esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Data Examination and Class Labeling  
In Section 3.1 datasets were briefly introduced. As part of the initial examination of useful features, each dataset was explored for statistics regarding its composition. Because network traffic is mostly assembled in Packet Capture (PCAP) formats, the tool employed to dissect and replay the packets was _Tshark_[52], broadly used traffic-analyzer software. Then, data were submitted into a controlled sandboxed environment where malicious traces were monitored in depth. With regard to the ISCX-Bot-2014 dataset, Table 1 provides the percentage of different botnet flows captured on-the-fly.  
By large-scale observing the replay of the ISCX-Bot-2014 dataset, suspicious connections were identified in accordance with incoming and outgoing connections from IP addresses mapped to anomalous behaviors. The report is depicted in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Botnet** & **Type** & **Flow Portion** \\ \hline Neris & IRC & 25,967 (5.67\%) \\ Rbot & IRC & 83 (0.018\%) \\ Menti & IRC & 2878 (0.62\%) \\ Sogou & HTTP & 89 (0.019\%) \\ Murlo & IRC & 4881 (1.06\%) \\ Virut & HTTP & 58,576 (12.80\%) \\ NSIS & P2P & 757 (0.165\%) \\ Zeus & P2P & 502 (0.109\%) \\ SMTP Spam & P2P & 21,633 (4.72\%) \\ UDP Storm & P2P & 44,062 (9.63\%) \\ Tbot & IRC & 1296 (0.283\%) \\ Zero Access & P2P & 1011 (0.221\%) \\ Weasel & P2P & 42,313 (9.25\%) \\ Smoke Bot & P2P & 78 (0.017\%) \\ Zeus Control (C and C) & P2P & 31 (0.006\%) \\ ISCX IRC bot & P2P & 1816 (0.387\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of different botnets used in dataset.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Botnet** & **IP Addresses Ranges** \\ \hline IRC & 192.168.2.112 \(>\) 131.202.243.84 \\  & 192.168.5.122 \(>\) 198.164.30.2 \\  & 192.168.2.110 \(>\) 192.168.5.122  
With the information presented above and by scrutinizing ISCX-Bot-2014 author specifications, useful features were determined in terms of the abnormal presence of packets during the request and responses from malicious sources, as shown in Table 3.  
The CIDDS-001 dataset is presented in plain-text format and was provided by prebuilt attributes. However, some values were missing or had insufficient information, yielding a large amount of noise. Manual inspection preceded to normalize the number of features and delete disproportional samples. Table 4 details each feature after the removal of noisy elements.  
Consequently, samples tagged with discrete or numerous categorical values were standardized into malicious and benign classes. For the CIDDS-001 dataset, a total of 49,554 samples were labeled as benign and 105,530 as malicious, while in ISCX-Bot-2014 benign samples were over 119,287, and 56,572 were malicious.
###### Abstract  
Simultaneous transmit and receive wireless communications have been highlighted for their potential to double the spectral efficiency. However, it is necessary to mitigate self-interference (SI). Considering both the SI channel and remote transmission (RT) channel need to be estimated before equalizing the received signal, we propose two adaptive algorithms for linear and nonlinear self-interference cancellation (SIC), based on a multi-layered joint channel estimator structure. The proposed algorithms estimate the RT channel while performing SIC, and the multi-layered structure ensures improved performance across various interference-to-signal ratios. The M-estimate function enhances the robustness of the algorithm, allowing it to converge even when affected by impulsive noise. For nonlinear SIC, this paper introduces an adaptive algorithm based on generalized Hammerstein polynomial basis functions. The simulation results indicate that this approach achieves a better convergence speed and normalized mean squared difference compared to existing SIC methods, leading to a lower system bit error rate.  
simultaneous transmit and receive; self-interference cancellation; digital cancellation; adaptive filtering; joint channel estimator 2024244249
# Vector Signals and Invariant Systems: Re-Tooling Linear Systems Theory
## 5 Frequency Domain Analysis for LRITI Systems  
As outlined in Section 1, numerous generalizations and modifications have been proposed to the conventional FT. However, for these FT generalizations, it is usually not clear how to define a compatible convolution definition for time-domain analysis (see pg. 10 in [PERSON] (1977)). On the other hand, our approach has been to develop LRITI systems theory in a way that follows the same structure of mathematical arguments and logical reasoning as used with the development of traditional LTI systems theory. Explicitly, we started by defining a representation of a VV signal using scaled and shifted impulses and used it to develop a convolution that aligns and generalizes the traditional convolution. In the section that follows, we continue the development of LRITI systems theory in the frequency domain in a way that follows the same structure of mathematical arguments and logical reasoning as used with the development of traditional LTI systems theory, as closely as possible.  
**Definition 23** (Fourier Transform of a VV Signal).: _We define the FT of the VV signal \(\mathbf{x}(t)\) with respect to arbitrary plane \(\mathbf{j}\) as_  
\[\mathcal{F}_{\mathbf{j}}\{\mathbf{x}(t)\}\triangleq\int_{-\infty}^{\infty} \!\!\!\mathrm{e}^{\mathbf{j}\frac{\omega}{2}t}\mathbf{x}(t)\mathrm{e}^{- \mathbf{j}\frac{\omega}{2}t}\,\mathrm{d}t. \tag{45}\]  
**Remark 15**.: _The plane used in the traditional FT is always the complex plane \(\mathbf{j}\), but in (45), the plane \(\mathbf{j}\) is an arbitrary unit bivector. Note that for \(\mathbb{L}^{2}\) space, the definition in (45) aligns with the traditional FT (9). See the proof in Appendix A.2._  
**Remark 16**.: _The traditional FT for CV signals in (9) provides the weights of complex exponential signal components in the complex plane as a function of frequency, effectively providing a decomposition of the signal \(\mathsf{x}(t)\) in terms of weighted complex exponential signal components. Unfortunately, while the FT for VV signals in (45) does provides the weights of bivector exponential signal components in the \(\mathbf{j}\)\  
**Remark 17**.: _For the complex exponential signal representation in (10), the complex plane \(\mathrm{j}\) comprises the entire space under consideration. For the bivector exponential signal in (47e), the rotation occurs in the plane \(\mathbf{j}_{0}\), but the space under consideration, \(\mathbb{L}^{n}\), generally has dimension \(n\) different than two._  
**Remark 18**.: _Unlike complex exponentials in Remark 3, bivector exponentials of different frequencies are not necessarily orthogonal because they may rotate in different planes. For bivector exponentials, orthogonality--as defined through rotations [33]--is not guaranteed._
# Exploring Data Model Relations in OpenStreetMap
## 3 Working with Relations in OSM
### Types of Relations in OSM  
According to both the OSM Wiki and TagInfo [26], the five most frequently occurring types of relations (where \(r_{t}\) contains the tag for type) are outlined as follows:  
* **Multipolygon:** A multipolygon relation can have any number of ways and these ways must somehow form valid rings to build a multipolygon from. Multipolygon relations are used to represent complex areas. Generally, the multipolygon relation can be used to build multipolygons in compliance with the OGC Simple Feature standard. Subsequently multipolygons allows for the expression of arbitrarily complex relations within OSM.
* **Restriction:** Relations with the type=restriction key allows the modeling of different types of traffic flow restrictions at junctions, intersections, etc. A turn restriction at a junction is represented by a relation that has a set of tags describing the type of turn restriction. This turn restriction relation is not necessarily limited to turns. It can also be used in a number of other situations.
* **Route:** This type of relation models a regular known line or path of travel. According to the OSM Wiki routes can consist of paths taken repeatedly by people and vehicles: a ship on the predefined shipping route, a car on a numbered road, a bus on its route or a cyclist on a national route.
* **Public Transport:** Relations with this type=public_transport allow for the description of relations used in the public transport tagging scheme in OSM. This relation corresponds to the description of all types of public transport stops, stations, halts, areas or similar. As stated in the OSM Wiki pages \"A stop area consists of everything regarding the embarkation and disembarkation of a specific public transport vehicle or service\" [27]. In the example of a specific railway line, this includes the adjacent platform, services on that platform, buildings, and information describing it (platform number, identification, etc.).
* **Route Master:** Relations of type type=route_master contain all the direction and variant routes and information belonging to a whole route service. Routes or services are represented by vehicles that always run the same way with the same reference number. Each direction of a route should be tagged as a separate relation. If a route has several variants (e.g., different way at weekend), these variants should also be in separate relations.  
In addition to relations with the type tag key assigned to one of the five values above, there are other frequently occurring types of relations in OSM including boundary, associatedStreet, site, waterway and bridge. However, these types of relations appear much less frequently within the global OSM database and subsequently we decided to omit them from our analysis.
# An Effective Blockchain-Based Defense Model for Organizations against Vishing Attacks
## 5 Implementation and Results
### Adoption of Hyperledger Fabric Blockchain Technology
#### 5.1.2 Peers Definitions  
Peers in the hyperledger fabric represent the fundamental entity of transaction endorsement, validation, or rejection. Peers represent the entities that receive and process the client's smart contracts. The network gateway service is responsible for managing the distribution of the smart contracts between all peers and its retransmission to the client for final endorsement. After that, the orderer is responsible for the new block addition to every peer ledger. The orderer is responsible for updating the blockchain ledger between all peers. No probabilistic approach is used within the fabric network, since any block validated by the peer is guaranteed to be final. This is explained by the fact that all participating peers are within a private network.  
In our case, the peers are the background persons (e.g., those who dual-control the transactions) who verify the recipient's request. Here, the recipient is the peer who initiates the interaction. Through a well-defined application, the recipient will create the smart contracts that contain the transaction details (i.e., the information provided by the caller) and then send the smart contract (request) to the verification process. The organization specifies the number and type of peers required to endorse the transaction. In this way, the system keeps its flexibility to deal with missing peers or endorsements, such as ruling that at least two peers must approve the transaction, instead of four, if they have privileges and the data equivalent to those with the missing peers. Thus, the processing time will be faster and avoid delay in a case when one or more peers do not respond quickly.  
Listing 1 shows an example of the bank channel configuration that defines the access list of permitted peers and their privileges. The peer's identities are verified through the MSP already detailed in the multi-access control management section. After adding their digital signature, the peer endorses a proposal response to a smart contract by using their private key to sign the entire payload. This endorsement serves as proof that this organization's peer generated that response. Therefore, the organization will ensure the non-repudiation of actions made by its employees.  
```
&&BankOrg
#DefaultOrg definesorganizationsinvolvedintheblockchainnetwork Name:BankOrgMSP
#IDtoloadtheMSPdefinition ID:BankOrgMSPDir:../organizations/peerOrganizations/BankOrg.bank.com/msp
#Policiesdefinesthesetofpoliciesatthisleveloftheconfigtree
#Fororganizationpolicies,theircanonicalpathisusually
#Channel/<Application|Order>/<OrgName>/<PolicyName> Policies: Readers: Type:Signature Rule:'AND('BankOrgMSP.admin','BankOrgMSP.peer','BankOrgMSP.employee')'' Writers:Signature Rule:'AND('BankOrgMSP.admin','BankOrgMSP.employee')'' Admins: Type:Signature Rule:'OR('BankOrgMSP.admin')'' Endorsement: Type:Signature Rule:'AND('BankOrgMSP.peer')''
```  
Listing 1: Sample of policies enforcing ACLs within the Bank organization.
## 7 Applications
### Linear Analysis of a Clamped Circular FGM Plate under a Uniform Pressure Load  
The accuracy of the present finite element model (PM) is illustrated in this numerical application. An FGM clamped circular plate, with thickness h and radius R and made of titanium/zirconium was studied (\(E_{m}\) = 110.25 GPa, \(\
u_{m}\) = 0.288, Ec = 278.41 GPa, \(\
u_{c}\) = 0.288). Based on the rule of mixtures, the effective modulus was estimated following [PERSON] et al. [1]: \(E(z)=(E_{m}-E_{c})\ V_{m}+\ \mathrm{E}_{c}\) with \(V_{m}^{k}=(0.5-\overline{z}/h)\ ^{\rho}\); \(V_{c}^{k}=1-V_{m}^{k}\). Table 1 shows the normalized maximum deflections (at the centre of the plate) when the plate was subjected to a uniform pressure \(q\), calculated by \(\overline{w}_{c}=w_{c}(64D_{c}/qR^{4}\), \(D_{c}=E_{c}h^{3}/12\big{(}1-\
u^{2}\big{)}\). An excellent agreement was observed between the results obtained with the present model and the results obtained using [PERSON] theory ([PERSON] et al. [1]), elasticity theory ([PERSON] et al. [2]) and HSDT theory ([PERSON] et al. [3]). The through-thickness distribution of the nondimensional radial stress is presented in Figure 4. A comparison with the solution obtained by [PERSON] et al. [2] revealed very good agreement.  
Figure 4: Through-thickness distribution of the nondimensional stress for different \(p\)-indexes. [PERSON] et al. [2].
# Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning
## 3 Improved Trimming Ant Colony Optimization (ITACO) Algorithm
### Improved Pheromone Increment Update Strategy  
In traditional ant colony algorithms, all ants share the same pheromone increment update rule, which limits the algorithm's ability to converge quickly. To overcome this limitation, this paper introduces a path-length-dependent pheromone increment mechanism to enhance the update rule, enabling faster and more efficient convergence.  
After each iteration, ants that are trapped in deadlock are discarded, and the average path length of ants that successfully reach the target node is calculated. The pheromone increment on the path planned by the optimal ant is updated according to Equation (9). By intensifying the pheromone concentration on the optimal path, the algorithm reinforces its attractiveness in subsequent iterations, thereby accelerating convergence toward high-quality solutions.  
\[\Delta\tau_{ij}=\lambda_{1}\frac{Q}{L_{k}},\quad L_{k}=L_{\text{best}} \tag{9}\]  
For ants whose path lengths are longer than that of the optimal ant but still shorter than the average path length, the pheromone increment on their paths is updated according to Equation (10). This approach ensures that paths with moderate performance receive a balanced pheromone update, maintaining diversity in the search process without overly rewarding suboptimal solutions.  
\[\Delta\tau_{ij}=\lambda_{2}\frac{Q}{L_{k}},\quad L_{\text{best}}<L_{k}\leq\bar{L} \tag{10}\]  
In cases where an ant's path length exceeds the average path length of the current iteration, the pheromone increment on that path is updated according to Equation (11). By reducing the pheromone increment for inferior solutions, the algorithm minimizes the misleading influence of poorer paths on subsequent iterations, thereby improving the overall efficiency and accuracy of the search process.  
\[\Delta\tau_{ij}=\lambda_{3}\frac{Q}{L_{k}},\quad L_{k}>\bar{L} \tag{11}\]where the following are expressed:  
* \(Q\) is the pheromone intensity coefficient;
* \(L_{\text{best}}\) is the length of the best path in the current iteration;
* \(\bar{L}\) is the average path length of successful ants;
* \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\) are positive constants satisfying \(\lambda_{1}>\lambda_{2}>\lambda_{3}>0\). In this paper, \(\lambda_{1}\) = 2, \(\lambda_{2}\) = 1.5, and \(\lambda_{3}\) = 0.5.  
This adaptive pheromone update mechanism dynamically prioritizes high-quality solutions and discourages the exploration of less promising paths. By assigning differentiated pheromone increments based on path quality, the algorithm not only accelerates convergence to the optimal solution but also maintains a balanced exploration-exploitation trade-off, leading to improved optimization outcomes.  
The same framework can extend to other optimization problems, such as path length maximization, by inverting the pheromone update rule to reward longer valid paths.
## 3 Active Fault-Tolerant Control Method for Configuration Transformation
### Worst Reliability Prediction Method  
The inversion model described in Section 3.2 is essentially a multi-level nested optimization process, which includes multi-objective optimization, worst reliability assessment, reliability calculation, and structural response analysis. The core challenge in decoupling this nested optimization process is efficiently predicting the worst reliability index, \(\beta^{\text{L}}\), under probability-interval hybrid uncertainty. This section introduces a method for worst reliability prediction based on the ARO-BP surrogate model, which is designed to provide a fast and accurate estimation of the worst reliability index in the presence of interval uncertainty. The FEA model is used for nonlinear structural response analysis during the construction process, and the ARO-BP surrogate model is employed to express the direct mapping relationship between design variables and the worst reliability index. This surrogate model simplifies the calculation of \(\beta^{\text{L}}\) and helps decouple the complex nested optimization process. The procedure for the proposed worst reliability prediction method is shown in Figure 5. The key steps are as follows:  
1. Structural response surrogate: Input samples are obtained through the LHS method, and corresponding structural responses are computed using FEA. These samples are then used to establish the ARO-BP surrogate model for the limit state function based on the required input-output sample set.
2. Reliability surrogate: Reliability calculations are performed using the First-Order Reliability Method (FORM). A surrogate model is created to represent the relationship between input samples and the reliability index. The explicit function for the reliability index \(\beta(UD)\) is extracted, allowing for efficient reliability analysis. Here, \(U=[u_{1},\,u_{2},\,\ldots,\,u_{m}]^{T}\) is the \(m\)-dimensional standard normal vector obtained by normalizing the random variable \(X\).
3. Worst reliability calculation: The interval variable \(D^{\text{I}}\) is introduced, and the reliability boundaries are calculated using the interval analysis method described in Equation (3) [27], ultimately obtaining the worst reliability index \(\beta^{\text{L}}\). \[\left\{\begin{array}{l}\beta^{L}(X,\,D^{I})=\beta(X,\,D^{c})-\sum\limits_{j =1}^{n}\left|\frac{\partial\beta(X,\,d^{c})}{\partial d_{j}}\right|d_{j}^{w}\\ \beta^{R}(X,\,D^{I})=\beta(X,\,D^{c})+\sum\limits_{j=1}^{n}\left|\frac{\partial \beta(X,\,d^{c})}{\partial d_{j}}\right|d_{j}^{w}\end{array}\right.\] (3)
4. Worst reliability surrogate: The ARO-BP model is used to build a surrogate model between the interval variable \(D^{\text{I}}\) and the worst reliability index \(\beta^{\text{L}}\). This surrogate model enables rapid prediction of \(\beta^{\text{L}}\) for any given set of input variables, thus decoupling the multi-level nested optimization process.
## 3 Results and Discussion
### FLC Determination
Figure 20: Strain evolutions along the section lines and DIC images for **(a)** TS275 and **(b)** TS290 under stretch forming condition at RT and 235 \({}^{\circ}\)C.  
strain paths. This can be attributed to the limited formability of the organosheets and its suppression effect of the plastic deformation on the outer steel sheet. In Figure 22b, the difference between the FLC levels of FML02 at different temperatures 200 and 235 \({}^{\circ}\)C is also shown to be negligible, except for the one at RT. Based on these results, preference can be given to forming at 200 \({}^{\circ}\)C, where good forming limits can be achieved while reducing the squeezing-out of the polymer matrix in the core at 235 \({}^{\circ}\)C, which leads to significant thickness irregularities (Figure 15). However, the temperature of 200 \({}^{\circ}\)C is not suitable for the one-step thermoforming of sandwich panels into complex shapes. The reason is that it is not possible to achieve both joining and forming processes at temperatures less than the melting point of the polymer matrix, i.e., 220 \({}^{\circ}\)C [40]. From Figure 22c,d, it can be seen that the FLC levels of the FML with different cover sheets TS275 and TS290 do not show significant differences at RT. In contrast, the FLC levels of FML01 and FML02 at 235 \({}^{\circ}\)C show a significant difference above the melting point of PA6, being significantly higher for FML02. Furthermore, the major-minor strain distribution at different strain paths for FML are similar to those of their monolithic steel sheets. Therefore, it can be stated that at RT, the forming limit of the organosheet dominates the FLC level to its FML because of early failure. However, at elevated temperatures, i.e., 200 and 235 \({}^{\circ}\)C, the cover sheet dominates the FLC level of the FML.
## 3 Background
### Overview of Target Algorithms
#### 3.2.3 Lea  
LEA is a lightweight block cipher algorithm developed considering high-speed environments such as big data and cloud computing services and lightweight environments such as mobile devices [3]. LEA has a fixed 128-bit plaintext block size, and the key length is 128, 196, and 256 bits. LEA has a feature that does not use S-box, and the round function consists of simple operations in 32-bit units. The number of rounds is 24, 28, and 32 for 128-, 196-, and 256-bit keys. The main operations are XOR, ADD, and ROTATE. Unlike CHAM using only left-rotates, LEA uses both right rotate and left rotate operations. Six 32-bit round keys are used for encryption per round. Figure 2 shows the encryption process of LEA.  
Figure 1: Two consecutive rounds beginning with the even \(i\)th round in CHAM.  
Figure 2: Round function of LEA.
# Transitional SAX Representation for Knowledge Discovery for Time Series
## 2 Preliminary: PAA and SAX  
In this section, we briefly explain preliminary information of SAX. SAX is a time series representation method using piecewise aggregate approximation (PAA) of time-series subsequences. Given a time series \(X\coloneqq\{x(t)\}_{t=1,\ldots,N}\), the PAA divides \(X\) into \(n\) equally sized segments, \(X_{p}\coloneqq\{x(t)\}_{t=K(p-1)+1,\ldots,Kp}\), \(p=1,\ldots,n\), where \(N\) is divisible by \(n\) (\(n\ll N\)) and \(K=N/n\). It evaluates its local average for the \(p\)-th segment, \(X_{p}\):  
\[\bar{x}_{p}=\frac{1}{K}\sum\
olimits_{j=K(p-1)+1}^{Kp}x(j). \tag{1}\]  
Next, the method transforms \(X\) into a representation vector \(\{\bar{x}_{p}\}_{p=1,\ldots,n}\), an efficient dimensionality-reduction from \(N\) to \(n\), which weakens the noise influence in \(x(t)\). The SAX method maps \(\bar{x}_{p}\) into a symbol in consideration of the value space of \(X\). For the mapping, it further divides the value range or value space of segments \(X_{p}\) into several non-uniform regions under the normality assumption and assigns a symbol to each region.