esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
## 4 Comparison between Cooperation and Wi-Fi Offloading in Small-Cell Network
### Cooperation in Small-Cell BSs
First, all the UEs present in the SC network find a single SBS with which it has the best channel gain from distance priority vector \(\bar{F}_{j}\). Without repetition of RBs, UEs associate with their first-priority SBSs as given in line 4 without violating the maximum association of each SC as stated in constraint (14c). After subtraction a certain portion (depending upon how much data rate of user \(j\) needs to be enhanced) of the total maximum power, it distributes the power uniformly between all the UEs associated with it. It saves a certain portion to allocate it to newly associated UEs. Now based on the association and power, it computes the achievable data rate of each user as given in (3). The MBS then verifies if constraint (14a) (QoS constraint) is satisfied or not for each user in the network. In case the achievable data rate of a user \(j\) is below the rate threshold values \(R_{j}^{th}\), to satisfy constraint (14a) then MBS provides it with more resources i.e., RBs, SBSs or power, as mentioned in line 9. All the resources should be completely used by first best link of UEs. If RBs of SBS \(i^{\prime}\) are not used then MBS provides its non-allocated RBs to UE \(j\). The second priority SBS \(i^{\prime\prime}\) from set \(I^{\prime}\) of user \(j\) is determined by the central node, where \(I^{\prime}\) is a set of \(\{I-i^{\prime}\}\) SBSs and checks if its channel gain difference with first-priority SBS \(i^{\prime}\) is less than a threshold value, after this the UE is associated with this SBS. Our decision of the next resource to be used depends on the difference of channel state of SBS a user is associated with and the next best potential SBS it can be associated with. We use this approach to distinguish most of the edge UEs in the network. In the case the channel gain difference is greater than threshold that means the user is far from the second best SBS and it should not be associated with it. The last resort to satisfy a user's data rate is to provide more power from already associated SBS \(i^{\prime}\). After all the options are considered the central node updates the RBs, power and association, and ensures the constraints are satisfied for each user in a round robin fashion. Thus, the resources (RBs, association and power) increase in a sequence based on the channel state conditions to maximize the sum data rate of the network. If UE achieves its data rate then its allocated power is decreased unless its QoS constraint is not violated, and this power adds to available power \(\Delta_{i}\) of SBS \(i\) it is associated with. The algorithm SCRE performs for all the UEs in a cooperative SC network. The computational complexity is of the order \(O((M\times N)(M_{j}+2K)+(M^{2}\times N)(3K))\).
## 5 Simulation Results
### Comparison with Existing Methods
Compared with the time used by the method in [34], it can be seen that the method in this paper can be used for fault recovery. The simulation time is related not only to the principle of the algorithm but also to the computational ability of the computer. In practical application, if a computer with better computational performance is used, the computational time used in each iteration process can be shortened, and the computational speed can be greatly improved. Under the three fault scenarios, all the three methods have been run continuously five times. The optimal solution, reliability and average computational time of the three methods are shown in Table 2.  
Through comprehensive comparison, it can be concluded that although all the three methods can find the optimal solution under certain conditions, the MPGA method has the advantages of computational speed and reliability, which can provide an effective fault recovery scheme for the power systems in floating nuclear power plants.
# Analysis on the Spatial-Temporal Distribution Patterns of Major Mine Debris Flows in China
## 2 Status of Research on Mine Debris Flow
### Progress of Mine Debris Flow Research in China  
Research on mine debris flows in China started in the 1950s. [PERSON] et al. [22] started early to evaluate the prediction of debris flows in mines through three aspects: storm intensity, solid material sources and topographic conditions, and established a qualitative prediction system for mine debris flows. [PERSON] [23] gave the first systematic overview of the application of fractal theory in geotechnics, providing a powerful new tool for the study of debris flow problems. [PERSON] et al. [24; 25] gave a more comprehensive discussion on the conditions and characteristics of mine debris flow formation, focusing on the analysis of mine debris flow and its distribution characteristics in Northwest China, and classified mine geological problems according to their occurrence in time, space, geological environment background, resulting outcomes, and recoverable degree in order to promote research on mine geological environment management.  
Many researchers have studied the distribution characteristics of mine geological hazards [26; 27; 28], and concluded that mine debris flow, as one of the common types of mine geological hazards, has the characteristic of being extremely destructive and a serious threat to normal production and human ecological environment in mining areas. Therefore, the distribution pattern was analyzed from both temporal and spatial perspectives, and it was concluded that major mine geological hazards are the main cause of human casualties and property losses, and should be focused on in future studies.
# A Systematic Analysis of Real-World Energy Blockchain Initiatives
## 2 Background  
### Energy Blockchain Applications  
The most prominent energy blockchain applications include peer-to-peer trading, electric vehicle charging, sustainability rewards, prepaid metering, energy performance, and demand-side management, to name a few [4]. Of these applications, peer-to-peer and transactive energy trading for smart grids and microgrids represent some of the most compelling and researched use cases, with research contributions focusing on efficient and robust designs to facilitate energy transactions across communities and wholesale markets [5; 6; 7; 8; 9].  
Many blockchain architectures and smart contracts have been proposed to enable transactive energy trading, auction management and security within smart grids [10; 11], while continuous double auction mechanisms have also been considered for microgrids [12]. In order to transition theoretical and experimental blockchain architectures to real-world environments, the identification and integration of engineering principles and market dynamics must also be considered. Examples of energy-specific requirements may include the ability to track and attribute energy losses across peer-to-peer transactions [1], automate the negotiation, settlement and payment of pre-time-of-use market pricing [3], or reward customers for demand-side management participation [3].  
Generally, early energy blockchain ideologies focused on the disintermediation of residential customers from traditional energy providers. However, energy blockchains can also deliver value and efficiencies for commercial and industrial customers. For example, energy blockchains and smart contracts have been proposed to manage energy performance contracts between clients and service providers, whereby measured data, analytics models and contractual obligations are stored within an immutable ledger to promote trust between stakeholders [2]. Another compelling set of energy applications are emerging around vehicle-to-grid integrations and services, where the adoption of blockchain technologies and smart contracts can be used to develop decentralised applications for (a) identifying charging stations based on journey routing, battery status and current traffic conditions, and (b) settling payments at charging stations [13].
## 4 Hardware Implementation and Tests of Proposed RMEDS-SP Simulation Model
### Test Results Analysis  
In the test, the maximum DFO is set as \(f_{max}=100\) KHz, the number of sinusoids and the number of simulations time are set as \(N_{1}=20\), \(N_{2}=21\), \(K=4\), the bandwidth of the test signal is set as 250 kHz. The theoretical simulation of RMEDS-SP is performed by the  
Figure 16: Test verification platform.  
MATLAB platform, which is taken as the reference results. The test results are shown in Figure 17.  
As Figure 17 shows, the simulation errors from the RMEDS-SP model implemented by hardware are only a little larger than the errors from RMEDS-SP model implemented by the theoretical simulation. We can see both implementations show the DPS is approximately symmetrical like the shape of \"U\". The truncation occurs near the +100 KHz and \(-100\) KHz. Above all, the proposed RMEDS-SP has shown a considerable performance of simulations and hardware implementation.
## 6 A Fault Diagnosis Algorithm
### Experiments' Results Analysis  
Next, we give experimental results for the execution of the t/h-Diag algorithm. Simulation experiments were performed on computers with Intel Core i7-11800H, 2.3 GHz, 16 GB DRAM, 64 bit Windows and x64 processor. The programming language was Python. Let \(BC_{n,k}\) be the target network, with \(k=2\), \(3\leq n\leq 7\) and \(1\leq h\leq 3\). In addition, we randomly selected \((h+1)(k-1)+n\) faulty nodes in \(BC_{n,k}\) by executing the algorithm 500 times and then calculating the single average time.  
Figure 10 gives the average execution time of the t/h-Diag algorithm in the simulation experiments. We chose \(k=2\) and \(3\leq n\leq 6\). The three lines show the average execution time for \(h=1,2,3\), respectively. It can be seen that the average execution time increases when more normal nodes are allowed to be faulty nodes and the simulation results are consistent with the time complexity of the t/h-Diag algorithm. According to the results of simulation, the execution time of the algorithm and \(n^{k+1}\) are directly proportional because the time complexity of the t/h-Diag algorithm is \(O(N(n+k-1))\) and \(N=(k+1)n^{k+1}\).  
Table 1 summarizes the number of fault nodes diagnosed by the t/h-Diag algorithm in simulation experiments. It can be seen that the algorithm can effectively diagnose all the faulty nodes. This demonstrates the accuracy and effectiveness of our algorithms. Table 2  
Figure 8: Fault nodes obtained after BFS.  
Figure 10: The average execution cost of the t/h-Diag algorithm.  
Figure 9: Fault nodes obtained after calling the UDiag algorithm.  
shows one of the experimental results of 500 simulation experiments of the algorithm. The order of the faulty nodes is the order in which the algorithm obtains them.
# Parasitic-Based Active Gate Driver Improving the Turn-On Process of 1.7 kV SiC Power MOSFET
###### Abstract  
This article discusses an active gate driver for a 1.7 kV/325 A SiC MOSFET module. The main purpose of the driver is to adjust the gate voltage in specified moments to speed up the turn-on cycle and reduce the amount of dissipated energy. Moreover, an adequate manipulation of the gate voltage is necessary as the gate current should be reduced during the rise of the drain current to avoid overshoots and oscillations. The gate voltage is switched at the right moments on the basis of the feedback signal provided from a measurement of the voltage across the parasitic source inductance of the module. This approach simplifies the circuit and provides no additional power losses in the measuring circuit. The paper contains the theoretical background and detailed description of the active gate driver design. The model of the parasitic-based active gate driver was verified using the double-pulse procedure both in Saber simulations and laboratory experiments. The active gate driver decreases the turn-on energy of a 1.7 kV/325 A SiC MOSFET by 7% comparing to a conventional gate driver (\(V_{\text{DS}}\) = 900 V, \(I_{\text{D}}\) = 270 A, \(R_{\text{G}}\) = 20 \(\Omega\)). Furthermore, the proposed active gate driver lowered the turn-on cycle time from 478 to 390 ns without any serious oscillations in the main circuit.  
active gate driver; medium voltage; power losses; SiC MOSFET +
Footnote †: journal: Applied Sciences  
14 January 2021  
Accepted: 26 February 2021  
Submitted: 3 March 2021
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model
### Approaches
#### 4.3.2 Network Overhead and Data Freshness Expressions for RENE  
RENE brings another sight of the ICN for vehicular networks. With this approach, the data are retrieved in \"push\" mode. Unlike the NDN model, the vehicle does not request for the data but the latter are brought when updated. Without the repetition of the interests, we reduce the use of the G5 bandwidth. Using the information pushing when generated, we also reduce the latency. However, the vehicle has to be in the range of a router when the data are generated, otherwise, it will never receive them. Figure 16 illustrates the exchanges that occur when using the RENE approach in our smart city model.  
At first (1), a vehicle subscribes to the server for the sensors that it is interested in. Then, a sensor generates new data (2). This sensor makes a request to the server to retrieve the list of subscribers and how to reach them (3). The server answers with this information (4).  
Figure 15: NDN Exchanges.  
Then, the sensor is able to transmit its data to the ad hoc routers (5), which will forward the data to the relevant vehicles (6).  
**Definition of \(CR\)**  
According to previous definitions (18), the network overhead dedicated to the ICN exchanges using the RENE approach is given by the following expression:  
\[CR=PO_{i}*I_{\text{s}}+\Big{(}\frac{\alpha}{100}*\delta_{RENE}\Big{)}*\sigma*PO_ {d} \tag{24}\]  
Which is equal to:  
\[CR=(S+P_{if})*\omega*F_{\lambda}+ \tag{25}\] \[\Big{(}\frac{\alpha}{100}*(\delta_{V}+\delta_{RSU})/2\Big{)}*\] \[\sigma*(S+P_{if}+P_{df}),\]  
**Definition of \(\alpha_{D}\)**  
In this approach, \(\alpha_{D}\) is given by the number of vehicles in the area when the data are generated, this gives the following expression:  
\[\alpha_{D}=\frac{\alpha}{100}*\delta_{RENE} \tag{26}\]  
**Definition of \(FR\)**  
We apply \(\alpha_{D}\) to the general definition of the network freshness (19), we obtain:  
\[FR=\frac{\frac{\alpha}{100}*(\delta_{V}+\delta_{RSU}/2)}{\alpha}*100 \tag{27}\]
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Rapid-DEM
#### 2.1.5 DEM Fusion  
UAS-based DEMs for modeling applications are limited due to their small spatial coverage and often irregular boundaries. Merging UAS-based DEM with existing, typically lidar-based DEM allows for more accurate viewshed or water flow analysis where larger spatial context plays an important role. However, simple mosaicing can lead to artifacts in the resulting DEM and subsequent analyses due to elevation differences along the irregular edge of the UAS-based DEM. DEM fusion [18], in which the transition between surfaces is controlled by distance-based weighted averaging along the DEM's blending overlap is applied to minimize these artifacts and generate a seamless DEM. More formally, given elevation surfaces DEMlidar and DEMUAS, we compute the euclidean distance \(d\) from the edge of the surface DEMUAS and use it as weight coefficients \(w\) for the linear combination of the two surfaces:  
\[\mathrm{DEM}_{\mathrm{new}}=\mathrm{DEM}_{\mathrm{UAS}}\cdot w+\mathrm{DEM}_ {\mathrm{lidar}}(1-w) \tag{3}\]  
Weight \(w\) is a function of overlap width \(s\) and distance \(d\). We assume \(w\) to be linearly dependent on distance \(d\), resulting in \(w=d/s\) for \(d\) in \([0,s)\) and \(w=1\) for \(d>s\). In the simplest case, \(s\) denotes a constant overlap width, however, \(s\) can be also spatially variable, derived from varying elevation differences along the blending seam. Spatially variable overlap width allows for a more gradual transition along the overlap where elevation differences are large while keeping a small overlap width and preserving the subtle features of both DEMs where differences are small.  
The fusion process has been developed as GRASS GIS addon r.patch.smooth for raster-based elevation surfaces using basic GIS functionality such as raster algebra. Thanks to that, the fusion process is relatively fast compared to other methods e.g., based on merging point clouds [18]. However, it assumes both surfaces have been resampled to the same resolution. Resolution can match the UAS-based DSM to preserve all features captured by the UAS, however since the ultra-high resolution of the UASDSM does not always correspond to the actual level of detail of the DEM, reducing the resolution is typically needed. The fusion process also presupposes good vertical alignment of the two DEMs. When GCPs are not available when capturing the images and processing the DSM, the UAS-based DEM typically does not vertically align with the lidar-based DEM. To proceed with the fusion process we estimate the vertical shift and correct for it by subtracting it from the UAS DEM. In case the surveyed area has been locally altered, for example by raising a new building, a simple mean or median of the elevation differences between UAS- and lidar-based DEM would not accurately represent the shift and GCPs or unaltered features can be used to ensure adequate alignment.
## 1 Introduction  
The Non-Orthogonal Multiple Access (NOMA) system has been characterized as an inspiring multiple access form for upcoming wireless approaches to enhance the spectral efficiency and throughput [1]. NOMA system can develop the available resources more realistically by efficiently, taking into consideration the users' channel environments and also giving support to several users with distinctive Quality of Service (QoS) needs [2]. The integration of NOMA and multiple antenna techniques can be exploited to improve and reinforce system performance [3], therefore, inspecting Multiple Input-Single Output (MISO) NOMA system can be a good example in the direction of characterizing the expected upgrade in achievable data rates [4]. In downlink NOMA structure, the receiver device can receive a multiplexing of signals transmitted to several user terminals in the NOMA cell, thus eliminating the interference generated by other user devices come to be essential for coordinated detection. Frequently in power domain NOMA (PD-NOMA), multiuser detection can be handled via successive interference cancellation (SIC) [5]. In the SIC procedure, symbols from numerous users are decoded successively on the basis of the Channel State Information (CSI) and power percentage designated for each user. A broad investigation of CSI for various users is demanding because pilot data that can be exploited in channel prediction, might interfere with symbols from other user terminals, therefore affecting the performance of a conventional prediction scheme, such as the Minimum Mean Square Error (MMSE) estimator [6]. Furthermore, power allocation policy is considered an essential issue for user devices when PD-NOMA is considered [7].  
Deep Learning (DL) or Reinforcement Learning (RL) techniques, have the ability to track the differences in the channels among users and BS, thus, they are recently considered a powerful tool for upcoming radio systems [8; 9]. Hence, allocating the power factors or estimating the CSI for user devices with the assistance of Machine Learning (ML) algorithms, triggered the authors for more deep investigations into this field in order to enhance the performance and detection process.
## 7 Application to UWB Doppler Radar Simulation
### Tracking of High-Maneuvering Target  
Finally, an application to high-maneuvering targets with relatively high accelerations is presented to clearly show the effectiveness of the proposed filter compared with the RA filter. Given the true target position of \((x_{\text{t}k},y_{\text{t}k})=(k^{2}T^{2},\,20+(kT)^{1.5}\cos(2\pi kT/10))\), the true acceleration is plotted in Figure 5a. Compared with the previous section, a high acceleration is assumed. We set \(B_{\text{x}}=0.3^{2}\), \(R_{\text{xv}}=1.0\), \(a_{\text{c}}=3\) m/s\({}^{2}\) and \(a_{\text{D}}=1.0\); other settings are the same as in the previous section.  
Figure 5b shows the simulation results for the high-maneuvering target. The difference in the RMS prediction error between the RA and proposed filters becomes large compared with the moderate-maneuvering target assumed in the previous section. \(E[\epsilon_{k}]\) in \(2<kT<4\) s of the best \(\alpha\)-\(\beta\), RA, PVM Kalman and proposed filters are 0.589, 0.459, 0.190 and 0.201 m, respectively. Whereas the PVM Kalman filter is slightly better than the proposed filter for the same reason as for a moderate-maneuvering target, the proposed filter achieves greater accuracy than the RA filter. This is because the RA model cannot track the abrupt motion of the high-maneuvering target because of limitations in expressing process noise. In contrast, the proposed filter can set gains corresponding to the appropriate process noises to accurately track the high-maneuvering target. Our theoretical analyses presented in Figure 1a show that the performance difference between the assumed filters becomes large when \(a_{\text{D}}\) is relatively large. The above simulation results are consistent with these analyses. In addition, with respect to the steady state accuracy of the PVM Kalman and the proposed filter, the same result with the moderate-maneuvering target is obtained. Thus, the applicability to performance predictions of the PVM Kalman filters using the analysis of the \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter is clearly indicated with these simulation results.
## 7 Current Challenges and Future Research Directions  
This section summarizes the current challenges for all research articles that propose approaches to detect LDDoS attacks based on machine learning and deep learning. Thus, researchers interested in this area can point out the challenges and propose efficient solutions for LDDoS attack detection.  
The recent attacks, such as the one on AWS (Amazon Web Services) [120], show that the nature of the DDoS attacks is changing from the typical high-volume flooding attacks to more stealthy low-rate attacks. Since the launch of the Shrew attack [121] categorized as performing an LDDoS attack, several variants of LDDoS attacks have been proposed and analyzed. An LDDoS attack is more difficult to detect since its average traffic volume is not significantly different from that of normal traffic. However, it cannot be ignored as it impacts legitimate traffic over time.  
Despite the fact that there has been a little study in the field of LDDoS attacks in SDN, the effectiveness of machine learning methods is more likely to classify and detect LDDoS attacks. This is why machine learning algorithms have outperformed manual checking methodologies when data amounts are large. Because threshold detection methods have usually depended on only a few measurements, it is simple to confuse a normal random burst in a real network for an attack. Furthermore, the outcomes of these approaches are susceptible to the detection threshold, which must be altered according to the network scene, or the correct detection probability will suffer. Several LDDoS attack detection using machine learning approaches [17; 76; 77; 78; 79; 80; 81; 82; 83; 84] have been investigated in this paper. They bring several recent results on LDDoS attack detection. These results can summarize as follows: