esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Experiments for Seen Chinese Character Recognition  
We pre-train our model on 3755 classes of Chinese characters. Then, the model is trained on HWDB1.0-1.1 and tested on full sets of ICDAR2013. At last, we combine the training parameters and pre-training parameters to fine-tune the model. The result is illustrated as SAE in the third block of Table 2. The first block of Table 2 shows the performance of human-beings [41] and character-based methods [44; 45; 46; 47] and the second block presents some few/zero-shot methods [1; 2; 12; 13; 17; 48]. The experimental results show that our SAE still outperforms few/zero-shot methods and some character-based methods, although it does not surpass all the character-based methods. This is probably because our model is mainly focusing on zero-shot recognition and therefore fails to achieve ideal results on seen recognition task.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Handwritten**} & \multicolumn{4}{c}{**CCR Accuracy under Different Seen Classes**} & \multirow{2}{*}{**Printed Article**} & \multicolumn{4}{c}{**CCR Accuracy under Different Seen Classes**} \\ \cline{2-2} \cline{4-11}  & **500** & **1000** & **1500** & **2000** & **2755** & **500** & **1000** & **1500** & **2000** & **2755** \\ \hline DenseRAN [12] & 1.70\% & 8.44\% & 14.71\% & 19.51\% & 30.68\% & DenseRAN [12] & 0.20\% & 2.26\% & 7.89\% & 10.86\% & 24.80\% \\ HDE [13] & 4.90\% & 12.77\% & 19.25\% & 25.13\% & 33.49\% & HDE [13] & 7.48\% & 21.13\% & 31.75\% & 40.43\% & 51.41\% \\ Stroke-based [1] & 5.60\% & 13.85\% & 22.88\% & 25.73\% & 37.91\% & Stroke-based [1] & 7.03\% & 26.22\% & 48.42\% & 54.86\% & 65.44\% \\ Ours & **5.91\%** & **14.35\%** & **24.32\%** & **30.17\%** & **40.22\%** & Ours & **8.25\%** & **32.24\%** & **50.72\%** & **57.13\%** & **68.88\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of zero-shot recognition with various approaches. Our model utilize the SAE based on ResNet and Transformer pre-trained on the second form of data sets.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline Human Performance [41] & 96.13 \\ HCCA-GoogLeNet [44] & 96.35 \\ DirectMap+ConvNet+Adaption [45] & 97.37 \\ M-RBC+IR [46] & 97.37 \\ Template+Instance [47] & 97.45 \\ \hline DenseRAN [12] & 96.66 \\ FewShotRAN [48] & 96.97 \\ HDE [13] & 96.74 \\ Stroke-based [1] & 96.28 \\ REZCR [17] & 96.35 \\ STAR [2] & 97.11 \\ \hline Ours & 97.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of seen Chinese character recognition with various approaches.
## 2 Nonlinear Feedback Control Analysis of Visual Information in Foggy Section of Expressway
\begin{aligned} & U_{1}(s)=& C_{11}(s)[X_{1}(s)-Z_{1}(s)]+C_{11}(s)[X_{2}(s)-Z_{2}(s)]+ \dots+C_{1i}(s)[X_{i}(s)-Z_{i}(s)]\\ &+C^{\prime}{}_{1,i+1}(s)[\Xi_{i+1}(s)-M_{i+1}(s)]+\dots+C^{ \prime}{}_{1n,i+1}(s)[\Xi_{n}(s)-M_{n}(s)]\\ & U_{2}(s)=& C_{21}(s)[X_{1}(s)-Z_{1}(s)]+C_{22}(s)[X_{2}(s)-Z_{2}(s)]+ \dots+C_{2i}(s)[X_{i}(s)-Z_{i}(s)]\\ &+C^{\prime}{}_{2,i+1}(s)[\Xi_{i+1}(s)-M_{i+1}(s)]+\dots+C^{ \prime}{}_{2n,i+1}(s)[\Xi_{n}(s)-M_{n}(s)]\\ &\dots\cdot\\ U_{n}(s)=& C_{n1}(s)[X_{1}(s)-Z_{1}(s)]+C_{n2}(s)[X_{2}(s)-Z_{2}(s)]+ \dots+C_{ni}(s)[X_{i}(s)-Z_{i}(s)]\\ &+C^{\prime}{}_{n,i+1}(s)[\Xi_{i+1}(s)-M_{i+1}(s)]+\dots+C^{ \prime}{}_{m,i+1}(s)[\Xi_{n}(s)-M_{n}(s)]\end{aligned} \right. \tag{16}\]  
According to Equation (16), \(\Xi(s)\) represents a predetermined value; \(M(s)\) represents the measured value of different instrument measurements; \(\Xi(s)-M(s)\) represents different error values due to different instrument measurements; and \(C_{a,b}(s)\) and \(C^{\prime}{}_{a,\delta}(s)\) represent two different control influencing factors. Equation (16) is decomposed into the control factor transfer function [31] matrix \(C(s)\), written as follows:  
\[C(s)=\left(\begin{array}{l}C_{11}(s)C_{12}(s)\cdot\dots\cdot C_{1i}(s)C^{ \prime}{}_{1,i+1}(s)\cdot\dots\cdot C^{\prime}{}_{1n}(s)\\ C_{21}(s)C_{22}(s)\cdot\dots\cdot C_{2i}(s)C^{\prime}{}_{1,i+1}(s)\cdot\dots \cdot C^{\prime}{}_{2n}(s)\\ \dots\cdot\\ C_{k1}(s)
# Iterative Learning Control for Vibration Suppression of a Robotic Arm
## 2 Control of Robotic Arm with Elastic Joints
### Elastic-Joint Dynamics Modeling
#### 2.1.2 Inverse Model Based on Recursive Newton-Euler Method  
Assuming that the elastic joint is linear, the elastic torque is expressed as  
\[\mathbf{\tau_{e}}=\mathbf{K}(\mathbf{G}_{r}\mathbf{\theta}-\mathbf{q}), \tag{3}\]  
where \(\mathbf{K}\) is the linear torsional spring coefficient. In practical application, \(\mathbf{\tau_{e}}\) is obtained from the torque sensor installed at the end of the link, \(\mathbf{\theta}\) is acquired by the transmission of the encoder of motor, and \(\mathbf{K}\) is obtained from the manufacturer. The angular displacement  
Figure 1: Illustration of the elastic joint for a robotic arm. (**a**) Elastic joint. (**b**) Flowchart of the VH-ILC-EJ method. (**c**) Robotic arm.  
An acceleration term is added to the proposed input-based ILC to suppress abrupt vibrations:  
\[\textbf{u}_{c,k+1}\textbf{=}\textbf{u}_{c,k}\textbf{+}\textbf{K}_{p}(\textbf{q}_ {d}\textbf{-}\textbf{q}_{k})\textbf{+}\textbf{K}_{d}(\hat{\textbf{q}}_{d}-\hat{ \textbf{q}}_{k})\textbf{+}\textbf{K}_{a}(\tilde{\textbf{q}}_{d}-\tilde{\textbf {q}}_{k}), \tag{7}\]  
where \(\textbf{K}_{a}\) represents the gain of the angular acceleration error. Subscript \(k\) denotes the \(k\)th data. \(\tilde{\textbf{q}}_{d}(t)\) and \(\tilde{\textbf{q}}_{k}(t)\) denote the desired acceleration of the link and the actual acceleration of the \(k\)th output, respectively. Further, the past information regarding the input, which contains the interference to which the system was subjected in the past, is added to improve the tracking performance in (8a). \(\alpha\) is the parameter that adjusts the ratio between the previous and present input in (8b). \(\lambda_{k}\) is a positive number. The function, \(\alpha\), decays as the iteration number, \(k\), increases. When \(k\) is larger than \(\lambda_{k}\), \(\alpha\) is 0. This property means that when the number of iterations is more than \(\lambda_{k}\), it no longer needs the information obtained from the previous iteration and guarantees the convergence of the ILC algorithm. Figure 3 shows the function of \(\alpha(k)\) for various gains \(\lambda_{k}\).  
\[\textbf{u}_{p,k+1}\textbf{=}(1-\alpha)\textbf{u}_{c,k}\textbf{+}\textbf{K}_{ p}(\textbf{q}_{d}\textbf{-}\textbf{q}_{k})+\textbf{K}_{d}(\hat{\textbf{q}}_{d}- \hat{\textbf{q}}_{k})\textbf{+}\textbf{K}_{a}(\tilde{\textbf{q}}_{d}-\tilde {\textbf{q}}_{k})+\alpha\textbf{u}_{c,k-1} \tag{8a}\] \[\text{where }\alpha(k)=\left\{\begin{array}{l}\frac{1}{\lambda_{k}} \left(\frac{\lambda_{k}}{k}-1\right),\,k<\lambda_{k}\\ 0\quad,\,k=1\,\text{or}\,k\geq\lambda_{k}\end{array}\right. \tag{8b}\]  
Figure 3: The parametric effect of \(\lambda_{k}\) on \(\alpha(k)\).  
Figure 2: Block diagram of the input-based ILC.
## 4 Discussion
### Subjective Assessment
#### 4.1.1 Assessment of Test Image A  
Test image A in Figure 5a mainly consists of various vegetation cover types and shadows with irregular shapes. In this situation, the resulting images using our proposed approach, the NSVDI-based method [18] and the SDI-based method [19], as shown in Figure 5e,g,h, respectively, demonstrate a similar visual appearance to the reference image in Figure 5b. When compared with the reference image in Figure 5b, the resulting image by the SRI-based method [11] in Figure 5c shows a good result. However, this resulting image still has some shortcomings: the shapes of many shadow areas are distorted to some extent because the shape information is only preserved by the intensity component rather than both the chromaticity and the intensity. Also, some small shadow regions are more or less omitted. In addition, the resulting image obtained using the NSVDI-based method [18] shown in Figure 5d demonstrates relatively good performance. Due to the utilization of invariant color properties of saturation and intensity, this NSVDI-based method accurately highlights most shadow regions and classifies shadows against vegetation for test image A, which has high vegetation coverage. The shadow omission problem still remains because some small shadow regions are omitted in the resulting image. Furthermore, the resulting image obtained with the MC3-based method [20] shown in Figure 5e appropriately distinguishes shadow pixels from vegetation pixels. However, this method fails to detect small shadows effectively, and misclassifies some bluish vegetation pixels as shadows, because bluish pixels are highlighted in the MC3 index. Similarly, the resulting image using the SI-based method [14] shown in Figure 5f generally delineates contours of large shadow regions. Nevertheless, the small shadow omission problem is still serious: most small shadows are confused with vegetation, because vegetation pixels may present similar reflectivity as shadows in the combination of near infrared, red and blue bands. Similar to the appearance in the resulting image using the NSVDI-based method [18] shown in Figure 5d, the resulting image by the SDI-based method [19] shown in Figure 5g presents a relatively accurate shadow detection result even though vegetation pixels are misclassified as shadow pixels to some extent in comparison with the reference image in Figure 5b. As for the resulting image using our proposed MPSI-based approach in Figure 5h, most small shadow regions are correctly identified, and most vegetation pixels are correctly classified. Due to the combined contribution of both invariant color components (hue and intensity), and the near infrared band and the red band, this resulting image shows a closer appearance to the reference image in Figure 5b compared with the other comparative methods mentioned above.
# Post-Quantum Authentication in the MQTT Protocol
## 2 Problem Statement
### Idea  
Current methods to provide authentication in other protocols, such as TLS ([[https://www.ietf.org/rfc/rfc5246.txt](https://www.ietf.org/rfc/rfc5246.txt)]([https://www.ietf.org/rfc/rfc5246.txt](https://www.ietf.org/rfc/rfc5246.txt)) (accessed on 26 January 2023)), use digital signatures, and therefore, this would be a logical way to provide authentication in the MQTT protocol.  
Digital signatures are especially important for IoT networks to verify that all devices are legitimate users and to prevent the access of intruders to the network, as well as to prevent them from making undetected changes to communications between IoT devices that are part of this network. Since IoT devices are focused on information exchange, it is important to not only provide proper security for the communication in the form of encryption, but also to prevent illegitimate users from gaining access to the communication channels through the use of authentication that is performed using digital signatures [22].  
NIST is currently in the process of defining a set of standard postquantum public key cryptographic algorithms ([[https://csrc.nist.gov/projects/post-quantum-cryptography/post-quantum-cryptography-standardization](https://csrc.nist.gov/projects/post-quantum-cryptography/post-quantum-cryptography-standardization)]([https://csrc.nist.gov/projects/post-quantum-cryptography/post-quantum-cryptography-standardization](https://csrc.nist.gov/projects/post-quantum-cryptography/post-quantum-cryptography-standardization)) (accessed on 26 January 2023)), including postquantum digital signature algorithms. Therefore, following a similar handshake process to TLS, postquantum digital signatures could be used to provide authentication in MQTT.  
However, as described in [23], we can expect significant overheads in terms of bandwidth, computational time and storage requirements with digital signatures. In [23], it is proposed that some efficiency gains can be made by authenticating using KEMs instead of signatures. KEMs are generally used to provide confidentiality through asymmetric encryption. This means that a public encryption key is available, which anyone can use to encrypt. However, only the owner of the public key has access to the private decryption key to read the encrypted message. The proposal is for TLS postquantum authentication, and promises less than 10% of the computational time for KEM-based authentication compared to a digital signature scheme.
## 4 Adaptive Disturbance Suppression Integrated Controller Design for UAV  
### NDI Controller Based on Real-Time Aerodynamic Parameter Identification  
Figure 4 shows the structure of adaptive NDI control.  
Considering that the change of airflow angle \(\alpha\), \(\beta\), \(\gamma\) is slower than that of angular velocity \(\omega_{x}\), \(\omega_{y}\), \(\omega_{z}\), the system is divided into a slow loop and fast loop. The inner slow loop dynamic inversion is to solve the next dynamic inversion command. According to the  
Figure 4: Adaptive NDI Controller Architecture.  
instructions of roll angle \(\phi\), pitch angle \(\theta\), and angle of sideslip \(\beta\), the instructions of \(\omega_{x}\), \(\omega_{y}\), and \(\omega_{z}\) are solved.  
\[\left\{\begin{array}{l}\omega_{xc}=K_{\phi}(\phi_{c}-\phi)-\tan(\theta)( \omega_{z}\sin(\phi)+\omega_{y}\cos(\phi))\\ \omega_{yc}=-K_{\beta}(\beta_{c}-\beta)-\frac{g}{V}\sin(\phi)\\ \omega_{zc}=\frac{1}{\cos(\phi)}(K_{\theta}(\theta_{c}-\theta)+\omega_{y}\sin (\phi))\end{array}\right. \tag{25}\]  
The lower-right corner '\(c\)' represents the instruction value of the variable, and this setting also takes effect in other parts of this article. \(K_{\phi}\), \(K_{\theta}\), \(K_{\beta}\) represents the gain of the controller.  
Bringing the above control instructions back to the dynamic equation, we can obtain:  
\[\left\{\begin{array}{l}\dot{\phi}=K_{\phi}(\phi_{c}-\phi)\\ \dot{\theta}=K_{\theta}(\theta_{c}-\theta)\\ \dot{\beta}=K_{\beta}(\beta_{c}-\beta)\end{array}\right. \tag{26}\]  
According to the solution formula of first-order linear non-homogeneous differential equation, we can obtain:  
\[\left\{\begin{array}{l}\phi=\phi_{c}+(\phi(0)-\phi_{c})e^{-K_{\phi}t}\\ \theta=\theta_{c}+(\theta(0)-\theta_{c})e^{-K_{\phi}t}\\ \beta=\beta_{c}+(\beta(0)-\beta_{c})e^{-K_{\beta}t}\end{array}\right. \tag{27}\]  
The time domain response function in the same form as the orbital angle can be obtained, and convergence can be achieved in a limited time.  
By solving the inverse of the dynamic equation of the inner loop to track these angular rate commands, the required angular acceleration proportional to the angular rate error can be obtained by:  
\[M_{c}=IK_{\omega}(\omega_{c}-\omega)-(\hat{M}-\omega\times I\omega) \tag{28}\]  
In the formula, \(M\) represents moment, \(\hat{M}\) is determined by the current identification model of the aircraft, and \(K_{\omega}\) is the control gain.  
Substituting the control torque command into \(\dot{\omega}\) in the kinetic equation gives, in the formula, \(M\) as the torque value obtained by the sensor.  
\[\dot{\omega}=K_{q}(\omega_{c}-\omega)+I^{-1}(M-\hat{M}) \tag{29}\]  
Combined with the results of real-time identification in the frequency domain, the final control rudder deflection solution can be expressed as:  
\[\delta_{c}=\frac{C_{m_{c}}-(C_{m_{x}}\times\alpha+C_{m_{\frac{g\omega}{V}}} \times\frac{g\omega}{2V}+C_{m_{0}})}{C_{m_{\delta}}} \tag{30}\]
# A General Framework for Visualization of Sound Collections in Musical Interfaces
## 4 Experiments
### Feature Learning  
#### 4.2.1 Settings  
We compared MFCCs to feature learning with the spherical k-means algorithm. In this case, we only measured KNN classification error, since the feature spaces are different, which would affect the comparison of continuity and trustworthiness. In both cases, we used windows of 1024 samples (\(\sim\)20 ms) with hops of 512 samples (\(\sim\)10 ms). In each case, we selected the best performing parameters when relevant. MFCCs were computed using 40 bands and 13 coefficients. For the spherical k-means features, we used 40 bands and 1024 clusters. We concatenated every consecutive pair of frames into a single frame (a technique often known as _shingling_). Larger numbers significantly impacted performance but would not improve results (a value of 2 did provide an improvement). With respect to pooling, the best results for MFCCs were obtained using mean, standard deviation, min and max for the original time series and first derivative. For spherical k-means features, we used only mean and standard deviation of the raw features.
## 6 Conclusions  
In this work, an autoencoder-based geometric shaping model framework is proposed to jointly optimize QAM signals at the symbol-wise and bit-wise levels for underwater visible-light communication with a green LED using CAP modulation. Traditional shaping methods always give shaping schemes based on theoretical models, which do not consider the specific characteristics of the actual transmission channel, resulting in errors caused by the discrepancy between theory and reality. To solve this problem, we use a neural-network-based model to model the entire underwater visible-light communication channel and use an autoencoder to automatically generate a symbol-wise globally optimal shaping scheme. A revised pairwise optimization (RPO) algorithm is then used for bit-wise optimization. In the experiment, a 2.05 Gbps transmission can be achieved under the 7% HD-FEC threshold by employing the autoencoder-based 8 QAM (AE-8 QAM) optimized by the AEGSM, which is 103 Mbps faster than the Norm-8 QAM. Besides, the AE-8 QAM enables the UVLC system to operate within a larger dynamic range of driving voltages, extending it by 58.37%, which demonstrates its ability to adapt to dynamic signals and different channel conditions. Such an auto-shaping framework can also bring new inspiration to other communication fields.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Up-Sampling** & **Roll-Off Factor** & **Bandwidth** & **Carrier Frequency** \\ \hline
4 & 0.205 & sampling rate 1/ up-sampling multiple & sampling rate/up-sampling multiple \(\times\) [(1 + roll-off factor) \(\times\) 0.5 + 0.01] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for DSP processing.  
Figure 11: BER performance versus bit rate with different shaping schemes.  
**Author Contributions:** Conceptualization, Y.W., L.Y., H.Z., C.S., N.C. and J.S.; methodology, Y.W., L.Y., H.Z. and J.S.; software, Y.W., L.Y. and H.Z.; validation, Y.W.; formal analysis, Y.W.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, J.S.; visualization, Y.W.; supervision, J.S.; project administration, J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the National Key Research and Development Program of China (2022 YFB2802803), the National Natural Science Foundation of China Projects (No. 61925104, No. 62031011, and No. 62201157), the Major Key Project of PCL, the China Postdoctoral Science Foundation (2021M700025), and the China National Postdoctoral Program for Innovative Talents (BX2021082).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Conclusions  
In satellite laser communication, beam-pointing accuracy is crucial for link stability. Thus, precise calibration is required before the terminal starts in-orbit operation, but the factors that affect the coaxiality error are unclear and lack effective adjustment methods. We analyzed the coaxiality error of a periscope-type APT system based on the CCR ray reflection method. The CCR geometrical error was established and analyzed, in which the dihedral angle and planeness errors were considered. The influence mathematical model of the CCR geometrical error on a wavefront was established, and the spot energy distribution was analyzed, the simulation data indicates that even if the wavefront distortion caused by CCR error is symmetrically distributed, the energy distribution of the diffraction faculae is not uniform. Subsequently, the influence of the mirror-element assembly error on APT beam-pointing accuracy was studied after the beam transformation model was established. Then, based on the above analysis, the coaxiality error under the influence of CCR geometrical and APT assembly errors was studied. The results showed that the increase in assembly error made the APT system's beam-pointing error more linear. The planeness error has a greater influence on the coaxiality error than that of the CCR dihedral angle error. However, if all errors take on different values, there will also be a certain amount of counteraction to the CE; therefore, error balancing can be adopted based on this principle to reduce CE. Moreover, the influence level of each assembly error of the APT system on coaxiality was broadly consistent, and the maximum coaxiality error can reach 700 \(\upmu\)rad under the current simulation condition, which is beyond the limit value for the APT system.  
We hope this study provides some reference for the design of an APT system.  
Conceptualization, D.L.; methodology, H.Z.; software, F.Z.; validation, L.S.; resources, Z.M.; data curation, D.L.; writing--original draft preparation, L.S.; writing--review and editing, F.Z.; supervision, D.L.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the General Special Projects of Shaanxi Provincial Department of Education (under Grant No. 21 JK0669) and the National Natural Science Foundation of China (under Grant Nos. 62101420).  
Not applicable.  
The authors declare no conflict of interest.