esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Passive Modulation of Metamaterial EIT-like Window
As shown in Figure 5a, the horizontal distance \(\delta_{x}\) was varied from 1 \(\upmu\)m to 9 \(\upmu\)m with fixed \(\delta_{y}\) of 30 \(\upmu\)m to show the influence of the modulation effect of the transparent window. It can be found that when \(\delta_{x}\) was 1 \(\upmu\)m, 3 \(\upmu\)m, and 5 \(\upmu\)m, the electric field was mainly confined in the dark mode. As the relative distance \(\delta_{x}\) increased, the coupling between the eigenmodes became weaker. Two resonance troughs of LSP resonance and LC resonance decrease and drew closer to each other at the same time, together with the transparent window blue-shifted. As \(\delta_{x}\) reached 9 \(\upmu\)m, the coupling disappeared completely, and the transparency window disappeared, too. Compared with the symmetric distribution of dark modes, the width of the EIT-like window was narrower, while the coupling distance \(\delta_{x}\) was smaller. Furthermore, for the influence of the vertical distance \(\delta_{y}\) between the USR pairs on the transmission characteristics, \(\delta_{xup}\) was fixed as 13 \(\upmu\)m and \(\delta_{xdown}\) = 4 \(\upmu\)m, the transparent window was modulated by varying \(\delta_{y}\) from 28 \(\upmu\)m to 12 \(\upmu\)m. It can be seen from Figure 5b that when \(\delta_{y}\) = 28 \(\upmu\)m, the incident THz  
Figure 4: The influence of structural parameters on transmission characteristics. (**a**) The transmission spectra at various coupling distances between CW and USRs, as the \(\delta x\) increases, the two resonance troughs of LSP and LC decrease and get closer, with the transparent window blue-shifted; (**b**) The transmission spectra at various coupling distances between USR pairs, as the \(\delta y\) decreases, the LC resonance trough gradually disappears, while the transparent window also disappears. wave and CW were strongly coupled, and the dark mode was also excited by near-field coupling. As USRs approached each other, the distance \(\delta_{y}\) decreased, so the coupling between bright and dark modes was weakened, while the LC resonance trough gradually disappeared. When \(\delta_{y}=12\)\(\upmu\)m, the EIT-like window disappeared. This is similar as that of the symmetric structure.
## Pattern-Guided \(k\)-Anonymity  
**[PERSON], [PERSON] * and [PERSON]**  
Institut fur Softwaretechnik und Theoretische Informatik, TU Berlin, Berlin, 10587, Germany;  
E-Mails: [EMAIL_ADDRESS] (R.B.); [EMAIL_ADDRESS] (R.N.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +49-30-314-73510 ; Fax: +49-30-314-23516  
_Received: 2 August 2013; in revised form: 9 October 2013 / Accepted: 9 October 2013 / Published: 17 October 2013_  
**Abstract:** We suggest a user-oriented approach to combinatorial data anonymization. A data matrix is called \(k\)-anonymous if every row appears at least \(k\) times--the goal of the NP-hard \(k\)-Anonymity problem then is to make a given matrix \(k\)-anonymous by suppressing (blanking out) as few entries as possible. Building on previous work and coping with corresponding deficiencies, we describe an enhanced \(k\)-anonymization problem called Pattern-Guided \(k\)-Anonymity, where the users specify in which combinations suppressions may occur. In this way, the user of the anonymized data can express the differing importance of various data features. We show that Pattern-Guided \(k\)-Anonymity is NP-hard. We complement this by a fixed-parameter tractability result based on a \"data-driven parameterization\" and, based on this, develop an exact integer linear program (ILP)-based solution method, as well as a simple, but very effective, greedy heuristic. Experiments on several real-world datasets show that our heuristic easily matches up to the established \"Mondrian\" algorithm for \(k\)-Anonymity in terms of the quality of the anonymization and outperforms it in terms of running time.  
**Keywords:** NP-hardness; parameterized complexity; integer linear programming; exact algorithms; heuristics; experiments
# Performance Analysis of Soft-Switching FSO/THz-RF Dual-Hop AF-NOMA Link Based on Cognitive Radio
## 5 Conclusions  
This paper introduces an innovative AF-NOMA-assisted soft-switching FSO/THz-RF double-hop link based on CR, addressing the scarcity of spectrum resources through FSO and THz. Firstly, a soft-switching FSO/THz system was built on the SR link, and both of them were paired with each other to largely mitigate the impact of atmospheric conditions on the SR link. In addition, NOMA allows signals of different powers to be superimposed on an identical frequency band, and CR allows that frequency band to be present in the main network, thus resolving frequency band resources and improving SE. The study primarily focuses on three key factors impacting link performance: \(P_{S}\), \(P_{Peak}\), and \(I\). Notably, \(P_{S}\) has a relatively minor influence on performance improvement due to its longer propagation distance. Additionally, the SN power is maintained at a constant level compared with the case without CR. It is evident that the performance without CR surpasses that with CR, mainly due to the imposed constraints. Nevertheless, the incorporation of CR significantly enhances SE and spectrum usage. The numerical results demonstrate the superiority of this link, which offers a fresh solution for next-generation communications.  
Conceptualization, methodology, R.L. and H.L.; software, validation, writing--original draft preparation, R.L.; formal analysis, investigation, data curation, R.W. and Z.W. ([PERSON]); writing--review and editing, visualization, Z.W. ([PERSON]), X.W., J.L., Y.W., and Y.Z.; supervision, project administration, funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 62175070 and 61875057), the Guangdong Basic and Applied Basic Research Foundation (No. 2021A151 5012652 and 2022A1515110752), and the Science and Technology Program of Guangzhou (No. 202201010340, and 2019050001).  
**Informed Consent Statement:** Not applicable.  
Figure 7: A comparison of the power of the SN network and OP at different \(I\).
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l l l} \hline Test route 1 & & \\ \hline  & F-score & \(p\)-value \\ Speed & 612.2230 & 0.00145 \\ Positive acceleration & 123.9876 & 0.00169 \\ Negative acceleration & 97.8761 & 0.00213 \\ No. of stops & 12.67 & 0.0315 \\ Test route 2 & & \\ \hline  & F-score & \(p\)-value \\ Speed & 997.2367 & 0.00345 \\ Positive acceleration & 432.2166 & 0.00193 \\ Negative acceleration & 398.1298 & 0.00321 \\ No. of stops & 2.11 & 0.0398 \\ \hline \end{tabular}
\end{table} 
Table 6: F-score and \(p\)-value for the different features over the two test routes. Figure 12: Reference speed and energy values. Figure 13: Energy consumption with the mixed cycle. Table 7 depicts the energy consumption of the six RDPs obtained through analysis of the EV model. For the highway type of roadway, the energy consumption falls under the range of 3.825-7.96 kWh for different road traffic conditions. Similarly, for the suburban and urban types of roadways, the energy consumption lies in the range of 0.985-0.964 kWh and 2.87-5.46 kWh, respectively. It can be observed that the error in the case of energy consumption obtained under real driving conditions and as calculated by the RDPs is relatively small. Figure 15 depicts a comparison between the energy consumption predicted with and without applying DPR. Table 8 further elaborates the optimum speed and average value of optimum energy consumption obtained with EMS from Tables 7 and 8. It is observed that the total energy consumption without EMS is significantly higher than optimum energy (with EMS). \begin{table}
\begin{tabular}{c c c} \hline \hline
**RDPs** & **Energy Consumption (kWh)** & **Error Observed (kWh)** \\ \hline Highway High Traffic & 3.825 & 5.1 \\ \hline Highway Low Traffic & 7.96 & 7.2 \\ \hline Suburban High Traffic & 0.985 & 0.9 \\ \hline Suburban Low Traffic & 0.964 & 0.5 \\ \hline Urban High Traffic & 5.46 & 3.3 \\ \hline Urban Low Traffic & 2.87 & 2.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Energy consumption and error calculation. Figure 14: Battery SOC with the mixed cycle. Figure 15: Energy consumption as predicted by the RDPs. Integration of DPR with EMS may lead to a significant improvement in energy savings. However, several practical challenges prevent the successful implementation of this technology. Firstly, large datasets are required, considering multiple factors related to the vehicle and the environment under which the vehicle is being driven, and how the vehicle is being driven. Secondly, low-latency and lightweight solutions are required to ensure the real-time applicability of the system. Thirdly, generalizability over multiple regions with varying conditions requires an adaptive approach. Finally, dynamic unknown factors related to the traffic, accidents, roadblocks, and driving habits need to be considered.
## 4 Experimental Results
### Datasets
#### 4.1.1 Massachusetts Road Dataset  
The Massachusetts road dataset, introduced by [PERSON] and [PERSON] [1], was selected as the first dataset. This dataset covers roughly 2600 square kilometers of the state of Massachusetts, comprising 1171 satellite images along with their corresponding road-extracted mask images. Each image has dimensions of \(1500\times 1500\) pixels, with a resolution of 1 meter per pixel. Given the high dimensions of these images, we employed a patchify procedure, cropping each image to obtain \(512\times 512\) tiles. This step was crucial to reduce computational demands and enable the training of complex deep learning models. From the entire dataset, 817 images were selected for our experiments, resulting in 3268 images and corresponding masks of dimensions of \(512\times 512\). For training and testing, the dataset was divided using an 80:20 split.
# Realistic Texture Mapping of 3D Medical Models Using RGBD Camera for Mixed Reality Applications
## 5 Conclusions  
* This study successfully achieves its objective of enhancing a 3D model reconstructed from DICOM images with real external texture using an RGBD camera.  
* The proposed method demonstrates reliable pointclouds registration, accurately aligning internal tissues related pointcloud and external textures with a mean registration error \(<\) 1 mm.
* The stable registration error across different sets of randomly chosen points reflects the method's robustness, reaching an average error of 1.1 \(\pm\) 1.3 mm for the \(x\)-axis, 0.7 \(\pm\) 0.8 mm for the \(y\)-axis, and 0.9 \(\pm\) 1.2 mm for the \(z\)-axis.
* The presented mixed reality visualization using the Microsoft HoloLens 2 device allows users to explore and manipulate the colourized 3D model with intuitive gestures and voice commands, showcasing the potential for practical applications such as surgical planning and medical imaging visualization.
* Since the pointcloud processing and registration method described does not rely on particular tools and predetermined RGBD camera setups or positions, it has the potential to be reproduced across other medical imaging modalities, including MRI or traditional CT scans, as well as different external texture capture techniques like photogrammetry or structured light cameras.  
Conceptualization, C.A., E.R., V.S., F.V. and L.B.; Data curation, C.A., A.M. and E.R.; Formal analysis, C.A.; Methodology, C.A., A.M., E.R., V.S. and F.V.; Project administration, L.B.; Software, C.A., A.M., E.R., S.L. and V.Y.C.; Supervision, L.B.; Writing--original draft, C.A. and E.R.; Writing--review & editing, C.A., A.M., E.R., S.L., V.Y.C., V.S. and F.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# BGPcoin: Blockchain-Based Internet Number Resource Authority and BGP Security Solution
## 5 BGPcoin Architecture for BGP Security
### BGPcoin_Client  
Every resource authority organization maintains a client with an Ethereum address to interact with the BGPcoin_Client smart contract to make operations on its own resource. The use right of a resource can be transferred from its RIR to an LIR and then be lent to some ISP for the use of binding with ASN. Every legal transfer operation triggers an event record on the blockchain.  
Owing to the aggregated Internet address repositing and updating function in BGPcoin, these IP addresses that are contiguous and have the same owner and the same status compose a whole IP block with one IP prefix representing the smart contracts. As Algorithm 2 demonstrates, if the owner of this IP block allocates a part of IP addresses to its sub-xIR authority, it firstly divides the IP block into several smaller IP blocks and then allocates one of them to the sub-xIR.  
**Path_end advertisement:** We support path_end record and filtering in BGPcoin by adding a list of approved adjacent ASes for every AS in the ASIst (the list we define for every AS to store its adjacent ASes)in BGPcoin. An AS's owner can update or delete the path_end records of its AS by sending adjASupdate or adjASdelete transactions to the BGPcoin_Client contract. Path_end advertisement helps one AS protect its valid last AS hop on the advertised BGP path. Therefore, the attacker cannot claim to be directly connected to the victim origin AS. Consequently, the path the attacker announces to a victim origin AS to his neighbors has a length of 2 at least.  
```
Input: BGPcoin contract address client_addr, BGPcoin_IPchain that is the IP assets storaged in the BGPcoin contract, _IPstart_ that its owner are going to allocate with the prefix length _prefixlen_ and _allocate_ that its owner allocates to. Output: TURE or FALSE
1 Search the IP prefix \(IPS\) that has been registered in BGPcoin that covers the _IPstart_ with the prefix length _prefixlen_;
2 Require msg.sender is BGPcoin_IPchain.\(IPB[IPS]\).owner;
3 Require BGPcoin_IPchain.\(IPB[IPS]\).state is Registed or Allocated;
4 if\(\_IPstart\
eq IPS\ or\_prefixlen\
eq BGPcoin\_IPchain.\(IPB[\_IPS].prefixlen\)then
5 Divide the IP asset with the IP prefix \(IPS\) to \(k+1\) small IP assets that contain the IP asset with the IP prefix \(\_IPstart\), where \(k=\_prefixlen-BGPcoin\_IPchain.\(IPB[\_IPS].prefixlen\);
6 set the IPBData state of the IP asset with the IP prefix \(\_IPstart\) to the new owner;
7 Emit the _IPAllocate_ Event;
8 final ;
9 returnTURE;
```  
**Algorithm 2**IPallocate function in BGPcoin_client contract.
## 5 Performance Analysis and Discussion  
The system detection performance of the proposed LRT-BER fusion rule is evaluated in this section. Here, we consider two cases: (1) sensors with identical local detection performance, _i.e_., the detection and false alarm probabilities are equal; and (2) sensors with different detection performance. For the former case, the closed-form performance analysis is carried out by deriving the distribution of the fusion statistic LRT-BER. For the latter case, the closed-form solutions are difficult to derive, since the distribution of the \(\Lambda_{\text{BER}}\) fusion statistic is a nonlinear function of \(y_{i}\). The central limit theorem (CLT) and the deflection coefficient are employed to carry out the performance evaluation.
## 4 Discussion
Clean-in-place and steam-in-place processes are furthermore commonly used in pharmaceutical and food processing industries, where plasma activated liquids could find use for sanitisation applications. Cytotoxicity results demonstrated that GD PAW had a better safety profile than SD PAW; using the same plasma activation times, a significantly higher cytotoxicity was observed by SD PAW for both cell lines. Hydrogen peroxide in SD PAW seems to be a central player in this process, but a role of nitrite and nitrate concentration or other reactive species (GD PAW) in cytotoxicity cannot be excluded as GD PAW showed growth inhibition in the absence of hydrogen peroxide. Several publications have reported hydrogen peroxide as a principle cytotoxic reactive species in PAL. Reduction of cell growth and viability of CHO-K1 and HaCaT cells showed a linear correlation to the concentration of peroxide in PAL in previous studies [19; 37]. Moreover, [PERSON] et al. investigated the cytotoxic effects of SD PAW combined with GD PAW and observed that cytotoxicity was reduced by GDtreatment in line with the reductions of hydrogen peroxide concentrations [37]. In the same study, PAW containing only nitrates of approximately 1.5 mM showed minor cytotoxicity, suggesting that nitrite could play a role in cell death attributed to GD PAW, even though the addition of nitrite and nitrate concentrations up to 1.2 mM showed no cytotoxic effects on HeLa cells in another investigation [25]. Other chemical reactive species, including less stable transient species, may also be responsible for inhibition of cell growth. Peroxynitrous acid can be formed by the interaction of nitrites and hydrogen peroxide, in weakly acidic to acid solutions [21]. The plasma treatment of water led to a decrease of pH, generating an acidic environment, which can promote peroxnitrous acid formation. Peroxynitrite/peroxynitrous acid can cause cell death by inducing both cellular apoptosis and necrosis [23]. Similar to the effects on mammalian cells, the impact of PAW on enzyme activity of lactate dehydrogenase appears to be dominated by hydrogen peroxide. Studies by [PERSON] et al. on plasma treated LDH suggested a role for long-lived species such as hydrogen peroxide and nitrate in changing the protein's secondary structure and activity [38]. Plasma reactive species can lead to modifications of protein secondary and tertiary structure [39] and induce changes to amino acids through reactions such as hydroxylation, nitration, or oxidation [40]. Solutions containing only nitrate and nitrite in our investigations not only retained enzymatic activity but even led to slight increases, suggesting that PAW could induce conformational changes which may not be detrimental to enzyme functionality. Studies conducted with the same plasma set-up on milk proteins found an increased affinity of \(\beta\)-lactoglobulin to an anti-\(\beta\)-lactoglobulin antibody after direct treatment with both SD and GD plasma discharge while antibody affinity of other milk proteins was reduced [41], indicating that plasma reactive species can affect different proteins in diverse ways.
## 3 Proposed Method
### Weibull FEA Stress Data Analysis
#### 4.1.2 Weibull Stress Family Determination  
The corresponding Weibull stress parameters (\(\beta_{s}\), \(\eta_{s}\)) are established as follows.  
First, by selecting a reliability of \(R(t)=0.9535\) to perform the analysis, from Equation (13), \(n=\frac{-1}{ln(0.9535)}=21\). Then by using \(n=21\) in Equation (15), the \(Y_{i}\) elements are generated, with a mean of \(\mu_{y}=-0.545624\) and a standard deviation of \(\sigma_{y}=1.175117\). Finally, from Equation (8), and by using the \(\mu_{y}\), \(\sigma_{1}\) and \(\sigma_{2}\) FEA values, the Weibull \(\beta_{s}\) value is:  
\[\beta_{s}=\ \frac{(-4)(-0.545624)}{0.99176*ln\Big{(}\frac{491.75}{184.8}\Big{)}}=2. 248519\]  
Likewise, since from Equation (10) the logarithm average value is \(\mu_{x}=ln(491.75*184.8)^{\frac{1}{2}}=5.708622\), then from Equation (9) the Weibull \(\eta_{s}\) parameter is \(\eta_{s}=exp[5.708622)=301.455469\) MPa. Thus, the Weibull stress family used to obtain the corresponding Weibull cycle distribution (Section 4.2) is W (2.248519, 301.455469 MPa). Using the Weibull stress family results, the strength random behavior can be determined. Because these parameters only depend on the applied \(\sigma_{1}\) and \(\sigma_{2}\) values, then by performing the Weibull analysis their random behavior can be determined as follows.
# Stabilization of an Unconventional Large Airship When Hovering
## 2 Modeling  
### Kinematic Description  
For the kinematic description we used a Galilean reference frame fixed to the ground \(R_{0}=(O,X_{0},Y_{0},Z_{0})\) and a mobile reference frame \(R_{m}=(O,X_{m},Y_{m},Z_{m})\) linked to the airship at its center of mass G. As we can see in Figure 2, the airship MC 500 is a quadrotor. Each rotor can swived around two axes: \(\beta_{1}\) around the \(Y_{m}\) axis (Figure 2), and \(\gamma_{1}\) around an axis \(Z_{\text{RI}}\), orthogonal to \(Y_{m}\) and which coincides in the beginning with the \(Z_{m}\) axis. The value of the thrust of the rotor i is denoted by \(||F_{i}||\), and its position in Rm is P1, such as: P1 = (a, b1, c)T; P2 = (a, -b1, c)T; P3 = (-a, b3, c)T; P4 = (-a, -b3, c)T.  
The orientation of the airship is described through three rotations defined by the classical Euler angles (yaw \(\psi\), pitch \(\theta\), and roll \(\phi\)). The rotation matrix between the mobile frame \(R_{m}\) and the fixed reference frame \(R_{0}\) is then given by:  
\[J_{1}^{T}=\left(\begin{array}{ccc}c\theta.c\psi&c\theta.s\psi&-s\theta\\ s\phi.s\theta.c\psi-c\phi.s\psi&s\phi.s\psi+c\phi.c\psi&s\phi.c\theta\\ c\phi.s\theta.c\psi+s\phi.s\psi&c\phi.s\psi-s\phi.c\psi&c\phi.c\theta\end{array}\right)\]  
where: \(c\theta=cos\theta\)\(and\phi=sin\phi\)  
Using the rotation matrix \(J_{1}\), the expression of the linear speed in the fixed reference frame \(\dot{y}_{1}=\left[\dot{x}_{0}.\dot{y}_{0}.\dot{z}_{0}\right]^{T}\) as a function of the speed expressed in the mobile frame \(v_{1}=\left[u,v,w\right]^{T}\) is given by:  
\[\dot{\eta}_{1}=J_{1}.v_{1} \tag{1}\]  
where \(\eta_{1}=\left[x_{0},\ y_{0},\ z_{0}\right]^{T}\) is the position vector of the origin of the mobile frame \(R_{m}\) expressed in the fixed frame \(R_{0}\).  
On the other hand, the angular speed of the airship \(v_{2}^{T}=\left[p,\ q,\ r\right]\) expressed in the mobile reference frame is the combination of the angular speeds around the three axes of yaw, pitch, and roll. It can be written related to \(\dot{\eta}_{2}^{T}=\left[\dot{\phi},\ \dot{\theta},\ \dot{\psi}\right]\) as:  
\[v_{2}=\left(\begin{array}{ccc}1&0&-s\theta\\ 0&c\phi&s\phi\ c\theta\\ 0&-s\phi&c\phi c\theta\end{array}\right).\left(\begin{array}{c}\dot{\phi}\\ \dot{\phi}\\ \dot{\psi}\end{array}\right) \tag{2}\]  
Conversely:  
\[\dot{\eta}_{2}=J_{2}.v_{2} \tag{3}\]  
Figure 2: Position of the rotors.  
where the transformation matrix \(J_{2}\) is represented by: \(J_{2}=\left(\begin{array}{ccc}1&\text{s}\phi\tan\theta&\text{c}\phi\tan\theta \\ 0&\text{c}\phi&-\text{s}\phi\\ 0&\text{s}\phi&\text{c}\phi\\ \end{array}\right)\).  
It is important to mention that the parametrization by the angles of Euler presents a singularity for \(\theta=\frac{\pi}{2}+k\pi\); however, this configuration is not reachable for the airship.  
The kinematics of the airship can then be written as:  
\[\left(\begin{array}{c}\dot{\eta}_{1}\\ \dot{\eta}_{2}\end{array}\right)=\left(\begin{array}{cc}J_{1}&0\\ 0&J_{2}\end{array}\right)\left(\begin{array}{c}\
u_{1}\\ \
u_{2}\end{array}\right) \tag{4}\]
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention
### Detection Stage 2: OCSVM + Deep Autoencoder (dAE)
#### 4.3.2 Deep Learning Classifier  
It thrives on processing lower-dimensional data, which showcases optimal performance, especially in clustered data scenarios. Its prowess lies in capturing meaningful patterns and behaviors, making it adept at discerning normal observations. Focused solely on non-attack packet data, the dAE stands out in this domain by effectively classifying attacks through its learned knowledge from the training dataset.