esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of HTTP DDoS Attacks Using NFStream and TensorFlow
## 3 Methodology
### TensorFlow
Therefore, the nfstream framework was used as an intermediate framework for data classification. Figure 7 shows the model we used to analyze samples in PCAP format. Pre-processed PCAP files were used as input data, where the NFstream framework allowed the creation of the desired netflow data, which were then tagged with the appropriate labels. The NFstream framework is a fast python module that provides fast, optimal and flexible data structures designed for analyzing ONLINE or OFFLINE network traffic. This framework is designed to provide a basic building block for network traffic analysis using NDPI classification and using established machine learning models. The advantage is the fast creation and structured reproducibility of the data in sub-applications for research purposes. Another advantage is the ability to perform deep packet analysis, which allows the detection fingerprinting of applications in network traffic, such as TLS, SSH, DHCP, HTTP etc. Network traffic analysis using nfstream was performed with the following parameters: streamer = NFStreamer(source=file_path, statistical_analysis=True, n_dissections=200) The options we enabled included the following: statistical_analysis--post-mortem flow statistical analysis. This option allows the storage of statistical data from measured network traffic, such as the amount of packets transmitted, minimum and maximum packet size, and other metrics n_dissections--the number of packets to analyze when detecting L7 protocols with NDPI. The default size is 20, which is insufficient for measurement purposes and the number of packets analyzed must be increased to improve accuracy. Once these data had been processed and saved in CSV format, an additional column was added to contain the given LABEL of the desired sample.
## 8 Conclusions  
In this paper, the matching problem between HAP drones and LEO satellites in the space-air-ground integrated network has been investigated. First, we introduced the network architecture and working mechanism, including the traffic generation model and the traffic transmission model. Then, a tripartite matching problem that takes comprehensive consideration of multi-dimensional characteristics has been formulated to maximize the average transmitted traffic value of network. Through mathematical simplification, the optimization problem is then simplified into two independent sub-problems: traffic-drone matching and LEO-drone matching. The former can be decoupled into multiple independent and easily solvable sub-subproblems. Considering the mixed stochastic and deterministic traffic generation model, the long propagation latency between LEO satellites and HAP drones, and in the continuous state space, we proposed a multi-agent deep reinforcement learning approach with centralized training and decentralized execution to solve the LEO-drone matching problem. In this approach, the value network is trained in a centralized manner at the terrestrial traffic center and the matching strategy is timely formulated in a decentralized manner at LEO satellites. Finally, the proposed approach has been compared with multiple state-of-the-art algorithms through simulations, and results have proven the effectiveness and efficiency of the proposed algorithm.  
**Author Contributions:** Conceptualization, X.H.; methodology, Z.W. and X.X.; validation, X.H., Z.W. and X.X.; formal analysis, X.H. and M.P.; investigation, X.H. and Z.W.; writing--original draft preparation, X.H.; writing--review and editing, X.H., Z.W., X.X. and M.P.; supervision, X.H. and Z.W.; project administration, X.X.; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the 2020 National Key R&D Program \"Broadband Communication and New Network\" special \"6G Network Architecture and Key Technologies\" 2020 YFB1806700.  
Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
Author [PERSON] is from the company, but all authors declare that there is no conflicts of interest.
## 3 Design of Maritime Data Security Management Platform
### Hardware Layer  
The hardware layer is composed of the network hardware, data platform, data acquisition sensor and so on. It runs on the acquisition equipment from the ship, shore, surface and underwater, and is the direct source of maritime data acquisition. For example, the ship is equipped with an Electronic Chart Display and Information System (ECDIS), VDES, radar, camera, Atmospheric and other equipment; the multipurpose buoy and Unmanned Surface Vehicles (USVs) are equipped with the Global Navigation Satellite System (GNSS), ADCP, Camera, Inertial Measurement Unit (IMU), Micro Electro Mechanical Systems (MEMS) and other equipment, and transmits the data collected by each sensor back to the server of the data storage center through the satellite link. The shore equipment includes Satellites, Observation Station, etc., and the Transmission Control Protocol (TCP) Server gateway is set up in the Cloud Server to receive data, process the data according to the data structure, save the data in the storage device and manage the data using security devices. What is more, Table 2 lists the hardware components on the ship, shore, surface and underwater.
## 3 Results
### Algorithm for Forming the Initial Trajectory Estimate  
As a result of the action of the proposed intelligent tracking method, which combines the Kalman filter, the GERT model, and the adaptive intelligent module, the system forms several parallel estimates of the target state at each cycle. To transfer these data to the radar executive modules, it is necessary to create a single initial trajectory estimate, as well as to formally assess its accuracy. To do this, we first implement the algorithm for  
Figure 10: Simulated range-angle map with a radar target and a non-coherent barrage jamming source.  
forming the initial trajectory estimate. This approach enables the system to adapt to environmental conditions. In a stable mode, the system trusts the Kalman filter more; in cases of interference, the GERT model forecast prevails.  
The algorithm is executed at each cycle \(t\) of the radar operation and consists of the following sequential steps.  
In the first step, input estimates are obtained. The system receives inputs from two main modules:  
* \(\hat{x}_{K}(t)\)--Kalman filter estimate after correction;
* \(P_{K}(t)\)--predictive error covariance;
* \(\hat{x}_{G}(t)\)--predictive estimate from the GERT model.  
In the second step, the reliability features of the measurements of the intelligent module are calculated and estimates of \(P_{meas}(t+1)\) and the number of missed measurements in the window \(N\) of the cycles \(L(t)\in N\) are formed.  
In the third step, the logic given in Table 1 is used. The mode is selected according to the conditions specified in this table.  
In the fourth step, after determining the mode, the weight coefficient \(\alpha(t)\) is calculated and assigned. Variants of the assignments are presented in Table 3.  
Alternatively, if continuity is required, the coefficient \(\alpha(t)\) can be calculated smoothly:  
\[\alpha(t)=0.2+0.7P_{meas}(t+1).\]  
In the fifth step, based on the obtained estimates and the coefficient \(\alpha(t)\), the final estimate of the target position is calculated:  
\[\hat{x}(t)=\alpha(t)\cdot\hat{x}_{K}(t)+(1-\alpha(t))\cdot\hat{x}_{G}(t). \tag{8}\]  
In the sixth step, data on the internal states of the system are updated as follows, after forming the estimate:  
* the loss window is updated;
* \(\hat{x}(t)\), \(P_{meas}(t+1)\), \(\alpha(t)\)are stored in the buffer;
* values for error analysis are formed.
# Behind the Door: Practical Parameterization of Propagation Parameters for IEEE 802.11 ad Use Cases
## 4 Experimental Results
### Case 3: Penalties for Locating the STA behind a Door  
In the third case under study, we evaluate the losses obtained by \"hiding\" the STA inside offices A, B, and C (see Figure 6). Measurements in this case are made without the presence of people on the entire floor. Figure 6 shows the angular power profiles obtained in the STA for the six positions under test: three positions where the STA is in the corridor (blue line) and three where the STA is inside the offices (magenta line). From the former configurations, we can deduce that, in LOS positions, the dominant lobe comes from the direction of the AP (270\({}^{\circ}\)), revealing a secondary lobe arriving from the end of the corridor (90\({}^{\circ}\)). By moving the STA away from the LOS, the maximum power entering the office is recorded near 0\({}^{\circ}\) (i.e., towards the corridor). The losses observed when \"hiding\" the STA are significant in relation to what is measured in the LOS: 35, 38, and 26 dB for offices A, B, and C, respectively, when comparing the maximums measured in each case.  
Figure 5: Standard deviation of positions 5–7: (**A**) Pos. 5 (LOS), (**B**) Pos. 6 (LOS), and (**C**) Pos. 7 (NLOS).
# Discrete Lattice Element Model for Fracture Propagation with Improved Elastic Response
## 4 Discussion  
The novel discrete lattice element model developed in this study represents a contribution in addressing challenges related to simulating the elastic response and fracture propagation with softening in materials and structures by using discrete lattice models. The model introduces improvements by ensuring that the local elastic lattice parameters correspond to global material elastic constants, allowing for the accurate simulation of various Poisson ratios and easier model characterization. At the same time, the model also successfully achieves mesh-independent post-peak softening responses, which is a nontrivial challenge in discrete lattice models. Here, this has been achieved through the integration of the embedded strong discontinuity framework. Through numerical examples, we demonstrated the model's capability to accurately represent stress tensors in shear-dominated scenarios. Additionally, the model presented the ability to reproduce typical failure mechanisms observed in uniaxial tension and compression tests, such as horizontal tensile cracking and diagonal shear fractures, respectively. The use of two different meshes in these tests revealed consistent macroscopic elastic responses, which is observed in the real-world behavior of engineering materials with inherent heterogeneities. While the global fracture softening responses exhibited close agreement, subtle deviations highlighted the influence of internal material heterogeneities on failure mechanisms, a phenomenon common in real engineering materials. The lattice element model naturally incorporates randomness in the mesh configurations, adding robustness to its ability to simulate various fracture mechanisms.  
The proposed discrete lattice element model with an improved elastic response demonstrates robust computational efficiency by using one-dimensional elements. The incorporation of the kinematics from the rigid-body spring model into a generalized beam lattice finite element aligns with computational efficiency as well. The softening based on a damage model [21; 22; 23] effectively captures monotonic failure in quasi-brittle and brittle materials. However, it should be noted that the model has limitations in representing cyclic  
Figure 17: Macro-crack at the end of compression test for: (**a**) Mesh1-1210; (**b**) Mesh2-2570.  
behavior. While the current framework favors a quasi-static regime, neglecting inertial effects and propagation rates [14; 34], potential extensions could be explored to incorporate cyclic behavior on the basis of proposed kinematics.
# A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments
## 4 Experiment and Analysis
### Comparison of Models  
To validate the performance of PBI-EdgeNeXt, we compare it with five lightweight SOTA network models. These models are trained and tested on the CPI dataset, and their performance is detailed in Table 4.  
It is obvious that our PBI-EdgeNeXt outperforms the other models as demonstrated by its top performance across the five indicators we selected. Our model not only achieves high average recall rates across various types but also ensures high precision, securing the highest F1 score among the six models. On the CPI dataset, it achieves a Top-1 accuracy of 87.12% and a Top-5 accuracy of 97.21%.  
Figure 5 displays the validation Top-1 accuracy curves for six model training processes. The graph reveals that our PBI-EdgeNeXt model exhibits the best performance. This superiority is evident not only in its highest validation accuracy but also in its rapid convergence rate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Top-1 Acc (\%)** & **Top-5 Acc (\%)** & **mP (\%)** & **mR (\%)** & **mF1 (\%)** \\ \hline EfficientNet-B0 & 85.50 & 97.21 & 86.00 & 85.50 & 85.47 \\ EfficientNetV2-B0 & 78.74 & 94.68 & 79.51 & 78.74 & 78.71 \\ MobileNetV2 & 82.88 & 96.76 & 83.51 & 82.88 & 82.70 \\ ShuffleNetV1 & 80.54 & 95.86 & 81.23 & 80.54 & 80.45 \\ ShuffleNetV2 & 81.08 & 95.23 & 81.59 & 81.08 & 81.01 \\ Ours & 87.12 & 97.21 & 87.32 & 87.12 & 87.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of models. This table presents the results of the comparative experiments between the PBI-EdgeNeXt model and the five selected models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Ablations**} & \multicolumn{5}{c}{**Metrics**} \\ \hline \hline
**PolyLoss** & **PE** & **OctConv** & **Top-1 (\%)** & **Top-5 (\%)** & **mP (\%)** & **mR (\%)** & **mF1 (\%)** \\ \hline  & & & 85.50 & 95.50 & 85.71 & 85.50 & 85.43 \\ ✓ & & & 85.95 & 96.40 & 86.20 & 85.95 & 85.87 \\  & ✓ & & 85.68 & 96.04 & 85.99 & 85.68 & 85.65 \\  & & ✓ & 86.13 & 96.85 & 86.32 & 86.13 & 86.07 \\  & ✓ & ✓ & 86.94 & 95.77 & 87.05 & 86.94 & 86.88 \\ ✓ & ✓ & & 86.31 & 97.12 & 86.70 & 86.31 & 86.30 \\ ✓ & & ✓ & 87.30 & 96.67 & 87.52 & 87.30 & 87.20 \\ ✓ & ✓ & ✓ & 87.39 & 96.49 & 87.58 & 87.39 & 87.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The table presents the results of the ablation experiments on the PBI-EdgeNeXt model. It demonstrates the effect of the loss function and two modules on enhancing the model’s performance.
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Anti-Cancer Effect of Piperine
#### 2.6.3. Genital Cancers  
When investigating the effect of piperine to voltage-gated K\({}^{+}\) channels (KV), which play an important role in regulating cancer cell proliferation and are considered as potential targets for the treatment of cancer, it was found that it blocks voltage-gated K\({}^{+}\) current. It was effective in doses of IC\({}_{50}\) = 39.91 uM in LNCaP and 49.45 uM in PC-3 human prostate cancer cells. This recorded blockade led to G\({}_{0}\)/G\({}_{1}\) cell cycle arrest and consequently inhibited cell proliferation and induced apoptosis [59]. In another study, on human prostate cancer DU145, PC-3 and LNCaP cells, piperine was also found to induce cell cycle arrest at G\({}_{0}\)/G\({}_{1}\), and to cause downregulation of cyclin D1 and cyclin A, while increased levels of p21 Cip1 and p27 Kip1 were found upon the piperine treatment in prostate cancer cells (LNCaP and DU145). Additionally, piperine treatment resulted in promoted autophagy as evidenced by the increased level of LC3B-II and the formation of LC3B puncta [60]. In LNCaP, PC-3, and DU-145 prostate cancer cells, piperine activated caspase-3 and cleaved PARP-1 proteins and reduced the expression of phosphorylated STAT-3 and NF-kB transcription factors [61]. A recent study determined that molecular mechanism responsible for observed repressed cell proliferation and migration (in PCa DU145 cell line) of piperine action was via affecting the expression of the Akt/mTOR/MMP-9 signaling pathway [62].
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 2 Related Works
Apparently, the discussed research works indicate a strong reliance on the ns-3 simulator. However, to the best of our knowledge, there is no other research work in the literature that examines the fidelity of MPTCP simulations using ns-3 DCE. This paper investigates the fidelity of ns-3 DCE by comparing the MPTCP performance results obtained by the ns-3 simulator and a real hardware testbed in two different real-world connectivity scenarios. Our study focuses on Cubic, LIA, OLIA, and wVegas algorithms and considers both long- and short-lived data flows.
## 3 Mathematical Modeling
### Dynamic Equations When the Launch Is On  
As shown in Figure 2, in the in-orbit launch design, the initial state (State 1) involves the transverse spring \(k_{2}\) being pre-compressed by \(\delta\). Thus, the total elastic potential energy of the system at initial state is:  
\[V_{0}=\frac{1}{2}k_{1}\Big{(}\sqrt{d^{2}+\delta^{2}}-l\Big{)}^{2}+\frac{1}{2}k _{2}\delta^{2}+\frac{1}{2}k_{3}\Big{(}\sqrt{d^{2}+\delta^{2}}-l\Big{)}^{2} \tag{9}\]  
When Equation (5) is substituted with Equation (9), the following occurs:  
\[V_{0}=k_{\text{\tiny p}}\Big{(}\sqrt{d^{2}+\delta^{2}}-l\Big{)}^{2}+\frac{1}{2 }k_{h}\delta^{2} \tag{10}\]  
The spacecraft \(m\) is released when it moves to State 2. According to the law of energy conservation, the elastic potential energy is completely converted into kinetic energy at State 2:  
\[T_{0}=\frac{1}{2}mv_{0}^{2}=V_{0} \tag{11}\]  
Therefore, the launch velocity \(v_{0}\) is:  
\[v_{0}=\sqrt{\frac{2k_{\text{\tiny p}}\Big{(}\sqrt{d^{2}+\delta^{2}}-l\Big{)}^{ 2}+k_{h}\delta^{2}}{m}} \tag{12}\]  
It is noteworthy from the above equation that when the pre-compression length \(\delta\) of the transverse spring \(k_{2}\) is zero, since \(\lambda=l/d>1(l>d)\), the two vertical springs have a pre-compression length. Hence the system still possesses unreleased elastic potential energy, which can also provide kinetic energy for launch. As \(\delta\) increases, the pre-compression length of the vertical springs is actually reduced, and the reduced potential energy is not enough to balance the increased potential energy from the compression of the transverse spring, resulting in a decrease in the system's total potential energy and launch velocity. During this process, there exists a minimum point \(\delta_{e}=\frac{\sqrt{(2k_{\text{\tiny p}}l-dk_{h}-2 dk_{\text{\tiny p}})(2k _{\text{\tiny p}}l+dk_{h}+2 dk_{\text{\tiny p}})}}{k_{h}+2k_{\text{\tiny p}}}\). When \(\delta>\delta_{e}\), the launch velocity increases with \(\delta\). When the pre-compression length reaches the critical value \(\delta_{c}\) (satisfying \(2k_{\text{\tiny p}}\Big{(}\sqrt{d^{2}+\delta^{2}}-l\Big{)}^{2}+k_{h}\delta^{2}= 2k_{\text{\tiny p}}(d-l)^{2}\)), the system's elastic potential energy is exactly equal to that when \(\delta=0\). Thereafter, the launch velocity increases approximately in direct proportion to \(\delta\). Moreover, it is obvious that the stiffness ratio \(\alpha=k_{\text{\tiny p}}/k_{h}\) determines the value \(\delta_{e}\) and \(\delta_{c}\).
## 1 Introduction
In the research field of geoscience, machine learning methods have been widely applied in the inversion of land surface parameters, especially in forest classification, forest aboveground biomass estimation, and other forest parameters estimation [26; 27; 28]. Previous research has shown that even a simple averaging or linear combination could improve the accuracy of estimation compared to single models, and machine learning methods such as RFs and SVMs can outperform linear combinations [29; 30; 31; 32; 33]. Although many LUE models have been developed and applied in terrestrial GPP estimation at ground-site, regional, or global scales [5; 10; 13; 16; 19; 20; 34], there is still a big gap between the actual and desired GPP estimation accuracy, and further improvements through machine learning are necessary for deeply understanding the contribution of forests in reducing greenhouse gas concentrations in the atmosphere. Thus, for the purpose of estimating accurate forest GPP, we ran five LUE models (CASA, GLO-PEM, EC-LUE, VPM, and C-Fix) and ensembled them to improve the capacity of forest GPP estimation across biomes by employing two widely used machine learning methods (SVMs and RFs) based on satellite, eddy covariance data, and the Koppen-Geiger climate zones. Our objectives were to (1) evaluate the five LUE models in estimating forest GPP with site observations; (2) fuse the five LUE models based on climate zones using an SVM and an RF; and (3) compare the accuracy of different machine learning methods with individual models to clarify the improvements raised by machine learning.
# Use of Total Least Squares Adjustment in Geodetic Applications
## 2 Solution of the Predetermined Model
### Total Least Squares Using SVD  
As we mentioned before, if we are looking for a solution for \(\mathbf{x}\) in system (1), then we want to achieve \(\text{rank}(\widetilde{\mathbf{A}})=\text{rank}([\widetilde{\mathbf{A}}| \widetilde{\mathbf{b}}])=u\). System (1), in its extended form, can be rewritten as:  
\[[\widetilde{\mathbf{A}}|\widetilde{\mathbf{b}}]\  
From Equation (48), the solution for the unknown vector can be derived when we are also taking into account the errors in variables:  
\[\widetilde{\mathbf{x}}_{\text{TLS}}=-\frac{1}{v_{u+1,u+1}}\big{[}v_{u+1,1}\quad v _{u+1,2}\ \ldots\quad v_{u+1,u}\big{]}^{\text{ T}} \tag{49}\]  
In this case, we find a solution without stochastic treatment. If we want to take into account the weights of the observations in the computation, both on the side of the vector \(\mathbf{b}\) and on the side of the matrix \(\mathbf{A}\), a problem arises. In the extension (2) or (44), we cannot take into account both the weights of the observations and the weights of the unknowns, as indicated in Section 2.1 by the Cholesky decomposition of the matrix \(\mathbf{P}\). Here, we consider two different weight matrices.