esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
It implies that when \(h\) is 15 cm the maximum value of \(a_{2}\) reaches 3.47 m/s\({}^{2}\) and 6.17 m/s\({}^{2}\) when the vehicle passes over the crest with a speed of 30 km/h and 40 km/h, respectively;
* \(i_{max}\) increases with the increase of \(h\) (i.e., the height of the sidewalk) regardless of the adopted criterion (Figures 16 and 17);
* Experimental data show that the greater the longitudinal slope \(i_{a}\) is, the lower the average speed will be (Figure 14). However, RPCs do not have (if properly designed) traffic-calming effects because they are the only features that allow pedestrians to cross the road without having to step onto the road itself;
* In regards to the Italian standards, less than 9% of the surveyed RPCs fit the criteria to design roads (i.e., site 2 and 3), while more than 90% of them comply with the traffic-calming criterion for designing speed tables (site 1a and 1b induce a vertical acceleration on occupants higher than the maximum value permitted for speed table features);
* The lack of regulation on geometrical and functional criteria for designing these raised pedestrian crossings result in an extreme variation: Effectiveness of these treatments is not guaranteed. The observed differences in terms of geometrical characteristics of longitudinal profiles and induced vertical accelerations highlight that two different approaches could be pursued to design RPCs, and they cannot be mixed:  
* Raised devices to enhance accessibility at crossing: The longitudinal profile of the RPC should comply with geometrical and functional criteria for designing roads. According to the Italian standard [47], the minimum vertical radius of crest and sag curves is 20 m and 40 m, respectively; the maximum the maximum vertical acceleration induced to vehicle occupants is 0.6 m/s\({}^{2}\). Therefore, this device has no application limits related to road type;
* Speed tables to reduce vehicle speeds (and to enhance the pedestrian environment) at crossings: It is not appropriate for bus routes or emergency routes because of the vertical accelerations induced for vehicle occupants. SCUs are traffic-calming units and aim to reduce noise and pollution from traffic in residential, local, or collector roads where the administrative speed limit is lowered compared to the ordinary administrative speed limit.
## 3 Materials and Methods
### Petrophysical Analysis  
Petrophysical analysis, also known as reservoir sensitivity parameter analysis, aims to identify one or more logging parameters that can effectively distinguish reservoirs by analyzing the distinct features displayed by different lithofacies on logging curves [73]. This provides geological guidance for the selection of pseudo-wave impedance reconstruction and well-seismic combined reservoir inversion methods, serving as an essential foundational task for reservoir prediction. Due to the myriad types of logging curves, it is crucial to conduct sensitivity analyses to determine which curve is the most effective at differentiating between reservoir and non-reservoir zones. This is typically accomplished using techniques such as histograms and cross-plots [74; 75].  
The lithological types prevalent in the study area include sandstone, siltstone, mudstone, limestone, and dolomite, among others. A single log curve is often insufficient for effective reservoir identification. Therefore, a combination of multiple logging parameters is necessary for the comprehensive identification of high-quality reservoirs [76]. Analysis of a single well reveals that the thin-layered sandstone in the lower second sub-member of the Dongying Formation is characterized by low acoustic wave velocity, low gamma radiation, and relatively high resistivity. Petrophysical analysis indicates that the resistivity and gamma measurements for the Dongying Formation do not effectively identify sandstone (Figure 6a,b). In contrast, the constructed lithology indicator curve successfully identifies sandstone, with a sandstone cutoff value of less than 83 (Figure 6c).  
Figure 5: Consistency processing of DT/GR Density and Gamma logging curves.  
Based on the results from the petrophysical crossplot analysis, the reconstructed lithology curve can effectively differentiate sandstone from other lithologies. Vertically, it can resolve sand bodies as thin as 5 to 10 m (or even thinner), while horizontally, it clearly delineates the boundaries and stacking relationships of sand bodies. The reconstructed curve is capable of distinguishing between sandstone and mudstone effectively [77, 78]. Building on this, seismic waveform-based inversion mechanisms are employed for predictions. By incorporating the concept of well-seismic integration, high-frequency information from the well is extracted to enhance the predictive accuracy for thin layers. This method facilitates the quantitative prediction of the distribution of thin-layer sandstones [79].
## 3 Models for Parameters' Identification and UFLS Schemes' Optimization
### Optimal UFLS Scheme Design  
Since the UFLS schemes are desired to be robust and efficient, a quantitative set of design criteria and quantitative performance criteria are considered, shown in the following [9]:  
1. Number of load shedding stages, \(k_{min}\leq k\leq k_{max}\);
2. Limits on frequency thresholds, \(f_{th,min}\leq f_{th}^{j}\leq f_{th,max}\);  
Figure 7: The concept of modification of a searching point by Particle Swarm Optimization (PSO).  
3. Range of load shedding sizes for each stage, \(\Delta d_{min}\leq\Delta d^{j}\leq\Delta d_{max}\);
4. Range of time delay for each stage, \(t_{d\_min}\leq t_{d}^{j}\leq t_{d\_max}\);
5. Margin between two consecutive UFLS threshold frequencies, \(f_{th}^{j}-f_{th}^{j+1}\geq f_{\varepsilon}\);
6. Minimum steady state frequency, \(f_{ss}\geq f_{ss\_min}\);
7. Minimum transient frequency, \(f_{ts}^{min}\geq f_{ts\_min}\);
8. Maximum transient frequency, \(f_{ts}^{min}\leq f_{ts\_max}\);  
Thus, the design of the UFLS can be configured into a framework optimization in which the objective function captures the trade-off between the frequency deviations and the amount of the total load shed. Then the optimization model for the robust UFLS scheme design can be described as follows:  
\[\text{min}F(Z)=C_{e}\cdot\int_{t}\frac{1}{2}\Delta f(t)^{2}dt+C_{ls}\cdot\sum_ {j=1}^{k}\Delta d^{j}, \tag{7}\]  
\[\left[\begin{array}{c}f_{th\_min}\\ \Delta d_{\text{min}}\\ t_{d\_min}\end{array}\right]\leq Z\leq\left[\begin{array}{c}f_{th\_max}\\ \Delta d_{\text{max}}\\ t_{d\_max}\end{array}\right], \tag{8}\]  
\[\left[\begin{array}{cccc|cccc|cccc}-1&1&\ldots&0&0\\ 0&-1&1&\ldots&0\\ \ldots&\ldots&\ldots&\ldots&\ldots\\ 0&0&\ldots&-1&1\\ \hline 0&0&\ldots&0&0\\ \end{array}\right]\begin{array}{c}0&\ldots&0\\ 0&\ldots&0\\ \ldots&\ldots&0\\ 0&\ldots&0\\ \end{array}\right]\cdot Z\leq\left[\begin{array}{c}-f_{\varepsilon}\\ \ldots\\ \ldots\\ -f_{\varepsilon}\\ \hline\Delta d_{\text{max}}^{s}\end{array}\right] \tag{9}\]  
\[\left[\begin{array}{c}f_{ss\_min}-f_{ss}\\ f_{ts\_min}-f_{ts}^{\text{min}}\\ f_{ts}^{\text{max}}-f_{ts\_max}\end{array}\right]\leq 0, \tag{10}\]  
The trade-off between minimizing the frequency deviation and the amount of the total load needed to be shed is captured in the developed objective function, shown as Equation (4). \(C_{e}\) and \(C_{ls}\) are the constant cost coefficient of system energy and load shed, respectively.  
As stated in the above description, the design of UFLS schemes can be defined as a standard nonlinear constrained optimization model. The PSO algorithm is applied to solve the optimization problem using Equations (4)-(7).
## 4 Model Development
### GEP Predictive Model  
The GEP, a class of machine learning tools developed by [PERSON] [61], is extensively used in the literature for developing robust predictive models in several engineering fields [16; 62]. Also, many researchers have employed it as a hybrid predictive tool along with other machine learning approaches [45; 46; 47; 48] due to its main feature of providing the model as an expression tree (ET), which can be converted into a simple mathematical equation. An ET of a GEP model consists of genes and chromosomes of constant length, where the genes are connected together by a predefined linking function, including addition, subtraction, multiplication, or division. Each gene is composed of a head and tails. The head is a mix of numbers, variables, and mathematical functions, whereas the tails include numbers and variables. More details on the GEP approach can be referred to in references [61; 63].  
In this study, GeneXproTools was utilized to generate the GEP model employing the combined datasets, as illustrated earlier. The datasets were divided randomly by the software into a training dataset (75%) and a validation dataset (25%). Many previous studies indicated no standard method for selecting the GEP setting parameters as it is based on the trial-and-error approach. However, some common parameters play an essential role in the model's performance, including the linking function, head size, number of chromosomes, and number of used genes [16; 64; 65]. Therefore, the model was developed after several iterations of trial and error to achieve the best-fitted model with the highest R\({}^{2}\) value and the fewest errors for both the training and validating datasets.  
Table 5 displays the ideal setting parameters that were employed in the proposed GEP model. The resulting ET of the model is shown in Figure 7. The notations \(d0,d1,d2\), and \(d3\) represent, in sequence, the input variables F, Ab, AC, and Va. The constants in the first gene (Sub-ET 1) are C\(7=-8.764,\text{C}5=-4.925,\text{C}1=-2.493\); in the second gene (Sub-ET 2), the constants are C\(1=-1.443,\text{C}0=9.396,\text{C}7=-9.768\); and in the third gene (Sub-ET 3), the constant is C\(9=-5.649\). The resulting tree has been simplified as a simple mathematical equation (Equation (2)) for predicting RSI values.  
\[\text{RSI}=A+B+C \tag{2}\]  
\[A=\left(\text{Va}\times\left(\left(\left(\frac{\text{Va}}{-8.764}\right)+( \text{AC}-4.925)\right)\times\tanh(\text{Va}-2.493)\right)\right)\]  
\[B=\left(\left(-1.443\times\text{Ab}\right)-(\text{AC}-91.778)\right)\]  
\[C=\left(\left(\left(\frac{-(\text{AC}+5.649)}{\text{AC}^{2}}\right)\times \text{F}\right)+\text{Va}\right)\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Linking Function** & **Addition** \\ \hline Number of genes & 3 \\ \hline Function selections & \(+,-,\times,\div,\tanh(x)\), \(x^{2},-x\) \\ \hline Head size & 8 \\ \hline Number of chromosomes & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: GEP parameters.
## 3 Staff Detection and Tracking System
### Pre-Processing for Staff Tracking  
The first stage guarantees that the input image for the rest of the process is a binary one with black background so that the morphological operators and counting stages perform as expected. Thus, the output images from the Pixelwise scheme, \(I(n,m)\), containing pieces of the staff lines are binarized by making use of the [PERSON]'s method [23]:  
\[I_{b}(n,m)=O_{b}(I(n,m)) \tag{1}\]  
where \(O_{b}(\cdot)\) stands for [PERSON]'s binarization scheme. Note that this is not a main stage because the data have been previously processed [5]. Nevertheless, since we need binary images as input, such a process needs to be carried out.  
The rest of the pre-processing scheme is aimed at removing small unconnected detections. To this end, the objects in the image were observed. Ideally, the Pixelwise algorithm removes all elements in the score and should return a set of unconnected broken staff segments, however, numerous other artifacts remain. Nevertheless, we expect each staff line piece to have the structure of a line segment of a minimum size built upon pixels connected in vertical, horizontal or diagonal (8-connected). This minimum size was defined as an area of \(\lambda=500\) pixels; this is approximately 20% of the area of the smallest element removed by the previous scheme, at the working resolution. This parameter is employed to apply an area opening algorithm [24], \(\gamma(\cdot)\), on the image:  
\[I_{o}(n,m)=\gamma(I_{b}(n,m),\lambda) \tag{2}\]  
to remove small artifacts. Thus, \(I_{o}(n,m)\) contains all the 8-connected components in \(I_{b}(n,m)\) with area larger than \(\lambda\).  
Then, a morphological closing step [25] with a disc-shaped structuring element of the same area as in the area opening stage, \(\lambda\), is performed [26]:  
\[I_{p}(n,m)=I_{o}(n,m)\bullet D \tag{3}\]  
where \(\bullet\) denotes the morphological closing operation and \(D\) the disc-shaped structuring element of area \(\lambda\).  
Figure 3 shows an illustration of the result of the application of these steps. The resulting image contains much fewer artifacts than the original one, although some small staff line segments can be missed.  
Note that, actually, there is no strict need for the area opening and morphological closing steps, however, their usage helps to obtain better estimations of the staff lines since they are less likely distorted  
Figure 2: General scheme of the staff reconstruction system.  
due to segments of letters or drawings not removed by the object detection algorithm. Figure 4 illustrates the influence of spurious pieces of objects left by the previous algorithm on the staff tracking scheme.
# Modeling and NVH Analysis of a Full Engine Dynamic Model with Valve Train System
## 4 Experimental Setup  
Noise and vibration verification experiments of the engine are conducted with an AVL electric dynamometer of type 204/8 SL in a semi-anechoic room. The sound pressure level of background noise is below 25 dB(A) in the semi-anechoic room. As shown in Figure 18, five noise measuring points are arranged around the engine, following ISO 3744-2010. The distance between the microphones and the corresponding end faces of the engine is set to 1 m. Three vibration measuring points are arranged on the surface of the intake side of the cylinder head, as shown in Figure 19. All the microphones of type PCB 378B02 and acceleration sensors of type PCB 356A16 are connected to the LMS SCADAS data acquisition front-end after calibration. The signal acquisition and analysis software is LMS Test.Lab. The sampling frequency is 20,480 Hz, and the sampling time is 5 s. Measure twice and take the average value for each working condition. During the experiments, the water temperature was guaranteed to keep in a normal state, and the intake, exhaust, and coolant pipes were all properly led out, so the influences of external factors on the experiments were minimized.  
Figure 17: The positions of sound level meters in the acoustic prediction model.  
Figure 18: The positions of the microphones in the noise experiments.  
The sound pressure in pascals is measured. The A-weighted sound pressure level is expressed in decibels:  
\[L_{pA}=10\lg\frac{p^{2}}{p_{0}^{2}}, \tag{18}\]  
where the reference value \(p_{0}=2\times 10^{-5}\) Pa.  
The A-weighted sound power level is expressed in decibels:  
\[L_{WA}=10\lg\left[\frac{1}{N}\sum_{i=1}^{N}10^{0.1L_{pA}i}\right]+10\lg\left( \frac{S_{1}}{S_{0}}\right), \tag{19}\]  
where \(N\) is the number of measuring points, \(L_{pA}i\) is the A-weighted sound pressure level at the \(i\)th measuring point, \(S_{1}\) is the area of the measuring surface, and \(S_{0}=1\) m\({}^{2}\).
## Appendix A Jacobi Integral Properties
### Expressions in Terms of Osculating Elements  
It may be useful to express the Jacobi integral via the osculating elements relative to one of the two primaries. To do this, we need to change the state variables by transforming to the inertial frame centered at the primary of interest, with its axes instantaneously parallel to the CR3 BP rotating frame axes. For instance, the inertial coordinates \((X,Y,Z,\dot{X},\dot{Y},\dot{Z})\) relative to the minor primary are related to the rotating frame coordinates as follows:  
\[x=1-\mu+X,\,\,\,y=Y,\,\,\,z=Z, \tag{17}\]  
\[\dot{x}=\dot{X}+Y,\,\,\,\,\dot{y}=\dot{Y}-X,\,\,\,\,z=\dot{Z}. \tag{18}\]  
Therefore, \(x^{2}+y^{2}=X^{2}+Y^{2}+(1-\mu)^{2}+2X(1-\mu)\) and \(v^{2}=V^{2}+2Y\dot{X}-2X\dot{Y}+X^{2}+Y^{2}\), where \(V^{2}=\dot{X}^{2}+\dot{Y}^{2}+\dot{Z}^{2}\). Substituting to Equation (10) yields  
\[J=2W_{z}-2E+(1-\mu)\Bigg{(}1+2X+\frac{2}{\sqrt{(1+X)^{2}+Y^{2}+Z^{2}}}\Bigg{)}. \tag{19}\]  
Here we denote by \(W_{z}=X\dot{Y}-Y\dot{X}\) the \(Z\)-component of the orbital angular momentum and \(E=V^{2}/2-\mu/r_{2}\) is the Keplerian energy relative to the minor body. When the spacecraft is close to this body,  
\[J\approx 3\,(1-\mu)+2W_{z}-2E \tag{20}\]up to second-order terms. If necessary, \(W_{z}\) and \(E\) can be expressed via the osculating values of semimajor axis, eccentricity, and inclination: \(W_{z}=\sqrt{\mu a(1-e^{2})}\cos i\), \(E=-\mu/2a\).  
When the spacecraft is far from the minor primary, it is better to introduce the inertial frame centered at the major primary. Keeping the same notation \((X,Y,Z,\dot{X},\dot{Y},\dot{Z})\) for such a frame and doing similar derivations gives  
\[J=2W_{z}-2E+\mu\Bigg{(}1-2X+\frac{2}{\sqrt{(1-X)^{2}+Y^{2}+Z^{2}}}\Bigg{)}. \tag{111}\]  
This time, \(W_{z}=\sqrt{(1-\mu)a(1-e^{2})}\cos i\), \(E=-(1-\mu)/2a\) and the orbital elements should be taken with respect to the major body. Neglecting the terms of first order in \(\mu\), we arrive at the well-known Tisserand formula  
\[J\approx 2W_{z}-2E\approx 1/a+2\sqrt{a(1-e^{2})}\,\cos i. \tag{112}\]  
In the important case when the major body is the Earth and a departure impulse is applied in a low-Earth parking orbit of geocentric radius \(r_{p}=R_{\oplus}+h_{p}\), the Tisserand formula becomes too inaccurate and should be replaced by  
\[J\approx 3\mu+2W_{z}-2E=3\mu+2r_{p}(V_{p}+\Delta V)\cos i-(V_{p}+\Delta V)^{2 }+\frac{2(1-\mu)}{r_{p}}, \tag{113}\]  
where \(V_{p}=\sqrt{(1-\mu)/r_{p}}\) and \(\Delta V\) is the departure impulse magnitude. Using this equation, the launch energy  
\[C_{3}=2E=(V_{p}+\Delta V)^{2}-\frac{2(1-\mu)}{r_{p}} \tag{114}\]  
can be expressed through \(J\) as  
\[C_{3}\approx 3\mu-J+2W_{z}\approx 3\mu-J+2r_{p}\sqrt{3\mu-J+\frac{2(1-\mu)}{r _{p}}}\,\cos i. \tag{115}\]  
The resulting value is almost insensitive to a specific choice of the parking orbit altitude \(h_{p}\): a good estimate of \(C_{3}\) can be even obtained by substituting the Earth's radius \(R_{\oplus}\) in place of \(r_{p}\).
## 2 Materials and Methods
### Mass Spectrometry  
Capillary high-performance liquid chromatography (Thermo Scientific EASY-nLC 1000 System, Nano HPLC, Waltham, MA, USA) was performed using the following equipment: mobile phase A: 100% ultrapure water, 0.1% formic acid; mobile phase B: 100% acetonitrile, 0.1% formic acid; (formic acid no. 56302, methanol no. 14262); pre-column: Acclaim PepMap100 column (2 cm \(\times\) 100 \(\upmu\)m, C18, 3 \(\upmu\)m); column: EASY-Spray column (12 cm \(\times\) 150 \(\upmu\)m, C18, 1.9 \(\upmu\)m); injection vials (Thermo, 11190533); cap (Thermo, 11150635); spray needle (Thermo, PN: ES542). Experimental procedure: The lyophilized components were reconstituted with 20 \(\upmu\)L of 2% methanol and 0.1% formic acid, followed by centrifugation at 12,000 rpm for 10 min, and then the supernatant was aspirated. The volume of sample was 10 \(\upmu\)L, and then auto-sampling was performed. The separation flow rate of the loading pump was 300 nL/min for 15 min, and the separation gradient is shown in Table S2A. Mass Spectrometry System (Thermo, Model: Orbitrap Fusion): Ion source parameters: spray voltage 2.2 kV; capillary temperature 350 \({}^{\circ}\)C; ion source: NSI. Full MS: resolution: 120,000 FWHM; full-scan AGC target: 2 \(\times\) 10\({}^{\circ}\); full scan max IT: 50 ms; scan range: 350-1550 m/z. dd-MS2: resolution: 30,000 FWHM; AGC target: 5 \(\times\) 10\({}^{\circ}\); max IT: 50 ms; fragmentation Methods: HCD.  
In this experiment, label-free Data-Dependent Acquisition (DDA) quantitative proteomic technology was employed for mass spectrometry [24]. Proteomics studies using a DDA scanning mode can obtain primary mass spectra and fragmentation information. However, this mode does not collect fragment ions from all parent ions; it collects the corresponding fragment ions by selecting specific parent ions into the collision cell. The limited DDA acquisition rate will cause a loss of quantification of low-abundance peptides [25; 26]. Fundamentally, the mass spectrometry method involves matching experimental spectra with theoretical spectra in the database to obtain potential peptide sequences. Maxquant (v2.2.0.0; [[https://maxquant.net/](https://maxquant.net/)]([https://maxquant.net/](https://maxquant.net/)), accessed on 10 September 2021) [27] was utilized to construct the transcriptome database and process the raw mass spectrometry files generated via label-free quantification (LFQ). We uploaded all raw data and set the grouping. On the Global Parameters screen, we clicked Sequences to add database files to be checked, set all parameters, and began searching the database. The LFQ algorithm first identified the peptide signals in the LCMS data, and then completed the qualitative data by performing a database search for all peptide signals in MS2 using the built-in Andromeda algorithm, with the specific parameter settings shown in Table S2B. The MS2 data were analyzed using Proteome Discoverer (v1.4; Thermo Fisher, Waltham, MA, USA) to quantify the peak intensity values of the peptide reporter ions (Table S2C, Figure S2).
# Fault Imaging of Seismic Data Based on a Modified U-Net with Dilated Convolution
## 4 Loss Function  
The neural network will produce deviation between prediction and reality during training, and the deviation value is represented by loss function. During the training, the stochastic gradient descent (SGD) algorithm is used to update the network parameters and  
Figure 5: The architecture of GSM.  
Figure 6: The architecture of the modified U-Net.  
reduce the value of the loss function, so that the prediction and the actual convergence gradually, tend to be consistent [26]. The final result of neural network output is fault probability body, where 1 represents fault and 0 represents non-fault. In this study, fault recognition is regarded as a binary segmentation task. In the fault probability body, the most part is non-fault, and the least part (less than 10%) has a value of 1. There are strong data imbalance and uneven fault distribution area. In this case, the binary cross entropy loss (BCE) function is most often used [6; 27]. Dice loss function is commonly used to serve the segmentation and recognition tasks of small-scale targets in medical research [28]. In this research, BCE and Dice are combined to solve problems such as data imbalance, uneven fault distribution area and insufficient accuracy in fault identification. The expression of the combined loss function is as follows:  
\[\left\{\begin{array}{c}L_{BCE}=-\frac{1}{N}\frac{N}{i}(g_{i}\log(p_{i})+(1- g_{i})\log(1-p_{i}))\\ \\ L_{Dice}=1-\frac{2\frac{N}{i}p_{i}g_{i}+\epsilon}{\frac{N}{i}p_{i}+\frac{N}{i }g_{i}+\epsilon}\\ \\ L=\lambda L_{BCE}+L_{Dice}\end{array}\right.\]  
where \(N\) is the total number of pixels in the input image. \(p_{i}\in[0,1]\) and \(g_{i}\in[0,1]\) represent the prediction probability and label value of pixel, respectively, \(\epsilon\) is the smoothing factor, whose value range is (0.1,1). \(\lambda\) is the balance coefficient of Dice loss and BCE loss.