esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Classification Performance of the Proposed Model  
#### 3.1.1 Time-Frequency Features Extraction  
To define the appropriate window function and window size, CNN network parameters should be set to default. Specifically, dropout is 0.5; regularization function is L2; batch size is 64; loss function is Cross entropy loss [40]. In the validation part of time-frequency features extraction, different types (Hann window, Hamming window and Gaussian window) [41] and sizes (64, 128, 256, 512) of window functions were adopted. The reason why we choose the four window functions is that they are the most widely used, and the choice of window function size is based on the relationship between the scan signal period and the sampling frequency.  
Figure 8 shows the average classification accuracy of four window sizes in three window functions respectively in the condition that experiments iterations generations are 100 and the evaluation approach is five-fold cross-validation. In Figure 8a, the best average classification accuracy achieved nearly 99.5% when window function is Hann and window size is 256. In terms of the Hamming window, the best average classification accuracy 99.8% is acquired in Figure 8b when the window size is 128. As for the Gaussian window in Figure 8c, the best average classification accuracy is 98% when the window size is set to 256. From the three figures, the blue and green lines are always in the lower position. It can be seen that the optimal window size should not be too large or too small. For the blue  
lines, we conclude that the window size is too small to cover even half of the response of the scanning pulse, which lead to invalid features extraction. On the contrary, when the window size is 512, we infer that large amounts of redundant features were collected and the effective features are difficult to exploit. In addition, large windows may affect the performance of the short-time Fourier transform.
# Clipping Noise Compensation with Neural Networks in OFDM Systems
###### Abstract  
The application of deep learning (DL) to solve physical layer issues has emerged as a prominent topic. In this paper, the mitigation of clipping effects for orthogonal frequency division multiplexing (OFDM) systems with the help of a Neural Network (NN) is investigated. Unlike conventional clipping recovery algorithms, which involve costly iterative procedures, the DL-based method learns to directly reconstruct the clipped part of the signal while the unclipped part is protected. Furthermore, an interpretation of the learned weight matrices of the neural network is presented. It is observed that parts of the network, in effect, implement transformations very similar to the (Inverse) Discrete Fourier Transform (DFT/IDFT) to provide information in both the time and frequency domains. The simulation results show that the proposed method outperforms existing algorithms for recovering clipped OFDM signals in terms of both mean square error (MSE) and Bit Error Rate (BER).  
OFDM; clipping; signal recovery; deep learning; interpretation of deep learning +
Footnote †: journal:  
_Article_  
Clipping Noise Compensation with Neural Networks in OFDM Systems
## 3 Results
### Descriptive Statistics
#### 3.1.2 Potential Users  
The sample of potential users consists of 46.6% females and 53.4% males. Most respondents (90.3%) are aged between 18 and 54, but there are also responses from older potential users. As in the case of the existing users, the two most popular answers concerning the occupation are \"private employee\" (28.2%) and freelancer (27.7%). However, in the case of the potential users also students consist an important proportion of the sample (16.5%). A similar pattern between the two samples is also followed regarding the education level since 35.9% of the potential users hold a bachelor's degree and 34.5% of them hold an MSc or PhD diploma. Also, the monthly household income of the potential users seems well distributed, with an adequate number of responses in the six classes. Potential users stated much lower frequency levels of performing exercise activities in comparison with the existing users since 52.9% of the potential users' sample exercise at least two times per week and 5.8% do not exercise at all. 65.5% of the potential users responded that they own a private car and 53.7% that they own a bicycle. Of the potential users, 38% revealed that they use a private car for the majority of their trips, while a significant proportion of the sample (23.9%) revealed that they use public transport for most of their trips. Concerning the frequency of cycling, it becomes obvious that potential users of the ThessBike system cycle much rarer compared to the existing users. More specifically, only 26.2% of the sample cycle at least once per week and a notable 47.6% do not cycle at all. It should be mentioned that the low usage levels of bicycle, which are observed in the case of the potential users are much more representative of the prevailing conditions in Thessaloniki. As in the case of the existing users, 65% of the potential users revealed that in most cases they use bicycle for leisure purposes.  
Concerning the assessment of the usefulness of the thirteen services, that a bike-sharing mobile app can provide, it becomes understood that potential users consider critical the provision of information about the location of the stations both in a static way and in a dynamic way, by taking into account users' location in real-time. In addition, potential users attach great importance to the provision of real-time information about the availability of bikes and free docks in every station and to the facilitation of the rental process, by conducting it through the mobile app. The development of a new and sophisticated mobile app seems crucial for the potential users too since 84.5% of them state that they would be guided in registering to the ThessBike system.
## 4 Object Classification Network DSDCN-ResNet
### Use of Transfer Learning  
The complexity of the underwater environment and the cost of data acquisition result in a lack of sonar images, and training the network with a small amount of data may lead to serious overfitting problems [38]. Therefore, the method of transfer learning is introduced here to migrate the trained network parameters to the small sample network, thus reducing the network overfitting, saving training time, and improving classification accuracy. The existing public datasets, such as ImageNet, differ greatly in image types from SAS images, and they are not suitable as training sets. The transfer learning steps adopted in this study are as follows: First, the SAS image simulation dataset was constructed according to the sonar imaging characteristics and used as the training set of the model. Second, the network designed in Section 4.2 was trained on the simulation data, and the trained model was saved. Finally, the trained model was used for feature extraction and the classification of real SAS images. The framework of the network training and object classification algorithm is shown in Figure 8.
# Transverse Vibration of Clamped-Pinned-Free Beam with Mass at Free End
## 2 Frequency Calculations
Substituting the first transverse displacement (Equation (4)) into the pinned boundary condition (Equations (10) and (11)) results in:  
\[X_{2}(x) =b_{1}\sin(\beta x)+b_{2}\cos(\beta x)+b_{3}\sinh(\beta x)+b_{4} \cosh(\beta x) \tag{5}\]6  
and  
\[X_{2}(x) =b_{1}\sin(\beta x)+b_{2}\cos(\beta x)+b_{3}\sinh(\beta x)+b_{4} \cosh(\beta x) \tag{5}\]7  
After, substituting the second transverse displacement (Equation (5)) into the boundary conditions defined by Equations (12) and (13) provides the following expressions:  
\[X_{2}(x) =b_{1}\sin(\beta x)+b_{2}\cos(\beta x)+b_{3}\sinh(\beta x)+b_{4} \cosh(\beta x) \tag{5}\]8 \[X_{2}(x) =b_{1}\sin(\beta x)+b_{2}\cos(\beta x)+b_{3}\sinh(\beta x)+b_{4} \cosh(\beta x) \tag{5}\]9\[X_{1}(0)=0 \tag{6}\]0 \[X_{1}(0)=0 \tag{6}\]1  
Aggregating Equations (14)-(21) into an \(8\times 8\) matrix (see Appendix A) and solving for the determinant leads to the transcendental equation expressed:  
\[X_{1}(0)=0 \tag{6}\]2 \[X_{1}(0)=0 \tag{6}\]3 \[X_{1}(0)=0 \tag{6}\]4 \[X_{1}(0)=0 \tag{6}\]5 \[X_{1}(0)=0 \tag{6}\]6 \[X_{1}(0)=0 \tag{6}\]7 \[X_{1}(0)=0 \tag{6}\]8  
where the natural frequencies (in Hz) are given by:  
\[X_{1}(0)=0 \tag{6}\]9  
To verify Equation (22), the first five natural frequencies were calculated for three well known cases:  
* Case 1: Clamped-free [14]: \(\frac{a}{L}=\frac{m_{\text{attached}}}{m_{\text{beam}}}=0\)
* Case 2: Clamped-free with mass at free end [7]: \(\frac{a}{L}=0\)
* Case 3: Clamped-pinned-free [6]: \(\frac{m_{\text{attached}}}{m_{\text{beam}}}=0\)  
The results are tabulated in Tables 1-3. The frequencies of the first five modes (\(\beta_{1}\)-\(\beta_{5}\)) are compared between what is found in literature against the results from Equation (22) (proposed model). The small differences are due to rounding errors of the beam vibration eigenvalues \(\beta\), which cause large changes in the calculated frequency (\(f_{n}\propto\beta_{n}^{2}\)). The precision for \(\beta\) in this paper is \(\pm 0.0002\). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Literature [14]** & **Proposed Model** & **Difference** \\
**Mode** & **(Hz)** & **(Hz)** & **(\%)** \\ \hline
1 & 19.64 & 19.63 & 0.051 \\
2 & 123.07 & 123.02 & 0.041 \\
3 & 344.64 & 344.45 & 0.055 \\
4 & 675.31 & 674.97 & 0.050 \\
5 & 1116.33 & 1115.79 & 0.048 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of analytical results: clamped-free (Case 1).
## 3 Simulation and Analyses
### Micro-Crack Signal Assessment under the Effect of Back-Surface \(R_{a}\)  
Figure 8a shows the non-linear change trend of mean amplitudes of micro-cracks caused by different depth cracks and different roughnesses \(\mathbf{R_{a}}\) with a rough back surface. \(\mathbf{R_{sm}}\) and size of crack parameters are the same as in Section 3.3 while the front surface is smooth. Compared to the influence of a rough front surface, the mean amplitudes of cracks drop more slowly with a rough back surface. According to the analysis in Section 2.2, there is only reflected attenuation caused by back-surface roughness for corner reflection wave of the crack while both reflected and transmitted attenuations exist when the ultrasound wave passes through the rough front surface. The relationship between SARF and \(\mathbf{R_{a}}\) is displayed in Figure 8b, and the procedure for calculation is the same as that in Section 3.3. It can be seen that the SARF critical values corresponding to micro-cracks with depths 100 \(\upmu\)m, 150 \(\upmu\)m, and 200 \(\upmu\)m, are 8.5 \(\upmu\)m, 15.8 \(\upmu\)m, and 24 \(\upmu\)m, respectively. This means that it can be accurately identified when the depth of the micro-crack on the rough back surface is larger than 200 \(\upmu\)m. On the other hand, the smaller the size of the micro-crack on the rough surface, the greater the effect of \(\mathbf{R_{a}}\) becomes on ultrasonic detection.
## 5 Experimental Design
### Real Case Datasets
For the sake of comparison, the MODIS cloud mask was also derived from the embedded MYD35 product and transformed to a binary mask assuming that \"confident clear\" and \"probably clear\" pixels are cloud-free whereas \"confident cloudy\" and \"probably cloudy\" pixels are filled with clouds. The 2B-GEOPROF-LIDAR and the MODIS-AUX products were split into two independent, temporally decoupled validation and training datasets acquired in 2006 and in 2007, respectively. The analyzed time period was selected due to the earliest possible joint operation time of the AQUA (launched in 2002), CALIPSO (launched in 2006) and CloudSat (launched together with CALIPSO) satellites. At that time, the instruments had experienced very small degradations apart from the MODIS AQUA 1.6 um channel. The full list of spectral and ancillary features used by the classifiers is presented in Table 3.  
The MODIS collocations with CALIOP and CPR were divided into two real cases. The first simple one consisted of daytime data (Sun zenith angle < 85\({}^{\circ}\)) over water, away from sun glint areas and excluding polar regions (60\({}^{\circ}\)-90\({}^{\circ}\) latitudes). The second, more difficult case consisted of daytime data (Sun zenith angle < 85\({}^{\circ}\)) over ice/snow. Information about sun glint areas as well as the land/water and snow masks was extracted from the MYD35 product. Each real case dataset was randomly subsampled into 100,000 training samples and 30,000 validation (hold-out) samples. \begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Full Name** \\ \hline B1\_645 nm & Band 1 reflectance at 645 nm \\ \hline B2\_858 nm & Band 2 reflectance at 858 nm \\ \hline B3\_469 nm & Band 3 reflectance at 469 nm \\ \hline B4\_555 nm & Band 4 reflectance at 555 nm \\ \hline B5\_1240 nm & Band 5 reflectance at 1.240 μm \\ \hline BY\_2130 nm & Band 7 reflectance at 2.130 μm \\ \hline B17\_905 nm & Band 17 reflectance at 905 nm \\ \hline B18\_936 nm & Band 18 reflectance at 936 nm \\ \hline B19\_940 nm & Band 19 reflectance at 940 nm \\ \hline B26\_1375 nm & Band 26 reflectance at 1.375 μm \\ \hline B20\_3750 nm & Band 20 brightness temperature at 3.750 μm \\ \hline B27\_6715 nm & Band 27 brightness temperature at 6.715 μm \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Spectral and angular features used for cloud discrimination over water surfaces using MODIS imagery.
###### Abstract  
The literature to date is limited regarding the implantation of VR in healthy young individuals with a focus on cognitive function. Thirty healthy males aged between 22.8 and 24.3 years volunteered to participate in the study randomly and were assigned to one of two groups with alike exercises: an experimental group (GE, \(n=15\)) that performed an exercise protocol with a VR game and a controlled group that performed the exercise protocol without the VR (CON, \(n=15\)). A 128-card computerized version of the Wisconsin Card Sorting Task (WCST) and the Stroop test were completed before and after the exercise protocol. There was a significant interaction effect between time and condition for WCST preservation errors (F\({}_{1,30}=4.59\), \(p=0.041\), \(\eta^{2}_{\text{P}}=0.141\)) and a significant time effect for all WCST and Stroop outcomes in GE. Results of preliminary findings suggest that the use of a VR platform offers effective benefits with respect to cognitive flexibility and selective attention. In addition, participants can achieve additional benefits in cognitive flexibility by engaging in a traditional exercise protocol of a similar volume.  
virtual reality; physical exercise; benefits; cognitive flexibility; attention; young males +
Footnote †: journal: Applied Sciences
# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 2 Bearing Fault Diagnosis Method Based on Knowledge Reasoning
### The Fault Diagnosis Method Based on an Expert System  
The fault diagnosis method based on an expert system, as the earliest intelligent diagnosis technology applied in the field of industrial motor bearing fault diagnosis, has the advantages of simple operation and short diagnosis time and also shows good logical reasoning in the process of partial bearing fault diagnosis and interpretation ability, but the expert system also has the limitation that it does not have the ability of autonomous learning. In particular, there are often problems of infinite expansion of rules in the knowledge base and compatibility problems between rules when faced with changing and complex operating conditions of industrial motor bearings.
# Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture
## 4 Proposed Solution
### Proposed DLEFN Algorithm
After estimating the expected average data size \(d_{ln}^{i}\) based on information on the volume of data generating from devices connected to a fog node \(i\) related to task \(tn\), the first procedure calculates the computational capacity required to handle the incoming data and the bandwidth that ensures QoS when transferring the intermediate result to cloud, and then decides the maximum number of layers that the fog node \(i\) can execute within usable resources. \(c_{i}^{able}\) and \(b_{i}^{able}\) indicate available computing capacity and bandwidth on fog node \(i\), respectively. \(L_{i}^{min}\) signifies the predefined compulsory minimum layer for fog node \(i\). In other words, fog node \(i\) should execute defined minimum layer or more for DL tasks. Defining maximum layers is tried for all tasks belonging to the new application in each fog node individually. During the above process for all tasks of a new application, if available computational capacity in fog node \(i\) is insufficient, the fog node reduces the already defined layers of tasks of other allowed and new applications. This work ensures minimum capacity to handle the task at the fog node. The work repeatedly cancels a layer that causes minimum loads among the highest layer for each task assigned to fog node \(i\) until capacity is guaranteed. \[L_{ta}^{i}\ =\ L_{ta}^{i}-1\quad s.t.,\ \ \min_{\forall{\text{{{t}}}} \subseteq T_{i}^{able}}\left(\left(l_{ta}^{i}-l_{ta}^{i}-1\right)\times d_{ta }^{i}\right) \tag{2}\]  
The above equation checks the layer for cancellation. \(T_{i}^{able}\) indicates a set of tasks assigned higher layers than the compulsory minimum layer defined for fog node \(i\). Namely, fog nodes should deal with at least the compulsory layer for their tasks, as shown in Equation (2). As some layers are canceled, the fog node regains its available capacity corresponding to the decreased loads. In contrast, more bandwidth will be required due to the larger number of intermediate results. The cancellation for the assigned layer is repeatedly carried out by Equation (2) until the fog node ensures computational capacity to handle the minimum layer for the task being attempted currently. If \(T_{i}^{able}\) is empty, i.e., assigned layers for all tasks on the fog node reaches the minimum layer, or a task has led to insufficient bandwidth on any fog node, the attempt to accommodate the new application to which this task belongs is considered a failure. Algorithm 1 indicates the pseudo code for the first procedure of the DLEFN, which defines the optimal layers for a new application.
# Atomic, Molecular and Cluster Science with the Reaction Microscope Endstation at FLASH2
## 3 Selected Results
### Two-Photon Double Ionization in Argon  
In a kinematically complete experiment, double ionization of argon by absorption of two photons was measured [30]. Neutral argon atoms were irradiated by FEL radiation with a photon energy of \(27.93\,\mathrm{eV}\) (averaged peak intensity \((3\pm 2)\times 10^{13}\,\mathrm{W/cm^{2}}\), pulse duration approximately \(50\,\mathrm{fs}\)). The angular distributions of the ejected photoelectrons during two-photon double ionization (TPDI) were measured. The dominant TPDI channel observed was sequential double ionization (SDI), in which both photoelectrons were emitted from the argon atom by sequential single-photon absorption (see also [38]). The two photoelectrons were confirmed to be correlated through polarization of the intermediate Ar\({}^{+}\) state. As a result, the measured angular distributions of both, the first and the second, photoelectron differ from that of the single ionization. In addition, the coincident detection method led to the discovery of the crucial role of autoinization in both steps of SDI in argon.  
Both the ion and the electron detector of the REMI have multi-hit capability. This is necessary for the coincident detection of the two photoelectrons emerging from one TPDI event. Additionally, Ar\({}^{+}\) ions were measured in coincidence with the photoelectrons. This allows to differentiate between particles stemming from single ionization and the SDI channel. As both processes occur during the same experiment, the influence of systematic errors can be greatly reduced by comparing results from these two ionization pathways.  
Figure 4 shows the two-dimensional kinetic energy spectrum of two photoelectrons detected in coincidence. Because the second ionization potential of argon lies just below the photon energy of the FEL radiation, there is one fast (first) and one slow (second) photoelectron expected during SDI [39]. This feature of correlated electrons in SDI is shown in Figure 4, where the kinetic energy of two photoelectrons created within one XUV pulse are plotted against each other. There is an increased yield of photoelectrons at positions where one photoelectron has a kinetic energy of \(12\,\mathrm{eV}\) and where the other electron has a kinetic energy of \(0.3\,\mathrm{eV}\). The increased photoelectron yield at \(E_{1}=12\,\mathrm{eV}\) and \(E_{2}=12\,\mathrm{eV}\) shows uncorrelated electrons and can be attributed to electrons from two independent single ionization processes. The capability to record such two-dimensional energy spectra of reaction fragments detected in coincidence underlines the aptitude of the REMI as a versatile AMO (atomic, molecular, and optical physics) science station. The dynamics following multi-photon absorption processes in the XUV range is kinematically completely accessible.
## 5 Conclusions  
In summary, this paper numerically analyzed the sensing characteristics of a high-performance midinfrared D-shaped PCF-SPR RI sensor. An FEM tool was employed to investigate sensing performance. The influence of the structural parameters, and the relationship between resonance wavelengths and analyte RI (1.33-1.39) were discussed for the sensor. Simulation results showed that the effect of central-hole diameter \(d_{2}\), gold-layer thickness \(T_{Au}\), channel length \(\Lambda_{2}\), and hole diameter d\({}_{3}\) was sensitive on the sensor. Using the WIM, average WS of 8850 nm/RIU and an average RI resolution of \(1.13\times 10^{-5}\) RIU were achieved; maximal WS and maximal RI resolution were 11,500 nm/RIU and \(8.7\times 10^{-6}\) RIU, respectively, when the analyte RI changed between 1.36 and 1.37. Using the AIM, maximal AS of 230 could be achieved. The sensor simultaneously had high linearity with the adjusted \(R^{2}\) of 0.99519 and an adequate FOM of 142. In addition, the sensor had good fabrication tolerance. So, we have no reason to doubt that our sensor could be applied as a photonic integrated component for real-time environmental monitoring.  
In future work, we intend to build an experiment setup to verify the simulated results. For simplicity, a basic midinfrared system comprises a supercontinuous light source covering wavelengths from 2.9 to 3.6 \(\upmu\)m, RI selection device, and midinfrared optical-spectrum analyzer (OSA). First, we aim to fuse a single-mode fiber at both ends of the sensor. Second, we will connect the light source, fiber, and OSA in sequence. Third, we will place the sensing section in the RI selection device, and then turn on the light source. Lastly, the corresponding spectrum could be obtained in the OSA by changing the RI.  
Conceptualization, N.C. and M.C.; methodology, N.C.; software, N.C.; validation, N.C., X.L. and M.C.; formal analysis, X.L.; investigation, J.Z.; resources, X.Z.; data curation, M.C.; writing--original draft preparation, N.C.; writing--review and editing, M.C.; visualization, J.Z. and N.C.; supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the National Key Scientific Apparatus Development of Special Item of China (project number 2016 YFF0101400).  
Figure 6: Loss spectra on variation of (**a**) cladding-hole diameter d\({}_{1}\) of \(\pm 3\%\) and (**b**) pitch \(\Lambda_{1}\) of \(\pm 3\%\).  
**Acknowledgments:** Sincerely thanks [PERSON], [PERSON] and [PERSON] in Shanghai University of Science and technology, for their kindly discussion with authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.