esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Factor Authentication Scheme for Mobile Money: A Review of Threat Models and Countermeasures
###### Abstract  
The proliferation of digital financial innovations like mobile money has led to the rise in mobile subscriptions and transactions. It has also increased the security challenges associated with the current two-factor authentication (2 FA) scheme for mobile money due to the high demand. This review paper aims to determine the threat models in the 2 FA scheme for mobile money. It also intends to identify the countermeasures to overcome the threat models. A comprehensive literature search was conducted from the Google Scholar and other leading scientific databases such as IEEE Xplore, MDPI, Emerald Insight, Hindawi, ACM, Elsevier, Springer, and Specific and International Journals, where 97 papers were reviewed that focused on the topic. Descriptive research papers and studies related to the theme were selected. Three reviewers extracted information independently on authentication, mobile money system architecture, mobile money access, the authentication scheme for mobile money, various attacks on the mobile money system (MMS), threat models in the 2 FA scheme for mobile money, and countermeasures. Through literature analysis, it was found that the threat models in the 2 FA scheme for mobile money were categorised into five, namely, attacks against privacy, attacks against authentication, attacks against confidentiality, attacks against integrity, and attacks against availability. The countermeasures include use of cryptographic functions (e.g., asymmetric encryption function, symmetric encryption function, and hash function) and personal identification (e.g., number-based and biometric-based countermeasures). This review study reveals that the current 2 FA scheme for mobile money has security gaps that need to be addressed since it only uses a personal identification number (PIN) and a subscriber identity module (SIM) to authenticate users, which are susceptible to attacks. This work, therefore, will help mobile money service providers (MMSPs), decision-makers, and governments that wish to improve their current 2 FA scheme for mobile money.  
two-factor; authentication scheme; authentication; mobile money; the mobile money system; mobile banking; threat models; countermeasures +
Footnote †: journal:  
_Future Internet_
###### Abstract  
Aiming at the nonlinear low-speed-jitter (LSJ) vibration suppression for a yaw system of a megawatt wind turbine, a kinematics mechanism of the yaw system is investigated from the perspective of tribology, and a kinematics model of the yaw system based on an equilibrium position is established. On the basis of the dynamic modeling of the yaw system, a nonlinear mathematical model of the LSJ system is deduced. Based on the two lead motors' driving of the conventional yaw motion, an innovative design with a special installation of two auxiliary motors for yaw transmission is carried out, which is integrated with a matching centralized lubrication system (CLS). Based on open-loop proportional-derivative (PD) control and the iterative learning control methods of the time-varying continuous system, the stability control and jitter amplitude suppression of the yaw system are realized by using a combined driving torque provided by the lead and auxiliary gears. From the stability and convergence of the time-domain response and the convergence of the iterative error, the effectiveness of the iterative learning control method with the PD-based regulation is verified, and its advantages for engineering applications are shown based on the algorithm solver improvement. The feasibility of the physical realization and engineering application of the control methodology is verified by using controller-hardware-in-the-loop (C-HITL) simulation technology.  
low-speed jitter; yaw system; dynamic modeling; PD control; iterative learning control; hardware-in-the-loop simulation
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 5 Anti-Tumor Immune Mechanism of LTP
### LTP Induces Immunogenic Cell Death  
Immune tolerance refers to the fact that tumor cells lack one or more components which are necessary to effectively stimulate the body's immune system. When some physical and chemical factors induce the apoptosis of tumor cells, tumor cells are transformed from non-immunogenic to immunogenic, which is called immunogenic cell death (ICD). ICD activates specific T lymphocytes and specific immune response to eliminate tumors. When ICD occurs, immunostimulatory molecules damage associated molecular patterns (DAMPs), are released from or displayed by the dying cells. Calreticulin (CRT), Adenosine Triphosphate (ATP) and High Mobility Group Box 1 (HMGB1) are well-known DAMPs that are released outside cells in response to ICD. More and more studies have shown that the oxidation produced by LTP induces immunogenic death of cells, causing an increase of DAMPs. In fact, ATP serves as a \"Find Me\" signal and CRT acts as an \"Eat Me\" signal for immune cells [48]. The DAMPs stimulate an immune response by stimulating antigen-presenting cells. The uptake of tumor antigens by dendritic cells was increased. Then, DCs presented tumor antigens to anti-tumor T cells [41] (Figure 3). [PERSON] [49] et al. used non-thermal plasma to induce ICD in A549 lung cancer cells, and found secreted danger signals from cells undergoing immunogenic death enhanced the anti-tumor activity of macrophages.  
Figure 2: The oxidative stress toxicity of LTP. LTP cause the increase of intracellular ROS, which leads to DNA damage and mitochondrial damage, thus leading to the cell apoptosis.
# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 3 Results and Discussion
### Ensemble Learners in Different Stations
As expected, resampling the data from 30-min intervals to hourly intervals led to a reduction in the number of samples. To compensate for this shrinkage and ensure statistical robustness, data augmentation was performed using PVLib to acquire additional samples. A total of 20,000 samples was used at this stage. All stations were retrained using a hybrid model combining the GradientBoosting and RandomForest algorithms. The results in Figure 12 demonstrate that, despite the coarser temporal resolution, the stacking model maintained strong performance across stations. The UNLV station achieved the best accuracy, with an \(R^{2}\) of 0.997 and an RMSE of 10.13, while the RAZON station showed relatively lower performance, with an RMSE of 98.29 and an \(R^{2}\) of 0.910. This variation suggests that, while the proposed model generalizes well, station-specific dynamics still influence performance, warranting further exploration of localized tuning or model adaptation strategies. In this step, confidence intervals (CIs) are calculated to evaluate the performance of the stacking-based ensemble model for predicting GHI in four different stations, incorporating bootstrap-based uncertainty estimates. The model's predicted mean aligns closely with the actual measurements, indicating strong predictive accuracy. A 95% confidence interval, standard in uncertainty quantification, captures the variability across bootstrap replicates. This interval remains narrow in most cases, reflecting high model confidence, but widens during periods of low solar radiation, indicating increased uncertainty. The visualization in Figure 13 clearly conveys both the accuracy and uncertainty of the forecasts, which is essential for risk-sensitive applications such as solar energy management. This performance, demonstrated at the UNLV station, can be confidently generalized to the NELHA station. At the ULL and NWTC stations, the model exhibits a marked improvement over earlier versions, achieving a much closer alignment between predicted and actual values. The updated plot features tighter, more consistent confidence intervals and captures fluctuations more effectively, suggesting enhanced robustness and generalization, likely due to refined bootstrapping and model calibration techniques. Figure 11: Comparison between single models and ensemble in terms of (**a**) \(R^{2}\) and (**b**) RMSE. Figure 12: Results from ensemble learning on all stations for 1-h shift. Figure 13: Analysis of stacking ensembles on 4 different stations with confidence intervals.
# Modular Robotic Design and Reconfiguring Path Planning
## 2 Robotic Design and Modeling
### Modular Path Planning Based on Optimal Ant Colony Optimization  
#### 3.3.1 Ant Colony Optimization  
The core idea of ant colony optimization is to simulate the working mechanism of a single ant colony foraging process to carry food to the anthill through mutual collaboration and information transfer, which is a kind of bionic intelligence algorithm. ACO belongs to a heuristic search, which can significantly reduce the difficulty of the problems and improve search efficiency. It also has the characteristics of distributed computing and positive feedback, which can achieve global search optimization [31].  
When the traditional ant colony optimization is used for path planning, the key to its implementation mainly includes the calculation of transfer probability, updating of pheromone concentration, storing the taboo table of nodes that ants are not allowed to choose, etc. [32; 33].  
1. Probability of state transfer \[p_{ij}^{k}(t)=\left\{\begin{array}{cc}\frac{\left[\tau_{ij}(t)\right]^{a} \left[\eta_{ij}(t)\right]^{\beta}}{\sum_{e\in A}\left[\tau_{is}(t)\right]^{a} \left[\eta_{is}(t)\right]^{b}},&if\;j\in A\\ 0,&otherwise\end{array}\right.\] (10) where \"\(p_{ij}^{k}(t)\)\" is the transfer probability of the ant \"\(k\)\" from node \"\(i\)\" to node \"\(j\)\" at time \"\(t\)\". The larger the value, the greater the probability that the ant chooses path \"\(I\to j\)\"; \"\(\tau_{ij}(t)\)\" is the pheromone concentration of the path at a time \"\(t\)\"; \"\(\eta_{ij}(t)\)\" is the heuristic function; \"\(a\)\" and \"\(\beta\)\" are the pheromone and the desired heuristic factor, respectively; \"\(\tau_{is}(t)\)\" is the total amount of information from node \"\(i\)\" to target node \"\(s\)\"; \"\(\eta_{is}(t)\)\" is the value of the desired heuristic function from node \"\(i\)\" to target node \"\(s\)\"; \"\(A\)\" is the set of nodes that the ant \"\(k\)\" is allowed to select the next one. For conventional path planning with the goal of distance minimization, the heuristic function can be described as the inverse of the distance of neighboring nodes.
2. Pheromone concentration The ant colony algorithm achieves the selection of the shortest path based on a positive feedback mechanism. As time passes, the concentration of pheromone left on the shortest path gradually increases, and the probability of that path being selected increases. However, if the pheromone concentration is too high, the heuristic information will be lost and thus fall into a local optimum. Therefore, the algorithm simulates the mechanism of pheromone volatilization in nature and achieves the pheromone update.
3. Forbidden table The forbidden table is a record to store the nodes that have been selected by ants during the search iteration of the ant colony algorithm, characterizing the ants are not allowed to select the node again in the subsequent path selection.  
Figure 7: Schematic diagram of the third type of collision avoidance. (**a**) Situation 1; (**b**) Situation 2; (**c**) Situation 3; (**d**) Situation 4.
## 2 Problem Formulation
### Communication Link Correlation  
In this part, we define the communication link correlation according to the relative mobility of UAVs. First, we need to obtain the relative states between each UAV and its neighbors. To estimate the relative state information, Ref. [24] designs a distributed predefined-time state observer. In addition, Ref. [25] adopts two consecutive HELLO information to estimate the relative velocity. In this paper, we simply assume that UAVs can get the speed of others through multi-hop communication. Then, UAV can calculate the relative speed between its neighbors and itself.  
The relative movement model between UAV \(i\) and UAV \(j\) is shown in Figure 2. \(A\) is the current position of UAV \(i\), while UAV \(j\) is located at \(B\) and moves towards \(C\) with velocity \(v_{ij}\) relative to UAV \(i\). Assume that the relative movement velocity remains for a short period. We define the communication link correlation between UAVs \(i\) and \(j\) as follows:  
\[f_{ij}^{c}=\begin{cases}\dfrac{dis_{BC}}{v_{ij}T_{th}},&\dfrac{dis_{BC}}{v_{ij} }<T_{th}\\ 1,&\dfrac{dis_{BC}}{v_{ij}}\geq T_{th}\end{cases} \tag{1}\]  
\(T_{th}\) is the link lifetime threshold. \(dis_{BC}\) denotes the distance between positions \(B\) and \(C\), which can be calculated from the geometric relationship in Figure 2:  
\[dis_{BC}=sqrt\left({r_{c}}^{2}-(dis_{AB}\sin\theta)^{2}\right)+dis_{AB}\cos\theta \tag{2}\]  
The greater the communication link correlation, the more stable the communication link is.
# Ultra-Precision Cutting Mechanism of KDP Crystal in Microplastic Region via Heating Assistance
## 6 Conclusions  
In this work, we studied the impact of heating assistance on the surface quality in the processing of KDP crystals.  
1. A processing method was developed to improve the surface quality of KDP crystals on the basis of the theory of ductile/brittle transition, wherein the surface temperature of KDP crystals is increased to enlarge the plastic zone. Several nanoindentation experiments were conducted on KDP crystals, and the critical depth of the ductile/brittle transition of KDP crystals was about 38 nm.
2. Nanoscratch experiments were performed on KDP crystal surfaces under heat. With the increase in the scratch length, the surface scratch changed from the plastic to the brittle zone, and the surface removal mode transformed from plastic slip to brittle fracture. The ductile/brittle transition depth of KDP crystals increased with increasing temperature. The surface morphology was affected by temperature, verifying the effectiveness of the heating-assisted method in improving the cutting performance of KDP crystal.
3. Finally, with heating, KDP crystal surfaces were processed using a UPDFC machine. The results showed that the surface roughness value of the material reached 5.275 nm as the surface temperature increased to 50 \({}^{\circ}\)C, indicating the superior surface quality compared with that at room temperature. These results verified the effectiveness of the heating-assisted machining method in improving the surface quality. As a typically brittle material, the processing of KDP crystals is a challenge. We proposed a heating-assisted method to improve surface quality, which provides a useful reference for the processing of other soft and brittle materials.  
Conceptualization, H.Y., S.F., M.H., and Z.J.; formal analysis, S.F., B.W., and G.Y.; investigation, Z.C., M.H., B.W., and G.Y.; methodology, S.F., H.Y., M.H., Z.C., and Z.J.; project administration, Z.J., H.Y., and G.Y.; resources, H.Y., G.Y., and Z.J.; software, S.F., Z.C., and B.W.; validation, H.Y. and Z.J.; visualization, S.F., H.Y., and B.W.; writing--original draft, S.F., Z.C., and G.Y.; writing--review and editing, H.Y., M.H., and Z.J. All authors have read and agreed to the published version of the manuscript.  
This study was financed the National Natural Science Foundation of China (Nos. 51305413, 52105490, and 51905507; funder: [PERSON], [PERSON], [PERSON]), Sichuan International Science and Technology Innovation Cooperation Project (Nos. 2020 YFH0006 and 2020 YJ0031; funder: [PERSON], [PERSON]).  
Not applicable.  
We are thankful for the support from the Institute of Machinery Manufacturing Technology, China Academy of Engineering Physics, Mian-yang 621900, Sichuan Province, China.  
The authors declare no conflict of interest.
## 4 Potential Applications of Peptide-Synthetic Polymer Hybrids
### Biological Evaluations (Cytotoxicity)  
The final hybrid conjugates can be classified as non-toxic and biocompatible depending on the nature of the synthetic polymer. Thus, multiple works involved methodologies so as to evaluate the cytotoxicity of the obtained synthetic copolymers in order to be sure that they can be used for biomedical applications. A recent review reported by [PERSON] [90], was precisely focused in the recent advances concerning the synthesis and self-assembly of PLA-containing amphiphilic block copolymers (e.g., including poly(meth)acrylates, poly(ethylene glycol), polypeptides, polysaccharides, and polyurethanes) and their bio-related applications (_i.e._, drug delivery and imaging platforms of self-assembled nanoparticles, and tissue engineering of crosslinked hydrogels). These kind of hybrid materials appeared as a promising approach to solve the problems associated to the inherent hydrophobicity of polylactide.  
Other recent studies, described in Section 2, performed also cytotoxicity evaluations of synthetic peptide-polymer hybrids. This is the case of amphiphilic poly(ethylene glycol)-_block_-poly(_y_-cholesterol-L-glutamate) diblock nanoparticles which were tested using a MTT assay (a standard colorimetric test for the evaluation of cell viability) [24]. The test showed that for two different cell lines (_i.e._, human embryonic kidney cell line HEK-293 and adenocarcinoma human alveolar basal epithelial cell line A549) the viability was practically the same and close to 100%, although a slight decrease (_i.e._, 80%-90%) was detected when the nanoparticles concentration was high (_i.e._, 43.75 mg/mL). This effect was related to the fact that positive charges of conjugates may induce cytotoxicity by disrupting and solubilizing cell membranes, and even that cationic polymers may interfere with critical intracellular processes of cells.  
Cytotoxicity studies using Bel7402 human hepatoma cells were also conducted on amphiphilic nanoparticles based on triblock copolymers constituted by hydrophobic polylactide and hydrophilic polylysine-poly(ethylene glycol) segments (_i.e._, poly(L-lactic acid)-_block_-poly(z-lysine)-_block_-monomethoxypoly(ethylene glycol) and poly(L-lactic acid)-_block_-poly(L-lysine)-_block_-monomethoxypoly(ethylene glycol) abbreviated as AZLE and ALE, respectively) [27]. It was found that cytotoxicity was dose- and time-dependent for both compounds. Although the cell activity decreased when the concentration of the sample increased, the degree of apoptosis caused by the deprotected ALE copolymer was low, and even at the high concentration of 100 mg/mL the cell death rate was only about 20%. The cell death rate of AZLE was relatively higher, suggesting that the use of cleavable polylysine has more advantage in biocompatibility than the protected one and that it should be useful for biological research. In fact, polylysine was positive charged in aqueous solution when deprotected and had a slight influence on the cell metabolism.
## 3 Methodology
### Experimental Environment Setup
#### 3.1.2 Network Isolation  
This is facilitated through a router that also behaves as a switch, creating a specialized environment exclusively for testing. Central to this setup is the gateway firewall housed within the router. It is armed with a specific rule that denies any connection attempts between the test environment and other devices or networks on the local grid.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Evaluation of Weighted Fusion Methodology  
To validate the efficacy of our proposed weighted fusion methodology, we conducted comprehensive evaluations under diverse operational scenarios. This section presents experimental results comparing the performance of our PCA-based weighted fusion approach against the direct aggregation of provincial forecasts across three representative seasonal periods: June (moderate temperature conditions), August (high temperature conditions), and December (winter conditions).
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 4 Experiments and Analysis
### Classification Results
#### 4.5.1 Cross-validation Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Features**} & **Number of Features** & **Classifier** & **Datasize** & **Average Acc** \\ \cline{1-1}  & & & & & & & _Acc_ \\ \hline Proposed & 2020 & Bispectrum & 3 & 4 & SVM-RBF & 75,604 & 87.8\% \\ Proposed & 2020 & Bispectrum+2D-GFT & 3 & 4 & SVM-RBF & 75,604 & 96.2\% \\
[38] & 2020 & Parameter +Visual Pattern & 9 & 15 & KNN & 104,986 & 97.7\% \\
[39] & 2019 & QRS complex & 13 & 5 & SVM & 100,467 & 89.0\% \\
[40] & 2018 & Power spectral density & 4 & 17 & SVM & 1000 (segments) & 90.2\% \\
[41] & 2014 & Multi-lead fused method & 3 & 5 & SVM & 49,664 & 86.0\% \\
[42] & 2014 & Morphology & 5 & 4 & SVM-RBF & 101,398 & 86.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison with other existing approaches in cross-validation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Year** & **Features** & **Type** & **Classifier** & **Datasize** & **Average Acc** \\ \hline Proposed & 2020 & Bispectrum & 4 & SVM-RBF & 75,604 & 87.8\% \\ Proposed & 2020 & Bispectrum+2D-GFT & 4 & SVM-RBF & 75,604 & 96.2\% \\
[43] & 2019 & STFT-Based Spectrogram & 5 & CNN & 2520 & 99.0\% \\
[44] & 2018 & Normalize & 5 & CNN & 16,499 & 98.1\% \\  & & & & & (segments) & 98.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison with non-feature-extraction-pattern classification approaches. The time cost of each method is listed in Table 13 which represents the time for one heartbeat to process. The running time of bispectrum combined with 2D-GFT is longer because of the higher algorithmic complexity and better performance of detection, which matches our anticipation. The 2D-GFT is very time consuming because of varied parameters.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 3 Results
### Model Classification Results and Performance Comparison
#### 3.2.5 External Validation  
In addition to internal testing and performance benchmarking, the model underwent external validation. A new dataset was constructed using the four preheating images presented prior to the formal experiment and was input into the XGBoost classification model, which demonstrated relatively high accuracy. This process aimed to assess the model's effectiveness in predicting the emotional quality of novel spatial stimuli. The model generated outputs in binary, ternary, and five-class classifications. By comparing these predicted classifications with the ground truth labels, we derived classification accuracy and confusion matrices, as presented in Table 7 and Figure 14.  
Figure 12: Comparison of accuracy and main performance indexes of binary, ternary, and five-element classification models of emotional valence.  
Figure 13: Comparison of accuracy and main performance indexes of binary, ternary, and five-element classification models of emotional arousal.  
The results indicate that the highest external validation accuracy was achieved in the binary classification, reaching 78.50%, followed by the ternary and five-class models with accuracies of 70.23% and 61.83%, respectively. Additionally, the classification accuracy for valence consistently outperformed that for arousal. The ternary confusion matrix reveals that samples labeled with a valence of \(-1\) were more frequently misclassified than those in other categories. In the five-class system, due to the limited number of samples labeled as \(-2\), the classification performance for labels 0 and 1 was comparatively more accurate.