esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Volatile Phenols From Wine Using Crosslinked Cyclodextrin Polymers
## 3 Materials and Methods
### Adsorption Experiments with CD Polymers  
Kinetic adsorption tests were carried out (in triplicate) by adding 1% _w/v_ polymer to model wine (10 mL) spiked with 1 mg/L of each of the volatile phenols (guaiacol, 4-methylguaiacol, 4-ethylguaiacol and 4-ethylphenol). The sample was agitated at 120 rpm at 25 \({}^{\circ}\)C for 2 h, during which time aliquots (1 mL) were periodically withdrawn (i.e., at 5, 20, 40, 80 and 120 min after the start of mixing) and centrifuged (2000 g for 5 min). The resulting supernatant was then analyzed by HS-SPME GC-MS (using established methodology (2018); with a Supelco DVB/CAR/PDMS fiber (Sigma Aldrich, Castle Hill, NSW) to determine volatile phenol concentrations.  
Since no significant difference was observed between RPAs for volatile phenols at 5 vs. 120 min after addition of CD-HDI polymers (i.e., adsorption equilibrium was achieved rapidly), subsequent analyses were conducted within 2 hours of incubation. To study the equilibrium adsorption capacity of the adsorbent in model wine, 2% _w/v_ of polymer was added to 10 mL of model wine, containing various concentrations of guaiacol, 4-methylguaiacol, 4-ethylguaiacol or 4-ethylphenol. The concentration of volatile phenols ranged from 0.05 to 1.0 mg/L. Controls were prepared without the addition of polymer. The equilibrium adsorption capacity of the polymer is expressed as \(\mathrm{q_{e}}\) (mg/g):  
\[\mathrm{q_{e}}=(\mathrm{C_{0}-C_{e}})^{*}\mathrm{V/W} \tag{4}\]  
where \(\mathrm{C_{0}}\) is the starting concentration of volatile phenols, \(\mathrm{C_{e}}\) is the concentration of volatile phenols remaining at equilibrium, \(\mathrm{V}\) is the sample volume, and \(\mathrm{W}\) is the weight of the polymer. The concentration of volatile phenols remaining in the model wine was determined by HS-SPME GC-MS (again using established methodology (2018)).  
To evaluate volatile phenol adsorption in a real wine environment, a batch experiment was conducted (in triplicate) to compare the adsorption efficiency (expressed as a percentage of the RPA) of the controls) using various amounts of CD-HDI polymers added to spiked red wine samples. The amount of CD-HDI polymer was 1%, 2% and 5% _w/v_. The reusability of the polymers was also tested. Pre-exposed polymers were collected from the wine samples and soaked in methanol at room temperature for 24 h with 120 rpm agitation, then filtered and dried for reuse. The regenerated polymers were subjected to five rounds of batch adsorption to test their regeneration efficiency.
## 2 Proposed STPA Configuration for JCAS, Its Design Pipeline, and Performance
### Proposed STPA and Its Design Pipeline  
#### 2.2.1 Design Objective for JCAS and the Proposed STPA  
The objective is to design a shared aperture for JCAS operating in the band of n261 of 5G NR. It is shown in Figure 2 that \(\theta\) is the elevation angle measured from \(z\)-axis and \(\phi\) is the azimuth angle measured from the \(x\)-axis. The design objectives are: a center frequency of \(f_{c}=28\) GHz with 400 MHz bandwidth, the field of view (FoV) of 30\({}^{\circ}\) in the elevation and of 360\({}^{\circ}\) in the azimuth angles [14], symmetrical 3 dB-beamwidth of 10\({}^{\circ}\) in azimuth and elevation domain.
## 6 Simulation Results and Discussion
### Performance of Inverter  
#### 6.2.1 Parameter Variations  
When \(t\) = 0.1-0.15 s, a random disturbance \(2\times 10^{-6}\)\(rand(1)\)\(PF\) is added to the capacitance parameter \(C_{ac}\), and while \(t\) = 0.3-0.35 s, the inductance parameter varies by adding a random disturbance with amplitude \(10^{-3}\), shown in Figure 12.
## 3 The Inversion of J-C Constitutive Parameters for Powder Liner  
### Numerical Simulation of Penetrating Steel Target  
#### 3.1.1 Numerical Calculation Model  
The numerical calculation model of the penetrating steel target is illustrated in Figure 5. Considering the geometric characteristics of the perforating process, a 1/2 axisymmetric model is established, comprising shell case, explosive, powder liner, steel target, and air domain. Due to the relatively thin wall of the perforating gun, its impact on jet energy loss is negligible and thus disregarded in numerical simulations.  
In numerical simulation, this paper employs the Arbitrary Lagrange-Euler (ALE) algorithm in numerical simulation, which integrates the merits of both Lagrangian and Euler algorithms, enabling faster and more accurate computation of explosion shock problems [13]. Among them, the Lagrange mesh is utilized for shell case and steel targets, while the Euler mesh is employed for explosives, powder liners, and air. Both grids are controlled during the calculation process to enable fluid-structure coupling of multiple substances. In addition, during the meshing process, it is imperative to utilize Euler mesh with co-nodes for explosives, powder liners, and air. Furthermore, a dense meshing of the powder liner and metal jet through the area is required [14]. Considering both computational cost and accuracy comprehensively, five tetrahedral meshes were created along the liner's wall thickness direction with a mesh size of approximately 0.03 cm. The mesh size for both the explosive domain and the air domain is set to 0.03 cm. The shell employs a mesh size of 0.06 cm, while the steel target utilizes a gradient mesh, with the central region having a mesh size of 0.06 cm. This model resulted in a total of 27,211 nodes and 26,219 units, as shown in Figure 5b.  
As the model is a 1/2 axisymmetric model, it is essential to define axisymmetric calculation commands separately for the Lagrange unit and Euler unit. Additionally, flow-out boundary conditions should be established at the periphery of the computational domain to simulate an infinite air domain environment, enabling unrestricted outflow of all substances from the model boundaries [15]. This approach prevents stress wave reflections during simulation and ensures maximum accuracy of the results.  
Figure 5: Numerical calculation model of penetrating steel target: (**a**) schematic diagram; (**b**) finite element model.
# Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter
## 3 Experiments and Results
As expected, when the primary user starts moving and the distance becomes larger, the probability of miss detection of the proposed approach decreases while it remains the same for the RSS-based localization technique. For example, when the distance is 50 m, the probabilities of miss detection of the proposed approach and the RSS-based localization technique are 76% and 84%, respectively. When the distance increases to 150 m, the probability of miss detection of the proposed approach decreases to 19% while it remains equal to 84% for the RSS-based localization technique.
# Communication-Efficient Federated Learning with Adaptive Consensus ADMM
###### Abstract  
This paper proposes utilizing federated learning (FL), a distributed learning paradigm, to process large, decentralized, and heterogeneous edge data in the context of Internet of Things (IoT) devices. However, heterogeneity and high communication costs are two primary challenges that hinder the efficacy of federated learning. To overcome these challenges, we have designed an algorithm, FedACADMM, which applies the adaptive consensus alternating direction method of multipliers (ACADMM) to federated learning clients (i.e., the edge mobile devices) to tackle the heterogeneity problem in federated networks. Importantly, the cost per round of communication for FedACADMM remains consistent with FedAvg and FedProx without adding any extra workload. Furthermore, our experimental results demonstrate that FedACADMM outperforms baseline methods with a realistic set of federated datasets, displaying enhanced convergence robustness. Notably, in highly heterogeneous scenarios, FedACADMM exhibits significant stability and rapid convergence, requiring at least 75% fewer communication rounds to achieve the target accuracy.  
Internet of Things (IoT); federated learning (FL); alternating direction method of multipliers (ADMM); communication efficiency +
Footnote †: journal: Accepted: 20 April 2023
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 4 The Proposed Solutions
### CRT Map Construction and Message Exchanges
#### 4.1.2 Information Exchange and Share  
To speed up the information distribution, vehicles exchange their own maps with each other at intersections. Each vehicle \(v_{i}\) maintains a counter \(CV_{i}\) to control the exchange frequency and cost. The counter can be dissimilar on different vehicles dependent on the vehicle's route-demand frequency. \(CV_{i}\) is then decreased by one per junction. Once \(CV_{i}\) is down to zero at a junction, \(v_{i}\) initiates a three-way exchange process at this junction. It is possible that there is already an exchange process taking place at this junction, thus, \(v_{i}\) simply joins the existing process. A request is broadcasted to require all vehicles at the junction to reply to their maps. A random back-off mechanism can be adopted to avoid collisions of responses. After a period of waiting time, \(v_{i}\) merges all received maps by Equations (1) and (2). Finally, \(v_{i}\) broadcasts the merged CRT Map to vehicles, and then they directly replace their maps with the merged one if they have joined in the exchange process. It is possible that  
Figure 4: A flowchart of Comprehensive Real-Time Traffic Map (CRT Map) construction and message exchanges for each vehicle.  
a vehicle arrived at the junctions too late to join, but overhears the merged map. The vehicle can still take advantage of the result by merging it with its own map. When the exchange process is completed, \(CV_{i}\) is reset.
# Robust CNN Compression Framework for Security-Sensitive Embedded Systems
## 4 Experiments
### Comparison with the State-of-the-Art Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network (Dataset)** & **Type** & \multicolumn{2}{c}{**Method Comp Rate Org Acc (\%) Adv Acc (\%)**} \\ \hline \multirow{2}{*}{VGG16 (CIFAR10)} & Pruning & FPGM & \(\times 1.3\) & **93.13** & 16.17 \\ \cline{2-6}  & Pruning + Defense & APD & \(\times\)**1.5** & 81.01 & **42.62** \\ \hline \multirow{2}{*}{LeNet (MNIST)} & Defense & DD & \(\times 1\) & 93.15 & 86.57 \\ \cline{2-6}  & Pruning + Defense & APD & \(\times\)**2** & **99.17** & **96.21** \\ \hline \multirow{6}{*}{LeNet (MNIST)} & Pruning + Defense & [PERSON] et al. & \(\times 2\) & 99.01 & 95.44 \\ \cline{2-6}  & Pruning + Defense & APD & \(\times 2\) & **99.17** & **96.21** \\ \cline{2-6}  & Pruning + Defense & [PERSON] et al. & \(\times 4\) & 98.87 & 94.77 \\ \cline{2-6}  & Pruning + Defense & APD & \(\times 4\) & **98.88** & **94.90** \\ \cline{2-6}  & Pruning + Defense & [PERSON] et al. & \(\times 8\) & 98.07 & 89.95 \\ \cline{2-6}  & Pruning + Defense & APD & \(\times 8\) & **98.08** & **91.06** \\ \hline \multirow{2}{*}{ResNet18 (CIFAR10)} & Pruning + Defense & [PERSON] et al. & \(\times 2\) & 81.83 & 48.00 \\ \cline{2-6}  & Pruning + Defense & APD & \(\times 2\) & **82.09** & **48.03** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of filter-wise pruning results of APD (ours) and other state-of-the-art methods. Figure 2: The original accuracy and the adversarial accuracy of AP and APD (ours) with respect to the epoch of the filter pruning procedure for **(a)** LeNet, **(b)** VGG16, and **(c)** ResNet18. The left of each row is the result in the compression rate of \(\times 1.5\), the middle of each row is the result in the compression rate of \(\times 2\), and the right side of each row is the result in compression rate of \(\times 2.5\). The blue line means the original accuracy and the red line indicates the adversarial accuracy. The solid line is the result of APD and the dashed line is the result of AP.
## 2 Wireless Multimedia Sensor Networks
### Real-Time Video Data
#### 2.2.4 Quality of Experience  
Quality of Experience (QoE) is a metric used to evaluate an end-user's satisfaction on the perceptions for the quality of multimedia services [55; 56]. In contrast to the aforementioned QoS provision methods that emphasize on technical terms for packet delivery, QoE stresses on the guarantee of a minimum level of quality of the real-time video data in order to meet the end-user's demands. As a result, providing QoE guarantees often requires cross-layer cooperations, from multimedia encoding in the application layer, to the QoS provisions in the communication layers. For example, in [57] a joint encoding and routing approach is studied which explores the correlation characteristics of video data in WMSNs to reduce data redundancy while also to improve frame delivery ratio. In [58] a cross-layer framework that jointly considers QoS and QoE is designed by using scalable high efficiency video coding (SHVC) with error concealment and multipath routing.
## 3 Outage Rate Analysis
### Maximal Ratio Combining  
Because the small-scale fading for the two links, Tx1-Rx and Tx2-Rx, are independent, the received SNR follows a hypo-exponential distribution for given shadowing realizations \(\Omega_{1}\) and \(\Omega_{2}\) with the conditional cumulative distribution function (CDF) [16]:  
\[F_{\gamma_{mrc}}(x|\Omega_{1},\Omega_{2})=1-\frac{\Omega_{1}}{\Omega_{1}- \Omega_{2}}\exp\left(-\frac{x}{\Omega_{1}}\right)-\frac{\Omega_{2}}{\Omega_{ 2}-\Omega_{1}}\exp\left(-\frac{x}{\Omega_{2}}\right), \tag{15}\]  
where \(x\geq 0\). Using this conditional probability density function, the outage probability for MRC is given by \[P_{out}^{mrc} =\int_{0}^{\infty}\int_{0}^{\infty}F_{\gamma_{mrc}}(\gamma_{th}| \Omega_{1},\Omega_{2})f_{\Omega_{1},\Omega_{2}}(\Omega_{1},\Omega_{2})d\Omega_{ 1}d\Omega_{2} \tag{16}\] \[=\int_{0}^{\infty}\int_{0}^{\infty}\frac{\xi^{2}}{2\pi\sigma^{2} \Omega_{1}\Omega_{2}\sqrt{1-\rho^{2}}}\left[1-\frac{\Omega_{1}}{\Omega_{1}- \Omega_{2}}\exp\left(-\frac{\gamma_{mrc}}{\Omega_{1}}\right)-\frac{\Omega_{2}} {\Omega_{2}-\Omega_{1}}\exp\left(-\frac{\gamma_{mrc}}{\Omega_{2}}\right)\right]\] \[\cdot e^{\frac{1}{\rho^{2}-1}\left(\frac{(10\log_{10}\Omega_{1}- \mu_{1})^{2}}{2\sigma^{2}}+\frac{(10\log_{10}\Omega_{2}-\mu_{2})^{2}}{2\sigma^ {2}}-\frac{\rho}{\sigma^{2}}(10\log_{10}\Omega_{1}-\mu_{1})(10\log_{10}\Omega _{2}-\mu_{1})\right)}d\Omega_{1}d\Omega_{2},\]  
which can be numerically integrated by applying the Gauss-Hermite formula [2, 44]. If the both links undergo the same shadowing realization (i.e., \(\rho=1\)), the corresponding outage rate is expressed as  
\[P_{out}^{mrc} =\int_{0}^{\infty}F_{\gamma_{mrc}}(\gamma_{th}|\Omega_{1}=\Omega_ {i},\Omega_{2}=\Omega_{i})f_{\Omega_{i}}(\Omega_{i})d\Omega_{i} \tag{17}\] \[=\int_{0}^{\infty}\left(1-\sum_{k=0}^{1}\frac{\exp\left(-\frac{ \gamma_{th}}{\Omega_{i}}\right)\left(\frac{\gamma_{th}}{\Omega_{i}}\right)^{k }}{k!}\right)\frac{\xi}{\sqrt{2\pi}\sigma\Omega_{i}}\exp\left[-\frac{(10\log _{10}\Omega_{i}-\mu_{i})^{2}}{2\sigma^{2}}\right]d\Omega_{i},\]  
which can be numerically calculated through the Gauss-Hermite polynomials [2, 44].
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### Incident and Reflected Waves  
Let \(a_{i}\) denote the incident wave at port \(i\), and \(b_{i}\) denote the reflected (or scattered) wave at port \(i\). In vector form, the relationships among these waves can be written as  
\[\begin{bmatrix}b_{1}\\ b_{2}\\ b_{3}\end{bmatrix}\ =\ \begin{bmatrix}S_{11}&S_{12}&S_{13}\\ S_{21}&S_{22}&S_{23}\\ S_{31}&S_{32}&S_{33}\end{bmatrix}\begin{bmatrix}a_{1}\\ a_{2}\\ a_{3}\end{bmatrix}. \tag{47}\]  
Expanding (47) in terms of its elements yields:  
\[b_{i}\ =\ \sum_{j=1}^{3}S_{ij}\,a_{j},\quad i\in\{1,2,3\}. \tag{48}\]  
Power Consistency Check  
The incident power \(P_{\text{in}}\) at port \(j\) can be related to \(|a_{j}|^{2}\), and the reflected power \(P_{\text{ref}}\) at port \(i\) can be related to \(|b_{i}|^{2}\). For a single excitation scenario (where all incident waves except \(a_{j}\) are zero), it follows from (48) that  
\[b_{i}\ =\ S_{ij}\,a_{j}. \tag{49}\]  
Thus, the ratio of reflected power at port \(i\) to the incident power at port \(j\) is  
\[\frac{P_{\text{ref},i}}{P_{\text{in},j}}\ =\ \frac{|b_{i}|^{2}}{|a_{j}|^{2}}\ =\ \bigl{|}S_{ij}\bigr{|}^{2}. \tag{50}\]If the SPDT switch (or multi-port network) is passive, the sum of reflected powers across all ports must not exceed the total incident power. Hence, if \(a_{j}\
eq 0\) and \(a_{k}=0\) for \(k\
eq j\):  
\[\sum_{i=1}^{3}|b_{i}|^{2}\ =\ \sum_{i=1}^{3}\bigl{|}S_{ij}\bigr{|}^{2}|a_{j}|^{2} \ \leq\ |a_{j}|^{2}, \tag{51}\]  
which is consistent with the passivity constraint in (42).  
These theoretical considerations guide the design and optimisation of SPDT switches used in reconfigurable radio front-ends, ensuring minimal loss, high isolation, and robust performance across the targeted frequency range.
## 6 Fault Decision  
After modeling the resampled RMS signal in a normal condition using the proposed AMRXU technique, and after estimating the normal and faulty resampled RMS signals using the proposed AMRXU-AVSPMI observation technique, the fault decision technique is used for anomaly detection and diagnosis. This procedure has two main subsections: (a) determination of the residual signal (the difference between the original resampled RMS signal in normal and abnormal states and the estimated signals), and (b) fault detection and classification using a machine learning-based approach, such as an SVM [44; 45].
## 3 Materials and Method
### Proposed Method
#### 3.3.2 Probability Density Extraction Module
If the reconstructed samples closely match the original data in terms of these statistical measures, it indicates that the module effectively retains the data's global distribution features. Furthermore, the module's ability to handle various data subsets is tested to ensure consistency across different types of data. This is done by applying KDE to different data subsets (e.g., different categories, time periods, or regions) and comparing the distribution properties of these subsets. If the density estimates exhibit consistent global statistical characteristics across different subsets, it further validates that the probability density extraction module is capturing the global distribution of the data in a comprehensive manner. This module's design offers several significant advantages. First, by combining kernel density estimation (KDE) with convolutional feature extraction, it ensures that the obfuscated data statistically retain the original data's distribution properties. The use of KDE allows for non-parametric estimation of the data's probability distribution, which is particularly effective for capturing complex and high-dimensional data distributions without making assumptions about the underlying distribution. Unlike parametric methods, KDE is flexible and can adapt to various data characteristics. Convolutional neural networks (CNNs) are employed to enhance this process by extracting high-level features that capture the underlying patterns in the data. CNNs are well suited for handling large, high-dimensional datasets such as images, where local patterns and hierarchical structures are important. This combination of KDE and CNNs ensures a robust, flexible approach to modeling data distributions that can adapt to different types of data while preserving the statistical characteristics of the original data. Second, as the output features represent global distribution characteristics rather than task-specific target features, the module achieves a high degree of task independence and adaptability. Finally, the module seamlessly integrates with the information entropy fusion module, providing high-quality probabilistic features to support the effectiveness of the obfuscation strategy.
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 3 Proposed Quality Control Method
### Neural Network Filter Design
There are many interesting features in Figure 5. Firstly, the FAR curves do not vary much with changes in the definition of the good population but there is a significant jump in PD with changes in the definition of the outlier population. This is due to the relatively small percentage of outliers when compared to the total sample population. Next, the FAR metric has the best performance when the good sample definition is set to 1 m/s and gradually degrades with increase in the difference. However, above a wind speed of \(\sim\)18 m/s, the trend reverses. This is due to the fact that, at higher wind speeds there is a greater degree of scatter in the data (as seen in Figure 1) resulting in poorer performance in terms of FARs at very stringent definitions of a good sample. Next, as mentioned earlier, the PD metric seems to have a strong jump with change in outlier definition; with the highest PD performance for an outlier definition of \(>\)7 m/s for wind speed difference from the GMF line. Again, the trend flips in nature at higher wind speed (\(>\)21 m/s), this is again attributed to increased scatter in the data. Finally, it is important to note that the general performance of the filter is not optimal at very low wind speeds (\(<\)3 m/s) for any definition of good and outlier sample. Thus, the ideal operating range for this filter is \(\sim\)5 m/s to 18 m/s; this were most of the samples lie. The choice of the definitions is dependent on the application. For instance, applications that require very high quality control like monitoring long term variations in wind speed data must go for highest PD performance. Applications at higher wind speeds which needs to retain as many higher wind speed samples as possible, must go for lower PD performance. In this work the assessment of wind retrieval performance is used as its definition of good sample a wind speed difference \(<\)=2m/s and defines outliers as \(>\)5 m/s. Figure 4: PD and FAR curves for 3 different network sizes (5, 10 and 15).
## 3 Methodology  
### Underwater Magnetic Induction MIMO Communication System Model  
#### 3.1.1 The Communication Procedure of Underwater MI  
Magnetic induction is a signal transmission method that utilizes a magnetic field as a carrier in aquatic environments. In this process, an electric current creates a magnetic field within a closed coil, and the receiving coil converts the magnetic field into an electric signal for communication. Figure 1 shows the process of magnetic induction communication, where \(T_{C}\) and \(R_{C}\) are the transmitting and receiving coils, respectively. \(W_{T}\) and \(W_{R}\) represent the number of turns in the transmitting and receiving coils, respectively, while \(A_{t}\) and \(A_{r}\) correspond to the radii of the transmitting coils. \(I_{1}\) denotes the current flowing through the transmitting antenna, while \(I_{2}\) refers to the induced current generated by the receiving antenna. The variable \(d\) represents the transmission distance, and \(\theta\) is the angle of deviation between the transmitting and receiving coils.  
The mutual inductance \(M\) between the coils can be expressed as (1):  
\[M=\frac{\mu\pi W_{T}W_{R}A_{t}^{2}A_{t}^{2}}{2\sqrt{\left(A_{t}^{2}+d^{2} \right)^{3}}}\left(2\cos^{2}\theta_{1}-\sin^{2}\theta_{1}\right) \tag{1}\]  
where \(\mu\approx 4\pi\times 10^{-7}\) H/m is the magnetic permeability. In this study, we found that the mutual inductance strength is maximum when the two coils have the exact specifications, which are shown in (2):  
\[M=\frac{\mu\pi W_{T}^{2}A_{t}^{4}}{2\sqrt{\left(A_{t}^{2}+d^{2}\right)^{3}}} \left(2\cos^{2}\theta_{1}-\sin^{2}\theta_{1}\right) \tag{2}\]  
Figure 1: Principles of magnetic induction communication.  
It is noted that these specifications apply when the two coils are aligned in parallel and coaxial with a separation distance of \(d\).