esa-sceva/satcom-chunk-collection huggingface dataset:
# Median Filter Aided CNN Based Image Denoising:
###### Abstract  
Image denoising is a challenging research problem that aims to recover noise-free images from those that are contaminated with noise. In this paper, we focus on the denoising of images that are contaminated with additive white Gaussian noise. For this purpose, we propose an ensemble learning model that uses the output of three image denoising models, namely ADNet, IRCNN, and DnCNN, in the ratio of 2:3.6, respectively. The first model (ADNet) consists of Convolutional Neural Networks with attention along with median filter layers after every convolutional layer and a dilation rate of 8. In the case of the second model, it is a feed forward denoising CNN or DnCNN with median filter layers after half of the convolutional layers. For the third model, which is Deep CNN Denoiser Prior or IRCNN, the model contains dilated convolutional layers and median filter layers up to the dilated convolutional layers with a dilation rate of 6. By quantitative analysis, we note that our model performs significantly well when tested on the BSD500 and Set12 datasets.  
image denoising; CNN; deep learning; Gaussian noise; median filter; BSD500 and Set12 datasets
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### Homogenous Macro/Small Cell Deployment
#### 3.1.1 Transmit and Receive Power  
For the above described network model, the UE's transmit power \(P_{\mathrm{Tx}}\) in dBm is given as:  
\[P_{\mathrm{Tx}}=P_{o}+\alpha.\mathrm{PL} \tag{2}\]  
and the received power \(P_{\mathrm{Rx}}\) in dBm at the eNodeB is:  
\[P_{\mathrm{Rx}}=P_{\mathrm{Tx}}-\mathrm{PL}=(P_{o}+\alpha.\mathrm{PL})- \mathrm{PL}=P_{o}+(\alpha-1).\mathrm{PL} \tag{3}\]  
As the network was analyzed for FPC mode as well as the full pathloss compensation mode, the \(\alpha\) value varied from 0.7 to 1. To model the wireless channel, a free space model was considered for the pathloss estimation depending on the distance of the UE from the serving eNodeB.  
A study carried out in Reference [35] clearly showed that for OL/TPC, the signal degradation due to the distance between UE and the serving eNodeB and the interference generated due to the improper power control settings were dominant. In this work, we investigated \(P_{\mathrm{max}}\) for the uplink using OL/TPC. Therefore, only the pathloss model was considered. The simulation parameters for each deployment scenario are summarized in Table 1. The mathematical expression for the free space model is given as:  
\[\mathrm{PL}=32.45+20\log_{10}(r)+20\log_{10}(f_{\mathrm{MHz}}) \tag{4}\]  
where \(r\) is the distance of the UE from the serving eNodeB in meters, and \(f_{\mathrm{MHz}}\) is the operating frequency. Equation (4) can be rewritten to calculate the distance from the UE to its serving eNodeB, when the corresponding pathloss and the frequency values are known:  
\[r=\log{10^{-1}(\mathrm{PL}-20\log_{10}(f_{\mathrm{MHz}})-32.45)}/20 \tag{5}\]  
Figure 1: Fluid model for uniformly distributed cells. Zoomed view shows a uniform UE distribution in each cell.
# Arbitrary Sampling Fourier Transform and Its Applications in Magnetic Field Forward Modeling
## 2 Methods
### 2d As-Ft  
Two-dimensional Fourier forward transform and inverse transform are performed on function \(f(x,y)\), respectively [29]:  
\[\left\{\begin{array}{l}F(k_{x},k_{y})=\int_{-\infty}^{\infty}\int_{-\infty }^{\infty}f(x,y)e^{-i(k_{x}x+k_{y}y)}dxdy\\ f(x,y)=\frac{1}{4\pi^{2}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}F(k_{x },k_{y})e^{i(k_{x}x+k_{y}y)}dk_{x}dk_{y}\end{array}\right.. \tag{8}\]  
where \(k_{x}\) and \(k_{y}\) represent the wavenumbers in the direction of \(x\) and \(y\). \(f(x,y)\) is a spatial domain function and \(F(k_{x},k_{y})\) is a wavenumber domain function. \(f(x,y)\) and \(F(k_{x},k_{y})\) are each other's Fourier transform functions.  
For the Fourier transform integral of Equation (8), because the kernel function is continuous in the integral interval, the double integral can be converted into two one-dimensional integrals: first, a one-dimensional integral in the x direction is performed, then a one-dimensional integral in the y direction, and then the 2D Fourier transform is realized.  
Taking the forward Fourier transform in Equation (8) as an example, a one-dimensional Fourier transform for the \(x\) direction is first made:  
\[F_{x}(k_{x},y)=\int_{-\infty}^{\infty}f(x,y)e^{-ik_{x}x}dx. \tag{9}\]Then a 1D Fourier transform of the \(y\) direction is performed:  
\[F(k_{x},k_{y})=\int_{-\infty}^{\infty}F_{x}(k_{x},y)e^{-ik_{y}y}dy. \tag{10}\]  
The integrals of Equations (9) and (10) above are solved by a 1D Fourier transform. The process of 2D inverse transform is the same as th forward transform, and will not be repeated.
# Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay
## 2 Materials and Methods  
### Test Soil and Equipment  
In this study, two types of clay contaminated with municipal waste were prepared: seeping municipal waste-contaminated clay and soaking municipal waste-contaminated clay. Seeping municipal waste-contaminated clay is contaminated with municipal waste pollutants (mainly leachate) and occurs at different seepage depths. The soaking municipal waste-contaminated clay was prepared by mixing clay with CaCO\({}_{3}\) and CH\({}_{3}\)COOH, which are the products of the later degradation stage of municipal waste pollutants. The uncontaminated clay used in this experiment was obtained from a southern suburb of Xuzhou city in Jiangsu Province, China, and was prepared for use by screening, natural drying, and eliminating impurities. The basic physical properties and composition of the clay are listed in Table 1.  
When preparing the municipal waste-contaminated clay with different seepage depths, the clay was compacted layer by layer into the test device, and the height of the clay column was approximately 100 cm. A gravel drainage layer was emplaced atop the compacted clay layer, and domestic waste was graded, compacted, and emplaced atop the gravel drainage layer. The experimental device was based on the liner construction of a domestic waste dump site as the prototype, and the preparation for the testing of seepage municipal waste-contaminated soil was designed using the similarity criterion, as shown in Figure 1. Leachate from municipal waste flowed through the clay layer at a rate of 38.5 L/d using a pressure pump, and the seepage level in the column was maintained at 1 m above the clay layer. Table 2 provides the water quality of the leachate. According to the research of [PERSON] [37], the properties of clay are obviously different at various seepage depths. In this paper, six clays with different depths were selected  
Contaminated clay in the later stage of municipal waste pollutant degradation was investigated to examine the pixel threshold of contaminated clay from a different angle. With the use of the clay soaking method, CaCO\({}_{3}\)- and CH\({}_{3}\)COOH-contaminated clay specimens with concentrations of 0.2, 0.4, and 0.6 mol/L were artificially prepared as soaking municipal waste-contaminated clay because municipal waste contaminants are continuously degraded in the seepage process and the main products of the late degradation stage are CaCO\({}_{3}\) and CH\({}_{3}\)COOH [38]. The CaCO\({}_{3}\) and CH\({}_{3}\)COOH concentrations were determined according to the content of soluble organic matter in the leachate of municipal landfills in different regions [39; 40].
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 5 Simulation Results
### Bit Error Rate Comparison of the Proposed Scheme  
In this study, we assume that the average transmit power of each transmit antenna is normalized to 1, i.e., the total transmit power is fixed to \(P_{total}=1\). For each method considered, the total transmitted power is distributed evenly among the number of waveforms. To calculate the BER, \(10^{6}\) pulses are considered in the embedding process. As a result, the performance of the various methods can be compared objectively and fairly. This ensures that the methods are evaluated based on the same SNR and that the average power of each transmit antenna remains the same.  
The performance of both cases explained in examples 1 and 2 of the proposed scheme remains the same, and no degradation is observed. For the case of four communication receivers as shown in example 2, there is a slight degradation in the BER compared to the single communication receiver due to interference. We compared the performance of the proposed scheme for a single communication receiver with side lobe control and waveform diversity cited in [29] and achieved improved BER performance. Moreover, the proposed scheme outperforms the ASK-based information embedding technique as cited in [27]. It can also be observed from the results shown in Figure 9, that the proposed scheme converges more quickly with the safe margin of \(3 dB\) when compared with the beam pattern PSK-based approach [26] in terms of BER and SNR.
# Portable RGB-D Camera-Based System for Assessing Gait Impairment Progression in ATTRv Amyloidosis
## 1 Introduction
### Contributions  
Our main contribution is studying the possibility of using an RGB-D camera-based system, which is an affordable, portable, and non-intrusive system, as a tool for supporting clinical gait assessment during follow-up of ATTRV amyloidosis patients, as an alternative to more expensive, time-consuming, and intrusive reference systems (as the Qualisys Motion Capture reference system), as well as to the present approach used in clinical practice (observation by the physician).  
In contrast with studies that evaluate the Kinect's validity for gait analysis and/or its reliability in a short time interval (1 to 7 days) [29; 30; 36; 39; 41; 42; 43; 44; 52], our aim was to explore if the Kinect can be used for gait assessment over a longer period of time (in this case, 18 months), closer to the typical interval between consecutive clinical follow-up appointments. Despite the known limitations of RGB-D sensors for this task, especially for measuring angular parameters, we pursue the hypothesis that their systematic measurement errors do not have a large impact when analyzing gait variation over time for a given patient.  
The present study includes the computation of the inter-rater agreement between the two systems (RGB camera and reference), based on the results regarding the presence or not of a significant difference between the two moments of assessment. To the best of our knowledge, besides our previous preliminary work, there is no similar study involving a ATTRV amyloidosis population.  
We analyzed the gait of six ATTRv amyloidosis patients with a varying degree of gait impairment and heterogeneous disease progression, using the following two systems simultaneously: a reference motion capture system (multi-camera marker-based Qualisys system) and our gait analysis system based on a single RGB-D camera (Kinect v2) [53]. The gait analysis results included twenty-three gait parameters computed for each gait cycle and system. The analysis was performed twice (T0 and T1) for each patient, with an 18-month interval.  
To assess the ability of our RGB-D camera-based system to detect significant changes in the patients' gait over time, we firstly investigated whether there was a statistically significant difference between T0 and T1, for each parameter, subject, and system. We then verified if our Kinect-based system was able to detect an actual significant or non-significant difference (Qualisys used as the ground truth) and, based on those results for both systems, we determined the inter-rater agreement for different sets of parameters, considering all subjects. This differs from typical validation studies that compare the systems directly and measure the error between them as shown in [49].
## 5 Simulation Setup and Experiments
### Varying the Attackers  
To inspect our proposed protocol's authentication delay, we set the transmission range to 400 m. This choice (400 m) is because moving vehicles face many distortion-related issues due to network instability. We counted the average authentication delay of clusters having congested, average, and sparse traffic, respectively. Each cluster's number of attackers or malicious vehicles varied from one to five. Figure 4a depicts the average authentication delay in milliseconds for all DDoS attacks and mitigation methods, including HTTP Flood and Ping of Death. The results indicated a better performance than the experiment by [PERSON] et al. [58]. The authors discussed a trust-based Authentication Technique (TBAT) and advanced Secure mechanism based on Clustering and Key Distribution (SCKD) in their research, but they did not test their protocol on different types of clusters. We compared authentication and Communication Protocol (ACP) results with the SCKD and TBAT experimental results. For comparison, we did not show the different authentication delays for each congested, average, or sparse cluster. We calculated each cluster's average authentication delay and compared it with the number of attackers. Our protocol also provided efficient results regarding Detection Accuracy and Keying Overhead. Figure 4b  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Notations** \\ \hline \(V_{i}\) & A Vehicle \\ \(ID_{i}\) & Vehicle Unique Identity \\ \(V_{j}\) & Closest Vehicle \\ \(POSITION_{i}\) & Vehicle-ID \\ \(VL_{i}\) & Closest Vehicles List \\ \(DISTANCE_{ij}\) & Distance Between \(V_{i}\) and \(V_{j}\) \\ \(NV_{j}\) & Number of Closest Vehicles to \(V_{j}\) \\ \(R\) & Range of Dynamic Transmission \\ \(\theta_{i}\) & Moving Vehicle Direction \\ \(S_{i}\) & Vehicle Speed \\ \(a,b,c,d\) & Assumed Weights \\ \hline \hline \end{tabular}
\end{table}
Table 9: Explanation of the notations.  
demonstrates Detection Accuracy, and it can be observed that when the number of attackers increased, the detection accuracy decreased in all three protocols. Figure 4c shows the Keying Overhead of our protocol concerning the scheme of [58]. Since the experiment of [PERSON] et al. [58] includes a complex keying generation process, their results do not provide better results than ours. In Figure 4d, we compared the packet delivery ratio in the context of varying attackers with the results of [PERSON] et al. [58]. Our results indicate a better performance compared to that obtained by the authors [58].
## 2 Related Work
### Traditional Remote Sensing Detection Methods
This approach may be limited and fail to fully capture the complex characteristics of targets, particularly when dealing with large-scale remote sensing data. Furthermore, traditional methods heavily depend on high-quality images, making them susceptible to interference from atmospheric factors like clouds and haze. This can result in lower image quality and increased noise, thereby impacting the accuracy and stability of detection outcomes. Additionally, traditional methods face challenges in processing large-scale data due to limitations in computing and storage resources. Since remote sensing images are often high-resolution and cover extensive areas, traditional approaches may struggle to handle such data demands efficiently. Deep learning-based remote sensing image target detection techniques have emerged to address these drawbacks. Deep learning models, built upon deep neural networks, can automatically learn and extract features from images without relying on manually designed features. This enables deep learning methods to better comprehend and represent complex features in remote sensing images, ultimately enhancing the accuracy and generalization capabilities of target detection.
## 4 Conclusions  
In this study, BSEA was utilized to efficiently improve the LOC and the comprehensive mechanical properties of EP at RT and cryogenic conditions. From the LOC test results, the EP/BCI/BSEA-4 and EP/BCI/BSEA-5 composites showed liquid oxygen compatibility. Moreover, the bending properties, fracture toughness and impact strength of BSEA-modified EP were greatly enhanced at RT and 77 K compared with pure EP. The bending strength (251.64 MPa) increased by 113.67%, the fracture toughness (2.97 MPa\(\cdot\)m\({}^{1/2}\)) increased by 81.10%, and the impact strength (31.85 kJ\(\cdot\)m\({}^{-2}\)) increased by 128.81% compared with pure EP at 77 K. All the above results indicate that BSEA-modified EP exhibits broad potential applications in the field of CFRP with high-performance equipment and liquid oxygen tanks.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/polym14204343/s1](https://www.mdpi.com/article/10.3390/polym14204343/s1)]([https://www.mdpi.com/article/10.3390/polym14204343/s1](https://www.mdpi.com/article/10.3390/polym14204343/s1)), Figure S1: O1s spectra of pure EP (a) before and (a1) after the liquid oxygen compatibility test; N1s spectra of pure EP (b) before and (b1) after, EP/BCI/BSEA-2 (c) before and (c1) after, EP/BCI/BSEA-5 (d) before and (d1) after the liquid oxygen compatibility test; Table S1: The bending properties of EP samples at RT and 77K; Table S2: The fracture toughness and impact strength of EP samples at RT and 77K; Video S1: burning; Video S2: no reaction.  
Conceptualization, N.L. and L.Q.; methodology, N.L. and H.W.; software, N.L.; validation, N.L., H.W. and S.W.; formal analysis, S.W.; investigation, L.Q.; resources, B.X.; data curation, N.L.; writing--original draft preparation, N.L.; writing--review and editing, L.Q.; visualization, H.W.; supervision, B.X.; project administration, B.X.; funding acquisition, B.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No.12090031) and the National Key Research and Development of China (No. 2018 YFA0702804).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 6 Applications to Cardiovascular Medicine
The latter was used as a target for fitting the material parameters through an iterative inverse formulation. The active contraction was simulated by a time-dependent homogeneous active stress model, and catheter measurements of ventricular pressure were used as boundary conditions. These studies found that fiber and cross-fiber stress are significantly larger at the infarct border zone relative to non-in-in- infarct regions. Additionally, the inverse model was employed to evaluate the benefits of diverse treatments and suggested that aneurysm plication decreases the myofiber stretch without compromising stroke volume, which the authors highlighted as one of the benefits delivered by such intervention. These early works present all the elements of more recent medical image-based inverse analyses: an image-based kinematic target, an optimization algorithm, and a parametric function to be optimized to estimate in vivo case-specific information that cannot be directly assessed without an invasive procedure. These studies were limited by the available computational power at the time. [PERSON] et al. reported a total of 16 h for each iteration of their forward cardiac model using a Silicon Graphics Octane II workstation with a capacity of about 250 MHz, which was a cutting-edge multiprocessor workstation at the time. Currently, the processing capacity of a desktop workstation is at least ten-fold greater (i.e., 3 to 4 GHz). Furthermore, many parallelization and cloud-computing options are now available to augment the speed of simulations. The technology is now mature enough for the evaluation of patient-specific inverse analyses on complex biomechanical models of clinical relevance. Though there are many instances of image-based inverse analyses on animal models and explanted tissues [137], in this review we aim to highlight the potential clinical applications of inverse methods. Thus, in this section, we present a detailed review of in vivo patient-specific inverse problems applied to elements of the human cardiovascular system along with a few pioneering and groundbreaking studies on animals. Figure 10 summarizes the anatomical references and location of focalized pathologies studied by the inverse-modeling applications reviewed herein.
## 4 Performance Evaluation
### Simulation Setup
#### 4.2.6 Node Localization  
In order to localize sensors, we adopt a gradient-based optimization method with weighted residuals, utilizing the RSS values as a key factor in the objective function. Neighbors at one hop of each node are positioned using the estimated inter-node distances. The sink's neighbors are localized based on the known position of the sink, and then the rest of the sensors are positioned. Figure 8 shows that our proposed methodology consistently surpasses other anchor-free localization techniques found in the literature. The NRMSE achieved by our approach falls within a range of \([0.02,0.045]\). In contrast, errors obtained using the SFO method vary between 0.089 and 0.16, [PERSON] et al.'s approach yields errors within \([0.12,0.18]\), and errors with the EAFLA range from 0.09 to 0.16. This highlights the superior accuracy and precision of our proposed method, consistently attaining lower NRMSE values.  
Figure 8: Localization NRMSE (\(\gamma=3,\sigma=4\), and \(m=4\)).  
Figure 7: Inter-node estimation NRMSE comparison (\(\gamma=3,\sigma=4\), and \(m=4\)).
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 3 System Model and Problem Definition  
### DSP Application Model  
A DSP application can be regarded as a directed acyclic graph (DAG), where data sources, operators, and consumers are connected by streams (see Figure 1a) An operator is a self-contained processing element that carries out a specific operation (e.g., filtering and POS-tagging), whereas a stream is an unbounded sequence of data (e.g., tuples). DSP applications are usually employed in latency-sensitive domains [2; 35; 36], where reduced response time is required. Although multiple definitions of response time exist in the context of DSP, the widely used one defines it as the overall processing and transmission latency from a data source to a final consumer on the application DAG. In this work, we assume that the DSP application exposes requirements in terms of response time, specifying a target value \(R_{max}\) that should not be exceeded.  
To improve performance, multiple replicas can be used to run an operator, where each replica processes a subset of the incoming data flow (_data parallelism_). By partitioning the stream over multiple replicas, running on one or more computing nodes, the load per replica is reduced, and so is the processing latency. Since DSP applications are long-running and subject to workloads that vary over time, the number of replicas should accordingly change at run-time to meet the performance target while avoiding resource wastage (_application elasticity_).