esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Channel Model
\begin{split}\mathbf{C}_{l}&=\mathbb{E}\left[\mathbf{ h}_{l}\mathbf{h}_{l}^{H}\right]\\ &=\frac{\beta_{l}K_{l}}{K_{l}+1}\left(\mathbf{b}\left(\rho_{x,l} \right)\mathbf{b}^{H}\left(\rho_{x,l}\right)\right)\otimes\left(\mathbf{a} \left(\rho_{y,l}\right)\mathbf{a}^{H}\left(\rho_{y,l}\right)\right)\\ &+\frac{\beta_{l}}{K_{l}+1}\int\limits_{\rho_{x,l}^{\min}}^{ \rho_{x,l}^{\max}}\int\limits_{\rho_{y,l}^{\min}}^{\rho_{y,\max}}S_{l}\left( \rho_{x},\rho_{y}\right)\left(\mathbf{b}\left(\rho_{x}\right)\mathbf{b}^{H} \left(\rho_{x}\right)\right)\otimes\left(\mathbf{a}\left(\rho_{y}\right) \mathbf{a}^{H}\left(\rho_{y}\right)\right)\mathrm{d}\rho_{x}\mathrm{d}\rho_{y} \\ &\overset{\Delta}{=}\mathbf{C}_{l}^{\mathrm{LoS}}+\mathbf{C}_{l}^{ \mathrm{NLoS}}\end{split}  \tag{5}\]  
with:  
\[ \begin{split}\mathbf{a}\left(\rho_{y}\right)&=\left[1,\exp\left(j2\pi\rho_{y}\right),\cdots,\exp\left(j2\pi\left(N_{y}-1\right) \rho_{y}\right)\right]^{T},\\ \mathbf{b}\left(\rho_{x}\right)&=\left[1,\exp\left(j2 \pi\rho_{x}\right),\cdots,\exp\left(j2\pi\left(N_{x}-1\right)\rho_{x}\right) \right]^{T},\end{split}  \tag{6}\]  
where \(\rho_{x,l}=\frac{d_{x}}{\lambda}\cos\theta_{l}\sin\varphi_{l}\) and \(\rho_{y,l}=\frac{d_{y}}{\lambda}\cos\theta_{l}\cos\varphi_{l}\) denote the virtual AoDs corresponding to the sensors' physical AoDs in the horizontal and vertical directions. \(S_{l}\left(\rho_{x},\rho_{y}\right)\) denotes the PAS with respect to the virtual AoDs \(\rho_{x}\) and \(\rho_{y}\). The integral boundaries are given by:  
\[ \begin{split}\rho_{x,l}^{\max}&=\max\limits_{\theta \in\left[\theta_{l}-\Delta\theta_{l},\theta_{l}+\Delta\theta_{l}\right], \varphi\in\left[\varphi_{l}-\Delta\varphi_{l},\varphi_{l}+\Delta\varphi_{l} \right]}\frac{d_{x}}{\lambda}\cos\left(\theta\right)\sin\left(\varphi\right), \\ \rho_{x,l}^{\min}&=\min\limits_{\theta\in\left[\theta_{ l}-\Delta\theta_{l},\theta_{l}+\Delta\theta_{l}\right],\varphi\in\left[\varphi_{l}- \Delta\varphi_{l},\varphi_{l}+\Delta\varphi_{l}\right]}\frac{d_{x}}{\lambda} \cos\left(\theta\right)\sin\left(\varphi\right),\\ \rho_{y,l}^{\max}&=\max\limits_{\theta\in\left[\theta_{ l}-\Delta\theta_{l},\theta_{l}+\Delta\theta_{l}\right],\varphi\in\left[\varphi_{l}- \Delta\varphi_{l},\varphi_{l}+\Delta\varphi_{l}\right]}\frac{d_{y}}{\lambda} \cos\left(\theta\right)\cos\left(\varphi\right),\\ \rho_{y,l}^{\min}&=\min\limits_{\theta\in\left[\theta_{ l}-\Delta\theta_{l},\theta_{l}+\Delta\theta_{l}\right],\varphi\in\left[\varphi_{l}- \Delta\varphi_{l},\varphi_{l}+\Delta\varphi_{l}\right]}\frac{d_{y}}{\lambda} \cos\left(\theta\right)\cos\left(\varphi\right).\end{split}  \tag{7}\]
## 2 Materials and Methods
### Problem of Seepage Field in Dry-Stack Tailings Ponds
#### 2.1.2 Water Balance Model of Seepage Field in the Dry-Stack Tailings Pond  
During the evolution of the seepage field of the dry-stack tailings pond, according to the principle of mass conservation, when the volume of inflow water is larger than the volume of discharge water, the water volume in the seepage field will increase and the phreatic line will increase. When the water into the seepage field is equal to the water discharge out of seepage field, that is, when Equation (9) is satisfied, the seepage field in the tailings pond is in a stable state.  
\[\mathrm{d}V_{in}=\mathrm{d}V_{out} \tag{9}\]  
By simultaneous Equations (1)-(9), when Equation (10) is satisfied, the seepage field of the tailings pond becomes stable, and the seepage field is in equilibrium. On the contrary, if Equation (10) is not satisfied, the seepage field of the dry-stack tailings pond does not reach a stable state.  
\[Q_{m}=Q_{d1}+Q_{c} \tag{10}\]  
From Equation (10), the main factors considered in establishing the calculation model should include the contribution of rainfall infiltration and the initial water content of tailings, as well as water drainage through the initial dam and water evaporation from the dam. In this study, the SEEP program of GeoStudio is applied to establish the numerical analysis model of the seepage field of the dry-stack tailings dam. The rainfall infiltration flow and evaporation are considered by the flow boundary, and the initial dam seepage is automatically calculated by the program. The Equation (10) is transformed into Equation (11)  
\[\Delta Q_{m}=Q_{d1} \tag{11}\]  
where \(\Delta Q_{m}\) is the net infiltration flow of rainfall deducting evaporation within a certain time (in this study, a certain time is a month). In the same region, the rainfall and evaporation are relatively stable in a year, so the annual average value of \(\Delta Q_{m}\) can be considered constant. \(Q_{d1}\) is the initial dam discharge flow. As the water level of the phreatic line of the seepage field rises, \(Q_{d1}\) will gradually increase. When \(Q_{d1}\) satisfies Equation (11), the seepage field of the dry-stack tailings pond will finally evolve to an equilibrium state, that is, the final state of the seepage field of the dry-stack tailings pond. Therefore, this seepage field can be used as the basis for the design of the drainage system and dam slope of a dry-stack tailings dam and also as the basis for predicting the long-term stability of a dry-stack tailings dam. It is worth noting that due to the fluctuation in seasonal rainfall, the final seepage field is relatively stable.  
In the following study, we will mainly study the evolution law of the seepage field, analyze the influencing factors, and judge the criteria of the final state of the seepage field.
# Electromagnetic Imaging of Uniaxial Objects by Two-Step Neural Network
###### Abstract  
The integration of electromagnetic imaging technology with the Internet of Things plays an important role in fields as diverse as healthcare, geophysics, and industrial diagnostics. This paper presents a novel two-step neural network architecture to solve the electromagnetic imaging for uniaxial objects which can be used in the Internet of Things. We incident TM and TE waves to unknown objects and receive the scattered fields. In order to reduce the training difficulty, we first input the gathered scattered field information into a deep convolutional neural network (DCNN) to obtain the preliminary guess. In the second step, we feed the guessed image into the convolutional neural network (CNN) to reconstruct high-resolution images. Our numerical results demonstrate the real-time imaging capability of our proposed two-step method in reconstructing high-contrast scatterers.  
Keywords:inverse scattering problems; two-step neural network method; deep convolutional neural network; convolutional neural network; electromagnetic imaging +
Footnote †: journal: Applied sciences
## 5 Microbial Corrosion by SRB
### Microbial Corrosion in Acidic Environment
#### 5.1.2 Microbial Activities
The authors also suggested the higher impact of acetate produced by SRB metabolism at low pH on materials corrosion than biogenic sulphide. However, when an experiment was carried out in sulphate free medium, bacteria changed their metabolic pathways to sulphate free metabolism, sulphide was not produced. Additionally, acetic acid was found to have inhibition effect on SRB metabolism as mentioned above. Hence, the comparison of impact of acetate and sulphide requires further study. Besides, most of the studies were conducted in near stagnant conditions in a specific volume in laboratory. When SRB achieve the growth, the bulk pH increased consequently. Thus, the effect of proton concentration on corrosion of materials was reduced. However, these conditions cannot simulate the actual condition in nature, e.g., in waste water. Further studies in corrosion of materials in open conditions where there is a bulk environmental should be taken undertaken. \begin{table}
\begin{tabular}{l l l c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material Type**} & \multirow{2}{*}{**SRB Species**} & \multirow{2}{*}{**Solution**} & \multicolumn{8}{c}{**Corrosion Rate at Different pH (10\({}^{-3}\) mm/Year)**} & \multicolumn{1}{c}{**Ref.**} & \multicolumn{1}{c}{**Note**} \\ \cline{3-14}  & & & **4.0** & **5** & **5.5** & **6.0** & **6.5** & **7.0** & **7.5** & **8.0** & **8.5** & **9.0** & **9.5** & **10** & \\ \hline DSS 2025 & _D. vulgaris_ & Artificial sea water & 6.1 & 1.0 & - & 0.6 & - & - & 0.5 & - & - & - & - & [17] & Calculated from current density \\ DSS 2025 & _D. vulgaris_ & Artificial sea water & - & - & - & - & - & 1.9 & 2.6 & - & 10.1 & - & 3.3 & [18] & Calculated from current density \\ CS AIT SL X70 & _D. vulgaris_ & Modified Baar’s medium & - & - & 13.1 & 8.5 & 6.8 & 12 & 7.5 & 9.4 & 11.4 & 13.1 & 14 & - & [102] & - \\ CS AIT SL X70 & Not identified & Artificial sea mud extract & - & - & - & 18.4 & - & - & - & 26.8 & - & - & - & 21.3 & [112] & Calculated from current density \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Corrosion rate of different materials according to different pH environment.
## 2 Materials and Methods
### Modeling Method  
This study proposes a multi-scale attention residual network (ReSE-AP Net) that synergistically integrates residual architecture, channel attention, and multi-scale feature fusion to efficiently decode complex spectral information. Centered on residual convolutions, the network employs skip connections to merge local spectral details with global abstract features, thereby alleviating gradient-vanishing issues. Within each residual block, a squeeze-and-excitation (SE) attention mechanism dynamically enhances responses at critical spectral bands through global feature statistics while suppressing noise. The model further incorporates atrous spatial pyramid pooling (ASPP) to extract spectral features at multiple scales in parallel, simultaneously capturing fine structures of weak absorption peaks and overarching trends of broad spectral ranges. Ultimately, feature fusion followed by nonlinear mapping enables end-to-end prediction, furnishing a robust deep-learning framework for spectral analysis.
## Abbreviations  
The following abbreviations are used in this article:  
\begin{tabular}{l l}
5G & Fifth Generation \\
6G & Sixth Generation \\ AWGN & Additive White Gaussian Noise \\ BER & Bit Error Rate \\ CSK & Chaos Shift Keying \\ DC & Direct Current \\ DCSK & Differential Chaos Shift Keying \\ IoT & Internet of Things \\ LPF & Low-Pass Filter \\ MIMO & Multiple-Input Multiple-Output \\ MSE & Mean Square Error \\ PLS & Physical Layer Security \\ QCSK & Quadrature Chaos Shift Keying \\ RSA & Rivest-Shamir-Adleman \\ SDN & Software-Defined Networking \\ SNR & Signal-to-Noise Ratio \\ WSN & Wireless Sensor Network \\ \end{tabular}
# Gold Nanoparticles for Drug Delivery and Cancer Therapy
## 4 Gold Nanoparticle-Based Drug Delivery
Cyclic peptide-capped Au NPs as a drug delivery system are an attractive and efficient choice due to their cell penetrating properties. They differ from regular Au NPs in various ways, such as the hydrophobic residues in the peptides. They are formed to perturb and/or permeate cell membranes by interacting with hydrophobic residues, which allow molecular cargo to have higher uptake than the conventional delivery. The Au NP surface with cyclic peptide requires no chemical modification or functionalization, nor any covalent conjugation between biologically active molecules and capped Au NPs (CP-Au NPs). The hydrophobic amino acids in the peptide produce a pocket for the noncovalent entrapment of the drug. Amino acid residues in cyclic peptide act as concurrent reductants and capping agents. Linear peptides--that in the amino acid contain nitrogen heteroaromatics, which are effective metal binders--and CP-Au NPs combined with anticancer and antiviral drugs can be used as potential scaffolds for the production of noncovalent prodrugs [67]. Recently, many studies have been carried out to find new innovative solution to develop highly efficient and reproducible fluorescent contrast agents for targeting specific cells [68]. Gold nano-carrier-based platinum (II) and platinum (IV) drugs have enhanced the loading capability on the surface of the nano-carriers, and release in a pH-dependent environment. A study introduced a system with Au NP-based platinum drugs, and Aminoanthraquinone was incorporated into the unit, where they are both acting as fluoroprobes and DNA intercalators. The unit has distinct, unusual and remarkable DNA binding properties [69]. Au NPs are also used on deeper sections of the eye such as the retina. A study was carried out to investigate how Au NPs can be used to deliver drug and biomacromolecules to the eye and how they can be used as active therapeutic tools [70]. A cell membrane coating strategy opened new opportunities for multifunctional drug delivery platforms. A system was proposed--making use of Au NPs combined with blood cell membrane--that can be used in cancer therapy for melanoma. Platelet membrane-coated Au nano-stars conjugated with curcumin were evaluated for whether R/P-cGNS had improved anticancer efficacy. Red blood cell coating provided self-antigens, so it evaded clearance by macrophages and platelet membrane coating provided targetability. The results show that it can be an effective system for cancer therapy [71]. On the other hand, a study was conducted to introduce a system that loads up doxorubicin-loaded oligonucleotides attached to Au NPs as carriers in chemotherapy for prostate cancer. The result shows that this is a viable method and has promising potential [72]. Moreover, three mercaptopropionic acid-capped Au NPs in resistant leukemia K562/ADM cells were tested for carrier capability and biomarkers. The experimental result is also promising [73].
# A Novel Deep Learning Approach for Deepfake Image Detection
## 9 Conclusions  
Deepfake detection using a novel deep learning-based approach is proposed to help cybersecurity professionals overcome deepfake-related cybercrimes by accurately detecting the deepfake content. The Xception, NAS-Net, Mobile Net, and VGG16 are the employed neural network techniques in comparison. The benchmark deepfake dataset containing the real and fake faces was utilized for building all research models. The proposed DFP approach outperformed with employed learning techniques and state-of-the-art studios. The proposed model achieved 94% accuracy for deepfake detection. The employed neural network techniques were validated using confusion matrix analysis and analyzed through a time series analysis. In the future, blockchain-based technology and cloud web system will be designed to configure our proposed system for detecting the deepfakes through a secure online system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Year** & **Learning Type** & **Technique** & **Accuracy Score (\%)** \\ \hline
[29] & 2020 & Deep learning & Traditional Convolutional Neural Network & 89 & 89 \\ \hline
[30] & 2021 & Transfer learning & VGG16 & 90 & 88 \\ \hline
[31] & 2020 & Transfer learning & Mobile Net & 88 & 86 \\ \hline
**Proposed** & **2022** & **Deep learning** & **Novel DFP** & **94** & **95** \\ \hline \hline \end{tabular}
\end{table}
Table 10: The comparative performance analysis of the proposed approach with the other state of art studies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**K-Folds** & **Accuracy Score (\%)** & **Loss Score** \\ \hline Fold 1 & 93 & 0.13 \\ \hline Fold 2 & 95 & 0.08 \\ \hline Fold 3 & 95 & 0.13 \\ \hline Fold 4 & 96 & 0.08 \\ \hline
**Average** & **95** & **0.10** \\ \hline
**Standard Deviation** & \(\pm\)**1** & \\ \hline \hline \end{tabular}
\end{table}
Table 9: The performance validation analysis of the novel proposed approach based on the k-fold cross-validation technique.  
**Author Contributions:** Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing, and original draft preparation, review and editing, and visualization, A.R.; supervision, project administration, and funding acquisition, K.M. and M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by University of Haft Albatin, Saudi Arabia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The supporting data for the findings of this study are available from the corresponding author on reasonable request.  
**Acknowledgments:** The authors would like to thank all participants for their fruitful cooperation and support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Coexistence Solutions for Sync-FT Operation
### Compensation of the Backoff Count  
The last feature compensates the backoff count of a free-riding link by a certain amount such that it will ultimately go through the full backoff time length on average. The operation of the solution that utilizes this feature is illustrated in Figure 15. This feature requires an STA to repick a new backoff count value to be added to the current count. Thus, we refer to the solution that includes this feature as Repick + Comp. In the example shown in the figure, the MLD repicks a backoff count value of eight (STR case) or ten (non-STR case) for Link 2 (free-riding link). After the free-riding transmission finishes, the link stores its current backoff count value as \(b_{old}\) and repicks a new backoff count \(b\). Then, it performs a new BO with an initial count of \(b+b_{old}\). In this example, the backoff count of Link 2 becomes eleven after compensation instead of three (STR case) or one (non-STR case).  
Figure 14: Switching to another CW set (Switch). (a) STR MLD, (b) Non-STR MLD.  
Figure 12: CW doubling (Double). (a) STR MLD, (b) Non-STR MLD.  
Figure 13: CW doubling and repicking a backoff count (Double + Repick). (a) STR MLD, (b) Non-STR MLD.  
```
1:while the STA has a packet to transmit do
2:\(b\leftarrow\text{Rand}[0,CW-1]\) # Pick a new backoff count value
3:while\(b!=0\)do
4:if Other STA has completed a backoff process and is about to transmit a packet then
5:if the channel is idle for PIFS then
6: Transmit the packet in the given channel
7:if the MLD uses the Switch Scheme then
8:\((CW_{min},CW_{max},CW)\leftarrow(CW_{min},CW_{max},CW_{min})\)
9: Start a timer with an expiration time of \(t_{switch}\)
10:endif
11:if the MLD uses the Doubling Scheme then
12:\(CW\leftarrow\min(2(CW+1)-1,CW_{max})\)
13:endif
14:if the MLD uses the Repick Scheme then
15:\(b_{old}\gets b\), \(b\leftarrow\text{Rand}[0,CW-1]\) # Repick a new backoff count value
16:endif
17:if the MLD uses the Compensation Scheme then
18:\(b\gets b+b_{old}\) # Add the remaining backoff count to the current value
19:endif
20:endif
21:endif
22:while the channel is sensed busy do
23: Sense the channel for DIFS
24:endwhile
25:\(b\gets b-1\) for every idle \(T_{slot}\) duration
26:endwhile
27: Transmit the packet in the given channel
28:if the MLD uses the Switch Scheme and \(t_{switch}\) has expired then
29:\((CW_{min},CW_{max},CW)\leftarrow(CW_{min},CW_{max},CW_{min})\)
30:endif
31:if the transmission succeeds then
32:\(CW\gets CW_{min}\)
33:else
34:\(CW\leftarrow\min(2(CW+1)-1,CW_{max})\)
35:endif
36:endwhile
```  
**Algorithm 1** Proposed multi-link channel access procedure of an STA.  
Figure 15: Compensation after repicking a backoff count (Repick + Comp). (**a**) STR MLD, (**b**) Non-STR MLD.
## 1 Introduction
developed a methodology to predict the angular distortion in multipass V-butt joint welding based on experimental and analysis results [20]. They analyzed the behavior of angular distortion along the passes performed. [PERSON] and [PERSON] investigated the effect of the welding process and heat input conditions on the angular distortion induced by bead-on-plate welding through a numerical approach [21]. They developed the parameter of mechanical melting region on the plate thickness and found that it was the dominant factor for accurately quantifying angular distortions. [PERSON] et al. revealed the mechanism of angular distortion in fusion welding and how the welding processes and factors influence the angular distortion [22]. They established a theoretical model concerning the melting-solidification process and formulated an expression for angular distortion. [PERSON] developed a systematic method to predict angular distortion in multilayer welding [23]. He defined the relationship between heat input, bead cross-section, angular distortion, and thickness through the bead-on-plate welding experiment and constructed databases from them. Then he proposed a method of predicting angular distortion in multilayer welding and verified it through welding experiments on V-groove butt joints. [PERSON] and [PERSON] et al. conducted studies on the butt-welding deformation using pieces [24; 25]. [PERSON] only conducted experimental studies on the butt welding deformation at the level of unit specimens. [PERSON] et al. simulated the butt-welding deformation of hull block joints, however they did not derive the optimal number of pieces and neither they simulate the butt-welding deformation of large hull blocks. In this study, a thermal elasto-plastic analysis based on the finite element method was employed to examine the welding deformation. The necessary parameters were chosen based on the experimental results, to guarantee the accuracy of the results. This study was carried out with the objective of maintaining quality accuracy of hull block and reducing temporary piece usage. The restraint effect of the temporary piece on the deformation of base plates during joint welding was reviewed both qualitatively and quantitatively through a series of butt-welding experiments. The calculation and analysis results were compared and verified with the experimental data. Lastly, the welding deformation on the hull block joint, considering the temporary piece, was simulated by using a verified analysis method, and the results obtained were compared with the experimental results. Based on these results and by placing the temporary pieces at the right places, an attempt to achieve quality accuracy of the butt joint welding area in panel block and saving the temporary piece usage at the same time was made.
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 3 Results
### Meta-Analysis
The findings of this study underscore the capability of models such as Random Forest and Logistic Regression to achieve clinically relevant predictive accuracy. For instance, the Random Forest model demonstrated superior performance in long-term follow-up scenarios, achieving an area under the curve (AUC) as high as 0.89. This level of accuracy suggests that ML models can serve as reliable tools in stratifying risk, enabling clinicians to intervene earlier in high-risk cases. Logistic Regression, a more traditional approach, remains a robust and interpretable model for clinicians, reinforcing its utility in settings where simplicity and ease of implementation are prioritized. Moreover, this review highlights the potential of ML models to incorporate diverse variables, including non-linear relationships, that may not be fully accounted for in traditional models. By doing so, these models can identify previously underappreciated risk factors, offering novel insights into the pathophysiology of mortality in HD patients. For example, factors such as albumin, hemoglobin, and body mass index, which were recurrently identified as key predictors, can inform clinical decision-making and enhance patient monitoring protocols. This study also emphasizes the importance of advancing personalized medicine in nephrology. ML models can provide individualized risk assessments, tailoring interventions to the specific needs of each patient. This aligns with the broader shift toward precision medicine, where treatments are customized based on unique patient characteristics rather than generalized protocols.