variation (COV) of 5.8%, whereas that of the crushed limestone base layer was between 0.1 and 34.2 m/day with a COV of 110%. The hydraulic conductivity of the geocomposite varied within a much smaller range compared to the base material, which may indicate that the segregation of the traditional base material can cause significant variation in the drainage performance of roadway systems.  
The APT device was used to quantify the improvement in drainage offered by the geocomposite layer (GC-2) for the unpaved road test sections. For each test location, APTs were performed at different depths within the overlying aggregate layer to determine the variation of saturated hydraulic conductivity (K\({}_{\text{sat}}\)) with depth. The test results are summarized in Figure 7. For the control section, the K\({}_{\text{sat}}\) values at the three test locations were similar (-19 m/day) and remained relatively constant with depth. However, for the geocomposite section, K\({}_{\text{sat}}\) in the surface aggregate layer increased consistently with depth from the roadway surface. The K\({}_{\text{sat}}\) of the GC-2 drainage layer is approximately 1920 m/day, which is more than two orders of magnitude greater than that of the control section. The field-measured K\({}_{\text{sat}}\) of the GC-2 agreed very well with the HPT test results shown in Figure 6.
## 4 Experiments and Evaluation
### Real Scenario  
#### 4.1.1 Basement Underperformance  
Figure 8 shows results of PTing of the lighting in the basement from February 2016 until the beginning of January 2017. The initial threshold was obtained using the BE10 model, as a function of the light electricity consumption per m\({}^{2}\), multiplied by the total area of the basement which is 1818 m\({}^{2}\). Thus, we get the initial threshold of 587,000 Wh/14 days. To factor in errors, we deliberately overestimate by adding 10% to the original threshold, achieving a value of 645,000 Wh/14 days. The results show a significant amount of data points that are higher than the threshold.  
As these failed performance tests might indicate a potential underperforming component or errors of different nature, two of the procedures in the Implementation Section were applied. The theoretical limit (Section 2) was calculated to discover whether the results from the test were higher than this limit. This calculation was done by querying each light component in the basement from the Brick model using the query from Figure 9 and summing up their wattages which is 3355W. We then calculated that the theoretical threshold is 1,127,000 Wh/14 days, assuming every lamp being on for the entire period. Considering that there are still many data points that are above the theoretical limit, we concluded that there were additional components being measured by the meter that the PT was not taking into account. This implies either an incorrectly configured PT as its configuration does not reflect reality or a physical miswiring error.  
Figure 8: Plots of performance test results.  
To further investigate these additional components, the query from Figure 10 was executed to the Brick model to examine the components that were being measured by the basement light meter, according to the Procedure to discover location of additional sources in Section 2.3.2. The query resulted in 117 components, 8 of which were found to be emergency lighting (which were not on during the timeframe under analysis). Further processing revealed that two of the 108 remaining components were not associated with a specific room.  
Reviewing the technical documents revealed the existence of a green wall and outdoor lighting components that were measured by the same meter. With this discovery, the reason for the underperformance of the basement was found to be a miswiring issue. After alerting the building managers, the outdoor lighting and the green wall were wired to a separate meter. The PT and the metadata model were changed accordingly.  
An alternative path for investigation could have been to apply the Procedure to identify an underperforming area. However, considering that the basement did not have any submeters for the lighting, the requirements for this procedure were unfulfilled.
## 2 Principles
### Inversion Algorithm  
Building upon Equation (6), to encode the high-frequency information from the \(m=1\) order frequency-shifted component into the low-frequency regime of the fundamental component \(I_{0}(\Delta)\), the FPI must satisfy \(\Delta_{FPI}\approx L\). Typically, the amplitude of interferogram \(I_{0}(\Delta)\) diminishes with increasing \(\Delta\), while the coefficients \(a_{m}\) progressively decrease at higher \(m\) values. Therefore, for the algorithmic simplification of Equation (6), components beyond \(m=0\), \(\pm 1\), and \(2\) can be neglected within the \(\Delta\in[0,L]\) domain due to their negligible energy contributions. Under this approximation, \(I_{2}(\Delta)\) can be expressed as follows:  
\[I_{2}(\Delta)=a_{0}I_{0}(\Delta)+\frac{1}{2}a_{1}I_{0}(\Delta_{FPI}-\Delta)+ \frac{1}{2}a_{1}I_{0}(\Delta+\Delta_{FPI})+\frac{1}{2}a_{2}I_{0}(2\Delta_{FPI }-\Delta). \tag{7}\]  
\(I_{2}(\Delta)\) arises from the linear superposition of the DC component and frequency-shifted components. To reconstruct \(I_{0}(\Delta)\), a system of linear equations relating \(I_{0}(\Delta)\) to both \(I_{1}(\Delta)\) and \(I_{2}(\Delta)\) must be formulated. Based on Equation (7), the following linear system can be established.  
\[\begin{bmatrix}I_{1}\\ I_{2}\end{bmatrix}=\begin{bmatrix}1&0\\ A_{1}&A_{2}\end{bmatrix}I_{0}=\text{A}I_{0}. \tag{8}\]  
In the equation, \(I_{1}\), \(I_{2}\in\mathbb{R}^{N}\) represent N-dimensional column vectors composed of discrete data points from \(I_{1}(\Delta)\) and \(I_{2}(\Delta)\) over \(\Delta\in[0,L]\), respectively. \(I_{0}\in\mathbb{R}^{2N}\) denotes a 2N-dimensional column vector constructed from \(I_{0}(\Delta)\) across \(\Delta\in[0,2L]\). The composite coefficient matrix \(A=[A_{1}\ A_{2}]\in\mathbb{R}^{N\times 2N}\) is derived from Equation (7), where A encapsulates the full-system coupling relationships. The computed \(I_{0}\) enables twofold spectral super-resolution by effectively doubling the OPD range. To mitigate the ill-posed nature of this inverse problem, Equation (8) is reformulated as the following optimization framework:  
\[\text{min }\|\text{A}I_{0}-I\|_{2}+\lambda\|I_{0}\|_{2}, \tag{9}\]  
where \(I=[I_{1};\ I_{2}]\) represents the vertically concatenated measurement vector, where \(\|\text{A}I_{0}-I\|_{2}\) constitutes the error term, and \(\lambda\|I_{0}\|_{2}\) serves as the regularization term to mitigate ill-posedness and measurement uncertainties, with \(\lambda\) denoting the regularization coefficient that balances the error and regularization terms.  
The system presented in this study achieves a twofold extension of the maximum OPD for \(I_{0}\) through the synergistic utilization of two interferograms \(I_{1}\) and \(I_{2}\), thereby achieving twofold spectral super-resolution. Theoretically, this approach can be extended by implementing additional optical paths or utilizing tunable FPIs to acquire more interferograms, thereby enabling threefold or multifold spectral super-resolution through the establishment of higher-dimensional linear systems of equations using the same data processing procedure as applied to \(I_{2}\).