esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Existing Mode-Pairing Criteria
### Modal Assurance Criterion (MAC)  
The Modal Assurance Criterion (MAC) is the most used criterion for pairing numerical and experimental modes of vibration [17; 21; 25]. It is defined as:  
\[\text{MAC}_{ij}=\frac{\left|\mathbf{\Phi}_{i}^{H}\mathbf{\Phi}_{j}\right|^{2}}{ \mathbf{\Phi}_{i}^{H}\mathbf{\Phi}_{i}\mathbf{\Phi}_{j}^{H}\mathbf{\Phi}_{j}} \tag{1}\]in which \(\mathbf{\Phi}_{i}\) and \(\mathbf{\[PERSON]}_{j}\) are the vectors containing the modal ordinates of modes \(i\) and \(j\), respectively, and \({}^{H}\) is the Hermitian transpose (conjugate transpose) operator.  
According to [12], the main advantages of applying the MAC parameter are: (i) simple implementation; (ii) experimental information is not required on all degrees of freedom of the structure; and (iii) it does not depend on the normalization of the modal vectors. However, according to [21], the value of the MAC parameter strongly depends on the dimension of the modal vectors and is also particularly sensitive to the change in the higher amplitude ordinates. Due to these issues, pairing by the MAC value may be not sufficient in cases of complex structures, as well as in continuous structures or structures with partial continuity, such as bridges with several spans, as demonstrated by [23]. Examples of the application of the MAC criterion in mode pairing can be found in references [1; 2; 26; 27].
## 4 VR-PEKS Scheme and Security Definition
### Security Model
#### 4.3.2 KT-IND-CKA Security  
The KT-IND-CKA security is defined by the following interactive game between adversary \(\mathcal{A}\) and challenger \(\mathcal{C}\).  
(1) The challenger \(\mathcal{C}\) inputs a security parameter \(\lambda\) and calls the _KeyGen_ algorithm to generate DR's key pair \((pk,sk)\) and key \(k\). Then, he sends \(pk\) to adversary \(\mathcal{A}\).  
(2) The adversary \(\mathcal{A}\) can perform the queries in steps (2) and (3) of the KC-IND-CKA game.  
(3) The adversary \(\mathcal{A}\) sends two keywords \(w_{0}\), \(w_{1}\) to challenger \(\mathcal{C}\) that he wishes to challenge, with the restriction that \(\mathcal{A}\) has not previously requested trapdoor \(T_{w_{0}}\) or \(T_{w_{1}}\). After receiving the keywords, \(\mathcal{C}\) chooses a random bit \(b\in\{0,1\}\) and sends the trapdoor \(T_{w_{0}}\) to \(\mathcal{A}\).  
(4) The adversary \(\mathcal{A}\) can continue to adaptively choose any keyword \(w^{\prime}\) to request its ciphertext \(CT_{w^{\prime}}\) and trapdoor \(T_{w^{\prime}}\) as long as \(w^{\prime}\
eq w_{0},w_{1}\).  
(5) The adversary \(\mathcal{A}\) outputs a guess \(b^{\prime}\in\{0,1\}\) of the keyword trapdoor. When \(b^{\prime}=b\), the adversary \(\mathcal{A}\) wins the game. The possibility that adversary \(\mathcal{A}\) can win the game is defined as  
\[Adv_{\mathcal{A}}^{KT-IND-CKA}(\lambda)=\left|\Pr\left[b^{\prime}=b\right]- \frac{1}{2}\right|\]
# An Energy-Efficient Convolutional Neural Network Processor Architecture Based on a Systolic Array
###### Abstract
Existing CNN processors have different implementation architectures, and they use various methods to implement data reuse. In most cases, the filter matrixes are placed next to the operation units and are obtained in a fixed way to be multiplied and added with different feature image pixels, such as Origami [19] and the processor proposed by [PERSON] et al. [20]. In addition, filter matrixes are also used by multiple operation units to realize reusing by broadcasting [4]. Similarly, the same input feature map can be used simultaneously by multiple identically constructed units to perform convolutions between output channels in parallel [19; 21]. The application of the RS (row-stationary) architecture [3] exploits local data reuse more fully, allowing power efficiency to be improved. To address the issues of a high hardware overhead and low energy efficiency due to intensive access to external memory in CNN computing, an energy-efficient configurable CNN processor architecture based on a systolic array is proposed in this paper, which can be configured by writing the instruction registers, supporting the acceleration for the operations of the convolutional layer and fully connected layer in different scales. According to the computation strategy based on systolic dataflow, the input image data and weight data are reused. Combined with the multi-level storage method, the power of memory access is significantly reduced, and the high energy efficiency is obtained accordingly. For large-scale CNNs, an image segmentation method is proposed, which enables the acceleration to be carried out on limited hardware through multiple processes. The rest of this paper is organized as follows. Section 2 overviews the architecture of the CNN processor and the computing strategy, and analyzes the configurability and low power achieved in this architecture. Section 3 describes the design details of processing modules. Then, the hardware implementation and performance evaluation are described in Section 4. Finally, the conclusions are given in Section 5.
## Appendix A Review of Vertiport Layouts  
Various vertiport layouts have been proposed by academia and the industry. In Table 11, a review of sources defining vertiport layouts is presented. The focus hereby lies on the number of pads (for take-off and landing) and gates/stands (for boarding of passengers and parking of vehicles). In our article, gates have the function of allowing passengers to board and de-board the vehicle, while stands are used to park idle vehicles. In contrast, most of the presented sources do not differentiate between gates and stands.  
As stated in Section 3.2, we chose a baseline vertiport layout of 4 pads, 12 gates and 20 stands. Four pads were chosen both by DLR (Germany) and NASA (U.S.), which we assumed to be the most trustworthy sources. Furthermore, using only one or two pads might not allow for more complex interactions to occur on the airfield, thereby rendering ABMS superfluous and preventing the results to be transferable to larger vertiport sizes. Four pads can be placed in the four corners of a square and allow for clear sectioning of the airspace; for more than 4 pads the airspace operations are not as straightforward anymore. For these reasons we chose four pads for our baseline layout, but varied the number from 2 to 5 pads, as shown in Section 4.1, to understand the broader design space.  
Next, we chose the number of gates corresponding to the number of pads. The lengths of approach and departure time (45 s) and boarding and de-boarding time (95 s) were derived from the preceding publication [14]. To match the theoretically possible throughput on one pad (and while assuming the pads to be the initial bottleneck of operations) there would be a need of three gates. This results in 12 gates for four pads as gates are grouped around pads. Stands were then lined up around gates, which results in 5 stands per pad and accordingly 20 stands for the baseline vertiport layout. The geometric arrangement of one group is displayed in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Publication Type** & **\# of Pads** & **\# of Gates/Stands** \\ \hline McKinsey [25] & report & 1–10 & 2–20 \\ Lilium [26] & website & 1–2 & 2–8 \\ Volocoopter [27] & website & 1–4 & - \\ DLR [28] & journal article & 1/4 & 8/36 \\ NASA [11] & conference paper & 4 & 20–24 \\ Virgina Tech [13] & conference paper & 1–3 & 8–24 \\ Purdue [29] & conference paper & 4 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Review of vertiport layouts.
## 5 Experiments and Results
### Classical Machine Learning Algorithms  
The classes are represented in the following order: Birth Certificate (BC), National Identity Card (CIN), and Passport (PASS). Confusion matrices of the algorithms tested are illustrated in Figures 7-9, which show promising results using hyperparameters, max-depth, min-samples-split, min-samples-leaf, max-features for decision tree, about SVM, kernel, and the C, gamma hyperparameters. The performance of machine learning algorithms within precision, recall, accuracy, and F1 score are presented in Table 2.  
Of all the algorithms evaluated, SVM and Decision Tree performed the best achieving an accuracy of 92%, followed by Naive Bayes with 91% accuracy.
# Dynamic Phase Comparison Planar Direction-Finding Algorithm on Satellite Radio Receiver
## 2 Description of the Algorithm
### Angular Ambiguity  
In both the phase interferometer algorithm and the dynamic phase comparison algorithm, the final DF angle range is \([0,~{}\pi)\). Because the DF angle is the angle between the azimuth and the interferometer baseline, in planar direction finding, there is a possibility of having two possibilities for the DF result, corresponding to \(\alpha\) and \(\pi+\alpha\).  
Figure 3 shows the antenna coordinate system of RPI, which is equipped with tri-orthogonal dipole antennas and a three-channel receiver. Moreover, RPI uses the amplitude comparison algorithm in the DF system.  
The direction of the EM waves can be obtained by comparing the intensity of the signals received by the three antennas. The expressions for the azimuth angle and elevation angle are given by [10]  
\[\begin{cases}\phi=\arctan\dfrac{E_{y}}{E_{x}}\\ \theta=\arctan\dfrac{\sqrt{E_{x}^{2}+E_{y}^{2}}}{E_{z}},\end{cases} \tag{18}\]  
where \(\phi\) is the azimuth angle and \(\theta\) is the elevation angle. The range of values for both angles is, respectively,  
\[\begin{cases}0\leq\phi\leq\dfrac{\pi}{2}\\ 0\leq\theta\leq\dfrac{\pi}{2}.\end{cases} \tag{19}\]  
Figure 3: Antenna coordinate system of the RPI (radio plasma imager) [10].  
In the amplitude comparison algorithm, the key to determining the arrival angle of EM waves lies in analyzing the ratio of signal amplitudes received by tri-orthogonal dipole antennas. Because signal amplitudes cannot be negative, the determination of the DF angle is subject to angular ambiguity.  
Taking the planar amplitude comparison algorithm as an example, when the actual arrival angle of the EM waves is \(\alpha\), \(\pi-\alpha\), \(\pi+\alpha\), or \(2\pi-\alpha\), the DF results obtained by this algorithm will not be able to distinguish between these four possibilities. Similarly, in the spatial amplitude comparison DF system, there are eight different possibilities for the result. Although the amplitude comparison algorithm can provide an estimate of the arrival angle, it has a \(\pi/2\) angular ambiguity in signal direction finding.  
Hence, introducing the dynamic phase comparison algorithm into the DF system on a space-borne plasma radio imager can achieve a larger effective DF range with high accuracy. This will be further discussed in the following section.
## 2 Background  
### Overview of PMIPv6  
The foundation of PMIPv6 is based on MIPv6 in the sense that it extends MIPv6 signaling and reuses many concepts such as the home agent (HA) functionality [10, 11]. However, PMIPv6 is designed to provide network-based mobility management support to a Mobile Node (MN) in a topologically localized domain [12]. Therefore, an MN is free from participation in any mobility-related signaling, and the proxy mobility agent in the serving network performs mobility-related signaling on behalf of the MN. Once an MN enters its PMIPv6 domain and performs access authentication, the serving network ensures that the MN is always on its home network and can obtain its home address on any access network. The serving network assigns a unique home network prefix to each MN, and conceptually this prefix always follows the MN wherever it moves within a PMIPv6 domain. From the perspective of the MN, the entire PMIPv6 domain appears as its home network. Accordingly, it is needless to configure the care of address at the MN. The new functional entities of PMIPv6 are the mobile access gateway (MAG) and local mobility anchor (LMA) [13]. The MAGtypically runs on the access router (AR). The main role of the MAG is to detect the MN's movements and initiate mobility-related signaling with the LMA on behalf of the MN. In addition, the MAG establishes a tunnel with the LMA for enabling the MN to use an address from it home network prefix and emulates the MN's home network on the access network for each MN. On the other hand, the LMA is similar to the HA in MIPv6. However, it has additional capabilities required to support PMIPv6. The main role of the LMA is to maintain reachability to the MN's address while it moves around within a PMIPv6 domain, and the LMA includes a binding cache entry for each currently registered MN. The binding cache entry maintained at the LMA is more extended than that of the HA in MIPv6 with some additional fields such as the MN identifier, the MN's home network prefix, a flag indicating a proxy registration, and the interface identifier of the bidirectional tunnel between the LMA and MAG. Such information associates an MN with its serving MAG, and enables the relationship between the MAG and LMA to be maintained.
## 4 Numerical Simulation Results
The NMSD curves are plotted in Figure 5, which shows that:  
* With \(\beta=5\), MCSS-APSA realizes better filtering accuracy at a transient process than \(\beta\in\{1,10,20\}\);
* With \(\beta=1\), MCSS-APSA becomes to CSS-APSA, while MCSS-APSA with other \(\beta\) values (such as \(\beta=5\) or \(10\)) outperforms CSS-APSA. This observation demonstrates the usefulness of modifications for CSS-APSA. * Increasing the \(\beta\) value from \(1\) to \(20\) cannot always improve the filtering performance of MCSS-APSA. Similarly to the effect of the \(C\) value, a \(\beta\) value which is too large leads to \(\mathbf{w}(n)\) dominated by \(\mathbf{w}_{2}(n)\) in iterations, and slows down the convergence speed of MCSS-APSA. Next, we compare the filtering performance between SMCSS-APSA and MCSS-APSA. In this part, we set \(\beta=5\) and \(C=6\), since such two parameters can improve the tracking performance of MCSS-APSA, as demonstrated in Figure 5. The compared NMSD results are plotted in Figure 6. From this figure, we can know that SMCSS-APSA achieves slightly better filtering behavior than that of MCSS-APSA, while the former is more computationally efficient than the latter, as demonstrated in Table 2.  
Figure 4: The NMSD learning curves of CSS-APSA and SCSS-APSA with different \(C\). (**a**,**c**): \(C=5\); (**b**,**d**): \(C=6\). Figure 3: The NMSD curves of CSS-APSA with different \(\mu_{\mathrm{s}}\) values. Finally, the filtering performance of SMCSS-APSA is compared with APSA, CAPSA, and SCSS-APSA (From [25], we know the VSS-APSA [21] need to reset algorithms when an abrupt change occurs, so we ignore the VSS-APSA's comparison in system identification). Figure 7 shows the NMSD curves of these algorithms. The results demonstrate that SMCSS-APSA outperforms APSA, CAPSA, and SCSS-APSA in terms of the convergence rate. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Parameters** & **Execution Time (s)** \\ \hline CSS-APSA & \(C=6\) & 1.7132 \\ SCSS-APSA & \(C=6\) & 1.6952 \\ MCSS-APSA & \(C=6,\beta=5\) & 1.7467 \\ SMCSS-APSA & \(C=6,\beta=5\) & 1.7108 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Execution time of CSS-APSA, SCSS-APSA, MCSS-APSA, and SMCSS-APSA at each iteration. Figure 5: The NMSD curves of MCSS-APSA with different \(\beta\), \(C=6\). Figure 6: The NMSD curves of MCSS-APSA and SMCSS-APSA with \(\beta=5\), \(C=6\).
# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 4 HMAO Algorithms
### Clustering and CH Selection Phase
The fitness function is defined as  
\[F_{CH}(i)=w_{1}\times E(i)+w_{2}\times MS(i)-w_{3}\times D(i)+w_{4}\times NH(i), \tag{11}\]  
where value of \(w_{1}\), \(w_{2}\), \(w_{3}\), and \(w_{4}\) are 0.4, 0.3, 0.2, and 0.1, respectively. To prevent CH node failure, \(w_{1}\) is considered to take residual energy as the highest priority. Following this, \(w_{2}\) and \(w_{3}\) are assigned second and third priority concerns to address UAV mobility patterns and minimize transmission costs between CMs and CHs, respectively. Finally, node degree \(w_{4}\) (the highest number of one-hop neighbors) is considered the fourth priority for choosing the CH. In this fitness function, a higher fitness score indicates a more suitable UAV for selection as the CH. UAVs with higher residual energy, greater mobility stability, closer proximity to CMs, and a higher number of one-hop neighbors achieve higher fitness values, making them more favorable candidates for CH selection. This prioritization ensures that the chosen CHs are energy-efficient, stable, centrally located, and well-connected, thereby promoting efficient and sustainable cluster management. HAMO-based clustering scheme is presented in Algorithm 1. First, the proposed algorithm initializes the clusters and optimal selection of CHs. This approach uses local optimization to achieve efficient and stable clustering suitable for high mobility and dynamic conditions in FANETs. This aims to organize UAVs into clusters managed by energy-efficient and stable CHs, minimizing frequent re-clustering, and effective intra- and inter-cluster communication. Following initialization of the UAV population, an initial cluster assignment is performed. At the initial stage, every UAV calculates its Euclidean distance to join in a cluster based on this distance. This assignment only provides a rough structure in which clustering begins, thus forming the basis for optimized CH selection. The best UAVs as CHs in each cluster are determined using local information during the CH selection phase. For each UAV, there are four key metrics for computing a fitness score: energy level, mobility stability, intra-cluster distance, and number of one-hop neighbors. These four metrics are aggregated as a weighted sum to calculate the fitness score of each UAV in the cluster. Higher energy, stable mobility, proximity, and sufficient connectivity lead a UAV to a higher fitness score. Then, among all UAVs in the cluster, the one with the maximum fitness score is designated as the CH for that cluster. This selection approach optimally balances energy efficiency, stability, and connectivity for a dynamically energy-constrained FANET environment.
## 3 Preliminary Investigations in a Virtual Environment
### Experimental Evaluation  
During the performance evaluation stage, all frames in a video were classified and the percentage of correct classification was estimated. Since at this stage, the aim was to assess the effect of different conditions rather than producing the final classification system, the experiment did not consider the frames where the view was split among two paintings to an extent that it was not possible to indicate the painting which the visitor focused on. Two experiments were run as described below: for the first experiment a single image per painting was used as part of the training procedure, whereas for the second, multiple images for each painting were used in the training set. In this case, a training set with approximately 100 images per painting was generated using a virtual environment setup. For this purpose, each painting was placed in a virtual wall and several images of each painting were captured from different angles, different distances and different lighting (see Figure 4). In effect, the attempt here was to introduce types of variations present in real conditions in the training set, which were not introduced through the usual data augmentation methods.  
It should be noted that in the pilot experiments carried out in a virtual environment setting, the aim was to determine the factors affecting the paintings identification accuracy, rather than obtaining optimized classification rates. For this reason, the standardized cross-validation methodology usually adopted in similar problems, where a data set is divided into training, validation and test subsets [20], was not used in this case. During the training stage, the training set was split into training and validation sets. However, in our experiments, the training and test sets were completely disjoined sets, where the training set was based either on a single image per painting (Experiment 1) or multiple images showing each painting under different conditions (Experiment 2), whereas the test set was a video showing the image frames as captured by a virtual camera in a way that simulated images captured by a wearable camera attached on visitors.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 1 Introduction  
In the ever-evolving landscape of telecommunications, the relentless pursuit of innovation and efficiency continues to drive transformative advancements [1, 2]. Within this dynamic realm, knowledge-based approaches represent a promising shift in how we design, manage, and optimize telecommunications networks. These approaches use advanced techniques like knowledge representation and machine learning/reasoning, along with domain expertise, to turn raw data into valuable operational insights. By analyzing vast and diverse datasets, knowledge-based systems enable informed decision-making, predictive analytics, and autonomous network orchestration and management. The potential impact on telecommunications is significant. Imagine networks that autonomously adapt to changing conditions, predict and mitigate anomalies, and optimize performance in real time [3]. This could lead to reduced signaling overhead, reduced operational costs, better resource allocation, and proactive identification of problems and vulnerabilities before they affect service quality. These intelligent systems have the potential to greatly improve the efficiency, reliability, and responsiveness of telecommunications networks [4, 5].  
This paper is an evaluation survey that aims to shed light on the importance of knowledge usage and knowledge management in telecommunication systems and telecommunications businesses. Firstly, based on a set of knowledge-related definitions, various forms of knowledge representations and reasoning (KRR) are classified and presented. The classification is used to associate the various representation and reasoning forms with the applications they are mostly suitable for supporting.  
Secondly, a set of assessment criteria is defined for the evaluation of the different approaches. These criteria correspond to KRR features characterizing the quality and usefulness of each approach given the tasks they are majorly used for. The evaluation according to the quality criteria is of assistance when determining the suitability of a KRR approach in different automation processes' demands.  
Finally, we consider the usage of KRR for the automation of telecommunication networks in three different levels of business automation abstraction. To this end, we define and reason on the usefulness and importance of the knowledge automation processes on three use-case categories including automation of network operation centers, telecommunication system solutions towards intent-driven operation, and distributed radio access networks (RAN) optimization functions. The paper concludes with a forward strategy and next steps.