esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Collective Detection Approach
### SPL Delta Corrected FFT Technique in CD  
In this paper, the frequency estimation technique developed in [11] is adopted. It has been demonstrated that the SPL method of Doppler frequency estimation with delta correction used in CD makes it possible to increase the sensitivity of the receiver [8]. Thus, with some better satellite measurements in view, we can indeed have greater sensitivity. This is very interesting while we can limit the number of satellites used in the CD process but not all available visible satellites. The receiver complexity is thus reduced. Details of this proposal are detailed in [8]. By applying the Doppler estimation in the CD, the correlation values and the CD metrics are expressed as follows.  
The delta-corrected Doppler is expressed as [11]:  
\[\hat{f}_{d}=\hat{f}_{d_{k}}+\hat{f}_{\delta} \tag{9}\]  
where \(\hat{f}_{d_{k}}\) represents the Doppler frequency of the \(k\)-th satellite signal, and \(\hat{f}_{\delta}\) represents the frequency correction term calculated by the fractional correction term, \(\delta\), as \(\hat{f}_{\delta}=\hat{\delta}\cdot\delta\hat{f}_{d}\).  
Thus, the new delta-corrected coherent output is defined as [11]:  
\[S_{\delta}\Big{(}\hat{\tau}_{k},\hat{f}_{d}\Big{)}=S\Big{(}\hat{\tau}_{k},\hat {f}_{d_{k}}+\hat{f}_{\delta}\Big{)}\ S_{\delta}\Big{(}\hat{\tau}_{k},\hat{f}_{ d}\Big{)}=\sum_{n=0}^{N-1}s[nT_{s}]\cdot c[nT_{s}-\hat{\tau}_{k}T_{s}]\cdot e^{-2\pi( \hat{f}_{d_{k}}+\hat{f}_{\delta})nT_{s}} \tag{10}\]  
\[S_{\delta}\Big{(}\hat{\tau}_{k},\hat{f}_{d}\Big{)}=N\cdot A_{s}\cdot\mathcal{R }(\Delta\tau_{k})\cdot\sin c(\Delta f_{g}T_{coh})\cdot e^{i\phi}+\widehat{w}^ {\prime}\]where \(N\) is the number of samples of the input signal \(s[\cdot]\) to be coherently processed, \(A_{s}\) is the signal amplitude, \(\mathcal{R}(\Delta\tau_{k})\) is the autocorrelation function of the signal spreading code evaluated at the code phase offset \(\Delta\tau_{k}\) (\(\Delta\tau_{k}=\tau_{k}-\hat{\tau}_{k}\)), \(\Delta f_{d_{k}}\) is the offset between the true and candidate carrier frequencies (\(\Delta f_{d_{k}}=f_{d_{k}}-\hat{f}_{d_{k}}\)), \(T_{coh}\) is the coherent integration time and \(\widetilde{w}\) is the resulting noise component. Thus, the new individual detection and the new CD metrics are respectively:  
\[D_{ind}\Big{(}\hat{\tau}_{k},\hat{f}_{d_{k}}\Big{)}=\left|S_{\delta}\Big{(}\hat {\tau}_{k},\hat{f}_{d}\Big{)}\right|^{2} \tag{12}\]  
\[D_{\text{CD}}\big{(}R_{i},\theta_{j},\Delta B_{n},\beta\big{)}=\sum_{k}D_{ind} \Big{(}\hat{\tau}_{k},\hat{f}_{d_{k}}\Big{)} \tag{13}\]  
where \(D_{\text{CD}}\) is calculated by the sum of the correlations of all visible satellites.
# Multi-Connectivity for Multicast Video Streaming in Cellular Networks
## 2 Multi-Connectivity in MBMS
### MC Multicast versus MBSFN  
5G NR uses MBSFN to enhance system efficiency by simultaneously transmitting identical content over the same radio resources within neighboring cells grouped in an MBSFN area. By leveraging the use of multi-connectivity, MC multicasting can provide the same advantages as MBSFN transmissions while employing a significantly simpler framework with reduced transmission overheads. Similar to MBSFNs, a UE can receive multicast content from multiple base stations, leading to an enhanced Signal-to-Noise Ratio (SNR), particularly for cell-edge users. However, unlike MBSFN operations, base stations under MC multicasting are not obligated to use the same Physical Resource Blocks (PRBs) for streaming multicast content. In MC multicasting, identical MBMS services are streamed through multiple base stations, and each base station independently allocates PRBs to the multicast streams. Consequently, each base station can optimize resource allocation for various services within its cell, resulting in significant frequency diversity that improves the probability of reliably receiving MBMS content. A multicast UE has the flexibility to decode any of the multiple copies of the content it receives. As demonstrated in Section 7, this diversity leads to substantial performance improvements in terms of the number of UEs served and the number of packets successfully delivered.  
In the following section, we discuss the resource allocation problem in the MC multicast system.
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 3 Materials and Methods  
### Sources of Data  
#### 3.1.1 Data on Elevation  
According to certain research, the elevation map with a resolution of 20-150 m should be chosen when using SWAT to model runoff (Kal
# Cybersecurity Risk Assessment in Smart City Infrastructures +
## 5 The Experimental Study Results  
In the simulation environment, two datasets were generated: a training one consisting of 10,000 vectors and a test one consisting of 10,000. The training was based on labeled data, and the vector size was 38 elements. When developing the ANN, Tensorflow and Keras frameworks were used. The neural network model has:  
* Input layer with 38 neurons;
* One hidden layer with 20 neurons and relu activation function;
* Output layer with one neuron and Softmax activation function.  
As a result of experimental studies, it was determined that the maximum classification accuracy of 97% was achieved with the following neural network parameters: three layers, 40 epochs of training, a training set equal to 10,000, and Adam optimizer. Figure 14 presents the results of comparing the quality of the ANN classification and classification according to Formula (2).  
\[R\left(U_{j}\right)T_{i}=P\left(U_{j}\right)\sum\
olimits_{k=1}^{n}I_{T_{i}T_{ k}}C_{T_{i}T_{k}}\times Q_{T_{k}}, \tag{2}\]  
where \(R\left(U_{j}\right)T_{i}\) is the security risk when implementing the threat \(U_{j}\in U\) for the device of the type \(T_{i}\in T\); \(P\left(U_{j}\right)\) is the probability of realization of the threat \(U_{j}\in U\); \(I_{T_{i}T_{k}}\) is a coefficient of influence of the connected devices on each other; \(C_{T_{i}T_{k}}\) is the coefficient of the number of the device interactions with each other; and \(Q_{T_{k}}\) is the amount of possible damage.  
When analyzing the confusion matrixes, a significant superiority of the neural network method over the method using Formula (2) was established. Such results can be explained by the fact that the neural network is able to establish hidden patterns and select optimal weights.  
Figure 13: The experimental setup.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Device number & 1 \\ Device type & Vehicle \\ \(Q_{T1}\) & 10,000 \\ Associated with \(T_{1}\) & 1 \\ Associated with \(T_{10}\) & 0 \\ Probability of BH & 20 \\ Probability of WH & 5 \\ \(I_{TIT1}\) & 1 \\ \(I_{TIT10}\) & 0 \\ \(C_{TIT1}\) & 10 \\ \(C_{TIT10}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vector example.
# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 5 Experimentation and Evaluation
### Performance Evaluation
From Figure 10, it is clear that MNN-SRC outperforms KNN-SRC for almost all \(K\) values. However, optimal values \(K\) = 20 and \(K\) = 5 were used for MNN and KNN, respectively, throughout our experiments. The detailed results with different \(K\) values of MNN-SRC are illustrated in Table 5.  
We have proven our results by calculating the execution time as illustrated in Figure 11. It is clear that the computational cost of our proposed algorithm MNN-SRC is much less with an execution time of 141.5 ms as compared to the SRC alone with an execution time of 761.8 ms. Furthermore, the execution time of MNN-SRC is less as compared to KNN-SRC. This is due to the fact that we are using the mean of nearest neighbors, which takes much less time to calculate its sparse coefficients as compared to traditional KNN. Although the execution time of MNN-SRC is a little higher as compared to MNN alone; however, as a compromise the recognition accuracy of MNN-SRC is comparatively better. The execution time of KNN is 130.7 ms, which is again higher as compared to MNN. Figure 10: Effect of nearest neighbors. Figure 9: Average accuracy using amplitude and phase information. (**a**) Scenario-I. (**b**) Scenario-II. In order to present the practical performance of our proposed framework, we perform a user independence test. We particularly adopt the Leave-One-Participant-Out Cross-Validation (LOPO-CV) scheme, in which the training data do not know about the test user. LOPO-CV is an effective technique for evaluating the generalization of results for unseen data [51]. In this experiment, all data are treated as the training data set, except a particular persons data that is selected as the test data. This process is repeated for each person. Figure 12, reveals the fact that our proposed method has acceptable performance even using LOPO-CV scheme with a recognition accuracy of 84.7%, 82.8% and 82.1% for scenarios I, II and III respectively. It can be concluded that our presented model is capable for the generalization of new users. The unbiased LOPO-CV estimator is difficult to implement due to its large amount of computation. However, it is suggested to acquire a large amount of training data from a variety of entities to get more better results.
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 5 Topology Control
### Need for Topology Control  
Stable and high-quality routes, along with proactively identified alternate routes in the pipe, can increase data throughput and reduce the number of rediscoveries of the routes, thus reducing packet loss, overhead and delay. Although the BS-CAP mobility model tries to provide a strong node degree and BS connectivity, it can be difficult to achieve both fast area coverage and stable connectivity for low- to medium-density UAV swarms. As a result, the route between the target UAV and the BS may not be stable or have sufficient quality to meet the QoS requirements. Additionally, the advantages of our pipe-based routing and proactive switching critically depend on alternative routes being available in the pipe, which is also less likely at lower densities.  
The BS-CAP mobility model uses a repel pheromone, in which UAVs fly away from the already covered areas to explore the entire map, without taking into account the current route(s) being used for data communication. This can lead to sections of the pipe becoming thin, where some intermediate node(s) in the current route have very few or no neighbors. We call this effect the 'pipe thinning' problem and illustrate it in Figure 5.  
Typically, existing node mobility schemes do not consider how to facilitate a robust set of alternative routes to improve the reliability and throughput of ongoing communication with the BS [8; 28; 32]. Our proposed topology control scheme uses the digital pheromone map and node degree information of the nodes along the current route between the target UAV and the BS to maintain a sufficient number of neighboring UAVs in the pipe region. Our topology control is performed by applying a pheromone mask that negates the repel pheromone (effectively acting like an attract pheromone) to bring UAVs toward regions of each current route's pipe where the UAV density is low. Combined with pipe-based routing, this reduces data flow interruptions and improves throughput. It can be particularly helpful when the targets are located far from the BS (a long route length) in a network of low node density.
# A Robust and Effective Two-Factor Authentication (2 FA) Protocol Based on ECC for Mobile Computing
## 3 Proposed Protocol
### Login and Mutual Authentication Phase  
After \(U_{i}\) registers with \(S_{j}\) effectively, \(U_{i}\) runs the login operation (L. 1) and subsequent authentication steps (A. 1-A. 2) with \(S_{j}\):  
L. 1 \(U_{i}\) inputs \(ID_{i}^{\prime},PW_{i}^{\prime}\) to \(SC_{i}\). Then, \(SC_{i}\) computes \(A_{i}^{\prime}=H\big{(}ID_{i}^{\prime}\parallel PW_{i}^{\prime}\parallel a_{i} \big{)}\) mod \(n_{0}\) and checks whether \(A_{i}^{\prime}=A_{i}\). If not, \(SC_{i}\) refuses the login request. Otherwise, \(SC_{i}\) computes \(RPW_{i}^{\prime}=H\big{(}ID_{i}^{\prime}\parallel PW_{i}^{\prime}\big{)}\) mod \(n_{0},V_{i}=B_{i}\bigoplus H\big{(}RPW_{i}^{\prime}\parallel a_{i}\big{)}\). Subsequently, \(SC_{i}\) picks \(a\epsilon Z_{p}^{*}\) and computes \(K_{1}=a\cdot P,K_{2}=a\cdot X,M_{1}=H(ID_{i}\parallel K_{1}\parallel K_{2} \parallel V_{i}),M_{2}=E_{K_{2}}(ID_{i}\parallel V_{i})\), where \(E_{K_{2}}(\cdot)\) is a symmetric encryption algorithm. Finally, \(SC_{i}\) sends \(\{M_{1},M_{2},K_{1}\}\) to \(S_{j}\);  
A. 1 After obtaining \(\{M_{1},M_{2},K_{1}\}\), \(S_{j}\) calculates \(K_{2}^{*}=x\cdot K_{1},ID_{i}^{*}\parallel V_{i}^{*}=D_{K_{2}}^{*}(M_{2})\), where \(D_{K_{2}}(\cdot)\) is a symmetric decryption algorithm. Then, \(S_{j}\) searches \(ID_{i}\) in its database. If \(ID_{i}^{*}\) cannot be found, this session is aborted. Otherwise, \(S_{j}\) moves to the next step. \(S_{j}\) extracts \(r_{i}^{*}\) stored in the database and checks whether \(V_{i}^{*}=H\big{(}ID_{i}^{*}\parallel x\parallel r_{i}^{*}\big{)}\). If they are unequal, \(S_{j}\) understands that \(U_{i}\)'s smart card has been broken. Otherwise, \(S_{j}\) moves to the next step. \(S_{j}\) computes \(M_{1}^{*}=H\big{(}ID_{i}^{*}\parallel K_{1}\parallel K_{2}^{*}\parallel V_{i }^{*}\big{)}\) and checks whether \(M_{1}^{*}=M_{1}\). \(S_{j}\) will end this session if they are unequal, which means that the integrity of \(M_{1}\) has been corrupted. Otherwise, \(S_{j}\) picks \(beZ_{j}^{*}\) and computes \(K_{3}=b\cdot P,K_{4}=b\cdot K_{1}\), \(M_{3}=H\big{(}K_{3}\parallel K_{2}^{*}\parallel V_{i}^{*}\parallel ID_{i}^{* }\parallel K_{4}\big{)},SK_{s}=H\big{(}K_{4}\parallel ID_{i}^{*}\parallel V_{ i}^{*}\big{)}\). Lastly, \(S_{j}\) sends the message \(\{K_{3},M_{3}\}\) to \(U_{i}\) openly.  
A. 2 On receiving the message \(\{K_{3},M_{3}\}\), \(U_{i}\) computes \(K_{4}^{\prime}=a\cdot K_{3},SK_{u}=H(K_{4}^{\prime}\parallel ID_{i}\parallel V _{i})\), \(M_{5}^{\prime}=H(K_{3}\parallel K_{2}\parallel V_{i}\parallel ID_{i}\parallel K _{4})\) and verifies if \(M_{3}^{\prime}=\)M3. If the verification fails, the integrity of \(M_{3}\) may be corrupted, and \(U_{i}\) ceases this session; otherwise, \(U_{i}\) thinks about a shared session key \(SK=SK_{u}=SK_{s}\).  
Again, the operations are summarized in Table 4 to provide researchers with a quick understanding of the login and authentication phase.
## 3 Results and Discussion
### Magnetization Analysis  
The magnetic properties of the synthesized magnetic nanoparticles were studied using a vibrating sample magnetometer (VSM) at RT. The magnetic parameters of MnFe\({}_{2}\)O\({}_{4}\) and Ce doped MnFe\({}_{2}\)O\({}_{4}\) NCs were undertaken at the field of \(-\)15 kOe to +15 kOe by VSM techniques and the obtained results of magnetization (M) vs. external field (H) plots are shown in Figure 7. The M\({}_{\rm s}\), H\({}_{\rm c}\), and M\({}_{\rm r}\) were also determined and are shown in Table 2[49; 50]. The increasing trend in M\({}_{\rm s}\), H\({}_{\rm c}\), and M\({}_{\rm r}\) was described on the basis of cerium substitution for prescribed nanoferrites and this fluctuation in values is due to the ionic size and electronic structure of Ce\({}^{3+}\) ions. Spinel MnFe\({}_{2}\)O\({}_{4}\) NCs exhibited ferromagnetism. The H\({}_{\rm c}\) of MnFe\({}_{2}\)O\({}_{4}\) and Ce doped MnFe\({}_{2}\)O\({}_{4}\) NCs were found to lie within the range of 260.72 Oe to 298.25 Oe. The H\({}_{\rm c}\) value is primarily organized by its influences, such as high anisotropy and cationic rearrangement [51; 52]. From the M-H hysteresis loop, the M\({}_{\rm r}\) values of pure and Ce doped MnFe\({}_{2}\)O\({}_{4}\) NCs were 5.757 emu/g (x = 0), 6.362 emu/g (x = 0.1), 7.521 emu/g (x = 0.3) and 8.533 emu/g (x = 0.5), whose values were dependent on the grain size and structural shape of MnFe\({}_{2}\)O\({}_{4}\). From the M-H loop, the M\({}_{\rm s}\) values for MnFe\({}_{2}\)O\({}_{4}\) and Ce doped MnFe\({}_{2}\)O\({}_{4}\) NCs were found to be within the range of 17.86 to 31.22 emu/g. With a smaller crystallite size (specific surface area becomes relatively higher), and once the magnetic field (H) is applied, it is easier to change the direction of magnetic domains, leading to a higher coercive field. In other words, the size effects in nanostructured materials could increase in M\({}_{\rm s}\) value. The existence of magnetic layers on the surfaces of materials and the effects of spin canting within the whole volume of the samples can produce a development in M\({}_{\rm s}\) value.  
Figure 7: M–H hysteresis loops of spinel MnCe\({}_{\rm s}\)Fe\({}_{2-x}\)O\({}_{4}\) (x = 0.0, 0.1, 0.3 and 0.5) NCs.
# Real-time Precise Point Positioning with a Xiaomi MI 8 Android Smartphone
###### Abstract  
The Global Navigation Satellite System (GNSS) positioning technology using smartphones can be applied to many aspects of mass life, and the world's first dual-frequency GNSS smartphone Xiaomi MI 8 represents a new trend in the development of GNSS positioning technology with mobile phones. The main purpose of this work is to explore the best real-time positioning performance that can be achieved on a smartphone without reference stations. By analyzing the GNSS raw measurements, it is found that all the three mobile phones tested have the phenomenon that the differences between pseudorange observations and carrier phase observations are not fixed, thus a PPP (precise point positioning) method is modified accordingly. Using a Xiaomi MI 8 smartphone, the modified real-time PPP positioning strategy which estimates two clock biases of smartphone was applied. The results show that using multi-GNSS systems data can effectively improve positioning performance; the average horizontal and vertical RMS positioning error are 0.81 and 1.65 m respectively (using GPS, BDS, and Galileo data); and the time required for each time period positioning errors in N and E directions to be under 1 m is less than 30s.  
Android smartphone; GNSS raw observations; Xiaomi MI 8; Real-time Precise Point Positioning +
Footnote †: journal: Sensors
## 4 Results
### Micro-Hardness Results  
Figure 5 and Table 1 show the values of the micro-hardness (Vickers Hardness) for the Ni-Ti-Ag SMAs with different weight percentages of Ag. For each sample, four readings were taken and then their average value was calculated. The results of this test showed a slight decrease in micro-hardness with the increase in weight percentages of Ag. The decrease in micro-hardness of the Ni-Ti-Ag SMAs was due to the ductility of silver and its lower scratching resistance than the Ni-Ti matrix.  
Figure 4: Compressive stress–strain curves of the Ni-Ti-Ag samples.
## 4 Numerical Examples  
In this section, examples of square, rectangular, circular, and L-shaped FGM plates are used to demonstrate the effectiveness of the proposed methodology. Specifically, in Section 4.1, the square plate example is utilized to verify the convergence performance of the SDF in terms of the two truncation numbers. In Section 4.2, the converged results of the rectangular plates are compared with exact 3D elastic solutions to validate the high accuracy of the SDF. In Sections 4.3 and 4.4, the circular and the L-shaped plate examples are tested to illustrate the application of the SDF-based IGA to analyze FGM plates with multi-patch and complex geometries. Table 1 shows the properties of the relevant materials.  
The penalty method is used to impose boundary conditions and handle patch coupling, in which the penalty coefficient is \(10^{4}\) times the maximum element of related stiffness matrices [44]. The \(k\)-refinement strategy [44] is applied to meshing the models and \((p+1)\times(q+1)\) in-plane Gaussian integration points are used in each element [48]. The number of integration points in the thickness direction is determined according to the value of the gradient index. The codes were compiled using MATLAB 9.10 and run on a computer with Intel(R) Core (TM) i7-9750H CPU (2.60 GHz), Win10 64-bit OS, 32 GB RAM, and 12 threads.  
As a preliminary to further discussions, the shear-locking-free property of the proposed SDF is demonstrated by considering a homogeneous square plate with length \(a\), thickness \(h\), and elastic properties \(E=1.092\) GPa, \(v=0\). The plate is subjected to a uniformly distributed load with amplitude \(\varrho_{0}=1\) N. Both simply supported and clamped boundary conditions are considered; SDF-based IGA using second-order NURBS basis functions with a 10 \(\times\) 10 mesh and SDF truncation numbers \(M=3\) and \(N=3\) prove accurate enough.  
Figure 4 compares the results obtained using the SDF-based IGA in this paper with the reference solutions presented in the relevant literature [49]. It is seen that for small aspect ratios, the central deflection results obtained using the SDF-based IGA are close to those of the FSDT-based IGA, which are considered to be accurate in practice. As the aspect ratio increases, the FSDT-based IGA displays obvious locking behavior. At the same time, the results obtained using the SDF-based IGA conform well with the CPT-based solutions for thin plates, demonstrating the shear-locking-free property.
# Review and Categorization of Digital Applications in the Energy Sector
## 4 Categorization of Digital Applications
### System Balance
Logically, the flexibility of generation units, especially renewables, can also be increased due to digitalization [7; 35; 38; 41], however in Germany this is rather an issue of renewable energy regulation. The flexibilities can be either part of the \"smart grid\", if controlled by the grid operator, or of the \"smart market\" if they are \"controlled\" via a price signal. These price signals could be variable tariffs for residential customers or direct market participation of industrial customers, enabled by \"smart meters\" and the previously described information and communication infrastructure [8; 37; 38; 39]. Flexibilities can be bundled to form Virtual Power Plants, offering financial benefits for the participants and new business possibilities for the service provider [7; 8; 34; 35; 37]. Overall, the digital data acquisition and transmission infrastructures enables trading and generation/consumption/grid controlling with a higher frequency, thus improving system stability. Besides converting the current electricity market into a \"smart market\" as indicated above, digitalization could also cause more disruptive changes such as a true peer-to-peer market, where decentralized prosumers (generator and consumer, e.g., household with photovoltaic units) exchange energy in a mostly regional setup [7; 8; 35]. This would require a digital platform, which offers basic market functionalities as well as direct communication and transaction channels between the control devices (e.g., \"smart home\" system) of the participants. By using \"smart contracts\", a high level of automation can be reached such that the user is not required to give frequent input [35; 37]. A blockchain technology could (if the technical challenges of high energy consumption and low transaction speed can be solved) offer an economically feasible way to perform these mini transactions in a secure manner [7; 37]. By using advanced analytics based on historical and current energy-related as well as external data, accurate forecasts for generation, demand, and grid conditions can be made. This reduces grid losses and the need of operating reserves, avoids unnecessary grid reinforcements and reduces the instances when renewable generation needs to be curtailed. In its effect, this reduces greenhouse gas (GHG) emissions and the use of resources [8; 34; 35; 38; 40]. The higher quantity and quality of information on the status of the energy system also allows for faster error detection and in some cases even remote fixing [7; 34; 35; 37; 38; 39]. Besides that, decisions on building further generation units or implementing grid enforcements can be made on a better factual basis [8; 38; 39; 42].