esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Brief Review of Habets Late Reverberant Spectral Variance Estimator  
The underlying theory for the present work is based on the LRSV estimator derived by [PERSON] [3]. The [PERSON] method is based on a generalized statistical model which is an improvement on [PERSON]'s statistical model [4]. Using \(H_{r}(k,l)\) represents early and late reflections. Then, the corresponding spectral variance can be written as Equation (3)  
\[\lambda_{h}(k,l)=E\Big{[}\big{|}H(k,l)\big{|}^{2}\Big{]}=\left\{\begin{array}[ ]{ll}\lambda_{h_{d}}(k),&l=0\\ \lambda_{h_{l}}(k,l),&l\geq 1\end{array}\right. \tag{3}\]  
\[\lambda_{h_{d}}(k)=E\Big{[}\big{|}H_{d}(k)\big{|}^{2}\Big{]},\lambda_{h_{l}} (k,l)=\kappa(k)\lambda_{h_{d}}(k)e^{\frac{-13\kappa R}{T_{60}(k)/\kappa}},\]  
where \(T_{60}(k)\) is the frequency-dependent reverberation time, \(f_{s}\) denotes the sampling frequency, \(R\) is the discrete time shift, and \(\kappa(k)\) is a prior parameter that is related to DRR.  
Assuming that the direct component \(Z_{d}(k,l)=H_{d}(k)S(k,l)\) and the reverberant component \(Z_{r}(k,l)=\sum\limits_{l^{\prime}=1}^{+\infty}H_{r}(k,l^{\prime})S(k,l-l^{ \prime})\) are uncorrelated, the corresponding spectral variance \(\lambda_{z}(k,l)=E\Big{[}\big{|}Z(k,l)\big{|}^{2}\Big{]}\) can be expressed as the sum of the direct component spectral variance \(\lambda_{d}(k,l)=E\Big{[}\big{|}Z_{d}(k,l)\big{|}^{2}\Big{]}\) and the reverberant component spectral variance \(\lambda_{r}(k,l)=E\Big{[}\big{|}Z_{r}(k,l)\big{|}^{2}\Big{]}\), such that Equation (4)\[\lambda_{z}(k,l)=\underbrace{\lambda_{h_{d}}(k)\lambda_{s}(k,l)}_{\lambda_{d}(k,l)}+ \underbrace{\sum\limits_{l^{\prime}=1}^{+\infty}\lambda_{h_{r}}\big{(}k,l^{ \prime}\big{)}\lambda_{s}\big{(}k,l-l^{\prime}\big{)}}_{\lambda_{r}(k,l)}, \tag{4}\]  
where \(\lambda_{s}(k,l)\) is the spectral variance of \(S(k,l)\). The reverberant component \(\lambda_{r}(k,l)\) can be further split into early reverberation \(\lambda_{e}(k,l)\) and late reverberation \(\lambda_{l}(k,l)\), as Equation (5)  
\[\lambda_{r}(k,l)=\underbrace{\sum\limits_{l^{\prime}=1}^{N_{e}}\lambda_{h_{r} }\big{(}k,l^{\prime}\big{)}\lambda_{s}\big{(}k,l-l^{\prime}\big{)}}_{\lambda_{ e}(k,l)}+\underbrace{\sum\limits_{l^{\prime}=N_{e}}^{+\infty}\lambda_{h_{r}} \big{(}k,l^{\prime}\big{)}\lambda_{s}\big{(}k,l-l^{\prime}\big{)}}_{\lambda_{ l}(k,l)}, \tag{5}\]  
and the main purpose is to derive an estimator for the LRSV \(\lambda_{l}(k,l)\). Combining Equations (3) and (4), \(\lambda_{r}(k,l)\) can be obtained by Equation (6)  
\[\lambda_{r}(k,l)=\exp\Big{\{}\frac{-13.8R}{T_{60}(k)f_{s}}\Big{\}}[(1-\kappa( k))\lambda_{r}(k,l-1)+\kappa(k)\lambda_{z}(k,l-1)]. \tag{6}\]  
Finally, according to Equations (3) and (5), \(\lambda_{l}(k,l)\) can be obtained using \(\lambda_{r}(k,l)\) as Equation (7)  
\[\lambda_{l}(k,l)=\exp\Big{\{}\frac{-13.8R(N_{e}-1)}{T_{60}(k)f_{s}}\Big{\}} \lambda_{r}(k,l-N_{e}+1). \tag{7}\]
## 4 Conclusions  
In conclusion, a compact and easy-to-operate method of generating frequency-tunable microwave pulses based on a femtosecond laser-excited unbalanced single-arm interferometer with frequency-to-time mapping has been demonstrated. Through simple spatial-optical group delay adjustment, the frequency of the pulsed microwave waveform was continuously tuned from 2.0 GHz to 19.7 GHz with high tuning linearity. Additionally, the pulse duration of the microwave waveform was measured to be 24 ns. Moreover, thanks to the designs of the single-arm optical path and the polarization-independent interference, the environmental disturbance and nonlinear effects caused by the optical devices were eliminated, which ensured that the microwave pulse train generated from this system had good stability in terms of frequency and electrical amplitude. Furthermore, a near-triangular-shaped microwave pulse at 4.5 GHz was synthesized by two sinusoidal signals generated by this system, which showed the potential to realize the generation of special microwave waveform pulses. In future research, phase tuning or phase encoding will be implemented by adding phase modulation between the PC pin and reflection mirror, based on the proposed scheme, to further improve the applicability of this system.  
Conceptualization, L.T. and J.W. ([PERSON]).; methodology, S.L.; validation, X.J. and T.T.; formal analysis, H.M.; resources, X.W.; data curation, L.C.; writing--original draft preparation, X.J. and L.T.; writing--review and editing, L.Z. and C.L.; visualization, J.W. ([PERSON]); supervision, J.W. ([PERSON]); funding acquisition, X.J., L.T. and J.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number: 62101518), the Foundation of National Key Laboratory of Shock Wave and Detonation Physics (grant number: JCKYS2019212004, JCKYS2019212005) and the Project supported by presidential Foundation of CAEP (grant number: PY20210006, CX2019003).  
Not applicable.  
The authors declare no conflict of interest.
# A Physical Layer Security Enhancement Scheme under the Ambient Backscatter System
## 3 IANT Scheme Design
### Design of Reader Side Scheme
#### 3.1.1 The First Sub-Slot  
In the first sub-slot, each tag sends a signal only within its corresponding time period, i.e., the \(k\)-th tag transmits \(N_{0}\) bits '1' to reader during \((K-1)N_{0}\) to \(KN_{0}\) symbols, while the other \((K-1)\) tag antennas are in the absorption state and do not backscatter the signal. Therefore, the signal received by the reader can be expressed as:  
\[y_{r}(n)=\left\{\begin{array}{cc}h_{r}s(n)+\omega_{r}(n)&B_{k}(n)=0\\ h_{r}s(n)+h_{k}d_{k}\eta_{k}s(n)+\omega_{r}(n)&B_{k}(n)=1\end{array}\right. \tag{3}\]  
Figure 2: Frame structure for the proposed IANT scheme in an ABC system.  
Next, the reader calculates the average power of the received signal:  
\[\Phi_{k}=\frac{1}{N_{0}}\sum_{n=1+kN_{0}}^{(k+1)N_{0}}\left|y_{r}(n)\right|^{2} \tag{4}\]  
The above formula represents the average power of \(N_{0}\) bits '1', which can be divided into the following two parts:  
\[\Phi_{0}=|h_{r}|^{2}P_{s}+N_{\omega r}+\frac{2}{N_{0}}\sum_{n=1}^{N_{0}}R\left\{ h_{r}s(n)\omega_{r}^{H}(n)\right\} \tag{5}\]  
\[\Phi_{k}=|\mu_{k}|^{2}P_{s}+N_{\omega r}+\frac{2}{N_{0}}\sum_{n=kN_{0}+1}^{(k+ 1)N_{0}}R\left\{\mu_{k}s(n)\omega_{r}^{H}(n)\right\} \tag{6}\]  
where \(N_{\omega r}\) is the power of artificial noise, and \(\mu_{k}\) is expressed as follows:  
\[\mu_{k}=h_{r}+h_{k}d_{k}\eta_{k} \tag{7}\]
## 2 The Evolution and Trends of 6G Network Architecture  
In order to address the requirements of upcoming applications, 6G is anticipated to integrate and expand on the revolutionary ideas beyond 5G. The standardization efforts of 5G have facilitated the implementation of flexible network structures, disrupting the traditional centralized hierarchy. With cutting-edge technologies like control/user plane separation, network slicing, and mobile edge computing (MEC), KPIs may be customized for particular uses. The architecture focused on services, featuring fragmented and extensively Application Programming Interface (API)-enabled software components, currently raises a more open innovation community, accelerating deployment. However, 6G will bring forth entirely new paradigms, encompassing novel features, capabilities, and innovative approaches to the underlying transport architecture infrastructure. The design process will also see novel philosophies aimed at expediting design and deployment, as further discussed in the conversation that follows.
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 4 Experiments and Analysis
### Evaluation Metrics  
To comprehensively evaluate the performance, this paper uses PSNR, SSIM, and MAE as the evaluation metrics. PSNR (Peak Signal-to-Noise Ratio) is used to quantify the absolute difference between the restored image and the original image, with higher values indicating better image quality. SSIM (Structural Similarity Index) assesses the fidelity of the image structure and texture, with values closer to 1 indicating better similarity. The MAE (Mean Absolute Error) calculates the average absolute difference between the predicted and ground truth values for each pixel position, with smaller values indicating more accurate restoration.
# Four-Qubit Cluster States Generation through Multi-Coin Quantum Walk
## 3 Generating Cluster States Using Quantum Gates Formed by Multi-Coin Quantum Walk
### CZ Gate  
CZ gate can be described as:  
\[U_{CZ}=\begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&-1\end{bmatrix} \tag{24}\]  
According to the mapping mentioned above, the qubit 2, 3, and 4 of a 4-qubit system are mapped to the position space and a position-dependent coin operator is implemented on the walker. Position-dependent coin means that for the same operation (such as CZ gate), the actual coin operator performed on the walker (qubit 1) is different according to which position the walker occupies. The possible operators required to form a CZ gate can be described as:  
\[\left\{\begin{aligned} Z=\sigma_{z}\otimes I_{p},\\ I=I\otimes I_{p},\\ V=e^{i\pi}I\otimes I_{p},\end{aligned}\right. \tag{25}\]  
where \(I_{p}\) is the identity operator in the position space. We can then map the CZ gate onto 8 positions in the position space, as shown in Figure 7. The directions of the arrows are determined by the shift operators, which only indicates how the walker moves instead of the state conversion.  
Take Figure 7a as an example. It describes the CZ gate between qubit 1 and 2, namely \(U_{CZ}^{12}\). If qubit 1 (the walker) is at \(|000\rangle\), then 2 possible operations might be carried out: if the walker follows trace 1, then coin operator \(C=I\) and shift operator \(S=I\) are performed on the walker, implementing a CZ gate and making the walker move from \(|000\rangle\) to \(|001\rangle\); if the walker follows trace 2, then coin operator \(C=-I\) and shift operator \(S=-I\) are performed on the walker, also implementing a CZ gate and making the walker move from \(|000\rangle\) to \(|010\rangle\).
## 4 Simulation System Design
### ROS2 Node and Topic Design  
In the ROS2 system, \"/controller\" is the controller node in Simulink, \"/wamv/ros_gz-bridge\" is the USV node in VRX, \"/navsat_transform\" is the transform node of GNSS data, and \"/ekf_localization\" is the state estimation node. Other nodes such as \"/wamv/frame_publisher\", \"/wamv/robot_state_publisher\", and \"/wamv/optical_frame_publisher\" are nodes related to coordinate transformation. The connection relationship between each node and topic is shown in Figure 10.  
The USV node publishes the GNSS and IMU topics \"/wamv/sensors/gps/gps/fix\" and \"/wamv/sensors/imu/imu/data\". The GNSS data conversion node subscribes to these two topics and the \"/odometry/filtered/global\" topic published by the state estimation node, then publishes the \"/odometry/gps\" topic (containing GNSS data directly usable by the state estimation node). The state estimation node subscribes to \"/odometry/gps\" and \"/wamv/sensors/imu/imu/data\", performs state estimation, and publishes the odometry topic \"/odom\" (including position, orientation, linear velocity, angular rate, etc.) for use by the controller node. The controller node subscribes to \"/od  
\begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Physical Quantity [Unit]** & **Value** \\ \hline Gyroscope Dynamic Bias Stability & \(\varepsilon\left[{}^{\circ}\cdot\text{h}^{-1}\right]\) & 4.1 \\ Accelerometer Dynamic Bias Stability & \(\
abla\left[{\text{\mu g}}\right]\) & 38.3 \\ GNSS Horizontal Position Standard & \(v_{\text{p}}\left[{\text{m}}\right]\) & 1.0 \\ Deviation & \(v_{\text{v}}\left[{\text{m}}\right]\) & 0.1 \\ GNSS Velocity Standard Deviation & \(v_{\text{v}}\left[{\text{m}}\right]\) & 0.1 \\ \hline \end{tabular}
\end{table}
Table 2: Device errors of IMU and GNSS.  
Figure 10: Connection relationships among system nodes.  
sponding motion status of WAMV, then publishes topics \"/wamv/thrusters/left/thrust\" and \"/wamv/thrusters/right/thrust\" (containing commanded thrust \(T_{\text{c}}\)) and topics \"/wamv/thrusters/left/pos\" and \"/wamv/thrusters/right/pos\" (containing commanded azimuth angle \(\delta_{\text{c}}\)), which are subscribed to by the USV node.
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 2 ESTCube-2 Mission
### Satellite Design
#### 2.3.3 Side Panel Systems  
A side panel is a proxy system of the ESTCube-2 nanosatellite, and there is a different configuration on each of the six sides of the satellite (see Figure 10). Each SP has a different functional block configuration depending on the necessary electronics hardware on each satellite's side (see Figure 11).  
Figure 10: Side panel locations and naming.  
The following are located on each SP system: STMicroelectronics STM32L4 series MCU, SS, magnetometer, one or more temperature sensors, FRAM memory, and ICP electronics.  
On three PCBs are custom maximum power point tracker (MPPT) solutions for energy harvesting, capable of fast power point tracking during the satellite spin-up to deploy the IPB tether.  
Driver electronics for three magnetorquers are located on those SPs closest to the actuators (except the bottom Z-axis side, see footnote 1 of Figure 11).  
Approximately in the middle of every side of the satellite, there is a two-axis Sun sensor. The Sun sensors have been developed in-house and are an evolution of the similar analog sensors used on ESTCube-1 [26].  
A single magnetometer on each of the SPs is used for better characterization of the residual magnetic field of the satellite and better-averaged measurement of the external magnetic field.
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 1 Introduction  
Radio Frequency Interference (RFI) jeopardizes satellite-based navigation applications. This is especially relevant in safety-critical activities where aspects such as availability, integrity, and/or continuity are of outmost importance. In order to mitigate and face an RFI in critical infrastructures/areas, the design of GNSS Interference Monitoring and Detection (GIMAD) was already presented in [1]. Now, this design has been implemented into a prototype.  
The use case from which the system requirements are derived is presented in Section 2. Considering the above-mentioned requirements, a GIMAD prototype description is presented in Section 3, including the different parts of the architecture. Algorithms implemented in the design stage have been verified, and the key verification results are shown in Section 4. Finally, Section 5 contains the main conclusions.
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 5 Conclusions  
The presented research proposes a supervision model customized for assembling processes, and introduces wireless sensor network technology for process management, i.e., for solving the localization problem during the process. The localization of assembled parts is based on the fingerprint localization method by using a received signal strength indicator. The proposed localization algorithms are based both on artificial neural networks and on weighted k-nearest neighbor method. The research results present a general solution to the problem of supervising the assembling process, which can be used in assembling systems regardless of the size and shape of the system.  
Based on the presented data, it can be concluded that the RSSI values obtained depend on the size and shape of the system, and that a more even signal distribution can be obtainedin the case of smaller and simpler layouts. Additionally, the density of the measured points correlates to the obtained accuracy. Obstacles, however, have a disturbing influence on the results.  
Comparing the ANN and WkNN localization algorithms, better results are obtained by using WkNN algorithm. Additionally, the initial higher number of fixed anchors gave worse results than a lower number of anchors. Changing the number of neurons in the hidden layer of ANN and changing the number of neighboring points in the case of WkNN did not changed significantly the localization accuracy.  
Based on the results of this analysis, the critical segments regarding the efficiency of the localization were determined. Based on this, insight was obtained regarding algorithm improvements and/or RSSI data filtering.  
Conceptualization: L.G.; methodology: L.G.; software: L.G.; validation: L.G. and I.F.; formal analysis: L.G. and I.F.; investigation: L.G. and I.F.; resources: L.G.; data curation: L.G. and I.F.; writing--original draft preparation: L.G. and I.F.; writing--review and editing: I.F.; visualization: L.G. and I.F. supervision: I.F.; project administration: L.G. and I.F; funding acquisition: L.G. and I.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data available in a publicly accessible repository.  
The authors declare no conflict of interest.
## 8 Technical Challenges for 6G Development
### Device Capabilities in 6G  
The forthcoming 6G system will introduce numerous novel features, demanding devices like smartphones to adapt to these advancements. Effectively supporting features such as Tbps throughput, AI, XR, and integrated sensing with communication functionalities poses a challenge for individual devices. Some 5G devices may not be equipped to handle certain 6G features, and enhancing the capabilities of 6G devices may come with increased costs. Considering that billions of devices are anticipated for 5G, ensuring the compatibility of these devices with 6G during the transition in communication infrastructure is a crucial concern. This compatibility facilitates user convenience and cost savings. Therefore, 6G should prioritize the development of integrated communication-computing devices and improvements in computing performance, ensuring technological compatibility with 5G.
# Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures
## 3 Traversability Cost Prediction
### Texture and Vibration Features Extraction/Association
The position used to take a new terrain image, denoted here by \(Z_{I_{i}(x,y)}\), is expressed as  
\[Z_{I_{i}(x,y)}=id_{s},\quad i=1,\cdots,N_{\mathrm{image}}, \tag{12}\]  
where \(d_{s}\) denotes the sampling distance, and \(N_{\mathrm{image}}\) is the total number of terrain images acquired during a run. Due to the camera tilt angle \(\alpha\), terrains will be covered from a certain position, expressed as  
\[Z_{I_{i}(x,y)}+Z_{\mathrm{BZ}},\quad i=1,\cdots,N_{\mathrm{image}}, \tag{13}\]  
where \(Z_{\mathrm{BZ}}\) is the blind spot. The motive behind focusing on a short distance range \(l\) covered by the images for texture feature extraction is that pixels further from the camera focus point are subject to more noise. Thus, visual information may fail to faithfully represent the environment. The acceleration signal sequence generated when traversing a distance \(l\) is used for motion feature extraction, and is limited according to the ROI as follows  
\[A_{i}=[Z_{I_{i}(x,y)}+Z_{\mathrm{BZ}},Z_{I_{i}(x,y)}+Z_{\mathrm{BZ}}+l], \tag{14}\]  
where \(A_{i}\) denotes the ROI. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Mean** & **Standard Deviation** & **SNR** \\ \hline
1.0346 & 0.0101 & 102.6474 \\
1.0339 & 0.0102 & 101.5350 \\
1.0250 & 0.0096 & 106.7354 \\
1.0335 & 0.0099 & 104.2580 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Signal to noise ratio (SNR) of acceleration signal at rest.
## 5 Conclusions  
In this study, the variations in spatial representativeness of the EC measurements were evaluated by using eight-year continuous flux data and remote sensing images in a subtropical coniferous plantation site of a monsoon area. We found that the spatial representativeness had been affected by the monsoon climate and the spatial heterogeneity around the flux site. Although the coniferous plantation was the dominant vegetation type over the flux site, the mixing of double-cropped rice changed the spatial representation significantly during a year. Our study demonstrates the effects of spatial heterogeneity on the spatial representation of EC measurements and highlights the importance of footprint awareness in the utilization of EC measurements for calibrating and validating EC data in a model.  
Figure 10: The location of the study site (red point) and the rectangular area is the solar-induced chlorophyll fluorescence (SIF) pixel (40 km \(\times\) 80 km) of the Global Ozone Monitoring Experiment-2 (GOME-2) in the same period, and the background is a land cover image derived from GlobeLand30. It can be seen that the vegetation type in SIF pixels is mainly cultivated land, and the composition proportion of each vegetation type still changed.  
**Author Contributions:** [PERSON] was the principal investigator for this study. He undertook data processing, carried out the main data analyses, and wrote the manuscript. [PERSON] contributed to the interpretation and presentation of the statistical results and revised the text. [PERSON] and [PERSON] assisted and contributed to the interpretation and presentation of the statistical results. [PERSON] assisted and contributed to the data analysis. [PERSON] was the main supervisor for the project. He supervised the work and helped in developing the methodology for the study, interpreting the results, and revising earlier drafts until the final approval. [PERSON] was the co-supervisor for the project. He assisted with developing the methodology for the study, supervised the development of the model, and revised the text. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China (Grant No. 2020 YFA0608103) and the National Science Foundation of China (Grant No. 31770765, 32160366).  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We thank the Qianyanzhou Ecological Research Station, which is a CERN and ChinaFLUX site, for sharing their data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
This paper proposes the lightweight autonomous vehicle self-diagnosis (LAVS) using machine learning based on sensors and the internet of things (IoT) gateway. It collects sensor data from in-vehicle sensors and changes the sensor data to sensor messages as it passes through protocol buses. The changed messages are divided into header information, sensor messages, and payloads and they are stored in an address table, a message queue, and a data collection table separately. In sequence, the sensor messages are converted to the message type of the other protocol and the payloads are transferred to an in-vehicle diagnosis module (In-VDM). The LAVS informs the diagnosis result of Cloud or road side unit(RSU) by the internet of vehicles (IoV) and of drivers by Bluetooth. To design the LAVS, the following two modules are needed. First, a multi-protocol integrated gateway module (MIGM) converts sensor messages for communication between two different protocols, transfers the extracted payloads to the In-VDM, and performs IoV to transfer the diagnosis result and payloads to the Cloud through wireless access in vehicular environment(WAVE). Second, the In-VDM uses random forest to diagnose parts of the vehicle, and delivers the results of the random forest as an input to the neural network to diagnose the total condition of the vehicle. Since the In-VDM uses them for self-diagnosis, it can diagnose a vehicle with efficiency. In addition, because the LAVS converts payloads to a WAVE message and uses IoV to transfer the WAVE messages to RSU or the Cloud, it prevents accidents in advance by informing the vehicle condition of drivers rapidly.  
in vehicle-diagnosis; random forest; IoV; integrated gateway; neural network +
Footnote †: journal: Sensors