esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulations
### Margin of Error for Minimum Aggregation Delay  
We calculate and display (see Figure 3) the margin of error values (confidence interval: 95%) for the minimum aggregation delay, the primary performance metric of interest in this paper. Since the aggregation delay is directly dependent on the other three key performance metrics (diameter, fraction of leaf nodes and the number of child nodes per intermediate node), the margin of error values for the minimum aggregation delay could be considered as a measure of the statistical accuracy of the simulations. The procedure followed to calculate the margin of error values (using a z-score of 1.96 for a 95% confidence interval) for the minimum aggregation delay is briefly explained below.  
The margin of error for the minimum aggregation delay under a particular operating condition (number of nodes and transmission range per node) is calculated as \(z*\sigma/\sqrt{n_{s}}\), where \(z\) is the z-score 1.96, \(\sigma\) is the standard deviation of the samples collected for the minimum aggregation delay under the particular operating condition and \(n_{s}\) is the number of samples collected (200 graph snapshots). If \(\overrightarrow{x}\) is the average value of the \(n_{s}\) samples collected for the minimum aggregation delay under a particular operating condition, then one could say that the range of values for the minimum aggregation delay is \(\left(\overrightarrow{x}-z*\sigma/\sqrt{n_{s}},...,\overrightarrow{x}+z* \sigma/\sqrt{n_{s}}\right)\) with a probability of 0.95 (95% confidence interval; z-score is 1.96).  
As the magnitude of the average value for the minimum aggregation delay increases, we observe the margin of error to also increase, especially in the case of the bottleneck node-weight based DG trees. For networks of high to very high density, we observe the margin of error values for the minimum aggregation delay to be relatively low for the bottleneck link weight-based DG trees, partly attributed to the lower values for the average minimum aggregation delay. Only for networks of  
Figure 3: Margin of Error Values for Minimum Aggregation Delay.  
moderate density, the margin of error values for the bottleneck node-weight base DG trees are slightly lower than that of the bottleneck link-weight based DG trees. We also observe negligible difference in the margin of error values incurred for the MaxBNW-DG trees and MinBNW-DG trees; likewise, there is negligible difference in the margin of error values incurred for the MaxBLW-DG and MinBLW-DG trees. This further vindicates our reason to just base our discussion of the results as bottleneck node-weight based DG trees _vs._ bottleneck link-weight based DG trees.
## 5 D-MSR Management Phases
### Coping with Dynamicity, Reservation Conflict and Interference in the Network (Phase-6)
#### 5.6.4 Coping with External Interference in the Network  
In case of interference in the network, different edges may experience a different packet loss ratio. In centralized approaches like WirelessHART, each node periodically reports on the status of its  
Figure 16: Resource reservation conflict sample.  
Figure 17: Internal interference caused by communication on edge (C, D) outside of the two-hop neighborhood of edge (A, B).  
communication with its neighbors to the network manager through a set of report commands. The network manager may re-construct new graphs, which include more reliable edges, based on the received reports from the network. It then releases former resources and constructs new communication schedule along these new graphs. These instructions will be forwarded to the network. This approach cannot cope with disturbance in large-scale networks in a real-time manner.  
However, in D-MSR, the RPL uses best Expected Transmissions values (the expected number of transmissions required to successfully transmit and acknowledge a packet on the edge), as a metric, to find the best paths in case of interference. Subsequently, after choosing the new path, the previous resources along the old path are released, and the new communication resources will be reserved along the new path in a distributed manner. In Section 6.5.2, we compare the performance of D-MSR to that of WirelessHART in terms of the ability to provide reliable communication in case of interference in the network.
## 2 Background and Related Concepts
### Security Policies  
Security policies refer to the rules established by organizational leaders to ensure the appropriate level of security is aligned with the organization's needs. The access control policy is crucial in safeguarding the organization's data and resources against internal and external threats, reducing vulnerability to cyber and physical attacks. Each access control type employs specific security techniques to meet the organization's security and privacy requirements, which will be discussed in detail in Section 3.2.
## 1 Introduction
Animal blood is also used as fertiliser [28]. Although blood meal contains only 0.22% P, it also has 12% N and trace elements [29]. In addition, animal blood is a good binder that can be used in fertiliser production [29]. P-fertilisers produced from secondary raw materials will be able to replace or supplement traditional fertilisers, provided that they are as effective as the latter, in other words, that they provide crops with P nutrition enough to build the same yield volume and do not impair the yield or endanger the environment. Many new P products have been tested for P plant availability and crop-enhancing efficiency [20; 21; 22; 23]. Although the results described seem to be optimistic, most of them were obtained in pot experiments and need confirmation in field trials [30]. Scientists from the Wroclaw University of Science and Technology, the Institute of New Chemical Synthesis in Pulawy and the University of Warmia and Mazury in Olsztyn (Poland) have developed an innovative technology for producing P-fertilisers from cheap renewable raw materials--SSA, animal (poultry) bones and dried animal blood. Most of them have been activated by phosphorus solubilising microorganisms (PSM). The additional potential of the PSM introduced into fertilisers is related to soil P solubilisation [31] and plant growth promotion [32]. New recycled fertilisers were evaluated for their agronomic utility in field experiments against traditional P-fertilisers. This paper addresses the dynamics of the total P content in soil under the influence of fertilisers from waste, as well as P content and accumulation in crop plants and accompanying weeds, and in the biomass of post-harvest residues. Based on the research, the following questions are expected to be answered: (i) Will waste-fertilisers not be worse than traditional fertilisers in maintaining soil P stock? (ii) Will they provide the same nourishment of crop plants with P and thus guarantee the same P content and accumulation in the main and by-product yields? (iii) Will they change the P content and accumulation by weeds and thus the weed competitiveness against crop plants? (iv) Will they ensure the same P pool in the post-harvest residues? (v) Will PSM present in fertilisers affect the soil P stock, and the P content and accumulation in the crop, weeds and post-harvest residues?
## 5 Reliability Verification of the Frequency Estimation Value of Midspan Deflection Acquisition  
Based on the error caused by the exponential distribution function simulation and the reliability probability integration method, it is necessary to further verify the calculation results of the bridge structure's acquisition frequency reliability under the above working conditions.  
For the midspan deflection time-history curve, the correctness of the reliability calculation results with a cutoff frequency of \(f_{1}=0.62\) Hz and an acquisition frequency of \(f_{s}=1.24\) Hz is verified.  
The signal acquisition interval \(\Delta t=0.00115\) s of the bridge simulation analysis is 869 Hz. Now, the acquisition frequency is changed to 1.24 Hz, which is equivalent to reducing every 603 items of data in the original signal to one item of data, and then forming a new original signal. Through Fourier transform, the time domain signal is converted into the frequency domain signal, and the amplitude spectrum curve of the frequency domain signal is obtained, as shown in Figure 16.  
When the acquisition frequency is 1.24 Hz, the cutoff frequency of the signal is 0.62 Hz, the mean value of the amplitude spectrum function is \(u_{1}=-1.4582\), the standard deviation is \(\varpi_{1}=0.94032\), the variance is \(\varpi_{1}{}^{2}=0.88421\), and the absolute maximum value is \(S_{1}=2.5895\). Compared with the quasi-static data, the error (absolute value) of each digital characteristic is as follows:  
Mean relative error:  
\[\alpha_{1}=\frac{u_{0}-u_{1}}{u_{0}}=\frac{1.65779-1.4852}{1.65779}=10.410\% \tag{18}\]  
Relative error of variance:  
\[\delta_{1}=\frac{\varpi_{0}^{2}-\varpi_{1}^{2}}{\varpi_{0}^{2}}=\frac{0.75695-0.88421}{0.75695}=16.812\% \tag{19}\]  
Relative error of standard deviation:  
\[\psi_{1}=\frac{\varpi_{0}-\varpi_{1}}{\varpi_{0}}=\frac{0.87003-0.94032}{0.87003 }=8.079\% \tag{20}\]  
Maximum relative error:  
\[\Delta_{1}=\frac{S_{0}-S_{1}}{S_{0}}=\frac{2.73976-2.5895}{2.73976}=5.484\% \tag{21}\]  
The reliability of acquisition frequency is calculated as follows:  
\[P_{r}=P(f<1.24\;\mathrm{Hz})=\frac{A_{1}}{A_{0}}=\frac{\sum_{i=1}^{n}g_{1}(x_ {i})f_{i}}{\sum_{i=1}^{n}g(x_{i})f_{i}}=\frac{0.86423}{0.94038}=91.900\% \tag{22}\]  
where, \(g_{1}(x)\) is the amplitude spectrum simulation function of the quasi-static curve of the original signal; \(g(x)\) is the extracted amplitude spectrum function simulated with a frequency of 1.24 Hz; \(A_{1}\) is the area of the amplitude spectrum function at a sampling frequency of 1.24 Hz; \(A_{0}\) is the area of the original amplitude spectrum function at a acquisition frequency of 869 Hz.  
As can be seen from Figure 17, when the acquisition frequency is 1.24 Hz, the digital characteristics of the midspan deflection signal of the bridge are relatively consistent with the quasi-static curve, and the change is relatively small, which shows that the reasonable acquisition frequency analysis method of the midspan deflection proposed in this paper is feasible.  
Figure 16: Frequency Distribution of Amplitude Diagram.
## 4 Conclusions  
A comparative analysis of natural frequency and FCM responses under varying damage conditions (extents and locations) was conducted to address the challenge of quantifying structural sealant damage in HFGCWs. It was demonstrated that although natural frequency exhibits high sensitivity to damage severity, it lacks spatial resolution for structural sealant damage. In contrast, the FCM was proved to be a robust indicator, enabling simultaneous quantification of damage severity and spatial localization.  
Figure 11: First-order curvature mode difference in side A under different damage degrees (**a**,**b**) at the boundary of (**a**) Condition 6; (**b**) Condition 7.  
1. The FCMD values in damaged sealant regions are significantly higher than in intact areas, allowing accurate determination of damage extent and location through peak distribution analysis.
2. Based on boundary node FCMD distribution characteristics, a damage threshold of 0.1205 is established for quantitative damage detection. This threshold is validated using boundary modal data, achieving 100% verification accuracy. Specifically, structural sealant damage is confirmed when FCMD exceeds 0.1205.
3. Modal data classification reveals that boundary modes are more effective for damage identification than internal modes. Consequently, the modal test is optimized by reducing measurement points to 24 boundary nodes, halving the testing workload while maintaining FCMD accuracy. This boundary-specific approach enhances diagnostic reliability and practical applicability, streamlining structural sealant inspection in HFGCWs.  
Methodology, Y.Y., X.W. and X.L.; Software, X.W.; Formal analysis, X.L.; Investigation, F.Y.; Resources, X.Z.; Data curation, X.W. and X.Z.; Writing--original draft, Y.Y. and X.W.; Writing--review & editing, J.S.; Supervision, J.S.; Project administration, J.S.; Funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Shandong Province (ZR2022 ME186), the National Natural Science Foundation of China (52001188) and Shandong Key R&D Program--Innovation Empowerment Initiative for Tech SMEs (2024 TSGC0979, 2024 TSGC0110).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This work was supported by the Natural Science Foundation of Shandong Province (ZR2022 ME186), the National Natural Science Foundation of China (52001188) and Shandong Key R&D Program--Innovation Empowerment Initiative for Tech SMEs (2024 TSGC0979, 2024 TSGC0110).  
The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 4 Discussion
### Opportunities  
The characteristics of IoT not only bring challenges to vulnerability analysis, but also new opportunities.  
(1) Application of AI Technology  
In recent years, two technological waves of AI and IoT have emerged and integrated, promoting society into the era of AIoT (AI + IoT). The development of AI technology has also brought new solutions and methods to the security of IoT. At present, there are related studies using AI technology for access control [111] and similarity detection [93; 94; 95; 96; 97; 98]. With the development of IoT and AI, new vulnerability discovery, detection, and mitigation technologies inevitably appear. When AI technology is applied to IoT devices, it is also a new opportunity of AI adversarial attack and defense. For example, the attacker pollutes the training set of the smart speaker and induces it to reply to a question with some negative information (abusive words). Security researchers prevent these problems by modifying AI algorithms.  
(2) The Dependency of Third-Party and Open-Source Code  
IoT firmware development relies heavily on third-party and open-source code. The manufacturers usually take new features, high performance, and low power consumption as the main targets of their products and shorten the development cycle as much as possible to enhance market competitiveness. Therefore, they adopt the agile development model. Many IoT manufacturers directly reuse open source code, refer to public code implementation, cross-compile PC platform code and rely on third-party libraries. [PERSON] et al. [113] found that 80.4% of printer firmware contained multiple known vulnerabilities at the time of release, and many of the latest released firmware updates still contained third-party library vulnerabilities that were announced eight years ago. Although this has exposed a large number of security issues, it has led to unique vulnerability discovery technologies. It is possible to mine homology vulnerabilities through the similarity of different levels of information. The similarity detection will also advance the application in the IoT field.  
(3) Development of Peripheral Systems  
IoT devices are becoming more interactive. It tends to improve to promote the development of IoT peripheral systems. IoT devices usually interact with terminals (mobile and PC), cloud endpoint, and other systems because of the characteristics of IoT. It not only adds new attack surfaces but also helps the development of peripheral analysis technology to solve the problem of difficult firmware acquisition and analysis. For example, the current research of _IoTFuzzer_[57] and access control framework [108; 109; 110; 111; 112] all have automated analysis and protection by peripheral systems.
## 6 Dynamic Canary-Relocation-Based Exploitability Evaluation
### Canary-Relocation-Based Exploitation Session Construction  
With the IO sequence and canary leakage metadata in mind, the canary-relocation-based exploitation session construction component makes just-in-time transformation on the analysis time exploit during the dynamic exploitation process, evaluating the exploitability of the target vulnerability in a completely automatic way.  
Algorithm 2 presents the inner working details. Provided with the communication channel \(proc\) on the vulnerable program, the analysis time exploit \(Exp_{analysis}\), the word size \(ptrSz\) of the target processor, the IO sequence \(ioSeq\) and the canary metadata \(canaryMd\), the algorithm simulates input and output events sequentially in the order specified by \(IOSeq\).  
When some READ event should be simulated, the relevant part in \(content\) is determined and then sent to the vulnerable program via \(proc.send\) (line 6-9). When some WRITE event should be simulated, we fetch the output data of the vulnerable program via \(proc.receive\), and then check if the desired canary exists in the output stream (lines 11-12). If it is confirmed existing, the actually leaked canary is calculated at line 13. This leaked canary is then utilized to relocate the input segment at the offset specified by \(canaryMd.offs\) at lines 14-16. The updated exploit would then be sent to the target in the following READ session, completing the vulnerability exploitability evaluation process in a canary sanity check bypassing way.  
```
0: The full duplex channel to connect to the target process, \(proc\).  The analysis time exploit input, \(Exp_{analysis}\).  The word size of the architecture of the target program, \(ptrSz\). 4 for 32 bit executables, 8 for 64 bit executables.  The IO sequence, \(ioSeq=\{IOEntry_{1},IOEntry_{2},\ldots,IOEntry_{n}\}\), where \(IOEntry_{i}\) (\(1\leq i\leq n\)) is the record describing the \(i\)th IO action.  The canary metadata, \(canaryMd=<loff,lfmt,offs>\), where \(loff\) and \(lfmt\) denotes the offset and format of the leaked canary in the output stream, \(offs=\{off_{1},off_{2},\ldots,off_{n}\}\) denotes the fields in the input stream that should be fixed with the leaked canary. Output: \(contents=Exp_{analysis}\) \(si=0\) \(ri=0\) \(canary=0\) for each \(IOEntry\in ioSeq\)do if\(IOEntry.type==READ\)then \(si\_end=si+IOEntry.len\) \(proc.\)send\((contents[si:si\_end])\) \(si=si\_end\) elseif\(IOEntry.type==WRITE\)then \(recvContent=proc.receive(IOEntry.len)\) if\(ri<=canaryMd.loff\)\(AND\)\(canaryMd.loff<=ri+IOEntry.len\)then \(canary=extractCanary(recvContent,canaryMd.loff-ri\), \(canaryMd.lfmt)\) for each \(off\) in \(canaryMd.offs\)do \(contents[off,off+ptrSz]=canary\) endfor endif \(ri=ri+IOEntry.len\) endif endfor
```  
**Algorithm 2** Runtime exploitation session with dynamic canary relocation
## 4 Conclusions  
In this work, we used atomistic simulation methods combined with clustering to study the influences of the concentration and microscopic distribution of hydrogen atoms on the elastic moduli in the interstitial solid solution of hydrogen in \(\alpha\)-Fe. Through this research, we made the following conclusions:  
The uniformly distributed hydrogen atoms slightly decreased the elastic constants and moduli of the \(\alpha\)-Fe, thereby causing softening, which was the result of the combined effect of the hardening of the Fe-H cohesion and the softening of the volume expansion of the lattice of \(\alpha\)-Fe.  
Different microscopic hydrogen distributions caused significant differences in the elastic moduli and induced softening or hardening. In the crystal planes determined by the Tet-sites on 10 NN, \(\rho_{[112]}>\rho_{[110]}\), and the increase of hydrogen atoms occupying these positions increased E and G. The positions in 5 NN affected those in 6 NN, and made \(\rho_{[112]}\) increased significantly, in the crystal planes which were determined by the Tet-sites of 5 NN and 6 NN, thereby, weakening the promotion effect of hydrogen on shears and slips. This explains why hardening can be induced by the hydrogen distributed in the 5 NN, 6 NN, and 10 NN positions.  
Hardening was not be induced by hydrogen atoms occupying the Tet-sites on 7 NN. In the crystal planes determined by such sites, \(\rho_{[112]}\) was relatively large, but still less than \(\rho_{[110]}\). This caused an increase in the shears and slips with the increasing hydrogen occupation. However, the enclosed decahedral structure by the DPs determined by these positions makes the effect of hydrogen on shears and slips tends to be isotropic; thus, the occupation of these positions by hydrogen will reduce B and make the \(\alpha\)-Fe easier to compress.  
Methodology, conceptualization, formal analysis, writing--original draft preparation, writing--review and editing, Z.W.; methodology, conceptualization, X.S.; writing--review and editing, funding acquisition, X.-S.Y.; writing--review and editing, Z.L.; writing--review and editing, S.-Q.S.; conceptualization, methodology, writing--review and editing, funding acquisition, X.M. All authors have read and agreed to the published version of the manuscript. This work is funded by the National Natural Science Foundation of China (Grant No. 11174030, 51701171). The authors acknowledge the foundation for financial support. The authors declare no conflict of interest.
## 3 Designing Control Strategy and Model
### Joint Control Model of Wind and Storage Based on TPA-BiGRU  
Firstly, the proposed model uses the deep BiGRU network to process wind power and energy storage data. Then, the temporal relationship of each feature quantity is extracted and processed for long-term memory. Moreover, it utilizes the TPA mechanism to strengthen the model memory function, highlighting the importance of the local information and ensuring the model's accuracy and stability. The control model proposed in this paper is shown in Figure 2.
## 3 Machine Learning Approaches for Slope Deformation Prediction
### Employed Statistical and Machine Learning Approaches  
#### 3.4.1 Statistical Approach: ARIMA  
The most popular statistical model for time-series prediction is the ARIMA. The ARIMA is a traditional linear prediction model that has three undetermined parameters: autoregressive term order \(p\), sequence difference order \(d\), and moving average term order \(q\). The process of determining \(p\), \(d\), and \(q\) is called the ARIMA model order determination.  
In the entire order determination and optimization process of the ARIMA model, three tests need to be conducted: (1) the sequence stationarity test; (2) the residual white noise test; and (3) the parameter significance test. The workflow of the ARIMA model is shown in Figure 5a. In this paper, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) were used to automatically determine the optimal parameters(_p_, \(d\), and _q_). The optimal parameters of the ARIMA model for the time-series displacement data of the Huanglianshu landslide are shown in Table 1.