esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Cyclostationarity
### Conventional Blind Adaptive RLS-FRESH Filtering  
In (22), the impulse response coefficients of the optimal FRESH filter, \(\rm{h_{p}(n)}\), and \(\rm{h_{q}(n)}\), can be obtained either through direct calculation by solving the multivariate Wiener filtering problem [19] or by performing adaptive updating of the filter weights. For direct calculation of the optimal filter weights, the autocorrelation of the frequency shifted input signal vector and the cross-correlation between the input signal vector and the desired signal are required. However, practically it is unlikely to know these values a-priori. Hence an adaptive implementation of the FRESH filter is necessary. The basic structure of the BA-LCL-FRESH filter is shown below (Figure 1):  
The steps involved in the Recursive Least Squares (RLS) based BA-LCL-FRESH filter in our NOMCR application can be tabulated as follows (Algorithm 1):  
```
Parameters: \(\lambda=\) forgetting factor, \(\delta=\) initializing factor for P(0) Initialization:- \(\textbf{P}(0)=\delta^{-1}\cdot\textbf{I}\), where **I** is the identity matrix of rank L \(\rm{d(n)}=r(n)\), i.e., received signal before cyclic frequency shift operation \(\rm{L}=\sum_{p=1}^{P}L_{p}+\sum_{q=1}^{Q}M_{q}\)
```  
**Algorithm 1**RLS Adaptation Algorithm Steps.  
Final Desired Signal Estimate: \(\hat{y}(n)=\textbf{w}^{H}\cdot\textbf{r}(n)\)
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Feasibility** & **Lowest Freq. (FBW)** & **Size *** & **Materials** \\ \hline
[33] & \(\times\) & 3.9 GHz (101\%) & 0.3 \(\times\) 0.3 \(\times\) 0.12 \(\lambda^{3}\) & Carbon-loaded ABS \\
[34] & \(\times\) * & 1.7 GHz (101\%) & 0.06 \(\times\) 0.06 \(\times\) 0.08 \(\lambda^{3}\) & FR4/Chip R, L **/**PIN diode** \\
[35] & \(\triangle\) & 1.85 GHz \({}^{\dagger}\) (105\%) & 0.06 \(\times\) 0.06 \(\times\) 0.14 \(\lambda^{3}\) & Form \& FR4/Chip R \\
[36] & \(\triangle\) & 1.89 GHz (113\%) & 0.25 \(\times\) 0.25 \(\times\) 0.1 \(\lambda^{3}\) & Form \& FR4/Chip R \\
[37] & \(\times\) & 0.21 GHz (130\%) & 0.02 \(\times\) 0.02 \(\times\) 0.005 \(\lambda^{3}\) & NiZn Ferrite \\
[38] & \(\triangle\) & 2 GHz (160\%) & 0.13 \(\times\) 0.13 \(\lambda^{3}\) & Carbon black \\
[39] & \(\triangle\) & 5.8 GHz (71\%) & 0.5 \(\times\) 0.5 \(\times\) 0.1 \(\lambda^{3}\) & PET, PDMS/CNT \\
[40] & \(\times\) & 1 GHz (93\%) & 0.078 \(\times\) 0.076 \(\lambda^{3}\) & FR4, Spacer \\ This work & \(\circ\) & 2.2 GHz (156\%) & 0.15 \(\times\) 0.15 \(\times\) 0.09 \(\lambda^{3}\) & Composite/Carbon paste \\ \hline \end{tabular}
\begin{tabular}{c c} \hline \multicolumn{3}{c}{\(\times\): Low (Lumped elements or bias are required, no planar structure). \(\triangle\): Mid (No planar structure, additional protective layers are required). \(\square\): High (possible to external exposure, low density, used actual reflectors). \(\blacktriangleright\): Unit-cell size based on the lowest freq. wavelength at the operating freq. **: Lumped resistor and inductor. \({}^{\dagger}\): Absorbance of over 85%. \({}^{\sharp}\): A multi-layer structure, and a spacer is required due to the PIN diode.} \\ \end{tabular}
\end{table} 
Table 2: Comparison of the proposed composite absorber and previous works.
## 1 Introduction
To better understand the feasibility of implementing DL wildfire detection onboard CubeSats, this study conducted the first systematic analysis of developing a CubeSat payload with a 5-m-resolution imaging mission. KITSUNE, which means fox in Japanese, is a 6U CubeSat platform developed as a collaboration project that was designed and developed by the Kyushu Institute of Technology (Kitakyushu), Harada Seiki Co., Ltd. (Hamamatsu), and Addnics Corp. (Tokyo) in Japan. The project started in September 2019, and KITSUNE was delivered to the Japan Aerospace Exploration Agency (JAXA) in November 2021. The satellite was deployed to the low Earth orbit (LEO) on 24 March 2022, and it is currently in the initial operation phase. Overall, the main purposes of the present paper are to validate the camera payload design for a 5-m-class imaging mission through the space environment and long-duration operation test, in addition to the study of the feasibility of classifying wildfire images onboard a CubeSat using the CNN approach. The imaging mission and DL execution onboard CubeSat during the ground functional tests showed the novelty of this study. The paper consists of five parts, starting with the mission design and requirements in Section 2. Section 3 explains the method of CNN fire detection and the functional test. The experimental results are presented in Section 4 and, finally, discussion and conclusions from the study are provided in Sections 5 and 6, respectively.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 4 Related Research: Modified Watermarking Approaches in the IoT Applications
### Robustness Image  
In order to improve the transfer of images amongst IoT layers, the authors in [97] propose a new watermarking approach based on the use of the biorthogonal family. The proposed approach uses Biorthogonal 2.2, Biorthogonal 3.5 and Biorthogonal 5.5 of different wavelet transforms. Symlets and coiflets wavelet transforms are also used. Different types of attacks are mitigated by the proposed scheme, so the piracy has been forbidden against digital images authentication in the emerging IoT-based systems. To investigate the proposed watermarking scheme robustness. multiple attacks are applied. In the same context, Biorthogonal wavelet transforms are used to preserve the authenticity of the image in the IoT. The researchers in [98] propose a new technique that uses four levels of the DWT where each level consists of a Biorthogonal wavelet, discrete meyer wavelet, reverse biorthogonal wavelet, coiflets wavelet transform and symlet wavelet.  
Additionally, the researchers in [32] describe a DCT digital image watermarking method, which mainly depends on embedding the watermark technique by using two different coefficient groups of the host image when using the DCT on the selected DWT coefficient groups. In [55], the data privacy and confidentiality are preserved while being transmitted in an IoT where a robust and secure framework are presented for hiding the required data. The random selection of both the coefficient and block approaches are implemented for embedding the data in order to improve the embedded data's robustness, and avoid the fragility weakness through different cyber-attacks, particularly, geometric attacks. A threshold factor can determine the strength of the robustness, which is optimally selected to maintain the perceptual quality of the watermarked image. The distribution of data over various areas of the watermarked image is confirmed by using multiple randomly selected DCT coefficients for embedding the watermark bits.
## 2 Related Work
### Attention Mechanism
#### 2.4. Residual Unit  
The classification accuracy of HSI has been substantially improved based on CNNs. However, as the depth of the network exceeds a certain extent, the phenomenon of gradient  
Figure 6: The structure of the spatial attention module.  
vanishing occurs, which directly leads to a deterioration in the network's performance. However, the skipping connection of residual blocks can retain the original image information, which is conducive to the back-propagation of the gradient, mitigating the phenomenon of accuracy decline and contributing to preventing network degradation. Therefore, some studies have built HSI classification networks through residual module connection and tried to solve the gradient vanishing problem of deep networks by means of stacking residual modules to achieve better classification results. The basic residual unit is shown Figure 7 and its expression as follows:  
\[X_{i+1}=F(X_{i},\ W_{i})+H(X_{i}) \tag{15}\]  
where \(X_{i}\) and \(X_{i+1}\) represent the inputs and outputs of the \(i\)-th residual unit. \(F\) is the residual function and \(H\) refers to the method of shortcut connection; \(H(X_{i})=X_{i}\) represents identity.  
Although some structures proposed by the above block have achieved relatively good results, the accumulation of basic modules leads to the explosive growth of parameters. In order to ensure that the features can be extracted further and the network load can be reduced simultaneously, we propose a hybrid residual module consisting of the residual block based on 2D-DSC and the residual block based on 3D-DSC, as shown in Figure 8.  
Figure 8: The structure of the hybrid residual module. (**a**) The residual block based on 3D-DSC; (**b**) the residual block based on 2D-DSC.  
Figure 7: The structure of the basic residual unit.  
For the hybrid residual module, different residual blocks are used for the input of data of different dimensions. Specifically, the 3D-DSC residual block and 2D-DSC residual block are, respectively, used for 3D data input and 2D data input.  
In contrast to the basic residual unit, our hybrid residual module omits the final rectified linear unit (ReLU). In the shortcut branch, we can express \(H(X_{i})=\mathit{Mish}(BN(Conv(X_{i})))\). In the lower branch, we introduce depth-wise convolution (DWC) and pointwise convolution (PWC) to replace standard convolution, which alleviates the problem of parameter surge when residual blocks are superimposed. Finally, the characteristics of the lower branch are obtained through the maximum pooling layer. Specifically, the order of execution of the layers can be described as follows: \(\text{DWC}\rightarrow\text{BN}\rightarrow\text{Mish}\rightarrow\text{ PWC}\rightarrow\text{BN}\rightarrow\text{Mish}\rightarrow\text{ MaxPool}\). As the network gradually goes deeper, the parameters do not increase significantly, so we can apply the lightweight residual module to our model, HDCDF.
###### Abstract  
Femtosecond laser ranging has drawn great interest in recent years, particularly based on an asynchronous optical sampling implementation where a pair of femtosecond lasers are used. High precision absolute ranging either relies on tightly-phase-locked optical frequency combs (a dual-comb setup) or multiple averaging of the measurements from two free-running femtosecond lasers. The former technique is too complicated for practical applications, while the latter technique does not apply to moving targets. In this report, we propose a new route to utilizing a powerful singular spectrum analysis (SSA) filtering method to improve femtosecond laser ranging precision for moving targets with acceleration. The SSA method is capable of separating complex patterns in signals without a priori knowledge of the dynamical model. Here, we utilize the basic SSA filter to extract the target trajectory in the presence of measurement noise both in numerical simulation and in the absolute ranging experiment based on a pair of free-running femtosecond lasers. The experimentally-achieved absolute ranging uncertainty of a moving target is well below 110 nm at a 200-Hz update rate by applying the basic SSA filter. This method paves the way to the practical applications of femtosecond absolute ranging for dynamic objects.  
Keywords:absolute ranging; optical frequency combs; filter; singular spectrum analysis +
Footnote †: journal: Applied Physics Letters
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 4 Proposed MS-FL Framework
### Intra-Server Training Phase  
#### 4.2.1 Local Training on Vehicles  
Every selected vehicle initiates local training on its own dataset for \(E\) epochs using iterative optimization methods, such as Stochastic Gradient Descent (SGD) [5, 6]. During local training, the vehicle's dataset is partitioned into batches of B data points. For each batch, the local model parameters are updated using (11) [5]:  
\[\mathrm{w_{i}(t+1)}\leftarrow\mathrm{w^{(s)}(t)}\ -\ \eta\
abla\mathrm{F_{i} (w^{(s)}(t))}, \tag{11}\]  
where \(\eta\) is the learning rate and \(\
abla\mathrm{F_{i}(w^{(s)}(t))}\) is the average gradient computed on vehicle i's local dataset \(\mathrm{d_{i}(t)}\) with respect to the server model \(\mathrm{w^{(s)}(t)}\). The updated model \(\mathrm{w_{i}(t+1)}\) reflects the improvements made during local training.
## 4 Experimental Results and Analysis
### Recognition Performance Comparison
When SNR was greater than 7 dB, the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of QPSK reaches 99.66%. Compared with the InceptionResNetV2 algorithm, the recognition rate of QPSK reaches 99.66%. Compared with the InceptionResNetV2 algorithm, the recognition rate is improved by 3%. When SNR is 5 dB, the recognition rate of 8 PSK reaches 99.66%, which is 3.33% higher than that of InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of the four neural networks for the three signals reached 100%, while the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of the four neural networks for the three signals reached 100%, while the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of QHLRT-UB was 100% higher than the InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of the four neural networks for the three signals reached 100%, while the recognition rate of QHLRT-UB was 10% higher than the InceptionResNetV2 algorithm. When  
only about 80%. This shows that the recognition rate of digital signal modulation by neural network is higher than that by classical algorithm. Figure 9 shows the overall recognition rates of the three signals of BPSK, QPSK and 8 PSK by the five algorithms. It can be seen from the figure that the overall recognition rates of the three signals by InceptionResNetV2-TA algorithm proposed in this paper were the highest. When SNR is higher than 4 dB, the recognition rate of the four algorithms for BPSK reaches the highest and remains stable, and the recognition rate is always 100%.When SNR is higher than 5 dB, the recognition rate of the four algorithms for QPSK reaches 100%.When SNR is higher than 6 dB, the recognition rate of the four algorithms for 8 PSK is 100%.This indicates that as SNR gets higher and higher, the recognition accuracy of various algorithms for three digital signal modulation modes will gradually increase and remain stable under a certain SNR.
# Integration of Production Planning and Scheduling Based on RTN Representation under Uncertainties
## 4 Case Study
### The Influence of Scheduling Layer Fuzzy Weight on the Optimization Results  
In the production scheduling layer, fuzzy theory is introduced to describe the uncertainty of utilities. It is assumed that triangular membership function is adopted to express the uncertainty of utilities. Using the knowledge of fuzzy theory, the uncertainty model with utility is transformed into a certainty model with fuzzy weights. The fuzzy parameter values of HS is \(u_{1}=62\), \(u_{2}=64\), \(u_{3}=68\); and the fuzzy parameter values of CW is \(u_{1}=67\), \(u_{2}=69\), \(u_{3}=73\). A visualized sensitivity analysis of fuzzy weights is illustrated in Figure 7. The optimization results of scheduling layer under different weights are shown in Table 5. It can be seen from Figure 7 that when the weight of the maximum possible value increases from 0.1 to 0.8, the optimal solution of the uncertain model is closer to the optimal solution of the deterministic model. This is because the weights \(w_{1}\), \(w_{2}\), and \(w_{3}\) represent the degree of inclination of the model to the most optimistic value, the maximum possible value, and the most pessimistic value in the membership function. In other words, if the weight of the boundary is large, the influence of the boundary on the optimization result will be greater. For the fuzzy weights, \(w_{1}\), \(w_{2}\), \(w_{3}\) represent the weights of the most pessimistic, the most possible, and the most optimistic value of the related fuzzy numbers, respectively. In practice, the suitable values for these weights as well as \(\delta\) are usually determined subjectively by the experience and knowledge of the decision maker. Based on the most likely value concept proposed by [PERSON] (2018), and considering several relevant works [PERSON] (2018); [PERSON] (2018), we set these parameters to: \(w_{1}=0.1\), \(w_{2}=0.5\), \(w_{3}=0.4\) in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Confidence Factor** & **Inverse Function** & **Objective Function (S)** \\ \hline
0.6 & \(-0.25\) & 99,206.36 \\
0.65 & \(-0.39\) & 98,945.22 \\
0.7 & \(-0.52\) & 98,702.73 \\
0.75 & \(-0.67\) & 98,422.94 \\
0.8 & \(-0.84\) & 98,105.84 \\
0.85 & \(-1.04\) & 97,732.78 \\
0.9 & \(-1.28\) & 97,285.11 \\
0.95 & \(-1.65\) & 96,594.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The objective function under different confidence factors.  
Figure 7: Scheduling layer optimization results under different weights.
## 5 Results
### Experiments with Different Target Intervals  
To evaluate the method performance for different human target intervals, three experimental results with three target intervals are provided in this subsection. For these three experiments, two human targets stand in the normal direction of the UWB impulse radar, in which target 2 is fixed at 6 m, and target 1 is standing at 5 m, 5.5 m, and 5.7 m, respectively. Therefore, the intervals between two targets correspond to 1 m, 0.5 m, and 0.3 m, respectively. For simplification, experiments with intervals of 1 m, 0.5 m, and 0.3 m are represented separately by Experiment 5.1, 5.2, and 5.3, respectively. The experimental results are provided in Figures 15-17 for Experiments 5.1-5.3, respectively. The target ranges extracted for each experiment are shown in Table 5. It can be seen that when the target interval is 1 m or 0.5 m, both targets can be detected by the proposed method with a maximum range error of 7 cm. Limited by the range resolution of UWB impulse radar, when the interval between two targets is shortened to 30 cm, the proposed method only extracts one target.
# A Comprehensive Review of Micro UAV Charging Techniques
## 8 Potential Challenges in UAV Charging Techniques
### The Maximum Power Point Tracking Algorithm  
A nonlinear relationship exists between the voltage and output power of PV panels. There is a specific maximum power point for these devices. Because of irregular illumination, there are several extremes in output characteristic curve of a PV panel. Thus, it is suggested to consider the maximum power point tracking algorithm to enhance the efficiency of PV panels. At present, conventional maximum power point techniques, such as open-circuit voltage, incremental conductivity and perturbation observation are used only for uniform illumination. Therefore, the research fraternity is focusing on maximum power point methods for irregular illumination.
## 3 Experimental Data and Evaluation Methods  
### ALS Point Cloud in Germany and USA  
In order to test and investigate the universality of the proposed method to different stand structures, we use the ALS point cloud provided by [PERSON] et al. (2018) in Germany and [[http://forsys.cfr.washington.edu](http://forsys.cfr.washington.edu)]([http://forsys.cfr.washington.edu](http://forsys.cfr.washington.edu)) (accessed on 11 June 2018) in the USA. In Germany, the ALS point cloud was acquired by MILAN Geoservice GmbH using a RIEGL VQ-780i installed on a Cessna C207 aircraft. The nominal specifications of the ALS instrument are as follows. The precision is 20 mm in the 250 m scanning range, the laser beam divergence is less than 0.25 mrad, and the point density is 115.2 points/m\({}^{2}\). In the USA, a small footprint, discrete return lidar system was used to map the study area. The contractor used a Saab TopEye lidar system mounted on a helicopter to collect data over the study site. Flying height is 200 m, flying speed is 25 m/s, scanning swath width is 70 m, forward tilt is 8 degrees, laser pulse rate is 7000 points/s, maximum returns per pulse is 4, footprint diameter is 40 cm, and point density is 4.86 points/m\({}^{2}\).
# Implementation of Efficient 5G AKA Protocol for Light-Weight Environment
## 1 Introduction
### Background
#### 1.1.2 Algorithm  
This section describes the algorithms used in the protocol. We describe only the purpose of using this algorithm and its core principles.  
MILENAGE is a set of algorithms developed by the 3 GPP security working group for authentication and key generation. Even though it is not a standard because it is a set presented in 3 GPP, we implemented the protocol using MILENAGE. It has seven cryptographic functions, f1, f1*, f2, f3, f4, f5 and f5* [19]. The *-labelled functions are re-synchronization functions used to verify whether the UE and HN have calculated the same values. The way of these functions is same with original function. The first three functions (f1, f1* and f2) are functions for authentication, while the remaining four functions (f3, f4, f5 and f5*) are functions used for key generation.  
As shown in Figures 3 and 4, we describe how f1-f5 functions work at the HN and UE side [21].  
Figure 3: MILENAGE algorithm set.  
The KDF is used to derive keys for secure communication. Table 1 shows the KDF used in the 5G AKA protocol [20].  
Ultimately, keys are required to generate K\({}_{\text{AUSF}}\) and K\({}_{\text{SEAF}}\). 3 GPP recommends using the AES algorithm when implementing MILENAGE.  
LEA is an international standard encryption algorithm developed by the National Security Institute of Korea [17]. It is a 128-bit block password that provides confidentiality in high-speed environments such as big data and cloud computing as well as in light-weight environments such as in mobile devices. Advances in the IT environment have required high-speed, lightweight and low-power cryptosystems on the IoT, mobile and cloud, resulting in the proposed efficiently powered LEA on a 32 bit platform [21]. When built with software, both ROM and RAM display a higher efficiency based on the ARM platform and a 1.5 times faster performance than AES. When built with hardware, it can also be highly efficient because bit XOR, rotations, and 32-bit adders without complex operations are possible. For security, it provides a 24/28/32 round count depending on the key length (128/192/256 bits). It provides 30% security margin performance for partial round attacks.