esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[17] explored a hybrid MEMD with the EEMD and independent component analysis (ICA) to identify the modal parameters from the numerical and experimental illustrations containing measurement noise, low energy and closely spaced modes. It is thus effective and reliable that the MEMD is adopted in this study to decompose the multi-dimensional nonstationary signal. After obtaining each single-frequency component, the peak-pick (PP), the half-power bandwidth (HPBW) methods and the HHT method are usually employed to identify the parameters of each component. Notably, SSI, an advanced output-only time domain method, can derive the modal parameters directly from the time history response. Based on the singular value decomposition (SVD), it is capable to filter extra noise from the dynamic response which causes a serious ending effect affecting the calculation accuracy of modal parameters by the HHT method. Additionally, as a more attractive tool, the Fast Bayesian FFT(FBFFT) method can obtain, not only the modal frequencies, but also corresponding posterior uncertainty, which is widely employed by many researchers [18; 19; 20] to identify the modal parameters. Thus, the SSI and FBFFT are reliable, and worth being adopted in this study. This paper aims to develop a more effective approach for accurate modal parameter identification with the output-only multi-dimensional nonstationary signal by integrating appropriate analysis tools and then comparing it with the commonly used methods. Firstly, a brief review of the MEMD is introduced. Then, the MEMD-based modal identification, including the SSI and the FBFFT methods is combined in Section 2. Notably, using MEMD, the manual-picked system order in SSI is significantly simplified. Section 3 employs a numerical illustration of a 5-storey frame structure and the experimental shaking table test to demonstrate the accuracy and robustness of the MEMD-based modal identification approach. In addition, the MEMD-based method is applied to a full-scale high-rise tower with multi-sensor data recorded under the earthquake in Section 4. Finally, some discussion and concluding remarks are provided.
# Molecular and Hormonal Mechanisms Regulating Fleshy Fruit Ripening
## 3 Ripening Is Influenced by Multiple Hormones and Light
### Auxin Delays Ripening and Antagonises the Effects of Ethylene
#### 3.3.2 Jasmonates  
Jasmonates (jasmonic acid (JA) and methyl jasmonate (MeJA)) are important plant hormones derived from linolenic acid [171]. Work on several fruits has supported a role for JA in modulating ethylene synthesis and ripening, and in apple, JA induces ethylene synthesis by enhancing expression of _MdMYC2_, which is known to be involved in JA signalling. MdMYC2 promotes ethylene biosynthesis by binding to the promoters of _MdACO1_ and _MdACS1_ and by regulating apple ERFs [172]. However, if the expression of _MdACS1_ was blocked by 1-MCP, the MeJA treatment was ineffective in enhancing ethylene production. More recently, [PERSON] et al. [173] showed that external ethylene and MeJA increased ethylene production, and this was correlated with higher transcripts of _ACS_ genes _AdACS1_ and _AdACS2_, and ACS enzyme activity in kiwifruit.
# Assessment of a Mid-Fidelity Numerical Approach for the Investigation of Tiltrotor Aerodynamics
## 4 Results and Discussion
### Wing-Prorotor Simulations  
The results that are obtained with DUST over the simplified configuration made by the wing and proprotor described in Section 4.1.1 are compared in the following to the outcomes of the work by [PERSON] [18] to investigate the ability of the mid-fidelity approach to capture the principal mechanisms of rotor/wing aerodynamic interactions. In particular, the effects on the proprotor performance due to the wing could be separated from the effect provided by the proprotor on the wing airloads. Considering the effects of the wing on the proprotor performance, in airplane mode the presence of a thick airfoil, as used in the XV-15 tiltrotor configuration, produces a blockage effect that generates an effective upwash when the blade passes over the wing. This results in a positive peak in the normal force produced by the proprotor that occurs once per revolution on each blade. Moreover, due to the wing aerodynamic circulation, a doublet wash on the normal velocity of the blades--i.e. an upwash followed by a downwash--occurs when the blade passes in front of the wing, thus producing a local unsteady doublet of the blade normal force [18].
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 4 Some Special Cases
### Other Channels  
In a similar manner, the secrecy performance for other fading channels in Table 1 can also be obtained. Due to the space limitation, the detailed derivations are omitted here.
# Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering
## 2 Methodology  
### Systems Engineering Approach  
Systems engineering was used to build and process all stages of the ROV life cycle, from its conception to its retirement stage, considering that it is a complex system in which multiple disciplines interact. Due to this complexity, its life cycle cannot be representedthrough a horizontal timeline, since, naturally, throughout the development of the stages, iterations can occur, hence giving different dynamics that are finally better represented by methods such as the Vee model.  
[PERSON] et al. [54] provided a description of how SE has been used to construct the functional division of the ROV system, using product design specification (PDS) and quality function deployment (QFD). Thanks to a cooperative/iterative effort between the client and the development team, extensive lists of requirements and engineering characteristics were built and evaluated through the use of the House of Quality (HoQ), a tool commonly associated with a QFD used to rank characteristics by quantifying the influence of the requirements on them. A comprehensive HoQ was built for the whole ROV system, but it is beyond the scope of this paper. In order to generate a set of engineering specifications (ES), the HoQ exercise was conducted for the hardware and software systems and reported by [54]; this ES list shows the complexity of the system that has to be developed to fulfill stakeholders' expectations and that requires the modular software architecture that is being reported:  
1. Power system autonomy time.
2. Average current (and range) of electrical power transmission.
3. Number of additional ports for connecting auxiliary equipment.
4. Energy storage capacity in the vehicle.
5. Power consumed by the propulsion system.
6. Supply voltage.
7. Maximum consumption current allowed.
8. Number of devices required for the power supply system.
9. Nominal power transmission voltage.
10. Control system response time.
11. Surface station size.
12. Average power demanded by the system.
13. Longitudinal advance thrust.
14. Additional ports available on the surface for connecting devices.
15. Power consumed by the lighting system.
16. Power consumed by the launch/recovery system.
17. Cable length.
18. Up/down thrust.
19. Umbilical cable gauge.
20. Existence of an electrical protection system.
21. Number of people needed to operate the system.
22. Number of lights.
23. Total illumination intensity.
24. Total number of thrusters.
25. Power (capacity/required availability) supply.
26. Vehicle dimensions.
27. Vehicle weight in air.
## 4 Seismic Risk Assessment
### Nonlinear Static Analysis Results  
The impact of the different masonry infill properties is quite visible, looking at the results of the pushover curves of the building, illustrated in Figure 6, for both directions (X and Y) and structural configurations, as a function of the masonry infill type. The seismic coefficient--base shear normalized to the total seismic weight--is quite affected by the material properties of the infills and is in the range of 0.44-0.10 and 0.43-0.095 for the completely infilled (Figure 6a,b) and partially infilled (Figure 6c,d) configurations, respectively. In particular, going from weak masonry infills to medium-strong and strong masonry infills, the seismic coefficient increases by almost 91% in the X direction and 100% in the Y direction for the completely infilled structural configuration [40]. The increment of the seismic coefficient is higher for the case in which the openings were accounted for in the numerical model, with an increment, going from weak masonry infills to medium-strong and strong masonry infills, of 168% in the X direction and 115% in the Y direction [40]. Moreover, in the case of partially infilled (Figure 6c,d), the seismic capacity decreases when compared with the completely infilled counterpart (Figure 6a,b).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Structural Typology** & **Infill Type** & **T\({}_{1}\) [s]** & **T\({}_{2}\) [s]** & **T\({}_{3}\) [s]** & **T\({}_{4}\) [s]** \\ \hline \multirow{4}{*}{**Completely Infilled**} & 1 & 0.644 & 0.471 & 0.388 & 0.238 \\  & 2 & 0.537 & 0.378 & 0.310 & 0.197 \\  & 3 & 0.463 & 0.322 & 0.264 & 0.170 \\  & 4 & 0.325 & 0.219 & 0.173 & 0.123 \\  & 5 & 0.264 & 0.171 & 0.131 & 0.094 \\ \hline \multirow{4}{*}{**Partially infilled**} & 1 & 0.800 & 0.643 & 0.534 & 0.305 \\  & 2 & 0.682 & 0.520 & 0.437 & 0.259 \\ \cline{1-1}  & 3 & 0.488 & 0.343 & 0.289 & 0.184 \\ \cline{1-1}  & 4 & 0.493 & 0.353 & 0.282 & 0.191 \\ \cline{1-1}  & 5 & 0.399 & 0.27 & 0.212 & 0.151 \\ \hline
**Bare** & - & 1.320 & 1.224 & 0.998 & 0.458 \\ \hline \end{tabular}
\end{table}
Table 2: Dynamic properties of the school building.  
For completeness, the capacity curves for the bare frame configuration are also reported. The differences between the bare frame configuration and the infilled configuration are evident. They are less expressive only for the partially infilled configuration with weak or weak-medium infill types, as expected. It is worth noting that once most of the infills are collapsed, the post-peak branch of the pushover curves for weak and weak-medium infill types is quite close to the bare frame configuration one.
## 2 Channel Capacity in the Presence of \(\eta\)-\(\mu\) Fading and CCI
### Derivation of the PDF of the Receiver's Output Signal-to-Co-Channel Interference Ratio
Since the \(\eta\)-\(\mu\) distribution is of Format 1 [3], its parameters are defined as:  
\[h_{1}=\frac{2+\eta_{1}{}^{-1}+\eta_{1}}{4},\ H_{1}=\frac{\eta_{1}{}^{-1}-\eta_ {1}}{4}. \tag{2}\]  
Applying Expression (2) to Expression (1), we obtained the PDF of the input signal to the SC combiner in the following form:  
\[p_{x_{i}}(x_{i})=\frac{4\sqrt{\pi}\,(\eta_{1}+1)^{2\mu_{1}}}{\Gamma(\mu_{1}) \mathrm{e}^{\frac{\mu_{1}(\eta_{1}+1)^{2}}{2\mu_{1}\Omega_{i}}x_{i}^{2}}}\sum _{i_{1}=0}^{+\infty}\frac{(1-\eta_{1}{}^{2})^{2i_{1}}x_{i}^{4i_{1}+4\mu_{1}-1 }(\mu_{1}/\Omega_{i})^{2i_{1}+2\mu_{1}}}{2^{4i_{1}+2\mu_{1}}\eta_{1}{}^{2i_{1} +\mu_{1}}\Gamma(i_{1}+\mu_{1}+1/2)i_{1}!}. \tag{3}\]  
In the observed environment, the input CCI envelopes, marked with \(y_{i}\), \(i\) = 1, 2,..., \(L\), also follow the \(\eta\)-\(\mu\) distribution:  
\[p_{y_{i}}(y_{i})=\frac{4\sqrt{\pi}\,(\eta_{2}+1)^{2\mu_{2}}}{\Gamma(\mu_{2}) \mathrm{e}^{\frac{\mu_{2}(\eta_{2}+1)^{2}}{2\mu_{2}\mu_{2}^{2}}y_{i}^{2}}}\sum _{i_{2}=0}^{+\infty}\frac{(1-\eta_{2}{}^{2})^{2i_{2}}y_{i}^{4i_{2}+4\mu_{2}-1 }(\mu_{2}/s_{i})^{2i_{2}+2\mu_{2}}}{2^{4i_{2}+2\mu_{2}}\eta_{2}{}^{2i_{2}+\mu_ {2}}\Gamma(i_{2}+\mu_{2}+1/2)i_{2}!}. \tag{4}\]  
The mean square values of the CCI envelopes are denoted by \(s_{i}\), \(s_{i}\) = \(\overline{y}_{i}^{2}\), \(i\) = 1, 2,..., \(L\). The ratio of the desired signal envelope and the CCI envelope at the \(i\)th input branch of the SC receiver is denoted by \(z_{i}\), \(z_{i}\) = \(x_{i}\)/\(y_{i}\), and has the following PDF [18]:  
\[p_{z_{i}}(z_{i})=\int\limits_{0}^{\infty}y_{i}p_{x_{i}}(z_{i}y_{i})p_{y_{i}}(y _{i})dy_{i}. \tag{5}\]  
After introducing Equations (3) and (4) into (5), we have the PDF of the SIR \(z_{i}\):  
\[\begin{array}{c}p_{z_{i}}(z_{i})=\frac{8\pi\,(\eta_{1}+1)^{2\mu_{1}}\,(\eta_ {2}+1)^{2\mu_{2}}}{\Gamma(\mu_{1})\Gamma(\mu_{2})}\sum\limits_{i_{1}=0}^{+ \infty}\sum\limits_{i_{2}=0}^{+\infty}\frac{(1-\eta_{1}{}^{2})^{2i_{1}}(1-\eta _{2}{}^{2})^{2i_{2}}\eta_{1}{}^{2i_{1}+
# Temporal Convolution Network Based Joint Optimization of Acoustic-to-Articulatory Inversion
## 3 Dataset  
### Dataset Description  
Electromagnetic articulograph (EMA) is one of the promising techniques to acquire acoustic-articulatory data. Two open-source EMA data sets, USC-TIMIT [30] and IEEE-EMA [14] corpus, which contain acoustic data in English along with EMA data are used in our AAI experiment. USC-TIMIT corpus provides EMA data for four native speakers (2 males and 2 females) of General American English, each person with 460 utterances. IEEE-EMA corpus consists of eight speakers' (4 males and 4 females) 720 utterances and EMA trajectories. The articulatory data of both datasets were collected using Northern Digital Inc. System. Thus, the articulatory features share the same dimensions and correspond to the same organs.
# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 4 Materials and Methods
### Dataset  
The CICIDS2017 dataset has drawn academic attention since its launch to develop new models and algorithms. The five-day normal and attack traffic data from the Canadian Institute of Cybersecurity were spread across eight separate files in the dataset. It includes 3,119,345 instances and eighty-three features with 15 class labels, 14 attack labels plus one normal class [70].  
In the CICIDS2017, data were captured across different periods. This dataset includes attacks that are not included in the UNSW-NB15, KDD Cup 1999, or NSL-KDD datasets. The CICIDS2017 attacks are categorized as DDoS, DoS, heartbleed, botnet, rhiltration, brute force SSH, brute force FTP, and web attacks. The features of this dataset set it apart from the others in terms of trustworthiness and realistic benchmarking. To ensure the reliability of the CICIDS2017 evaluation, the benchmarking used 11 criteria involving available protocols and complete traffic [71]. [PERSON] et al. [72] revealed that a range of modern multi-stage attacks, including heartbleed and various DoS and DDoS attacks, were included in this dataset. Moreover, a range of modern protocols were included.  
[PERSON] et al. [70] outlined some drawbacks of this dataset, including its scattered presence, a large volume of data, missing values, and high class imbalance. Accordingly, they developed an enhanced version of this dataset considering the aforementioned drawbacks. The enhanced version of the dataset developed by [PERSON] et al. [70] was used. This was received from the author after a request was made. The dataset consists of 91,830 instances with 79 features. Six types of attacks were included in this dataset, in addition to the normal instances. Table 6 lists the number of instances for each attack including the number of normal instances.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attack Type** & **Number of Instances** \\ \hline Botnet ARES & 1873 \\ \hline Brute Force & 10,201 \\ \hline Dos/DDos & 26,066 \\ \hline Infiltration & 29 \\ \hline Normal & 26,185 \\ \hline PortScan & 25,409 \\ \hline Web Attack & 2067 \\ \hline Total of instances & 91,830 \\ \hline \hline \end{tabular}
\end{table}
Table 6: CISIDS2017 Dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Parameters** \\ \hline KNN & Number of neighbors = 5 \\ \hline RF & Number of trees = 10, tree depth = 5 \\ \hline MLP & Neurons in hidden layers = 100, maximal number of iterations = 200 \\ \hline DT & Min. number of instances in leaves = 2, limit the maximal tree depth to 100 \\ \hline AdaBoost & Number of estimators = 50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classifiers Parameters.
## 1 Introduction  
RMS is a pediatric tumor divided into fusion-positive (FPRMS) and fusion-negative (FNRMS) subtypes depending on the presence or absence of t(2;13) or t(1;13) chromosomal translocations [1]. Multiagent chemotherapy (vincristine, actinomycin D, and cyclophosphamide or ifosfamide) followed by radiotherapy represents the standard treatment of RMS patients. Although this approach results in \(>\)70% overall 5-year survival for localized tumors, intrinsic or acquired radioresistance is a predictor of poor (less than 30%) survival [2;3]. Deregulation of receptor tyrosine kinase (RTK) signaling through the RAS/extracellular signal-regulated kinases (ERK) and serine/threonine Akt/mammalian target of rapamycin (mTOR) pathways plays a key role in the pathogenesis of RMS [4]. Enhanced signaling through these pathways influences various cellular functions, including proliferation, migration, and invasion [5]. In particular, the Akt pathway, which induces both genome instability and DNA damage repair [6], displays oncogenic and antiapoptotic activities [7]. In RMS, Akt activation is negatively associated with patient survival [8]. Recently, we demonstrated that stable expression of an active myrAkt1 form in human RD cells correlates with enhanced DNA damage repair via activation of DNA-dependent protein kinase (DNA-PK) [9]. In a significant proportion of RMS cases, members of the non-receptor tyrosine kinase Src family are found to be abnormally activated [10;11], often promoting resistance mechanisms [12; 13; 14]. Interestingly, the scaffolding protein Cav1, a cholesterol-binding protein involved in the formation of caveolar lipid rafts [15;16] that also plays a role in RMS malignancy [17; 18; 19], is a well-known target of the Src family [20]. Src-mediated phosphorylation of Cav1 on tyrosine-14 activates a variety of pathways related to cancer aggressiveness, influencing cell migration, invasion, and resistance to oxidative stress [21;22]. In recent years there has been growing interest in the mechanisms underlying radiation resistance in RMS [3]. In previous works, we demonstrated that increases in Cav1 and Akt levels support radioresistance [9;23]. Furthermore, their expression was increased in radioresistancet RD and RH30 lines developed with a hypo-fractionated radiotherapy protocol [24]. The experiments in this work were undertaken to evaluate whether and how crosstalk between Akt1, Src, and Cav1 may influence oxidative stress levels and response to IR treatment. We additionally investigated whether inhibition of the mevalonate pathway by statins (which causes reduction of isoprenoid intermediates and cholesterol [25]) could affect the Akt1/Src/Cav1 signaling network and oxidative stress, thereby influencing radioresistance mechanisms. In this context, we evaluated whether the combination of statins with IR or actinomycin D could increase the efficacy of therapy. For this purpose, we used human RD and RH30 lines, representative of FNRMS and FPRMS, respectively, as well as previously generated RD lines characterized by radioresistancet traits due to their respective overexpression of myrAkt1 [9] and Cav1 [23].
# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 3 Proposed Method  
Figure 1 illustrates the overall structure of the proposed SS-Net. After facial coordinate localization, RGB video sequences and NIR video sequences are processed to retain Region of Interest (ROI) sequences, which are stored in the form of spatiotemporal feature maps and serve as inputs to the network. The spatiotemporal sensitive (SS) module captures spatiotemporal information that is relevant to physiological signals from both RGB and NIR spatiotemporal feature maps. Subsequently, the multi-level spatiotemporal context fusion (MLSC) module assesses the importance of different modalities, spatiotemporal dimensions, and semantic levels for guiding the fusion. Finally, the Refine module is employed on respective task branches to predict rPPG waveforms and HR values.
# A Brief Review of Mueller Matrix Calculations Associated with Oceanic Particles
## 3 General Scattering Method for Suspended Particles  
The governing principle for light scattering by particles is [PERSON]'s equations. The scattering solution is called Rayleigh scattering if x \(\propto\) 1 and \(\mid\)mx \(\mid\propto\) 1 [16; 20], where the size parameter x is defined as \(2\pi r_{\
u}/\lambda\) with \(r_{\
u}\) being the radius of a sphere or volume-equivalent sphere and \(\lambda\) the incident wavelength in the surrounding medium. The analytical solutions to [PERSON]'s equations are only effectively available for spheres [16; 20]. For a prolate or oblate spheroid, the analytical solution is given in a series of the spheroidal wave functions by [PERSON] and [PERSON] [24] and [PERSON] and [PERSON] [25]. However, the analytical solutions for a spheroid are only computationally effective for small particles due to numerical instability in computing the spheroidal wave functions for large particles. For an infinite circular cylinder, the analytical solution can be easily computed [16]. However, the infinite morphology does not exist in nature. For a particle with spherical symmetry, such as a homogeneous sphere or a multi-layered sphere, the analytical solution can be obtained by using the Lorenz-Mie theory for any size [16; 20]. The advantage of using the spherical model is the computational efficiency, while the disadvantage is the appearance of spherical artifacts, such as the rainbow or glory, which have seldom been observed for ocean water (e.g., [26]). For a non-spherical particle, the solution of [PERSON]'s equations consists of two categories: rigorous and approximate solutions. The rigorous solutions can be further divided into numerically exact solutions and semi-analytical T-matrix solutions.