esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Experiment Results of Physical Motor Platform  
The most appropriate operation speed at no load can be found to be 15 rpm (0.5% rated speed), as shown in Figure 21a, and the SSSE is 31.2344 (rpm). When the online torque compensation strategy is applied, the SSSE drops significantly to 6.3968 (rpm), as shown in Figure 21b. The amplitude of the observed cogging torque is about 0.04 Nm, as shown in Figure 21c. An offset of \(-0.045\) Nm can be observed from the estimated torque, which may be caused by the nonlinear friction of the PMSM and the modeling uncertainty of the observer model. As the online compensate strategy reaches steady state, the offline torque table capture technique is applied. The cogging torque information of five rotary cycles is used to create the offline table. When the offline torque compensate strategy is applied, the SSSE drops significantly to 4.5703 (rpm), as shown in Figure 21d. As can be seen in Figure 21b,d, the resulting SSSE of offline compensation is lower than online compensation, which verifies the noise and uncertainty reduction capability.  
Figure 21: _Cont._  
Figure 20: Speed response: (**a**) at rated load; (**b**) at rated load after offline torque compensation.  
In order to create a rated load torque condition, a hysteresis brake is installed as a power measuring device, as shown in Figure 22. The specifications of the hysteresis brake are shown in Table 3.  
Figure 21: Speed response: (**a**) 15 rpm no load; (**b**) 15 rpm no load by online torque compensation; (**c**) Estimated cogging torque by online torque compensation strategy under 15 rpm no load; (**d**) Offline torque compensates speed response of 15 rpm no load.  
The most appropriate operation speed can be found at about 30 rpm. Since the hysteresis brake is a PM machine, the estimated cogging torque of the PMSM and the brake are combined together. After applying the online torque compensate strategy, the offline cogging torque table can be obtained by averaging the observed information for five turns, as shown in Figure 23a. It can be found that an amplitude of about 0.04 Nm of the PMSM's cogging torque is observed at a low frequency, while an amplitude of about 0.3 Nm of the hysteresis brake's cogging torque is observed at a high frequency. A DC offset of about \(-0.01\) Nm can be observed from the estimated torque, which may be caused by nonlinear friction or the modeling uncertainty of the platform model. Applying a rated load torque of 1.1 Nm, the resulting speed response is shown in Figure 23b where the SSSE is 81.4123 (rpm). After applying the offline torque compensation strategy, the SSSE drops significantly to 8.3834 (rpm), as shown in Figure 23c.  
Figure 23: Cont.  
\begin{table}
\begin{tabular}{c c} \hline
**Brake Spec.** & **Value** \\ \hline Rated Output Torque & \(6\ (\mathrm{Nm})\) \\ Max. Speed & \(25,000\ (\mathrm{rpm})\) \\ Max. Power & \(3400\ (\mathrm{W})\) \\ Poles Number & \(36\) \\ \hline \end{tabular}
\end{table}
Table 3: Power meter specification.  
Figure 22: Power meter platform.
# Fault-Tolerant Visual Secret Sharing Schemes without Pixel Expansion
## 2 Experimental Section  
### Visual Cryptography Concepts  
Different from previous secret sharing scheme techniques, when the shares are stacked, the confidential content can be interpreted with human vision directly. That is, a VC scheme can restore the secret without additional computation. Figure 1 shows the encryption and decryption process model of a VC scheme.  
In 1995, [PERSON] and [PERSON] proposed visual cryptography, by introducing a simple and perfectly-secure way that allows secret sharing without any cryptograph computation [1]. To decrypt the secret message, the reader should photocopy each pattern on a separate transparency, align them carefully and project the result with an overhead projector. This basic model can be extended into a visual variant of the \(k\) out of \(n\) secret sharing problem. The grey level of this combined share is proportional to the Hamming weight \(H(V)\) of the \"or\" \(m\)-vector \(V\). A solution to the \(k\) out of \(n\) visual secret sharing scheme consists of two collections of \(n\times m\) Boolean matrices \(C_{0}\) and \(C_{1}\). To share a white pixel, the dealer randomly chooses one of the matrices in \(C_{0}\), and to share a black pixel, the dealer randomly chooses one of the matrices in \(C_{1}\), The chosen matrix defines the color of the \(m\) subpixels in each one of the \(n\) transparencies. The solution is considered valid if the following three conditions are met:  
Figure 1: The visual cryptography (VC) encryption and decryption process model.  
For any \(S\) in \(C_{0}\), the \"or\" \(V\) of any \(k\) out of \(n\) rows satisfies \(\mathrm{H}(V)\leqslant d-a\,\times\,m\).
2. For any \(S\) in \(C_{1}\), the \"or\" \(V\) of any \(k\) out of \(n\) rows satisfies \(\mathrm{H}(V)\geqslant d\).
3. For any subset \(\{i_{1},i_{2},\ldots,i_{q}\}\) of \(\{1,2,\ldots,n\}\) with \(q<k\), the two collections of \(q\,\times\,m\) matrices \(D_{t}\) for \(t\in\{0,1\}\) obtained by restricting each \(n\,\times\,m\) matrix in \(C_{t}\) (where \(t=0,1\)) to row \(i_{1}\), \(i_{2}\), \(\ldots\), \(i_{q}\) are indistinguishable in the sense that they contain the same matrices with the same frequencies.  
In the above conditions, parameter \(a\) means the relative difference between stacked shares that come from a white pixel and a black pixel in the original picture. We would like \(a\) to be as large as possible. This scheme is perfectly secure and very easy to implement. However, it causes pixel expansion. That is, the size of the shares and the reconstructed image will be \(m\)-times the size of the original secret image, and it deforms the secret image. Therefore, we have the following discussion.
# A Novel Optimization Framework to Replicate the Vibro-Acoustics Response of an Aircraft Fuselage
###### Abstract  
In this work, a novel optimization framework, based on a Multi-Disciplinary Optimization (MDO) procedure, applied to the vibro-acoustic Finite Element Method (FEM) model of an aircraft fuselage mock-up, is proposed. The MDO procedure, based on an Efficient Global Optimization (EGO)-like approach, is implemented to characterize acoustic sources that replicate the sound pressure field generated by the engines on the fuselage. A realistic sound pressure field, evaluated by aeroacoustic simulations, was considered as the reference acoustic load, whereas two equivalent sound fields, displayed by two different arrays of microphones and generated by the same configuration of monopoles, were calculated by the proposed vibro-acoustic FEM-MDO procedure. The proposed FEM-MDO framework enables to set up ground experimental tests on aircraft components, useful to replicate their vibro-acoustic performances as if tested in flight. More in general, such a procedure can also be used as a reference tool to design simplified tests starting from more complex ones.  
vibro-acoustics; FEM; MDO; aircraft fuselage +
Footnote †: journal: Applied Sciences
## 3 Proposed Method
To evaluate the effectiveness of the optimization, the error was calculated based on the validation accuracy for the best hyperparameter combination. After employing the Bayesian optimization method, our model consists of 4 layers with 512, 256, 256, and 512 units, respectively. The DNN model begins with an input layer of 512 dimensions. This number represents the dimension of features at the output of ElasticNet after the feature selection block. The second layer consists of 256 units with ReLU activation and kernel L2 regularization set to 0.0296. This layer is followed by batch normalization and a dropout layer with a 0.1 dropout rate. Both L2 regularization and dropout are used to prevent the overfitting problem. The second layer includes 256 units. Here, the ReLU activation function and the same L2 regularization value of 0.0296 are used as in the first layer. Batch normalization and a dropout layer with a 0.1 dropout rate are used after the second layer. The third layer of the network has 512 units with the same L2 regularization value. After that, batch normalization and a dropout layer with a rate of 0.4 are again utilized. The layer at the output of the network includes a single unit utilizing a sigmoid activation function. This activation function is especially appropriate for binary classification tasks. The Adam optimizer is selected as optimizer of the DNN model, and the learning rate of the optimizer is chosen as 0.001. We choose binary cross-entropy, which is compatible with the sigmoid activation function, as the loss function. As stated in our results, this Bayesian-optimized DNN architecture achieves higher accuracy and generalization across test sets than other classical methods
# Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems +
## 2 Related Work
### LSGO Tackling Approaches  
#### 2.2.1 Non-Decomposition Methods  
There are two general groups of methods for solving LSGO problems [12]: non-decomposition and CC methods. The first group optimizes the complete problem without its decomposition. Non-decomposition methods are focused mainly on creating new mutation, crossover, and selection operators, applying specialized local search algorithms, reducing the size of the population, and other techniques for enhancing the performance of the standard optimization algorithms. Some popular non-decomposition methods are IPSOLS [19], LSCBO [20], FT-DNPSO [21], GOjDE [22], LMDEa [23], C-DEEPSO [24].
# Research on the Pavement Performance of Slag/Fly Ash-Based Geopolymer-Stabilized Macadam
## 3 Results and Discussion
### Mechanical Tests  
#### 3.2.1 Influence of Curing Age on Compressive Strength and Elastic Modulus  
The effect of curing age on compressive strength and elastic modulus of slag/fly-ash-based geopolymer-stabilized macadam (GFA-4) and cement-fly-ash-stabilized macadam (CFA-4) is shown in Figure 1a,b. It can be seen that the strength and elastic modulus of cement and geopolymer-stabilized macadam increased with curing ages. The compressive  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Code of Mix** & **Dosage of Geopolymer** & **Dosage of Fly Ash** & **Optimal Water Content** & **Maximum Dry Density** \\
**Proportion** & **(\%)** & **(\%)** & **(\%)** & **(g/cm\({}^{3}\))** \\ \hline CFA-3 & 3 & 12 & 5.76 & 2.36 \\ CFA-4 & 4 & 16 & 6.17 & 2.32 \\ CFA-2 & 2 & 8 & 5.31 & 2.30 \\ CFA-3 & 3 & 12 & 5.82 & 2.41 \\ CFA-4 & 4 & 16 & 6.14 & 2.29 \\ CFA-5 & 5 & 20 & 6.49 & 2.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of heavy compaction test of geopolymer-stabilized macadam.  
strength and elastic modulus of geopolymer-stabilized macadam showed a sharply increase in the first 14 d of curing, and then increased gently. The compressive strength increases from 8.76 MPa to 13.2 MPa, an increase by 50.7% in the age range from 7 d to 14 d, which is accompanied by an increase of the elastic modulus from 2220 MPa to 2912 MPa (31.1%). Furthermore, the strength and elastic modulus of geopolymer stabilized macadam are higher than those of cement-stabilized macadam.
## 4 Crosstalk Analysis
### Analysis of Signal-to-Noise Ratio between Forward and Backward Information Transfer  
In order to analyze the influence of the relationship between noise interference and information transmission, the ratio between signal power and noise is defined as the signal-to-noise ratio. Taking the forward transmission of information as an example, then we get:  
\[SNR=20\log\frac{V_{R}\left(\omega_{d},\ V_{d}\right)}{V_{R}\left(\omega_{p},\ V_{1}\right)} \tag{30}\]  
The signal carrier amplitude of the secondary side information receiving end is \(V_{R}(\omega_{d},V_{d})\), where \(\omega_{d}\) is the information transmission angular frequency. According to the information forward transmission channel gain \(G_{\mu}\), we have:  
\[V_{R}\left(\omega_{d},\ V_{d}\right)=\frac{V_{dp}}{1+\frac{L_{1}}{L_{2}}\frac {R_{d}/\omega L_{T1}-\omega C_{R}k_{d}+j}{R_{d}/\omega L_{T1}}}=\frac{V_{dp}} {1+\frac{L_{1}}{L_{2}}\frac{Q-\omega C_{R}k_{d}+j}{Q}} \tag{31}\]  
where \(Q\) is the quality factor of the \(LC\) resonant circuit when the signal is accepted.  
According to the transfer functions \(G_{NT1}\) and \(G_{NT2}\) of the power carrier to the transmitting end and the receiving end of the information forward transmission process, the power carrier amplitude of the signal receiving end is \(V_{r}(\omega_{p},V_{o})\), where \(\omega_{p}\) is the angular frequency of electric energy transmission. According to Formulas (24) and (25).  
\[V_{r}\left(\omega_{p},V_{1}\right)=\frac{\omega^{2}MV_{1}L_{T1}}{R_{2}\left(R_ {e}+R_{d}\right)+\omega^{2}M^{2}} \tag{32}\]Incorporating the above two formulas into \(SNR\), the signal-to-noise ratio formula is:  
\[SNR=20\log\frac{V_{d}}{V_{1}}+10\log\frac{L_{1}}{L_{2}}+20\log\frac{K\left[R_{2} \left(R_{ep}+R_{d}\right)+\omega^{2}M^{2}\right]}{(1-K^{2})\omega^{2}ML_{T1} \left(1+\frac{L_{1}}{L_{2}}\frac{Q+j-\omega C_{d}R_{d}}{Q}\right)} \tag{33}\]  
Among them, \(V_{d}\) is the voltage of the transmission source of the information forward transmission signal, and \(V_{1}\) is the DC power supply of the power transmission loop. According to Formula (33), when the system coil and circuit parameters are determined, the signal-to-noise ratio of information transmission is related to the coil coupling coefficient \(K\), the quality factor of the \(LC\) resonance circuit and the information transmission frequency. In order to improve the signal-to-noise ratio of signal transmission, the coil coupling coefficient \(K\) and quality factor \(Q\) can be improved. Therefore, when designing a signal transmission system, the system parameters should be fully and reasonably designed to reduce the system frequency transmission crosstalk and improve the signal-to-noise ratio, thereby reducing the bit error rate in the transmission, so that the signal demodulation module can successfully receive the transmitted signal and complete the information transmission.
## 3 System and Scenario Description
Let us take Switch B as an example. The flow tables in Switch B are shown in the figure. Flow Table 0, with the highest priority, is the \"topology selection table\". Flow Table 1 stores the forwarding rules for packets in Topology 1, while Flow Table 2 stores the forwarding rules for packets in Topology 2. It is assumed that a data packet contains a field that stores the content identifier (EID) of the content it belongs to. Packets belonging to the same content form a data flow. When Switch B receives a data packet, it first matches Flow Table 0. It identifies which data flow the packet belongs to by matching the EID field of the data packet. The packet is then forwarded to either Flow Table 1 or Flow Table 2 according to the corresponding action. Finally, it is forwarded through the corresponding port according to the matching destination address. This enables the data packets belonging to a specific data flow to be directed to the specified topology, and the path between a node pair in a logical topology is configured by the controller. It is assumed that the new network address of a mobile device can be obtained within a precise delay from a distributed on-site resolution system [21], and the packets' destination addresses can be updated to the new network address in some way. If a switch is part of a logical topology, the corresponding flow table needs to be installed in the switch. The number of flow entries in the flow table is equal to the number of paths from the source node to the destination node that traverse the switch in the topology. It should be noted that in a logical topology, there is at most one path between any source-destination node pair.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 3 Security Analysis
### Information Entropy Analysis  
The information entropy can be used to measure the randomness of the images. For grayscale images, the ideal information entropy is 8 [14]. The definition of information entropy is given as [15]:  
\[H=-\sum\limits_{i=0}^{255}p_{i}\log_{2}(p_{i}) \tag{17}\]  
where \(p_{i}\) represents the frequency of occurrence of pixel value \(i\).  
Figure 8: Peppers decryption diagram except for the bit-scrambling stage: (**a**) ours; and those algorithms proposed in (**b**) [1]; (**c**) [2]; (**d**) [4]; (**e**) [7]; and (**f**) [8].  
Tables 3 and 4 show the results of the information entropy test. It should be noted that the scrambled intermediate cipher image of this algorithm is a one-dimensional sequence of length \(MN\). For the convenience of comparison, it is rearranged into an image with a size of \(M\times N\) for processing. Compared with the algorithm put forth in [4] that uses single-pixel bit scrambling and those described in [7,8] that use adjacent-pixel bit scrambling, the information entropy of the scrambled intermediate cipher image and final cipher image is closer to the ideal value 8. After encryption by the algorithm in this paper, the pixel value has an excellent random distribution characteristic.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **Plain Image** & **Ours** & **Ref. [4]** & **Ref. [7]** & **Ref. [8]** \\ \hline Lena & 7.5683 & 7.9974 & 7.9969 & 7.9973 & 7.9973 \\ Text & 0.4912 & 7.9971 & 7.9961 & 7.9969 & 7.9967 \\ Fruits & 7.4782 & 7.9973 & 7.9970 & 7.9972 & 7.9972 \\ Peppers & 7.5701 & 7.9976 & 7.9973 & 7.9972 & 7.9971 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Information entropy test results of cipher image.  
Figure 9: Histogram comparison of Lena: (**a**) plain image; (**b**) scrambled intermediate cipher image; (**c**) cipher image.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **Plain Image** & **Ours** & **Ref. [4]** & **Ref. [7]** & **Ref. [8]** \\ \hline Lena & 7.5683 & 7.9691 & 7.9261 & 7.7532 & 7.9674 \\ Text & 0.4912 & 7.1349 & 2.9977 & 2.8157 & 6.9499 \\ Fruits & 7.4782 & 7.9650 & 7.8868 & 7.6300 & 7.9635 \\ Peppers & 7.5701 & 7.9499 & 7.8984 & 7.8290 & 7.9499 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information entropy test results of scrambled intermediate cipher image.
# Hiding Stealth Optical CDMA Signals in Public BPSK Channels for Optical Wireless Communication
## 3 The Proposed Optical Steganography System Setup and Simulation Results
### Structure of Public and Stealth Channels
Figure 10b,c indicates that the spectrum of SPC-OCDMA is flatter than that of SAC-OCDMA. In Figure 10c, the encoded signal is visible near the peak of the public signal. The spectrum of the public channel varies slightly after the SPC signal is transmitted in Figure 10a,b so that it can be buried in the public channel in the spectral domain. The encoded stealth signals are combined using a coupler and passed through a CFBG for further pulse broadening to lower the peak power. To effectively conceal the stealth channel beneath the public channel, the CFBG is used to produce a total dispersion of 1600 ps/nm. The waveforms before and after this stretching are displayed in Figure 11, which shows that the pulse is broader and the signal power is much lower after stretching. \[S_{1}(t)=\sqrt{\frac{2E_{b}}{T_{b}}}\cos(2\pi f_{c}t),\ \text{for bit 1}, \tag{11}\]9 Spectrum of public channel: (**a**) without stealth signal; (**b**) with SPC signal; and (**c**) with SAC signal. \[S_{0}(t)=\sqrt{\frac{2E_{b}}{T_{b}}}\cos(2\pi f_{c}t+\pi)=-\sqrt{\frac{2E_{b}} {T_{b}}}\cos(2\pi f_{c}t+\pi),\ \text{for bit 0}, \tag{12}\]0 Spectrum of encoded signal with: (**a**) SPC code; and (**b**) SAC code. Figure 12 presents the waveform of the public channel in the time domain. The waveform reveals the necessity of adopting CFBGs. Figure 12a,b shows that the stealth signal can be favorably hidden in the public channel using a CFBG with 1600 ps/nm dispersion. Without dispersion, the existence of the stealth signal is exposed, as shown in Figure 12c. The source powers of the stealth and public signals are \(-5\) and 0 dBm, respectively. Figures 10 and 12 demonstrate that the spectra and waveform of the public BPSK signal with and without the stealth signal are negligibly different when the noise power is \(-3\) dBm. Therefore, the stealth signal can be buried underneath the system noise and transmitted in the public channel without being detected.
# Adaptive Imputation of Irregular Truncated Signals with Machine Learning
## 5 Discussion
Like MAR, at 90% missing data especially, the accuracy of classification on the \"Error_Code\" class is notably lower than that of \"Status\", a trend that is consistent across all percentages of missing data. These results prove that our adaptive imputation system is effective on a simulated dataset specifically designed to reflect realistic data from an advanced manufacturing facility. Although our conclusions discussed in the next section are based on this singlesimulated dataset, there are several reasons to believe that the proposed method can be successfully applied to other cases. Firstly, our approach is based on well-established imputation techniques and ML algorithms that have been extensively validated in various contexts. Secondly, the adaptive nature of our imputation framework ensures that it can be tailored to the specific characteristics of new datasets, thereby enhancing its generalizability. Additionally, the modular nature of our framework allows for the incorporation of newer and potentially more effective imputation methods as they become available. This ensures that the framework remains relevant and capable of handling future data challenges.
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 5 Simulation Results  
In this section, we introduce the verification algorithm for evaluating the performance of the newly derived calibration factors. Also, we verify the performance of the proposed method in Sections 3 and 4 through system throughput simulations.