esa-sceva/satcom-chunk-collection huggingface dataset:
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 5 Tensor-Based Approaches for Nonlinear Systems
### Volterra-PARAFAC Models
Then the median value over the \(R\) runs is computed as \(\epsilon(t)=\text{median}\{e_{\rho}(t),\ \rho=1,2,\cdots,R\}\). This allows discarding outliers due to ill convergence of EKF. Indeed, depending on the initialization, the EKF sometimes failed to converge with the selected number of samples. This is particularly the case for PCWS. Finally, \(\epsilon(t)\) is smoothed with a moving average filter: \(\epsilon_{L}(t)=\frac{1}{L}\sum\limits_{\tau=0}^{L-1}\epsilon(t-\tau)\). The obtained results are given in Figure 3 where a comparison between PCWS and Volterra-PARAFAC in terms of the square error \(\epsilon_{L}(t)\), with \(L=100\), is depicted. In general, EKF converges faster with Volterra-PARAFAC than with PCWS in the noiseless case._  
**Evaluation in steady state:** _To evaluate the steady state performance, the NMSE (normalized mean square error) is calculated as \(NMSE=\frac{\xi^{2}}{\hat{y}^{2}}\), with \(\epsilon^{2}=\frac{1}{f_{-l}}\sum\limits_{t=l_{0}}^{f_{\rho}}\epsilon^{2}(t)\) and\(\hat{y}^{2}=\frac{1}{t_{f}-t_{0}}\sum\limits_{t=t_{0}}^{t_{f}}y^{2}(t)\), where the interval \([t_{0},t_{f}]\) characterizes the steady state. The evaluation was carried out with two types of input signals: the composite sum of sines previously used and a random input. The random input was drawn from a uniform distribution between \(-1\) and \(1\). The number of iterations needed for convergence of the EKF with the random input was much less than with sum of sines; 10,000 samples were generated for a random input and the steady performance was evaluated from the 1000 last samples of the reconstructed output. In the case of sum of sines, 100,000 samples were generated and the steady state was evaluated from the 10,000 last samples. A white Gaussian noise was added to the output; its variance depends on a specified signal-to-noise ratio (SNR). For different values of SNR, Figures 4 and 5 depict the NMSE in steady state for sum of sines and for the considered random input, respectively. It can be noticed that in steady state, both Volterra-PARAFAC and PCWS give the same performance whatever the input signal. With a lower SNR value, Volterra-PARAFAC is slightly better than PCWS.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### Pseudonym Update  
To prevent attackers from contacting multiple messages from the same vehicle, the pseudonym of a vehicle needs to be updated periodically. The detailed steps for updating are as follows. Firstly, CA selects a new random number \(r_{v}^{n}\) for a vehicle. And CA generates the pseudonym \(AID_{v}^{n}\) for the vehicle, where \(AID_{v}^{n}=H(sk_{c}||r_{v}^{n})\oplus ID_{v}\). Then, CA deletes the old random \(r_{v}\) and the old pseudonym \(AID_{v}\) from its database and updates with the new random number \(r_{v}^{n}\) and the new pseudonym \(AID_{v}^{n}\). Finally, CA sends \(\{AID_{v}^{n},ID_{v}\}\) to the vehicle via a secure method.
## 6 Prediction of HL by the Particle Swarm Optimization (PSO)-ANN Model  
Like the GA-ANN model, the selected initialization ANN model was optimized by the PSO algorithm for predicting HL of EEB systems, called PSO-ANN model. In this regard, the parameters of the PSO algorithm were set up before optimization of the ANN model (i.e., ANN 8-24-18-1 model), including the number of particle swarms (_Sw_), maximum particle's velocity (\(V_{max}\)), individual cognitive (\(\phi_{1}\)), group cognitive(\(\phi_{2}\)), inertia weight (\(w\)), and maximum number of iteration (\(m_{l}\)). Then, the weights and biases of the initialization ANN model were optimized by the PSO algorithms, as those applied for the GA-ANN model above. Similar to the GA-ANN model, a TAE procedure of _Sw_ was implemented, with _Sw_ of 100, 200, 300, 400, 500, respectively; \(V_{max}\) = 1.8; \(\phi_{1}\) = \(\phi_{2}\)= 1.7; \(w\) = 1.8, and \(m_{i}\) = 1000. The similar techniques as those used for the GA-ANN model were also applied for the PSO-ANN model in developing the model (i.e., back-propagation algorithm, min-max scale [-1,1]). Finally, the best PSO-ANN model was determined with the lowest RMSE. Figure 10 shows the performance of the PSO-ANN model in the training process. Figure 11 illustrates the structure of the PSO-ANN model. Note that, although the number of input neurons, hidden layers, and neurons, as well as the output layer, is the same as Figure 8; however, the weights and biases of them are different. Eventually, the HL predictions on the training dataset and testing dataset were conducted based on the developed PSO-ANN model, as shown in Figures 12 and 13, respectively.  
Figure 10: Particle swarm optimization (PSO-ANN) performance for estimating HL of EEB systems in the training process.  
Figure 9: HL predictions on the testing dataset of the GA-ANN model.  
Figure 11: Structure of the PSO-ANN model for estimating HL of EEB systems.  
Figure 12: HL predictions on the training dataset of the PSO-ANN model.
# Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning
## 1 Introduction
An image and its augmentation are taken to be in the same class (positives), and all other images are considered to be of different classes (negatives). Noticeably, contrastive loss objective function and aggressive data augmentation are the other two key factors influencing the success of self-supervised representation learning [16; 32]. In addition, Ref. [34] showed that contrastive loss encourages consistent representation of augmented view and matches prior distribution. There are two main practical advantages of contrastive learning. First, the agreement is estimated between only the learned representations of various views, which lie on a lower dimensional space than the original one. Second, various views can be chosen to capture different aspects and modalities of the data with plenty of modeling flexibility [35; 36]. These properties can be especially beneficial for feature matching that helps to retrieve information in retrieval tasks. **Contribution**: Our research aimed to find a solution for the challenging problem of category image retrieval tasks on low-resolution images. The problem can be hypothesized into a general question about learning effective visual representations in an embedding space where similar images with different resolutions are kept close to each other and dissimilar ones are placed far away from each other. In this paper, we present a framework that consists of contrastive learning trained over the Visual Transformer encoder (ViT). The benefits of using contrastive learning are expected to maximize positive pairs' agreement,which are samples from the same class or augmenting resolution samples from the same source, via contrastive loss. However, it is usually a huge challenge when training a contrastive framework from scratch since it requires large-batch training for a long period [16]. Therefore, a possible solution to the problem at hand is proposed in this paper. We used a powerful pre-trained encoder to extract visual representations and fine-tune contrastive learning to learn embeddings for feature matching. Our contribution can be summarized as follows:  
1. We adapted the Visual Transformer to the image retrieval task when the embedded vectors were calculated using attention weights. The main advantage of this method is that the attention mechanism of the ViT model helps to focus more on an object of interest when comparing two images. 2. We addressed the problem of retrieval with degraded samples such as low-resolution. We proposed using a contrastive learning framework to learn an embedded space where the same samples are close together with respect to Euclidean distance. 3. We conducted extensive experiments on CARS196, Stanford Online Products, and CUB200-2011 datasets under various circumstances. Both quantitative and qualitative results show that the proposed framework is efficient.
# Power Control Optimization of an Underwater Piezoelectric Energy Harvester
## 7 Conclusions  
In the present study, a new control algorithm based on the circular cylinder lift coefficient, water velocity, and piezoelectric voltage has been developed and implemented into an underwater energy harvester system. Firstly, a computational study of a piezoelectric energy harvester for underwater applications has been studied for using the kinetic energy of water flow. The device consists of a piezoelectric beam assembled to an oscillating body, a circular cylinder that is 1 cm in diameter. Therefore, two-dimensional computational simulations around a circular cylinder have been carried out in the current work at four different Reynolds numbers Re = 3000, 6000, 9000, and 12,000. Unsteady state simulations were performed for a period of time of 20 s to investigate the evolution of the wake and the vortex shedding as well as to check the dynamic behavior of the cylinder. The lift coefficient of the circular cylinder has a sinusoidal time waveform and it has been used as an input variable in the system control. Secondly, a new control law strategy has been developed to extract as much energy as possible from the energy harvester. The maximum extracted power is 996.25 \(\mu\)W and corresponds to the case with Re = 12,000. The results show that thanks to the new control law and optimization scheme the power output of the harvester has significantly increased in comparison with previous studies. The power output of the underwater energy harvester seems to follow an exponential law at the Reynolds numbers studied. Furthermore, the proportional gain of the control law K\({}_{\text{P}}\) maintains approximately constant at the water velocities studied in the present work, which is an indicator of the robustness of the control system.  
The following are available online at www.mdpi.com/2076-3417/8/3/389/s1, Video S1: Vortex Shedding.  
The funding from the Government of the Basque Country and the University of the Basque Country UPV/EHU through the SAIOTEK (S-PE11 UN112) and EHU12/26 research programs, respectively, is gratefully acknowledged. The authors are very grateful to [PERSON] of UPV/EHU and European funding (ERDF and ESF) for providing technical and human.  
[PERSON], [PERSON] and [PERSON] performed the computational simulations and developed the new control algorithm. [PERSON] and [PERSON] supervised the methodology and the preparation of the current study.  
The authors declare no conflict of interest.
## 5 Implementation and Evaluation  
We implemented a prototype system and conducted real-world experiments to validate the identification capability. We collected response data (port open, protocol response data, and web feature information) from 53,000 real IoT devices using the Shodan API [3]. As shown in Table 2, the dataset captures common IoT device brands well, covering a wide range of device categories. The ratio of training data to test data in our experiments was 9:1.  
As mentioned earlier, cyber search engines can identify device type with an accuracy of over 95%. Our approach focuses on fine-grained identification, such as device brand, model, and firmware version. We utilize the dataset to implement model training and to evaluate the success rate and efficiency of identification. Then, we validate the identification capability of our approach by comparing it with another method based on protocol popularity.
# An Enhanced Time Series Analysis to Improve the Performance of 5G Communication Systems +
Footnote †: Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
[PERSON]  
1 Department of Business Management, Vignana Bharathi Institute of Technology, Ghatkesar Mandal, Medical Malkangir 501301, India; [PERSON]
[PERSON]  
2 Department of Electronics and Communication Engineering, Manavracha International Institute of Research and Studies, Faridabad 121004, India; [EMAIL_ADDRESS]  
[PERSON]  
3 Faculty of Management, SRM Institute of Science and Technology, Katankulathur 603203, India; [EMAIL_ADDRESS]  
[PERSON]  
4  
[PERSON]  
5 Department of Electronics and Communication Engineering, Mahatma Gandhi Institute of Technology, Hyderabad 500075, India; [PERSON]##### Abstract  
The 5G communication systems are rapidly becoming integral in numerous areas, such as user experience, productivity, and performance, due to increased bandwidth, lower latencies, and superior signal coverage. As such, ensuring a high-performance 5G network has become more important than ever before. To this end, different performance metrics, such as throughput, latency, and packet error rate, must be measured and monitored on a regular basis. Time series analysis has emerged as a promising tool to measure, diagnose, and predict the performance of 5G communication systems. By considering the time dimension of metrics such as latency, throughput, and packet error rate, time series analysis provides a comprehensive view of the system and can potentially uncover patterns that are otherwise hidden in isolated metrics. Moreover, this type of analysis can also be used to fine-tune system parameters to improve system performance, detect faults, and identify trends in the system. In this way, time series analysis is an ideal tool for understanding, optimizing, and maintaining 5G communication systems.  
5G communication; bandwidth; latency; signal coverage +
Footnote †: Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 8 Conclusions  
The aim of this paper is to show the impact of flooding on the AODV routing protocol (which is already designed to deter flooding) and to demonstrate how a very simple direct trust mechanism (which requires no new operations in the protocol and has a low overhead) can significantly improve performance.  
Figure 6: Throughput of TAODV vs. AODV under flooding attack (95% CI).  
Figure 7: End-to-end delay of TAODV vs. AODV under flooding attack (95% CI).  
In this paper we have explained what MANETs are and how the routing protocols AODV works. It has described the vulnerabilities that AODV suffers from, which can be exploited by malicious nodes to run flooding attacks, leading to negative effects on performance. The paper then introduced trust management as a promising principle that could solve many issues in MANETs. Then proposed a direct trust management scheme to detect and isolate the flooding attack, which improved the performance of the network in the presence of the attack. The proposed scheme shows a considerable improvement in the performance of MANETs, but with some overhead. The increase in overhead was expected as adding any tasks to the routing protocol will likely consume more resources such as the memory for example.  
There are clearly other means to counter flooding attacks and other forms of attack against MANET routing, as discussed in Section 2. The advantage of the TAODV scheme proposed here is its simplicity and, hence, ease of implementation and low overhead. Despite this simplicity, TAODV is shown to be effective in countering flooding attacks. The authors are not aware of any works which show a comparison between different trust-based approaches or other mechanisms for defending against attacks. An empirical comparison of the performance of different approaches to defending against flooding attacks in AODV-based MANETs would be a substantial but worthwhile task, but one which lies beyond the scope of this current paper.  
Direct trust management has been used to identify malicious behaviour in many other different contexts in order to defend against different forms of attack. The mechanism used in this paper is only intended to identify a flooding attack, but other approaches to calculate a trust value could easily be added to counter other attacks against AODV. Furthermore, the approach used here could be adapted for use in other network routing protocols and other forms of trust management, indirect or global, could be used.  
Conceptualization, N.T. and A.A.; Methodology, N.T. and A.A.; Formal analysis, N.T. and A.A.; Investigation, N.T. and A.A.; Writing--original draft, A.A.; Writing--review & editing, N.T.; Project administration, N.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
## 4 Experimental Evaluation  
In this section, we first introduce the dataset and evaluation metrics used for the experiments, and then conduct comparison experiments with other gesture recognition algorithms on the EgoHands complex background gesture dataset [53] to demonstrate the effectiveness and superiority of the proposed method. This is followed by ablation experiments to demonstrate the effectiveness of the proposed improvements, then experiments on the TinyHGR dataset [49] to demonstrate the generalization of the proposed method, and, finally, practical testing experiments to demonstrate the robustness of the proposed method.
# FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
## 3 Definitions and Models
### Node Energy Model  
We employ the classical simplified node energy model [14; 15; 16]. In this model, a free space and a multi-path fades channels that are defined depending on the distance between the source and the destination nodes. The required energy for transmission depends on a threshold distance d\({}_{0}\). If the distance from the source to the destination is less than d\({}_{0}\), the free space model is used and the energy required by the amplifier to send data directly to the recipient is defined as \(\varepsilon_{\rm fs}({\rm pJ}/({\rm bit/m^{2}}))\). Otherwise, the multi-path \(\varepsilon_{\rm mp}({\rm pJ}/({\rm bit/m^{2}}))\) parameter is employed and represents the energy required to send data by using other sensors in a multi-hop way.  
To transmit b-bits of data over a distance d\({}_{0}\), the energy of transmission E\({}_{\rm T}\) is given by Equation (1).  
\[{\rm E}_{\rm T}=\left\{\begin{array}{ll}{\rm b}\times{\rm E}_{\rm elec}+{\rm b }\times\varepsilon_{\rm fs}\times{\rm d}^{2}&\mbox{if }{\rm d}<{\rm d}_{0}\\ {\rm b}\times{\rm E}_{\rm elec}+{\rm b}\times\varepsilon_{\rm mp}\times{\rm d} ^{4}&\mbox{if }{\rm d}>{\rm d}_{0}\end{array}\right. \tag{1}\]  
In which E\({}_{\rm elec}\) (nJ/bit) is the sum of electrical energy required by the transmitter front-end to transmit one bit of data. The amount of energy expended by the front-end radio to receive b-bits of data is defined as:  
\[{\rm E}_{\rm R}={\rm b}\times{\rm E}_{\rm elec} \tag{2}\]  
In addition, we also assume E\({}_{\rm sen}\) to be the energy required to sense the environment, and E\({}_{\rm com}\) to be the energy needed for data aggregation.
## 3 Proposed Extended Chaotic-Map-Based User-Authentication and Key-Agreement Scheme
### Registration Phase  
Figure 2 illustrates the processes of registration phase of the proposed scheme. Each patient _Pat_ signs a privacy contract \(w\) that includes the patient's information and instructions on how to be stored and used, and then performs the following steps for registration.  
Step 1: Patient _Pat_ selects an identity \(ID_{P}\) and password \(PW_{P}\), computes \(W_{P}=h(ID_{P}\|PW_{P})\) and sends \(\{w,ID_{P},W_{P}\}\) to _MCS_.  
Step 2: On receiving \(\{w,ID_{P},W_{P}\}\) from _[PERSON]_, MCS selects a random number \(R_{\text{MCS}}\) and computes \(NID_{P}=E_{r_{\text{MCS}}}(ID_{P}\|W_{P}\|R_{\text{MCS}}\|w)\) and \(X_{P}=h(ID_{P}\|R_{\text{MCS}})\) for authentication. Then, _MCS_ sends \(\{NID_{P},X_{P}\}\) to _[PERSON]_.  
Step 3: On receiving \(\{NID_{P},X_{P}\}\) from _MCS_, _[PERSON]_ obtains his/her smartcard and computes \(Y_{P}=X_{P}\ \oplus\ W_{P}\), and replaces \(X_{P}\) with \(Y_{P}\).
# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
###### Abstract  
Deep learning has been used to improve intelligent transportation systems (ITS) by classifying ship targets in interior waterways. Researchers have created numerous classification methods, but they have low accuracy and misclassify other ship targets. As a result, more research into ship classification is required to avoid inland waterway collisions. We present a new convolutional neural network classification method for inland waterways that can classify the five major ship types: cargo, military, carrier, cruise, and tanker. This method can also be used for other ship classes. The proposed method consists of four phases for the boosting of classification accuracy for Intelligent Transport Systems (ITS) based on convolutional neural networks (CNNs); efficient augmentation method, the hyper-parameter optimization (HPO) technique for optimum CNN model parameter selection, transfer learning, and ensemble learning are suggested. All experiments used [PERSON]'s public Game of Deep Learning Ship dataset. In addition, the proposed ship classification achieved 98.38% detection rates and 97.43% F1 scores. Our suggested classification technique was also evaluated on the MARVEL dataset. This dataset includes 10,000 image samples for each class and 26 types of ships for generalization. The suggested method also delivered an excellent performance compared to other algorithms, with performance metrics with an accuracy of 97.04%, a precision of 96.1%, a recall of 95.92%, a specificity of 96.55%, and a 96.31% F1 score.  
transfer learning; convolutional neural network; deep learning; ensemble learning; particle swarm optimization