esa-sceva/satcom-chunk-collection huggingface dataset:
# Transfer Learning-Based Fault Diagnosis under Data Deficiency
## 4 Conclusions  
Collecting the fault data during the real operation is difficult because of safety and cost issues, which results in the imbalance issue in the number of normal versus fault data, and even data deficiency. The performance of fault diagnosis is then greatly deteriorated due to this. On the other hand, a good number of normal and fault data can be acquired in a similar condition such as the lab test or maintenance inspection. In this study, a transfer learning-based fault diagnosis is proposed to solve this problem, which is to pre-train the NN using the large datasets as the source domain, and transfer the knowledge to train the NN using the imbalanced datasets in the real target domain. To illustrate this, normal and fault datasets are acquired from the run-to-fail test of the LM guides, in which the data at high and low speeds are regarded as those for the real operation (target) and maintenance inspection (source), respectively. To study the effect of imbalance, the number of fault data in the target domain is reduced by multiplying the imbalance ratio (IR), and the accuracy of diagnosis is explored as a function of IR. From the study, it is concluded that TL is truly more effective than ordinary ML when there is a large imbalance between the fault and normal such as smaller than 0.1. When the imbalance ratio is 0.1, the variance is improved substantially such that the IQR is about 60% of the others. However, to our surprise, it soon becomes less so, reversing the performance as the IR is increased, which is a too small benefit to employ TL as the solution. This seems to be attributed to the negative transfer arising when the fault characteristics differ too much between the source and target domain. How to figure out and overcome this will be another future research topic, which involves numerous implementations of various experimental datasets.  
Conceptualization, S.H.C. and S.K.; methodology, S.H.C. and S.K.; writing--original draft preparation, S.H.C.; writing--review and editing, J.-H.C.; supervision, J.-H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No. 2020R1A4A407990411).  
The authors declare no conflict of interest.
## 2 Modeling and Problem Formulation  
We will first start with a motivating example, introduce the models of entities in CPS and then formulate the problem in planning a process in a sustainable CPS. We consider several factors in the process-planning problem, including operational requirements, time for executing operations, energy consumption of operations and constraints of the processes.  
A Motivating Example: Consider a CPS consisting of a number of process agents, resource agents and optimization agents. The role of a process agent is to specify the requirements of the target production process to be planned and submit the requirements to the optimization agent. The role of a resource agent is to specify the operations that it can perform and submit bids to the optimization agent to indicate the potential operations that can be performed by it. The optimization agent aims to determine the optimal configuration of the process agent and resource agents based on the bids submitted by the process agent and the resource agents. For example, suppose the workflow \(\Omega_{n}\) of process agent \(n\) requires five operations to be performed. Suppose the upper bound of the processing time of the process to be composed for process agent \(n\) is \(\omega_{n}=200\). Suppose there are nine resource agents in the CPS to perform operations. Depending on the type of resource agents, the capability varies. That is, the function of different types of resource agents is different in general. Therefore, each type of resource agent can only perform some of the operations. The planning problem for process agent \(n\) is to find the configuration of resource agents that can completely perform the required operations, satisfy the processing time requirement and optimize energy consumption. To address this process-planning problem in the CPS, several Cyber World models must be defined to capture the operations, the workflow of the process agent and the capability of resource agents (activities that can be performed by resource agents).  
The above motivating example used to illustrate the proposed solution method is based on a real application scenario similar to the one reported in [48]. The difference between the illustrative example used in this paper and the one in [48] is the number of resource agents and the number of operations in the process. Please refer to [48] for the details of the application scenario.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 5 Parametric Study
### Influence of Rebar Strength  
Figure 19 shows the influence of the rebar strength on the pull-out behavior of notched perfobond shear connectors with the other parameters kept constant. When the yield strength of the rebar was increased from 335 MPa to 400 MPa and to 500 MPa, the pull-out resistance of notched perfobond shear connectors increased by 13% and 9%, respectively. It was revealed that the increase of the rebar strength would lead to an increase in the pull-out resistance of the notched perfobond shear connector. This was possibly because when a higher strength rebar was used in the hole, the dowel effects of the rebar and the concrete in the hole were both increased. However, when the yield strength of the rebar was greater than 400 MPa, the pull-out resistance tended to stop increasing. The reason for this might be that higher strength concrete that matched the higher strength rebar was required to achieve full resistance of the perforating rebar in the hole.
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 2 Communication Signal Description and Dataset Generation
### Signal Time-Frequency Description
#### 2.2.2 Amplitude-Phase Modulation Signal Time-frequency Description  
For MPSK, MAPSK, and MQAM signal, since they all belong to amplitude-phase modulation, the derivation processing of the signal time-frequency characteristics is the same as MPSK. Hence, we specify the time-frequency characteristics of the MPSK signal, and the STFT can be expressed as:  
\[\begin{array}{ll}STFT_{s_{PSK}}(t,w)&=\int_{-\infty}^{+\infty}\left[s_{PSK }(\tau)\gamma^{*}(\tau-t)\right]e^{-jw\tau}d\tau=\\ &=\int_{-\infty}^{+\infty}\left[\sum_{k=-\infty}^{+\infty}Ag(\tau-kT_{b})e^{j( w_{\tau}+\phi_{k}+\phi_{k})}\gamma^{*}(\tau-t)\right]e^{-jw\tau}d\tau\end{array} \tag{14}\]  
As the derivation of MFSK signal time-frequency characteristics, when \(\gamma(t)\) is in a symbol duration, the Equation (14) can be simplified as:  
\[\begin{array}{ll}STFT_{s_{PSK}}(t,w)=&\int_{-T/2}^{T/2}Ae^{j(w_{\tau}(\tau+t) +\phi_{k}+\phi_{k})}e^{-jw(\tau+t)}d\tau=ATe^{-jw\tau}e^{i(w_{\tau}t+\phi_{k}+ \phi_{k})}Sa(\frac{w-w_{k}}{2}T),\\ kT_{b}+T/2<t<(k+1)T_{b}-T/2,k=0,1,2,\ldots\end{array} \tag{15}\]When \(\gamma(t)\) spans two symbols, the Equation (14) can be simplified as:  
\[\begin{split} STT_{s_{PSK}}(t,w)=\int_{-T/2}^{d}Ae^{i(w_{c}(t+1)+ \phi_{c}+\phi_{k})}e^{-iw(\tau+t)}d\tau+\int_{d}^{T/2}Ae^{i(w_{c}(\tau+t)+\phi_{ c}+\phi_{k+1})}e^{-iw(\tau+t)}d\tau=\\ =Ae^{i((w_{c}-w)t+\phi_{c}+\phi_{k})}\int_{-T/2}^{d}e^{-i(w-w_{c}) \tau}d\tau+Ae^{i((w_{c}-w)t+\phi_{c}+\phi_{k+1})}\int_{d}^{T/2}e^{-i(w-w_{c}) \tau}d\tau=\\ =A\frac{T+2d}{2}e^{i((w_{c}-w)t+\phi_{c}+\phi_{k})}e^{\frac{i(w-w_ {c})(T-2d)}{4}}S(\frac{(w-w_{c})(T+2d)}{4})+\\ +A\frac{T-2d}{2}e^{i((w_{c}-w)t+\phi_{c}+\phi_{k+1})}e^{\frac{i(w- w_{c})(T+2d)}{4}}S(\frac{(w-w_{c})(T-2d)}{4}),\\ (k+1)T_{b}-T/2<t<(k+1)T_{b}+T/2,d=(k+1)T_{b}-t,k=0,1,2,\ldots\end{split} \tag{16}\]  
where \(\phi_{k+1}\) is the phase of the k+1-th symbol. And if \(\phi_{k+1}=\phi_{k}\), Equation (15) is equal to Equation (16). We take the modulus square of (15), and the result can be expressed as:  
\[\begin{split} SPEC_{s_{PSK}}(t,w)=\left|STFT_{s_{PSK}}(t,w)\right| ^{2}=A^{2}T^{2}Sa^{2}(\frac{w-w_{c}}{2}T),\\ KT_{b}+T/2<t<(k+1)T_{b}-T/2,k=0,1,2,\ldots\end{split} \tag{17}\]  
And for (16), it can be expressed as:  
\[\begin{split} SPEC_{s_{PSK}}(t,w_{c})=\frac{A^{2}T^{2}}{2}(1+\cos (\phi_{k}-\phi_{k+1}))+2A^{2}d^{2}(1-\cos(\phi_{k}-\phi_{k+1}))\leq A^{2}T^{2},\\ (k+1)T_{b}-T/2<t<(k+1)T_{b}+T/2,d=(k+1)T_{b}-t,k=0,1,2,\ldots\end{split} \tag{18}\]  
We take the partial derivative for Equation (18):  
\[\begin{split}\frac{\partial SPEC_{s_{PSK}}(t,
## 3 Quadcopter Control
### Attitude Control
The dynamics model specifications of the employed Parrot quadcopter are listed in \[J(\mathbf{d}_{k})=J_{RT}(\mathbf{d}_{k})+J_{u}(\mathbf{d}_{k})+J_{\Delta u}(\mathbf{d}_{k}) \tag{33}\]0  
This research study employs a linear model predictive control. Hence, a linear state-space model of the quadcopter is required. Equation (40) describes the standard form of a linear time-invariant (LTI) state-space model, which has \(\mathbf{p}\) inputs, \(\mathbf{q}\) outputs, and \(\mathbf{n}\) state variables. \[ \begin{array}{l}\dot{\mathbf{x}}=\mathbf{A}\mathbf{x}+\mathbf{B}\mathbf{u}\\ \mathbf{y}=\mathbf{C}\mathbf{x}+\mathbf{D}\mathbf{u}\end{array}  \tag{40}\]  
where \(\mathbf{x}\), \(\mathbf{y}\), and \(\mathbf{u}\) are the state vector, the output vector, and the input vector, respectively. In the equation, \(\mathbf{A}\), \(\mathbf{B}\), and \(\mathbf{C}\) are (\(n\times n\)), the state matrix; (\(n\times p\)), the input matrix; and (\(q\times n\)), the output matrix, respectively. It should be noted that \(\mathbf{D}\) is a (\(q\times p\)) feedforward matrix. The \(\mathbf{D}\) matrix is zero in this study, as there is no direct feedthrough. The linear model was computed around the operational point in the state-space model using the MPC Designer app in the Model Predictive Control Toolbox. The toolbox included in the MATLAB software provides control blocks for developing not only linear model predictive control but also nonlinear and adaptive model predictive controllers. In addition, the MPC Designer app included in the software package facilitates the design of an LMPC by automating the processes for plant linearization and tuning the controller parameters. The \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specification** & **Parameter** & **Unit** & **Value** \\ \hline Drone Mass & \(m\) & kg & 0.063 \\ Lateral Moment Arm & \(l\) & m & 0.0624 \\ Thrust Coefficient & \(b\) & N-s\({}^{2}\) & 0.0107 \\ Drag Coefficient & \(d\) & N-m-s\({}^{2}\) & 0.7826 \(\times 10^{-3}\) \\ Rolling Moment of Inertia & \(lx\) & Kg-m\({}^{2}\) & 5.82857 \(\times 10^{-5}\) \\ Pitching Moment of Inertia & \(ly\) & Kg-m\({}^{2}\) & 7.16914 \(\times 10^{-5}\) \\ Yawing Moment of Inertia & \(lz\) & Kg-m\({}^{2}\) & 0.0001 \\ Rotor Moment of Inertia & \(lr\) & Kg-m\({}^{2}\) & 0.1021 \(\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Parrot aerial robot model specifications [48]. state-space parameters of the linearized plant can be found in the MPC object (MPCobj) generated by the MPC Designer app. The Simulink MPC Designer linearizes each block in the model independently, then combines the outputs of the individual linearized models to produce the linearized model of the whole plant. Table 3 presents the resulting values for the state-space model generated by the app.
## 4 Analysis of Security and Performance
### Security Analysis  
In the proposed schemes, the public key is formed by the hash value of the identity of the issuer, an anonymous identity, and the time slot, according to Equation (3). Any vehicle's public key can be calculated by any other vehicle. A vehicle's private keys are formed by the indexing secret and the vehicle's public key, according to Equation (4). Vehicles know the secret index but cannot retrieve the indexing secret because the ECDLP (Elliptic Curve Discrete Logarithm Problem) is hard. The pairing values are used to establish mutual trust and perform negotiation. The pairing values are bilinear mappings of one vehicle's private key and another vehicle's public key, which can be calculated by a vehicle without any negotiation. In the processes of requesting a new registration and building a neighbor set, the only exposed information is anonymous identity \(\{ID_{Ra},T_{t},ID_{vi},DS_{vi}\}\). \(ID_{Ra},T_{t}\) and \(ID_{vi}\) can not be fake because they will be used to calculate the public key and the associated private key. \(DS_{vi}\) is exposed but does not include any information of secret value in secret pool. The information that is involved in message authentication is the message and pairing values that are derived by the vehicle. The following section discusses security in greater detail.
## 4 Conclusions  
Nanostructuring silicon surface using modified anodization method produce several interesting morphologies that can find applications in many different fields. For example, it can be applied to the development of photonic or optoelectronics devices. Beside to the conventional anodization parameters namely, the applied current density, electrolyte composition, temperature and substrate type, we investigate another important parameter which is the cathode type. The symmetric anode/cathode setup leads to a more uniform and larger pore size distribution compared with structures produced by conventional techniques. Using certain fabrication conditions and substrate type, the Si symmetric cathode reveal bilayer microstructure composed of micro flakes top and spongy structure underneath. The modified setup reduces the number of shallow and interlinked holes that found in samples produced by conventional Pt cathode. The spectral reflectivity is lowered over the entire UV-VIS spectrum with a red shift to the UV reflectance dip and blue shift for the peaks at the VIS-NIR region when using Si cathode. The fabricated PSi structure incorporated in a Schottky diode of the form Al/Si/PSi/Au and the diode parameters were modified when using Si cathode, as for the example the reverse saturation current is reduced to its half value, also, the ideality factor was decreased.  
Besides, to the morphology, optical, and electrical changes found in samples produced by the Si cathode the modification in the configuration allows the production of PSi structures on large scale at low cost compared with the conventional setup based on Pt cathode.  
Conceptualization, R.R. and R.J.M.-P.; methodology, R.R. and R.J.M.-P.; software, R.R., M.H.E. and R.J.M.-P.; validation, R.R., M.H.E. and R.J.M.-P.; formal analysis, R.R. and M.H.E.; investigation, R.R., M.H.E. and R.J.M.-P.; resources, R.J.M.-P.; data curation, R.R. and M.H.E.; writing--original draft preparation, R.R., M.H.E. and R.J.M.-P.; writing--review and editing, R.R., M.H.E. and R.J.M.-P.; visualization, R.R. and M.H.E.; supervision, R.J.M.-P.; project administration, R.J.M.-P.; funding acquisition, R.J.M.-P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 8: The spectral reflectance of sample S3 anodized using both configurations, it show a broadband reduction in the optical reflectance for the symmetric Si electrode case (red line) when compared with the PSi fabricated using the Pt cathode (black line).  
Data Availability StatementAll data included in graphics and tables within the manuscript and are available upon reasonable request to the authors.  
The authors declare no conflicts of interest.
## 2 Results and Discussion
### Saponins  
LC-HRMS and LC-MS/MS mapping of saponins in 70% methanol extracts of the leaves of our _I. aquifolium_ and _I. meserveae_ samples against a reference sample of Yerba Mate identified about 180 peaks as highly probable saponins. Due to the high number of components and numerous possibilities of identifying similar substances, each was named using an acronym containing its pseudomolecular ion and the averaged retention time under experimental conditions (e.g., 911, 12.60 min).  
Summarising the extensive results presented in Supplementary Table S2, 10 saponins with RA% higher than 75% were recorded. Among them, the main saponins, clearly differentiating _I. aquifolium_ and _I. meserveae_ samples from _I. paraquiensis_ based on their relative concentrations, were (1073, 8.11 min), (927, 8.29 min), (911, 12.6 min), and (765, 15.25 min), as well as one standout compound containing the SO\({}_{3}\) group (887-S, 10.71 min). Another 27 saponins with RA% higher than 25% were recorded.  
For the group of _I. aquifolium_ cultivars, the distinctive saponin markers may be the following three compounds: (969, 9.07 min), (969, 9.5 min), and (887-S, 10.71 min).  
For the group of _I. meserveae_ cultivars, the distinctive saponin markers may be the following nine compounds: (927, 7.07 min), (1235.61, 7.51 min), (825, 7.71 min), (809, 8.11 min), (793, 12.6 min), (911, 13.98 min), (911, 14.25 min), (765, 15.25 min), and (647, 16.50 min).  
For the reference (PA), the most outstanding saponins were (809, 9.07 min), (809, 9.27 min), (1115, 11.32 min), (777, 11.5 min), (1057, 12.18 min), (911, 12.6 min), (895, 14.43 min), and (953, 14.96 min).  
According to the results of the GC analysis of the terpenoid fraction (reported above), the main aglycones of saponins in the investigated _Ilex_ taxons are unsolic and oleanolic acids.  
Similarities and differences between the saponin patterns of the analysed _Ilex_ samples were visualised using hierarchical clustering (Supplementary Materials Figure S1) and principal component analysis (Figure 7). PCA explained about 62% of the variation, and the graph shows three groups in different locations. The _I. paraquariensis_ reference sample has strong negative PC1 and PC2 scores, while the three varieties of _I. meserveae_ are locatedon the positive side of PC1. In contrast, _I. aquifolium_ varieties are located in the upper left quarter of the graph (negative PC1 and positive PC2 scores).
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Ice (SI)
#### 2.2.4 SAR  
Generally, a newly formed thin ice would have a smooth surface which causes specular reflectance and appears very dark in SAR images. The specular reflectance makes it challenging to distinguish thin and new SI on a calm water surface as a specular reflector [58; 94]. When SI is covered with moist snow, it usually has volume or composite scattering, making such areas appear bright in SAR images [94]. The aged SI can also cause volume scattering. Moreover, the morphology of the SI can change due to temperature fluctuations and SI movement [94]. These changes would roughen the SI surface and create small pressure ridges. Therefore, aged SI would appear brighter in SAR images because of the rough surface [58; 94].  
The imaging frequency and polarization of SAR systems are important for SI mapping. For example, Figure 3 illustrates various properties of SI in C-, L-, and P-bands SAR images [94]. As is clear, C-band was a better discriminator for new and aged SI. Additionally, the penetration of the microwave radiation in L- and P-band would complicate the interpretation of SI scattering characteristics, especially for new and aged SI discrimination. However, L-band was generally reported to be a better demonstrator of the pressure ridges of the SI [94]. In terms of polarization types, the Horizontal transmit and Horizontal receive (HH) polarization is generally the preferred polarization for discriminating SI from ocean water because it is less sensitive to water roughness than the Vertical transmit and Vertical receive (VV) polarization [94]. For example, [PERSON]. [95] extracted 12 different polarimetric features from the HH-VV dual polarization TerraSAR-X images and trained an Artificial Neural Network (ANN) for pixel-wise SI type classification. A comprehensive statistical analysis of the correlation between the extracted polarimetric features and their relevance for SI classification was conducted in this study. It was observed that the features purely based on the covariance matrix were more informative for SI classification than the features involving eigen decomposition of the coherency matrix. The in situ data acquired during the N-ICE2015 field campaign was also used for validation. The percentages of in situ samples of each SI class that were assigned to the respective SI type by the classifier were computed to assess the stability of the classification procedure. Over 90% stability for almost all SI types indicated that the approach was consistent and stable.  
So far, many SAR satellites have so far provided valuable data for SI mapping and monitoring. For example, RADARSAT-1 and -2, Sentinel-1, ENVironmental SATellite (ENVISAT), TerraSAR-X, and Advanced Land Observing Satellite (ALOS) satellites have been extensively used for SI studies.
## 4 Materials and Methods
### Chemotaxis Assays Using Olfactometers  
We designed an olfactometer (Figure 4) to evaluate the preference of _S. australe_ for the selected compounds based on their chemotaxis. Our design comprised two plastic Petri dishes, with the external section consisting of the bottom piece of a 90 mm plastic Petri dish. The internal components included a 50 mm plastic Petri dish divided into halves. We set the halves in the center of the 90 mm plate opposed to each other, with a separation of 16 mm (decision area), flanged by two plastic pieces (80 mm \(\times\) 10 mm) to fix the position. Both halves and the in-between portion were filled with ca. 2.5 g of moistened sand (15% humidity \(w\)/\(w\)). One half was randomly designated as the treatment area and the other half as the control. The section in between was considered the decision area. At five millimeters from the distal border of the treatment area, we added 50 \(\upmu\)L of a standard solution of the selected compounds, and the control area received 50 \(\upmu\)L of hexane. The olfactometers remained open to the air for 15 min to allow solvent evaporation. Then, 300 (\(\pm\)50) IJs of _S. australe_ were suspended in one milliliter of distilled water and inoculated over the decision area. The labeled olfactometers were covered with a 90 mm Petri dish lid and randomly oriented in a dark room at 14 (\(\pm\)2 \({}^{\circ}\)C) for 15 h. The sand of each section (treatment and control) containing the IJs was individually recovered using 50 mL of tap water and kept in 500 mL plastic cups (Hefty\({}^{\text{\textregistered}}\), 18 oz) until use. These cups were gently hand-shaken for 10 s and maintained for 45 min at 30\({}^{\circ}\) inclination for IJs decantation. Finally, the water containing the IJs was carefully recovered using a 1 mL plastic Pasteur pipette. A second wash of the sand was performed using 30 mL of tap water, repeating the previous procedure. The number of IJs collected was counted using a Nexius Zoom microscope (Euromex, The Netherlands). We performed 15 replicates for each compound and concentration. The olfactometer described in Figure 4 was considered a replicate.