esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Ambient Backscatter NOMA Systems
## 1 Introduction
### Motivations and Contributions  
The above studies provide an excellent basis for AmBC-assisted NOMA systems. RF components are susceptible to damage from phase imbalance, phase noise, and other defects in actual scenes [37], yet there is little research on the impact of residual hardware impairments (RHIs) on system performance. The effects of imperfect successive interference cancellation (ipSIC) on system performance need to be investigated, considering that there are residential interference and SIC process quantization errors, which will lead to incomplete decoding. Inspired by these, we examined the impact of RHIs and ipSIC on the OP, throughput, and EE for AmBC-NOMA systems. The major contributions are outlined as follows:  
1. We suggest a model of the AmBC-NOMA system, with the channel modeled as a multi-user ordering of the Rayleigh channel. We derived the closed-form and asymptotic expressions for the OP of the \(d\)-th user and for a BD with ipSIC/perfect successive interference cancellation (pSIC), respectively. On this basis, we calculated the diversity order of the \(d\)-th user, which was heavily influenced by the level of residential interference and RHIs;
2. We show that AmBC-NOMA systems have better outage behavior than AmBC-OMA systems. In addition, due to the existence of the ipSIC and RHIs, the error floors of OP for a BD and the \(d\)-th user appear in the high signal-to-noise ratio (SNR) regime;
3. We investigated the throughputs of users and BD with ipSIC/pSIC, respectively. Furthermore, we analyzed the impact of the ipSIC and reflection coefficients on the throughputs for the users and the BD. We observed that the throughput of the far users in the AmBC-NOMA systems was lower than in the AmBC-OMA systems.
4. We evaluated the EE of the AmBC-NOMA systems with ipSIC/pSIC in the delay-limited transmission mode, where the performance of AmBC-NOMA systems under pSIC was better than that of its counterpart. Moreover, we examined the effect of RHIs on AmBC-NOMA systems' EE.
## 5 Lattice Boltzmann Methods (LBMs)
### Two Relaxation Time (TRT) Collision Model  
Alternative to SRT schemes, the TRT-LBM adopts a collision operator involving two relaxation times. Hence, in the space of discrete velocities, the expression for the collision operator (_without body force term_) reads [149]:  
\[\Omega_{i}(f_{i})=\tau_{s}^{-1}(f_{i}^{s}-f_{i}^{s,\ \alpha q})-\tau_{a}^{-1} (f_{i}^{a}-f_{i}^{a,\ \alpha q}) \tag{12}\]  
where \(f_{i}^{s}\) and \(f_{i}^{a}\) are symmetric and antisymmetric parts of \(f_{i}\) (\(=f_{i}^{s}+f_{i}^{a}\)), which are expressed as:  
\[f_{i}^{s}=\big{(}f_{i}+f_{i}\big{)}/2;\qquad f_{i}^{a}=\big{(}f_{i}-f_{i} \big{)}/2 \tag{13}\]where the index \(\tilde{i}\) is in the direction opposite to \(i\). These parts are, respectively, associated with relaxation times \(\tau_{s}\) and \(\tau_{a}\). The former (_called the viscous relaxation time_) is used to determine kinematic viscosity, and the latter (_called the free relaxation time_) is tuned to improve numerical stability and accuracy. Moreover, the symmetric and antisymmetric components of the distribution function are linked by \(f_{i}^{s}=f_{\tilde{\imath}}^{s}\) and \(f_{\tilde{\imath}}^{a}=-f_{\tilde{\imath}}^{a}\) for a link. It should be pointedout that the relations (13) hold for the equilibrium distribution function \(f_{i}^{eq}\). It can be shown that relaxation times need to satisfy a monitor factor \(\Lambda\) (\(=(\tau_{s}-1/2)(\tau_{a}-1/2)\)) whose role is to control the algorithm stability. Obviously, TRT-LBM degenerates into SRT-LBM when \(\tau_{s}=\tau_{a}\). In addition, modeling of the Navier-Stokes equations with the TRT operator is discussed in detail in Refs. [154; 155; 156], to name a few. Nonetheless, although such an approach can draw its advantages from those of SRT and MRT methods (_simplicity and stability_), it remains much less popular due to its tedious mathematical derivation, which can hamper a computer code implementation.
# CAL: Core-Aware Lock for the big.LITTLE Multicore Architecture
## 1 Introduction
We can observe that the throughput of four locks increases with the core number but drops quickly when the core number is from 6 to 10, that the first 6 cores are the big core, and the last 4 cores are the little cores; we analyze this phenomenon further in Section 2.3. In this paper, we study the lock fairness issue in the big.LITTLE Multicore Architecture, and we introduce the slowdown ratio [5] into lock design to measure the fairness of entering the critical section, and design a core-aware lock for this novel big.LITTLE multicore architecture. The goal of CAL is to make the big core and the little core subject to the same slowdown ratio as possible to achieve fairness and improve the throughput of the program running on both the big core and the little core. This paper makes the following contributions:  
* We conduct a preliminary experiment to observe the fairness issue of traditional locks in big.LITTLE multicore architecture, and carry out an in-depth analysis of their performance problems. We point out the core-unaware lock on big.LITTLE multicore architecture induces performance degradation. * Motivated by our observation, we designed a core-aware lock for the big.LITTLE multicore architecture to trade-off lock assignment between the big core and little core. We build this scheme based on ticket lock and MCS ([PERSON] and [PERSON]) lock, and then reorder the lock request according to our lock fairness model. Furthermore, we implemented CAL on top of the open-source lock framework named Litt library, which is widely used for studying lock optimization. We conduct a series of experiments on the benchmarks and key-value Store engine named LevelDB. The experimental result validates the effectiveness of our scheme. Compared to SOTA (state-of-the-art), the CAL's fairness has increased by up to 67%; and its throughput is 26% higher than FIFO-based (First In First Out) lock and 53% higher than competition-based lock, respectively. In addition, the tail latency of CAL is always kept at a low level. In the rest of the paper, Section 2 discusses the lock background and motivates our design. Section 3 presents the detailed scheme. Section 4 describes the experiment methodology and analyzes the results. Section 5 gives the related work, and Section 6 concludes the paper.
## 2 Datasets and Method
### Overview of PointNet++ Model  
PointNet++ is recognized as a state-of-the-art DL model and superior to other DL models for semantic segmentation and classification using point cloud data. PointNet++ is an improved DL version of the PointNet since PointNet does not extract the local features of the point clouds successfully [9]. The PointNet++ is a hierarchal network applying recursive PointNet for partitioned point clouds to capture fine-grained patterns in the data for classification and semantic segmentation. In PointNet++, the farthest point is selected for the given point cloud sets to form centroids for determining nearest neighbor points for each centroid. Then, the PointNet is applied to the local regions to generate a feature vector for each region [23]. PointNet++ adopts the U-Net architecture using skip link concatenation (or skip connection) [24].  
The set-abstraction levels within the encoder network of PointNet++ are composed of sampling, grouping, and PointNet layer. The PointNet component contains a set of convolution, normalization, and rectified linear units (ReLU) followed by a max-pooling layer. The feature vectors are created from the local region patterns during the encoding process. On the other hand, the decoder network is composed of interpolation and PointNet layer. Figure 9 represents the architecture of PointNet++, and Figure 10 illustrates the PointNet configuration in the PointNet++ model.  
Figure 10: PointNet++ architecture showing configuration of the PointNet layers.  
Figure 9: PointNet++ architecture for segmentation and classification.  
The sampling layer selects a set of points that can define the centroids of local regions through selecting a subset of the farthest points to each other from the point clouds. The grouping layer organizes local regions by searching neighboring points around the centroids. Then, the subsampled data are fed to the PointNet for encoding the local region patterns into the feature vectors. By stacking several set abstraction levels, PointNet++ can learn features from a local geometric structure and abstract the local features layer by layer [5]. Details of PointNet++ including multi-scaling grouping and multi-resolution grouping that are to solve point data with non-uniform density efficiently are described in [25; 26].
## 4 Biomedical Applications of ML-Assisted SWIR Techniques
### Assistance in Diagnosis  
SWIR techniques offer promising solutions for biomarker detection in various diseases, particularly in cardiovascular diseases (CVDs) and cancers, which are leading causes of global hospitalization and mortality. ML as a transformative technology to analyze complex clinical data can assist in the interpretation of results from SWIR techniques, thereby enhancing diagnostic precision [82]. Remarkably positive outcomes have emerged from ML's application in diagnosing both CVDs [83] and cancers [84], spanning various clinical stages. The text below scrutinizes recent advancements in ML-assisted diagnostics of SWIR images based on a clinical area of application.
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 4 Proposed Method
### Learning Process  
Scale-invariant source-to-noise ratio (SISNR) [39] is usually used as the basic separation evaluation index to measure the performance of blind source separation. SISNR measures the ratio of the signal to the separation error. The higher the SISNR, the lower the separation error and the better the separation performance. Before the calculation,the source signal and the separated source signal are normalized to a zero mean value to ensure that the scale remains unchanged.  
The gradient descent method is generally used during network training. This method needs to minimize the loss function. As is shown in Formula (15), a negative SISNR is used as the loss function. This ensures that in the end-to-end training, the loss is minimized and SISNR is maximized to ensure the accuracy of model training.  
\[\underset{\theta}{\text{min}}\,\ell_{SISNR}(\mathbf{s},\mathbf{s}^{*})=-10\log_{10} \left(\frac{\left\|\mathbf{s}_{aim}\right\|^{2}}{\left\|\mathbf{s}^{*}-\mathbf{s}_{aim} \right\|^{2}}\right) \tag{15}\]  
\[s_{aim}=\frac{\langle\mathbf{s}^{*},\mathbf{s}\rangle\mathbf{s}}{\|\mathbf{s}\|^{2}} \tag{16}\]  
The parameters are updated through the back-propagation gradient descent algorithm:  
\[\theta_{encoder} \leftarrow\theta_{encoder}-\eta\frac{\partial\ell_{SLSNR}}{ \partial\theta_{encoder}} \tag{17}\]  
\[\theta_{mask} \leftarrow\theta_{mask}-\eta\frac{\partial\ell_{SLSNR}}{ \partial\theta_{mask}} \tag{18}\]  
\[\theta_{decoder} \leftarrow\theta_{decoder}-\eta\frac{\partial\ell_{SLSNR}}{ \partial\theta_{decoder}} \tag{19}\]  
The back-propagation gradient descent algorithm updates the parameters of the encoding part, the mask part and the decoding part \(\theta=\{\theta_{encoder},\theta_{mask},\theta_{decoder}\}\). The encoder parameter set, mask parameter set, and decoder parameter set are \(\theta_{encoder}=\{\mathbf{w}_{1},\mathbf{b}_{1}\},\theta_{mask}=\{\mathbf{w}_{2},\mathbf{b}_{ 2}\},\theta_{decoder}=\{\mathbf{w}_{3},\mathbf{b}_{3}\}\).
## 3 Solution and Analysis of Longitudinal Deformation Curve of Surrounding Rock
### Data Comparison and Equation Optimization Analysis
#### 3.2.3 Analysis of Optimization Results of Theoretical Equation  
A compare and analysis of the monitoring data of Class-III surrounding rock sections YK30+960 and YK30+911 and Class-IV surrounding rock sections YK31+645 and YK31+422 with the calculation data of Equation (8) and modified Equation (10) are shown in Figure 7.  
The curve in Figure 7 is fit to obtain the variance and correlation coefficient of each fitting curve, as shown in Table 3:  
The results show that the fitting effect of the theoretical equation is effectively improved by adding the \"expanded convergence function\" (the correlation coefficient is increased from about 0.8 to about 0.95); The fitting results show that the deformation law  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fitting** & \multicolumn{2}{c}{**III**} & \multicolumn{2}{c}{**IV**} \\ \cline{2-5}
**Parameters** & **YK30+960** & **YK30+911** & **YK31+645** & **YK31+422** \\ \hline RMSE & 0.0310 & 0.0322 & 0.0375 & 0.0361 \\ \hline SSE & 0.0122 & 0.0134 & 0.0183 & 0.0170 \\ \hline R-square & 0.9650 & 0.9549 & 0.9386 & 0.9428 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitting parameters of surrounding rock.  
Figure 7: Monitoring and theoretical fitting diagram of vault displacement release coefficient: (a) Class–III surrounding rock; (b) Class–IV surrounding rock.  
of surrounding rock is not only related to Poisson's ratio, but is also related to the elastic-plastic modulus, internal friction angle, cohesion and blasting parameters. At the same time, it is also verified that the worse the quality of the surrounding rock, the more cracks, the lower the bearing capacity, the less significant the spatial constraint effect, the earlier the radial displacement of the surrounding rock in the excavation process, the greater the final deformation of surrounding rock and the later the final convergence is achieved.
## 3 Results
### Hydrological Evaluation of Discharge Simulation Using Two SPPs
#### 3.3.4 Event of 16 December 2016  
The event represents a winter rain-storm characterized by liquid precipitation downstream and snow upstream of the watershed. This type of rain-storm is very frequent during the winter, especially in the high mountains of the Atlas.  
The hydrographs of calibration in Figure 8 represent a simulated flow curve quite illustrative; the rising curve and the recession were well reproduced for both products 3B42V7 and IMERG V05, contrary to the peak flow which has not been reached for the 3B42V7. However, the evaluation criteria are acceptable and represent an NSE of 63.50% and 61.90%, respectively.  
On the other hand, Figure 9 illustrates the validation graphs of each product, although the curves of the simulated flows are not well reproduced for both products, which underesti  
Figure 8: Calibration of the episode 16 December 2016, using 3B42V7 and IMERG V05 rainfall products and observed runoff data as input.  
Figure 7: Validation of the episode of 3 May 2016, of 3B42V7 and IMERG V05 rainfall products and observed runoff data as input using the means calibration parameters of the gauge precipitation calibration.  
mated winter precipitations since they are a mixture of rainfall and snowfall. The evaluation criteria are acceptable with an NSE of 63.20% for 3B42V7, and 57.20% for IMERG V05.  
In this paper, an efficient method has been developed for the first time in a country with a Mediterranean climate, on the mountainous watersheds of the Moroccan High Atlas with low density and irregularity of precipitation and flow measurement stations. This is a good method to apply to solve the problem of deficiency of observed data in these regions.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 3 Proposed Method
### Optimization Methods
#### 3.3.2 Parameter Norm Penalty  
The basic idea of parameter norm penalty is to add a parameter norm penalty \(\Omega(\theta)\) that describes the complexity of the model into the loss function \(J\), the regularized loss function \(\overline{J}\) is shown in Equation (18).  
\[\overline{J}(\theta;X,y)=J(\theta;X,y)+\alpha\Omega(\theta), \tag{18}\]  
where \(\alpha\) is the parameter that measures the proportion of norm penalty term \(\Omega(\theta)\) in the regularized loss function. By using the strategy of parameter norm penalty, the model cannot arbitrarily fit the random noise in the training data. The effects are different with different parameter norm \(\Omega(\theta)\) whose regularization methods include \(L1\) regularization and \(L2\) regularization. Where \(L1\) regularization represents the sum of the absolute values of all the elements in the vector, as shown in Equation (19). While \(L2\) regularization is the sum of squares of each parameter, as shown in Equation (20). In this paper, \(L2\) regularization is adopted.  
\[\Omega(\theta)=||W||_{1}=\sum_{i}|W_{i}|, \tag{19}\]  
\[\Omega(\theta)=\frac{1}{2}||W||_{2}^{2}=\sum_{i}W_{i}^{2}, \tag{20}\]  
Figure 9: Schematic diagram of Dropout: (**a**) a standard neural network; (**b**) the neural network using Dropout.
# Nanoformulation of Peptides for Pharmaceutical Applications: In Vitro and In Vivo Perspectives
## 6 Mechanism of Nanoformulated Peptides in Disease Theranostics
### Cell Targeting Peptide  
In recent times, targeted peptides were used in disease therapeutics to target specific receptors that were overexpressed in cells affected by a disease, specifically due to their low molecular weights and abundant versatility [147]. Recently, researchers have focused on conjugating peptides with nanocarriers, such as micelles, polymers, or liposomes, to improve their therapeutic drug delivery toward cells affected by a disease [148]. In case of cancer therapy, [PERSON] et al. (2016) designed T7 peptide-modified core-shell nanoparticles (T7-LPC/siRNA NPs) for an effective systemic delivery of siRNA, especially for the targeted breast cancer therapy. The characteristic core shell structure aids in siRNA encapsulation by preventing them from RNAse degradation. Hence, siRNA remained intact for more than 36 h of incubation for T7-LPC/siRNA NPs, compared to standalone siRNA, which were completely degraded after 3 h of incubation. An in vivo experimental analysis revealed an enhanced tumor accumulation for T7-LPC/siRNA NPs, attributed to their active targeting via receptor-mediated interactions through T7-peptide modification and are effective in tumor growth inhibition. Moreover, T7-LPC/siRNA NPs have been identified to be effective and safe systemic delivery agents for siRNA with negligible body weight changes in mice models, less hemoglobin (HGB) levels, interferon alpha (IFN-a), and interleukin-6 (IL-6) levels [149]. Similarly, [PERSON] et al. (2019) successfully developed a peptide-based targeting ligand with docetaxel (DTX) nanoparticles, EN1-iPeps, and RGD peptides (EN1-RGD-iPeps NP) for the treatment of chemo-resistant triple negative breast cancer (TNBCs). This DTX-NP formulation has reduced TNBC growth in vivo, with no alteration in organ appearance, body weight, liver enzyme profile, and lack of evidence for tissue damage at the histopathological level, which proves the fewer side effects of these NPs [150]. It is noteworthy that these cell-targeting peptides were either formulated in nanoparticles or coated on nanoparticle surfaces to be utilized for the desired disease treatments in the future [151; 152; 153].
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 3 The Proposed Model
### Matrix Co-Factorization
\[\hat{R}_{i,j} =g(U_{i}^{T}V_{j}) \tag{19}\] \[\hat{M}_{i,j} =g(U_{i}^{T}Z_{j})\]  
```
Input: Observed data matrix, regularization parameter \(\lambda_{U}\), \(\lambda_{V}\)\(\lambda_{Z}\), \(\lambda_{Q}\), \(\lambda\), numbers of iterations \(T\), and learning rate \(\theta\) Output: latent factor matrixes \(U\), \(V\), \(Z\), and \(Q\)
1 Derive the implicit feedback matrix \(M\) and the explicit feedback matrix \(R\) from the observed data;
2 Initialize elements of \(U\), \(V\), \(Z\), \(Q\) by zero-mean Gaussian distribution;
3 for\(i=1\) to \(T\)do
4 Shuffle training data;
5 foreach user-item pair (\(i\),\(j\)) in \(R\), \(P\), and \(N\)do
6 Calculate gradients based on Equations (15)-(18);
7 update latent factor matrix as follows:
8\(U_{i}\gets U_{i}-\theta\frac{\partial\mathcal{L}}{\partial U_{i}}\);
9\(V_{j}\gets V_{j}-\theta\frac{\partial\mathcal{L}}{\partial V_{j}}\);
10\(Z_{j}\gets Z_{j}-\theta\frac{\partial\mathcal{L}}{\partial Z_{j}}\);
11\(Q_{k}\gets Q_{k}-\theta\frac{\partial\mathcal{L}}{\partial Q_{k}}\);
12
13 end for
14
15 end for return\(U\), \(V\), \(Z\), and \(Q\)
```  
**Algorithm 1**Stochastic gradient descent learning for probabilistic matrix co-factorization
# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 2 Review of the State-of-the-Art Technology
### Comparative Analysis of UAV Sensing Technologies  
Next, we summarize the main properties of the described technologies to summarize the previous sections. The summary takes the form of Table 14.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Radar detection range & 1.5 km \\ RF detection range & 1 km (urban) \\  & 5 km (rural) \\ Acoustic detection range & 200 m \\ Camera detection range & 600 m (small UAVs) \\  & 2 km (large UAVs) \\ RadarZero field of view & \(\geq\)90\({}^{\circ}\) azimuth \(\times\) 80\({}^{\circ}\) elevation \\ RadarZero angle resolution & \(\pm\)1\({}^{\circ}\) azimuth \(\pm\) 3\({}^{\circ}\) elevation \\ RadarZero frequency & 24.45–24.65 GHz (multichannel) \\ RadarZero target detection & \(\geq\)20 targets simultaneously \\ DroneOpt pan rotation & 360\({}^{\circ}\) continuous \\ DroneOpt pan speed & 0.2\({}^{\circ}\)/s\(\geq\)120\({}^{\circ}\)/s \\ DroneOpt tilt range & \(-\)55\({}^{\circ}\)\(\rightarrow\)90\({}^{\circ}\) \\ DroneOpt tilt speed & 0.2\({}^{\circ}\)/s\(-\)90\({}^{\circ}\)/s \\ DroneOpt position accuracy & \(\pm\)0.07\({}^{\circ}\) \\ DroneOpt zoom & 30\(\times\) optical zoom \\ DroneOpt field of view (min–max) & 2.3\({}^{\circ}\)–63.7\({}^{\circ}\) \\ DroneOpt resolution & 640 \(\times\) 480 \\ DroneOpt frame rate & 30 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 13: DroneSentry integrated system specifications.
# Spatial Correlations of Global Seismic Noise Properties
## 1 Introduction  
Information about seismic noise makes it possible to study the processes preceding strong earthquakes [1; 2; 3]. Atmosphere and ocean (cyclone movement and the impact of ocean waves on the shelf) are the main sources for the energy of seismic noise [4; 5; 6; 7; 8; 9; 10]. At the same time, processes inside the earth's crust are reflected in changes in the properties of seismic noise and studying these properties helps in investigating the structural features of the earth's crust [11; 12; 13].  
The Donoho-Johnston index (DJ-index) of a random signal can be defined as the ratio of the number of the coefficients of orthogonal wavelet decomposition, which in absolute value exceed the threshold introduced in [14], to the total number of wavelet coefficients. The threshold that separates \"large\" wavelet coefficients from others was defined first in [14] and is used to shrink noise from signals and images using wavelets. It has turned out that the DJ-index has the ability to most clearly highlight the effects of the spatial correlation of seismic noise, and it outperforms other noise statistics (such as entropy and the width of the multi-fractal spectrum of the singularity) in terms of highlighting predictive effects [15; 16; 17; 18]. This article presents a detailed study of the DJ-index of global seismic noise, both the change in time and space of the correlation properties of noise and its response to the irregularity of the Earth's rotation, as well as the possible use of this response to assess the current seismic hazard.
## 3 Spatial-Frequency Model of the Point-Wise FRF
### Complex-Valued Gaussian Process
As demonstrated in Equation (8), \(\mathbf{g}_{r}\) has a structure similar to the FRF \(\mathbf{G}_{0}\); hence, it can be characterized by the same kernel parameters \(\alpha\) and \(\beta\), but it is described by a different scaling factor \(\gamma_{r}\).