esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Digital Modulation Classification Based on Curriculum Learning
## 2 Signal Model and Relative Theories
### Deep Residual Network  
For the neural network, its classification accuracy depends on the depth of network. With the increase of depth, the classification accuracy firstly improves and then reduces. Researches show that the reduction of classification accuracy is caused by the disappearance of variation in network weight gradient. Aiming at solving this problem, we employ a deep residual network (ResNet), which contains multiple residual blocks as shown in Figure 1. The residual block mainly includes three convolution layers (Conv layer 1, Conv layer 2 and Conv layer 3) and a summator. There are two routes between these layers and summator: Sequential connection and shortcut connection. Firstly, the sequential connection conducts three consecutive convolutions on \(x\) to get \(F(x)\), which is used as an input for the summator. Then, the other input of the summator, \(x\), is obtained by shortcut connection. Finally, the output of whole residual block can be expressed as \(H(x)=F(x)+x\). As \(F(x)=0\) indicates the gradient disappearance of network weight, \(H(x)=x\) is an identity mapping that removes the three convolution layers and decreases the depth while the classification accuracy is ensured.  
Figure 1: The architecture of the (**a**) residual block and (**b**) ResNet.  
The complete architecture of ResNet used in this paper is shown in Figure 1b. It contains a convolution layer, a full connection layer and 33 residual blocks. Every residual block contains three convolution layers. Therefore, the utilized ResNet is a 101-layer DNN. The detailed parameters of ResNet are the same as the 101 layers ResNet parameters [19]. We only modified the input size and output size of the network.
## 3 Simulation Case  
### Simulation Structure  
In this study, the proposed optimum methodology is applied on a 5-story structure (shown in Figure 3) to verify the feasibility and desirability. The 5-story RC frame structure with 3 bays is designed according to CSDBC [4]. The building is assumed to be constructed in a region of earthquake fortification intensity 8 with 0.2 g peak ground acceleration and a site soil classification Class II with a first design earthquake group of CSDBC [4]; and this building has a story height of 3 m on all stories. The lateral-force resisting structure system consists of two orthogonal systems. One is three bays with a bay span of 6 m on both sides and a small bay span of 3 m in the middle bay. The other direction is six bays with the same bay span of 6 m. The plan dimensions are 15 m and 36 m. The dead loads for interior and top story are 6.0 kN/m\({}^{2}\) and 5.0 kN/m\({}^{2}\), respectively, and the live separately are 2.0 kN/m\({}^{2}\) and 1.5 kN/m\({}^{2}\). HRB335 with the design tension strength 335 N/mm\({}^{2}\) and elastic modulus \(2.0\times 10^{5}\) N/mm\({}^{2}\) is selected as the reinforcement material for all the beams and columns. The cross section and reinforcement of the beams and columns, which are designed by the CSDBC [4], are shown in Table 1.  
Figure 2: Flowchart of the proposed optimization method.  
The dynamic characteristic analysis and nonlinear time history analysis of the structure were developed by the OpenSees software platform (Version 2. 5. 0, The Regents of the University of California, Berkeley, CA, USA, 2016) [44]. Nonlinear fiber elements are used to model the structure beams and columns. Uniaxial materials Concrete02 and Steel02 are selected for concrete and steel simulation, respectively. The Rayleigh damping ratio of 0.05 recommended by CSDBC [4] is adopted. The P-Delta transformation of geometrics stiffness is used for the P-Delta effects. Dynamic characteristics analysis is conducted based on this numerical model, and the fundamental period of this building is 0.79 s.
## 4 Experiments and Discussion
### Data Sets  
We perform experiments on DAGM data sets. The German DAGM 2007 dataset includes 10 types of woven fabrics, as shown in Figure 4. These texture defects usually exist on the surface of textile cloth and wallpaper. The red lines in the figures indicate the defect. There are 8050 training and testing sets each, and the ratio of positive and negative samples for each type is approximately 1:7. This dataset is often used in industrial optical defect detection.
## 4 Results and Discussion
### High Density Straight NWs and Rare NWs with Thick 2D-Layer  
First numerical experiments for nanowires-in-polymer samples have been carried out for the two samples shown on Figures 1 and 2. The first sample presents straight NWs, relatively homogeneously distributed in plane and embedded in the HSQ polymer. The second has rare NWs, very dispersive in form and in sizes (\"_pyramidal_\" NW). Table 2 displays the effective parameters of the coating and core layers of these samples obtained by minimizing cost function (7) constructed on the basis of three-layer model (\(M=3\)). Figures 8 and 9 illustrate the dispersion properties of these samples simulated with these parameters. The left parts (a) of these figures are level-line images of the \(|K_{33}|\) surface, and the right parts (b) are SAW dispersion curves in the frequency-slowness plane. The experimental results are shown on the both parts by bold markers. One can see that in the case of nanowired interlayers, the theoretical plots are also in good agreement with them.  
As was expected, the effective moduli of the HSQ-NW layers of these samples exhibit large difference for \(x\) and \(z\) directions (e.g., there is a strong contrast between Young moduli \(E_{x}\) and \(E_{z}\) in Table 2) confirming a strong anisotropy of this new composite material. If we take a basic isotropic approximation of bulk values for the two constituting materials weighted by the filling factor (33-40% for Figure 1 and 6-8% Figure 2), the estimated in-plane and out-of-plane Young moduli of our HSQ-GaN NW composite layer are in the range 103-123 GPa and 26-32 GPa respectively. Comparing these values with the restored values of Table 1 for the first morphology (Figure 1), we observe that \(E_{z}\) is close to this basic estimation, whereas the \(E_{xy}\) is one o  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \(\mathbf{C_{11}}\) & \(\mathbf{C_{33}}\) & \(\mathbf{C_{13}}\) & \(\mathbf{C_{44}}\) & \(\mathbf{C_{66}}\) & \(\rho\) & \(\mathbf{h}\) & \(\mathbf{E_{x}}\) & \(\mathbf{E_{z}}\) & \(\mathbf{\mu_{z}}\) & \(\mathbf{\
u_{x}}\) & \(\mathbf{\
u_{z}}\) \\ \hline sample Figure 1 & 2.00 & 129 & 1.26 & 7.12 & 0.74 & 2.06 & 1059 & 1.85 & 128 & 7.12 & 0.26 & 0.499 \\ sample Figure 2 & 20.8 & 106 & 0.00 & 10.4 & 5.35 & 1.77 & 887 & 15.9 & 106 & 10.4 & 0.485 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effective parameters of the HSQ-NW composition obtained for the samples Figure 1 (straight NWs, \(t=400\)\({}^{\circ}\)C.) and Figure 2 (rare NWs and thick 2D-layer, \(t=400\)\({}^{\circ}\)C.).  
Figure 8: Experimental points (bold dots), \(|K_{33}|\) level-line image (**a**) and slowness dispersion curves (**b**); straight-NW sample Figure 1.  
Concerning the second morphology (Figure 2), the both values are far from the estimated with bulk moduli. This discrepancy may arise from the use of three layers model. Indeed, the parasitic rough layer changes considerably the elastic properties of the lower part of the composite medium. The simulation can be refined by dividing the composite medium into two sublayers.
# Vertex Cover Reconfiguration and Beyond +
## 6 FPT Algorithms
### Compression via Reconfiguration  
**Theorem 4**: _Vertex cover reconfiguration is at least as hard as vertex cover compression._  
We give a reduction from the latter to the former. For \((G,C,k)\), an instance of vertex cover compression, we let \(V(G)=\{v_{1},\ldots,v_{n}\}\) and form \(G^{\prime}=(V_{G}\cup V_{A}\cup V_{B},E_{G}\cup E_{J})\), where \(G^{\prime}\) consists of the disjoint union of a copy of \(G\) and a biclique \(K_{k,k}\). Formally, we have:  
\[V_{G} =\{g_{1},\ldots,g_{n}\}\] \[V_{A} =\{a_{1},\ldots,a_{k}\}\] \[V_{B} =\{b_{1},\ldots,b_{k}\}\] \[E_{G} =\{g_{i}g_{j}\mid g_{i}\in V_{G},g_{j}\in V_{G},v_{i}v_{j}\in E(G)\}\] \[E_{J} =\{a_{i}b_{j}\mid a_{i}\in V_{A},b_{j}\in V_{B},1\leq i\leq k,1 \leq j\leq k\}.\]  
We let \((G^{\prime},S,T,3k-1,6k-2)\) be an instance of vertex cover reconfiguration, where \(S=V_{A}\cup\{g_{i}\mid v_{i}\in C\}\) and \(T=V_{B}\cup\{g_{i}\mid v_{i}\in C\}\). Clearly, \(|S|=|T|=2k\) and both \(S\) and \(T\) are vertex covers of \(G^{\prime}\). We claim that \(G\) has a vertex cover of size \(k-1\) if and only if there is a reconfiguration sequence of length \(6k-2\) or less from \(S\) to \(T\).  
Before we can remove any vertex from \(V_{A}\), we need to add all \(k\) vertices from \(V_{B}\). However, \(2k+k=3k>3k-1\), which violates the maximum allowed capacity. Therefore, if there is a reconfiguration sequence from \(S\) to \(T\), then one of the vertex covers in the sequence must contain at most \(2k-1\) vertices. Of those \(2k-1\) vertices, \(k\) vertices correspond to the vertices in \(V_{A}\) and cover only the edges in \(E_{J}\). Thus, the remaining \(k-1\) vertices must be in \(V_{G}\) and should cover all the edges in \(E_{G}\). By our construction of \(G^{\prime}\), these \(k-1\) vertices correspond to a vertex cover of \(G\).  
Similarly, if \(G\) has a vertex cover \(\widehat{C}\) such that \(|\widehat{C}|=k-1\), then the following reconfiguration sequence transforms \(S\) to \(T\): add all vertices of \(\widehat{C}\), remove all vertices of \(C\), add all vertices of \(V_{B}\), remove all vertices from \(V_{A}\) and finally add back all vertices of \(C\) and remove those of \(\widehat{C}\). The length of this sequence is equal to \(6k-2\) whenever \(C\cap\widehat{C}=\emptyset\) and is shorter otherwise.
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
## 2 Methodologies and System Description
### Chebyshev Filter  
An equiripple response in either the passband or the stopband characterizes the Chebyshev filter. Compared to the Butterworth filter, this filter provides a steeper transition in the transition band at the cost of introducing ripples in the passband or stopband. Chebyshev filters are categorized into two types based on their ripple characteristics.  
1. Type I Chebyshev Filter  
This filter has equal ripples in the passband and remains flat in the stopband. This design suits applications that can tolerate passband oscillation but require high stopband attenuation.  
2. Type II Chebyshev Filter  
This kind of filter is also called an inverse Chebyshev filter. It has equal ripples in the stop band and remains flat in the pass band. It is suitable for situations where a stable response needs to be maintained in the pass band.  
Chebyshev filters can be designed using mathematical formulas such as transfer functions, polynomials, and others [15]. The transfer functions are expressed as in Equations (8) and (9), respectively.  
\[H_{1}(s)=\frac{1}{\sqrt{1+\epsilon^{2}T_{n}^{2}\left(\frac{\omega_{c}}{\omega _{c}}\right)}} \tag{8}\]  
\[H_{2}(s)=\frac{1}{\sqrt{1+\epsilon^{2}U_{n}^{2}\left(\frac{\omega_{c}}{\omega }\right)}} \tag{9}\]  
* Equations (8) and (9) are only true when \(s=j\omega\), \(\epsilon\) is the ripple coefficient, \(\omega_{\text{C}}\) is the cutoff frequency, and \(n\) is the order of the filter. \(T_{n}\) is a Chebyshev polynomial of type I, and \(U_{n}\) is a Chebyshev polynomial of type II.
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 8 Conclusions  
This paper has presented a new energy-efficient LDPC decoding method called partial LDPC decoding by taking into account the characteristics of the NAND flash channel. The proposed algorithm decodes by using a portion of the full H-matrix in order to save the energy consumed in the decoding. The partial decoding can provide a range of error-correcting capabilities by adjusting the size of the partial H-matrix, enabling a trade-off between energy consumption and error-correcting capabilities. The existence of a partial H-matrix, which achieves almost the same decoding performance as that of the full H-matrix for a specific SNR, has theoretically been analyzed and proved by intensive simulations. A prototype decoder to implement the proposed algorithm has been developed for 4 KB LDPC codes using a 65 nm CMOS process. The proposed decoder reduces energy consumption by 93% compared to recent LDPC decoding architectures.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Wearable Light-and-Motion Datalogers for Sleep/Wake Research: A Review
## 3 Review
### Communication Interface and Software  
All dataloggers have computer (PC) software that allows researchers to program the device to record data offline and download it after the recording. On MMC and MMS, this can also be done using a smartphone application (where data is sent live). The way of communication with PC varies among units (Table 1). A simple and secure direct connection via USB cable is available for devices having a USB port (N = 9). Four devices have metal contacts on them (ActTrust/ActTrust2, Activatch 2, GENEActive) or infrared optical contacts (Daysimeter-D), requiring an intermediate docking station (cradle, base, hub, reader--through which these devices are also charged) to communicate with a PC. The remaining four devices have exclusively wireless connection: using an infrared adapter plugged into a PC (N = 2) or Bluetooth (N = 2). The readout usually takes less than a minute. The collected data remains saved on the device until it is reinitialized.  
The software of nine devices provide both graphical and analytical reports--actogram and circadian and/or sleep indices. An actogram is a 24-h graphical representation of motion, light values, event markers, and program-defined marks for the sleep onset and offset (Figure 2). The circadian and sleep indices are calculated using built-in algorithms selected by manufacturers to analyze activity score dynamics. The list of sleep indices may include sleep latency, onset, offset, duration, fragmentation, efficiency, and the list of circadian indices may include relative amplitude, interdaily stability, intradaily variability (e.g., [58]). Some of these analytics integrated into the software (for example, Activware,MotionWare) give researchers the possibility to drag the event markers that outline the sleep episode to the appropriate positions on the actogram with automatic recalculation of the output measures, which is very convenient for researchers. Researchers may want to redefine the sleep onset/offset times based on the subject's sleep log data, event marker's position, and light levels at that time. As the built-in algorithms may vary across devices, the calculated values may also vary.  
The software of the other eight devices do not provide circadian or sleep analysis. They execute only operational actions (programming the device run and reading the recorded data), graphical display of data (not all devices), and access/export of data.  
Most of the software (applications) provide the opportunity to freely export original data either in converted units (e.g., lux) or row units (specified partly in Table 1). Various software exist that can compute circadian, sleep and other rhythmic variables from the imported data, sometimes obtained using different types of devices and large populations. Many such tools are based on the cosionor method [59] and use non-traditional packages such as Python [60, 61]. Some computational methods and software tools are presented in Supplementary Table S2 (includes references [59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]).
## 6 Future Research and Work
### Reduction in Required Number of Reference Signals \(N\)  
The computational time of the proposed nonlinear compensation method increases in proportion to the required number of reference signals, \(N\). The optimal value of \(N\) variesdepending on nonlinear parameter \(\xi\) and SNR. Therefore, if the SNR and the degree of distortion are known in advance, the required \(N\) can be estimated. By estimating the SNR and equipping the receiver with color filters corresponding to the transmitter LEDs to pre-limit the received color components, the necessary number of reference signals can be reduced, thereby decreasing the computational time.
## 3 Experimental Results
### Quantitative Evaluation
#### 3.4.5 Overall Comparison with Other Segmentation Methods  
A comparison with other methods (REF [18], Snake [15], Flood fill [28]) using all 200 test samples is carried out to ensure adaequate experimentation in proving the effectiveness of TongueNet in a quantitative way (refer to Table 4). The mean IOU (MIOU), mean PA (MPA), mean F-score (MFscore), mean Precision (MPrecision), and mean Recall (MRecall) are utilized. The mean value is the average of all test samples:  
\[Mmetric=\frac{1}{n}\sum_{i=1}^{n}metric(i), \tag{13}\]  
Figure 14: F-score, precision and recall comparison of TongueNet and REF using radar charts. (**left**) F-score; (**center**) precision; (**right**) recall.  
Figure 13: PA comparison of TongueNet (green) and REF (red).  
where \(metric(i)\) represents the \(i\)th value of a specific metric and \(n\) represents the number of test samples. The highest value of each metric is marked in bold font.  
From Table 4, it can be seen that the proposed TongueNet outperforms other methods, especially in MIOU, which is over 10% higher than others on average, implying a higher accuracy in tongue region prediction. In terms of MF-score, the gap between TongueNet and other comparison methods range from 3.25% to 36.27%, indicating the superiority of TongueNet (from a comprehensive perspective way). As for MPA, MPrecision, and MRecall, the improvements achieved by the proposed method varied from 1.63-7.50%, 9.13-15.04%, and 0.81-49.36%, respectively. All three of these metrics proved the superiority of TongueNet not only in reliability and completeness, but also in overall pixel prediction. In the statistical significance test, we set the significance level to be \(\beta=0.05\), where the \(p\)-values for different metrics have been calculated to show the significance difference of TongueNet and REF.
## 5 User Study
### Participants  
A total of 40 participants took part in the experiment, including 26 women and 14 men. Demographic information (age and gender) was collected at the start of the session. The age distribution was as follows: 19 participants were aged 18-24, 11 were 25-34, 4 were 35-44, 5 were 45-54, and 1 participant was aged 55-64.  
The participants were randomly assigned to one of the two experimental conditions (RJAS Active or RJAS Inactive). Among them, 18 participants had previously interacted with the robot Mini, while 22 had not. Table 2 shows the distribution of participants across gender, age groups, and prior experience with the robot, separated by experimental condition. We believe the sample was well balanced across these demographic variables, with a comparable number of participants in each condition and no major disparities in background characteristics that could systematically bias the results. As a result of the assignment, both conditions included 20 participants.  
The University Carlos III of Madrid approved this experiment (under the name _PI_perception_JA_I) It was performed under the Spanish Organic Law 3/2018 of December 5 th on the Protection of Personal Data and Guarantee of Digital Rights (LOPDGDD) and the European General Data Protection Regulation (Regulation (EU) 2016/67, abbreviated GDPR), and informed consent was obtained from all participants and/or their legal guardians.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Gender** & **Age Group** & **RJAS Inactive** & **RJAS Active** & **Total** \\ \hline Male & 18–24 & 2 (5.0\%) [1] & 3 (7.5\%) [1] & 5 \\  & 25–34 & 2 (5.0\%) [0] & 5 (12.5\%) [3] & 7 \\  & 45–54 & 0 (0.0\%) [0] & 1 (2.5\%) [1] & 1 \\  & 55–64 & 1 (2.5\%) [0] & 0 (0.0\%) [0] & 1 \\ Female & 18–24 & 10 (25.0\%) [2] & 4 (10.0\%) [2] & 14 \\  & 25–34 & 2 (5.0\%) [2] & 2 (5.0\%) [2] & 4 \\  & 35–44 & 1 (2.5\%) [1] & 3 (7.5\%) [1] & 4 \\  & 45–54 & 2 (5.0\%) [1] & 2 (5.0\%) [1] & 4 \\ \hline
**Total** & – & 20 (50.0\%) [7] & 20 (50.0\%) [11] & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of participants by gender, age group, and experimental condition. Values are reported as _count (percentage of total)_; values in brackets indicate how many had prior interaction with the robot Mini.
## 4 Results
### Sensitivity
#### 4.3.1 Analysis of the Compression Ratio  
In order to perform this analysis, the compression ratio was maintained at values of 10, 12 and 15 while the other variables were left to vary within their range (10-30 for Pp, 1420-1520 for Gt and 850-950 for Ct). Figure 10 shows that at Cr = 15, maximum values for the net power and exergy efficiency are obtained but at the cost of a high emission of CO2. It could also be seen that reducing the value of Cr from 15 to 10 would cause a reduction in the CO2 emission that is more significant than a reduction of the net power and exergy efficiency. This suggests that employing a lower compression ratio will greatly minimize the CO2 emission with negligible effects on the optimal values of the net power and exergy efficiency. It is interesting to note that, in comparison with other decision variables, the Cr produces the greatest change/effect in the CO2 emission and yields the least value.
# Design Options to Improve the Dynamic Behavior and the Control of Small H-Darrieus VAWTs
## 2 Background
### Response to Aerodynamic Unsteadiness  
The capability of the turbine to react promptly to rapidly changing incoming wind conditions depends on both the time scale of the air velocity fluctuations and the response characteristics of the turbine itself. VAWTs generate a highly unsteady aerodynamic environment. The incident air velocity is not constant in both intensity and direction, resulting in varying chordwise and normal blade velocity components [34,35]. Besides this intrinsic unsteadiness induced from VAWT kinematics, even in steady-state flow, another source is given by the vortexes generated by the blade-to-tower and blade-to-blade interaction. An additional unsteadiness is provided by the wind shear, the natural turbulence, and the gusts. This complex flow field reflects the periodic and random variation of driving and normal blade forces and pitching moments. With concern for instance to the typical operational context of a small wind turbine within the urban environment, both flow distortions and turbulence are created by natural and anthropic elements, such as trees, buildings, etc. [7,35]. Even though just a few works have investigated the impact of turbulence on small wind energy conversion systems [36,37], it is recognized that the turbulence spectrum in the urban roughness sub-layer is characterized by a peak between \(10^{-1}\) Hz and 1 Hz, thereby determining a characteristic time scale on the order of seconds [38]. Several characteristic times can be identified for a small wind turbine, governing the transient process between two steady-state operating conditions. They can be related to the relaxation of the boundary layer around the blades (leading to a time constant on the order of \(10^{-2}\) to \(10^{-3}\) s [39]), as well as the one of the rotor wake (where a time constant on the order of 0.1 to 1 s can be inferred, see again [39]). The inertial response of the turbine appears to be one (or even two) orders of magnitude higher than those related to the aerodynamic phenomena described above (being on the order of tens of seconds, or even a minute; see [18]), especially in the case of VAWTs.