esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of Voltammetrical Parameters  
#### 3.4.1 Effect of pH  
As known to all, the voltammetric responses of DA and UA highly depend on the solution pH. Therefore, it's worthwhile to optimize pH. The dependences of pH on the anodic peak currents of DA and UA are shown in Figure 5A. In the pH range of 2.58 to 3.93, the \(i_{\text{pa}}\)(DA) gradually increased with the increase of pH, then decreased slowly as the pH rose to 7.01, and suddenly decreased when the pH exceeded 7.01. Obviously, the maximal \(i_{\text{pa}}\)(DA) was achieved at pH 3.93. As for UA, the \(i_{\text{pa}}\)(UA) show a downward trend, with pH varying from 2.58 to 8.52. To ensure the highest possible anodic peak current for DA and UA, pH 3.93 was selected for the following experiments. Moreover, the anodic peak potentials of DA and UA linearly decreased as pH was rising (Figure 5B). The linear relationships of \(E_{\text{pa}}\) versus pH can be expressed as \(E_{\text{pa}}\)(DA) = \(-\)0.0685 pH + 0.839 (R\({}^{2}\) = 0.974) and \(E_{\text{pa}}\)(UA) = \(-\)0.0639 pH + 0.679 (R\({}^{2}\) = 0.976), respectively. Their slopes (68.5 pH/mV and 63.9 pH/mV) are close to 59 mV/pH, demonstrating the equal numbers of electron (e\({}^{-}\)) and protons (H\({}^{+}\)) involved  
Figure 4: CVs of 10 \(\upmu\)M Dopamine (DA) and uric acid (UA) mixture solution (1:1) on different electrodes.  
in their electrooxidation processes [16]. As reported, the oxidation of DA and UA are two electron transfer processes [45]. Hence, the electrooxidation of DA and UA involves two electrons (2e\({}^{-}\)) and two protons (2H\({}^{+}\)). The electrochemical oxidation process of DA and UA at the MnO\({}_{2}\)NFs/NrGO/GCE are illustrated in Scheme 1.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 5 Methods for Improving Photocatalytic Efficiency
### Metal or Non-Metal Ion Doping
#### 5.3.3 Mixed Doping  
Nonmetallic ions can also be used in combination with metal ions. Theoretically, the simultaneous doping of metal ions and non-metal ions can simultaneously obtain the advantages of metal ions and non-metal ions. [PERSON] et al. [321] applied the hydrothermal method to prepare Fe, N, and C co-doped TiO\({}_{2}\) photocatalysts. In the study of the reduction and degradation of a Cr\({}^{6+}\) solution, the conversion of the Fe-N-C-TiO\({}_{2}\) catalyst reached 100%, which was much higher than other photocatalysts such as C-TiO\({}_{2}\) and N-C-TiO\({}_{2}\). [PERSON] et al. [322] prepared TiO\({}_{2}\) co-doped with Ce and N via a sol-gel method and then incorporated diatomaceous earth particles into Ce-N-TiO\({}_{2}\) to form a composite photocatalyst. N ion doping reduces the forbidden bandgap of the TiO\({}_{2}\) composite catalyst, and the composite TiO\({}_{2}\) becomes a photoelectron trap due to the special 4f energy level of the Ce element. Therefore, under visible light irradiation for 240 min, the degradation rate of oxytetracycline by Ce-N-TiO\({}_{2}\)/G is almost 100%, which is much higher than other catalysts. [PERSON] et al. [323] used a sol-gel method to prepare Zr, Ni and N co-doped TiO\({}_{2}\) catalyst. Under visible light irradiation, the conversion of NO and SO\({}_{2}\) gas by the three element co-doped TiO\({}_{2}\) photocatalyst is much higher than that of the single or two element (Zr, Ni, N) doped TiO\({}_{2}\) photocatalyst. Mixed doping can effectively increase the light absorption of the composite in the visible region, which is the main reason for the enhanced photocatalytic activity.
## 6 Conclusions  
As Agriculture 5.0 evolves, DTs is expected to play an increasingly important role in the livestock farming industry. By helping farmers achieve the goals of Agriculture 5.0, DTs can contribute to a more sustainable and resilient food system. Indeed, DTs has the potential to revolutionize livestock production systems by providing real-time monitoring, analysis, and simulation capabilities. By integrating advanced technologies and data-driven decision-making, farmers gain insights into feeding patterns, stress factors, and optimal breeding strategies, thereby ensuring not only productivity but also the welfare of the livestock while promoting sustainable farming practices. Yet, despite their benefits, the applications of DTs in livestock production systems seem to be fragmentary and in the early stages of development, likely due to some open issues that have to be addressed. The initial costs of adoption, concerns regarding data security, and the imperative of educating farmers about these advanced tools necessitate careful consideration. Moreover, issues such as interoperability and ethical considerations need to be addressed for the widespread adoption of digital twins in livestock production systems. Future research should focus on developing standardized models, addressing ethical concerns, and exploring new applications of digital twins in livestock farming. In addition, a concerted effort toward developing user-friendly interfaces and comprehensive training programs is crucial to unlocking the full potential of digital twins in agriculture. As technology advances, DTs are expected to offer increased connectivity and seamless interoperability with other smart farming technologies. The collaboration between technology developers, agricultural experts, and policymakers will be pivotal in shaping a future where DTs play a central role in sustainable, efficient, and resilient agricultural practices.  
Conceptualization, E.S., C.M. and K.G.A.; Data curation, E.S. and C.M.; Methodology, E.S., C.M. and K.G.A.; Supervision, C.M. and K.G.A.; Validation, E.S. and C.M.; Visualization, E.S.; Writing--original draft, E.S.; Writing--review and editing, E.S., C.M. and K.G.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 6 Case Studies  
### A Case Study of the Blontology Construction  
This case study is provided to quantitatively test the efficiency of the proposed Blontology construction method. The Qingzhou Channel Bridge (QZCB) in the Hong Kong-Zhuhai-Macao Bridge is selected as a case study bridge. Figure 8 gives the overview of the QZCB, which is a two-pylon, five-span, cable-stayed bridge with a main span of 458 m. The superstructure of the QZCB is divided into 695 elements according to the _Standards for Technical Condition Evaluation of Highway Bridges_ in China. Each bearing is regarded as an element. Each segment of main beam or pylon is divided into an element. Each stay-cable system is divided into four elements: a tensile element, an anchor element, a cable encapsulation and a damping device.  
Table 4 presents the comparison of average time spent on creating an individual \"bridge element\" using the proposed automatic mapping method and the manual method. It can be seen that the average time using the proposed automatic mapping is no more than 0.0033 s, which is much less than the minimum average time (37.5674 s) using the manual method. This is because the manual method needs to first check elements information and then enter data into the Protege. In the data entry process, data-entry errors are almost inevitable, and a small amount of time is also wasted on fixing mistakes. In contrast, the automatic mapping method is not affected by artificial factors since this method can transform the relational data to OWL ontology without human operation. Hence, the automatic mapping method can dramatically increase the efficiency of the Blontology construction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bridge Component**} & \multirow{2}{*}{**Number of Elements**} & \multicolumn{2}{c}{**Automatic Mapping**} & \multicolumn{1}{c}{**Manual Method**} \\ \cline{3-5}  & & **Total Time (s)** & **Average Time 1 (s)** & **Average Time 1 (s)** \\ \hline Bearing & 16 & 0.0529 & 0.0033 & 39.6024 \\ \hline Main beam & 85 & 0.1147 & 0.0013 & 45.2090 \\ Bridge pylon & 146 & 0.1775 & 0.0012 & 41.5220 \\ Stay-cable & 448 & 0.4608 & 0.0010 & 37.5674 \\ system & 695 & 0.80589 & 0.0012 & 40.9752 \\ \hline \hline \end{tabular}  
* Average time is equal to total time divided by the number of elements.  
\end{table}
Table 4: Comparison of the Blontology construction times.  
Figure 8: The overview of the QZCB from the bridge decomposition perspective.
# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
###### Abstract  
Deep-learning models require large amounts of accurately labeled data. However, for medical image segmentation, high-quality labels rely on expert experience, and less-experienced operators provide noisy labels. How one might mitigate the negative effects caused by noisy labels for 3D medical image segmentation has not been fully investigated. In this paper, our purpose is to propose a novel hybrid robust-learning architecture to combat noisy labels for 3D medical image segmentation. Our method consists of three components. First, we focus on the noisy annotations of slices and propose a slice-level label-quality awareness method, which automatically generates label-quality scores for slices in a set. Second, we propose a shape-awareness regularization loss based on distance transform maps to introduce prior shape information and provide extra performance gains. Third, based on a re-weighting strategy, we propose an end-to-end hybrid robust-learning architecture to weaken the negative effects caused by noisy labels. Extensive experiments are performed on two representative datasets (i.e., liver segmentation and multi-organ segmentation). Our hybrid noise-robust architecture has shown competitive performance, compared to other methods. Ablation studies also demonstrate the effectiveness of slice-level label-quality awareness and a shape-awareness regularization loss for combating noisy labels.  
deep learning; noisy labels; medical image segmentation; robust learning Article
###### Abstract  
This research aimed to develop coding metamaterials to reduce the Radar Cross-Section (RCS) values in C- and Ku-band applications. Metamaterials on the macroscopic scale are commonly defined by effective medium parameters and are categorized as analogue. Therefore, coding metamaterials with various multi-layer and cuboid designs were proposed and investigated. A high-frequency electromagnetic simulator known as computer simulation technology was utilised throughout a simulation process. A one-bit coding metamaterial concept was adopted throughout this research that possesses '0' and '1' elements with 0 and \(\pi\) phase responses. Analytical simulation analyses were performed by utilising well-known Computer Simulation Technology (CST) software. Moreover, a validation was executed via a comparison of the phase-response properties of both elements with the analytical data from the High-Frequency Structure Simulator (HFSS) software. As a result, promising outcomes wherein several one-bit coding designs for multi-layer or coding metamaterials manifested unique results, which almost reached 0 dBm\({}^{2}\) RCS reduction values. Meanwhile, coding metamaterial designs with larger lattices exhibited optimised results and can be utilised for larger-scale applications. Moreover, the coding metamaterials were validated by performing several framework and optimal characteristic analyses in C- and Ku-band applications. Due to the ability of coding metamaterials to manipulate electromagnetic waves to obtain different functionalities, it has a high potential to be applied to a wide range of applications. Overall, the very interesting coding metamaterials with many different sizes and shapes help to achieve a unique RCS-reduction performance.  
coding metamaterial; electromagnetic waves; phase response; Radar Cross-Section +
Footnote †: journal: materials  
0000-0001-9993-3880][PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Design and Analysis of Multi-Layer and Cuboid Coding Metamaterials for Radar Cross-Section Reduction. Materials 20222154282. [[https://doi.org/10.3390/ma15124282](https://doi.org/10.3390/ma15124282)]([https://doi.org/10.3390/ma15124282](https://doi.org/10.3390/ma15124282))
# EMG Based Gesture Recognition Using the Unbiased Difference Power
## 2 Materials and Methods
### EMG Gesture Recognition  
Many EMG gesture recognitions are based on signal representation and classification [5]. In various studies, feature extraction is used for signal representation. In this study, the difference power is used as the feature. Let \(x[n]\)be the input signal, then the difference power is expressed as  
\[E[\Delta x[n]]=E[(x[n]-x[n-1])^{2}]. \tag{1}\]  
Classification is a stage for inferring the class of an unknown observation (test data) using information from earlier observations (training data). In this study, we use linear discriminant analysis (LDA) as a classifier. Although LDA may not be the most efficient classifier, LDA is a commonly-used and basic method due to its simplicity, robust classification to long-term usage, and its reasonable performance in gesture recognition [25; 26; 27]. Considering the main contribution of the paper is to propose the unbiased difference feature, we tested the proposed feature using the LDA classifier. However, for more rigorous performance evaluation and practical usage, it would be necessary to evaluate the performance using various classifiers. For LDA, we use a discriminant function with the mean value and pooled covariance of the training data to predict the test data class. For the performance evaluation we use classification accuracy. The classification accuracy is calculated as \(\frac{100 Nc}{N}\), where \(Nc\) is the number of correct trials and \(N\) is total number of trials. We use the leave-one-out validation 100 times and we set \(N\) at 100.  
One reason for the deterioration in classification performance is the noise added to the signals. Figure 1 shows the feature variation for different noise levels. SNR, a measure of noise level in Figure 1 is \(SNR=10 log_{10}\frac{P_{signal}}{P_{noise}}\), where \(P_{signal}\) and \(P_{noise}\) is power of noiseless signal and noise respectively. It is observed in Figure 1 that the difference power feature is highly deteriorated by noise and increased as SNR decreases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \# & **Description** \\ \hline \multirow{4}{*}{**Category**} & & 1 & Flexion of ring and little finger \\  & & 2 & Thumb opposing base of little finger \\  & & 3 & Abduction of the fingers \\  & & 4 & Fingers closed together \\ \hline \multirow{4}{*}{**Category**} & & 5–6 & \begin{tabular}{c} Wrist supination and pronation \\ (rotation axis through the middle finger) \\ \end{tabular} \\  & & 7–8 & \begin{tabular}{c} Wrist supination and pronation \\ (rotation axis through the little finger) \\ \end{tabular} \\  & & 9 & \
\begin{tabular}{c} Wrist radial and ulnar deviation \\ \end{tabular} \\  & & 10 &
\begin{tabular}{c} Wrist extension with closed hand \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Synthetic descriptions of 10 gestures.
# Screening of Plants and Indigenous Bacteria to Improve Arsenic Phytoextraction
## 3 Results and Discussion
### Microcosm Tests with Phosphate
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Soil**} & \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{**Z. _mays_**} & \multicolumn{2}{c}{**C. _sativa_**} & \multicolumn{2}{c}{_B. juncea_} \\ \cline{3-8}  & & **Root** & **Shoot** & **Root** & **Shoot** & **Root** & **Shoot** \\ \hline \multirow{3}{*}{S2} & CT & 0.10 \(\pm\) 0.01a & 0.05 \(\pm\) 0.01a & 0.05 \(\pm\) 0.01a & 0.03 \(\pm\) 0.01a & 0.11 \(\pm\) 0.01a & 0.05 \(\pm\) 0.01a \\  & P & 0.77 \(\pm\) 0.07b & 0.38 \(\pm\) 0.03b & 0.19 \(\pm\) 0.02b & 0.15 \(\pm\) 0.03b & 1.27 \(\pm\) 0.11b & 0.61 \(\pm\) 0.06b \\  & P+ & 0.82 \(\pm\) 0.07b & 0.35 \(\pm\) 0.04b & 0.21 \(\pm\) 0.02b & 0.16 \(\pm\) 0.02b & 1.22 \(\pm\) 0.10b & 0.62 \(\pm\) 0.05b \\ \hline \multirow{3}{*}{S6} & CT & 0.12 \(\pm\) 0.02a & 0.06 \(\pm\) 0.01a & 0.03 \(\pm\) 0.01a & 0.02 \(\pm\) 0.01a & 0.21 \(\pm\) 0.03a & 0.10 \(\pm\) 0.01a \\  & P & 0.47 \(\pm\) 0.05b & 0.23 \(\pm\) 0.03b & 0.14 \(\pm\) 0.02b & 0.11 \(\pm\) 0.02b & 0.85 \(\pm\) 0.08c & 0.41 \(\pm\) 0.04c \\  & P+ & 0.57 \(\pm\) 0.05b & 0.24 \(\pm\) 0.02b & 0.12 \(\pm\) 0.01b & 0.09 \(\pm\) 0.01b & 0.69 \(\pm\) 0.06b & 0.35 \(\pm\) 0.03b \\ \hline \multirow{3}{*}{SW} & CT & 0.07 \(\pm\) 0.01a & 0.04 \(\pm\) 0.01a & 0.09 \(\pm\) 0.01a & 0.03 \(\pm\) 0.01a & 0.18 \(\pm\) 0.02a & 0.11 \(\pm\) 0.01a \\  & P & 0.29 \(\pm\) 0.03b & 0.15 \(\pm\) 0.02b & 0.19 \(\pm\) 0.02b & 0.09 \(\pm\) 0.01b & 0.39 \(\pm\) 0.04b & 0.41 \(\pm\) 0.05b \\  & P+ & 0.40 \(\pm\) 0.04c & 0.16 \(\pm\) 0.01b & 0.25 \(\pm\) 0.03c & 0.10 \(\pm\) 0.01b & 0.34 \(\pm\) 0.03b & 0.38 \(\pm\) 0.04b \\ \hline \multirow{3}{*}{PZ} & CT & 0.12 \(\pm\) 0.02a & 0.07 \(\pm\) 0.01a & — & — & — & — \\  & P & 0.21 \(\pm\) 0.03b & 0.11 \(\pm\) 0.01b & — & — & — & — \\ \cline{1-1}  & P+ & 0.25 \(\pm\) 0.03b & 0.10 \(\pm\) 0.01b & — & — & — & — \\ \hline \end{tabular}
\end{table} 
Table 3: Arsenic concentration (mg kg\({}^{-1}\)) in the roots and shoots of _Z. mays_, _C. sativa_, and _B. juncea_ grown on control (CT) and As-contaminated soil treated with phosphate (P). The addition of PGPR bacteria (first set of isolates) is indicated with the “+” sign. Values are reported as mean (\(n=3\)) \(\pm\) SD. Values with different letters are significantly different at the 5% probability level (Tukey’s test). evident effect was also obtained with the addition of PGPR bacteria. This effect, which occurs with all soils, also depends on the plant species. Under the growth conditions of the microcosm test, where biomass production is limited, the total removal value confirms that hemp is the plant species with the lowest arsenic phyto-extraction capacity.
# Theory of Regions for Control Synthesis without Computing Reachability Graph
## 2 Background
The TR is expressed by a linear system composed of three classes of conditions:  
* Reachability conditions: \[M(P_{c})=M_{0}(P_{c})+C(P_{c},.)\overrightarrow{\Gamma}_{M}\geq 0\] (4) \(\overrightarrow{\Gamma}_{\text{M}}\): Is the path between \(M_{0}\) and \(M\). * MTSI condition: \((M,t)\in\Omega\). \(\Omega\) is the set of prohibited state transitions. The transition \(t\) may lead to a forbidden marking \(M^{\prime}\in\text{MI}\). MI is the set of markings which do not comply with a GMEC: \[M^{\prime}(P_{c})=\ M_{0}(P_{c})+C(P_{c},.)\overrightarrow{\Gamma}_{M}+C(P_ {c},t)<0\] (5)
* Basic cycle equations: \[\sum_{t\in\Gamma}C(P_{c},t)\cdot\overrightarrow{\sigma}[t]=0,\ \forall\sigma\in R\] (6) \(\overrightarrow{\sigma}[t]\) is the algebraic sum of occurrences of t in \(\sigma\), and R is the desired behavior of the controlled system. The application of the classical approach of the theory of regions [9] is given in the following example (i.e., see Figure 1). For a given PN (Figure 1a) [23], we firstly generate the reachability graph (Figure 1b). Next, one can analyze the RG to determine existing basic cycles, the legal markings and the forbidden ones (marking \(M_{7}\in\text{MI}\)). The linear system of TR is given as follows:  
\[M_{0}(P_{c})\geq 0\]  
\[M_{1}(P_{c})=M_{0}(P_{c})+C(P_{c},t_{1})\geq 0\]  
\[M_{2}(P_{c})=M_{0}(P_{c})+C(P_{c},t_{1})+C(P_{c},t_{2})\geq 0\]  
\[M_{3}(P_{c})=M_{0}(P_{c})+C(P_{c},t_{1})+C(P_{c},t_{2})+C(P_{c},t_{3})\geq 0\]  
\[M_{(\overrightarrow{w},k)}=\left\{M\in M\left|\overrightarrow{w}^{T}M\leq K\right\}\right. \tag{2}\]0  
\[M_{(\overrightarrow{w},k)}=\left\{M\in M\left|\overrightarrow{w}^{T}M\leq K\right\}\right. \tag{2}\]1  
\[M_{(\overrightarrow{w},k)}=\left\{M\in M\left|\overrightarrow{w}^{T}M\leq K\right\}\right. \tag{2}\]2  
\[M_{(\overrightarrow{w},k)}=\left\{M\in M\left|\overrightarrow{w}^{T}M\leq K\right\}\right. \tag{2}\]3  
\[M_{(\overrightarrow{w},k)}=\left\{M\in M\left|\overrightarrow{w}^{T}M\leq K\right\}\right. \tag{2}\]4  
\[M_{(\overrightarrow{w},k)}=\left\{M\in M\left|\overrightarrow{w}^{T}M\leq K\right\}\right. \tag{2}\]5 Example of PN model: (**a**) PN model; (**b**) Reachability graph; and (**c**) Controlled PN model. The resolution of this linear system generates the following PN controller \(P_{c}:M_{0}(P_{c})=2\); \(C(P_{c},.)=(-1,0,0,1)\). The addition of this PN controller gives the controlled PN model shown in the Figure 1c. In the following section, an effective method to avoid the combinatorial explosion of states in the presumed reachability graph will be introduced, where the generation step of RG is overlooked.
# Security Analysis of the MQTT-SN Protocol for the Internet of Things
## 2 MQTT-SN Protocol
### Format of the Main Packets
#### 2.1.4 Pingreq Message  
This type of message is used to know whether a client is connected to the broker. Additionally, in MQTT-SN, it allows taking the device out of sleep mode.  
Table 5 shows the format of a _Pingreq_ type packet. This format is simpler than those described above:  
* **ClientId**: It is an optional field that is used for changing the status of a client from _sleeping_ to _awake_. It contains the client identifier.
# BGPcoin: Blockchain-Based Internet Number Resource Authority and BGP Security Solution
## 7 Implementation and Deployment
### Overview  
The prototype consists of a set of smart contracts that perform all typical operations for the Internet resource and a client written in Python to interact with the smart contracts deployed in the Ethereum blockchain.  
**Methodology**: We firstly reproduce the process of the Internet number allocating and assigning on our experiment BGPcoin blockchain as the setup phase. We collect the real resource delegations (IP addresses and ASNs) from IANA to RIRs, RIRs to NIRs, NIRs to ISPs. Before the deployment of BGPcoin on the blockchain, we generate an Ethereum address and the corresponding private key for every entity. Then, we map the resource delegations in the real world to three kinds of transactions, IPregister, IPallocate and IPassign, that the resource delegators send to the delegates. We also collect the mappings from the IP prefix to ASN from the public available source and give a addROA demonstration of some mappings to evaluate the cost. After the setup phase, BGPcoin is able to serve as a resource query system.  
**Data:** We collect the ASN and IP block registers and allocations from the public archives RIPE RIS [35] and the other four RIR datasets (ftp.arin.net/pub/stats/, ftp.afrinic.net/pub/stats, ftp.apnic.net/pub/stats/, ftp.lacnic.net/pub/stats). Since the RPKI currently only overs less than 10% mappings from AS to IP in its ROAs, we collect the compatible IP-ASN-mappings information from a service ([[http://www.team-cymru.com/IP-ASN-mapping.html](http://www.team-cymru.com/IP-ASN-mapping.html)]([http://www.team-cymru.com/IP-ASN-mapping.html](http://www.team-cymru.com/IP-ASN-mapping.html))) dedicated to mapping IP numbers to BGP prefixes and ASNs.  
Figure 5: A case of RPKI authority misbehaving.  
The distribution of the registered, allocated and assigned IP numbers deployed in BGPcoin is shown in Figure 6a. The left pie chart shows the IP prefixes' distribution where most of the IP blocks that the RIRs register are of the prefix length of eight, except the IP numbers near 193.0.0.0, where the registered IP block has the prefix length of 24. The right pie chart shows the allocated or assigned IP prefixes' distribution in five RIRs, where we observe that a few IP numbers have not been allocated or assigned. The IP numbers held by ASes and the number of ROAs bound with ASes are shown in Figure 6b. Most of the ASes (\(>\)212) only hold one ROA, and there exists some AS that hold more than 210 ROAs and more than 225 IP numbers covered in its ROAs.