esa-sceva/satcom-chunk-collection huggingface dataset:
# MOCA: A Network Intrusion Monitoring and Classification System
## 4 MOCA Design
### Performance Metrics  
To effectively evaluate the performance of MOCA, four performance metrics, accuracy, precision, recall, and F1-score, were critical. More specifically, the following four metrics are considered for evaluating the performance of intrusion detection methods:  
* Accuracy: the sum of flows classified correctly with respect to the total number of flows. It is written by: \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}}\] where TP is the number of true positives or simply the true positives, TN is the true negatives, FP is the false positives, and FN is the false negatives.
* Precision: positive predictive value, equal to: \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\]* Recall: sensitivity or Detection Rate (DR), which is equal to: \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]
* F1-Score: the harmonic mean of the Precision and Recall. In other words, it is a statistical technique for examining the system's accuracy by considering both precision and recall: \[\text{F1}_{\text{score}}=2\times\frac{\text{precision}\times\text{recall}}{ \text{precision}+\text{recall}}\]
## 2 Sensor Antenna Design
### Optimized Antenna Dimension  
The design and modelling of the sensor antenna were performed using HFSS in the Ansys Electromagnetic Suite(r) 2023 R1(Ansys Inc., Canonsburg, PA, USA). The excitation port of each antenna employs a coaxial probe that is powered with a 1 mW source. To find the feeding points of each antenna, the position of the coaxial probe is adjusted iteratively using the optimetrics feature in HFSS. The theoretical and optimized values are presented in Table 1. All patch antenna designs use an Alumina substrate with dimensions of 50 \(\times\) 50 \(\times\) 2.45 mm, while the conductor parts are made of silver conductive paste with a thickness of 0.06 mm. The bottom surface of the substrate is fully covered with a ground plane. The design and corresponding manufacturing results are presented in Figure 1.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 2 Background and Related Research
### Energy Resilience
[22] where the Expected Electrical Distribution Mission Impact (EEDMI) quantifies the resilience of an energy system versus all expected initiating events, threats, disruptions, etc. In [PERSON] et al. 's approach, MDI is used to understand the value of each critical load to national security where it provides the input to Mission Impact (MI) on a per unit time (T) basis. MI is the impact to a mission on a per unit time basis for if a specific electrical load is not served. A single scenario (specific initiating event, threat, etc. ), \(M_{s}\), is defined by the MI per unit time (T) that is _not_ served electricity throughout the duration of the scenario [22],  
\[M_{s}=\sum_{t=1}^{T}M{I_{t}} \tag{1}\]  
During normal operations and with no electrical interruptions, \(M_{s}\) is zero. During a scenario where MDI = 50, the unit of time is hours, and power is not delivered for 2 h, \(M_{s}=100\). In scenarios where not enough power is available to serve all loads, load shedding occurs. The details of how load shedding occurs (e.g., which loads are shed first, rotating blackouts, etc.) is dependent upon behavior of the energy system and controller(s). In such situations where load shedding occurs, \(M_{s}>0\) which indicates an impact to national security and thus warrants further investigation. The aggregate of all scenarios (\(S\)) calculated in Equation (1) is EEDMI which includes the probability (\(Pr\)) of the specific threat or initiating event occurring over the course of a year:  
\[EEDMI=\sum_{s\in S}Pr(S=s)M_{s} \tag{2}\]  
The total EEDMI value for a specific electrical system configuration is used as a way to compare between different potential electrical system architectures from the perspective of energy resilience. A lower EEDMI value is more desirable because it means that the electrical system is less susceptible to initiating events and threats disrupting power to critical loads [26]. The process of developing EEDMI is the same whether analyzing a very small nanogrid or a very large microgrid on a military base.
## 2 Materials and Methods
### Image Collection and Dataset Construction
#### 2.1.2 Dataset Augmentation and Construction  
In the original dataset, images of each weed type were divided into training, validation, and test sets in a 7:2:1 ratio, with 84 images for training, 24 for validation, and 12 for testing. Deep learning models depend heavily on the image features within a dataset. Rich image data improve model accuracy and help prevent overfitting. However, noise in the images and errors in annotations can decrease the accuracy of the model. To compensate for insufficient data and prevent network overfitting, this paper enhances the dataset by applying 90\({}^{\circ}\) clockwise and counterclockwise rotations, adding 0.1% Gaussian noise, and varying \(\pm\)25% saturation and \(\pm\)15% brightness. These methods expanded the number of training images from 924 to 2772, as shown in Table 1.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Training Images** & **Validation Images** & **Test Images** & **Total Images** \\ \hline Before Augmentation & 924 & 264 & 132 & 1320 \\ After Augmentation & 2772 & 264 & 132 & 3168 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of images before and after data augmentation.  
Figure 1: Images of eleven types of weeds.
## 5 Key Enablers  
Computing, networking, and storage are used to focus on data processing, data communication, and data storing, respectively. Now, because of the cloudification, all three areas are merging together. Due to the highly distributed nature of the edge clouds, resource allocation is more difficult than in a centralized data center. Moreover, edge cloud needs to handle users with significant randomness in applications. Therefore, to improve the scalability, utilization, and efficient management of computing, storage, and networking resources at the edge cloud platform, virtualization and SDNs are the two key synergistic enabling technologies. In addition, use of virtualization and SDNs in edge cloud can facilitate the access of applications, dynamic resource provisioning based on the density of the user request, migration, and security. In the next couple of sections, we discuss virtualization and SDNs in the context of edge cloud network infrastructure [13; 23].  
Figure 6 shows a general idea of the integration of SDNs with the edge cloud infrastructure. In the edge cloud network, the SDN controller can manage physical network devices, while simultaneously coordinating and controlling virtual resources through APIs and orchestration. We discuss more details in the following sections.
## 3 Results
### High Temperature Performance: Softening Point and Viscosity  
In general, softening point and viscosity have positive correlation to service years, as found in previous studies of RAP binder [1]. This indicates that service years influence high temperature performance much more than milling depth and some other factors. It was assumed that the RABs were subject to similar temperature histories because they had the same source material and were collected from similar climactic conditions in Southern China.  
Due to differences in traffic volume, surface layer and mixture gradation, the increase in softening point and viscosity for sample 8 was lower than the other samples as shown in Figures 6 and 7. The aging process makes asphalt binders become harder, but sample 8 only consisted of middle and lower surface layers, giving it less exposure to the aging experienced by the upper layers. However, softening point and viscosity of samples 1 and 2 became lower than original SBS modified asphalt due to the presence of base asphalt in one of the component layers.  
A comparison between sample 1 and 2 shows that the latter has slightly high softening point and viscosity because it covers an upper surface layer, more prone to aging than middle and lower surface layers. The service year and the thickness of the upper layer of sample 3, 5 and 7 are the same, so the longer service time of the middle layer results in a more serious aging degree. Sample 11 and 12 have the same service years and thickness but were located in different layers, showing again that upper layers are more subject to aging. The effect of service year on aging degree can also be seen in samples 12 to 16.
## 5 Ablation Study and Discussions  
### Ablation Study  
To further examine the effectiveness of the feature processing and the optimization algorithm of the proposed method, an ablation study was conducted and reported in Table 4. As shown, the accuracy of any deep feature-based method is higher than that of the SIFT-based approach, including the proposed method without optimization. It proves that the selected filters still have the ability to extract useful features comparable to local feature descriptors. Moreover, the accuracy of the proposed method without an optimization module is similar to that of Rodriguez-Pardo but with slightly less time cost. It means that the designed filter selection method using edge results can substantially lower the computational cost yet maintain reasonable features for repeated pattern detection. Furthermore, the time cost with and without optimization was almost the same (both were 40 s), demonstrating that the proposed optimization algorithm dramatically improves detection accuracy without increasing the time consumption.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Image Type**} & \multicolumn{2}{c}{**SIFT-Based**} & \multicolumn{2}{c}{**The Current Work w/o**} & \multicolumn{2}{c}{**Rodriguez-Pardo**} & \multicolumn{2}{c}{**The Current Work**} \\  & \multicolumn{2}{c}{**Approach [7]**} & \multicolumn{2}{c}{**Optim. (Steps (a)–(c))**} & \multicolumn{2}{c}{**et al. [2]**} & \multicolumn{2}{c}{**(Steps (a)–(e))**} \\ \cline{2-7}  & **Acc. (\%)** & **Time (s)** & **Acc. (\%)** & **Time (s)** & **Acc. (\%)** & **Time (s)** \\ \hline Computer-generated & 30.8 & 65.4 & 32.2 & 46.2 & 37.9 & 55.0 & 84.4 & 46.3 \\ Scans of actual fabrics & 33.5 & 61.0 & 33.7 & 39.2 & 40.9 & 49.0 & 65.8 & 39.4 \\ All images & 33.3 & 61.3 & 33.6 & 40.0 & 40.7 & 49.4 & 67.3 & 40.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative comparison of the current work without applying the optimization algorithm and when another feature-based approach is being applied.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **Floating-Point Operations (FLOPs)** & **Memory Usage** \\ \hline [PERSON] et al. [2] & 0.48 G & 40 MB \\ [PERSON] et al. [12] & 0.72 G & 60 MB \\ The current work & 0.10 G & 10 MB \\ \hline \hline \end{tabular}
\begin{tabular}{l} _Computer config._ CPU Intel e5-2673 v3, GPU GeForce RTX 3060. Evaluate using input image sized 224 \(\times\) 224. \\ \end{tabular}
\end{table}
Table 3: Efficiency comparison between current and other deep-learning based methods.
# Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control
## 2 Compartmental Model of the Spread of the COVID-19 Epidemic in Hungary
### Transitions between the Phases of the Disease
The recovered people are assumed to be immune to reinfection. We assumed that the disease is transmitted by the members of compartments **P**, **A**, and **I**, such that the relative infectiousness of the asymptomatic individuals (**A**) is \(\delta=0.75\), compared to those who produce symptoms (**I**). The transmission rate \(\beta\) of the disease is the most prominent parameter of the epidemic spread, which is typically time-dependent. The nominal values of the above-mentioned model parameters and their assumed uncertainty are collected in \[\mathbf{L}_{k+1} = \mathbf{L}_{k}\ +\ \ \beta_{k}(\mathbf{P}_{k}+\mathbf{I}_{k}+ \delta\mathbf{A}_{k})\mathbf{S}_{k}/\mathbf{N}\ -\ \alpha\ \mathbf{L}_{k},\]1 For simplicity, each uncertain parameter was assumed to have a normal distribution, such that its nominal value (\(\mu\)) is the expectation and its uncertainty (\(\pm\alpha\%\)) gives the \(2\sigma\) confidence interval with the standard deviations \(\sigma=a\mu/200\). \[\mathbf{L}_{k+1} = \mathbf{L}_{k}\ +\ \ \beta_{k}(\mathbf{P}_{k}+\mathbf{I}_{k}+ \delta\mathbf{A}_{k})\mathbf{S}_{k}/\mathbf{N}\ -\ \alpha\ \mathbf{L}_{k},\]2 Transition graph of the epidemic model. Compartments and transitions are represented by the nodes and edges, respectively. Here, we examined the evolution of the epidemic in a fixed time window between 1 March 2020 (\(k=0\)) and 30 June 2021 (\(k=T\)). This interval contains the first three waves of the epidemic in Hungary. Subscript \(k\in\{0,1,\ldots,T\}\) denotes the number of days elapsed in the given time window of length \(T+1\).
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 3 Experiments
### Capacity  
In conventional watermarking systems, the embedding capacity is positively correlated with the size of the cover image. In contrast, our proposed method takes the latent representation of an image to hold the watermark. Since it is a low-dimensional representation of the image, it is much smaller in size than the cover image, limiting the embedding capacity of the watermark. On the flip side, embedding watermarks in the latent space ensures the robustness of watermarking systems, since transmission distortions and regeneration attacks tend to affect the high-frequency components of the image. To further evaluate the impact of the increased embedding capacity, we employ watermarks of 64 and 96 bits for the test. For 64 bits, the sixth coefficient on the main diagonal of each DCT block is additionally chosen for watermark embedding while the eighth coefficient is additionally selected for 96-bit embedding. From the results in Table 3, it is observed that a higher capacity like 96 bits leads to a significant degradation of image quality, in spite of a robustness gain as the reward. However, the watermarked image with an embedding capacity of 64 only suffers from slight image degradation, suggesting the trade-off between embedding capacity and imperceptibility of watermarks with our proposed scheme.
# Ten Years of TerraSAR-X Operations
## 8 Performance Monitoring
### Long Term System Monitoring  
Long-term system monitoring (LTSM) is an important task to ensure the quality of the SAR products and the appropriate operation of the satellite and the instrument. The LTSM visualizes the most significant parameters of the satellite system.  
There are a number of parameters that are checked for each data take in order to monitor the quality of a single acquisition as described in the previous section. In contrast, the LTSM shows the long-term evolution of the system parameters in order to identify trends in the data and to be able to take countermeasures before the occurrence of critical situations.  
The most important parameters monitored by LTSM are:  
* Instrument parameters:
* Utilization of the satellites
* Quality of the Replica amplitude and the Bias of the In-phase and Quadrature Channel
* Calibration parameters like geometrical accuracy or the absolute calibration factor, including antenna pattern accuracy
* Status of the Transmit/Receive Modules
* Drift of the radar frequency
* Drift of the Doppler of the SAR signal
* Coverage fulfillment of data takes compared to the user requests
* Satellite parameters:
* Amount of switches of the traveling wave tube amplifier for X-band downlink
* Battery performance and utilization
* Bit error rates of the Solid State Mass Memory
* Mission parameters:
* Mission phases (for information)
* SAR outages (for information)
* Parameters concerning the TanDEM-X Mission like DEM performance  
Figure 16 shows, as an example, the evolution of the In-Phase Channel Bias, as visualized by the Long-Term System Monitoring. The In-Phase Channel Bias is a measure for the correct utilization of the dynamic range of the receivers. A drift of 20% over six years was observed with the long-term evaluation. In June 2014, the origin for the In-Phase Channel Bias was readjusted in the system, and the bias is since then again well centered within the dynamic range.  
In general, the TerraSAR-X satellite still performs very well. The quality of the data is as good as at the beginning of the mission, both in terms of overall performance and in terms of radiometric accuracy [32].
## 1 Introduction
SHS facilitated the blades' surface with much less ice under both glazed and rimed icing conditions, while icephobic coating prevents large ice-chunk formation in the leading edge as ice chunks easily shed from the leading edge, compared with the SHS blade and blades with a hydrophilic coating. In this context, the present work reports an experimental test campaign performed in the Italian Aerospace Research Centre (CIRA) IWT with the aim to evaluate the effect of a SHC on the ice accretion occurring on the nacelle lip-skin belonging to the M28 PZL vehicle, possessing dimensions of 50 cm \(\times\) 50 cm \(\times\) 20 cm and is made of stainless-steel (the PTAI) and composite (the back side). To the best of our knowledge, no previous work focused on this topic. IWT tests were performed at the takeoff and first climb conditions [9], which represent the worst case for ice accretion due to the switched off PTAI system in these two flight phases, at T = -5 \({}^{\circ}\)C and \(-\)12 \({}^{\circ}\)C, v = 70 and 95 m/s, at an altitude of 3000 m, with a median volumetric diameter (MVD) equal to 20 \(\upmu\)m and liquid water content (LWC) equal to 0.3 and 1 g/m\({}^{3}\). Tests were performed before and after the application of a SHC, in order to study the effect of the SHC application on ice accretion. Accreted ice was measured after each test in terms of thickness and impingement length. In a previous work [11], the authors studied the effect of the SHC on the ice accretion occurring on two wing profiles of NACA0015 made of acrylonitrile butadiene styrene and manufactured with a 3D printing machine, having a length of 135 mm and a chord of 100 mm. Broader ranges of IWT parameters were considered in the previous work, i.e., temperatures ranging between \(-\)3 \({}^{\circ}\)C and \(-\)23 \({}^{\circ}\)C, speed 50 and 95 m/s, and LWC 0.3 and 0.6 g/m\({}^{3}\). The present work was developed in the framework of the Clean Sky 2 SAT-AM (More Affordable Small Aircraft Manufacturing) project, whose main goal was to investigate new technologies for a future small aircraft able to fly with low fuel consumption. The reference vehicle for this project was the M28 designed and manufactured by Consortium Partner Polskie Zaklady Lotnicze (PZL), Mielec (PL). It is a commuter category 19 passenger, twin-engine high-wing cantilever monophane, suited for passenger and/or cargo transportation and certified under EU CS-23 and USA FAR 23 requirements.
## 4 Experiment and Analysis  
### Comparison Experiments of Algorithms on the SCB-Dataset-S  
Table 1 presents the comparison results of different algorithms on the SCB-Dataset-S. The SCB-Dataset-S is a publicly available dataset of real student classroom behaviors, containing a total of 5010 images with annotations for three types of behaviors: hand raising, reading, and writing. Specifically, the dataset includes 10,078 labels for hand raising, 5882 labels for reading, and 2539 labels for writing. The models evaluated included YOLOv5n, YOLOv8n, RT DETR-ResNet18, and RT DETR-ResNet18+LPE+DySample. Their performance was assessed using four standard object detection metrics: precision, recall, mAP50, and mAP50-95. The results were as follows:  
The results clearly indicated that the RT DETR-ResNet18+LPE+DySample model outperformed the others across all evaluated metrics, demonstrating superior precision, recall, and consistency in object detection. This highlighted the model's enhanced capability in handling complex scenes and detecting small targets, making it particularly suitable for practical applications such as monitoring student behavior in classrooms.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **mAP50** & **mAP50-95** \\ \hline YOLOv5n & 0.681 & 0.675 & 0.711 & 0.483 \\ YOLOv8n & 0.679 & 0.677 & 0.724 & 0.550 \\ RT DETR-ResNet18 & 0.759 & 0.754 & 0.781 & 0.555 \\ RT DETR-ResNet18+LPE+DySample & 0.817 & 0.783 & 0.801 & 0.585 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of algorithms on the SCB-Dataset-S.
# Hybrid Improved PSO Algorithm for Soil Property Parameter Estimation
## 3 Improvement of the PSO Algorithm
### Comparative Test Experiment
In summary, compared to PSO, SPSO, PSO-GA, and PSO-WOA, the PSO-EDO algorithm significantly improves convergence accuracy. Compared to the EDO algorithm, it reduces computation time and enhances the ability to escape from local optima. Figure 4 shows the average fitness curves of different algorithms during the iterative evolution of the F1-F6 test functions, providing a more intuitive comparison of the convergence speed and accuracy of the algorithms. As observed in Figure 4 for F2, F4, and F5, the convergence speed of PSO-EDO is significantly better than that of other algorithms. Figure 4: The mean F1–F6 fitness evolution curves by using PSO, SPSO, PSO-GA, PSO-WOA, EDO, and PSO-EDO in 30 independent runs. Table 4 presents the average fitness of the PSO-EDO algorithm over the first 15 iterations for the F1-F6 test functions, showing that the improved algorithm nearly completes convergence within the first 10 generations. Although other algorithms, except for PSO-GA, also exhibit relatively fast convergence for F1, F3, and F6, their performance is still inferior to that of the improved algorithm. To further verify the statistical significance of the performance differences among algorithms, Wilcoxon signed-rank tests were performed between PSO-EDO and the other four comparison algorithms (PSO, SPSO, PSO-GA, and PSO-WOA) based on the fitness data presented in Table 5. The results show that all pairwise comparisons yielded \(p\)-values of less than 0.01, indicating that the performance improvements achieved by PSO-EDO are statistically significant. These differences are unlikely to be caused by random fluctuations, thereby reinforcing the robustness and effectiveness of the proposed method.