esa-sceva/satcom-chunk-collection huggingface dataset:
# A Signaling-Free Underwater Code Division Multiple Access Scheme
## 5 Probabilistic Channel Access  
We assumed that the system has a predefined CDMA system capacity--the number of nodes that are allowed to transmit simultaneously--which we denote as \(C\). The nodes examining the channel availability estimate the number of currently transmitting nodes \(N_{e}\) (obtained by Equation (8)). If \(N_{e}\) is less than \(C\), then the examining nodes are allowed to join the transmission. However, if they try immediate joining, it may cause overflow--total number of transmitting nodes including the newly joining nodes exceeds the capacity \(C\). Therefore, a probabilistic channel access in a \(p\)-persistent manner is employed to avoid such overflow. The channel access probability is proportional to the availability of the channel and is given by  
\[p_{tr}=\left\{\begin{array}{ll}\frac{C-N_{e}}{C},&C>N_{e}\\ 0,&\text{otherwise}\end{array}\right. \tag{9}\]  
where \(P_{tr}=0\) means the transmission is delayed by a random back-off and restarts from the beginning, i.e., channel estimation.
## 3 Results  
The one-dimensional time-domain signal and two-dimensional wavelet time-frequency map are input to TC-CNN, and Figures 11 and 12 show the resulting training results. The accuracy of the training and validation sets is about 100%, and the accuracy of the test set is about 99.17%. The loss function curves show the training results of the training and validation sets, the accuracy curves, and the confusion matrix of the test set: the accuracy graph shows the improvement of accuracy in the 200 epochs of training, and the confusion matrix shows the correct recognition rate and specific error recognition of the six types of defects. Almost all types of defects are correctly recognized, and only 4% of the first type of defects were mistakenly entered into the third type of defect category.  
Figure 10: Structure of TC-CNN classifier. Referring to other TC-CNN models, * is to distinguish layers with the same name that have appeared before.  
Figure 13 shows the mapping characteristics of the input data and output results. Using the T-distributed random neighbor embedding (T-SNE) method, we visualized the hierarchical feature learning process of TC-CNN. The learning features of the input data and output results of the test set are mapped to two dimensions. It shows the change in feature mapping of different classes during the model learning process. The input data features are relatively scattered and difficult to distinguish. After convolution, pooling, feature fusion, and other operations, the characteristics of the same class are very concentrated. Figure 13b shows good classification results.  
Figure 11: TC-CNN’s overall training of 200 epochs. (**a**) Loss function curve; (**b**) Accuracy curve.  
Figure 12: Confusion matrix.  
In addition, to verify the method's effectiveness for multi-defect pile feature extraction and classification, the 1D time-domain signal and 2D wavelet time-frequency map are input into the exact specification of conventional CNN for comparison experiments. Furthermore, using the same dataset compared the recognition accuracy. The recognition results of different methods are shown in Table 6.  
From the data in Table 6: the defect recognition accuracies of the three methods on the training set are very close. However, there is a significant difference in recognition accuracy between the validation and test sets. TC-CNN is higher than 2D-CNN, while 2D-CNN is higher than 1D-CNN. Overall, TC-CNN outperforms single-channel CNN.
# Analytical Modeling of the Maximum Power Point with Series Resistance
## 7 Conclusions  
In this work, a new analytical expression for the maximum power point voltage that explicitly accounts for the effect of the series resistance was derived. Approximate analytical expressions for the current and power at the maximum power point were also presented. To derive these expressions, it was shown that [PERSON]'s W function may be approximated by its argument, as long as the value of the series resistances is not excessively large. This makes what otherwise would be a transcendental problem analytically solvable. The accuracy of the new expressions was tested with a numerical single-diode model. It was shown that the new model accurately predicts the maximum power of all the investigated cases with small discrepancies between the analytical model and the numerically simulated values. This was the case even when considering values of the series resistance above \(2\ \Omega\cdot\text{cm}^{2}\), which is larger than typical values for laboratory and commercial cells [18]. The accuracy of the approximation was shown to decrease with increasing series resistance, but also to increase with increasing bandgap energy. This makes the derived model of particular interest for semiconductors with large bandgaps such as perovskite or organic solar cells. Based on the results presented in this work, together with the results published in [10], it may be concluded that the derived analytical model can successfully be utilized to predict the maximum power point for solar cells that follow the diode equation when series resistance is accounted for. Moreover, the employment of Lambert's W function allowed for accurate and simpler expressions than what is currently found in the scientific literature. With respect to new developments, the derived model opens the possibility of analytically studying the effect of the series resistance on the temperature coefficients of the maximum power point. Further enhancements may also include attempts to generalize the model derived in the present work to also include the effect of the shunt resistance.  
Conceptualization, A.S.G. and R.S.; methodology, A.S.G.; validation, A.S.G.; formal analysis, A.S.G.; investigation, A.S.G.; writing--original draft preparation, A.S.G.; writing--review and editing, A.S.G. and R.S.; supervision, R.S.; project administration, R.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 1 Introduction
* Despite generating relatively small-sized images with limited information using selective features, our CNN model achieved high accuracy and a very low false positive rate (FPR). * We propose a CNN-based malicious network traffic detection framework that utilizes images processed with dimensionality reduction, and we present comparative experiments to examine the impact of feature correlation during image generation. * We evaluated the system resource usage of our proposed framework by measuring and comparing the training and testing times with models using one-hot encoding. The results demonstrated the superiority of our proposed framework. The rest of this paper is structured as follows: In Section 2, we introduce various preceding studies on IDSs based on machine learning and deep learning. In Section 3, we provide a detailed explanation of the overall architecture of the malicious network traffic detection framework with feature engineering. Section 4 describes the experimental environment, presents the performance evaluation results of the proposed framework, and demonstrates the superiority of our framework through a performance comparison with other CNN-based IDSs. Finally, in Section 5, we summarize the research and discuss the future research directions we plan to perform.
## 5 Experimental Results
(**a**) The first row is the original image, and (**b**) the other images are reconstructed by a combination of various \(\mathbf{z}_{p}\) and \(\mathbf{z}_{c}\). Figure 8: The condition-invariant image generation results using the constant feature vector \(\mathbf{z}_{c}\) of the Nordland dataset. Figure 7: The independence visualization result of the Kaist dataset. (**a**) The first row is the original image, and (**b**) the other images are reconstructed by a combination of various \(\mathbf{z}_{p}\) and \(\mathbf{z}_{c}\). As expected, we can see that similar images are generated regardless of time or season changes if we use the same \({}^{o}\mathbf{z}_{c}\). The visualization results showed that the independent assumption between \(\mathbf{z}_{p}\) and \(\mathbf{z}_{c}\) is reasonable because the reconstructed images are mainly influenced by the condition-sensitive feature \(\mathbf{z}_{c}\). Therefore, we can conclude that our model can extract the condition-invariant feature \(\mathbf{z}_{p}\) and perform robust place recognition in changing environments using this feature. To compare the place recognition performance, precision-recall analysis was conducted. Various thresholds were applied to the values of the similarity matrix. We compared the proposed method C-VAE (_VAE+_C_) and CP-VAE (_VAE+_C+_P_) with the sum of the absolute difference (SAD) [24], FAB-MAP [25], AlexNet [10], and VGG19 [26]. The precision-recall results are shown in Figures 10 and 11. Figure 10: The precision-recall results in various seasons of the Nordland dataset. Figure 9: The condition-invariant image generation results using the constant feature vector \(\mathbf{z}_{c}\) of the Kaist dataset. Precision-recall results showed that the proposed method CP-VAE outperformed other methods in most cases. Existing handcraft features such as SAD and FAB-MAP showed they are not suitable for place recognition in a changing environment. Pre-trained deep learning models such as AlexNet and VGG19 showed reasonable performance in various situations. However, the performances were degraded when environmental changes between images were substantial, such as winter images with snow and other seasonal images without snow. This is a fatal weakness of the pre-trained model from the viewpoint of securing stability for long-term operation of the robot. Since the proposed method recognizes a place using condition-invariant features, it shows robustly high performance even in these cases. From the results of the precision-recall analysis, we were able to verify the validity of the proposed method's place recognition performances in a changing environment.
## 4 Analysis of Experimental Results  
### Strain Distribution in the Reinforcement  
In this study, the failure behavior of the specimen was analyzed using the strain distribution of strain gauges attached to the longitudinal and transverse reinforcement. Figure 7 shows the strain distributions of longitudinal reinforcement in relation to lateral load. Regardless of yield strength of transverse reinforcement, configuration of transverse reinforcement, and axial force ratio, all longitudinal reinforcement of specimens experienced yield at the critical section (d = 250 mm) of both ends under a drift angle in the range of 0.45 to 0.6%. After reaching the peak load, the strain of longitudinal reinforcement increased at the critical section, which indicates plastic hinge formation at both ends of the critical section.  
Although the configuration and yield strength of transverse reinforcement did not influence strain distribution of longitudinal reinforcement, longitudinal reinforcement showed greater strain in the plastic hinge zone with increasing axial force ratio.  
Figure 8 shows the strain distribution of transverse reinforcement in relation to lateral load. Regardless of yield strength of transverse reinforcement, configuration of transverse reinforcement, and axial force ratio, the transverse reinforcement of all specimens did not experience yield before reaching the peak load. Transverse reinforcement strain was more prominent at the critical section, where the plastic hinge develops, than the center of specimens. As a result, flexural failure occurred at the critical section due to the yielding of longitudinal reinforcement at the critical section.  
All specimens showed greater strain of transverse reinforcement at the critical section with higher axial force ratio. Compared to the H-series, the S-series specimens exhibited greater strain of transverse reinforcement at the critical section with higher yield strength of transverse reinforcement and axial force ratio. In particular, the transverse reinforcement of NS30 specimen experienced yield after the peak load. The proposed spiral transverse reinforcement with alternating squares and octagons was thus verified as providing effective lateral confinement within the critical section even under high axial force ratio, and lateral confinement grew more pronounced with increasing yield strength of transverse reinforcement.  
Figure 7: _Cont._Figure 8: _Cont._  
Figure 7: Strain distributions of longitudinal reinforcement: (**a**) NH20; (**b**) NH30; (**c**) UH20; (**d**) UH30; (**e**) NS20; (**f**) NS30; (**g**) US20; (**h**) US30.
## 2 Computational Details  
Hydrogen molecules were placed in a 2 \(\times\) 2 \(\times\) 2 sII-clathrate supercell with a box size of 3.42 nm on each side, with 5\({}^{12}\)6\({}^{4}\) large cages (i.e., with 12 pentagonal faces and 4 hexagonal ones) and small-cage 5\({}^{12}\) dodecahedra (each composed of 12 water pentamers). The Periodic Boundary Condition (PBC) was applied for the x, y and z directions. The calculation was performed in four different configurations, with each large cage holding one to four (1, 2, 3 and 4) molecules as a guest, and each small cage holding only one molecule as a guest. The other set of four calculations was performed using D\({}_{2}\) molecules as the substitute for H\({}_{2}\) molecules. The parameters for both calculations were the same; however, the D\({}_{2}\) is a stable isotope of hydrogen, i.e., D\({}_{2}\) contains one proton and one neutron, and H\({}_{2}\) contains only one proton. In order to properly reproduce the experimental gas-phase quadrupole moment of H\({}_{2}\) and D\({}_{2}\), the same intermolecular potential parameters were used as in previous MD investigation of sII hydrogen hydrate [25]. A Lennard-Jones 12-6 with a 6.8 A cut-off was implemented, whilst the smooth particle-mesh Ewald method [26] was used to handle the long-range electrostatics. The selected four-site water model, TIP4P/2005, was also used in many simulation studies on hydrate structures [27; 28]. The velocity Verlet algorithm was used for the molecular dynamics (MD) simulations with a time step of 3.33 fs using the canonical NVT ensemble, featuring a Nose-Hoover relaxation time of 5 ps, for a total time of 50 ns [29]. The trajectories were performed at nine different temperatures: 80 K, 100 K, 120 K, 140 K, 160 K, 180 K, 200 K, 220 K and 240 K. All of the simulations were performed in the GROMACS 5.1 package [30; 31; 32; 33; 34]. The sII-clathrate lattice [25] was used in our calculations, and it is shown in Figure 1.  
The diffusion coefficients (D) [35] of the H\({}_{2}\) and D\({}_{2}\) molecules were calculated by the limiting-slope analysis of the mean squared displacement (MSD) for the entire temperature range--from 80 K to 240 K--at intervals of 20K (see Figures S1 and S2, Supporting Information). The travel of the H\({}_{2}\) and D\({}_{2}\) molecules inside the clathrate-hydrate structure, facilitated by inter-cage hopping migration [36; 37; 38], was analysed and captured as Markov-chain models [39]. It is important to realize that not all states will make a significant contribution to the configurational properties of the system. In order to accurately determine the properties of the system in the finite time available for the simulation, it is important to sample those states that make the most significant contributions. This is achieved by the generation of a Markov chain.
## 4 Performance Evaluation
### Performance Evaluation
#### 4.4.3 Energy Efficiency of Routing Protocols  
Measured as the ratio of the percentage of packets successfully delivered to the average of total energy consumed during the entire simulation time, improving this parameter is one of our highest concerns. As it can be seen in Figure 6(a), the performance of all three routing schemes gradually drops due to the decrease in the packet delivery ratio  
Figure 5: Packet delivery ratio performance.  
Figure 6: End-to-end delay performance.  
and the corresponding increase in average energy consumption during transmission as traffic flow increases. Regardless, the proposed scheme, with its ability to consider energy-efficient objectives, has the potential to outperform the OSPF and Traditional RL in terms of energy consumption. By selecting objectives that prioritize energy efficiency, the DOS-RL can route traffic through paths that minimize energy usage. As the simulation results exhibit, the DOS-RL scheme achieves a successful transmission of data packets to the sink node with a lower average energy consumption. By leveraging the DOS algorithm and incorporated shaped rewards, the scheme provided is capable of adjusting the objective priority while meanwhile selecting relatively shorter paths, unlike those selected by the OSPF and SDN-Q scheme. The DOS-RL scheme effectively monitors node energy levels, detects energy depletion or bottlenecks, and redirects traffic along more energy-efficient paths. This adaptability allows it to align its objectives with the energy efficiency goals of the network, surpassing the other two routing schemes.  
Furthermore, as shown in Figure 6(b), the DOS-RL algorithm exhibits better energy efficiency in successfully delivering data packets compared to the counterparts. With an increasing number of nodes, more nodes participate in routing which affects the average energy consumption. Additionally, factors such as the increased overhead and interference contribute to a decrease in the packet delivery ratio, impacting the overall energy efficiency of the network. Among the considered algorithms, OSPF performs the worst, as it operates without considering real-time network conditions and solely selects the shortest path, which is not always the most efficient choice.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 4 Security Model  
In this section, we formalize the security model for our privacy-preserving K-means clustering protocol under the semi-honest (honest-but-curious) adversary setting. The protocol involves three parties: a client, a cloud server (CS), and a computation service provider (CSP). The client owns private input data and receives the final clustering results, while CS and CSP collaboratively perform computations over encrypted data.
## 4 Case Study of an IoT Enabled Bridge
### Analysis and Results
When results in Figure 4 are compared to Figure 6, the greatest deviation is depicted in Scenario 1, with an underestimation of cyber-physical risk of 15.1% in mean values (i.e., 387.2 in Figure 4 and 328.8 in Figure 6), and in Scenario 4 with an overestimation of cyber-physical risk of 73.3% in mean values (i.e., 109.1 in Figure 4 and 189.1 in Figure 6). Finally, a sensitivity analysis for the proposed threat source characteristic of Terrorism experience, in the Monte Carlo simulations was undertaken to demonstrate the relevance of the decision score of this characteristic. By keeping constant all the inputs (i.e., \(X_{i}\), \(W_{i}\)) from Scenario 1, as in Table 4, three categories of threat source related to intensity score of Terrorism experience are considered. The specific categories capture real examples from a threat source and its potential association with terrorist organizations that target critical infrastructure as described by [PERSON] et al. (2018). Specifically, the threat source could either have no terrorism experience, (\(X_{\text{Te}}\) is assigned a Low level) or being manipulated online using social media or online communities (\(X_{\text{Te}}\) is assigned a Medium level), or is a terrorist organization (\(X_{\text{Te}}\) is assigned a Very high level). Results for the cyber-physical attack, as shown in Figure 7, indicate an increase of 58.05% in mean values of cyber-physical risk, when they are undertaken by a terrorist organization in comparison to threat sources with no previous terrorism experience. Figure 6: Cyber-physical risk when no importance index is considered (i.e., \(W_{i}=1\)).
## 2 Literature Review
### Overview of PV Power Prediction
#### 2.1.1 Physical Models  
Physical models for PV power estimation predict energy production by modeling the interaction of meteorological variables, such as solar radiation, temperature, wind speed, etc., with PV panels using mathematical and physical principles. These models are based on fundamental laws of physics to predict the performance of PV systems and often include numerical weather prediction, sky imaging, and satellite imaging techniques [18]. In their study, [PERSON] et al. developed a physical model that incorporated tilted radiation, diffuse radiation, PV cell models, and inverter models for estimating PV power. Validated on a 250 kW PV plant, the model demonstrated high accuracy, with a monthly error rate of 5% and a daily error rate of 10%, while offering flexibility. [PERSON] and [PERSON] [19] comprehensively compare different physical PV power forecasting models. Using one year of data from 16 PV plants, the analysis found that radiation separation and transposition modeling are the most critical factors in forecasting accuracy. A difference of 13% Mean Absolute Error (MAE) and 12% Root Mean Square Error (RMSE) was found between the best and worst models. [PERSON] et al. [20] evaluated the performance of the physical method PVPro in day-ahead power estimation on four different PV systems and reported that the mean normalized absolute error (nMAE) rate was 1.4%, indicating a 17.6% reduction in error compared to other techniques. It was also emphasized that PVPro demonstratedrobust performance across different seasons and weather conditions and could operate effectively with limited historical production data (3 days). Physical models of the processes involved in producing electrical energy in PV systems can be used to estimate the daily power output based on the expected weather on a specific day. However, complex nonlinear interactions and unpredictability may not be sufficiently taken into account by the empirical formulas and assumptions that this approach frequently employs. Moreover, physical models are often challenging to apply due to the need for numerous costly variables and equipment that are not always readily available in many parts of the world.
## 3 Results and Discussion
### Spectral Indices  
Considering the most frequently used VIs (Figure 8), NDVI [23] was used most frequently in 46% of the studies. The Enhanced Vegetation Index (EVI) [24] was also  
Figure 7: Remote sensing platforms (**a**) and sensors (**b**) employed in studies, categorized by remote sensing applications. TSPE: tree segmentation and parameters extraction; IC: imagery classification; HMDD: health monitoring and disease detection; WM: water management; OTR: other; SAT: satellite; UAV: unmanned aerial vehicle; MAV: manned aerial vehicle; RGB: red, green, blue; MSP: multispectral; TIR: thermal infrared; HSP: hyperspectral; LDR: LiDAR.  
frequently used in 16% of the studies, followed by the Soil Adjusted Vegetation Index (SAVI) [25] in 9%, the CWSI [26], and the Green Normalized Difference Vegetation Index (GNDVI) [27] in 8% each. When analyzing the RS applications, NDVI and EVI were used in all types of applications, especially in WM. On the other hand, CWSI was mainly used in WM as it is an efficient parameter to evaluate crop water use and water stress [28]. Green Leaf Index (GLI) [29], Plant Senescence Reflectance Index (PSRI) [30], and Red Edge Normalized Difference Vegetation Index (REN) [31] were only implemented in IC applications, while Canopy Chlorophyll Content Index (CCCI) [32] and Wide Dynamic Range Vegetation Index (WDRVI) [33] were specifically used for TSPE applications.