esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Analysis of the Fit of the Measured Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Assessment Metrics**} & **MoM** & **ZlogZ** & **MFoM** & **ZlogZ** & **MLE** & **MLE** \\  & **(2/4)** & **(1)** & **(05/1)** & **(0.25)** & **(1D)** & **(2D)** \\ \hline MSD & \(3.30\times 10^{-3}\) & \(2.40\times 10^{-3}\) & \(8.36\times 10^{-4}\) & \(4.17\times 10^{-4}\) & \(1.20\times 10^{-3}\) & \(1.20\times 10^{-3}\) \\ RMSD & \(5.70\times 10^{-2}\) & \(4.90\times 10^{-2}\) & \(2.89\times 10^{-2}\) & \(2.04\times 10^{-2}\) & \(3.43\times 10^{-2}\) & \(3.44\times 10^{-2}\) \\ K-S & \(5.84\times 10^{-2}\) & \(6.09\times 10^{-2}\) & \(3.37\times 10^{-2}\) & \(2.84\times 10^{-2}\) & \(3.00\times 10^{-2}\) & \(3.01\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The fitting test results of a domestic open source measured data. the above. The fitting test results indicate that the PSO algorithm used in this paper not only efficiently and accurately finds the parameter estimates of each low-order moment estimation method but also improves the fitting effect of the generalized Pareto distribution model on the measured data. In addition, some scholars used the segmented mean square deviation (SMSD) test [43; 44] to observe the local fitting effect of the distribution model on the measured data, and Figure 12 shows the SMSD test effect of the two sets of data. From Figure 12, it can be seen that the generalized Pareto distribution is relatively stable in fitting the real measurements within each interval segment without substantial fluctuations, so the target detector design based on the generalized Pareto distribution model can be used to obtain a more robust performance.
# Analysis and Prioritization of the Floating Photovoltaic System Potential for Reservoirs in Korea
## 1 Introduction
In addition, K-water operates South Korea's first commercial system by producing 500 kW of electricity for approximately 170 households. K-water plans to install 550-MW capacity dam lakes by 2022, and the Korea Rural Community Corporation (KRC, Naju, Korea) is planning 1900 MW of capacity for the reservoirs under management. Various studies have been conducted to improve and utilize floating PV. There have been many studies on mooring systems to maintain the balance of panels on the water [8; 9], and a solar tracking system has been actively studied to improve efficiency [10; 11]. In recent years, it has become possible to transmit and receive data from a floating PV through the integration of ICT technology [12]. In addition, there have been several studies where Geographical Information System (GIS) techniques have been applied to determine a suitable location for field application and evaluation of the PV potential [13]. [PERSON] and [PERSON] [14] evaluated the potential and economic feasibility of a pit lake on the abandoned mine site in Gangwon-do, South Korea, by assessing shadow effects based on GIS techniques and a fish-eye lens camera. [PERSON] and [PERSON] [15] evaluated the applicability of floating PV by region based on the analytic hierarchy process (AHP), considering geographical conditions and weather conditions. [PERSON] et al. [16] evaluated additional parameters to be considered because of the difference between conventional and floating PV systems and evaluated the suitability of the Hapcheon dam area. There have been many studies regarding individual reservoirs, lakes, and dams, but little research has been conducted on a national scale. Particularly in countries like Korea where large-capacity floating PV systems will be installed in the long term, the PV potential of each available area should be assessed. The purpose of this study is to evaluate the applicability and potential of floating PV for the approximately 3400 reservoirs registered and managed in Korea.
# On the Design of Bionic Hierarchical H-Type Whip Restraints for Nuclear Power Plants
## 4 Parametric Analysis  
### Effect of Hierarchical Portion Combination Mode  
It has been elucidated in the previous sections that NBDHMC has superior energy absorption capacity compared with NBHMC. This section mainly considers the influence of the hierarchical portion combination mode on the crashworthiness of the structure. Three basic hierarchical structures are combined in pairs to obtain different composite structures, namely, NBIOHMC, NBISHMC, and NBOSHMC, as shown in Figure 16.  
Figure 15: Sectional views of representative lobes folding for 5 different structures under oblique impact: (**a**) NBHMC, (**b**) NBIHMC, (**c**) NBOHMC, (**d**) NBSHMC, (**e**) NBCHMC.  
The combined structures are designed with different wall thicknesses to achieve the same mass (14 kg). Figure 17 presents the force-displacement curves and energy absorption curves of the combined structure and the previous structures. From Figure 17a, the IPF of all structures is very similar. With the exception of NBIHMC and NBHMC, the plateau force remains consistent across the other structures. From Figure 17b, with the exception of NBIHMC and NBHMC, the energy absorption curves for the others are similar. Particularly, the energy absorption curves of NBISHMC and NBSHMC closely align and represent optimal performance. The model parameters and crashworthiness parameters of the combined structures are displayed in Table 3. By comparing Tables 2 and 3, the influence of the hierarchical portion combination mode can be explored. The plots show that the crashworthiness parameters of NBIOHMC and NBSHMC are lower following combination, whereas those of NBISHMC are higher. The phenomenon indicates that the presence of the outer hierarchical portion exerts a negative effect on the other hierarchical components. Notably, NBISHMC exhibits the highest energy absorption capacity. It is reasonable to assume that the hierarchical structure formed by the combination of the inner tube and spiral portion represents the most rational configuration. However, the crashworthiness of NBSHMC closely resembles that of NBSHMC. Therefore, other factors, such as cost, can be considered in practical applications to choose between them.  
Figure 16: Three composite structures and parameter Settings: (**a**) NBIOHMC, (**b**) NBISHMC, (c) NBSHMC.  
Figure 17: Force-displacement and energy absorption curves of composite structures under the same mass: (**a**) force-displacement curve, (**b**) EA–displacement curve.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Structure** & **t (mm)** & **m (kg)** & **EA (kj)** & **IFF (kN)** & **SEA (kj/kg)** & **MCF (kN)** & **CFE (\%)** \\ \hline NBIOHMC & 0.886 & 14 & 217.992 & 731.272 & 15.571 & 500.674 & 68.466 \\ NBISHMC & 0.965 & 14 & 236.574 & 731.407 & 16.989 & 543.851 & 74.357 \\ NBOSHMC & 0.685 & 14 & 221.410 & 734.032 & 15.815 & 508.430 & 69.265 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Crashworthiness characteristics of three composite structures.
## 3 Results  
### Spatial Distribution of Soil-Sediment Total Nitrogen  
Overall, the soil-sediment total nitrogen content was 0-2360 mg/kg, the average content was 90 mg/kg, the variation coefficient was 3.88 and the fluctuation range was large. In other regions, the total nitrogen content in the Qilihai Lagoon in Hebei Province ranges from 90 to 1180 mg/kg [8], the total nitrogen content in the sediments of Dianchi Lake in Yunnan Province ranges from 1600 to 5560 mg/kg [17] and the total nitrogen content in the sediments of Puppet Lake in Jiangsu Province ranges from 670-2570 mg/kg [18]. A comparison indicated that the soil-sediment total nitrogen content in the lagoon of the Samet Inland Sea was low. Using ArcGIS software, the spatial distribution of nitrogen content in the study area was obtained by kriging interpolation analysis. In Figure 2a, it can be seen that the total nitrogen content of terrestrial soil is higher than that of aquatic sediments, and the highest concentration of total nitrogen appeared in the southern part of the inland soil, followed by the Wanquan River estuary and the adjacent land areas in the north. Overall, the distribution of total nitrogen showed characteristics of diffusion from inland to offshore waters. This indicated that a large amount of nitrogen-containing nutrients carried by the rivers that enter the lagoon may be input into the lagoon from the inland sea of Samet, and at the same time, under the influence of hydrodynamics, nitrogen becomes enriched near the estuary of the lagoon. The overall nitrogen deficiency in the offshore area may be related to the exchange of water between the lagoon and the ocean, and frequent coastal currents and tidal action accelerate the process of nitrogen volatilization. This result confirmed that the denitrification effect in the offshore area is strong [19]. The concentrated distribution area of total nitrogen is basically consistent with the distribution of organic matter, suggesting the connection between the two in terms of genesis and enrichment, which is consistent with previous research results [20].
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 6 Open Issues
### Stochastic Learning  
Stochastic learning algorithms are a class of optimization algorithms that utilize random variables to optimize objective functions or identify the fixed points of functions that are only accessible through noisy observations. These algorithms are commonly used in reinforcement learning to improve the performance of networks, such as in the case of IDT SDVN. It is one of the most popular stochastic learning algorithms used in reinforcement learning. Using this method, agents learn the optimal Markov decision process policy to follow. The optimal policy can be determined by the fixed point of an expectation function, which is the optimal state-action value function. However, in the context of the SDVN, the optimal state-action value function is not directly accessible due to the complex and dynamic nature of the network environment. The network state may change rapidly due to various factors, such as traffic patterns, network topology, and network failures. This makes it challenging to accurately estimate the state-action value function for each state and action in the network.  
To address this challenge, researchers have proposed several extensions of the Q-learning algorithm, such as the Deep Q-Network (DQN) algorithm, which uses deep neural networks to estimate the state-action value function [157]. The DQN algorithm has been shown to be effective in learning the optimal policy for complex and dynamic network environments, such as in the case of SDVNs. In summary, stochastic learning algorithms, such as the Q-learning algorithm, can be used to optimize network performance in IDT SDVN. However, the dynamic and noisy nature of the network environment requires modifications to these algorithms to make them effective in practice. Extensions of these algorithms, such as the DQN algorithm, have been proposed and shown to be effective in addressing the challenges of dynamic and noisy network environments.
## 3 Results
### Optimization Results and Analysis
#### 3.1.4 Optimization Design of LEO Polar Constellation
Taking into account the altitude and inclination of the constellation comprehensively, Scheme 4, i.e., Walker 72/8/1:1176/85 (\(N=72\), \(P=8\), \(F=1\), \(h=1176\) km, \(i=85\)), was chosen as the deployment and construction scheme for the LEO polar constellation.
## 1 Introduction  
Toxin-antitoxin systems (TA) are small modules generally composed of two elements: a stable toxin that targets an essential cellular process and a labile antitoxin that inhibits the toxin's deleterious activity [1; 2; 3]. These modules were originally discovered on low copy number plasmids [1,4,5] and coined \"addiction\" modules (Figure 1).  
Later on, with massive sequencing of bacterial genomes, computer searches led to the discovery of chromosomally-encoded systems [2,6]. Quite surprisingly, TA systems were found to be widespread and abundant in bacterial chromosomes, and this stimulates researcher's curiosity. So far, up to 88 TA systems were predicted for _Mycobacterium tuberculosis_, while some cyanobacteria encode more than 70 predicted toxins and antioxins. In some Proteobacteria and green sulfur species, up to 2.5% of total open reading frames (ORFs) are predicted to be type II systems [7,8]. Note that the occurrence of type II systems does not depend on the chromosome size. Recent unpublished bioinformatics analyses estimate that a bacterial chromosome and plasmids encode, on average, 3.8 and 0.4 type II systems, respectively [9].  
Figure 1: The addiction phenomenon. Toxin-antitoxin (TA) systems participate in plasmid maintenance in growing bacterial populations by a mechanism called addiction or post-segregational killing. Addiction relies on the differential stability of the toxin and antitoxin. **A**: Daughter-bacteria that inherit a plasmid copy encoding the _ccd_ (control of cell death) toxin-antitoxin system grow normally. **B**: Daughter-bacteria that do not inherit a plasmid copy still have antitoxin-toxin complexes in their cytoplasm. **C**: The CcdA antitoxin (light green) is degraded by the Lon protease, while the CcdB toxin (dark green) is stable. CcdB is, therefore, liberated from the CcdA-CcdB complex and is able to interact with DNA-gyrase, a class II topoisomerase. The interaction of CcdB with DNA-gyrase inhibits DNA replication and leads eventually to cell death. Addiction leads to the selective killing of plasmid-free daughter bacteria and increases plasmid prevalence in the bacterial population.  
Many research groups became interested in these systems at different levels; toxin activities, gene expression regulation, diversity and, of main interest, the biological roles of these abundant entities [10; 11; 12; 13; 14; 15; 16]. This active field of research led to the identification of different types of TA systems and functions (see below).
# A Modality-Enhanced Multi-Channel Attention Network for Multi-Modal Dialogue Summarization
## 3 Model
### Modality-Enhanced Attention Mechanism  
One of the challenges the multi-modal summarization task faces is effectively integrating information from different modalities [7]. Some existing methods only include a single text decoder similar to text summarization, which limits the effective utilization of multi-modal input information. This paper proposes a decoder framework based on a modality-enhanced attention mechanism, which aims to strengthen cross-modal interaction  
Figure 2: Multi-channel attention mechanism.  
and improve the quality and relevance of the summary to process and fuse information from different modalities.  
Before computing attention, the cross-modal fusion feature (\(G_{f}\)) and image embedding (\(G\)) filter video frames by calculating fusion weights through cascade gates [21]. Specifically, the image selector fuses the text-aware visual embeddings (\(G_{enc}\)) with \(G_{f}\), and \(G\) and calculates the image ranking score, taking the top-10 images and feeding them into the attention calculation as follows:  
\[\lambda=\sigma(FFN(G_{f}))\lambda^{*}=\sigma(FFN(G)) \tag{8}\]  
\[\hat{I}=\lambda G_{f}+\lambda^{*}G+(1-\lambda-\lambda^{*})G_{enc} \tag{9}\]  
\[I_{score}=softmax(FFN(\hat{I})) \tag{10}\]  
\[\mathcal{L}_{image}=-\mathbf{y}_{I}^{T}\log(I_{score}) \tag{11}\]  
where \(\lambda\) and \(\lambda^{*}\) are the balance weights and \(\sigma\) is the Sigmoid activation function. \(I_{score}\) is the final image ranking score, and the images with the top-10 scores are considered relevant images. \(\mathcal{L}_{image}\) is the image selection cross-entropy loss.  
\[\widetilde{H}=MEAtt(Q,K,V) \tag{12}\]  
\[MEAtt=softmax\left(\frac{H_{audio}H_{vision}^{T}}{\sqrt{d_{k}}}\right)H_{text} \tag{13}\]  
where \(H\) is the input vector; MEAtt is modality-augmented attention; \(Q\in\mathbb{R}^{d_{x}\times d_{h}}\), \(K\in\mathbb{R}^{d_{x}\times d_{h}}\), and \(V\in\mathbb{R}^{d_{mask}\times d_{h}}\) are the query, key, and value matrices, respectively; \(d_{h}\) represents the implicit representation dimension; and \(d_{e}\) represents the attention embedding dimension.
## 7 Discussion
### Input-Output (I-O) Model  
The I-O model is often used in the analysis of industrial systems or macroeconomic systems to examine the structure of intersectoral linkages. The I-O model can show the size of the flow of intersectoral linkages in an economy. The relationship between the input arrangement and the output distribution is the basic theory underlying the model.  
The I-O model describes the transaction stream between sectors in which each sector provides a certain 'output' while also consuming 'inputs' from other sectors. In our case, the 5G-mmWave private network deployment CAPEX and OPEX cost serves as the input, and the economic impact as the output.  
In the I-O model, the parameter of'multiplier' calculates the ratio of output changes in equilibrium as a result of a change in final demand. Thus, the multiplier calculates the overall change in the economy as a result of a unit change in final demand. Changes in final demand may result from private consumption, government spending, investment, and export. A change in output will be greater than a change in final demand as a result of production linkages. For example, if the information and communication technology (ICT) sector's final demand (e.g., broadcasting and programming services, film and sound recordings, telecommunication services, and computer and information technology consulting services) rises by 10 unit values of money, the economy's output will rise by more than 10 unit values of money or by the multiplier coefficient of these sectors.
## 4 Results
### Performance Analysis by Video Type  
This section analyzes the performance of drone detection models based on various types of videos present in the database. Each video type provides a practical example, showcasing its corresponding \"bounding box\" along with the detections made by the models in that image. Furthermore, the overall results of the models for that category are presented.  
To conduct this analysis, the Average Precision (AP) metric is selected as the primary evaluation tool. AP is chosen for its relevance to the task, its simplicity, and its ability to capture the critical aspects of model performance in drone detection. This selection facilitates a coherent and comparative analysis in accordance with the standards of the computer vision community.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Epochs** & **FPS** & **Inference Time** & **Training Time** \\ \hline YOLO & 100 & 134 & 7.5 ms & 48 h y 5 min \\ \hline GELAN & 75 & 95 & 10.5 ms & 50 h y 32 min \\ \hline DETR & 55 & 38 & 26 ms & 93 h y 6 min \\ \hline ViTDet & 55 & 36 & 28 ms & 97 h y 48 min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the models in terms of time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AP@50** & **mAP@50:95** & **F1-Confidence** \\ \hline YOLO & 74.7\% & 72\% & 75\% at 0.102 \\ \hline GELAN & 81\% & 77\% & 77\% at 0.255 \\ \hline DETR & 78.3\% & 75.7\% & 81\% at 0.063 \\ \hline ViTDet & 65\% & 62\% & 75\% at 0.137 \\ \hline UTTracker & 68.8\% & — & — \\ \hline Baseline & 69.7\% & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of the models.  
By concentrating on a key metric such as AP, a more comprehensive and detailed analysis of the results can be conducted, thereby avoiding the dispersion that may arise from the inclusion of multiple metrics. This approach facilitates the maintenance of a clear and precise focus on the model's performance regarding accuracy, which is the most critical aspect of this study.  
It should be noted that, in the group of videos without targets (VE), where no drones are present, the model's performance is evaluated in terms of its ability to avoid false detections. This performance is measured using the True Negative Rate (TNR) metric, which quantifies the frequency with which the model accurately does not detect a drone in the absence of one. This evaluation is crucial to ensure that the model does not generate false alarms in scenarios in which no drones are present.
# SDMw: Secure Dynamic Middleware for Defeating Port and OS Scanning
## 2 Background and Aims  
The Internet uses the Transmission Control Protocol and the Internet Protocol (TCP/IP) suite of protocols that were designed with no security in mind. Both the research community and developers of this protocol did not anticipate this exponential growth. Thus, these protocols offer minimal protection against eavesdropping. Today, the Internet has become part of our day-to-day life. We use the Internet to shop, book holidays, file tax returns or work on a complex collaborative project. A wealth of corporate and personal information is stored online and is accessible through the Internet. Breach of privacy and confidentiality of data may cause huge financial loss and possibly human loss.  
Network security is essentially a battle between a Penetrator who tries to find holes and the Administrator who tries to close them before it is being found. Security is only as strong as the weakest link. The attacker often uses the weakness found in the protocol, hardware, or the combination of both to penetrate the network.  
Fingerprinting is a process of identifying the remote network devices, services running on the devices, their Operating system (OS), hosts running different OSs and so on. Fingerprinting a network equipment has several potential applications and benefits in network management and security. It is also useful to understand the network structures and their behaviour. However, to penetrate a corporate network, it is essential to fingerprint their network topology, the software system and the services used in the network. Fingerprinting is done either from a single remote computer connected to the Internet or from a group of computers. The attackers often collaborate from different parts of the world to launch an attack against a network. The attackers use either a spoofed IP address or some compromised proxy servers to hide their identity. To launch a distributed scanning, the attacker may install zombies and control them from a central remote computer.  
Several research proposals and commercial products are available in the market to defeat fingerprinting. Some of them have performance limitations, whereas others expose themselves to the attacker. In this paper, we propose a plug-and-play adaptive middleware architecture to defeat attackers fingerprinting a network, without exposing itself to the attackers. We also demonstrate that our proposed scheme works seamlessly and requires zero-configuration at the client side.