esa-sceva/satcom-chunk-collection huggingface dataset:
# SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective
## 4 Experimental Analysis
### Vulnerability Discovery  
In order to verify whether our proposed method can effectively improve the efficiency of vulnerability discovery and can be applied in reality, we compared SATFuzz with BooFuzz, AFLnwe, and AFLnet. BooFuzz is a stateful blackbox fuzzer, AFLnwe is a stateless coverage-guided fuzzer, and AFLnet is a stateful-coverage-based fuzzer. They are all open source and widely used in practice. For all fuzzers, we counted the numbers of vulnerability found and measured the time they took to expose these vulnerabilities. To reduce the impact of randomness, we ran 10 isolated instances of each group for each subject. We set the test time to 12 h, and the experimental results are shown in Table 6.  
discovery. In addition, SATFuzz can be applied in practice to find high-risk vulnerabilities of real software in a shorter time.
## 3 Results
### Parameter Estimation of the SCOPE Model
We compared GPP and SIF simulated by the SCOPE model based on the default and optimized parameters against flux tower GPP and OCO-2 SIF observations at Willow Creek (Figure 8). The model based on the optimized parameters estimated both GPP and SIF fairly well (R\({}^{2}\) = 0.88 and RMSE = 1.47 gC m\({}^{2}\) day\({}^{-1}\) for daily GPP, R\({}^{2}\) = 0.70 and RMSE = 0.15 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\) for SIF at 771 nm, R\({}^{2}\) = 0.61 and RMSE = 0.09 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\) for SIF at 771 nm). The simulated GPP and SIF based on the optimized parameters were more consistent with flux tower GPP and OCO-2 SIF in seasonal trajectories than the simulated GPP and SIF based on the default parameters (Figure 8).
## 2 Signal Model  
The schematic diagram of the idealized PBR system is shown in Figure 2. The system has two antennas to receive the reference signal and surveillance signal, respectively.  
In a PBR system, the idealized baseband reference signal \(u_{ref}(t)\) comprises two components: the baseband signal from IO and noise. Its expression is given by  
\[u_{ref}(t)=A_{ref}u(t-\tau_{0})+n_{ref}(t) \tag{1}\]  
where \(A_{ref}\) is the amplitude of the reference signal, \(u(t)\) is the baseband signal from the IO, \(\tau_{0}\) is the time delay between IO and radar, and \(n_{ref}(t)\) is the noise in the reference channel.  
The baseband surveillance signal \(u_{surv}(t)\) received by radar comprises four components: the reference signal component, multipath clutter, target echo, and noise. Its expression is given by  
\[u_{surv}(t)=A_{surv}u(t-\tau_{0})+\sum_{k=1}^{K}A_{k}u(t-\tau_{k})+\sum_{p=1}^ {p}A_{p}u(t-\tau_{p})e^{j2\pi f_{p}t}+n_{surv}(t) \tag{2}\]  
where \(A_{surv}\) is the amplitude of the reference signal component; \(K\) is the number of multipath clutter components; \(A_{k}\) is the amplitude of the \(k\)-th multipath clutter; \(\tau_{k}\) is the time delay of the \(k\)-th multipath clutter; \(P\) is the number of targets; \(A_{p}\) is the amplitude of the \(p\)-th target echo, \(\tau_{p}\) and \(f_{p}\) are the time delay and Doppler frequency of the \(p\)-th target echo, respectively; and \(n_{surv}(t)\) represents the noise in the surveillance signal.  
To obtain the position and velocity of the target, the PBR system suppresses the reference signal and multipath clutter components in the surveillance signal. Subsequently, it estimates the target's position and velocity by identifying the delay and Doppler frequency corresponding to the peak of the CAF. Let \(u^{\prime}_{surv}(t)\) represent the idealized surveillance signal with the reference signal and multipath clutter components suppressed. The expression is  
\[u^{\prime}_{surv}(t)=\sum_{p=1}^{p}A_{p}u(t-\tau_{p})e^{j2\pi f_{p}t}+n_{surv }(t) \tag{3}\]  
Figure 2: The schematic diagram of the idealized PBR system.  
So the expression of CAF is  
\[\begin{split}\chi(\tau,f_{d})&=\int_{0}^{T}u_{ref}^{*} (t-\tau)u^{\prime}{}_{surv}(t)e^{-2\pi f_{d}t}dt\\ &=\sum_{p=1}^{p}A_{ref}^{*}A_{p}\chi_{p}(\tau,f_{d})+n_{\chi}( \tau,f_{d})\\ &=A_{ref}^{*}\sum_{p=1}^{p}A_{p}\int_{0}^{T}u^{*}(t-\tau_{0}-\tau )u(t-\tau_{p})e^{-2\pi\left(f_{d}-f_{p}\right)t}dt+n_{\chi}(\tau,f_{d})\end{split} \tag{4}\]  
where \(\tau\) and \(f_{d}\) are the delay and Doppler frequency, respectively; \(T\) is the integration time; \(\chi_{p}(\tau,f_{d})\) is the CAF of the \(p\)-th target; and \(n_{\chi}(\tau,f_{d})\) represents the impact of noise on the CAF calculation. Equation (4) indicates that the CAF between the reference signal and the surveillance signal can be represented as a linear combination of the CAFs between the reference signal and copies of itself with added delays and Doppler frequencies. This forms the basis for the sparse representation of CAF.
## 4 Validation of the Real-Time Optimization Based Strategy
### Simulation Results
The results are summarized in Figure 12. The SC allows us to drastically reduce the RMS value of the battery current. This reduction is strongly linked with the value of the weighting factor \(A_{b}\). For the considered value (\(3.3\times 10^{-7}\)), a reduction of 77% can be obtained (Figure 12a). It is clear that any others values of the weighting factor \(A_{b}\) will change the reduction. A value of \(6.6\times 10^{-7}\) (star in Figure 10) will reduce for instance the RMS value to 83%. Nevertheless, as the Pareto optimal front shown, the hydrogen consumption will be higher. As far as the performance index is concerned, there is a hydrogen reduction of 7% in comparison with a FC-based electric vehicle (without any secondary source; Figure 12b). To sum up, the simulation results shows that the association of the three sources reduces the hydrogen consumption, while avoiding high dynamics on the FC. Furthermore, the SC allows us to significantly reduce the solicitations of the battery. Figure 11: (a) WLTC class 2 low-speed phase. (b) Fuel cell currents \(i_{\textit{LFC}}\) for the fuel cell–battery–super capacitor (FC-BAT-SC)-based vehicle (black line) and fuel cell (FC)-based vehicle without any secondary source (green line). (c) Voltage of the super capacitors \(u_{\textit{SC}}\). (d) State of charge of the battery \(SoC_{\textit{D}}\). Figure 12: Performance index: (a) H2 consumptions of the FC-BAT-based vehicle (green color) and FC-BAT-SC-based vehicle (black color); (b) battery current (root mean square (RMS) value) of the FC-based vehicle without any secondary source (green color) and FC-BAT-SC-based vehicle (black color).
## 4 Protocols Protecting Terminal Devices in Zero-Trust Networks
### Description of the Protocols with CRP Mechanisms
#### 4.1.1 Enrollment Cycle  
The input data in Algorithm 6 shown below are: d, N, P, and prime numbers \(\alpha\), \(\beta\). The ephemeral key K is picked randomly. K is used to select the orderly sequence of f responses \(\mathrm{K_{R}}\): \(\left\{r^{\prime}1,\ldots,r^{\prime}_{f}\right\}\) from the full set of responses \(\left\{r_{1},\ldots,r_{N}\right\}\) by skipping the \(N-f\) positions with a state of 0. \(\mathrm{K_{R}}\) is kept secret by the controlling party. Let \(\mathrm{Kc}\) be the stream of the nonce \(\left\{\omega\right\}\), the seed S, and the ciphertext M*. Both C and \(\mathrm{Kc}\) are distributed to the terminal device. All other information and cryptographic keys are erased, namely {C*, Sk, Pk, K} and the N-f responses with a state of 0.  
```
1:Input data: Some file \(F\)
2:Nonce \(\left\{\omega\right\}\), stream {S}, seed {L} \(\leftarrow\) random number generator
3:Generate ephemeral public-private key pair {Pk, Sk} from \(L\) (ex: PQC algorithm)
4:\(C\leftarrow\) Encrypt(F, Sk)
5:\(M\gets Pk\)
6:\(\rightarrow\)Use Module 1:Generate a set of N responses with C, and {\(\omega\),S}: * Static input data: Positive integers \(d\), \(N\), \(P\), \(\alpha\), \(\beta\) * Output data: the N, P-bit long, responses \(\left\{r_{1},\ldots,r_{N}\right\}\)
7:\(\rightarrow\)Use Module 2:Generate a subset of responses from \(M=Pk\) and {\(r_{1},\ldots,r_{N}\)}: * Key \(K\) with \(f\) states of \({}^{*}1^{\prime}\leftarrow\) random number generator Filter the \(f\) responses \(\left\{r^{\prime}1,\ldots,r^{\prime}_{f}\right\}\) * \(M^{\star}\leftarrow\) Encrypt(\(M\), \(K\))  Erase: {Sk, Pk, K} * Let subkey \(K\) be the \(f\), \(P\)-bit long, responses \(\left\{r^{\prime}1,\ldots,r^{\prime}_{f}\right\}\) * Let subkey \(Kc\) be the streams {S, \(\omega\), \(M^{\star}\)}
```  
**Algorithm 6**Enrollment cycle for file \(F\), generate subkeys \(\mathrm{Kc}\) & \(\mathrm{Kr}\)
###### Abstract
The major difficulty in the described process is the great number of various factors determining the growth rate of microorganisms and thus influencing the efficiency of GDL synthesis. Despite the interest in the optimization of biotechnological methods for the biotransformation of castor oil into peach-like fragrance, most experiments have been carried out by means of comprehensive experimental plans. There are only a few scientific reports applying a design of experiment protocols in order to choose the batch culture conditions [21; 28]. In one study, it was attempted to optimize the conditions for the biotransformation of castor oil to GDL by yeast _Yarrovia lipolytica_ using the Taguchi robust design method. The approach allowed the researchers to determine the influence of several factors on the efficiency of the process provided in a laboratory-scale bioreactor, with a small number of experiments, based on orthogonal array design [29]. _Yarrovia lipolytica_, used in the research, is one of the best known \"unconventional\" yeasts. It is considered non-pathogenic, and the biotechnological processes involving it are generally recognized as safe (GRAS status) by the Food and Drug Administration (FDA, Silver Spring, MD, USA). The unique physiological and biochemical properties of these microorganisms contribute to their wide application in food biotechnology [30; 31]. _Yarrovia lipolytica_ is characterized by, among others, high extracellular lipolytic activity, which allows the use of castor oil (a cheap and easily available substrate) in GDL biosynthesis. The yeast produces three extracellularly lipases, named Lip2, Lip7, and Lip8 [32; 33]. This article presents the results from batch cultures in which the impacts of medium pH, mixing speed, and castor oil concentration were analyzed based on the statistical Taguchi method. Although this method has already been applied [22], to the best of our knowledge, no study has used the minimal orthogonal array design in the batch bioreactor cultures.
# Real-Time Vision through Haze Based on Polarization Imaging
## 1 Introduction  
Environmental pollution has become a matter of great concern in recent years. In order to protect the environment, the inspection of the environmental quality, especially the sensing and detection of the emission of pollutants, is a problem that must be faced [1; 2]. In view of this situation, many countries have set up environmental stations to monitor companies with high pollutant emissions [3]. The optical sensing and detection method has become the most popular monitoring method owing to its high resolution, abundant information, and use of simple equipment. However, in bad weather conditions, such as hazy and rainy weather conditions, the atmospheric aerosols and various gases, especially the haze particles, have a serious scattering effect on the light wave [4; 5]. The image obtained under these conditions will go through a serious degradation process, which will reduce the efficiency of optical sensing and detecting, as well as the monitoring distance [6; 7]. Therefore, without increasing the number of monitoring stations, how to improve the sense and detect distance of the monitoring station under the condition of bad weather and how to remove the influence of gases and aerosols on the monitoring efficiency have become urgent problems to be solved.  
This paper proposes a real-time optical sensing and detection system based on the Stokes vectors through analyzing the scattering characteristics and polarization characteristics of gases and aerosolsin the atmosphere. This system can sense and detect the emission of pollutants in real time in bad weather and improve the monitoring distance of the environmental monitoring system. In this study, we first analyzed the principles of polarization imaging algorithms and established a physical model for polarization images through gases and aerosols in bad weather based on the Stokes vectors. Next, we developed a real-time optical sensing and detection system with four cameras aligned parallel to the optical axis. By solving the linear polarization parameters of the Stokes matrix, we achieved real-time registration of different polarization images. Further, we achieved visual enhancement of polarization degree images using an image registration algorithm based on the speeded up robust features (SURF) algorithm and a bilinear interpolation based on the contrast-limited adaptive histogram equalization (CLAHE) algorithm. Our results show that the proposed system can achieve real-time high-contrast, high-definition imaging under bad weather conditions without a loss of spatial resolution.
# Short-Form Videos for Public Library Marketing: Performance Analytics of Douyin in China
## 2 Literature Review
### Douyin in Libraries Context  
Current research on Douyin in the library field tends to separate public and academic libraries because of the differences in their objectives and service subjects. For public libraries, studies mainly focus on operating short-form video accounts in libraries [14]. Early TikTok video contents include brief event promotions and snapshots of the libraries and collections [15]. Librarians utilizing TikTok recognize its creative social media nature for interacting with the newer generation frustrated with Facebook, Instagram, and Twitter [16]. There is relatively little research on how public libraries use TikTok and more research on how public libraries use Douyin.  
[PERSON] et al. [17] studied the Zhejiang Library Douyin for reading promotion and suggested that library short video operations should have a clear positioning, a professional team, an emphasis on content quality, an understanding of the platform algorithm, and active message interaction. Based on the Technology Acceptance Model (TAM), [PERSON] [18] proposes a Douyin strategy to identify patrons' needs, cultivate their interests, and drive their attention to practice. [PERSON] [19] used data envelopment analysis (DEA) to evaluate the operational efficiency of provincial public libraries' Douyin, with operation days, numbers of dynamics and replies as input index, and numbers of followers, likes, forwarding, and comments as output index. [PERSON] et al. [20] constructed an evaluation model of public library short video dissemination capability by combining the entropy weighting method to determine the index weights around the four aspects of users, network, video, and communication characteristics of Douyin accounts.  
For academic libraries, TikTok can help strengthen the relationship between libraries and academic departments [16]. Librarians already use TikTok to disseminate videos to recommend their favorite books [21]. For example, Nicholls Library has employed the application to promote its facilities at the university, and the TikTok account@nichollslibrary is essential to the library's marketing identity [22]. Some libraries already have active interaction and followers on their TikTok accounts [23]. As a new user engagement platform, librarians inevitably consider its security, privacy, and safety. A library media specialist at the Great Valley High School opined that TikTok is as safe and secure as any other social media app [24]. Based on the SOR framework, [PERSON] [25] proposed influencing factors of short video users' continuous attention intention in university libraries, and used structural equation analysis and fuzzy set qualitative comparison analysis to conduct empirical analysis. That study found that communication interaction, content quality, social environment, and perceived trust constitute the influencing factors of the continuous attention intention of short video users in university libraries.
## 5 Conclusions  
In this paper, we proposed ECDAB to deal with distortion and edge discontinuity caused by 360\({}^{\circ}\) omnidirectional images through equirectangular projection and also proposed CRCAB, which can capture global contextual information. To address the shortcomings of CRCAB, ICRCAB was proposed, and then networks were built based on these modules. The performance of these networks was demonstrated to outperform the baseline model through classification and object detection experiments based on the Omni-MNIST and ERP-Detect datasets. Our proposed method can be combined with existing excellent pre-training models without additional overhead time to train pre-training weights and is computationally much less expensive than the proposed network for 360\({}^{\circ}\) omnidirectional images, since our proposed method does not require multi-projection or resampling.  
In the future, for 360\({}^{\circ}\) omnidirectional images, ECDAB and ICRCAB can be migrated to the scene segmentation task to improve the performance of the network. Moreover, although we only performed classification experiments on CIFAR-10, CRCAB and ICRCAB can be applied to more complex models for other tasks relating to traditional images.  
Conceptualization, X.Z., D.Y. and T.S.; methodology, X.Z., D.Y., T.S. and Y.Y.; software, X.Z.; validation, X.Z.; formal analysis, X.Z.; investigation, X.Z.; resources, D.Y., T.S. and Y.Y.; data curation, T.S., J.Z. and Y.S.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z., D.Y., T.S. and Y.Y.; visualization, X.Z., T.S. and J.Z.; supervision, T.S. and Y.Y.; project administration, T.S.; funding acquisition, T.S. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Chongqing (CSTB2022 NSCQSX1200), the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202200537), Chongqing Normal University Ph.D. Start-up Fund (21 XLB035), and the Special Funds for Postdoctoral Research of Chongqing (2010010004713415).  
Not applicable.  
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# A Compact Sub-GHz Wide Tunable Antenna Design for IoT Applications
## 3 Results of Simulation and Measurements
### Current Density Analysis  
For single antenna designs, current density analysis is usually performed to understand the radiation characteristics, determine the active parts of the radiating structure, and find the effective electrical length of the antenna. For the proposed antenna design, the current density distribution is plotted to understand the antenna's behavior at the resonating frequency bands. It has been observed that current density distribution varies over different operating bands. It has been observed that a large portion of the antenna is radiating at lower frequency bands and vice versa. Such analysis can be utilized to optimize the antenna's dimensions by eliminating non-radiating antenna parts. The surface current distributions of the proposed antenna design were analyzed at various bands of resonance. Figure 6a,b shows the current distribution for non-optimized design for frequency bands at 1.95 GHz and 1.75 GHz, respectively. For the optimized design, the surface current densities at frequency bands of 1029 MHz and 764 MHz are shown in Figure 6c,d, respectively. From the given figures, it can be seen that the current density has a different distribution for different frequency bands. Figure 6d shows the current density distribution at 764 MHz. It can be seen that the maximum current variation is along the outermost part of the loop slot structure. The effective electrical length can be mapped to the corresponding resonating band. The slot length variations affect the first and other resonating bands for different values of capacitive loading. Thus, the analysis helped in understanding the antenna's operation and gave useful insights about operating at different frequency bands.
# Robust Backstepping Control of Wing Rock Using Disturbance Observer
## 2 Problem Formulation and Preliminaries
### Neural Networks  
The RBFNN has been widely employed to tackle unknown nonlinear functions due to its satisfactory approximation ability. Considering an unknown function \(f\left(x\right):R^{m}\to R\), the RBFNN approximation output over a compact set \(\Omega_{m}\subseteq R^{m}\) can be written as [22]  
\[f(x)={W^{*}}^{T}S(x)+\iota^{*} \tag{4}\]  
where \(W^{*}\in R^{p}\) is the optimal weight value and \(\iota^{*}\) is the smallest approximation error; \(S(x)=[s_{1}(x),s_{2}(x),\cdots,s_{p}(x)]^{T}\in R^{p}\) is a basis function vector, which can be written in the form of  
\[s_{i}(x)=\exp[-(x-c_{i})^{T}(x-c_{i})/(2b_{i}^{2})]\ \ i=1,2,\cdots,p \tag{5}\]  
where \(c_{i}\) and \(b_{i}\) are the center and width of the neural cell of the ith hidden layer.  
The optimal weight value of the RBFNN is given by [42]  
\[W^{*}=\arg\min_{\hat{W}\in\Omega_{f}}\Big{[}\sup_{x\in\Omega_{m}}\Big{|}f\left( x\right)-\hat{W}^{T}S\left(x\right)\Big{|}\Big{]} \tag{6}\]  
where \(\hat{W}\) is the estimation of the optimal weight value \(W^{*}\); \(\Omega_{f}=\left\{\hat{W}:\left\|\hat{W}\right\|\leq M\right\}\) is a valid field of the parameter and \(M\) is a design parameter; \(\Omega_{m}\) is an allowable set of the state vector. Using the optimal weight value yields  
\[\Big{|}f(x)-{W^{*}}^{T}S(x)\Big{|}=|\iota^{*}(x)\ |\leq\ t \tag{7}\]  
where \(\imath>0\).
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
## 4 Results and Discussions  
The experimental results are organized into two sections: The first section details the impact of a double-sided exponential jammer (Transient EMI) on an LTE-A link. In contrast, the second section examines its effect on a 5G-NR link. Finally, a comparison is performed to determine which communication link is more susceptible to this type of interference.
# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 4 Methods
### Logistic Regression  
In Logistic Regression, the used technique was SMOTE-temek [50] and the following hyperparameters: an l2 penalty, a tolerance for stopping criteria (tol) of 0.0001, an inverse of regularization strength C of 10,000, the algorithm _newton_cg_ for the optimization problem, and a maximum iteration of 1000, which is the number of iterations taken for the solvers to converge. Logistic Regression hardware architecture is formed by a Multiplier-Adder 1, a Sigmoid function, and a comparator, as is shown in Figure 11.  
Multiplier-Adder 1 receives 15 inputs that give information about CQI. They were multiplied by the weights and added until a single value was obtained, which is the input to the _Sigmoid_ module. Finally, the output of Sigmoid output was connected to the comparator input, which determines if CLASS0 or CLASS1 is activated, depending on if the output of _Sigmoid_ is higher or lower than 0.5, respectively.