esa-sceva/satcom-chunk-collection huggingface dataset:
# Quadcopter Adaptive Trajectory Tracking Control:
## 3 Trajectory Tracking Control: Adaptive Backstepping Design and Stability Analysis
### Horizontal Position Controller  
Considering the desired trajectory is projected in the \(xy\)-plane. The purpose is that horizontal position \((x,y)\) is able to track this reference asymptotically. \[X_{d}=[x_{d},y_{d}]^{T} \tag{13}\]  
The horizontal state variables (14), (15) and virtual control input (16) can be shown as:  
\[X =[x,y]^{T} \tag{14}\] \[V =[\dot{x},\dot{y}]^{T}\] (15) \[\dot{\phi} = \begin{bmatrix}\cos(\phi)\sin(\theta)\\ \sin(\phi)\end{bmatrix}  \tag{16}\]  
It is easily seen that the two first equations in (8) becomes (17) and (18) by using (14), (15), and (16):  
\[\dot{X} =V \tag{17}\] \[\dot{V} =\mathbf{A}(U_{1},\psi)\dot{\phi}\] (18) \[\mathbf{A}(U_{1},\psi) =\frac{U_{1}}{m} \begin{bmatrix}\cos(\psi)&\sin(\psi)\\ \sin(\psi)&-\cos(\psi)\end{bmatrix}  \tag{19}\]By calculating the virtual control (16), this system change its \(\theta-Pitch\) or \(\phi-Roll\) angle to move along the \(x-axis\) or \(y-axis\), respectively. The tracking error is chosen as:  
\[\xi =X-X_{d} \tag{20}\] \[\dot{\xi} =V-\dot{X}_{d}\]  
The first Lyapunov candidate function (21) and its time-derivative (22) along the solution of model (20):  
\[V_{1} =\frac{1}{2}\xi^{T}\xi \tag{21}\] \[\dot{V}_{1} =\xi^{T}\left(V-\dot{X}_{d}\right) \tag{22}\]  
The virtual control input for (22) with positive definite matrix \(\mathbf{K}_{1}\in\mathbb{R}^{2\times 2}\) can be selected as:  
\[r_{1}=\dot{X}_{d}-\mathbf{K}_{1}\xi \tag{23}\]  
The second Lyapunov candidate function (24) and its time-derivative along solution of model (20) and (23):  
\[V_{2}= V_{1}+\frac{1}{2}\left(V-r_{1}\right)^{T}\left(V-r_{1}\right) \tag{24}\] \[\dot{V}_{2}= \xi^{T}\left(V-\dot{X}_{d}+r_{1}-r_{1}\right)+\left(V-r_{1} \right)^{T}\left(\mathbf{A}(U_{1},\psi)\vartheta-\dot{r}_{1}\right)\] \[= -\xi^{T}\mathbf{K}_{1}\xi+\left(V-r_{1}\right)^{T}\left(\mathbf{ A}(U_{1},\psi)\vartheta-\dot{r}_{1}+\xi\right) \tag{25}\]  
It can be seen that when the input \(U_{1}\) satisfies Assumption 1, the matrix \(\mathbf{A}(U_{1},\psi)\) is definitely invertible. The virtual control input for (25) with positive definite matrix \(\mathbf{K}_{2}\in\mathbb{R}^{2\times 2}\) can be selected as:  
\[r_{2}=\mathbf{A}(U_{1},\psi)^{-1}\left[\dot{r}_{1}-\xi-\mathbf{K}_{2}\left(V- r_{1}\right)\right] \tag{26}\]  
The Roll and Pitch dynamic models in (9) are considered to find the control input:  
\[\dot{\vartheta} =\mathbf{B}(\phi,\theta)\dot{\vartheta} \tag{27}\] \[\mathbf{B}(\phi,\theta) = \begin{bmatrix}-\sin(\phi)\sin(\theta)&\cos(\phi)\cos(\theta) \\ \cos(\phi)&0\end{bmatrix} \] \[\dot{\vartheta} = \begin{bmatrix}\dot{\phi}\\ \dot{\theta}\end{bmatrix}  \tag{28}\]  
**Remark 1**. : _Generally, the horizontal movement is divided into position and attitude subsystems with separate control laws. Previous studies presented a stability standard for each subsystem which led to the possibility of the appearance of the finite escape time phenomenon.
# On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method
## 2 LED Fingerprint Verification Mechanism  
### LED Fingerprint Model  
It is noteworthy that there is little difference between the components of a VLC system except for the LEDs. The nonlinearity and saturation characteristic support the possibility to distinguish LEDs, even those from the same batch [26]. Thus, the LED fingerprint model has been established to characterize the distinctive feature of each device. A small-signal equivalent circuit model of the LED in the VLC system is illustrated in Figure 1 According to the equivalent circuit, \(p_{signal}\) is the power of the input signal, and \(p_{out}\) is the power of the output optical signal. \(R_{s}\) and \(L_{s}\) are the parasitic resistance and parasitic inductance. \(C_{d}\) and \(C_{b}\) represent the diffusion and the barrier capacitance of the LED, respectively. \(\eta\) is the photoelectric conversion efficiency and \(r_{d}\) is the small-signal diode resistance of an LED. The equations defining these elements using SPICE parameters are the following [29; 30]:  
\[P_{optical}=\eta P_{electric} \tag{1}\]  
\[g_{d}=\frac{1}{r_{d}}=\frac{dI_{d}}{dV_{d}}=\frac{qI_{s}}{NkT}e^{\eta V_{D}/NkT }\approx\frac{I_{D}}{NV_{th}} \tag{2}\]  
\[C_{d}=TT\cdot g_{d} \tag{3}\]  
\[C_{b}=\left\{\begin{array}{ll}\frac{C_{l0}}{(1-V_{D}/V_{B})^{M}}&V_{D}<FC \cdot V_{B}\\ \frac{C_{l0}}{(1-FC)^{1+M}}\Big{[}1-FC(1+M)+\frac{MV_{D}}{V_{B}}\Big{]}&V_{D} \geq FC\cdot V_{B}\end{array}\right. \tag{4}\]  
\[C=C_{b}+C_{d} \tag{5}\]  
\[\lambda=[C,\eta] \tag{6}\]  
The values of the diffusion capacitance \(C_{d}\), the barrier capacitance \(C_{b}\) and the photoelectric conversion efficiency \(\eta\) are the inherent and stable values of an LED. Once the fabrication of an LED is accomplished and the quiescent operation point was set, these values are fixed. Due to the industrialized and standardized production of LEDs, the dissimilarity of parasitic resistance and inductance values are too subtle to distinguish between different LEDs. Therefore, the values of \(\eta\) and \(C\) constitute a two-dimensional feature vector \(\lambda\). It can represent the inherent characteristics of the LED and, as a result, we are able to define the feature vector of the LED and thus its fingerprint.  
Figure 1: A Small-Signal Equivalent Circuit Model of an LED in the VLC System.
## 2 Methods  
### Background: Types of VR Setups and Level of Immersion  
We provide a general scale ranking different VR setups on the level of immersion (Figure 1). However, the immersiveness still depends on the physical conditions of the setup. For example, if the setup causes perceptual incongruencies, it can still cause a less immersive experience [23].  
We rank the setups such that HMD are more immersive than CAVE [24], which is in turn more immersive than modalities that are presented through a flat-screen [25]. For content presented through a flat-screen, 360-degree panorama is more immersive than 2D pictures [26]. Similarly, for content presented through HMD, virtual 3D space is more immersive than 360-degree video [27].  
Head-mounted displays are in general more immersive than flat-screen displays because of stereoscopic vision and the responsiveness of the field of view to head movements [28]. In HMD, the field of view changes in response to head movements, just like in natural visual perception, helping to create a sense of immersion [29].  
We rank the CAVE as lower than HMD setups because the CAVE is still projected onto flat-screens [24]. The CAVE is ranked higher than 2D pictures and 360-degree panorama on flat-screens because it allows greater free movement [24].  
In 360-degree videos, specialized 360-degree cameras are used to capture a spherical panorama of a real-world scene [30] (Figure 2, top). Users watching the video through HMD can look around in every direction as if they were physically present at the location [30]. These videos capture the entirety of the surrounding environment, providing a passive panoramic experience [30].  
Virtual 3D spaces (Figure 2, bottom), on the other hand, are created as 3D-modelled environments that the user can move around in [31]. As they move to different positions and move their head to look in a different direction, a view is rendered to be displayed through the HMD based on their position and viewing angle and the 3D virtual model [31]. The objects in the modeled environment may also be developed with natural movements and behaviors and respond with natural behavior when touched [31]. Thus, unlike  
Figure 1: Different types of virtual reality setups are used to simulate the experience of nature through VR ordered on a scale of level of immersion. At the top, three modes of presentation are shown: a flat-screen such as a computer monitor, a CAVE VR setup where the user is placed in a room and the environment is projected onto each of the three walls and onto the floor, and a head-mounted display device. In the second row, examples of stimuli presented are shown for each of the five setups ranked.  
360-degree videos, these environments support a higher level of interaction, allowing users to engage with the virtual world in a more dynamic and responsive manner.  
While each of the five types of VR setups detailed above and other types of setups are used in different studies, this review only includes studies using HMD as these are the most common and most canonical types of VR. One study that used CAVE is also included.
## 4 Discussion
The constant evolution of malware families makes it difficult for these models to maintain high accuracy when exposed to novel malware samples not seen during training. GRU-GAN models, while effective with known data, may struggle with new malware behaviors that differ significantly from their training sets. Addressing this issue requires exploring several research directions. One possible approach is to develop advanced data augmentation techniques using GANs to generate diverse and realistic synthetic malware samples. This would help expose models to a wider variety of potential malware behaviors during training. Additionally, integrating transfer learning techniques could enable the models to adapt more rapidly to new malware families. Another promising direction is the design of meta-learning algorithms, which could improve the model's ability to generalize from limited examples of new malware types. Furthermore, ensemble methods, where multiple GRU-GAN models are trained on different subsets of malware families, could enhance generalization and robustness across a broader range of threats. Another pressing issue is the adversarial robustness of GRU-GAN models in malware detection. As the use of these models becomes more widespread, they are likely to face intentional attempts to evade detection, with malware authors developing more sophisticated evasion techniques. Ensuring robustness against adversarial attacks is crucial to the long-term effectiveness of GRU-GAN-based detection systems. To improve resilience, researchers could explore adversarial training techniques tailored to the unique characteristics of API call sequences and the GRU-GAN architecture. This would involve generating adversarial examples that simulate potential evasion strategies and incorporating them into the training process. Moreover, developing detection mechanisms for identifying adversarial inputs, perhaps through latent space analysis in the GAN or by examining the confidence levels of the GRU-based discriminator, could further enhance robustness. A third major challenge involves dynamic adaptation in GRU-GAN models. Given the rapidly evolving nature of cyber threats, it is essential to develop models that can adapt to new malware variants and changing API call patterns without requiring frequent retraining. Retraining models are often computationally expensive and time-consuming, making it impractical for real-time security applications. One promising direction for addressing this challenge is to investigate online learning techniques that allow the model to update its parameters incrementally as new data becomes available. This approach would involve designing specialized loss functions and optimization algorithms that balance the retention of previously learned knowledge with the incorporation of new patterns. Finally, developing efficient techniques for identifying when adaptation is necessary, such as drift detection algorithms or monitoring the model's real-time performance metrics, could help in creating more responsive and dynamic malware detection systems.
# Block-Wisely Supervised Network Pruning with Knowledge Distillation and Markov Chain Monte Carlo
## 5 Conclusions  
In this paper, we introduce a block-wisely supervised network pruning approach to effectively eliminate structural redundancy of a baseline network. The KD technique is employed to independently mimic block-wise feature representations of baseline networks, and an MCMC scheme is employed to sample substructures from the posterior. Extensive benchmarking tests of BNP in pruning popular neural network architectures on three datasets demonstrate our method has advantages over the SOTA. The limitation of BNP lies in that pruning ResNet networks on ImageNet with MCMC sampling is computationally expensive, and we plan to improve its efficiency by designing more efficient sampling methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha\) & **Paras\_\(\downarrow\)\%** & **FLOPs\(\downarrow\)\%** & **Baseline Acc** & **Acc\(\downarrow\)\%** \\ \hline
0.01 & 30.32 & 38.74 & 92.17 & 0.84 \\
0.02 & 54.83 & 51.28 & 92.17 & 1.34 \\
0.03 & 58.26 & 60.89 & 92.17 & 2.16 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pruning results of ResNet-20 on CIFAR-10 when hyperparameter \(\alpha\) is set to 0.01, 0.02 and 0.03.  
Figure 3: The effects of hyperparameter \(\alpha\) on network pruning results. (**a**) ResNet-20; (**b**) ResNet-56.  
**Author Contributions:** Conceptualization, H.L. and Z.Y.; methodology, F.D. and Z.Y.; software, F.D.; validation, H.L., F.D. and Z.Y.; formal analysis, H.L.; data curation, F.D.; investigation, H.L., F.D. and L.S.; project administration, H.L., L.S. and Z.Y.; supervision, Z.Y.; resources, F.D. and L.S.; writing--original draft, H.L. and Z.Y.; visualization, H.L.; writing--review and editing, H.L., F.D. and Z.Y.; funding acquisition, F.D., L.S. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (61901238, 62062058), the Key Research and Development Program of Ningxia (2019 BEB04023, 2021 BEE03013), and the West Light Foundation of the Chinese Academy of Sciences (XAB2019 AW12).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** The ILSVRC-2012, CIFAR-10, and CIFAR-100 datasets used to support the findings of this study are available at [[https://image-net.org/challenges/LSVRC/2012/](https://image-net.org/challenges/LSVRC/2012/)]([https://image-net.org/challenges/LSVRC/2012/](https://image-net.org/challenges/LSVRC/2012/)) (accessed on 10 August 2022) [[http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz](http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)]([http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz](http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)) (accessed on 10 August 2022) and [[http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz](http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz)]([http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz](http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz)) (accessed on 10 August 2022).  
The authors declare no conflict of interest.
## 1 Introduction
They also realized that the impeller performance is independent of the radial gap size. Furthermore, it was elucidated that pressure recovery (C\({}_{\text{P}}\)) increases in a smaller radial gap ratio. [PERSON] et al. [PERSON] et al. (2011) reported that decreasing the radial gap ratio substantially increase the efficiency. However, when the diffuser is closer to the impeller compared with the optimum value, the impeller part most probably endures mechanical excitation. According to [PERSON] et al. [PERSON] et al. (2017, 2018), reduction in the Mach number at the diffuser vane leading edge is a result of enlarging the radial gap size. The efficiency, pressure ratio and mechanical loading are some other factors that are affected by the radial gap ratio. The main conclusion is that by adjusting the radial gap ratio in each individual case, the flow configuration in the diffuser channel can be optimized. For determining the value of a radial gap ratio for an optimum diffusion, the loading of the vane pressure and suction side should be balanced. [PERSON] et al. [PERSON] et al. (2018) executed a numerical study to peruse the interaction between the impeller and diffuser in a high-pressure ratio centrifugal compressor, concluding that the compressor with the radial gap ratio of 1.1 has a higher pressure ratio and pressure rise in comparison with the compressor with the radial gap ratio of 1.05. Although so many researchers have investigated the effect of a radial gap ratio on the performance of centrifugal compressors, few works have examined to how to select a suitable radial gap ratio for a specific situation. In this paper, the effect of the various design of a radial gap on the performance of the high pressure ratio centrifugal compressor and the flow phenomena inside the compressor components is investigated. For this purpose, vast numerical investigations using steady three-dimensional Reynolds-averaged Navier-Stokes simulations at six different radial gaps for five rotational speeds have been conducted. Different designs of the radial gap are studied thoroughly. Furthermore, as the flow behavior in the interaction between the impeller and diffuser may be critical, the steady simulation should predict it carefully. Therefore, in order to verify the reliability of results obtained by the steady simulation, unsteady simulations for two radial gap ratios at transonic tip-speed Mach numbers were executed and the results are compared with steady ones. At the end, although different investigations showed that choosing an optimal radial gap ratio is strongly depending on the geometry [PERSON] et al. (2017); [PERSON] et al. (2017),optimum radial gap ratio for a specific range of tip-speed Mach number (M\({}_{\text{u}}\)) for a high-pressure ratio centrifugal compressor are suggested according to the results of the survey in this paper.
## 2 Materials and Methods
### Measurements  
X-ray diffraction (XRD) data were collected using a D/MAX 2200 diffractometer (Rigaku, Tokyo, Japan) with Cu K\(\alpha\) irradiation operated at 40 kV and 45 mA. The samples were scanned in the 2 range of 5 to 75\({}^{\circ}\) and step size of 0.02\({}^{\circ}\) at 4 s for each step. Fourier transform infrared spectra (FTIR) were collected using a Nicolet FTIR360 spectrometer (Thermo Nicolet, Madison, WI, USA) (KBr pellet method, 4 cm\({}^{-1}\) resolution, number of scans is 32 times). Scanning electron microscope (SEM) observations of LDHs, LDHs/EVA composites and the cone calorimetric residue were conducted on an FEI-Sirion instrument (FEI, Eindhoven, the Netherlands) with a field emission of 20 kV. The specimens were examined in a HITACHI 1H-7650 transmission electron microscope (TEM) (Hitachi, Tokyo, Japan) operated at an accelerating voltage of 100 kV. Contact angles were measured using a JC2000A contact angle and interface tensile measurer (Shanghai photoelectric, Shanghai, China). Samples were pressed into disks with a diameter of 11 mm and thickness of 0.4 mm. Deionized water (3 \(\mu\)L) was dripped onto each disk, and the drops were observed for 30 s. Each measurement was repeated five times. Thermogravimetric (TGA) and differential thermogravimetry (DTG) analyses were conducted using a Perkin Elmer Pyris 1 Thermal Analyzer (Perkin Elmer, Norwalk, CT, USA) at a linear heating rate of 10 \({}^{\circ}\)C/min in pure nitrogen from 50 \({}^{\circ}\)C to 800 \({}^{\circ}\)C, using a sample mass of 3 to 4 mg. Calorimetry tests were conducted in a cone calorimeter at an incident heat flux of 50 kW/m\({}^{2}\) according to the ISO 5660-1 standard. The polymer sample (100 \(\times\) 100 \(\times\) 5 mm\({}^{3}\)) was placed horizontally on the balance holder.
# LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite
## 3 Algorithmic Principle
### FG Optimization Positioning Method Assisted by SFLA  
#### 3.4.1 BDAPS FG Model  
The data fusion factor model of BDAPS is built on the basis of the existing original observation data. The system factor graph model is constructed by observing the navigation state in the time domain and all available navigation measurements. On this basis, two kinds of models are established, which are measurement model and process model. The measurement model is a posteriori probability estimation model, which represents the conditional probability of the measured value obtained at time K under the \(\mathbf{X}_{k}\) of the navigation state set at that time. In a factorial graph, each measurement corresponds to a factor node in the factorial graph. The ranging process is the primary process used to construct the positioning equation. It is assumed that the ranging value at k-time is \(\rho_{r,t}^{s}\). When there is n-channel signal, the pseudo-range of n-channel signal is obtained  
\[\rho_{r,k}^{s}=[\rho_{r,k}^{1}\quad\rho_{r,k}^{2}\quad\ldots\quad\rho_{r,k}^{N }] \tag{18}\]  
Since the indoor environment is usually near-earth, the effect of ionospheric delay can be ignored. The influence of troposphere generally defaults that the tropospheric delay is invariable under the same environment region. The measurement equation based on single difference of distance can be expressed as  
\[ \begin{bmatrix}r_{r,k}^{21}\\ r_{r,k}^{31}\\ \ldots\\ r_{r,k}^{N1}\end{bmatrix} = \begin{bmatrix}\|\mathbf{p}_{r,k}-\mathbf{p}_{k}^{2}\|-\| \mathbf{p}_{r,k}-\mathbf{p}_{k}^{1}\|\\ \|\mathbf{p}_{r,k}-\mathbf{p}_{k}^{3}\|-\|\mathbf{p}_{r,k}-\mathbf{p}_{k}^{1} \|\\ \ldots\\ \|\mathbf{p}_{r,k}-\mathbf{p}_{k}^{N}\|-\|\mathbf{p}_{r,k}-\mathbf{p}_{k}^{1} \|\end{bmatrix} +\mathbf{w}_{r,k}^{s} \tag{19}\]  
In the formula, \(\mathbf{p}_{r,k}\) is the user's position at k-time, \(\mathbf{p}_{r,k}=[x_{k}\quad y_{k}\quad z_{k}]\), and \(\mathbf{p}_{k}^{s}\) is the space position of the s-type BeiDou pseudo-satellite at k-time. Therefore, the pseudo-range measurement information equation is a function related to the position of the pseudo-satellite and the position of the receiver. The position of the BeiDou pseudolite is usually demarcated by outdoor RTK and total station in advance. The position of the BeiDou pseudolite is known here. We can see that \(h(\mathbf{p}_{r,k})\) is only a function of the user's position at K time. \(\mathbf{w}_{r,k}^{s}\) is the noise error introduced by spatial noise, and we consider it a random Additive white Gaussian noise.