esa-sceva/satcom-chunk-collection huggingface dataset:
## Adaptive Multi-Node Multiple Input and Multiple Output  
(MIMO) Transmission for Mobile Wireless Multimedia  
**[PERSON] \({}^{1}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{3,*}\)**  
\({}^{1}\) Department of Computer Science and Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan, Gyeonggi-do 426-791, Korea; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Information and Communication Engineering, DGIST, 333 Techno jungang-daero, Hyeonpung-myeon, Dalsung-gun, Deagu 711-873, Korea; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Department of Information and Communications Engineering, Myongji University, 116 Myongji-ro, Cheoin-gu, Yongin, Gyeonggi-do 449-728, Korea  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-31-330-6771; Fax: +82-31-335-6478.  
_Received: 2 September 2013; in revised form: 25 September 2013 / Accepted: 26 September 2013 / Published: 2 October 2013_  
**Abstract:** Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase.  
**Keywords:** mobile wireless multimedia sensor networks; multi-node MIMO; diversity; spatial correlation
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 4 Accuracy Enhancing Methods
### Optical and Mechanical Aspects
#### 4.1.3 Combination of Laser Interference and White Light Interference  
As described in Section 3, the laser interference and white light interference have respective advantages. The white light interference has surface positioning advantages for its incoherence and the laser interference has a good interference fringe because of its good coherence. The combination of two ways of interference has advantages for improving measurement results. The signal obtained by the combination of two ways of interference is shown in Figure 12.  
The signal center of zero-order interference fringes of white light represents the position of surface. It is easy to ensure the rough position by processing the white light interference signal first. Then according to the rough position obtained from white light interference signal, accurate position is further calculated from the laser interference signal. The processing method for signal is critical to obtaining good repeatability. The methods include center strategy, the signal filtering, etc., which will be described in the following section.  
Figure 11: The semiconductor laser wavelength stability monitoring.
## 2 Related Work
Based on trained dictionaries, conventional SR methods show better performance in image quality using the dictionary-based high-frequency energy for details of HR images, and simulation results prove that our estimator achieves more precise texture reconstruction in high resolution. **Denoising followed by super-resolution**: Few research studies have been conducted on the resolution of noisy images. [PERSON] et al. [64] performed the two tasks separately and then joined the noisy super-resolution task and denoised super-resolution task. [PERSON] et al. [65] performed the combined task by combining the denoising task with a newly introduced filter-based algorithm for super-resolution. [PERSON] et al. [66] performed the super-resolution task with a simultaneous denoising task derived from a multi-scale noise reduction method. These methods do not leverage neural networks and fail to fully utilize edge information constraints. [PERSON] et al. [67] used GAN for super-resolution and denoising tasks. They utilized the residual network to directly map the image with the original noise map. However, this approach does not fully exploit the constraints of edge information.
## 5 The Group-Aware Query Region Tree (GQR-Tree)
### Cooperative Evaluation of CM Range Monitoring Queries
#### 5.4.1 Server-Side Tasks  
The server performs three main tasks: (i) query registration (or de-registration); (ii) domain assignment; and (iii) query result update.  
**Query Registration (or De-Registration)**. When a new query \(q=(q.R,q.V)\) is issued by a client, the server assigns an identifier to \(q\), inserts \(q\) into the query table, and invokes Algorithm 3 (_i.e._, insert algorithm), after which it broadcasts the \(InsertQuery(q.id,q)\) message to all the moving objects that are registered at the server, where \(q.id\) denotes the identifier of \(q\). On the other hand, when an existing query \(q\) is terminated by a client, the server deletes \(q\) from the query table and invokes Algorithm 6 (_i.e._, delete algorithm). Then, the server broadcasts the \(DeleteQuery(q.id)\) message.  
**Domain assignment**. In addition to the main data structures, namely the query table and the GQR-tree, the server maintains an _object table_ (hashed on object identifiers), which stores for each moving object \(o\), an identifier, a location \(o.loc\) (from the last \(RequestDomain\) message or \(UpdateResult\) message), a set of non-spatial attribute values \(o.A\), and a capability \(o.Cap\). When a new moving object \(o=(o.loc,o.A)\) is registered at the server with its capability \(o.Cap\), the server assigns an identifier to \(o\), inserts \(o\) into the object table, and invokes Algorithm 1 (_i.e._, search algorithm). Then, the server assigns a resident domain \(N\) to \(o\) together with query identifier and spatial query range pairs. When the server receives the \(RequestDomain(o.id,o.loc_{new},o.Cap,N_{old})\) message from \(o\), where \(o.id\), \(o.loc_{new}\), and \(N_{old}\) denote the identifier, current location, and previous resident domain of \(o\), respectively, it visits the object table and sets \(o.loc\) of \(o\) (referred to by \(o.id\)) to \(o.loc_{new}\). Next, the server invokes Algorithm 1 and assigns a new resident domain \(N_{new}\) to \(o\) together with new pairs of query identifiers and spatial query ranges. Finally, the server visits \(N_{old}.full\_qid\_table\) and gets the tuple \(\langle g.bv,full\_qid\_list\rangle\) such that \(g.bv\wedge o.bv=o.bv\), after which it checks if the movement of \(o\) affects the result of each query \(q\) referred to by each query identifier contained in \(full\_qid\_list\). (Note: the object bit-vector \(o.bv\) of \(o\) has already generated in Algorithm 1.) If so, the server update the result of \(q\).  
**Query Result Update**. When the server receives the \(UpdateResult(o.id,o.loc_{new},q.id)\) from a moving object \(o\), it visits the query table and checks if the result of the query \(q\) (referred to by \(q.id\)) contains \(o.loc_{new}\). If so, the server inserts \(o\) into the result of \(q\). Otherwise, the server removes \(o\) from the result of \(q\).
## 3 Ambient Parameter Monitoring and Sensor Technology
### Shock and Vibration
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Ambient** & \begin{tabular}{c} **Sensor Technology Suitable** \\ **for IoT** \\ \end{tabular} & \begin{tabular}{c} **Solutions Commercially** \\ **Available** \\ \end{tabular} & **Examples** & **Relevant Fruit and Vegetables** & **References** \\ \hline \multirow{4}{*}{Oxygen} & \multirow{4}{*}{\begin{tabular}{c} Electrochemical sensor \\ Fluorescence sensor \\ \end{tabular} } & \multirow{4}{*}{Yes} & \begin{tabular}{c} DC\({}_{\text{S}}\)exponity EC Sense (Wi-Fi) \\ PS-3217 by Pasco (Blatetooth) \\ \end{tabular} & \begin{tabular}{c} During CA storage/transport: \\ applies, pears, exchanges, onions, \\ kivirful, urvocados, melons, \\ nectarines, peaches, broccoli, \\ asparagus, mangoes, \\ plums, strawberry \\ \end{tabular} & \begin{tabular}{c} **References** \\ \end{tabular} \\ \hline Carbon Dioxide & \begin{tabular}{c} NDIR sensor \\ Metal oxide sensor \\ \end{tabular} & Yes & \begin{tabular}{c} PS-3208 by Pasco (Blatetooth) \\ SenseCAP Wireless CO\({}_{2}\) Sensor by Seed \\ Studio (LoRa) \\ \end{tabular} & \begin{tabular}{c} During CA storage/transport: \\ same as for oxygen \\ Climacter fruit \\ \end{tabular} & \begin{tabular}{c} **References** \\ \end{tabular} \\ \hline Shock/ vibration & \begin{tabular}{c} MEMS accelerometer \\ \end{tabular} & Yes & \
\begin{tabular}{c} SentirusTM (BT510 LairdTM \\ Connectivity (Blatetooth) \\ BeamDevice WiFi111 Wlow\({}^{\mu}\) AX-3D by \\ BeauAir (Wi-Fi) \\ \end{tabular} &
\begin{tabular}{c} All commodities, particularly for: \\ borders, mushrooms, \\ conquettes, bannas \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Conf.
## 8 Conclusions  
WVSNs are next generation WSNs that enable more visual data-based applications, such as security surveillance, home care and environment monitoring. On the other hand, WVSNs also introduce new challenges and research opportunities. First, instead of sensing range coverage, the FoV coverage in WVSNs is determined by the camera's view angle, focal depth and occlusion caused by the obstacles. Second, visual data is much bigger and more complicated than scalar data. Processing and transmitting visual data consumes much more resources than scalar data. Third, visual data reconstruction at the sink node relies on the cooperation among multiple correlated data sources nodes to reconstruct the whole picture of the interested objects. Fourth, the QoS requirements (e.g., resolution, delay, jitter, packet loss) for the visual data applications are more stringent than for scalar data applications. These four new challenges make the traditional WSN algorithms or protocols inapplicable to WVSNs. Existing researches in WVSNs are still at an early stage and there are still many open issues that need to be addressed in the aspects of sensor coverage, visual data capturing/processing/transmission. We believe that novel WVSN algorithms or protocols should be a multi-disciplinary (i.e., multimedia processing, wireless communication and networking, distributed processing and intelligent system) and cross-network-layered design.
# Analysis of Behavioral Characteristics of Smartphone Addiction Using Data Mining
## 4 Data Analysis Based on Data Mining
### Data Mining for Smartphone Usage Pattern  
In order to confirm the relationship between addiction and smartphone usage pattern, the classification criterion was analyzed using the gain ratio. For supervised learning, the label variables were assigned to the addiction level. The input variables were demographic characteristics such as gender and grade and usage time of the smartphone (0-6 h, 6-12 h, 12-18 h, 18-24 h). The final result indicates that, among the 64 users who provided accurate data for smartphone usage pattern measurement, 4.7%, 18.7%, and 76.7% of the participants were high-risk, potential-risk, and general or normal smartphone users, respectively; it is evident that the general smartphone users were the majority. The accuracy of our prediction was 89.7% as listed in Table 4.  
The decision for the individuals to be grouped in the high-risk user group was made based on the following points. First, the screen is turned on for more than 110 times per day. Second, users spent more than 6.07 h and less than 8.29 h per day, while they spent more than 72.5 h per week using their smartphone. In particular, the third-grade students are smartphone-addicted, for whom, the results show that their phone screen was turned on more than 1971 times and most of their time is spent on entertainment applications. For the risk group, the usage time was less than 0.215 h and the number of the times the screen was turned on was less than 565 times; therefore, The input variables that have the greatest influence on the high-risk user group are the \"number of screen turns\" and the \"actual use time--use time\" variable. These results are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy: 89.57\%** & **General Users (True)** & **Potential-Risk Users (True)** & **High-Risk Users (True)** & **Class Precision** \\ \hline General users (pred.) & 83 & 12 & 0 & 87.37\% \\ Potential-risk users (pred.) & 0 & 12 & 0 & 100.00\% \\ High-risk users (pred.) & 0 & 0 & 8 & 100.00\% \\ Class recall & 100.00\% & 50.00\% & 100.00\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Decision tree confusion matrix.  
Figure 4: Decision tree graph results for smartphone user usage pattern.  
The results of applying the decision tree using the app are as follows. As the 'Number of screen turns\" variable increases, the higher-risk users increase. In addition, \"Entertainment\" and \"Actual use time\" variables belonged to high-risk users. The greater the difference between the actual use time and the cognitive use time, the more potential-risk users were. In the existing studies, the cases where the usage amount was high were found to be addictive; however, the new variables influencing the addiction were found. In addition, the addicts found that there was a large difference from the actual use time than the perceived use time. The high-risk users did not recognize the use of smartphones.
# Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems
## 2 System Model  
This section describes the LS-MIMO model and the channel model. These are necessary to introduce and analyze the proposed scheme.
## 4 Results
### Analysis and Discussion
From the numerical comparison results, it can be observed that the algorithm designed in this work outperforms all benchmark algorithms on energy management problems of varying scales. None of the compared algorithms shows significantly better performance than NSGA-II-MC according to the Wilcoxon rank-sum test. Note that CTAEA failed to find feasible solutions for most problems. In particular, the energy management problem with six controllable loads involves more decision variables and stricter power constraints, making it more challenging to solve. Results show that many algorithms failed to find feasible solutions for the six-scale problem, whereas the NSGA-II-MC algorithm proposed in this work consistently obtained feasible solutions. Additionally, it can be seen that the advantage of the NSGA-II-MC algorithm is relatively small for small-scale controllable load problems. However, as the number of controllable loads and problem constraints become more complex, the performance advantage of the NSGA-II-MC algorithm becomes more pronounced. Compared to the NSGA-II algorithm, the proposed NSGA-II-MC method achieved a 49.7% improvement in the Hypervolume metric on large-scale problems of six controllable loads. This demonstrates the effectiveness of the proposed algorithm, which can significantly improve the convergence performance and constraint-handling effectiveness of multi-objective optimization algorithms. Furthermore, Figure 2 provides a comparison of the Pareto fronts between NSGA-II-MC and the traditional NSGA-II under different scenarios. The optimization objectives in the problems are minimizing operating costs and reducing dependence on the power grid. Each point on the Pareto front represents a management solution, with the horizontal and vertical axes representing the values of the two objective functions. Therefore, points closer to the origin indicate better convergence (optimality) of the algorithm. Multi-objective optimization also considers the diversity of the Pareto front, which involves how well a series of solutions can cover different objective preferences. This is reflected in the distribution of the Pareto front formed by a series of solutions.
## 3 Methodology and Principle
### The Optimal Number of Principal Components  
The selection of principal components directly affects the actual prediction ability of PLS model [22]. Too few principal components can not fully reflect the information of independent variables, resulting in inadequate fitting and low prediction accuracy. Excessive calculation of principal components is complex, and noise information is mixed in, which leads to overfitting and reduces the prediction ability of the model [23; 24]. Due to the existence of Hughes phenomenon [25], when the feature dimension increases to a certain critical point, the classification accuracy will decrease and the prediction error of the model will also increase.  
PLS method usually does not need to select \(u_{1},\ldots,u_{r}\) with \(r\) components to establish regression equation, but just select the first \(l\) components (\(l\leq r\)) like PCA to get a regression model with better prediction ability. The optimal number of principal components can be determined by cross validation, according to the cumulative variance contribution of principal components to dependent variable matrix Y and the sum of squares of predicted residuals (Press). Greater contribution of cumulative variance and smaller residuals can lead to better prediction effect of PLS method.
# Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems
## 4 Simulation Results
In addition, all NOMA schemes can still meet the user requirements at a minimum data rate of 0.5 Mbps, but OFDMA cannot continue to work. Figure 8: Bit error rate versus SNR. Figure 7: Outage probability versus SNR.
## 1 Introduction
In the first step, the EUT is placed in the working volume of the RC and the radiated field is measured in a number of selected points on the RC walls. In the second step, from the knowledge of these field samples, the values of the equivalent sources that characterize the EUT radiation are evaluated. In the third step, the equivalent currents are considered to be radiating in free space, and the emission of the EUT is finally evaluated. In this paper, we focus our investigation on step two, concerning the evaluation of the equivalent sources. In particular, the aim of this paper is to improve the efficiency of the method, reducing the number of equivalent sources used to reconstruct the emission of the EUT. In fact, in the previous version of the method, six equivalent sources (three electric and three magnetic dipoles) were needed in each grid point, and the grid points were regularly placed in the whole volume containing the EUT. In the current formulation of the method, the surface including the EUT is discretized into sub surfaces, and in each of them four equivalent sources (magnetic and electric current densities) are considered. The paper is organized as follows: Section 2 describes the scenario of an RC where the MMSS technique is implemented, the method, and the analytical formulation, using the equivalence principle; Section 3 reports the experimental results obtained, applying the proposed method in a real scenario, and their comparison with numerical simulations and more traditional anechoic environment measurements. Finally, discussion on the results and conclusions are reported in Sections 4 and 5, respectively.
# Pilot Sequence Allocation Schemes in Massive MIMO Systems Using Heuristic Approaches
## 4 Proposed Solutions
### PSO-Smallest Position Value  
To solve the optimization problem (13), we used the Smallest Position Value method along with the PSO algorithm (PSO-SPV). In the SPV, the value of each problem dimension is exchanged by the index of the sorted values. In this specific problem, it is equivalent to the SPV value designating the pilot sequence allocated to each user. The sorting process which is responsible for swapping the real values for their ordinal ones is performed separately for each cell in the system. To illustrate the application of the SPV method, we present Table 1 which relates the value of each PSO particle (Real Value) to the pilot sequence assignment when four dimensions (users) are accounted for in a single cell.  
Using an SPV changes the problem domain from a binary scenario to integers values such that \(\theta_{k,\ell}=x\) where \(k\) is the dimension of the SPV scheme and \(x\) is the SPV value, while \(\ell\) designates the cell of interest.  
The PSO is used along with the SPV through the application of Equation (16) and the update position equation:  
\[\mathbf{x}_{i}[t+1]=\mathbf{x}_{i}[t]+\mathbf{v}_{i}[t+1] \tag{22}\]  
It is important to point out that the velocity limits defined by Equation (19) are also applied to the PSO-SPV solution. Furthermore, from the nature of this approach, it is unlikely that two users will have the same pilot sequence assigned, or the other way around, since the sorting process would only place two users in the same position if their real values are exactly the same. To avoid this situation, in case two dimensions of an individual present the exact same real value, the tie break is the user index: the smallest user indexer is sorted as the smallest value. Due to these two facts, the PSO-SPV fitness function is the objective function presented in (9), which means that the BPSO and PSO-SPV  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dimension (User)** & 1 & 2 & 3 & 4 \\
**Real value** & 4.85 & \(-\)2.15 & 145 & \(-\)1.333 \\
**SPV (Pilot Sequence)** & 3 & 1 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of operation of SPV in a single cell, \(K=4\) users scenario.  
use different objective functions although the algorithms objective is the same: maximizing the system spectral efficiency.
# Image Steganography Using LSB and Hybrid Encryption Algorithms
###### Abstract  
In today's era of widespread web technology and cloud computing, ensuring data security has become a crucial concern across various industries. Instances of data breaches and vulnerabilities in cloud storage have emphasized the need for robust data protection and communication protocols, particularly in sectors like social media, military, and research. This research proposes a Multi-Level Steganography (MLS) algorithm that employs two encryption algorithms, AES and Blow-Fish, to secure the cover image and embed encryption keys as key images within the stego image. The proposed MLS algorithm incorporates a robust pixel randomization function to enhance the security of the encrypted data. Experimental results demonstrate that the proposed algorithm effectively protects data with high Peak Signal-to-Noise Ratio (PSNR) and low Mean Square Error (MSE) values, ensuring superior image quality, reliable encryption, and decryption of secret messages. The utilization of hybrid encryption with AES and Blow-Fish algorithms further strengthens the algorithm's security by augmenting the complexity of the encryption process.  
AES; steganography; encryption; decryption; peak signal-to-noise ratio +
Footnote †: journal:  
Article