esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method  
This research focuses on the development of a real-time intrusion detection system primarily built upon the architecture delineated in [22]. An additional module incorporating XAI is introduced, which is specifically designed for anomaly detection and multi-class classification within the system initially proposed in [22]. This system is built in accordance with the network traffic sensor for risk management focused on cyber situational awareness [7]. The structure of the proposed system in this research adheres to a distributed and modular framework, guaranteeing the autonomy of each primary component.  
This article introduces an additional module utilizing XAI based on anomalies and multiclass classification for the system mentioned before in [22]. The proposed system is mainly based on the system architecture proposed in [22] and presented in this research, which is based on a distributed and modular structure, ensuring each primary component maintains autonomy. The proposed system proposes a classification in real-time of network flow as either normal or anomalous traffic and types of attacks based on a multiclass classification according to the attacks defined in the UNSW-NB15 dataset. Consequently, an evaluation of the performance of machine learning algorithms developed within this research and supported by the framework of Spark [7].  
An additional objective is to enhance user customization of the software by facilitating the incorporation of proprietary artificial intelligence algorithms without component alteration, preserving the explanatory aspect of the work, and allowing modifications to the traffic monitoring mechanism without systemic disruption. This component is based on a real-time acquisition and transmission of network traces.  
Concurrently, this research additionally introduces a system with the capabilities to utilize an artificial intelligence module for real-time analysis and transmission of received traces, along with their results, for graphical representation and historical recording in a database. The visualization component is designed to display classified data along with their explanations, the aforementioned database, and system customization settings for user personalization.  
The system architecture is defined by four primary blocks. These blocks are categorized as trace monitoring, data transmission, real-time processing, and result representation.
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 3 Delay Tolerant Network Protocols
### Black and Grey Hole MaxProp  
The MaxProp routing protocol is a state-of-the-art algorithm and provides an effective way to route DTN messages [11]. Whereas Spray and Wait replicates a message a fixednumber of times at the source node, the MaxProp protocol replication occurs each time nodes meet and the conditions for copying a message from one node to another are met. These conditions are based on prioritising packet movements through the network based on their hop count. This includes both the schedule of those packets to be transmitted and those to be dropped [32]. In a MaxProp network, all nodes carry all messages until the next transfer occurs. A node will continue to forward a message to multiple others until either the message times out, it is notified of delivery, or is deleted due to a full buffer. The key to the routing algorithm is the nodes knowing which messages should be transmitted and dropped first (Figure 2).  
To work out the chance of being on the transitive path, each node has a vector with a number of elements equal to one less than the total number of nodes within the network. Initially each node is equally likely to meet any other node next, once a node meets another (node x) the xth element of its vector is incremented by one. Then the entire vector is normalised so that the magnitude of the whole vector is equal to one. Upon initial contact between two nodes, they exchange their vectors, in an optimal solution, every node has an up-to-date, accurate element within the vector containing the probabilities linked to every other node [37]. Then the vectors can be used during a depth-first search in order to find the shortest path to the destination node.  
Acknowledgements can be used in the protocol as once a node has successfully received a message, they can be injected into the network [38]. Once it has been flooded, all nodes carrying the original message can free up space by deleting it. Messages with low hop-counts are given priority to be transmitted as this helps get new messages spread quickly and efficiently. The message also contains a list of nodes that it has already visited so that it does not revisit a node.  
A node communicating using the standard Maxprop algorithm will only communicate with others using the same protocol. The black and grey hole vehicles use a protocol based upon Maxprop that is modified so that either all or half of the messages can be dropped based upon the parameters set by the attackers. The black and grey hole Maxprop algorithm communicates with standard nodes by impersonating the standard protocol that they are using. The attacking nodes can falsely increase their published chance of delivery so that they receive (and attack) more messages.
# A Precise Modeling Method for PMSM Servo System of Spacecraft
## 4 Experimental Testing and Validation
### Nonlinear Identification
#### 4.1.1 Elastic Model Parameter Identification  
The stiffness of the spacecraft servo system is a complex number that varies with the excitation frequency, known as dynamic stiffness. The dynamic stiffness of the PMSM servo system of spacecraft refers to the dynamic torque required to generate a unit angle at the output end of the steering gear, which reflects the characteristic that the support stiffness will change with the frequency of the external load. The dynamic stiffness has a significant influence on the vibration characteristics of the spacecraft servo system. Therefore, fine modeling and dynamic stiffness simulation analysis of the spacecraft servo system are very necessary.  
The method to obtain the dynamic stiffness in the elastic model involves maintaining a constant setpoint angle, applying a sinusoidal torque excitation to the output shaft of the servo system, and simultaneously measuring the magnitude of the excitation torque and the response of the output shaft. These data are then used for frequency response analysis to calculate the magnitude and phase of the system stiffness. For \(n\) sampling points, a set of overdetermined equations can be formulated as follows:  
\[\boldsymbol{Ku=f} \tag{11}\]  
where \(K=\begin{bmatrix}a\sin(\omega_{i}t_{1})&a\cos(\omega_{i}t_{1})\\ a\sin(\omega_{i}t_{2})&a\cos(\omega_{i}t_{2})\\ \vdots&\vdots\\ a\sin(\omega_{i}t_{n})&a\cos(\omega_{i}t_{n})\end{bmatrix}\), \(u=\begin{bmatrix}A_{i}\cos\phi_{i}\\ A_{i}\sin\phi_{i}\end{bmatrix}\), \(f=\begin{bmatrix}y_{i}(t_{1})\\ y_{i}(t_{2})\\ \vdots\\ y_{i}(t_{n})\end{bmatrix}\), \(y_{i}(t)=A_{i}a\sin(\omega_{i}t+\phi_{i})\). \(a\) is the amplitude of the excitation signal, \(\omega_{i}\) is the \(i\)-th frequency of the excitation signal, \(A_{i}\) is the amplitude of the output signal, and \(\phi_{i}\) is the phase difference between the output signal and the input signal.  
The least squares method is used to solve the overdetermined equation \(\boldsymbol{u}=\left(\boldsymbol{K}^{T}\boldsymbol{K}\right)^{-1}\boldsymbol {K}^{T}\boldsymbol{f}\), from which the frequency response function for the corresponding frequency is obtained:  
\[G(j\omega_{i})=A_{i}\cos\phi_{i}+jA_{i}\sin\phi_{i} \tag{12}\]  
The dynamic stiffness of the servo system is the reciprocal of \(G(j\omega_{i})\).  
Select a stepwise sinusoidal sweep excitation torque with an amplitude of 10 N\(\cdot\)m from 1 to 25 Hz and measure the rotation angle response of the output shaft of the steering gear. Use the method described in this article to calculate the dynamic stiffness of the system, as shown in Figure 9. According to the experimental results, it can be seen that  
Figure 8: Picture of the experimental setup.  
the stiffness changes slowly with the excitation frequency. The static stiffness is about 3270 N\(\cdot\)m\(\cdot\)rad\({}^{-1}\).
## 3 Integration of Multi-Domain MEC Environments
### Business Implications
#### 4.2.3 Discovery of Domains  
Despite the fact that manual configuration can be used for establishing peer sessions between MEC providers--as is typically done, e.g., by ISPs for establishing BGP peering sessions--automatic procedures are desirable for speeding up service provision in the network softwarized era.  
An autonomic coordination between administrative domains requires mechanisms such as discovery and bootstrapping. Descriptors have to be defined for populating available multi-domain components, together with criteria for allowing the association among them. Specific identifiers can be expected for those components and the administrative domains in general. Forwarding to and reachability of remote components and domains can be also foreseen, in a similar way as IP prefixes are advertised nowadays for Internet peering and transit.
# Reversible and Plausibly Deniable Covert Channels in One-Time Passwords Based on Hash Chains +
## 5 Experiments
### Performance  
To measure the performance impact of the covert channel, especially on the receiver, we use a hash chain of length \(n=16\),384 and a string of length \(t=16\),382 (plus termination symbol), i.e., we embed a symbol in each password except the first. As seed \(s\), we use the string _Wonderful seed for Hash Chain_. As the secret message, we use a string solely comprising the repeated letter '?' (symbol 31). As the cryptographic key, we use the same string as for the seed.  
The secret message is chosen such that, no matter if we use encryption or not, the number of hash function evaluations by CR is 32 (on average if encryption is used, with standard deviation 18), as we use only 63 of the 64 possible symbols during the message, and can ignore the influence for recognizing the last symbol because of the message length.  
The results can be seen in Table 1. If a CC is used, the runtime is notably higher than without, while the runtimes for CC with encryption only marginally deviate from runtimes for CC without encryption. The difference between runtimes with and without CC, divided by 32 - \(n\), amounts to 0.63 \(\upmu\)s and 12.1 \(\upmu\)s per evaluation of MD5 and SHA-3, respectively, which, multiplied by \(n\), amounts to about half the runtime without CC.
## 3 Performance Analysis of iPyroxy Block
### Electromagnetic Wave Transmission Rate  
Applying the following formula to a material with a dielectric constant (\(\epsilon_{r}\)) of 9.8 will provide the electromagnetic wave transmission rate (\(T\)) for a 2.4 GHz signal. This means that the projected electromagnetic wave transmission rate for a 2.4 GHz signal is around 31.9% for a material having a dielectric constant of 9.8. This indicates that at a frequency of 2.4 GHz, around 31.9% of the electromagnetic waves pass through the material and depending upon other conditions, the remaining portion may reflect or absorb. Note that this is an oversimplified estimate and that the natural transmission qualities may vary depending on other parameters, including the characteristics of the material, behavior at different frequencies, and the particular circumstances surrounding the interaction of the electromagnetic wave with the material.  
\[\begin{array}{l}T=\frac{1}{\sqrt{\epsilon_{r}}}\\ T=0.319\end{array} \tag{1}\]
## 3 Compressive Sensing Based Preamble Channel Estimation
### CS Theory for Channel Estimation  
CS theories [37; 38; 39] state that a sparse signal \(h\) can be recovered steadily from linear measurement  
\[y=\Phi h+\eta \tag{8}\]  
where \(\Phi\) is the measurement matrix, and denotes the matrix with \(M\times N\); here, \(M\ll N\), and \(\eta\) denotes the noise, by minimizing the \(\ell_{1}\)-norm of \(h\). The prerequisite is that \(\Phi\) satisfies the Restricted Isometry Property (RIP), that is, for all \(K\)-sparse signal \(h\),  
\[1-\delta_{K}\leq\frac{\parallel\Phi h\parallel_{2}^{2}}{\parallel h \parallel_{2}^{2}}\leq 1+\delta_{K} \tag{9}\]  
where \(\delta_{K}\) is the RIP parameter, \(0<\delta_{K}<1\).  
The signal \(r(t)\) in Equation (5) can be given in matrix form as [31]  
\[R=XH+\Phi \tag{10}\]  
where \(X=diag(x(0),x(1),...,x(N-1))\), \(R=[r(0),r(1),...,r(N-1)]^{T}\), and \(H\) denotes the multipath channel frequency response sampling value, \(H=F_{NL}h\). \(F_{NL}\) denotes a \(L\) row discrete Fourier Transform matrix, \(L\) is the channel length; and \(\Phi\) denotes the noise matrix, with zero mean and variance of \(\sigma^{2}\), and the matrix with \(N\times N\).  
We set \(P\) for the number of pilots, \(\varphi=\left(\mathrm{e}_{\mathrm{s}_{1}},\mathrm{e}_{\mathrm{s}_{2}},\ldots, \mathrm{e}_{\mathrm{s}_{P}}\right)\) denotes a pilot selection matrix with \(P\times N\), and \(\varphi\) is utilized to seeking the pilots position from the whole sub-carriers. \(\mathrm{s}_{i}\left(i=1,2,\ldots,P\right)\) indicate the \(i\)th pilot's position. Rewrite Equation (10) as  
\[R_{P}=X_{P}F_{P}h+Z_{P} \tag{11}\]  
where \(R_{P}=\varphi R\) is received pilot signal; in this paper, \(R_{P}\) is the LS estimation channel values, \(\Phi_{P}=\varphi\Phi\), with \(P\) column vectors. \(F_{P}=\varphi F_{NL}\), and \(X_{P}=\varphi X\varphi^{T}\) denotes a diagonal matrix, where the diagonal elements are pilot values.  
Let us assume that \(F=X_{P}F_{P}\), rewrite Equation (11) as  
\[R_{P}=Fh+Z_{P} \tag{12}\]  
where \(h\) denotes sparse multipath channel response, and we can obtain \(R_{P}\) and \(F\) in the transmission process. Then, we can use the CS recovery algorithm to recover sparse signal \(h\).
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 4 Dynamic Spectrum Access Model
### Spectrum Access Model Under Two Types of Radio Systems  
In this model, we consider the CR-enabled LPWAN overlapped with a primary radio network (PRN) such as the mobile cellular network. In this spectrum access model, frequency bands are used by two types of radio systems. One is the cellular network system operating in the licensed bands and the other is the wireless sensor network system operating in the unlicensed bands.  
Similarly, there are two types of radio users, licensed PUs and unlicensed CUs. Typically, the unlicensed CU tries to use the available licensed channel. That is, the unlicensed users can access the licensed channel as long as they do not cause interference to the licensed users. Hence, when a PU begins to use an available licensed channel, the unlicensed CUs on IoT devices in CR-LPWAN, which operate on licensed bands in an opportunistic manner, must immediately detect the activity of licensed PU via spectrum sensing. That is, cognitive radio capability should be able to operate in licensed bands as well as unlicensed bands since the PUs do not always fully utilize the licensed spectrum bands.  
Now let there be two types of radio users, PU and CU. In this model, the entire spectrum for two types of radio users actually consists of \(M\) sub-band units. Each primary user uses an A-band channel consisting of \(m\) sub-bands and each cognitive user uses a B-band channel consisting of only one sub-band. That is, one A-band channel is divided into \(m\) B-band channels. The A and B-band channels overlap with each other in the same part of the entire spectrum, as indicated in Figure 3, and the overlapped spectrum is used by both PUs and CUs. In more detail, a single cell of the primary radio network has \(C_{1}\) (\(=N\)) total channels, and CUs can only use some channels in the primary radio network beyond the channels (\(C_{2}\)) allocated in a CR-LPWAN. The PUs have priority to use the overlapped spectrum and can reclaim any sub-bands used temporarily by CUs. Therefore, the presence of CUs is entirely transparent to the PUs.  
In Figure 3, the primary users can use \(N\) channels and the cognitive users can use \(r\) (\(=mK+C_{2}\)) channels where \(K=0\), \(1\), \(2\), \(\ldots\), \(C_{1}\). The process of spectrum occupation is modeled as a continuous time Markov chain. It is characterized by its states and transition rates. The \(mC_{1}\)-(\(M\)-\(r\)) sub-band channels are shared by the primary users and cognitive users. In this case, the states are described by an integer pair (\(i\), \(j\)), where \(i\) is the total number of primary users and \(j\) is the total number of the cognitive users. We assume the arrivals of primary users and cognitive users are both Poisson  
Figure 3: Spectrum band used by two types of radio systems.  
processes with arrival rates \(\lambda_{1}\) and \(\lambda_{2}\). The corresponding service times are exponentially distributed with rates \(\mu_{1}\) and \(\mu_{2}\).
# A New Computational Method for Arabic Calligraphy Style Representation and Classification
## 4 Proposed Method  
In this paper, a new tool is proposed for the task of ACSR. The proposal is capable of recognizing nine different AC styles and might be extended to other styles. After analyzing Arabic calligraphy, we found out that the styles are geometrically different. Some styles have a unique shape, some share similar letter shapes, and some others have special diacritics. For the visually different styles, it is easy to separate them, whilst for the other fine-grained styles, we needed to extract one or more distinctive characteristics to separate them. The proposed solution was inspired by the calligraphy expert. A calligraphy expert uses tools such as brushes, pens, or markers to create a special writing style that is artistic  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Author (Year)** & **Language** & **Dataset** & **N°****S****S****S** \\ \hline [PERSON] et al. (2020) [15] & Arabic & 1685 line image & 9 \\ \hline [PERSON] et al. (2012) [16] & Arabic jawi & 700 blocks image (privet) & 6 \\ \hline [PERSON] et al. (2013) [17] & Arabic-jawi & 700 block image (privet) & 7 \\ \hline [PERSON] et al. (2011) [18] & Arabic & 14 documents images (privet) & Unknown \\ \hline [PERSON] et al. (2011) [19] & Arabic & 100 line image (privet) & 6 \\ \hline [PERSON] et al.(2011) [20] & Arabic & 700 line images & 7 \\ \hline [PERSON] et al. (2011) [21] & Arabic-jawi & 100 character image (privet) & 5 \\ \hline [PERSON] et al. (2011) [22] & Arabic-jawi & 1019 character images (privet) & 4 \\ \hline [PERSON] et al. (2017) [23] & Arabic & 330 character images (privet) & 6 \\ \hline [PERSON] et al.(2016) [24] & Arabic & 267 line/word images (privet) & 3 \\ \hline [PERSON] et al. (2020) [25] & Arabic & 421 line/word images (privet) & 3 \\ \hline [PERSON] (2020) [26] & Arabic & 2653 documents images (privet) & 6 \\ \hline [PERSON] et al. (2019) [27] & Arabic & 1685 line image & 9 \\ \hline [PERSON] al. (2012) [28] & Arabic & 27709 word image & Unknown \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of related works on ACSR.  
and expressive. After an in-depth study of each style, looking for the visual characteristics used by calligraphers to distinguish it, we did come up with the main distinctive features that need to be extracted for each style or set of styles. In the feature extraction section, we explain these visual features and the relationship with their corresponding styles.  
Our proposed tool consists of three main steps: (a) image preprocessing: perform several transformations to prepare the image for feature extraction, (b) feature extraction: extract the proposed descriptor from each image, and (c) classification: use the extracted features to predict the style of the input image. Figure 3 illustrates the general scheme of our proposed tool.
# A Hybrid Watermarking Approach for DICOM Images Security
## 3 Proposed Method
### Watermark Embedding Process
#### 3.4.2 Embedding the Watermark in the Black Background Region  
As mentioned earlier, the zero-watermark ZW of size 16 \(\times\) 16 generated using the zero-watermarking approach is first encoded into a watermark W of size 32 \(\times\) 32 before embedding into the original image. W is divided into K blocks of size n \(\times\) m (W1, W2, W3,...,WK), and the algorithm mentioned in the subsection above is used to find the regions (R1, R2, R3,...,R2K) of size n x m in the black background region for the insertion of the blocks of the watermark. The blocks are embedded into the regions selected using a linear interpolation technique [22]. In this technique, the embedding of a watermark W into a host image I uses the following equation (Equation (1)) to generate the watermarked image I\({}_{w}\):  
\[\text{I}_{w}=(1-t)\text{ W}+t\times\text{ I},\text{ where t is the interpolation factor and t }\in\text{]0,1[} \tag{1}\]  
The imperceptibility of the embedded watermark is stronger when the value of t is close to 1. For this reason, a value of t close to 1 has been chosen for the embedding of the blocks of the watermark in the selected regions of the black background of the medical image.  
After embedding the watermark blocks into the black background region, the anatomical object image and the watermarked black background image are merged to obtain the watermarked image Iw.  
Figure 6 shows the watermark embedding process. Algorithm 1 presents the pseudo-code of the watermark embedding process.  
Figure 5: Example of division of the black background region and the watermark with n \(\times\) m = 8 \(\times\) 8.  
```
Input: host image I, the selected 2k blocks block\({}_{\rm{i}}\) (i = 1,... 2k) of size n \(\times\) m in the black background region, original zero-watermark image ZW and interpolation factor t Output: watermarked image (Iw) Begin \(\rm{W}\leftarrow\) Encode ZW  Divide W into k blocks \({}_{\rm{W}}\) (i = 1,...,k) of size nxm for i = 1 to k loop \(\rm{block}_{\rm{iw}}=(1-t){}_{\rm{W}}_{\rm{i}}+t\times\rm{block}_{\rm{i}}\) \(\rm{block}_{\rm{(k+i)w}}=(1-t){}_{\rm{W}}_{\rm{i}}+t\times\rm{block}_{\rm{(k+i) }}:t\in\left]0-1\right[\) end loop \(\rm{Iw}\leftarrow\) Merge Anatomical region of host image I and watermarked black background  End
```  
**Algorithm 1**The pseudo code of the watermark embedding in the black background region of the image.  
Figure 6: The watermark embedding process.