esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Segmentation Framework for Detecting Adversarial Attacks for Autonomous Driving Cars
## 4 Experimental Setup
### U-Net Model Setup  
A U-net model was trained to generate segmentation masks for each data set. The U-net architecture featured a contraction path for downsampling with feature extraction and an expansive path for upsampling with segmentation, each composed of 4 blocks. Each block contained two consecutive convolutional layers with ReLU activation, followed by a \(2\times 2\) max-pooling layer in the contraction path. The upsampling path utilized transpose convolution layers followed by two convolutional layers. All convolutional layers used \(3\times 3\) filters with batch normalization. The same U-net architecture was applied to all data sets, and segmentation masks were generated based on the training data for each class.  
The training process for the U-net model involved two phases. In phase 1, the U-net model was trained for 30 epochs using 90% of the training data, while 10% was used for validation. The model was further trained for 20 epochs on the entire training data set in phase 2. Data augmentation techniques, including random clipping and horizontal flipping, were applied during the U-net training to increase the model's robustness. The performance of the U-net models used for generating segmentation masks is presented in Table 3, evaluated using Intersection over Union (IoU) and Dice metrics. The MNIST data set achieved the highest scores, as the training set used to train the U-net model was good enough for the learning process. However, the U-net model for CIFAR-10 achieved lower scores, indicating that the model did not generalize well to generate masks for this data set.
## 3 Generation of Equations
### Graph Encoding  
#### 3.1.1 Netlists  
Each line of a netlist describes an element of the corresponding schematic, with: identification label, list of connection nodes, type of element, and list of parameters. We divide netlists into two blocks: internal components (dissipative and storage) and external ports (supplies and ground). In the first block (components), each line includes a reference to the appropriate entry in the dictionary and a list of the parameters for the corresponding model. Each line of the second block (external ports) provides the label of the externalized node, the type of supply (voltage or current), and the symbol\(\sim\)if the supply is modulated (typically, the input signal), or a value if constant (typically, a battery).  
As an example, the netlist corresponding to the circuit in Figure 2 is given in Table 1. Here, the components are given lines \(\ell_{1}\) to \(\ell_{3}\) (gray). The first two lines describe dipoles: a linear capacitor between \(N_{1}\) and \(N_{2}\) with label \(C1\) and capacitance value \(20e^{-9}\) F; and a resistor between \(N_{3}\) and \(N_{4}\) with label \(R1\) and resistance value \(1.5e^{3}\)\(\Omega\). The third line describes a npn bipolar-junction transistor. From the dictionary (Appendix B), the base terminal appears to be connected to the circuit's node \(N_{2}\), the emitter terminal to \(N_{3}\), and the collector terminal to \(N_{5}\). For this component, the list of parameters is: forward and reverse common emitter current gain, reverse saturation current, and thermal voltage. External ports are given lines \(\ell_{4}\) to \(\ell_{7}\). Line \(\ell_{4}\) describes a constant 9 V voltage supply (labeled \(Vcc\)) on the circuit's node \(N_{4}\). \(\ell_{5}\) describes a modulated voltage supply (here considered as the input signal) on \(N_{4}\). \(\ell_{6}\) describes a constant 0 A current supply on node \(N_{3}\); this permits the recovery of the voltage on that node \(N_{3}\) as an output to the circuit. \(\ell_{7}\) describes the connection of the circuit's node \(N_{5}\) to the ground.  
Figure 2: Schematic and corresponding graph of a simple bipolar-junction transistor (BJT) amplifier with feedback. The grey part corresponds to the components, and the outer elements correspond to the external ports, or sources (as in Table 1).
## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### DL for Channel Estimation  
In a MIMO-NOMA system, an accurate channel estimate is crucial since it influences the system's performance. Appropriate CSI is necessary for interference cancellation at the receiving end. DNN may be a good option for calculating precise CSI and channel estimates. The researchers of [103] created an algorithm that automatically assesses and seeks the best logical plan for AWGN channels and MIMO Rayleigh fading channel-state information to recover the signal. For every conceivable scenario, including power allocation parameters, it was demonstrated that DL-based approaches might outperform SIC receivers in terms of symbol error rate (SER) performance. Channel estimation and detection were carried out in batches throughout the training phase. In the testing phase, channel error was included, and the authors investigated how the DL method behaved when the estimated CSI and the actual channel state were different. Throughput is decreased because channel estimate errors cause residual and SIC decoding errors. Impact channel estimate error and reference signaling are reduced by the transmission rate back-off method (in which the transmission rate is regulated). Random beamforming is a useful technique for lowering CSI feedback [107]. Since flawless interference cancellation depends on the correct CSI estimate, NOMA system performance is impacted. Practically speaking, it is challenging because of the complicated fluctuations in channel conditions brought on by high mobility. Utilizing the spatial diversity of massive MIMO, DL methods may be utilized to evaluate the DOA and real-time channel estimates. The sparse features may be fully extracted and efficiently used in the DL technique to learn the entire system. DL also performs better than traditional approaches when SNR is high [35].  
The articles make it clear that estimate accuracy is a performance parameter and that DL is highly preferred to estimate CSI in real-time with less complexity and pilot overhead than the conventional alternatives. Large datasets with different channel conditions are still difficult for supervised learning, and offline network training takes time. Its efficacy in cases with high mobility is constrained since it is challenging to predict the channel.
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 2 Motivation and Related Work  
In recent years, various congestion control algorithms in data centers have been proposed by researchers in order to alleviate network congestion and enhance network transmission performance [2; 6; 13]. In this section, we delve into the limitations of previous work and provide an overview of the design features of our proposed scheme.
# Learning-Assisted Rain Attenuation Prediction Models
## 5 Comparison of LARA Models of Rain Attenuation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy** & **R2** \\ \hline SL FFBP [4] & RMSE = 0.06 (2000 epoch) & \(\times\) \\ \hline FFBNN [6] & RMSE = 0.037749\(\sim\)0.71591 & 0.97226\(\sim\)1 \\ \hline SML [8] & \(\times\) & \(\times\) \\ \hline kNN+ANN [10] & kNN: RMSE = 0.8 & \(\times\) \\ ANN: RMSE = 0.5 & \(\times\) \\ \hline BPNN [11] & \(\times\) & \(\times\) \\ \hline FFMLP+SL [12] & RRMSE = 8.5\% & 0.9989 \\ \hline LS-SVM+BPNN [13] & RMSE = 0.4362 & \(\times\) \\ \hline SL ANN [5] & RMSE = 0.2320\(\leftarrow\)23.20\% (YEO) & \(\times\) \\  & RMSE = 0.2578\(\leftarrow\)25.78\% (DBSG3) & \(\times\) \\ \hline AANN+ILA [14] & Accuracy = 95\% (20 s interval) & \(\times\) \\ \hline RA [16] & Accuracy = 97\% & \(\times\) \\ \hline \multicolumn{3}{c}{\(\times=\) unavailable.} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy and R2 of the LARA models. Figure 8: These radar maps show the supported maximum frequency limit, types of supported links (earth-space or terrestrial or both), support for polarization characterizes, and the number of input parameters associated with LARA models. This radar map is visualized with the data from Tables 2 and 3. For better visualization, data range was rescaled from \(-\)5 to \(+\)15.
## 4 Results
### T-Test Results  
The quantitative research carried out during the creation of the framework was focused on measuring the latency medical devices would have when different microsegmentation methods were used. For this, ping tests were used to measure the roundtrip time (RTT) of packets before and after the addition of a fully configured firewall or proxy server. The RTT was measured in milliseconds, and the results from this analysis should not be presumed to be an exact representation of the latency in a real-world implementation of the framework, as a real network would have a constant flow of traffic. The results from these tests will instead give healthcare organisations a better idea of what percentage the latency can be expected to increase by.  
The objective of testing the latency was to find out which was the most appropriate method of microsegmenting medical devices and vulnerable workstations. In this scenario, the tests were pinging the PACS workstation from the device representing the MRI scanner and pinging one of the nurse workstations from the device representing one of the patient monitoring systems.  
The tests to measure latency involved sending 60 packets from one of the devices representing the medical device to a workstation representing either the PACS workstation or a nurse workstation. To make it clearer where the devices are in this topology, Figure 6 shows the flow of packets in these tests. Only the patient monitoring device was used inthe tests, but since the topology was identical to that of the radiology department, there was no need to use two different devices.  
A two-tailed \(t\)-test was used to compare the RTT data from packets sent with and without a proxy server in place. After this, a \(t\)-test was used to compare the RTT data from packets sent with and without a firewall and with and without a firewall cluster. Table 3 provides a list of the null and alternative hypotheses formed before the \(t\)-test, while Table 4 shows the results of the \(t\)-tests.
## 4 Conclusions  
The removal of EDTA, using positively charged AERs, was investigated in a solution containing pure EDTA and in one mixed with EDTA and Cs ions, which was a simulant of RLWs, generated during nuclear decontamination. The EDTA adsorption experiments were carried out under various conditions of adsorption time, amount of adsorbent, and initial EDTA concentration. The \(q_{\text{max}}\) for EDTA removal was found to be 0.47 mmol/g for initial EDTA concentrations in the range of 0.01-1 mM in the mixed EDTA/Cs solution. The EDTA adsorption on AERs occurred via chemical interaction, rather than physisorption, considering that the pseudo-second-order kinetic model was found to be suitable for the EDTA adsorption. Langmuir adsorption was found to be preferable, providing the monolayer adsorption model on the AER surface. EDTA removal, using negatively charged CERs, was also performed to verify the charge interaction between the anionic EDTA and the CERs, resulting in a considerably lower EDTA removal efficiency (Scheme 2). The results showed that the AERs were able to remove EDTA efficiently, regardless of the presence of Cs ions in the solution, indicating their potential usefulness for a separation technique for EDTA in RLWs generated from decontamination processes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Isotherm Models** & **Isotherm Constants** & **Value** \\ \hline \multirow{3}{*}{Langmuir} & \(q_{\text{max}}\) (mmol/g) & 0.4725 \\  & \(b\) (L/mmol) & 15.130 \\  & \(\text{R}^{2}\) & 0.9988 \\ \hline \multirow{3}{*}{Freundlich} & \(1/n\) (L/mmol) & 0.6494 \\  & \(K_{\text{F}}\) (mmol/g) & 1.081 \\ \cline{1-1}  & \(\text{R}^{2}\) & 0.9721 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Langmuir and Freundlich constants for the adsorption of EDTA on AERs.  
Figure 13: (**a**) Langmuir and (**b**) Freundlich isotherm models for the adsorption of EDTA on AERs in EDTA/Cs (1:1) mixed solution. [AERs] = 2 g/L; [EDTA] = 0.01–0.5 mM; adsorption time = 5 h.  
**Author Contributions:** All authors discussed the results and reviewed the manuscript. Drafting of manuscript: J.K. and T.S.L.; testing: J.K. and C.W.P.; planning and supervision of the research: K.-W.L. and T.S.L.  
**Acknowledgments:** This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government through the Basic Science Research Program (2018R1A2A2A14022019) and Nuclear R&D Project (2016M2B2B1945085).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
###### Abstract  
5G New Radio (NR) systems promise to expand offered services to enable industrial automation scenarios. To enable ultra-low latency at the air interface and to exploit spatial redundancy for applications such as synchronization and motion control, user equipment (UE) will naturally require device-to-device (D2D) and base station (BS) to UE communications and directional transmissions provided by millimeter wave (mmWave) frequencies. However, the performance of such systems is affected by the blockage phenomenon. In this paper, we propose a simple line-of-sight (LoS) blockage model for Industrial mmWave-based industrial Internet of Things (IIoT) deployments. The model is based on two sub-models, where each part can be changed/replaced to fit the scenario of interest. The first part is based on photogrammetry and provides the transparency probability for a single element on the factory floor. The second one utilizes these models of industrial elements to form the deployment and then applies stochastic geometry to derive the blockage probability. The proposed model can be utilized for any type of industrial machine, accounts for their inherent regular deployments on the factory floor, and provides the final results in an easy-to-compute form. Our results indicate that direct UE-UE communications are feasible in sparse deployments (less than 0.1 machine/m\({}^{2}\)) or at small communications distances (less than 5-10 m) or in deployments with highly transparent machines (machine transparency less than 0.5). Otherwise, BS-UE communications need to be utilized. In this case, BS height becomes a critical parameter affecting the LoS probability. Specifically, using a BS height of 10 m allows blockage probability to be improved by 20-30% as compared to a BS of height 4 m. Finally, as UE height produces more impact on the blockage probability as compared to a machine height, in real deployments, one needs to ensure that the height of communications modules at UEs are maximized.  
5G; New Radio; industrial IoT; blockage; millimeter wave +
Footnote †: journal:  
_Future Internet_2022, 14, 193. [[https://doi.org/10.3390/f14070193](https://doi.org/10.3390/f14070193)]([https://doi.org/10.3390/f14070193](https://doi.org/10.3390/f14070193))
# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 4 Performance Evaluation
### Simulation Results and Discussion
#### 4.3.2 Impact of UAV Node Density  
We also investigated the impact of different node densities in FANETs. The number of UAV nodes varied between 20 and 100, while the velocity of the UAV nodes remained fixed at 10 m/s.  
Figure 6 presents the PDR of the compared routing protocols for different amounts of UAV nodes. Initially, due to the low density of the UAV nodes, the network experienced frequent disconnections, resulting in a low packet delivery ratio. However, as the number of UAV nodes increased, the network connectivity improved, leading to an increase in the packet delivery rate. Throughout this process, the AO-AOMDV consistently outperformed traditional routing protocols in terms of the packet delivery rate.  
Figure 7 shows the average E2E delay of the compared routing protocols for different amounts of UAV nodes. In low-density networks, it is difficult to form a sufficient number of paths to utilize the arithmetic optimization algorithm, resulting in the AO-AOMDV exhibiting the highest latency. However, as the number of UAVs increases, the latency of all three protocols decreases and tends to stabilize.  
Figure 8 indicates the network lifetime as the number of nodes increases. When the number of nodes exceeds 70, the lifetime of the AO-AOMDV protocol surpasses that of the traditional protocols and tends to stabilize.  
Figure 6: PDR for varying node densities.  
Figure 7: Average E2E delay for varying node densities.
## 2 Experimental
For this purpose, microstructure images of the sample surface of at least 0.1 cm in length (\(l\), cm) were analyzed, and the number of cracks (\(n\), pc) and their depth (\(d\), \(\mu\)m) were recorded. When cracks were not seen on the surface of the studied samples, the \(k\)-parameter was taken to be zero. Otherwise, the arithmetic mean (\(d_{aver. }\), \(\mu\)m) was calculated for all recorded cracks, and the \(k\)-parameter (pc. \(\times\)\(\mu\)m/cm) was calculated using the following formula (a detailed description of the applied method was given previously [49]):  
\[k=n\cdot d_{aver.}/l. \tag{3}\]  
In the present study, four industrial nickel-chromium-molybdenum-based corrosion-resistant low-carbon alloys were tested, as shown in Table 3. These alloys can be categorized into different families depending on chromium and molybdenum content. Hastelloy\({}^{\circledR}\) G-35\({}^{\circledR}\) is the alloy from the G-group and is characterized by higher chromium content. VDM\({}^{\circledR}\) Alloy 59 and the Russian alloy KhN62M-VI [50] belong to the C-family. The last alloy, Hastelloy\({}^{\circledR}\) B-3\({}^{\circledR}\), represents B-family, with increased molybdenum content and low chromium content. The G-family was designed for application in highly oxidizing aqueous systems, the C-group family offers versatility in both oxidizing and reducing media, while the B-family excels in reducing environments [51]. The possibilities of their application at relatively high temperatures in contact with aggressive molten halides are still not clear and require deeper investigation. The composition of the alloys in their 'as received' state (according to the manufacturer's data) is listed in Table 3. Analysis of the structure of the alloys in the initial state was also carried out (Figure 2). The microstructure of the Hastelloy\({}^{\circledR}\) G-35\({}^{\circledR}\) (Haynes, Kokomo, IN, USA), VDM\({}^{\circledR}\) Alloy 59 (VDM-Metals, Werdohl, Germany), KhN62M-VI (RUSPOLYMET, Kulebaki, Russia), and Hastelloy\({}^{\circledR}\) B-3\({}^{\circledR}\) (Haynes, Kokomo, IN, USA) alloys in the 'as received' state represented a nickel-based fcc solid solution with a small amount of inclusions, which is typical for this class of materials [51]. \begin{table} \begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Alloy**} & \multicolumn{8}{c}{**Content, wt. \%**} \\ \cline{2-13}  & **Ni** & **Cr** & **Mo** & **Fe** & **Mn** & **Ti** & **Co** & **Al** & **Si** & **S** & **C** & **P** \\ \hline
**Hastelloy\({}^{\circledR}\) G-35\({}^{\circledR}\)** & 57.2 & 33.1 & 8.2 & 0.8 & 0.22 & 0.01 & 0.07 & 0.19 & 0.04 & 0.002 & 0.012 & 0.003 \\
**(UNS N06035)** & 63.2 & 23.2 & 12.8 & 0.47 & 0.03 & 0.08 & — & 0.11 & 0.06 & 0.003 & 0.005 & 0.004 \\ \hline
**VDM\({}^{\circledR}\) Alloy 59** & 60.4 & 22.6 & 15.4 & 0.9 & 0.19 & — & 0.01 & 0.23 & 0.02 & 0.002 & 0.002 & 0.002 \\
**(UNS N06059)** & 67.1 & 1.6 & 28.5 & 1.41 & 0.68 & 0.01 & 0.05 & 0.33 & 0.04 & 0.001 & 0.002 & 0.004 \\ \hline \hline \end{tabular}  \({}^{(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\
## 3 Reconsideration of Reflected and Transmitted Fields:  
The calculation of the reflected field \(G_{r}(x,0)\) that is generated at the junction plane z = 0 between two waveguide is carried out using a combined spatial-spectral formalism in TE [12] and TM [14] cases as follows:  
\[G_{r}(x,0)=\Delta r(x,0)\cdot\Im^{-1}[g_{i}(k_{x},0)\cdot\rho(k_{x},0)] \tag{9}\]where \(g_{i}\) (\(k_{x}\), 0) is the angular spectrum, i.e., the transverse (with respect to x) Fourier transform of the incident field \(G_{i}\) (\(x\), 0) upon the junction plane, namely the guided mode of the incoming waveguide. This plane wave spectrum takes the form:  
\[g_{i}(k_{x},0)=\int\limits_{-\infty}^{\infty}G_{i}(x,0)e^{-jk_{x}x}dx \tag{10}\]  
where, \(k_{x}\) is the variable of the Fourier transform (the transverse wavenumber of the representative plane waves). The spectral part \(\rho(k_{x}\),0) is written as [14]:  
\[\rho(k_{x},0)=\left\{\begin{array}{ll}\frac{k_{x}^{-}-k_{x}^{+}}{k_{x}^{-}+k _{x}^{+}}&(TE)\\ \frac{k_{x}^{-}(n_{0}^{+})^{2}-k_{x}^{+}(n_{0}^{-})^{2}}{k_{x}^{-}(n_{0}^{+})^ {2}+k_{x}^{+}(n_{0}^{-})^{2}}&(TM)\end{array}\right. \tag{11}\]  
where, \(n_{0}^{+}\) and \(n_{0}^{-}\) are certain reference refractive indices just immediately after and before the junction plane z = 0, as will be explained later. \(k_{z}^{+}\) and \(k_{z}^{-}\) are the z-components of wave vectors of plane waves having transverse x-components \(k_{x}\) in z \(>\) 0 and z \(<\) 0 respectively.  
Namely,  
\[k_{z}^{-}=\sqrt{k_{0}^{2}(n_{o}^{-})^{2}-k_{x}^{2}} \tag{12}\]  
\[k_{z}^{+}=\sqrt{k_{0}^{2}\Big{(}n_{o}^{+}\Big{)}^{2}-k_{x}^{2}} \tag{13}\]  
The spatial dependent part of the reflection operator \(\Delta r(x,0)\) in Equation (9) assumes the form:  
\[\Delta r(x,0)=\frac{r(x,0)}{r_{o}} \tag{14}\]  
where:  
\[r(x,0)=\left\{\begin{array}{ll}\frac{n(x,0^{-})-n(x,0^{+})}{n(x,0^{+})+n(x,0 ^{-})}&(TE)\\ \frac{n(x,0^{+})-n(x,0^{-})}{n(x,0^{+})+n(x,0^{-})}&(TM)\end{array}\right. \tag{15}\]  
and:  
\[\mathbf{r_{o}=r(x_{o},0)} \tag{16}\]  
where \(x_{o}\) is the coordinate at which \(\left|r(x,0)\right|\) is maximum. Moreover, the reference refractive indices in Equation (11) are: \(n_{o}^{+}=n(x_{o},0^{+})\) and \(n_{o}^{-}=n(x_{o},0^{-})\). Obviously, the refractive index distribution \(n(x_{o},0^{-})\) is the index profile of the waveguide to the left of the junction, and \(n(x_{o},0^{+})\) is that one to the right of the junction. The transmitted field \(H_{t}\) (\(x\), 0) across z = 0, follows directly from the boundary condition:  
\[H_{t}(x,0)=H_{i}(x,0)+G_{r}(x,0) \tag{17}\]  
where  
\[H_{i}=\left\{\begin{array}{ll}E_{y}&\text{in $TE$ case}\\ H_{y}&\text{in $TM$ case}\end{array}\right. \tag{18}\]