esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Topology Optimization Using Adaptive Neighborhood Simulated Annealing
## 3 Topology Optimization  
Application of TO includes a wide range of problems with different physics. The problems of compliance TO in beams have been used in several research works [6; 17; 37]. The optimized topologies based on compliance are a good starting point for other TO problems, such as maximum stress, maximum deflection, etc. [3]. Minimizing the compliance of a structure is equal to minimizing the strain energy \(S\) which is the external work performed by the external force \(F\) with elastic deformation \(U\) from an unstressed state as shown in:  
\[\begin{split}\min S=U^{T}KUI\\ \text{Subjected to }KUI=F\end{split}. \tag{3}\]  
For a discretized domain with \(N\) elements, the density of each element \(x_{e}\) are the TO variables. The total strain energy can be calculated as a strain energy summation in each element with penalized design variable \(p\). According to the SIMP method, it is calculated as:  
\[S=\sum_{e=1}^{N}(x_{e})^{p}u_{e}^{T}k_{e}u_{e} \tag{4}\]  
where \(u_{e}\) is the element displacement vector and \(k_{e}\) is the element stiffness matrix. The volume fraction constraint determines the final volume regarding the design domain. The optimization process includes the generation of new candidates and objective function evaluation. The SA with crystallization heuristic is used as an optimizer for TO. The algorithm is presented in detail in the next section.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 4 Applications of the LiFi Network
### Sound System Communication  
Other types of LiFi applications include sound system communications. Studies on sound system applications are fewer as compared to other applications. However, they are discussed because of their significance in integrating LiFi. In [150], various coloured LEDs may relay sound and even execute an audio signal transmitting scheme at the Pulse width modulation (PWM) base [153].
# A Review on Hot-IP Finding Methods and Its Application in Early DDoS Target Detection
## 2 Hot-IP Finding Methods
### Application in Finding Hot-IPs
#### 2.2.1 Experimental Data Sets and Parameters  
The UCLA data sets [13] were collected from simulated DDoS attacks using DoS/DDoS attack tools. Raw data of collected packets were processed, whereby each packet is converted into a record. For TCP packets, each record consists of 11 fields, including _Packet_TIME_, _IP_from_, _IP_to_, _PORT_from_, _PORT_to_, _LENGTH_, _FLAG_, _SEQ_from_, _SEQ_to_, _ACK_ and _WIN_. In the experiments in this section, the _IP_from_ (source IP address) is used to find Hot-IPs.  
The experimental data set extracted from the UCLA data sets [13] consists of 200,000 records (also contains \(M\) = 200,000 source IP addresses), in which there are \(N\) = 2214 unique IP addresses.  
For the Count-Sketch and Count-Min methods, select \(\varepsilon\) = 0.005, \(\delta\) = 0.00000001 and the number of IPs with the highest occurrence frequency \(k\) = 10.  
For the Group Testing method, select the maximum number of unique IPs \(L=\) 10,000, using the Reed Solomon code \(RS[n=15,\,k=3]_{d=16}\) and the code concatenation to construct the \(d\)-disjunct matrix \(m_{txN}\)[5]. The \(d\)-disjunct value is computed as: \(d=(n-1)/(k-1)=(15-1)/(3-1)=7\).  
We use a one-dimensional matrix \(C_{N}\) to be the counter of the occurrence frequency of IP addresses in the IP packet stream. An IP is considered a Hot-IP if \(C[i]>L/(d+1)\).
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Source Simulation (SoSi)  
This technique [52] assumes that the global adversary can only trace the presence of a moving asset. The adversary tracks every trace in the network whether it is real or fake. Moreover, each trace is assumed to be a candidate of a real trace. The main aim is to create several traces to mislead the adversary about the presence of the real event. Additionally, _SoSi_ has its own definition for privacy. Privacy occurs when the adversary observes a set of transmissions, which indicates that the asset is nearby one of the nodes. Virtual assets are created to copy the behavior of the real asset to confuse the adversary. Another purpose of these virtual assets is to create dummy traces. To implement the virtual assets, some sensor nodes are selected randomly. Each selected node obtains a token in the deployment stage. These nodes are called the token nodes. _SoSi_ forces the _WSN_ to function in rounds; each round has a fixed time. In each round, a fake event is transmitted to the sink by the token node through its neighboring nodes, which create a stream of traffic. By the end of each round, the token node selects the next node that will act as a token node. The _SoSi_ technique does not increase the delay since the dummy streams do not affect the real event. However, the use of dummy streams increases overhead.
## 3 Materials and Methods
### Ensemble Empirical Mode Decomposition (EEMD)  
Mode mixing is the main disadvantage of the classical EMD while decomposing complex signals into different subgroups. To remove this drawback, [PERSON] and [PERSON] [52] suggested the ensemble empirical mode decomposition (EEMD) technique in 2009. The procedure of this technique is outlined below:  
Step 1: Select the ensemble number, \(m\), and the white noise amplitude, \(n\).  
Step 2: To create a new series, \(\overset{\prime}{y_{i}}(t)\), add a white noise series, \(w_{i}(t)\) to the actual signal, \(y(t)\).  
\[\overset{\prime}{y_{i}}(t)=y(t)+w_{i}(t) \tag{5}\]  
Step 3: Divide the signal \(\overset{\prime}{y_{i}}\) using EMD into several IMFs and a monotone residue.  
Step 4: Repeat the above steps 2 and 3 by adding diverse white noise series, respectively, and  
Step 5: Attain the (ensemble) means of the appropriate IMFs of the decompositions as the concluding outcome.
## 2 Design and Modeling  
### Design of \"S\" Type Power Generation Beam of Energy Harvester  
In order to improve the performance of the energy harvester, nonlinear technology is the main way used to enhance the efficiency of energy conversion. The common method is to alter the potential shape of the harvester by providing pre-tightening force or pre-deformation. A generator beam designed as a bistable \"S\" type energy harvester is proposed here to effectively convert low-frequency, low-amplitude, random vibrations into electricity, as shown in Figure 1a.  
The modal properties of the generator beam are analyzed using the finite element method, as shown in Figure 1b. The first mode of the beam is a torsional motion occurring at 2.11 Hz, the second mode is a local small range torsional motion occurring at 3.32 Hz, and the third mode is a bistable motion occurring at 7.45 Hz, which is an ideal state for energy capture as required by the design of the generator beam in the energy harvester. The fourth mode is a torsional motion at 37.63 Hz.  
The third mode of generator beam is used in the design of energy harvester as an ideal energy harvesting motion. The generator beam is imposed a pre-deformation of the \"S\" shape by the clamped columns on both sides, thus having two steady states, when the harvester is excited by the environment, it can perform either small-amplitude motions within a single potential well (intra-well motion) or large-amplitude motions between the two potential wells (inter-well motion), as shown in Figure 1c. The occurrence of inter-well motions requires breaking through potential energy barriers (snap-through),  
Figure 1: The “S” type bistable power generation beam: (**a**) Design sketches of structure composition; (**b**) Modal properties of the beam; (**c**) Steady state of the beam.  
which constitutes a typical bistable structure. Upon suitable environmental stimulus, snap-through occurs and the beam changes from one stable state to the other. A large amplitude of oscillation and a local high-frequency vibration accompanies this process, allowing the harvester to output greater electrical energy.
# Multi-Information Fusion Indoor Localization Using Smartphones
## 4 Fusion Localization Architecture
### Step-Counting Detection  
The peak and valley are used to calculate the pedestrian's step counting. To guarantee the validity of these peaks and valleys, we define four thresholds \(\mathrm{A}_{acc}^{pl},\ \mathrm{A}_{acc}^{uu},\ \mathrm{T}_{time}^{u}\), and \(\mathrm{T}_{time}^{l}\) for the peak and valley determination, where \(\mathrm{A}_{acc}^{pl}\) is the lower bound of the acceleration peak value; \(\mathrm{A}_{acc}^{uu}\) is the upper bound of the acceleration valley; and \(\mathrm{T}_{time}^{u}\) and \(\mathrm{T}_{time}^{l}\) are the upper bound and lower bound of the time interval between two adjacent peak or valley values. The step-counting detection method is characterized in detail as follows:  
1. Peak and Valley Detection; If \(\mathrm{A}(m)>\mathrm{A}(m-1)\)_and_\(\mathrm{A}(m)>\mathrm{A}(m+1)\), then \(\mathrm{A}(m)\) is the peak. If \(\mathrm{A}(m)<\mathrm{A}(m-1)\)_and_\(\mathrm{A}(m)<\mathrm{A}(m+1)\), then \(\mathrm{A}(m)\) is the valley where \(\mathrm{A}(m),\ \mathrm{A}(m-1),\ \mathrm{and}\ \mathrm{A}(m+1)\) are the acceleration values at times \(m,\ m-1\), and \(m+1\), respectively.
2. Threshold Judgment: All detected peaks must be greater than \(\mathrm{A}_{acc}^{pl}\); otherwise, they are discarded. All detected valleys are less than the preset valley threshold \(\mathrm{A}_{acc}^{uu}\); otherwise, they are discarded.
3. Time Interval Detection: If \(\left(T_{A(m)}-T_{A_{step}(m-1)}\right)\in\left(\mathrm{T}_{time}^{u}\mathrm{T }_{time}^{l}\right)\), then the acceleration at time \(m\) is peak or valley; otherwise, the acceleration is discarded.  
The peak and valley values at time \(m\) are determined when conditions one to three are satisfied. The step counting can be obtained from the peak and valley.
###### Abstract
After computational analyses or experiments are performed as many times as the number of design of experiments (DOE), metamodels for objective functions and constraints are established based on the results. The generated metamodels are in the form of approximated functions and are combined with an optimization algorithm to find an optimal solution to a design problem. Since there are various types of metamodel techniques for optimal design, it is important to apply a proper metamodel technique with high accuracy according to each design problem [9]. However, in most of the previous studies, optimization was usually performed using only one metamodel technique, e.g., the response surface method or Kriging [10; 11; 12; 13; 14; 15; 16]. The distinguishing points of this paper from the previous research cases mentioned above are as follows. First, we optimized the PMSM design considering the demagnetization characteristics of the permanent magnets. The previous studies have not considered demagnetization characteristics as objective functions or constraints of the optimal design because the analysis process is complex. However, in this study, the demagnetization characteristic was set as the objective function of the optimal design by using an automated linkage analysis procedure with a finite element analysis (FEA) software and an optimal design software [9]. Second, the effects of the weight of the multi-objective function on the optimal design result were carefully examined by applying the multi-objective genetic algorithm (MOGA). Therefore, it was possible to select the desired optimal design result according to the user's purpose or application. Furthermore, we approximated the multi-objective functions and constraints using various metamodeling techniques. The accuracy of the optimal design result was ensured by performing optimal design using metamodels with the best prediction performance. In this study, a multi-objective optimization of a PMSM for electric vehicles was conducted considering the demagnetization characteristics. A multi-objective function was established to maximize the average torque and minimize the demagnetization rate. As constraints, the torque ripple and efficiency were selected. Five design variables were selected to satisfy the multi-objective function and constraints. Fifty experimental points were determined through the optimal Latin hypercube design (OLHD) [17]. Design of experiments (DOE) for 50 experimental points was carried out through an automated linkage analysis procedure using an FEA software and an optimal design software [9]. From the DOE results, we examined the correlation between design variables and responsesusing a screening technique. Metamodels of multi-objective functions and constraints were established using the DOE results, and the metamodels with the highest accuracy were selected through accuracy evaluation. These metamodels were combined with the MOGA to find various optimal solutions according to the weight [18].
# Nonlinear Optical Microscopy of Interface Layers of Epitaxial Gamet Films
## 5 Conclusions  
Summing up, we performed experimental studies of the magnetic domain structure of 10 \(\upmu\)m thick \(Bi\), \(Lu\)-substituted epitaxial garnet films by a combination of magnetic force microscopy and nonlinear optical microscopy techniques based on the effects of the second and third harmonics generation. In the latter case, strong localization of the pump radiation of a pulsed femtosecond laser source along with the nonlinear power dependence of the registered harmonics' signal allowed to the visualization of the magnetic stripe domains in the bulk of the garnet film through a pronounced contrast in the average second and third harmonics' intensity in adjacent domains. Based on the symmetry analysis of the nonlinear optical response, we conclude that magnetization of this type of domain is tilted with respect to the film's normal as well as to the stripe domains.  
Moreover, we applied the nonlinear optical technique to approach and investigate the magnetic organization of the buried interface of the \((LuBi)_{3}Fe_{5}O_{12}\) epitaxial layer with the gallium gadolinium garnet substrate, which is inaccessible for the MFM studies. We found that a zigzag-shape interface domain appear above the bulk stripe ones, similarly to the upper \((LuBi)_{3}Fe_{5}O_{12}\)/air interface studied quite recently. This confirms that the main role is played here by the discontinuity of the garnet crystalline structure for the magnetic domains' organization, while the type of neighbour medium is less important.  
We also present the first experimental demonstration of the efficiency of the the third harmonic generation microscopy for the study of magnetic domains in garnet films. In spite of relatively small linear values in magnetization contribution to the third harmonic response, the THG microscopy for the nearly crossed polarizations of the fundamental and THG beams allows the visualization of the surface magnetic closure domains at the hidden garnet/GGG interface and provides even higher spatial resolution of the magnetic structure as compared to magnetization-induced second harmonic generation.  
Conceptualization, A.T., A.M. and T.M.; methodology, M.T. and A.M.; software, A.T. and A.M.; validation, M.T., T.M. and A.M.; formal analysis, A.T. and A.M.; investigation, M.T., A.M. and A.T.; resources, A.M.; data curation, M.T. and A.M.; writing--original draft preparation, T.M. and A.T.; writing--review and editing, A.T. and A.M.; visualization, A.T.; supervision, T.M.; project administration, A.T. and M.T.; funding acquisition, T.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation, Grant 19-72-20103-P. MFM studies were supported by Russian Science Foundation, Grant 19-19-00607-P.  
Not applicable.  
The data presented in this paper are available upon request.  
The authors acknowledge useful discussions with [PERSON].  
The authors declare no conflict of interest.
## 3 Simulation Results
### Bit Error Rate  
The bit error rate of a QPSK-OFDM transmission was calculated by the authors, as a function of the signal-to-noise ratio at the transmitter and distance between the modems. We considered 900 carriers between 2 and 30 MHz, and we calculated the BER of each carrier corresponding to the SNR at the receiver after the attenuation of the channel response previously determined. Then, we evaluated an average BER of the channel over the whole frequency band, and the results are shown in Table 2 and Figure 15.  
\begin{table}
\begin{tabular}{c c c} \hline
**Termination** & **Characteristic Impedance** & **Open Circuit** \\ \hline \(L=25\) m & \(5.2117\times 10^{-8}\) s & \(5.2131\times 10^{-8}\) s \\ \(L=50\) m & \(3.1263\times 10^{-8}\) s & \(2.9852\times 10^{-8}\) s \\ \(L=75\) m & \(5.4672\times 10^{-8}\) s & \(6.2786\times 10^{-8}\) s \\ \(L=100\) m & \(1.5101\times 10^{-7}\) s & \(1.8992\times 10^{-7}\) s \\ \(L=125\) m & \(3.1154\times 10^{-7}\) s & \(3.9555\times 10^{-7}\) s \\ \(L=150\) m & \(5.1555\times 10^{-7}\) s & \(5.0826\times 10^{-7}\) s \\ \(L=175\) m & \(6.0195\times 10^{-7}\) s & \(4.1073\times 10^{-7}\) s \\ \hline \end{tabular}
\end{table}
Table 1: RMS Delay Spread.  
Figure 14: Cumulative density function (CDF) relating to each channel.  
For a data transmission system, a reliability threshold is usually taken as BER = \(1\times 10^{-3}\); higher BER means a less reliable transmission that might lead to unacceptable performances. It can be observed that for a reliable transmission, that is, BER values lower than \(1\times 10^{-3}\), the signal can cover 75 m in the less noisy condition of 60 db SNR, and less than 25 m in the worst scenario of 25 db SNR. Also, a gain loss of about 10 db every 25 m can be observed from Table 2, in order to maintain a fixed BER level.
## 2 Materials and Methods  
Figure 1 shows the overall algorithm proposed in our study. When an image is received, the algorithm converts the RGB color space into a HSV color space and applies histogram equalization to the input image. This preprocessing alleviates the sensitivity to illumination changes [31]. A depth image is then generated from the input image using a supervised learning technique. In the depth image, a human form is represented as a cluster area having the same or similar depth, which makes it easy to detect shapes and locations of the human. To automatically detect a human form and initialize tracking, we use a global optimization method called particle swarm optimization (PSO) that calculates the optimum size of a bounding box (window) enclosing the human and the box location simultaneously. This information serves as the input for the foreground (inside) and background (outside) information of the graph-cuts method, which helps detect a person and initialize the parameters of an ellipse model, thus enclosing the human to be tracked. In the next step, the ellipse (human) is tracked through iterative predictions and update steps of the Rao-Blackwellized Particle Filter (RBPF). While tracking, our fall detection algorithm determines whether a fall occurs or not by analyzing both 2D and 3D features of the ellipse model (human). Once the algorithm detects a fall, our system raises an emergency alarm in case the duration of the subsequent no-motion phase is prolonged. In the following flowchart, each step of the algorithm is explained.  
Figure 1: Proposed fall detection algorithm.
## 4 Results and Discussion  
This section presents and analyses the performance of the proposed algorithms for both RFI and modulation classification. All the simulations have been performed with MATLAB (Version R2019b), in a Core i5-5257U CPU computer system, operating at 2.70 GHz with \(RAM=8\) GB. Moreover, the dataset used for the classification has been generated at AWGN power \(-140\) dBm, and approximately SNR value is 9 dB. As shown in Table 2, the power of the received signals (dBm/Hz) is measured using a signal analyzer in a 3.84 MHz Bandwidth (\(BW\)).  
The Jamming to Signal Ratio (JSR) is computed as  
\[JSR=Power_{Sol}-Power_{Jamming} \tag{13}\]  
Therefore, the measured JSR for CWI, MCWI, and CI is 7 dB, 5 dB, and 8 dB, respectively.
# An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer
## 4 Experiments
### Popularity Prediction Based on Time Series
#### 4.2.1 Validity Check  
The number of hidden layer units of the initial structure of IDBN proposed in this paper is set to 40, and the number of iterations of the initial structure and the new structure are both 300, so that the DBN is fully trained, the learning rate k = 0.1, and the threshold of the stopping criterion is set to 0.03. Finally, in the LFM-1k data set through the independent construction of DBN, it is determined that the hidden layer is 3 layers, that is, the stopping criterion is met when the depth is 4. Therefore, the final IDBN structure was determined to be 20-80-160-160-1. In the ML-20M data set, the hidden layer is determined to be 4 layers, that is, the stop criterion is met when the depth is 5. Therefore, the final IDBN structure was determined to be 40-80-160-320-320-10. In order to compare the accuracy of the network depth determined by IDBN, the number of hidden layers of IDBN is sequentially increased. Table 1 shows the test results of IDBN at different depths. The number of neurons in each layer is the same as the IDBN model.  
According to Table 1, as the network depth increases, the reconstruction error continues to decrease, but the running time is gradually increasing. For LFM-1k, when the depth is 4, the accuracy of the test data reaches the maximum; for ML-20M, when the depth is 5, the accuracy of the test data reaches the maximum, which further proves the correctness of the self-built algorithm.  
In order to further show the effectiveness of IDBN prediction, a representative popularity sequence is selected from LFM-1k and ML-20M. Take the music \"All I Need\" as an example: select 1 month as the time interval, and use the number of comments obtained in each month as the popularity size to obtain the popularity time series. The popularity time series from 2006 to 2007 is used to train IDBN and predict the popularity in 2008. The forecast results are shown in Figure 7. Taking the movie \"Forrest Gump\" as an example, half a year is selected as the time interval, and the number of comments obtained in half a year is used as the popularity size to obtain the popularity time series. The popularity time series from 1996 to 2008 was used to train IDBN and forecast the popularity from 2009 to 2016. The forecast results are shown in Figure 8. Among them, the training result is the result after the implementation of PLSR fine-tuning. It can be seen from the experimental results that all time series points have been accurately predicted.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Network Depth**} & \multicolumn{3}{c}{**Last.fm-1K (LFM-1k)**} & \multicolumn{3}{c}{**Movielens-20M(ML-20M)**} \\ \cline{2-7}  & **Error** & **Correct Rate** & **Running Time (s)** & **Error** & **Correct Rate** & **Running Time (s)** \\ \hline
2 & 1.8867 & 80.6\% & 21.65 & 1.9542 & 81.2\% & 25.42 \\
3 & 0.5346 & 84.3\% & 23.96 & 1.2914 & 84.5\% & 26.84 \\
4 & 0.5123 & 89.6\% & 27.56 & 0.6722 & 86.8\% & 29.51 \\
5 & 0.4554 & 86.7\% & 30.67 & 0.