esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Steps
### Data Sample Processing Method
#### 2.3.2 Data Denoising Processing Method Based on the Wavelet Analysis Method  
Based on the RKNN algorithm, this paper realizes the filling of missing data in the sample data and improves the number of samples. In order to improve the accuracy of sample data and reduce the deviation of subsequent data analysis caused by noisy data as far as possible, this paper needs to further carry out noise reduction analysis and preprocessing of noisy data. Considering data noise types and data noise reduction  
where \(j\) represents the number of original signal decomposition layers; \(n\) represents the number of original signal sampling points; and \(h\) and \(g\) represent the orthogonal filter bank.  
(2) The selection of the wavelet threshold  
The signal noise of truck running state data is unknown, so it needs to be estimated. Considering the GCV (Generalized Cross Validation) method only through the data input and data output to determine the threshold value, it has nothing to do with noise ability or the real data. Therefore, the GCV method is adopted in this paper to determine the noise reduction threshold. The GCV expression is as follows:  
\[GCV(\delta)=\frac{\left\|W-W_{\delta}\right\|^{2}}{N(N_{0}/N)^{2}} \tag{11}\]  
where \(N\) represents the number of wavelet coefficients, \(N_{0}\) represents the number of wavelet coefficients 0, \(W\) represents the wavelet coefficient of the signal input polluted by noise, and \(W_{\delta}\) represents the wavelet coefficient after threshold processing.  
Then the optimal threshold can be expressed as:  
\[T=\text{argmin}GCV(\delta) \tag{12}\]  
(3) Wavelet reconstruction process  
Contrary to signal decomposition, wavelet reconstruction solves\(f_{N}\) with known \(g_{j}\) and \(F_{N-M}\), and the relevant expression is as follows:  
\[\left\{\begin{array}{l}c_{kj}=\sum\limits_{l}\left[p_{k-2l}c_{l,j-1}+q_{k-2 l}d_{l,j-1}\right]\\ c_{j}=\left\{c_{kj}\right\}\in l^{2}\end{array}\right. \tag{13}\]  
where \(p(z)\) and \(q(z)\) represent the corresponding scale functions.  
In order to improve the data accuracy and verify the reliability of the wavelet analysis method, this paper takes the measured mining truck running state data as an example for data noise reduction. Considering the wavelet transform basis function, the DB5 wavelet is orthogonal, tightly supported, an approximately symmetric wavelet, with a linear phase, good smoothness, simple calculation, and many other advantages. In this paper, the DB5 wavelet is used to reduce the noise of the mining truck running state data signal. Through multi-scale decomposition, wavelet threshold selection, and wavelet reconstruction of data signals, real data can be obtained to characterize the running state information of mining trucks. After wavelet analysis of 5260 sets of sample data processed by the RKNN algorithm, the data accuracy is expected to increase by 10.3% after comparison and analysis with the original data. Taking the result of some data noise reduction processing as an example, the results are shown in Figures 4 and 5.
# MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model
## 5 Use Cases
### Critical Infrastructure Defense System  
HERALD is a proof-of-concept system designed to prevent attacks to critical infrastructures (CI) through early detection/identification of threatening targets and short-term prediction of the target's activities and threat level for the region where the critical infrastructures are located [23]. In 2014, Samsung Thales coined the design of a critical infrastructure defense system which is the predecessor of the HERALD system. Requirements from Samsung Thales were to design and develop a next-generation system for critical infrastructure defense by means of integration for previous knowledge with evidence from multiple sensors (e.g., MTI (Moving Target Indicator) system, IMINT (Imagery Intelligence) sensor system, and GEOINT (Geospatial Intelligence) system).  
HERALD consisted of an inference module, a control module, and a scenario simulator. The inference module used a HERALD MTheory to infer current and future situations. The HERALD MTheory was designed by domain experts and learned using an MEBN learning process (HMLP) [23]. Also, HERALD contained a relational database that was developed by the domain experts and used for a simulation. The HERALD simulator simulated ground truth information of a situation in which our forces and enemies operated against each other. To develop the HERALD MTheory, the relational database was used to develop a partial HERALD MTheory. Figure 4 shows the schema of the relational database.  
The relational database schema contained 18 relations (e.g., _MTI_Report_, _Target_, and _TargetTemporalProperty_). For example, the relation _MTI_Report_ represented knowledge from MTI about reported locations (e.g., _LatitudeReport_) of and reported moving aspects (e.g., _DistanceToClReport_) of a target. The relation _Target_ represented knowledge about a target's information that was not time-varying. The relation _TargetTemporalProperty_ represented knowledge about a target's information that was time-varying.  
MEBN-RM was used to convert the relational database schema to the partial HERALD MTheory. Listing 5 shows some MFrags of the partial HERALD MTheory. These three MFrags correspond to the three relations in the relational database schema. Please note that each name of the resident nodes is changed to a form that includes an abbreviation derived from the name of the MFrag (e.g., the prefix MR for resident nodes in the MFrag MTI_Report) to prevent construction for resident nodes with the same name. The domain experts, then, used the partial HERALD MTheory to construct a complete HERALD MTheory by adding CLDs for resident nodes and conditional dependence relationships between the resident nodes.  
To evaluate the mapping accuracy, we compared the numbers of elements in the source RDB (Figure 4) and the numbers of elements in the target MTheory (Table 6). These elements in Table 6 were mapped as expected. The total mapping time was 0.0136 s (running on the 3.40 GHz Intel Core i7-3770 processor).
# Improved Sound Radiation of Flat Panel Loudspeakers Using the Local Air Spring Effect
## 2 Introduction and Modeling of a Standard System
### Validation of the Simulated Pressure Response  
This subsection presents the performance of the numerical simulation in comparison to experimental data. Figure 5 shows the simulated and measured SPL transfer functions referenced to an input voltage of 2.83 Vrms. A small shift occurs between the simulation model and the real measurement. To estimate the accuracy of a simulation model the total fitting error (TFE) is used [21]. If the average of the TFE is lower than \(-\)10 dB, which corresponds to a deviation of 10%, the model is accurate enough to simulate the behavior of the panel. The calculated average TFE is \(-\)10.68 dB in the frequency range from 20 to 1000 Hz, which leads to a sufficient fit between the simulation and measurement results. This shift is acceptable since the shape of the curve fits the measurement, and conclusions regarding frequency-dependent behavior can be extracted. However, the steep decay at low frequencies can be accurately described. Furthermore, an increase at 90 Hz and a deep dip of approximately 25 dB in the range of 110-150 Hz are clearly displayed in the simulation model. Additionally, the pressure level increases again by 20 dB at 180 Hz, and the following individual peaks and dips show only a minimal frequency shift between the simulation model and the measurement. For these reasons, the simulation model is sufficiently accurate for analyzing the sound radiation behavior in further applications. However, the acoustic measurement of the pressure level has a substantial disadvantage. It does not provide any information about the causes of large deviations in the response. Therefore, modal and sound pressure-related decomposition is performed in the following subsection.  
Figure 4: (**a**) Generating the compensation function based on a reference measurement with Klippel near field scanner (NFS); and (**b**) an in situ test for low-frequency measurements in the test environment.  
Figure 5: Validation of the measured and simulated pressure response of the panel with the exciter (standard construction) with a closed backside at a 1 m distance and with 2.83 Vrms input.
## 4 Proposed Method
### Procedure of the Proposed Method
#### 4.4.1 How to Stimulate Mobile Hosts at Suitable Positions to Volunteer to Serve as REPs  
In the proposed method, mobile hosts decide whether to volunteer to serve as a REP based on the messages exchanged through D2D communication with its one-hop neighbors. The procedure to stimulate mobile hosts to volunteer to serve as REPs is explained here and in Figure 14.  
1. Each mobile host decides whether it should volunteer to serve as a P-REP at every time step \(\mathcal{T^{\prime}}_{q}\). The start of the cycle is selected randomly by each mobile host. Specifically, a mobile host volunteers to serve as a P-REP and sends a Bid Invitations Query message to the server if it has neither received any D2D Primary Notification message from its one-hop neighbors nor been a P-REP within time \(\mathcal{T^{\prime}}_{q}\).
2. On becoming a P-REP, the mobile host sends a D2D Primary Notification message through one-hop D2D broadcast to notify its one-hop neighbors of its current status as a P-REP.
3. For volunteering to serve as an S-REP, a mobile host (MH-X) that receives a D2D Primary Notification message waits for a random wait time between 0 and configuration parameter \(T_{s}\). If the number of D2D Secondary Notification messages received by MH-X while waiting is less than \((M-1)\), where \(M\) is a configuration parameter, MH-X volunteers to serve as an S-REP. When volunteering to serve as an S-REP, the S-REP sends a Bid Invitations Query message to receive the same bid invitations as a corresponding P-REP.
4. At the same time, the S-REP sends a D2D Secondary Notification message through two-hop D2D broadcast to notify the one-hop neighbors of the P-REP that it is currently an S-REP.
5. If the number of D2D Secondary Notification messages received by MH-X while waiting is equal to or greater than \((M-1)\), MH-X does nothing. Thus, at least \((M-1)\) mobile hosts among the one-hop neighbors of a P-REP volunteer to serve as S-REPs.  
Figure 14: Example of the selection of a P-REP and S-REPs (in the case of \(M=2\)).
## 4 Experiment Setup and Result Analysis
### Result Analysis
#### 4.2.1 Simulation Test
On the contrary, it is easy to identify the satellite 14 as the fault satellite for the reason that only the detection values of satellite 14 exceed the alarm threshold in Figure 12b. Furthermore, 100 sets of simulation data are used for Monte Carlo test. The test result is that the accuracy of fault identification is up to 97%, which is increased by 25% after using a robust algorithm.
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 4 Methodology
### User Allocation Module  
The user allocation module is influenced by the model presented in [32]. As depicted in Figure 3, we created a CNN-based model consisting of three convolutional 128-unit layers with ReLU activations and three dense layers with widths of 256, 128, and 18, respectively. CNNs succeed in extracting spatial features from geospatial data such as base station positions, as well as processing multi-dimensional data. The input of this model is the geographical longitude and latitude of each user as derived from the DeepMIMO dataset, while the output is an 18 one-hot encoded output, corresponding to 18 stations where the users are allocated. This model was trained over a maximum of 1000 epochs with a batch size of 32. An early stopping mechanism with a patience of 25 was employed; thus, the 1000 epochs were never reached. [PERSON] [29] was selected as the optimizer with a learning rate of 0.001 to improve the convergence of training and reduce the risk of gradient descent being stuck in local minima. The way this is achieved is through the adaptive learning mechanism in the root mean squared error propagation method and the momentum mechanism employed in the gradient descent process. Multi-class cross-entropy [33] was employed as the loss function for this multi-class task. The above architecture and hyperparameters are optimal as arises from the analysis carried out in [32].  
In order to allocate users to base stations based on user traffic predictions, we rank the base stations based on their perceived future traffic. Base stations with very high traffic have a virtual increase in user distance to that base station relevant to the intensity of the predicted high traffic. That distance is analogous to the percentage of total base station allocated users compared to the user capacity of that base station. So if we would want the users of a base station to fall by some percentage point, we would virtually position them that much farther away from the high traffic station than they are. Hence, user traffic predictions are used in an adjusted virtual position mechanism in the user allocation module. With this approach the models for allocating users take into account the future traffic of base stations.  
The virtual distance is calculated through the equation below. \(Da\) is the actual distance, \(Dv\) is the resulting virtual distance, \(Lp\) is the load predicted in total users for that base station and \(Lmax\) is the capacity of that base station. A parameter \(\lambda\) can be used to intensify this effect depending on the performance in a production environment. When \(\lambda=1\) is set for simplicity, the equation performs calculations that would put base stations at exact capacity, so setting \(\lambda\) to be a small percentage higher than 1 would be better. For example setting \(\lambda=1.005\) would allocate in a way that 0.5% of the base station capacity is left available.  
\[Dv=\lambda*Da*(Lp/Lmax)\]
## 4 IRS-Aided Wireless Network Performance and Deployment
### Deployment Strategy and Networking  
As discussed previously, an IRS when deployed in a wireless network improves the network performance by reflecting the incident signals. However, the overall network performance depends on the deployment strategy used in the wireless network. Deployment strategy includes factors such as deployment locations, size of the IRS, user distribution and demand, cost of deployment, operational cost, availability of space and the propagation environment [81]. IRS deployment strategy is completely different from the deployment strategy used for active communication nodes such as APs, BS and relays. Unlike an IRS, active BSs and APS are spaced geographically to achieve maximum coverage while active relays are deployed between the transmitter and receiver to balance the SNR of the two-hop link. It is typically ideal to deploy an IRS in the close vicinity of the BS and users since IRSs are passive (only reflect and lack amplification and signal processing power). Moreover, due to the low cost, power and negligible interference of an IRS, it can be densely deployed in a wireless network and spaced sufficiently since its signal power rapidly decays with distance [81]. An IRS can be deployed in a distributed or centralized manner. Two IRS deployment strategies widely discussed for a multiuser scenarios based on the number of reflecting coefficients are briefed below [81].  
* Distributed IRS deployment strategy [102]: The IRSs are densely deployed around one BS in a wireless network to serve multiple user clusters. The achievable rate is reduced since the user receives passive beamforming gain from only the closest IRS due to significant distance from the other deployed IRSs.
* Centralized IRS deployment Strategy [42]: All IRS reflection coefficients are kept central to the BS or AP in one location. The beamforming gain in this scenario will be larger due to more IRS reflecting elements. However, the gain may be reduced as the number of served users increases.  
These deployment strategies have varying achievable rates in different scenarios, and it is difficult to choose a single best strategy [42].
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Features
#### 4.2.2 Area Based  
Area-based techniques are useful for detecting one or more areas, such as indoors or outdoors, as well as detecting specific scenes in that area, such as mountains, nature, skyline, etc. [PERSON] and [PERSON] [14] proposed a video summary system that takes into account user preferences based on events, objects, and area. Through a user-friendly interface, the user can adjust the frame number or time limit. [PERSON] et al. [67] proposed a personalized video summarization system. The proposed system includes 25 detectors for semantic concepts based on objects, events, activities, and areas. Each captured video is assigned shot relevance scores according to the set of semantic concepts. Based on the time period and shots that are most relevant to the user's preferences, the video summary is generated. [PERSON] et al. [60] proposed a system consisting of three layers of server-middleware-client to produce a video summary according to user preferences based on objects, events, and scenes in the area. Many studies [53; 54; 62; 66; 68] have proposed that personalized video summarization is also based on area.
# A Weighted Turbo Equalized Multi-Band Underwater Wireless Acoustic Communications
## 2 The Multi-Band Underwater Wireless Acoustic Sensor (UWAS) Communication  
The multi-band acoustic propagation method uses frequency-selective fading to modulate signals that are to be transmitted into different frequencies, by dividing the same coded bit string in parallel. As shown in Figure 1, when coded bits are divided into each block and a preamble bit is input for synchronous acquisition, a single packet is created. Each packet, consisting of \(N_{T}\) bits, is reorganized as \(N_{b}\) packets, and then different \(N_{b}\) frequencies, that is, different bands, are used to combine and propagate the modulated signals.  
Figure 1: Packet structure for multi-band transmission.  
The packet structure is shown in Figure 1. This paper generated a single packet consisting of coded and preamble bits. Besides, the same \(N_{b}\) packets were configured for multi-band acoustic propagations and different \(N_{b}\) frequencies were used to add up and propagate the modulated signals.
# An In-Line Coaxial-to-Waveguide Transition for Q-Band Single-Feed-Per-Beam Antenna Systems
## 1 Introduction
Hence, the waveguide traversal geometry in proximity of the transition needs to be modified in order to both reduce its impedance and match its field distribution with the end launcher [28]. Then, the transition is matched to the full WR-22 through a stepped impedance transformer. The performance has been analyzed in terms of return and insertion loss.
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
## The Proposed Fuzzy Keyword Search Scheme Based on MinHash  
### Preliminaries  
LSH (Locality Sensitive Hashing) was first proposed by [PERSON] et al [21] to solve the problem of approximate nearest neighbor search. The basic idea is to generate the same hash key value for the points that are close in terms of their distance in the dataset. It has been shown that LSH algorithm has the obvious advantages of lowering the consumption of space and improving the efficiency of query processing compared to other schemes. The LSH method can thus help to return approximate nearest neighbor query results quickly in a probabilistic way, ensuring the accuracy of query results as well as the efficiency in terms of space and time.  
LSH provides an effective way of retrieving one or more data points that are adjacent to the query data point in a massive and high dimensional dataset. LSH can not only find the most adjacent data to the query point while minimizing the number of data points that need to be matched, but also ensure a high probability of finding the nearest neighbor data points. As one of the most commonly used methods of LSH, the MinHash algorithm uses Jaccard similarity as a measurement standard to detect the similarity of two objects. The higher the Jaccard similarity, the more similar the two objects. In addition, the smaller the distance between two objects, the greater the possibility that two objects belong to the same class. Traditional methods are not very efficient in measuring the similarity between two datasets with a large number of elements, i.e., the dimension of the feature space is very large. In such cases, MinHash can deal with the problem with great efficiency.
## 4 Experimental Setup
### Evaluation Metrics
#### 4.3.3 Calinski-Harabasz Index  
The Calinski-Harabasz index [65] assesses a clustering based on the mean between and inside cluster sum of squares, and a model with better defined clusters will have a higher Calinski-Harabasz index. For a set of data \(E\) of size \(n_{E}\) which has been clustered into \(k\) clusters, the Calinski-Harabasz index is defined as the ratio of the between-clusters dispersion (the sum of distances squared) mean and the within-clusters dispersion:  
\[\frac{tr(B_{k})}{tr(W_{k})}\times\frac{(n_{E}-k)}{(k-1)}\, \tag{7}\]  
where \(tr(B_{k})\) is the trace of the between cluster dispersion matrix, \(tr(W_{k})\) is the trace of the within-group dispersion matrix, \(E\) is a set of data, \(n_{E}\) is the size of data, and \(k\) is the number of clusters.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 6 Machine Learning in Voltage Optimization
### Approaches Using ML in Real Time Systems
The advantage of TD models is that they do not require the model of the environment, of its reward and next-state probability distributions, and functioning in an incremental fashion. Also, TD methods have usually been found to converge faster than Monte Carlo methods on stochastic tasks [43]. The convergence performance of the model is due to the form in which current and next states are determined by estimations using known true values of the current state and determine its next states.