esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 1 Introduction
The recently introduced Mamba [13] has an impressive linear complexity in long-range modeling, making it more suitable for handling high-resolution images with spatially varying defocus blur, compared to the quadratic complexity of Transformer. Its linear complexity makes it possible to conduct efficient global modeling with limited computational resources. Based on this, this paper proposes a CNN-Mamba hybrid architecture for defocus deblurring, named CNN-Mamba Defocusing Deblurring Network (CMDDNet). CMDDNet adopts a parallel structure that combines the advantages of CNNs in local detail recovery with Mamba's ability to model long-range dependencies. This approach enhances the accuracy of local detail restoration in defocused images and effectively handles complex blur patterns, achieving competitive deblurring results with low resource consumption (see Figure 1), making it highly efficient for practical applications. The main contributions of this paper are as follows:  
1. To overcome the limitations of a single structure in handling defocus blur tasks, particularly in local detail recovery and global dependency modeling, we propose a hybrid architecture for defocus deblurring, called CMDDNet. This network enablesthe simultaneous modeling and processing of local features and global context in different blurred regions of defocused images. 2. To address the high computational complexity of Transformers on high-resolution defocus images, we introduce Mamba as a replacement for the Transformer. Its linear complexity design significantly reduces both computational and memory costs, while retaining the ability to model global dependencies when handling complex spatially varying defocus blur. 3. To enhance defocus deblurring performance, we design a CNN-Mamba parallel feature extraction structure that fully leverages fine-grained image features. Combined with a multi-scale training strategy, this structure improves the model's adaptability and generalization ability.
## 2 Preliminary Knowledge
### Hyperchaotic System  
For the encryption schemes based on chaotic systems, the corresponding chaotic systems play an important role in generating the pseudorandom sequences for permutation and diffusion. Due to the better pseudo-randomness and higher unpredictability, the logistic Feigenbaum non-linear cross-coupled hyperchaotic map (LF-NCHM) proposed by the authors of [33] is used here. The mathematical expression is shown in (1), where the parameter \(\mu\in(0,4]\).  
\[\begin{cases}x_{n}=mod(\mu\times(\delta\times y_{n-1})\times(1-\delta\times y _{n-1}),1)\\ y_{n}=mod(\lambda\times sin(\pi\times(x_{n-1}+y_{n-1})),1)\end{cases} \tag{1}\]  
Figure 1: The encryption process of ECIES.  
Figure 2: The decryption process of ECIES.
# Durability Investigation of Fiber-Reinforced Functionally Graded Concretes in Cold Regions
## 2 Test Methods and Proportioning Model
### Impermeability Test
#### 2.3.1 Water Penetration Resistance Test  
The water penetration resistance test was carried out on the upper \(\varphi\)175 mm \(\times\) lower \(\varphi\)185 mm \(\times\) height 150 mm round test piece, using the step-by-step pressurization method. The test process is shown in Figure 7. The water pressure started from 0.1 MPa and increased by 0.1 MPa every 8 h. The specimen's water seepage was observed during the stabilization process, and the time when the water seepage occurred was recorded. The specimen height was taken as the water seepage height. If there was no water seepage at the end of the test, the test was stopped after 24 h. No water seepage occurred in any of the specimens during the test, and the water penetration resistance results are shown in Table 4.  
Figure 6: Comparison of early crack resistance of concrete in groups #1–#6: (**a**) Initial cracking time; (**b**) number of cracks per unit of area; (**c**) total cracked area per unit of area.  
Figure 7: Water penetration resistance test of concrete.  
It can be seen that the seepage pressure of #3 and #6 reached 1.3 MPa when anti-freezing and anti-cracking mass concrete was used in this test. In addition, the impermeability grade was more remarkable than W10, representing excellent water infiltration resistance. The seepage pressure of concrete #5 for the inner pier reached 1.4 MPa, and the impermeability grade was higher than W10. All of the above water infiltration resistance values meet the requirements of bridge engineering applications. It can be explained that the mineral admixture mix achieves the densest skeletal buildup, and the micro-aggregate effect of fly ash fills its internal pores, blocking the seepage channels and enhancing the seepage resistance of the concrete.
# Basins of Convergence in a Multi-Perturbed CR3 BP +
## 2 Description of the Dynamic Model
### Oblateness of the Primary Masses  
The original CR3 BP problem established the gravitational potential generated by two primary masses when they were considered as spherical shapes. However, not all celestial bodies have a perfectly spherical form but rather have certain flattening or oblateness, so several authors have modified the effective potential in order to consider these new geometries ([PERSON], 1970; [PERSON], 1971; [PERSON], 1972; [PERSON], 1973). To define these parameters, three semi-axes are considered for each primary mass, denoted as \(a_{j}\), \(b_{j}\), and \(c_{j}\) and measured on a right-hand trihedral axis, as are the relationships between them. In this work, it is assumed that the x-axis of the reference frame aligns with the line joining the primary masses. This alignment is consistent with tidal effects, which naturally orient the primary masses along their principal axes due to mutual gravitational interactions. Thus, the triaxiality parameters \(\sigma_{i_{j}}\) are defined as follows:  
\[\sigma_{1_{j}}=\frac{a_{j}^{2}-c_{j}^{2}}{5R^{2}}\qquad\qquad\sigma_{2_{j}}= \frac{b_{j}^{2}-c_{j}^{2}}{5R^{2}} \tag{10}\]  
where \(a_{j}\) and \(b_{j}\) represent the semi-axes in the \(x\) and \(y\) directions, respectively, and \(c_{j}\) is perpendicular to them.  
Therefore, each primary mass \(j\) has two triaxiality functions \(f_{i_{j}},i=1,2\), given by  
\[f_{1_{j}}=2\sigma_{1_{j}}-\sigma_{2_{j}}\qquad\qquad\qquad f_{2_{j}}=\sigma_{ 2_{j}}-\sigma_{1_{j}} \tag{11}\]  
The terms of the potential function associated with each of the primary masses are modified by means of these triaxiality functions:  
\[\Omega^{*}(x,y)=\frac{n^{2}}{2}(x^{2}+y^{2})+\frac{1-\mu}{r_{1}}\left(q_{1}+ \frac{f_{1_{1}}}{2r_{1}^{2}}+\frac{3y^{2}f_{2_{1}}}{2r_{1}^{4}}\right)+\frac{ \mu}{r_{2}}\left(q_{2}+\frac{f_{1_{2}}}{2r_{2}^{2}}+\frac{3y^{2}f_{2_{2}}}{2r_ {2}^{4}}\right) \tag{12}\]  
where the corrected dimensionless mean movement is  
\[n=\sqrt{1+\frac{3}{2}(2\sigma_{1_{1}}-\sigma_{2_{1}})+\frac{3}{2}(2\sigma_{1_ {2}}-\sigma_{2_{2}})}. \tag{13}\]
## 2 Material and Methods
### Microodosimetric Spectra  
The acquired microodosimetric spectra were represented as the distributions of the lineal energy \(y\), the stochastic variable defined as the energy deposited by a single event in a specific volume, divided by the mean chord length [34]. Frequency \(f(y)\) and dose \(d(y)\) distributions of the lineal energy obey the usual normalization rules of probability distributions:  
\[\int_{0}^{\infty}f(y)dy=1 \tag{2}\]  
and  
\[\int_{0}^{\infty}d(y)dy=1 \tag{3}\]  
with  
\[d(y)=\frac{yf(y)}{\int_{0}^{\infty}yf(y)} \tag{4}\]  
The frequency and dose-mean lineal energy values, \(y_{F}\) and \(y_{D}\), are calculated as:  
\[y_{F}=\int_{0}^{\infty}yf(y)dy \tag{5}\]  
and  
\[y_{D}=\int_{0}^{\infty}yd(y)dy \tag{6}\]  
The microodosimetric lineal energy distribution was used to estimate RBE, following the approach of the biological weighting function [35]. A weighting function \(r(y)\) was applied to the measured dose distribution in lineal energy \(d(y)\) in order to determine a single parameter, \(RBE_{\mu}\), that estimates the biological effectiveness:  
\[RBE_{\mu}=\int_{0}^{\infty}r(y)d(y)dy \tag{7}\]  
In this work, the Loncol's function \(r(y)\)[36] was adopted to weight the lineal energy. The Loncol's function was numerically determined by an unfolding procedure on empirical and statistical bases. The results of a RBE-microdosimetry intercomparison study between experimental measurements (performed with a TEPC simulating 2 \(\upmu\)m) and in-vivo experimental data of intestinal tolerance assessment by crypt cells regeneration was adopted. The unfolding procedure allowed to determine \(r(y)\) by minimizing the differences between biological RBE and \(RBE_{\mu}\) calculated applying the Equation (7). Despite the function \(r(y)\)was derived for a specific biological end-point in vivo (early intestinal tolerance in mice), previous works have already shown that the same function allows to reproduce the trend of the \(RBE_{10}\) as a function of depth for a variety of cell lines [22; 23]. In this work the \(RBE_{\mu}\) was here calculated at positions P0 (entrance) and P1 (mid-SOBP). These two results were afterwards multiplied by a constant factor so that \(RBE_{\mu}\) at position P0 (entrance) equal the clinical value of 1.1:  
\[RBE_{\mu}(P_{i})=\int_{0}^{\infty}r(y)d(y,P_{i})dy\times[1.1\div\int_{0}^{ \infty}r(y)d(y,P_{0})dy] \tag{8}\]  
Applying the LQ model, the estimated \(RBE_{\mu}\) and the \(\alpha\) and \(\beta\) derived from cell survival assessments for reference radiation, were used to calculate \(\alpha_{\mu}\), starting from the expression for \(RBE_{\mu}\):  
\[RBE_{\mu}=\frac{\alpha_{\mu}+\sqrt{\alpha_{\mu}^{2}-4\beta_{\mu}ln(0.1)}}{ \alpha_{\gamma}+\sqrt{\alpha_{\gamma}^{2}-4\beta_{\gamma}ln(0.1)}} \tag{9}\]  
assuming a constant \(\beta_{\mu}\)=\(\beta_{\gamma}\) and using Equation (9), \(\alpha_{\mu}\) can be calculated as follows:  
\[\alpha_{\mu}=\frac{RBE_{\mu}^{2}\cdot A_{\gamma}^{2}+4\beta_{\gamma}ln(0.1)}{ 2 RBE_{\mu}\cdot A_{\gamma}} \tag{10}\]  
with  
\[A_{\gamma}=\alpha_{\gamma}+\sqrt{\alpha_{\gamma}^{2}-4\beta_{\gamma}ln(0.1)} \tag{11}\]
## 2 System Model of DBT-MC-FTN
### Equalization Scheme  
The mixed interference experienced at the DBT-MC-FTN receiver is composed of the ISI induced by the fading channel and the ISI/ICI introduced by time-frequency packing. Benefitting from the special structure of the DBT-MC-FTN, the mixed interference can be tackled separately in this paper. Since the effect of channel estimation and channel equalization are not the focus in this section, we assumed that a perfect channel state information (CSI) is applied and the simple minimum mean squared error (MMSE) algorithm is utilized to the channel equalization. To further eliminate the ISI/ICI introduced by the two-dimensional packing, a soft SIC scheme has been designed in this section. \(\mathbf{\hat{a}}_{m}=\{\hat{a}_{m}(0),\hat{a}_{m}(1),\cdots,\hat{a}_{m}(D-1)\}\) is assumed as the frequency domain soft estimated signal generated from the extrinsic log-likelihood rate (LLR) \(\mathbf{L}_{e}\) obtained by the decoder at the end of the last iteration. Based on the interference cancellation principle in [35], the received signals on the \(m\)th subcarrier after ICI cancellation can be expressed as  
\[\mathbf{r}_{m}=\mathbf{z}_{m}-\sum_{m^{\prime}=0,m^{\prime}\
eq m}^{M-1} \mathbf{C}_{m,m^{\prime}}\mathbf{\hat{a}}_{m^{\prime}}, \tag{10}\]  
where \(0\leq m\leq M-1\), \(\mathbf{C}\) is the correlation matrix in the case of ideal channel estimation as mentioned in Section 2.2.  
Then, the ICI soft canceled symbols are fed up to the ISI cancellation process. The system model containing the color noise in Equation (9) is called the Ungerboeck model [36]. As a result, a MAP equalization combined with a Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm based on the Ungerboeck observation model is adopted in this paper to deal with the ISI introduced by time domain packing. The conditional probability density function of the modulated sequence vector \(\mathbf{r}\) and the modulation symbol vector \(\mathbf{a}\) with the BCJR algorithm is proportional to  
\[\begin{split} p(\mathbf{r}|\mathbf{a})&\propto exp \left(\frac{2}{N_{0}}\left(\mathcal{R}\left(\sum_{k}a_{k}^{*}r_{k}\right)-\sum_ {k}\mathbf{C}_{k,k}|a_{k}|^{2}-\sum_{k}\sum_{l=1}^{k}2\mathcal{R}\left(\mathbf{ C}_{k,l}a_{k}^{*}a_{l}\right)\right)\right)\\ &\propto\prod_{k}exp\left(\frac{1}{N_{0}}\mathcal{R}\left(a_{k}^{ *}r_{k}-\frac{1}{2}\mathbf{C}_{k,k}|a_{k}|^{2}-a_{k}^{*}\sum_{l=1}^{k}\mathbf{ C}_{k,l}a_{l}\right)\right),\end{split} \tag{11}\]  
where \(N_{0}\) is the channel noise power of the AWGN channel, \(\mathbf{C}\) is the correlation matrix obtained from Equation (9) according to the proposed DBT-MC-FTN transceiver. It can be observed from Equation (11) that the BCJR algorithm based on the Ungerboeck observation model can deal with colored noise instead of a whitening filter with acceptable complexity. Then, the iterative operation is following to further eliminate the residual interference in the currently demodulated symbols to improve the accuracy.
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 5 Experimental Results and Analysis
### Baselines  
We compare NPIVGAE and its variant NPVGAE with several state-of-the-art methods, including using a standard normal distribution as the prior and solving the posterior collapse problem.  
* **GAE**[1] is a non-probabilistic graph autoencoder model, which reconstructs the adjacency matrix through the inner product of the node representations. It minimizes the reconstruction error to learn the parameters in the neural networks.
* **VGAE**[1] assumes that the latent variables follow a normal distribution. The mean and variance of latent variables are encoded by a two-layer GCN, and the structure is reconstructed by a decoder.
* **ARGE**[29] introduces an adversarially regularized term into the graph autoencoder. The adversarial module aims to discriminate whether the node embeddings come from the prior distribution or the graph encoder.
* **ARVGE**[29] realizes regularization and promotion of node embeddings by drawing an adversarial module into the variational graph autoencoder.
* **EVGAE**[10] divides the dimensions of the latent variables into multiple groups, called epitomes, and only penalizes one group each time. This method alleviates the posterior collapse problem by increasing the activity of the hidden units.
## 3 Algorithm Design and Analysis
### TCP-SCTP Switch Process
#### 3.2.2 Process Design  
The TCP-SCTP switch process was used to select protocol and store the type of the current protocol. It is shown in Algorithm 2.  
```
0: ProtocolType
1: Process begin
2: If ProtocolType == 0
3: Program apply SCTP to establish the connection
4: Let ProtocolType = 1
5: Else
6: Program apply TCP to establish the connection
7: Let ProtocolType = 0
8: End Else
9: End If
10: End Process
```  
**Algorithm 2. TCP-SCTP Switch Process**  
* Line 1 indicates that the process started running when the TCP-SCTP method judged that it needed to make protocol switch.
* Line 2 shows that the process needed to judge which type of protocol was running. If the current ProtocolType value was zero, it indicated that the TCP protocol was currently running. The protocol needed to be switched to SCTP.
* Line 3 indicates that the current transport protocol was set to SCTP.
* Line 4 was used to record the protocol type for process switching. The current usage protocol was set as SCTP, and the ProtocolType was assigned a value of one. The TCP-SCTP switch process ended.
* Line 5 indicates that Line 2 was false. The current ProtocolType value was one, therefore the SCTP protocol was currently running. The protocol needed to be switched to TCP.
* Line 6 shows that the current transport protocol was set to TCP.
* Line 7 was used to record the protocol type for process switching. The current usage protocol was set as TCP, and the ProtocolType was assigned a value of zero. The TCP-SCTP switch process ended.
# Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm
## 4 Experiments and Results
### Prediction the Risk of CKD  
The predicted creatinine value and the original 23 features will be combined to predict the risk of CKD. We trained a logistic regression model using XGBoost and sampling-4 method for training.  
First, a test set of 1592 samples, which contains 555 CKD samples and 1037 non-CKD samples, is used for testing. The Receiver Operating Characteristic (ROC) curve is drawn in Figure 10, by varying the threshold. The area under the ROC curves (AUC)  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Test Set** & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** & **Average** \\ \hline
1 & 0.5562 & 0  
was 0.90. In order to display the results more precisely, Table 10 shows the results of true positive (TP), false positive (FP), true negative (TN), false negative (FN), true positive rate (TPR), and true negative rate (TNR) by varying the threshold. If the threshold is set at 0.3, we could achieve a TPR of 84% and a TNR of 83% under the condition that creatinine data is not available.  
As the ratio of CKD and non-CKD in 1592 samples is different from reality, next the whole test set of 90,112 samples, which contains 3248 CKD samples and 86,864 non-CKD samples, is used for testing. The Receiver Operating Characteristic (ROC) curve is shown in Figure 11, by varying the threshold. The area under the ROC curves (AUC) was 0.76. Table 11 shows the detailed results with varying threshold. Obviously, when the whole test set of 90,112 samples is used for testing, the overall result is worse. This is because the test data are very unbalanced. If we use the same threshold of 0.3, the TPR decreases from 84% to 56% and the TNR decreases from 83% to 80%. This is because there are many samples that are difficult to clearly define the class. Even so, if the threshold is set at 0.4, 13,540 (1598 + 11,942) samples will be classified as positive. There will be 49.1% (1598/3248) positive samples detected, and only 15.6% (13,540/86,864) of the population need to be  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Threshold** & **TP** & **FN** & **TN** & **FP** & **TPR** & **TNR** \\ \hline
0.0 & 555 & 0 & 0 & 1037 & 100\% & 0\% \\ \hline
0.1 & 523 & 32 & 570 & 467 & 94\% & 55\% \\ \hline
0.2 & 488 & 67 & 761 & 276 & 88\% & 73\% \\ \hline
0.3 & 464 & 91 & 861 & 176 & 84\% & 83\% \\ \hline
0.4 & 435 & 120 & 909 & 128 & 78\% & 88\% \\ \hline
0.5 & 401 & 154 & 944 & 93 & 72\% & 91\% \\ \hline
0.6 & 348 & 207 & 975 & 62 & 63\% & 94\% \\ \hline
0.7 & 290 & 265 & 996 & 41 & 52\% & 96\% \\ \hline
0.8 & 218 & 337 & 1016 & 21 & 39\% & 98\% \\ \hline
0.9 & 127 & 428 & 1027 & 10 & 23\% & 99\% \\ \hline
1.0 & 0 & 555 & 1037 & 0 & 0\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance with varying threshold value on classification of 1592 samples.  
Figure 10: The ROC curve of 1592 testing samples.  
checked. In this way, we can reduce the impact of CKD through a compromise of testing creatinine for those identified in the CKD risk class.
###### Abstract  
This article provides an overview of the fifth generation of cellular communications (5G) and beyond. It presents the transmission techniques of current 5G communications and those expected of future developments, namely a brief study of non-orthogonal multiple access (NOMA) using the single carrier with frequency domain equalization (SC-FDE) block transmission technique, evidencing its added value in terms of spectral efficiency. An introduction to the sixth generation of cellular communications (6G) is also provided. The insertion of 5G and 6G within the Fourth Industrial Revolution framework (also known as Industry 4.0) is also dealt with. Consisting of a change in paradigm, when compared to previous generations, 5G supports a myriad of new services based on the Internet of things (IoT) and on vehicle-to-vehicle (V2V) communications, supporting technologies such as autonomous driving, smart cities, and remote surgery. The new services provided by 5G are supported by new techniques, such as millimeter waves (mm-wave), in addition to traditional microwave communication, and by massive multiple-input multiple-output (m-MIMO) technology. These techniques were not employed in the fourth generation of cellular communications (4G). While 5G plays an important role in the initial implementation of the Fourth Industrial Revolution, 6G will address a number of new services such as virtual reality (VR), augmented reality (AR), holographic services, the advanced Internet of things (IoT), AI-infused applications, wireless brain-computer interaction (BCI), and mobility at higher speeds. The current research on systems beyond 5G indicates that these applications shall be supported by new MIMO techniques and make use of terahertz (THz) bands.  
5G; 6G; NOMA; Industry 4.0; massive MIMO; mm-wave; IoT 1
## 1 Introduction  
### 1.1 AM in the New Technological Scenario  
Rapid industrial advancements in technology are addressing innovative and simultaneously sustainable and cost-saving manufacturing solutions. Additive manufacturing (AM) is a digital fabrication method involving the realization of three-dimensional objects by building them layer upon layer and linked together one at a time. AM processes are classified into the following categories based on the feedstock, layer deposition technique, and raw materials: direct energy deposition (DED) [1, 2, 3], powder bed fusion (PBF) [4, 5, 6, 7], material jetting (PolyJet) [8], binder jetting [9], material extrusion [10], VAT photopolymerization [11], and sheet lamination [12, 13, 14, 15]. AM is currently proposed as an alternative technique for the 3D prototyping of geometrically complex shapes, creating or finishing components and parts in many sectors [16, 17, 18, 19, 20], especially in aerospace and automotive applications [21]. The main interest of the aerospace industry is oriented toward safe weight optimization methods to maximize the payload and reduce gas emissions. The aviation programs ACARE 2020 (Advisory Council for Aviation Research and Innovationin the EU) and Flightpath 2050 raise awareness of the need for \"greening\" via optimizing aircrafts' fuel consumption and CO2 and NOx emissions over the course of the next several years [22; 23]. The aviation sector should undergo demanding technological development to meet these targets, such as by incorporating cutting-edge materials, design methodologies, and manufacturing processes, all while maintaining rigorous standards for structural durability and safety. Currently, the main engineering task is the maximization of a material's efficiency while improving the functional and manufacturing complexity, which may not always be achievable through conventional approaches. Establishing effective methods to ensure that dfAM (design for additive manufacturing) strategies are developed is the primary challenge faced by the industry. For this reason, worldwide standards are in place to sustain the certification of these new manufacturing procedures, defining a comprehensively accepted framework [24; 25]. Several in-depth reviews have reported on the progress in the AM sector, particularly focusing on aircraft and spacecraft applications [26; 27; 28]. The benefits of AM have been investigated extensively in the aerospace industry for decades; for example, Boeing [29; 30; 31; 32], Airbus [33; 34; 35; 36; 37; 38], Embraer [39], and General Electric [40; 41] employed AM techniques for the manufacturing and repair of several parts. Spaces agencies, such as NASA [42; 43; 44; 45], Thales Alenia Space [46], and SpaceX [47], are currently exploring the feasibility of utilizing AM igniters, injectors, and combustion chambers on their rocket engines.