esa-sceva/satcom-chunk-collection huggingface dataset:
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 5 Detailed System Description
### Requester Meets an Agent
#### 5.3.1 Calculation of Agent's DT
Based on the Encounter Average \(EA_{(R,A)}\) which is 10 h, future encounters can be predicted to happen between intervals of approximately 10 h (\(T_{1}=10,T_{2}=20,T_{3}=30\)). Delivery is expected to occur on the meeting that follows the Agent's visit to the Ad-Dealer that is anticipated to take place 24 h later, as per \(TTA_{Agent}\). Consequently, \(DT_{Agent}\) is equal to the time of the first meeting that will take place after a period that is greater than \(TTA_{Agent}\). The \(TTA_{Agent}=24\), therefore, \(DT_{Agent}=T_{3}=30\) (\(T_{3}>TTA_{Agent}\)) as the previous encounter \(T_{2}=20\) is less than \(TTA_{Agent}\). The Requester is now able to decide whether or not to use the services of that particular Agent by comparing the Agent's \(DT_{Agent}\) with the average time that it takes him/her to collect adverts, i.e., his/her own \(TTA_{Requester}\). In this particular scenario, the Requester visits the Ad-Dealer between average time intervals \(TTA_{Requester}=60\) h, which is significantly more than the Agent's \(DT_{Agent}=30\)  
Figure 3: Calculation of \(DT_{Agent}\) (Agent’s Delivery Time). Based on this observation, the Requester can deduce that it is within his/her best interest to send his/her requests through that Agent.
## 3 The Optimality Box
### An Example of the Problem \(1|p_{i}^{L}\leq p_{i}\leq p_{i}^{U}|\sum C_{i}\)  
Following to [15], the notion of a block for the jobs \(\mathcal{J}\) may be introduced for the problem \(1|p_{i}^{L}\leq p_{i}\leq p_{i}^{U}|\sum C_{i}\) as follows.  
Definition 3: A maximal set \(B_{r}=\{J_{r_{1}},J_{r_{2}},\ldots,J_{r_{|B_{r}|}}\}\subseteq\mathcal{J}\) of the jobs, for which the inequality \(\max_{J_{r_{i}}\in B_{r}}p_{r_{i}}^{L}\leq\min_{J_{r_{i}}\in B_{r}}p_{r_{i}}^{U}\) holds, is called a **block**. The segment \([p_{r}^{L},b_{r}^{U}]\) with \(b_{r}^{L}=\max_{J_{r_{i}}\in B_{r}}p_{r_{i}}^{L}\) and \(b_{r}^{U}=\min_{J_{r_{i}}\in B_{r}}p_{r_{i}}^{U}\) is called a **core** of the block \(B_{r}\).  
If job \(J_{i}\in\mathcal{J}\) belongs to only one block, we say that job \(J_{i}\) is **fixed** (in this block). If job \(J_{k}\in\mathcal{J}\) belongs to two or more blocks, we say that job \(J_{k}\) is **non-fixed**. We say that the block \(B_{v}\) is **virtual**, if there is no fixed job in the block \(B_{v}\).  
Remark 1: Any permutation \(\pi_{k}\in S\) determines a distribution of all non-fixed jobs to their blocks. Due to the fixings of the positions of the non-fixed jobs, some virtual blocks may be destroyed for the permutation \(\pi_{k}\). Furthermore, the cores of some non-virtual blocks may be increased in the permutation \(\pi_{k}\).  
We demonstrate the above notions on a small example with input data given in Table 1. The segments \([p_{i}^{L},p_{i}^{U}]\) of the job processing times are presented in a coordinate system in Figure 1, where the abscissa axis is used for indicating the segments given for the job processing times and the ordinate axis for the jobs from the set \(\mathcal{J}\). The cores of the blocks \(B_{1}\), \(B_{2}\), \(B_{3}\) and \(B_{4}\) are dashed in Figure 1.  
There are four blocks in this example as follows: \(\{B_{1},B_{2},B_{3},B_{4}\}=:B\). The jobs \(J_{1}\), \(J_{2}\), \(J_{3}\), \(J_{4}\) and \(J_{5}\) belong to the block \(B_{1}\). The jobs \(J_{4}\), \(J_{5}\) and \(J_{7}\) are non-fixed. The remaining jobs \(J_{1}\), \(J_{2}\), \(J_{3}\), \(J_{6}\), \(J_{8}\), \(J_{9}\) and \(J_{10}\) are fixed in their blocks. The block \(B_{2}\) is virtual. The jobs \(J_{4}\), \(J_{5}\) and \(J_{7}\) belong to the virtual block \(B_{2}\). The jobs \(J_{4}\), \(J_{6}\), and \(J_{7}\) belong to the block \(B_{3}\). The jobs \(J_{7}\), \(J_{8}\), \(J_{9}\) and \(J_{10}\) belong to the block \(B_{4}\).
# Machine-Learning-Based Path Loss Prediction
## 3 Materials and Methods
### Data Processing
As a result of this normalization process, all input and output parameters were scaled between \(-1\) and \(1\). Normalization was performed to allow fast and effective learning performance. After the initial stages of machine learning, such as data cleaning and preprocessing, 85% of the dataset was allocated as the training set, and the remaining 15% was set aside as test data to validate the accuracy of the model. In order to accurately assess the generalizability of the model, a five-fold cross-validation method was applied. Figure 5 shows the distribution of the 192,000 observations used in the path loss estimation on the input parameters. After data preprocessing, normalization, and division of the data into 85% training and 15% test sets, we conducted hyperparameter optimization using grid search [51], which is crucial because it enables the adjustment of parameters that are not directly learned during training, thereby resulting in the best possible model performance. This step positively affects the ability of the model to perform efficiently with new and unexplored data, contributing to improved accuracy, a lower risk of overfitting, and guaranteeing the optimal functionality of the model. In this study, hyperparameter optimization was conducted for the ANN, RF, LR, GB, SVR, and AB machine-learning models, as presented in detail in Table 4. The optimal ANN configuration contained a certain number of layers and neurons, and the ReLU, Sigmoid, and Tanh activation functions were fine-tuned. RF was optimized for surrogate decision bins and minimum leaf size. Thus, flexibility and learning abilities increased. When SVR was optimized with RBF, Linear, and Polynomial kernel functions, regularization parameter (C), tolerance margin (epsilon), and kernel-scale adjustments, a balanced error tolerance and good performance were achieved. AB optimization was optimized by adjusting the learning rate, the base predictor, and the number of weak learners (n-predictors), whereas GB showed an optimal performance by optimally selecting the learning rate, the maximum tree depth, and the number of weak learners. The optimization process for LR ensured efficient model convergence by determining the maximum number of steps, limiting the terms used, and defining the initial terms. In summary, all of these parameter optimizations contributed to increasing the accuracy of path loss prediction and the robustness of each model. Figure 5: Distribution of input parameters.
## 2 Modeling Methods
### Spectral Analysis  
As Figures 9 and 10 show, the spectra of the damaged conditions differed from the spectra of the healthy one, mainly in the excitation of new frequencies below the GMF. In order to better visualize this effect, Figure 11 shows the differential spectra of the damaged gearbox (damaged minus healthy) for different pitting severities (A to F). The increase of the vibration amplitude at certain frequencies with the increasing level of damage was evident.  
On the other hand, Figure 12 shows the spectra predicted for damage level D and different positions on the flank (PI 3, 4, 5, and 6).  
Figure 8: Pressure profile on the meshing gears of the testing gearbox: (**a**) nonpitted spur gears; (**b**) pitted (PitF) spur gears.  
Figure 10: Comparison of the vibrational spectra on one bearing between the healthy transmission and the damaged one (frequency domain).  
Figure 9: Comparison of the vibrational spectra on one bearing between the healthy transmission and the damaged one (time domain).  
Figure 11: Differential spectra for different damage levels.  
Figure 12: Spectra for damage level D and different positions on the flank.  
Positions 3 (having coordinates S = 36 and T = 0.5) and 4 (S = 36, T = \(-\)0.5) had the same radial coordinate (S = 36) but different axial coordinates (they lay on opposite sides of the flank). The two spectra were very similar and overlapped, as shown in Figure 12. The same could be observed for the spectra of positions 5 (S = 12, T = 0.5) and 6 (S = 12, T = \(-\)0.5). Therefore, it can be concluded that the data were not significantly affected by the axial position.  
On the contrary, positions 4 (S = 36, T = \(-\)0.5) and 6 (S = 12, T = \(-\)0.5) differed in the radial coordinate only. The impact of the radial position was visible in the spectra (Figure 12). The same effect could be observed between position 3 (S = 36, T = 0.5) and 5 (S = 12, T = 0.5).  
Moreover, the smaller pit with code A did not produce meaningful differential spectra. In other words, the impact of such small pits on the vibration could not be captured by the numerical model.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Chemical Methods
#### 2.2.8 Non-Thermal Methods  
Non-thermal methods for the synthesis of MNPs refer to techniques that do not involve high temperatures during the production process. These methods are often preferred for their ability to produce NPs with unique properties and for avoiding issues related to thermal decomposition or aggregation [61].  
Common non-thermal methods used for MNP synthesis are:  
* **Chemical Reduction** Magnetic nanoparticles are formed through the reduction of metal ions by chemical agents without the need for high temperatures. This method is simple, cost-effective, and can be used for a variety of metals. It is also commonly used for the synthesis of metal nanoparticles, such as gold, silver, and platinum nanoparticles [62].
* **Electrochemical Synthesis** MNPs are produced through electrochemical reactions at the electrodes. Using this method, size and morphology control, simplicity and scale-up ability can be achieved. Also, it is worth mentioning that it is suitable for various materials, including metals, metal oxides, and conducting polymers [63]. Such methods have gained confidence in relation to conventional ones by allowing the synthesis of superparamagnetic nanoparticles in surfactant or stabilizing agent-free conditions [64]. Moreover, new methods showed increased efficiency in obtaining multimetallic nanoparticles [65].
* **Microwave-Assisted Synthesis** Microwave radiation is used to heat reaction mixtures, facilitating rapid and uniform heating. This method possesses faster reaction rates, higher yields, and reduced energy consumption compared to conventional heating methods. It is applied to a wide range of materials, including metals, metal oxides, and carbon-based nanoparticles [66].
* **Ultrasound-Assisted Synthesis** Ultrasonic waves are applied to a reaction mixture, leading to acoustic cavitation and promoting particle formation. This technique has rapid reaction rates, control over particle size, and improved homogeneity. It is used for metals, metal oxides, and organic nanoparticles [67].
## 4 Discussion
In fact, once identified and well positioned the PSs on the building volume, different techniques can be used to investigate the possible displacements of the structure, based on the combination of the _ASC_ and _DES_ datasets (discussed, e.g., in [PERSON] et al. [2] and [PERSON] et al. [36]), under the assumptions explained in Section 2.1. For example, spatial interpolation techniques can be used for mapping the mean velocity components, under specific hypotheses, to create preliminary deformation maps (as alternative to the procedure performed in this work, based on the selection of PSs couples, only if the PSs have a good spatial density). One of the limits of the proposed methodology is its inability to sharply distinguish single buildings units, when they are built-in, as hinted in Section 2.3.1. Specifically, from a structural point of view, if two buildings have a common wall, or common structural foundations, they constitute a single structure. In this case, no approximation subsists in the methodology. When two buildings are separated by a seismic joint, instead, they have completely different structural behaviors and do not interact with each other. In this case, it is not possible to distinguish buildings so close through the input satellite data, and an approximation is done. So, if the output clusters are very large, it is suggested to focus on them to understand if it is a single building, or an aggregate, or joined buildings, to make appropriate considerations for the specific case. Another limit of the proposed methodology is the difficulty in identifying the clusters referred to one-floor buildings, since they are classified as noise in order to avoid the creation of false clusters, as explained in Section 2.3.1. This assumption is necessary since it strengthens the correct identification of the two-to-n floors buildings, by eliminating any interaction with the ground points. Nevertheless, it eliminates the possibility to identify one-floor buildings, and some very few points of the n-floors buildings' facades. This limit could be exceeded by integrating the proposed clustering algorithm with an additional clustering, to execute by opportunely setting the cut on \(\delta\) and the _eps_. For example, the clustering could be repeated excluding only the PSs supposed to be on the ground. Then, the new clusters, not overlapping those obtained through the first clustering, should be added to the previous result. Maybe, in some cases, this second clustering could also help in finding a more refined shape of the two-floor buildings, since some points can be excluded in the initial cut, as consequence of the topographic error. More in-depth studies are in progress to pursuit this scope. However, it is worth noting that it is very rare to find one-floor buildings in very urbanized areas (in fact, as it has been shown in this work, only one has been found in three areas). Then, neglecting them in a preliminary phase is not a very strong limitation. Finally, it is worth specifying that the clustering result is quite sensitive to small hyper-parameters changes, so the latter should be set with attention.
## 5 Experimental Results
### Addressing Parameter Selection  
The selection of the particular parameters of the addressing scheme depends heavily on the particular application. In most cases, the specifications will require the selection of three minimum requirements: (1) address space size (\(S\)); (2) yield (\(Y\)); and (3) error rate (\(p_{E}\)). After these requirements are specified, the other parameters of the masked addressing scheme can be determined. The parameters of a hypothetical system are determined here using experimentally derived data, to give an indication of the performance of this work.  
Consider a system with the specifications \(S=1000\), \(Y=99.9\%\), \(p_{E}=10^{-6}\), and a possible \(\Delta T=10\)\({}^{\circ}\)C. Given the requirement \(S=1000\) and Equation (15), the following constraint is imposed  
\[N-U\geq 10 \tag{25}\]  
Given this constraint, a lower limit for \(N\) can be found using Equation (20), with \(Y=0.999\) and \(p_{U}=0.41\). The smallest value that satisfies these conditions is \(N=31\), setting \(U=21\). Finally the total error rate can be found using Equation (19) to be \(p_{E}=1.03\times 10^{-7}\).
## 8 Conclusions  
In this study, available MT-HVdc transmission system topologies, as well as the one proposed by this paper, have been compared for offshore wind farms integration to onshore substations. Each topology has been characterized in terms of techno-economic factors, such as number and rating of dc circuit breakers and VSCs, the necessity of extra offshore stations, number, length and rating of dc-link, stability, flexibility, redundancy, cost, and meeting the maximum loss criterion. Simulations are developed in EMTDC/PSCAD to analyze the operation and control of the configurations in terms of (i) permanent GS-VSC disconnection and (ii) dc line-line faults. Results have shown that WFRT and 2 WF-ISST are not conforming to the GBSQSS and hence not viable during GS-VSC disconnection and dc-link fault within remote/far-end/the last unit (unit-2 or unit-3 in Figure 5). Therefore, a more dynamic topology for the MT-HVdc grid is proposed and named as two wind farm and substation ring topology to overcome the aforementioned shortcomings. The proposed 2 WF-SSRT is able to improve the stability, utilization, and redundancy while fulfilling the maximum loss criterion in the normal and abnormal conditions by providing an alternate path for the power flow with the least number and ratings of HVdc circuit breakers and grid side VSC-HVdc stations. Therefore, the anticipated 2 WF-SSRT is a promising topology for the future wind farm MT-HVdc grids.  
Conceptualization, A.R.; formal analysis, A.R., M.Y., K.R., and G.A.; resources, A.R., A.A.; Investigation, A.R., M.Y., Y.L., A.A., K.R., and G.A.; software, M.Y.; Validation A.R., M.Y., A.A., K.R., and G.A.; Methodology, A.R. and M.Y.; writing--original draft preparation, A.R., M.Y., Y.L., and A.A; writing--review and editing, A.R., M.Y., Y.L., A.A., K.R., G.A., and G.A.; supervision, A.R.; project administration, A.R.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant No. (DF-468-611-1441). The authors, therefore, gratefully acknowledge DSR technical and financial support.  
The authors are thankful to the Department of Electrical Engineering, The University of Lahore--Pakistan, and Department of Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia for providing facilities to conduct this research.  
The authors
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 2 Theory and Method
### GA-Elman Model  
GA has three main applications in the field of neural networks: training connection weights, designing network structures, and finding optimal learning rules. The first two applications have been widely studied by many scholars and have achieved good results. However, most published studies focus on feedforward neural networks rather than recursive neural networks. In this study, we use GA to optimize the initial weight and threshold of the Elman neural network. The traditional Elman model adopts the gradient descent method when updating the weight. This method not only runs slowly, but also easily falls into local optimization, which cannot ensure the accuracy of the model's prediction results [41]. The GA's global search for the optimal solution can make up for this deficiency of the Elman neural network [42]. Using the data optimized by the GA as the weight and threshold of the Elman neural network input can greatly improve the accuracy of the model [43].
## 2 Materials and Methods
### Integrated Drought Monitoring with Multi-Sensor Based Statistical Simulations
Equation (9) can be generalized with a weight order (\(r\)) as Equation (11). \[\delta_{\mu}(r)=\widetilde{\mu}(r)-\hat{\mu}=\frac{1}{\psi_{r}}\sum_{i=1}^{n}W_{i,n}^{r}X_{i}-\frac{1}{n}\sum_{i=1}^{n}X_{i} \tag{11}\]  
The selection of the weight order (\(r\)) can be performed by using the self-organizing migrating algorithm (SOMA; [42]) with the following objective function (Equation (12)):  
\[minimize\ \big{[}\delta_{\mu}-\delta_{\mu}(r)\big{]}^{2}. \tag{12}\]  
Note that if \(r<0\), then \(\delta_{\mu}(r)<0\), which implies a drier state, and if \(r>0\), then \(\delta_{\mu}(r)>0\), which implies a wetter state. When \(r<0\), lower values indicating the dry state are resampled more frequently than higher values indicating the humid state, causing \(\delta_{\mu}(r)\) to decrease. In addition, to objectively determine the accuracy of drought monitoring using three satellite-based drought indices, this study conducted a receiver operation characteristics (ROC) analysis. The ROC analysis was performed to evaluate the validity of the RSIDI calculations using the three drought indices (SPI, ADCI, and WBDI). The range of drought indices used in this study is given in Table 2.
## 3 IA for Two-Unit Parallel System with Three States
### Numerical Solution and Error Analysis  
Because the dimension of the matrix \(M\) is large, it is difficult to get the analytic solutions of IA. Therefore we will calculate out the numerical solution of \(A(t)\). Next we will use four-order _Runge-Kutta_ formula to get IA for the two-unit parallel system with three states.  
**Lemma 4** ([20]).: _For the ordinary differential equation with initial value_  
\[\left\{\begin{array}{l}\frac{dy}{dx}=f\left(x,y\right),\\ y\left(x_{0}\right)=a,\end{array}\right. \tag{37}\]  
Figure 4: The state transition diagram of two-unit parallel system with three states.  
_Four-order Runge-Kutta formula is as follows_  
\[\left\{\begin{array}{l}y_{n+1}=y_{n}+\dfrac{1}{6}h\left(k_{1}+2k_{2}+2k_{3}+k_{ 4}\right),\\ k_{1}=f\left(x_{n},y_{n}\right),\\ k_{2}=f\left(x_{n}+\dfrac{1}{2}h,y_{n}+\dfrac{1}{2}hk_{1}\right),\\ k_{3}=f\left(x_{n}+\dfrac{1}{2}h,y_{n}+\dfrac{1}{2}hk_{2}\right),\\ k_{4}=f\left(x_{n}+h,y_{n}+hk_{3}\right).\end{array}\right. \tag{38}\]  
_The error of four-order Runge-Kutta formula is \(O(h^{5})\)._  
Now we will apply four-order _Runge-Kutta_ formula to (35) to get \(A(t)\). According to Lemma 4, we have  
\[\left\{\begin{array}{l}P(t_{i+1})=P(t_{i})+\dfrac{1}{6}h(k_{1}+2k_{2}+2k_{3}+ k_{4}),\\ k_{1}=(P(t_{i}))M,\\ k_{2}=(P(t_{i})+\dfrac{1}{2}hk_{1})M,\\ k_{3}=(P(t_{i})+\dfrac{1}{2}hk_{2})M,\\ k_{4}=(P(t_{i})+\overline{h}k_{3})M.\end{array}\right. \tag{39}\]  
Therefore \(P(t_{i})\) can be obtained, so that numerical solution of \(A(t)\) can be gotten as follows:  
\[A(t_{i})=P_{0}\left(t_{i}\right)+P_{1}\left(t_{i}\right)+P_{2}\left(t_{i} \right)+P_{3}\left(t_{i}\right)+P_{4}\left(t_{i}\right), \tag{40}\]  
and the error of \(A(t)\) is \(O(h^{5})\).
## 2 Methods  
### Impedance Measurement System  
For the setup of the electrical impedance measurement system, the configurable impedance network analyzer (ADuCM350, Analog Devices, Norwood, Massachusetts, U.S) was prepared [19]. ADuCM350 mainly consists of a 16 MHz ARM CORTEX M3 processor and analog front end specially designed for high precision data acquisition with digital to analog converter (DAC) and analog to digital converter (ADC). A 12-bit DAC converts the digital data into an analog signal, having a frequency range of 200 Hz to 50 kHz, which is applied to the biological tissue using the monopolar injection needle (25 gauge, Chalgren Enterprises Inc., U.S) as a working electrode and the Ag/AgCl as the counter electrode. The needle has the outer diameter of 0.51 mm, inner diameter of 0.260 mm, and length of 50 mm. The outer wall of the needle electrode was insulted. The response analog signal as converted to digital data by a 16-bit ADC which has the maximum sampling rate of 150 kS/s. A DFT core carried 2048 DFT points and presented the complex value of the electrical impedance as real and imaginary parts. The maximum amplitude of the current injected into the biological tissue was restricted to 63 uA at 12.73 mV lower than the current value required for the safety of medical electrical equipment regulated by the International Electrotechnical Commission (IEC-60601) [20]. The DFT-based impedance measurement system was controlled through serial communication by a LabVIEW based graphical user interface (GUI). Although the basic code for the impedance measurement was given by the manufacturer, the code was modified to measure the impedance spectrum at a logarithmic frequency sweep and to continuously monitor the impedance data at a frequency and sampling time. The accuracy of the impedance measurement system was evaluated by measuring the impedance spectra of the RRC (resistor-resistor-capacitor) circuit model in comparison with the commercialized product (SP-200, Biologic Science Instruments, France).