esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Inverse Problem
### Markov Chain Monte Carlo  
In many cases it is not possible to obtain an analytical solution for the posterior probability distribution within the Bayesian structure. In such cases, simulation methods, which use a sampling process to obtain information about the probability density posteriori, can be used, as is the case with the Markov chain Monte Carlo (MCMC) method [43].  
The MCMC combines the properties of Monte Carlo and the Markov chain. The first is estimates the properties of a distribution by examining random samples from the distribution. The second is based on the idea that a special sequential process generates random samples, where each random sample is used as a step to generate the next one. A special property of the chain is that, although each new sample depends on the previous one, new samples do not depend on any sample before the previous one [44; 45; 46; 47; 48; 49; 50].  
One of the most widely used algorithms for implementing the Markov chain Monte Carlo is the Metropolis-Hastings algorithm. This algorithm uses a proposal distribution, \(\overline{q}\left(\mathbf{P}^{*}|\mathbf{P}^{(\mathrm{i})}\right)\), to generate a vector of candidate parameters, \(\mathbf{P}^{*}\), based on the current state of the chain, \(\mathbf{P}^{(\mathrm{i})}\). The Markov chain then moves to \(\mathbf{P}^{*}\) with the probability of acceptance given by Equation (22).  
\[\alpha\Big{(}\mathbf{P}^{(\mathrm{i})},\mathbf{P}^{*}\Big{)}=\min\left[1,\frac{\pi( \mathbf{P}^{*}|\mathbf{Y})\overline{q}\left(\mathbf{P}^{(\mathrm{i})}\middle|\mathbf{P}^{*} \right)}{\pi\Big{(}\mathbf{P}^{(\mathrm{i})}\middle|\mathbf{Y}\Big{)}\overline{q}\left( \mathbf{P}^{*}|\mathbf{P}^{(\mathrm{i})}\right)}\right] \tag{22}\]The Metropolis-Hastings algorithm can be specified using the following steps [44; 51]:  
1. Start the iteration counter, i = 1, and specify an initial value, \(\mathbf{P}^{\rm i}\);
2. Generate a vector of candidate parameters from the proposal distribution, \(\mathbf{P}^{*}\).
3. Calculate the probability of acceptance, \(\alpha\left(\mathbf{P}^{\rm(i)},\mathbf{P}^{*}\right)\), given by Equation (22);
4. Generate an auxiliary random sample of a uniform distribution, \(\overline{u}\sim U(0,1)\);
5. If \(\overline{u}\leq\alpha\), then accept the new value and update the parameter vector, \(\mathbf{P}^{\rm(i+1)}=\mathbf{P}^{*}\). Otherwise, \(\mathbf{P}^{\rm(i+1)}=\mathbf{P}^{\rm(i)}\);
6. Increment the counter from i to i + 1 and go back to step 2.
## 3 Algorithm Simulation Comparison
### Simulation Comparison of ASD Estimation Algorithms
that observed in the original algorithm, reflecting a reduction of 87%. Additionally, in comparison to the LPSD algorithm, its MSE is approximately 0.36 times lower, signifying a reduction of 64%. Conversely, the MSE of the LPSD algorithm is the same as that of the LISA-LPSD algorithm. Moreover, the adaptive sine multi-taper algorithm achieves an MSE approximately 0.36 times that of the LISA-LPSD algorithm, reflecting an impressive reduction of 64%, as shown in Table 1 under 'Noise Metric Simulation: Adaptive Sine Multi-Taper vs. Others'. Further analysis, as depicted in Figure 6b, supports conclusions akin to those drawn from Sections 1-5. By selecting the longest data processing duration of 5 h to assess computational speed, the enhanced adaptive sine function algorithm diminishes computation time to a mere 32% of what is necessitated by the original algorithm, reflecting a reduction of 68%. Additionally, it requires only 14% of the LPSD algorithm's processing time, indicating a reduction of 86%, and 69% of the LISA-LPSD algorithm's computation duration, corresponding to a reduction of 31%, as shown in Table 1 under 'Noise Metric Simulation: Adaptive Sine Multi-Taper vs. Others'. In both simulation scenarios, the adaptive sine multi-taper algorithm shows a reduced MSE compared to the original adaptive sine multi-taper, LPSD, and LISA-LPSD algorithms. It also features lower complexity and faster computation. Notably, in comparison to the LISA-LPSD algorithm, the MSE is reduced by approximately 64% to 90%, with run time decreasing to about 70% of the latter's duration, as shown in Table 1. Overall, the enhanced adaptive sine multi-taper algorithm exhibits commendable performance for applications in gravitational wave detection system assessments. Nevertheless, considering the intrinsic complexity of the noise, further experimental validation is merited.
# Lower-Latency Screen Updates over QUIC with Forward Error Correction
## 6 Empirical Results on Emulab
The **latency improvements of Partial+FEC grew as a function of the packet-loss rate** for both the streaming and non-streaming workloads. Higher packet-loss rates mean waiting for retransmissions more frequently with total delivery, which hurts the latency metrics. For example, with a 1% packet-loss rate, which is double the baseline loss and streaming workload, the 99-percentile of the message-blocking time for partial+FEC (n/k = 1.1) was 0.4 ms, while the 70-percentile of total was already 99 ms, and the 99-percentile was 264 ms (Figure 11). 4. The **latency improvements of Partial+FEC grew as a function of the RTT** of the network for both the streaming and non-streaming workloads. A higher RTT means lingering waiting time for each retransmission with total delivery, while using FEC we can let us avoid it. For example, with a 200 ms RTT, which is double the baseline RTT and streaming workload, the 99-percentile of the message-blocking time for partial+FEC (n/k = 1.1) was 0.4 ms, while the 80-percentile of total was already 225 ms, and the 99-percentile was 429 ms (Figure 13). We evaluated two metrics for each workload and present them in the next two sections. In Section 6.1, we measured the message-blocking times and message inter-delivery times for the streaming updates workload. The message-blocking time is the time from receiving a message to its delivery time, during which it needs to be blocked in the client's buffer. The message inter-delivery time is the time between delivering every two consecutive messages. Each noticeable message inter-delivery time can have a negative impact on the user experience as a glitch in that screen update. In each figure, we compare four delivery options: (1) total signifies delivering the messages in the exact order that they have been sent, (2) partial signifies delivering the messages within each update in arrival order but keeping the sequence of updates as they have been sent, and (3) and (4) denote Partial+FEC with two different redundancy rates--the same as partial but using FEC to recover lost messages sooner. In Section 6.2, we measured the update response times and message inter-delivery times for the non-streaming updates workload. Response time was introduced in [36] as an important metric for interactive performance over VNC. The response time is the time from getting an input (e.g., mouse and keyboard) from the client to the time we finish downloading the update.
## 2 Materials and Methods
### In Vitro Drug Release from Chrysin-Loaded Microemulsion  
The in vitro drug release from CS-ME was studied in a Franz diffusion cell. The effective diffusion surface area and receptor compartment mean volume were 0.785 cm\({}^{2}\) and 12 mL, respectively. Synthetic cellulose acetate membranes with 0.45-\(\upmu\)m pore diameter (Millipore, Barueri, Brazil) were washed in phosphate buffer, pH 7.4, for 2 h before being mounted between the compartments. A 1:1 (\(v/v\)) ethanol/water solution was used as the receptor fluid to assure sink conditions, considering that chrysin concentration in the medium represented \(\leq\)10% of the saturation solubility. This medium was constantly stirred and maintained at 37 \(\pm\) 0.5 \({}^{\circ}\)C to simulate body temperature on the membrane surface. After placement of cellulose acetate membranes in the Franz diffusion cell, 0.3 mL of CS-ME formulation was placed in the donor chamber and sealed with paraffin wax to minimize environmental disturbances. Successful collector conditions were ensured by replacing the receptor solution after withdrawing it from the receptor compartment at predefined times and magnetically stirring at a rotational speed of 300 rpm. One mL of the receptor solution was withdrawn after 0.25, 0.5, 1, 2, 4, 6, 8, 10, and 12 h, and then replaced. The amount of drug passed through the membrane was measured spectrophotometrically as described previously. All experiments were carried out in triplicate, and kinetic parameters of release were compared by one-way ANOVA at 95% confidence interval.  
The cumulative amount of chrysin released (\(R_{t}\)) was divided by the surface exposed area and expressed in \(\upmu\)g/cm\({}^{2}\), and data were plotted as a function of time (\(t\)). The percentage of drug release (\(P\)) was determined by the equation:  
\[P\left(\%\right)=\left(R_{t}/L\right)\times 100, \tag{2}\]  
where \(L\) the initial chrysin content of CS-ME.  
Kinetic Modeling of Drug Release from Chrysin-Loaded Microemulsion  
The experimental data of chrysin release were fitted to different kinetic models including zero-order (\(R_{t}\) versus time), first-order (\(\ln R_{t}\) versus time), and Higuchi (\(R_{t}\) versus square root of time) models [33]. The mathematical model ensuring the highest coefficient of linear correlation (\(\tau^{2}\)) was selected to describe the release profile. The flux of chrysin across the synthetic membranes (\(J\)) was calculated by the linear regression of steady-state data corresponding to the linear portion of the slope [32; 34]. The latency time was assumed to correspond to the point of intersection on the time axis of the steady-state line. The release coefficient (\(K_{r}\)) was calculated as the ratio of the flux of chrysin at steady state (\(J_{ss}\)) to the initial drug concentration (\(C_{o}\)) in the vehicle according to the equation  
\[K_{r}=J_{ss}/C_{o}, \tag{3}\]
# Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing
## 2 Related Works
### Offloading Management for MAR
[PERSON] et al. suggested a joint optimization of task offloading and resource allocation scheme for energy-efficient MAR [13]. The main goal of this paper is to minimize the energy consumption of user terminals in the task offloading process. This paper designs a novel AR application task model consisting of several dependent subtasks and implements a Deep Reinforcement Learning (DRL)-based optimization solution. These research works still focused on the specific factor and goal. However, in the real MAR environment, users' requirements are more complicated. Most MAR users require lower latency, lower power, and more accurate AR services and applications. With this trend of demand, service providers are also forced to consider more factors. Therefore, recent research works on the MEC framework for MAR have considered not only the architecture that one MEC server takes multiple MDs but also the impact of the trade-off among multiple factors, such as computation latency, energy consumption of clients or MEC server, and service accuracy. First of all, [PERSON] et al. considered the mobility of mobile devices used by mobile users [15]. They aimed to minimize both task completion latency and energy consumption in the long term with stochastic computation tasks and dynamic network conditions. To solve this complicated problem, they also adopted the DRL approach. Also, they focused on the ask offloading placement problem for AR overlay rendering in multi-party MAR system [19]. [PERSON] et al. proposed a task offloading algorithm under end-edge-cloud three-tier architecture [20]. In this paper, a genetic algorithm-based solution was suggested to optimize energy consumption and latency. They first presented the observation about performance bottlenecks of edge devices and explained the necessity of splitting the AR overlay rendering pipeline and then suggested a DRL-based offloading decision solution for optimizing both QoS and service cost. With these studies, we can conclude that the recent MAR applications on the MEC system should consider QoS, energy consumption of MAR users, and service latency. [PERSON] et al. designed an energy-aware edge-based MAR system that enables MDs to dynamically change their configuration parameters, such as CPU frequency and computation model size, based on their user preferences, camera sampling rates, and available radio resources at the edge server [21]. In this paper, their proposed algorithm can minimize the per-frame energy consumption of multiple MDs without degrading their preferred MAR performance metrics, such as service latency and detection accuracy.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 2 Methods
### Evaluation Metrics  
To evaluate the performance of the epileptic detection model, cross-validation can reduce overfitting to a certain extent and obtain as much adequate information as possible under a small sample size. Three evaluation metrics (accuracy, sensitivity and specificity) were used to evaluate the model's performance. Accuracy predicts correct results as a percentage of the total sample. Sensitivity represents the proportion of true positive samples in actual positive samples. Specificity represents the proportion of true negative samples in actual negative samples.  
To better fit the proposed epileptic detection model, the epileptic dataset is stored in a text file, and the training and test datasets are read through the panda's library and stored in a data frame data structure. This model's training and testing processes were tested on a windows desktop with 32 GB of RAM and an NVIDIA TITAN RTX GPU (NVIDIA Corporation, Santa Clara, CA, USA) using the Python 3.8.8 environment and TensorFlow-GPU 2.3.0 for good computational efficiency.
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 5 Dataset Construction and Experimental Results  
In this section, datasets are constructed for network training and testing, including equivalent MEO SAR ship data and simulated MEO SAR ship data. The experimental results for coarse focusing-classification and fine focusing-classification are given to show the effectiveness of the proposed method.
## 7 Conclusions  
The development and prototyping flow of NISC-based architectures dedicated to MMSE turbo equalization and Max-Log-MAP turbo demapping have been presented. The described designs are efficient, flexible and support different communication modes defined in the WiFi, WiMAX, DVB-RCS, LTE and LTE-Advanced wireless communication standards. The proposed designing flow is detailed starting from architecture specification till FPGA implementation. Using this flow, the NISC-based architectures of the equalizer and demapper are prototyped targeting the Xilinx Virtex-7 XC7 VX485T. The proper functionality of both architectures has been verified, and their corresponding performances have been evaluated for different system configurations by conduction on-chip validation. The FPGA prototype of the proposed equalizer architecture achieves a throughput of 115.8 mega symbols per second for 2 \(\times\) 2 and 62.4 mega symbols per second for 4 \(\times\) 4 spatially-multiplexed (SM) MIMO systems when operating at a clock frequency of 202.67 MHz. Moreover, the FPGA prototype of the proposed demapper architecture enables a maximum throughput of 234.6 mega LLRs per second adopting the 16-QAM modulation scheme when operating at a clock frequency of 293.242 MHz. The two proposed architectures are compared to relevant state-of-the-art implementations. The comparison results illustrate the effectiveness of the proposed design approach, which allows shortening development cycles, while ensuring high implementation efficiency.  
This work is done in collaboration between Telecom Bretagne and Lebanese University, and funded by the Electronics Department of Telecom Bretagne.  
[PERSON] developed the design and prototyping flow, designed and implemented the architectures, and wrote the paper. [PERSON] scientifically supervised the work, contributed in implementing the architectures, and participated in writing the paper. [PERSON], [PERSON], and [PERSON] academically supervised the work for Telecom Bretagne and Lebanese University.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methodology
#### 2.1.3 Projection of Covariance Matrices to Tangent Plane  
Inherently possessing the attributes of symmetric positive definite (SPD), the SCM matrices \(C\), of dimensions \(K\ \times\ K\), are of full rank and nonsingular. If the space of SCM matrices is equipped with a differential structure, it becomes a Riemannian manifold \(C_{p}\), defined as  
\[C_{p}=\Big{\{}C\ \in\ \mathbb{R}^{K\ \times\ K}\ :C=\ C^{T}\text{ and }x^{T}Cx>0,\ \forall x\ \in\mathbb{R}^{K}\{0\}\Big{\}}\]  
Figure 2 represents the mapping of a Riemannian space of a geodesic path to tangent space. It illustrates the projection of points \(C_{1}\) and \(C_{2}\) from the Riemannian manifold to tangent space, with the reference point M transformed into the null matrix \(\varnothing_{S}\), which serves as the origin of \(T_{M}C_{p}\). As shown in Figure 2, the Riemannian manifold space is nonlinear, and SPD matrices inherently belong to a non-Euclidean domain. To accurately map the datapoints of an SCM on a Riemannian manifold \(C_{p}\) to tangent space \(T_{M}C_{p}\), the log-Euclidean kernel is utilized.  
For every point M on manifold \(C_{p}\), the transformation to the tangent space \(T_{M}C_{p}\) is defined as  
\[\left\{\begin{array}{ll}f:&C_{p}\to T_{M}C_{p}\\ &C\to\log_{M}(C)=M^{\frac{1}{2}}\log\Big{(}M^{-\frac{1}{2}}CM^{-\frac{1}{2}} \Big{)}M^{\frac{1}{2}}\end{array}\right.\]  
The selection of an appropriate reference point is crucial for projecting datapoints into \(T_{M}C_{p}\). In this study, the Riemannian mean \(M_{rm}\), calculated using Equation (2) [21], serves as the reference point for the manifold.  
\[\mathrm{M}_{\mathrm{rm}}=\arg\min_{\mathrm{M}\in\mathrm{C}_{\mathrm{P}}}\sum_{i=1 }^{\mathrm{P}}\delta_{\mathrm{r}}^{2}\big{(}\mathrm{M},\mathrm{C}_{\mathrm{P}} \big{)} \tag{2}\]  
Riemannian distances between two SPD matrices \(\mathrm{C}_{1}\) and \(\mathrm{C}_{2}\) are defined as follows:  
\[\delta_{\mathrm{r}}(\mathrm{C}_{1},\mathrm{C}_{2})=\|\log\left(\mathrm{C}_{1} ^{-1}\mathrm{C}_{2}\right)\|_{\mathrm{F}}\]  
The transformed SCM matrix is then represented as matrix \(\mathrm{S}\):  
\[\mathrm{S}=\begin{bmatrix}\mathrm{Var}(\mathrm{e}_{\mathrm{i}})&\mathrm{Cov} \big{(}\mathrm{e}_{\mathrm{i}},\mathrm{e}_{\mathrm{j}}\big{)}\\ \mathrm{Cov}\big{(}\mathrm{e}_{\mathrm{i}},\mathrm{e}_{\mathrm{j}}\big{)}& \mathrm{Var}\big{(}\mathrm{e}_{\mathrm{j}}\big{)}\end{bmatrix}\]
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods
### YOLO Object Detection
#### 3.2.1 YOLOv4: Object Detection for UGV Component  
In the UGV component of our system, we implement YOLOv4 as the primary object detection algorithm. YOLOv4 is renowned for its state-of-the-art accuracy and its compatibility with the darknet framework and Robot Operating System (ROS). This compatibility enables seamless integration within the UGV system, leveraging the darknet-ROS package. Furthermore, considering the resource limitations of the system, we choose to utilize YOLOv4-Tiny, a compressed version designed for CPU-enabled devices. YOLOv4-Tiny achieves high-speed object detection with a reduced number of convolutional layers and YOLO layers, along with fewer anchor boxes for prediction.
# Graphene-Based Membranes for CO2/CH4 Separation: Key Challenges and Perspectives
## Mixed-Matrix Membranes
### Perspectives
The filler labels, namely, (1) ideal, (2) exemplary, (3) competent, (4) moderate, and (5) inadequate, were created to cover \(F_{\text{index}}\) values of \(>\)8.00, 8.00-4.00, 4.00-1.50, 1.50-0.00, and \(<\)0.00, respectively. To date, no filler has achieved the highest accolade of the ideal label [5]. Among the surveyed graphene-based fillers, 31% of them achieved a label of either exemplary or competent, while 41% and 28% achieved a moderate and inadequate label, respectively. These accomplishments are slightly less stellar than the metal-organic frameworks (MOFs), which are also prominently used as fillers in mixed-matrix membranes [5]. Among the 148 MOF fillers surveyed thus far [5], 36% of them has a label of either exemplary or competent and 51% obtain a moderate label. Only 13% of the MOFs is labelled inadequate. Although the figures are less compelling, we believe that graphene-based fillers can add value in their own ways. One of them is to utilize the 2D morphology of graphene-based fillers to enhance the membrane selectivity polymericmatrices with high intrinsic permeability. Typically, one of the drawbacks of polymers with high intrinsic permeability, such as PIM-1 and 6 FDA-based polymers, is the low CO\({}_{2}\)/CH\({}_{4}\) selectivity [79]. By using graphene-based fillers to enhance the selectivity while sacrificing the CO\({}_{2}\) permeability to a limited extent, it will be easier to realize high-performance mixed-matrix membranes that surpass the 2008 Robeson upper bound. In this respect, we foresee that the \(F_{\text{index}}\) will be able to empower researchers with higher competencies in making data-driven decisions for designing mixed-matrix membranes. Explicitly, this entails leveraging the \(F_{\text{index}}\) values of specific graphene-based materials to help match with suitable polymer matrices of the right intrinsic separation performances so as to elicit the ideal polymer/filler pair that best optimize the performances of the mixed-matrix membranes. Furthermore, with this ability to align graphene-based filler materials with polymer matrices, it will also help accelerate the translation of flat-sheet to hollow fiber graphene-based mixed-matrix membranes, as well as the development of more complex membrane designs, such as the thin-film nanocomposite membranes where graphene-based materials are incorporated into the thin selective layer of the membranes (Figure 4**e**), and which is of particular interest. We believe that by strategically optimizing membrane design from a data-driven approach, high-performance graphene-based thin-film nanocomposite membranes, especially those in the hollow fiber configuration, can be realized. This will be a game-changer, considering that the amount of graphene-based materials used will be relatively lower than the mixed-matrix membranes and this will give a low-cost competitive advantage over all the other membrane designs discussed in this article [80].