esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Anti-Jamming Algorithm Based on Transmit/Receive Time-Sharing Technology
## 4 Robust Anti-Jamming Algorithm Based on the Transmit/Receive Time-Sharing  
### Robust Anti-Jamming Algorithm  
Under long-term continuous interference, there are usually multiple T/R time-sharing periods. Navigation receivers usually receive navigation signals from random satellites in different T/R cycles, but the interference characteristics, such as interference bandwidth, power, and center frequency, are stable. Therefore, the interference characteristics can be used as the preliminary information to provide the initial value for the next re-convergence to ensure that the receiver can quickly converge in the following T/R time-sharing period. The time-domain anti-jamming algorithm of the satellite navigation receiver based on the time-sharing signal system is shown in Figure 8. When the burst signal clock level is 1, the weight memory is enabled and the previous convergence weight is transmitted to the anti-interference module as the initial weight. This is also the innovation of this algorithm.  
Figure 7: The effect of signal reception interval on data loss.  
Suppose that the weight vector of the converged anti-jamming filter in the \(i\)-\(th\) period is stored and applied to the \((I+1)\)-\(th\) initial weight vector:  
\[\mathbf{W}_{M}^{i+1,1}=\mathbf{W}_{M}^{i,end} \tag{13}\]  
The weight convergence of the robust time-domain anti-jamming algorithm is shown in Figure 9. The navigation receiver converges slowly in the preliminary information stage. After entering the next receiving time slot, the filter weight does not increase significantly but achieves rapid convergence and almost no information loss.  
The estimated CNR of the prior signal and the burst signal are shown in Figure 10. When the anti-jamming filter converges to stability, the stable value of the filter weight vector can provide a priori information for the burst signal. Therefore, the interference in the subsequent periods can be effectively suppressed once the burst signal is transmitted to the anti-jamming filter. The figure shows that the output estimated CNR increases rapidly, and only 0.0132 ms is required when the CNR loss margin is 1 dB\(\cdot\)Hz.  
Figure 8: The flow chart of the robust anti-jamming algorithm.  
Figure 9: The convergence of the robust anti-jamming algorithm.
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
## 4 Implementation and Evaluation
### Comparison to Other Approaches
#### 4.3.3 VC-Less Approach  
An alternative approach for achieving the same functionality of our system is to not use VC at all and instead include the DIDs of endpoint owner to the DID documents of \(Group_{DID}\). This has the advantage that CoAP responses become shorter (since they do not include a membership VC), and revocation becomes simpler (since the group owner has simply to update the corresponding DID document). On the other hand, adding or removing a new endpoint owner requires modification of the corresponding DNS record, so depending on the frequency with which endpoint owners are added or removed to a group, the VC-less approach may be a better or worse option. In any case, VCs allow to express more complex relationships than a DID document. For example, there can be cases where an endpoint owner is allowed to participate in a group only during specific times: such advanced trust relationships can be easily expressed using VCs.
## 5 Discussion on Sensitivity Parameters
### Effect of \(t\)  
The analytical method obtained from this paper was used to calculate the groundwater drawdown difference induced by dewatering time under the unsteady flow, and the dewatering times are 6 h, 12 h, 24 h, 48 h, 96 h, and 192 h, respectively. As shown in Figure 13, it can be seen that the drawdown difference decreases with the increase of the dewatering time. When the time increases from 6 h to 48 h, the drawdown difference decreases rapidly. When the time exceeds 48 h or more, the drawdown difference decreases slowly, and finally maintains a steady balance. When the time decreases from 6 h to 192 h, the decrements of \(\Delta/H_{0}\) are 0.122 (\(S_{h}/H_{0}\)), 0.078 (\(L/H_{0}\)) and 0.072 (\(R_{\rm w}/H_{0}\)), respectively, and the groundwater drawdown inside the pit is most significantly affected by the dewatering time. The length of the waterproof curtain and the radius of the foundation pit are weakly affected by the dewatering time.  
For foundation pit dewatering with a suspended waterproof curtain, the decrement of \(\Delta/H_{0}\) is larger when the dewatering time is within 48 h; the data of observation wells inside and outside the foundation pit should be recorded in a timely manner to eliminate adverse effects on the foundation pit and surrounding environment. The groundwater drawdown difference of different dewatering times can be calculated by the analytical method proposed from this paper, which provides a reference for foundation pit dewatering in phreatic aquifers with a suspended waterproof curtain.
## 2 Materials and Methods
### Data Extraction  
After identifying and excluding duplicates, the results were exported to an Excel document (Microsoft Office, 2016. Microsoft. Inc., Redmond, Washington, USA), and three tables were created, one for each database. These tables recorded the author's name, date, title, and keywords of all conducted studies, arranged alphabetically based on the first author's name. After reviewing the title and results, the inclusion and exclusion criteria were applied. Data extraction was performed independently by two reviewers (EFG and ARF). The studies meeting the inclusion criteria were reviewed to extract relevant data regarding the effects of CT on physical fitness. The means, standard deviations (SDs), and sample sizes (\(n\)) were extracted by one author (EFG) from the included papers and were corroborated by a second (ARF). Any discrepancy between the authors was resolved through discussion with a third author (AV). Alongside the results of the fitness tests, the intervention characteristics (i.e., training frequency, total duration, and type of training protocol) and sample details (i.e., sample size, age, and body mass) were extracted and recorded. In instances where data were not explicitly provided in the text but rather were only in a figure [21; 29; 30], the extraction was performed using a validated graphical software program (WebPlotDigitizer version 4.5; Automeris LLC, Pacifica, CA, USA), previously validated (r = 0.99, \(p\) < 0.001) [31] by a single experienced researcher.
# Biochar Affects Heavy Metal Uptake in Plants through Interactions in the Rhizosphere
## 3 Results  
### Plant Growth and Metal Uptake  
The addition of 2% biochar to soil did not increase plant biomass compared to unamended control treatments for non-polluted and weakly polluted soil (Table 2). In highly polluted soil, plants grown without biochar grew very poorly and only reached a biomass of 8-10% of plants in the corresponding biochar amended treatment. Plants growing in the weakly polluted soil reached 85% of the shoot biomass in the non-polluted soil, whereas corresponding growth of plants in the biochar-amended highly polluted soil was 67% of that in the non-polluted soil. The metal content of plants increased with increasing degrees of metal pollution in the soils. The highest concentrations of metals in barley were found when plants were growing on highly polluted soil, and for this soil the biochar amendment reduced the metal concentration in shoots by 82% for Cd, 66% for Cu, 91% for Pb and 53% for Zn, compared to plants growing on the same soil without biochar amendment (Table 3). There were no significant differences in shoot metal concentrations between plants grown on biochar-amended and non-amended soil for the two soils with lower metal content. For the weakly polluted soil, root Cu concentrations were lower in plants grown on amended compared to non-amended soil, while there were no significant differences for the other metals. Roots of plants grown on non-amended highly polluted soil were too small to allow analyses in two out of three replicates. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Soil**} & \multirow{2}{*}{**Treatment**} & \multicolumn{3}{c}{**Shoots**} & \multicolumn{3}{c}{**Root**} \\ \cline{3-7}  & & **(g/pot)** & **SEM** & **(g/pot)** & **SEM** \\ \hline Unpolluted soil & Control & 1.63 & a & 0.05 & 0.56 & a & 0.03 \\  & Biochar & 1.76 & a & 0.12 & 0.52 & a & 0.08 \\ Weakly polluted soil & Control & 1.39 & ab & 0.08 & 0.39 & b & 0.03 \\  & Biochar & 1.34 & b & 0.13 & 0.29 & c & 0.04 \\ Highly polluted soil & Control & 0.09 & d & 0.01 & 0.02 & d & 0.01 \\  & Biochar & 1.16 & c & 0.12 & 0.25 & c & 0.02 \\ \hline \hline \end{tabular} Values (means and SEM, \(n=3\)) for either shoots or roots followed by the same letter are not significantly different (\(p<0.05\), Tukey’s test).  
\end{table} 
Table 2: Biomass of shoots and roots of barley grown in three differently polluted soils amended or not with biochar (2% by weight).
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 3 Fouling in MD, OD and OMD
### Fouling in Omd  
Fouling in OMD may occur following the same mechanisms mentioned previously in DCMD and OD fouling, provided that OMD is a non-isothermal process using an osmotic solution in the permeate side of a porous and hydrophobic membrane. Similar to any other membrane process, the presence of fouling in OMD may vary according to the nature of the feed solution to be treated and the characteristics of the used membrane. It is worth noting that fouling investigation in OMD has received little attention as can be concluded from the number of published papers summarized in Table 3. It was reported that fouling in OMD comprises a major obstacle for its industrial implementation and the determination of the OMD performance during long-term run is required [7].  
[PERSON], et al. [81] used the OMD process to treat OMW with PTFE membranes of different pore sizes (TF200, TF450 and TF1000) at 40 \({}^{\circ}\)C feed temperature and 20 \({}^{\circ}\)C of 5 M CaCl\({}_{2}\) osmotic permeate solution. To study membrane fouling, OMD experiments were carried out using distilled water as feed before and after each OMW treatment [81]. Long-term experiments of 30 h OMW processing were performed. The permeate flux showed a decrease from 3.9 to 1.3 L/m\({}^{2}\)h (i.e., a decline of 67%). However, after rinsing the fouled membrane with distilled water, a reduction of only 5.7% of the permeate flux of water was detected indicating that membrane fouling is of less importance when using OMD for the treatment of OMW by PTFE membranes [81].  
In a recent study, [PERSON], et al. [86] conducted OMD-crystallization to obtain Na\({}_{2}\)CO\({}_{3}\)\(\cdot\)10H\({}_{2}\)O as a solid product using NaCl as an osmotic solution and a feed solution containing Na\({}_{2}\)CO\({}_{3}\). A hollow fiber PP membrane with an effective pore size of 0.04 \(\upmu\)m and a porosity of 40% was employed. The feed temperature ranged between 20 and 40 \({}^{\circ}\)C while that of permeate was kept at room temperature. Different experiments were carried out varying the concentration of Na\({}_{2}\)CO\({}_{3}\) and NaCl in the feed and permeate, respectively. A decrease of the permeate flux was detected during the first minute of each experiment, and scaling was observed in some experiments due to the accumulation of crystals on the membrane surface, coming from the feed reservoir because of the recirculation of the feed stream [86].
## 5 Continuous-Trajectory-Tracking Controller Design
### Main Theorem  
Firstly, we characterize an asymptotically stabilizable domain of system (25) subject to the input constraint \(\tilde{u}\in\tilde{U}(t,e)\). According to Assumption 3, there exists a positive definite proper function \(\tilde{V}(e)\) such that \(V(t,e)\leq\tilde{V}(e)\) for all \((t,e)\). In this paper, we consider thefollowing asymptotically stabilizable domain, which is defined by using the sub-level set of \(\bar{V}(e)\):  
**Definition 4** (Asymptotically stabilizable domain).: _Let \(D\subset\mathbb{R}^{n}\) be the domain of Assumption 3, and let \(r>0\) be a constant such that \(\{e\in\mathbb{R}^{n}\ |\ \|e\|\leq r\}\subset D\). Let \(\alpha_{max}\in(0,\min_{\|e\|=r}\|e\|)\) be the maximum constant such that_  
\[\bar{V}(e)<\alpha_{max}\ \Rightarrow\ \omega_{1}(t,e)+L_{g}V(t,e)\cdot\bar{k}(t,e)<-Q (e),\ \forall t\in[0,\infty), \tag{60}\]  
_where \(\bar{k}(t,e)\) is the minimizing input given by (54). Then, we define the asymptotically stabilizable domain of system (3) subject to \(\bar{u}\in\bar{U}(t,e)\) as follows:_  
\[W=\{e\in\mathbb{R}^{n}\ |\ \bar{V}(e)<\alpha_{max}\}\subset D. \tag{61}\]  
To design a continuous-trajectory-tracking controller satisfying \(\bar{u}\in\bar{U}(t,e)\), we introduce the following scaling function \(\mu:[0,\infty)\times\mathbb{R}^{n}\to\mathbb{R}\) proposed in [13]:  
\[\mu(t,e)=\begin{cases}\frac{P(t,e)+|P(t,e)|+C(\|L_{\bar{g}}V\|)}{2+C(\|L_{g}V \|)}&(L_{\bar{g}}V(t,e)\
eq 0)\\ 0&(L_{\bar{g}}V(t,e)=0)\end{cases}, \tag{62}\]  
where \(P:\mathcal{X}_{1}\to\mathbb{R}\) is given by  
\[P(t,e)=\frac{\omega_{1}(t,e)+Q(e)}{-L_{g}V(t,e)\cdot\bar{k}(t,e)}, \tag{63}\]  
and \(Q(e)\) is a positive-definite function of Assumption 3. Moreover, \(C:\mathbb{R}\to\mathbb{R}\) is any continuous positive-definite proper function.  
**Remark 9**.: _The composition of the scaling function \(\mu(t,e)\) is almost the same as the one proposed in [13]. The only difference is that the function \(P(t,e)\) contains the positive-definite function \(Q(e)\) to deal with the stabilization of time-varying systems._  
Then, by using the scaling function \(\mu(t,e)\), we can formulate the following main theorem of this article:  
**Theorem 2**.: _Consider the error system (25) subject to \(\bar{u}\in\bar{U}(t,e)\). Let \(W\subset D\) be the domain of Definition 4, \(\bar{k}(t,e)\) the minimizing input given by (54), and \(\mu(t,e)\) the scaling function defined by (62). Then, the state feedback controller_  
\[\bar{u}=\bar{k}(t,e)=\mu(t,e)\bar{k}(t,e) \tag{64}\]  
_locally asymptotically stabilizes the origin \(e=0\) on \(W\) and satisfies the input constraint \(\bar{u}\in\bar{U}(t,e)\)._  
The proof of Theorem 2 will be given in Section 5.2.  
**Corollary 1**.: _Consider the nonlinear system (1) subject to a convex input constraint \(u\in U(x)\). Then, the state feedback controller_  
\[u=k(t,x):=\bar{k}(t,x-x_{d}(t))+u_{r}(t) \tag{65}\]  
_locally achieves trajectory tracking and satisfies the input constraint_  
\[k(t,x)\in U(x),\ \forall(t,x)\in[0,\infty)\times\mathbb{R}^{n}. \tag{66}\]
# Prevention of Controller Area Network (CAN) Attacks on Electric Autonomous Vehicles
## 3 CAN Bus Attack Mitigation Schemes
Fourthly, IDS systems have detection latency because of the complexity of the underlying machine learning and deep learning algorithms. Finally, IDSs rely on data training, which generally requires a large data set, or they may be ineffective. Unlike the techniques discussed in the literature, this paper proposes a CAN bus attack prevention scheme to be implemented at the ECU level rather than at the CAN bus level. This paper suggests the usage of secure boot process on all ECUs that are connected to the network; this is to prevent any CAN bus attacks that may result due to the presence of malicious software on a connected ECU. The secure boot should detect an attack at its root level and apply the necessary countermeasures to prevent the attack from continuing to the CAN bus. The proposed scheme can mitigate the limitations of existing state-of-the-art cryptographic encryption and authentication algorithms, and intrusion detection systems. Because the secure boot scheme is implemented during the boot process only, it does not require changes in CAN bus behavior and does not add latency to CAN bus communication, as the only delay introduced is at the startup of the ECUs. While the secure boot scheme may use a secret key, the vulnerability associated with using secret keys is not the same as that of cryptographic encryption and authentication because the secure boot key is not shared between ECUs. Because secure boot schemes prevent the execution of malicious codes, secure boot is able to prevent all CAN bus attacks that occur through the path of injecting a malicious code into an existing ECU. Additionally, secure boot has the added benefit of mitigating the risk of executing a malicious code in a vehicle's ECUs even if it does not affect the CAN bus. Finally, as secure boot algorithms are built on strong cryptographic functions, secure boot is able to detect minor changes in the code. A comparison between the existing state-of-the-art CAN attack prevention schemes and the proposed secure boot scheme is summarized in Table 2.  
The proposed secure boot scheme's limitations are that it cannot detect attacks that involve adding a malicious ECU to the CAN bus and it has a high cost of implementation. The high implementation cost is because all ECUs of the system have to incorporate the secure boot algorithm for the scheme to be effective, as indicated in Figure 3.
## 5 Discussion
### Limitations and Future Work  
This study primarily employs qualitative methods to evaluate the design and application of MultiScaleAnalyzer, focusing on foundational questions such as whether the tool enables educators to identify meaningful patterns in spatiotemporal learning data and what specific patterns can be observed. While qualitative analysis is well suited for these exploratory objectives, it does not provide the quantitative metrics necessary to systematically assess the tool's efficiency and accuracy. In our future work, we plan to conduct a quantitative evaluation to compare MultiScaleAnalyzer with other analysis methods, utilizing a larger student sample size and involving a broader group of educators to provide a more comprehensive and statistically robust assessment of its capabilities and impact on educational practices.
## 4 Results  
This paper proposes FAPSO as a feature extraction method and SVM as the hierarchical GPCR protein classification model. The proposed model is different from the traditional classification model as it is a combination of the following parts: (1) hybridization of the Firefly algorithm and Particle Swarm Optimization to select the suitable wavelet family and decomposition level and (2); a hierarchical classification strategy (LCPN with VC) to stop the incorrect classifications that occur with non-mandatory leaf node prediction (NMLNP) in internal hierarchy levels before reaching the leaf node. The results are analyzed according to three hierarchy levels: family, subfamily, and sub-subfamily. The family level consists of 5 classes; the subfamily level contains 38 classes; and the sub-subfamily level contains 87 classes.  
The PseAAC features vector graph for one of the class A GPCR proteins is shown in Figure 4a. Using the Coiflet wavelet family 4 and 3 levels of decomposition, the DWT converted the PseAAC features into approximation coefficients and detail coefficients. The detailed wavelet coefficients for decomposition levels 1, 2, and 3 that contain local protein features are shown in Figure 4b-d. While Figure 4d shows the wavelet approximation coefficient with global features or rough characteristics of the protein. The number of global and local features produced depends on the number of decomposition levels. Decomposition level can be performed up to the log2 (N) level, where N is the length of the studied data. In this study, considering the size of the PseAAC feature used is 170, the maximum decomposition size allowed is seven levels. Here, we can see the advantage of the wavelet's multiresolution analysis, which enables protein sequences to be studied using multiple decomposition levels.  
In this paper, the results are separated into two sections. The first section describes the chosen wavelet family and decomposition level, while the second section describes the algorithm's performance.
## 4 Experimental Results  
### Datasets  
To obtain the experimental dataset, we collected two weeks of traffic logs from the boundary route [28] of an enterprise network that contains 150 active users. The dataset contained approximately 30 million traffic flows, including 209 intranet nodes and 54,147 outer network nodes. The logs were stored in a format similar to Netflow and exported as readable text.
## 3 UAV AG Communication Channel Modeling and Characterization
### A UAV AG Multi-Mobility Multi-Trajectories Channel Model
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(K(t)\) & The number of clusters \\ \(L_{k}\) & Rays number of the \(k\)th cluster \\ \(\mathbf{C}_{k}^{A}(t)/\mathbf{C}_{k}^{Z}(t)\) & Coordinates of the center of clusters \(\mathbf{C}_{k}^{A}/\mathbf{C}_{k}^{Z}\) \\ \(\mathbf{C}_{k}^{A}(t)/\mathbf{C}_{k}^{Z}(t)\) & Coordinates of the scattering points of the \(l\)th ray in the \(k\)th cluster \\ \(\phi_{A,l_{k}}^{T_{l_{k}}}(t)/\phi_{E,l_{k}}^{E}(t)\) & AAoD and EAoD of the \(l\)th ray in the \(k\)th cluster \\ \(\phi_{A,l_{k}}^{T_{l_{k}}}(t)/\phi_{E,l_{k}}^{E}(t)\) & AAoA and EAoA of the \(l\)th ray in the \(k\)th cluster \\ \(\phi_{A,l_{k}}^{T_{l_{k}}}(t)/\phi_{E,l_{k}}^{T_{l_{k}}}(t)\) & AAoD and EAoD of the LoS ray \\ \(\phi_{A,l_{k}S}^{T}(t)/\phi_{E,l_{k}S}^{E}(t)\) & AAoA and EAoA of the LoS ray \\ \(\phi_{l_{k}}^{T}\) & Initial phases subject to uniform distribution in \((0,\,2\pi]\) \\ \(\mathbf{x}_{l_{k}}\) & Cross-polarization power ratio \\ \(D_{l_{k}}^{T,MB}(t)/D_{l_{k}}^{R,MB}(t)\) & Distance of the \(l\)th ray between Tx/Rx and clusters for the MB case \\ \(D_{pq}^{L_{k}}(t)/\mathbf{A}_{g}^{R}(t)\) & Distance of the LoS path between the \(p\)th antenna and the \(q\)th antenna \\ \(\mathbf{A}_{p}^{T}(t)/\mathbf{A}_{g}^{R}(t)\) & Coordinates of \(p\)th Tx or \(q\)th Rx antenna \\ \(\mathbf{C}_{l_{k}^{T}}(t)\) & Coordinates of the single cluster for the SB case \\ \(D_{p,l_{k}^{T}}^{T,SB}(t)/D_{q,l_{k}^{T}}^{R,SB}(t)\) & Distance of the \(l\)th ray between the Tx/Rx and the single cluster for the SB case \\ \(\mathbf{V}_{p}(t)\) & The speed vector of the Tx \\ \(\mathbf{V}_{Q}(t)\) & The speed vector of the Rx \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of channel model parameters.
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
## 4 Experimental Results and Analysis
### Natural Image Denoising
We used the PSNR, SSIM, and FSIM as the objective indicators of image quality evaluation, and the data results are shown in Tables 1-6. In order to show the denoising performance of our method more intuitively, we drew histograms for the average of evaluation indexes of eight natural images under different noises according to the six methods, as shown in Figure 7. Our method achieved the best results in different noise experiments as indicated by the data from the objective indicators. In the impulse denoising experiment, our average PSNR was 23% higher than that of WNNM\(-\)RPCA, and the average SSIM was even 31% higher than that of WNNM\(-\)RPCA. In the Poisson noise denoising experiment, the average PSNR of our method was 11% higher than that of the classical algorithm RPCA, and the average FSIM was 32% higher than the TDV. In studies including Gaussian noise denoising, the average PSNR of our method was 51% better than that of AMlogtv. Figure 6: Denoising images of Lena by different methods (Gaussian noise). (**a**) Singular value comparison; (**b**) original clean image; (**c**) noisy image (Gaussian noise); (**d**) RPCA (PSNR = 33.61 dB, SSIM = 0.9416, FSIM = 0.9698); (**e**) AMlogtv (PSNR = 25.24 dB, SSIM = 0.7445, FSIM = 0.9847); (**f**) NonRPCA (PSNR = 37.56 dB, SSIM = 0.9437, FSIM = 0.9712); (**g**) WNNM\(-\)RPCA (PSNR = 38.86 dB, SSIM = 0.9539, FSIM = 0.9766); (**h**) TDV (PSNR = 23.03 dB, SSIM = 0.6737, FSIM = 0.7617); (**i**) our method (PSNR = 42.37 dB, SSIM = 0.9795, FSIM = 0.9890).