esa-sceva/satcom-chunk-collection huggingface dataset:
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 5 Experimental Results
### Intermediate  
Unlike the previous map, which was rectangular, the intermediate map had an elbow-shaped structure with more rooms, as well as more obstacles to reaching the destination. When finding the path to the destination, when the same path was found for the preprocessed topology map, 147.2 nodes were visited on average, representing a reduction of 99.86% compared with the average of 107,462.6 nodes visited in the existing grid environment. It is assumed that the average number of visited nodes in the image map decreased despite the further increase in resolution because the amount of internally accessible free-space data was smaller than the image resolution. Applying A* to the image map took 183.8 ms on average, which is shorter than for the map with Simple complexity. However, because the number of generated nodes and resolution are higher, the preprocessing time through the CNA was further increased to 90.4 ms. When the same algorithm was applied to the topology map generated by the CNA, it took 2.8 ms (a decrease of 98.48%) when preprocessing and pathfinding were performed together versus alone.
## 3 Real-Coding PBIL Algorithm for Multi-Satellite Scheduling
### Algorithm Steps  
The algorithm flow chart is shown in Figure 6.  
Figure 5: Mutation operator.  
Figure 6: The algorithm flow chart of the PBIL algorithm.  
**Step 1.**: Encode each task according to the real-coding format, and initialize the probability matrix.
**Step 2.**: According to the above update method, use the probability matrix to guide the generation of the new population.
**Step 3.**: According to the constraints in the mathematical model, each of the genetic positions of each chromosome in the population, that is, each task is checked for conflict. A task that does not pass the conflict check will be abandoned; that is, the value of this gene position of the chromosome will be set as zero.
**Step 4.**: The fitness value of each individual is calculated to obtain the best individual with the highest fitness value.
**Step 5.**: If the end condition is satisfied, shift to Step 6. Otherwise, according to the best individual generated in the previous step, update and normalize the probability matrix and shift to Step 2.
**Step 6.**: The algorithm ends. Obtain the best individual and output the corresponding mission planning scheme.
## 4 Numerical Stability  
Before we proceed to the computational results obtained from our numerical algorithms, it is fundamentally important to demonstrate the numerical stability of the underlying algorithms. Specifically, we need to demonstrate that the computational results are independent of both mesh size and time-step size. Figures 2 and 3, respectively, illustrate that our numerical algorithms are indeed independent of mesh size and time-step size.  
The results of Figures 2 and 3 indeed illustrate, as expected, that our numerical algorithms are independent of mesh size and time-step size. The mesh sizes used in Figure 2 range from \(101\times 101\) to \(401\times 401\) grid points. These grid points respectively correspond to \(100\times 100\) to \(400\times 400\) computational cells. The time-step sizes employed in Figure 3 range from \(0.0001\) to \(0.1\).  
The values for temperature and velocity plotted in Figures 2 and 3 correspond to the values at the midpoint of the computational domain at time \(t=5\), i.e.,  
\[\Theta(x,y,t)=\Theta\bigg{(}\frac{L_{x}}{2},\frac{Ly}{2},5\bigg{)},\quad u(x, y,t)=u\bigg{(}\frac{L_{x}}{2},\frac{Ly}{2},5\bigg{)},\quad v(x,y,t)=v\bigg{(} \frac{L_{x}}{2},\frac{Ly}{2},5\bigg{)}.\]
## 4 Complexity and Performance
### Space Overhead  
The space overhead of this algorithm mainly involves the \(\mathbf{f}_{\text{efv}}\) and the reliability determination results of the LPU based on \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\). Specifically, the space requirement of \(\mathbf{f}_{\text{efv}}\) is directly related to the codeword length, \(n\), and its space complexity is linear. This is because each codeword bit requires 1 bit to store the corresponding entropy feature value. In addition, the classification result of each LPU also only requires 1 bit to store. Compared with the prior LLR and posterior LLR that need to store the entire page in the joint-page decoding optimization methods, the proposed method significantly reduces the storage requirement. This shows that these space overheads are all linear space complexities, indicating that, as the problem size increases, the required storage space grows linearly and controllably. This design ensures the algorithm's high efficiency in storage resource utilization and wide applicability.
## 5 Conclusions  
In this paper, a TanDEM-X bi-static InSAR algorithm was proposed to calculate glacier surface elevation changes, and we used the algorithm of glacier mass balance estimation to obtain the glacier mass loss in the SETP. We added the non-local mean filtering method and phase difference strategy to the TanDEM-X bi-static InSAR algorithm to facilitate the suppression of phase noise and to preserve texture details, and to solve the problems of unwrapping phase jump and discontinuity, and the presence of dense interferometric fringes which all hinder the traditional InSAR method. The approach followed by this study improves the accuracy and reliability of glacier topography and glacier surface elevation change data. The correction of systematic errors such as seasonality and radar penetration differences were carried out, which improved the accuracy of the glacier mass balance estimation. In addition, the uncertainty of the glacier surface elevation change, the uncertainty of the seasonal systematic error, and the uncertainty of the radar penetration difference error were evaluated. We used 13 pairs of TanDEM-X bi-static InSAR images to estimate the glacier surface elevation change for the entire SETP, where two pairs of bi-static InSAR images covering the Parlung No. 4 glacier were used for seasonal systematic error correction, and the X-band and C-band SRTM DEMs covering the study area were used for radar penetration difference systematic error correction; the values were \(-2.295\) m and \(1.908\) m, respectively. A regional average glacier surface elevation change of \(-0.505\)\(\pm\)\(0.005\) m/yr was obtained. Based on this, the regional average net mass balance was \(-454.5\)\(\pm\)\(13.1\) mm w.eq. during the observational period. Compared with published results obtained by satellite altimetry, optical stereo image pairing, and bi-static InSAR, the glacier mass balance estimation reported in this paper is moderate. We also analyzed the spatial distribution of the glacier mass balance within the SETP using the sub-watershed analysis method. The results showed that the mass loss rate of glaciers in the SETP was larger in the southeast than in the northwest. We used temperature and precipitation data to analyze the climatic factors affecting the glacier mass balance. The results showed that the spatial distribution of glacier mass balance was consistent with the spatial distribution of temperature, i.e., increased temperature in the southeastern part of the study area, and a slight decrease in temperature in the northwestern part of the study area. The results from this study contribute to our understanding of glacier dynamics in this region and can also provide scientific support for major national goals such as the rational utilization of water resources and construction of important infrastructure projects.
# Optimal Pricing in a Rented 5G Infrastructure Scenario with Sticky Customers
## 3 The 5G Infrastructure: Network Operators, Pricing Plans, and Customer Preferences
In our case, we have opted to study a typical situation where we have a single MNO and \(N_{R}\) MVNOs, where MNOs and MVNOs compete for end customers and charge them a flat-rate plan (which encompasses the prepaid, postpaid, and monthly plans described above), where customers pay a fixed amount (respectively, \(p_{\text{MNO}}\) for the MNO and \(p_{\text{MVNO}}^{(i)}\) for the \(i\)-th MVNO) for each period of time. On the other hand, the MNO charges a flat rate \(\gamma B\) with a fixed amount \(B\) of bandwidth allotted to each customer. The revenues coming from end customers are then proportional to the number of customers, whereas the revenues of the MNO coming from MVNOs are proportional to the number of MVNO's customers and the bandwidth allotted to them. The relationship between customers, the MNO, and MVNOs is illustrated in Figure 1.  
We also assume that customers prefer the MNO, so they may wish to subscribe to its services though its prices are higher. We call this phenomenon stickiness and state the proper demand function in Section 4.2. There are several reasons why mobile customers may prefer an operator even though its prices are higher:  
* Network coverage and reliability, because customers may prefer an operator with better network coverage and reliability; a good network coverage allows the customers to have seamless access to services and to be able to connect in more places;
* Quality of service, because customers may prefer an operator that offers a higher quality of service, such as faster internet speeds or better customer service;
* Brand reputation, because customers may prefer an operator that has a good reputation and a long history of providing quality service;
* Offered services, because customers may prefer an operator that offers additional services such as international roaming plans, more flexible plans, more family plans, more roaming plans, and more data plans;
* Device availability, because customers may prefer an operator that offers the latest and most desirable devices for purchase or on contract;
* Bundles and discounts, because customers may prefer an operator that offers bundled services such as TV, internet, and home phone services or offers discounts for combining services;
* Loyalty programs, because customers may prefer an operator that rewards loyal customers with discounts or other benefits. In the following, we do not provide a detailed analysis of the reasons that may lead a customer to choose the MNO, notwithstanding its (possibly) higher prices. Rather, we consider a demand function that may incorporate any of these factors. Figure 1: Relationships between customers, the MNO, and MVNOs.
## 5 Results and Discussion
### Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Alive Nodes [\%]** & **LEACH-C** & **PSO-C** & **SMOTECP** & **SSMOECHS** \\ \hline
99 (FND) & 2100 & 2234 & 2348 & **2522** \\
90 & 2230 & 2347 & 2413 & **2838** \\
80 & 2301 & 2447 & 2444 & **2970** \\
70 & 2344 & 2486 & 2464 & **3021** \\
60 & 2396 & 2550 & 2490 & **3065** \\
50 (HND) & 2431 & 2662 & 2510 & **3076** \\
40 & 2486 & 2725 & 2533 & **3085** \\
30 & 2550 & 2851 & 2559 & **3094** \\
20 & 2649 & 2905 & 2637 & **3103** \\
10 & 2725 & 2922 & 2738 & **3107** \\
0 (LND) & 2771 & 2947 & 3008 & **3112** \\ \hline \hline \end{tabular} Values in bold indicate the best results. FND, first node dead; HND, half node dead; and LND, last node dead.  
\end{table} 
Table 4: Alive nodes according to protocol execution round under homogeneous setup. Figure 7: Energy consumption according to protocol execution round under homogeneous setup. Figure 8: Alive nodes according to protocol execution round under heterogeneous setup. Figure 9: Energy consumption according to protocol execution round under heterogeneous setup. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Alive Nodes [\%]** & **LEACH-C** & **PSO-C** & **SMOTECP** & **SSMOECHS** \\ \hline
99 (FND) & 1294 & 1463 & 2027 & **2071** \\
90 & 1515 & 1590 & 2101 & **2172** \\
80 & 1633 & 1675 & 2109 & **2200** \\
70 & 1695 & 1741 & 2119 & **2218** \\
60 & 1754 & 1943 & 2126 & **2235** \\
50 (HND) & 1843 & 2068 & 2132 & **2242** \\
40 & 1924 & 2147 & 2135 & **2248** \\
30 & 1976 & 2174 & 2139 & **2250** \\
20 & 2026 & 2195 & 2142 & **2254** \\
10 & 2078 & 2206 & 2150 & **2255** \\
0 (LND) & 2201 & 2210 & 2243 & **2259** \\ \hline \hline \end{tabular}  
* Values in bold indicate the best results. FND, first node dead; HND, half node dead; LND, last node dead.  
\end{table} 
Table 5: Alive nodes according to protocol execution round under heterogeneous setup.
# A Traffic Equilibrium Model for Multi-Modal Networks with Uncertain Demands
## 3 Generalized Travel Cost Function
### Physical Links  
#### 3.1.1 Transport Mode 1 (Bicycle)  
The travel time \(t_{l}^{1}\) considering congestion on road traffic can be modelled using the widely used Bureau of Public Road function as ([PERSON] (1985) [19])  
\[t_{l}^{1}=t_{l}^{1^{0}}\left[1+\alpha\left(\frac{v_{l}^{1}}{B_{l}}\right)^{ \beta}\right] \tag{3}\]  
where \(t_{l}^{1^{0}}\) (min) is the free-flow travel time of mode 1 on physical link \(l\); \(v_{l}^{1}(\textit{passengers}\cdot(h^{-1}))\) is the traffic volume of mode 1 on physical link \(l\); \(B_{l}(pcu\cdot(h^{-1}))\) is the road capacity for mode 1; and \(\alpha\), \(\beta\) are model parameters with dimensionless units, usually with values of 0.15 and 4.  
In many cases, crowd discomfort is considered to be associated with many factors, such as travel time, passenger volume of physical links, capacity of the vehicle, and so on. For simplicity, in this study, crowd discomfort is assumed to be a linear function of travel time:  
\[u_{l}^{1}=\gamma u^{1^{0}}t_{l}^{1} \tag{4}\]  
where \(\gamma\) is the crowd discomfort-time conversion coefficient, a dimensionless unit, with a usual value of 0.5, and \(u^{1^{0}}(min^{-1})\) is the crowd discomfort per time unit.  
In this study, the travel fares of the bicycles are considered to be directly proportional to the travel distance:  
\[m_{l}^{1}=\pi m^{1^{0}}h_{l} \tag{5}\]  
where \(\pi\) is the travel fare-time conversion coefficient, a dimensionless unit, with a usual value of 0.5; \(m^{1^{0}}\) (km\({}^{-1}\)) is the travel fare of mode 1 per distance unit; and \(h_{l}(km)\) is the length of link \(l\).  
Combining the above, the travel cost function of mode 1 on link \(l\) can be written as follows:  
\[c_{l}^{1}=\left(\lambda_{t}+\lambda_{u}u^{1^{0}}\right)t_{l}^{1^{0}}\left[1+ \alpha\left(\frac{v_{l}}{B_{l}}\right)^{\beta}\right]+\lambda_{m}\pi m^{1^{0}} h_{l} \tag{6}\]
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 5 Research Trends
### Development of Time Convolution  
The advancement of time convolution techniques is pivotal for precise extraction of temporal features, especially essential for capturing the dynamics of lip movements. Techniques such as ACNet, Temporal Shift Module (TSM), Grouped Spatial Filtering (GSF), and Tied Block Convolution have emerged as promising methods for temporal feature enhancement. However, challenges remain in optimizing these techniques for real-time processing. The complexity of accurately modeling temporal dependencies within lip movements necessitates significant computational resources, which can hinder performance in practical applications. Therefore, future research must focus on refining these algorithms to balance accuracy and efficiency, ensuring that they can operate effectively in dynamic environments where lip movements occur rapidly and with varying intensity.
###### Abstract  
Based on the engineering problem of large deformation and support failure in the roadway of Ronghua No. 1 Mine, on-site in situ stress testing was carried out to understand the distribution characteristics of the in situ stress around the roadway. Then, a stability analysis of the roadway was conducted on the key factors causing support failure. Combining the on-site situation and the results of the stability analysis, the principle of surrounding rock stability control in a high horizontal stress roadway was proposed. Based on this principle, an optimized scheme was designed. The optimized scheme and the original scheme were comparatively analyzed through numerical simulation to verify the applicability of the optimized scheme. Finally, the optimized scheme was applied on-site, and the roadway was monitored. The results were as follows: (1) from the results of the in situ stress test conducted in Ronghua No. 1 mine, the horizontal tectonic stress field is dominant. (2) Based on the characteristics of in situ stress distribution, the angle between the direction of the maximum horizontal principal stress and the roadway orientation, along with varying lateral pressure coefficients, is directly proportional to the stress and deformation of the surrounding rock. (3) Through numerical simulation analysis, an optimized support scheme was proposed based on the original design. The roof subsidence was reduced by 37.3%, the floor heave was reduced by 49.5%, and the side convergence was reduced by 34.7%. The surrounding rock stability of the roadway was significantly improved. (4) The optimized support scheme was applied in the 6A# Left Second roadway at Ronghua No. 1 Mine. Through on-site monitoring of the perimeter rock deformation, significant reductions in deformation and increased stability were observed. The research content provides a theoretical basis and practical experience for the stabilization of high horizontal stress roadways.  
high horizontal stress; mining roadway; rock fracture and deformation; support optimization 2025
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 8 Challenges and Future Directions
### Adoption of Synthetic Storm Technique  
Synthetic Storm Technique (SST) is one of the most reliable methods to estimate rain attenuation time series [127]. Rain attenuation time series are usually generated through SST with the input rain rate data measured by RGs or other equipments. Based on the generated rain attenuation time series, the seasonal, annual, or daily statistics which reflect the dynamic patterns of the received power of the signal in a communications link are obtained, and then the rain attenuation can be compensated more effectively. The implementation of the SST technique can yield more accurate predicted values of rain attenuation as compared to the ITU model [128; 129]. On the other hand, SST may also be used to estimate the rain rate in short time intervals (e.g., 1 min) by inverting the convolution integral. Therefore, SST should be considered and given more attention in the future in the CML-based rainfall measurement field.
# Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning
###### Abstract  
As the number of Internet of Things (IoT) devices connected to the network rapidly increases, network attacks such as flooding and Denial of Service (DoS) are also increasing. These attacks cause network disruption and denial of service to IoT devices. However, a large number of heterogenous devices deployed in the IoT environment make it difficult to detect IoT attacks using traditional rule-based security solutions. It is challenging to develop optimal security models for each type of the device. Machine learning (ML) is an alternative technique that allows one to develop optimal security models based on empirical data from each device. We employ the ML technique for IoT attack detection. We focus on botnet attacks targeting various IoT devices and develop ML-based models for each type of device. We use the N-BaIoT dataset generated by injecting botnet attacks (Bashlite and Mirai) into various types of IoT devices, including a Doorbell, Baby Monitor, Security Camera, and Webcam. We develop a botnet detection model for each device using numerous ML models, including deep learning (DL) models. We then analyze the effective models with a high detection F1-score by carrying out multiclass classification, as well as binary classification, for each model.  
internet of things; botnet attacks; N-BaIoT; machine learning; deep learning Article
# Recent Development of Aptamer Sensors for the Quantification of Aflatoxin B1
## 2 Colorimetric, Electrochemical, and Fluorescent Aptamer Sensors to Monitor AFB1
### Fluorescent Aptamer Sensor for AFB1
Figure 10: **(A)** Sensing mechanism of fluorescent aptasensor for determining the presence of AFB1. **(B)** Fluorescence spectra of the carbon dots/aptamer/AuNPs with the addition of AFB1. **(C)** The relative fluorescence intensities of the fluorescent aptasensor in presence of various relevant species. Reproduced from [28]. Copyright 2016, Elsevier. [PERSON] et al. reported a fluorescent aptasensor to monitor the level of AFB1 by taking advantage of the peroxidase-like properties of DNAzymes (Figure 12) [30]. When hemin and K\({}^{+}\) were present, two probe DNAs partially hybridized with the aptamer to form a G-quadruplex. This complex was capable of catalyzing the formation of fluorescent 2, 3-diaminobenz (DAP) from phenylenediamine. Free hemin in the aqueous media could be adsorbed and then magnetically separated through magnetic multiwall carbon nanotubes. When AFB1 was present, the aptamer bound to the AFB1, causing the separation of the two probe DNAs. With the decrease of the catalytic ability of the DNAzymes, the fluorescence of DAP decreased. The aptasensor achieved a limit of detection of \(2.0\times 10^{-8}\) g\(\cdot\)L\({}^{-1}\), indicating high sensitivity toward AFB1. Compared with other strategies, the aptasensor was more selective for the determination of AFB1 and could be applied to detecting AFB1 in food. Figure 11: Schematic representation of the dual-terminal proximity aptamer probe for detecting AFB1. Reproduced from [29]. Copyright 2019, Elsevier. Figure 12: Schematic illustration of AFB1 determination using an aptamer/G-quadruplex DNAzyme probe. Reproduced from [30]. Copyright 2019, Elsevier. Combination of other fluoresgens with aptamers has been considered to be an effective strategy for detection of AFB1. For instance, [PERSON] and [PERSON] devised an aptasensor on the basis of aptamer-functionalized CdZnTe QDs and complementary strand-modified Au nanoparticles (AuNPs) [31]. The performance of the aptasensor for detecting AFB1 was observed to be linear, ranging from \(5.0\times 10^{-8}\) to \(1.0\times 10^{-4}\) g\(\cdot\)L\({}^{-1}\), with a low detection limit of \(2.0\times 10^{-8}\) g\(\cdot\)L\({}^{-1}\).