esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Cognitive Control: Advancement, Definition, Framework, and Prospect
## 2 Cognitive Control Research Overview
### Cognitive Control Based on CDS
#### 2.3.1 Cognitive Control for CBTC Systems  
CBTC systems transmit information through train-ground wireless communication, and the wireless local area network (WLAN) is generally adopted as the transmission medium. However, the time delay and packet loss caused by transmission errors and media access control (MAC) layer handoffs are inevitable because most of the current IEEE 802.11-based WLAN standards are not designed for high-mobility environments. Consequently, the state information of the front train cannot be received by the current train in time, resulting in unnecessary traction, braking, and energy loss.  
In [110], cognitive control was used to alleviate the influence of uncertain communication on CBTC systems. The wireless channel was modeled as a finite state Markov model, the entropic state was used to quantify the time delay and packet loss, and the multiplexing gain and handoff action in multiple-input multiple-output (MIMO) wireless communication was adjusted by the cognitive controller. In [111], the interference in a CBTC system coexisting with a passenger information system (PIS) was mitigated based on cognitive control. Owing to the interference of the PIS, the signal-to-noise ratio (SNR) in the  
Figure 16: The CRC system architecture [108].  
wireless transmission process becomes the signal-to-interference-plus-noise ratio (SINR). Hence, in addition to adjusting the multiplexing gain and handoff action, the packet size and packet interval were also controlled by the cognitive controller.  
In [112], a cognitive control approach for CBTC systems was proposed based on a semi-Markov decision process. Two communication periods were set: regular and short. In each PAC, the MAC layer retry limit and communication period for the next PAC were adjusted by the cognitive controller. Specifically, when packet loss occurs, the retry limit was increased appropriately to reduce the packet loss rate, and the short communication period was chosen for the next PAC to shorten the time interval of the adjacent sampling times.  
In [113], a cognitive control method for cost-efficient CBTC systems with smart grids was established. The train uses regenerative braking technology to feed energy back to the power grid; therefore, the cognitive controller needs to control the motion of the train and reduce the total financial cost of consuming electrical energy. A joint objective function composed of the information gap and the total financial cost was applied to generate an optimal policy [113]; the MAC layer retry limit and traction strategy were adjusted by the cognitive controller to decrease the packet loss rate and optimize the traction and braking process.
Electronics_**2015**, \(4\), 359-379; doi:10.3390/electronics4020359  
_electriconics_  
**ISSN 2079-9292**  
www.mdpi.com/journal/electronics  
_Article_  
**HMC-MAC Protocol for High Data Rate Wireless Sensor Networks**  
**[PERSON] *, [PERSON] and [PERSON]**  
Clermont University, University of Auvergne, LIMOS, BP 10448, F-63000, Clermont-Ferrand, France  
CNRS, UMR 6158, LIMOS, F-63173 Aubiere, France  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-473-177-048; Fax: +33-473-177-111.  
Academic Editor: [PERSON]  
_Received: 23 April 2015 / Accepted: 9 June 2015 / Published: 15 June 2015_  
**Abstract:** Using multiple channels in wireless sensor networks helps increase the overall throughput and avoid interferences. In addition, introducing multi-interface nodes further helps in increasing the packet delivery rate for those specific nodes. In this paper, we evaluate a channel allocation method based on neighborhood discovery up to 3 hops and a hybrid MAC protocol designed for high data rate wireless sensor networks. We propose a network segmentation that takes into consideration the existence of multi-interface sink in order to further increase to packet delivery ratio. Our protocol, called HMC-MAC, uses Time Devision Multiple Access (TDMA) for sequencing nodes activity, and Carrier Sense Multiple Access (CSMA)/CA with Frequency Devision Multiple Access (FDMA) for simultaneous data exchange. We evaluated our method using NS2 simulator and results show that HMC-MAC protocol improves the overall network performance compared to other protocols especially with high data rate and burst traffic.  
**Keywords:** wireless sensor networks, MAC protocol, multi-channel, multi-hop
# Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems
## 1 Introduction
### Related Work
In the case of IM and its variants with OAM and OFDM, detection methods become complex with an increasing number of users and a different activation mode because the signal power of activated modes reduces, and it becomes difficult for traditional methods to accurately detect. In this regard, AI has played an important role due to its capabilities of learning the environment and channel conditions to improve detection accuracy. The system's performance increases, as the algorithm can learn in contrast to traditional methods that use one-time mathematical function definitions. In [35], VLC-based indoor positioning systems using KNN, ANN, and clustering are discussed, and the effectiveness of these techniques is given. In [36], regression in machine learning is used for indoor positioning using a VLC system. To improve the overall performance of VLC systems, [37] presents machine learning techniques to mitigate non-linear effects. Inspired by the advantages of the low bit-error-rate (BER), low complexity, and high data rate mentioned in the recent literature, we implement OAM-IM and OFDM-IM with a CNN-based approach for SIC to retrieve data for a NOMA-based multiuser VLC system. In this study, a four-user VLC system is proposed using OAM-IM and OFDM-IM. The BER performance for the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) VLC channels with both the maximum likelihood (ML) method and CNN-based detection method is compared. Our primary experiments demonstrate the benefits and drawbacks of OAM-IM and OFDM-IM at various SNR levels. The results show that OAM-IM performs better than OFDM-IM for multiuser VLC systems. In the comparison of CNN- and ML-based systems for NOMA with SIC, the CNN-based system performs better than the ML method. The remainder of this article is as follows: First, we discuss the system model, which includes a detailed mathematical discussion on OFDM-IM, OAM-IM, the VLC channel, NOMA, and CNN-based receiver design. Then, we present the BER results for four users in a MIMO configuration followed by the conclusion.
# Time Series Feature Selection Method Based on Mutual Information
## 2 Methodology
### Feature Selection Based on Mutual Information  
Mutual information is a measure of the interdependence between two variables, which mainly represents the correlation between them. Assuming that the joint probability distribution of two random variables \(X\) and \(Y\) is \(p(x,y)\), and their marginal probability distribution is \(p(x)\) and \(p(y)\), respectively, then the mutual information \(MI(X,Y)\) is the Kullback-Leibler divergence of the joint probability distribution \(p(x,y)\) and the marginal probability distribution \(p(x)\) and \(p(y)\), which is defined as follows [28]:  
\[MI(X,Y)=-\int_{x}\int_{y}f_{x,y}(x,y)\log\frac{f_{x,y}(x,y)}{f_{x}(x)f_{y}(y)}dxdy \tag{3}\]  
From the above definition, when variables are completely independent or mutually independent, their mutual information is the smallest, and the result is 0. The greater the mutual information between variables, the stronger the correlation. In data processing, features with large mutual information with the target variable should be selected as much as possible to improve the prediction ability of the algorithm, and features with small mutual information should be eliminated to reduce data redundancy, that is, so-called feature selection based on maximum correlation and minimum redundancy [44].
# Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations
###### Abstract  
Images may be corrupted by salt and pepper impulse noise during image acquisitions or transmissions. Although promising denoising performances have been recently obtained with sparse representations, how to restore high-quality images remains challenging and open. In this work, image sparsity is enhanced with a fast multiclass dictionary learning, and then both the sparsity regularization and robust data fidelity are formulated as minimizations of L0-L0 norms for salt and pepper impulse noise removal. Additionally, a numerical algorithm of modified alternating direction minimization is derived to solve the proposed denoising model. Experimental results demonstrate that the proposed method outperforms the compared state-of-the-art ones on preserving image details and achieving higher objective evaluation criteria.  
Keywords:impulse noise removal; sparse representation; dictionary learning; L0 norm +
Footnote †: journal:
# EMF Assessment Utilizing Low-Cost Mobile Applications
## 1 Introduction  
The evolution of wireless communication technology, as well as the expansion of mobile networks in urban environments, has led to increasing public concern about electromagnetic exposure and its potential health effects [1]. While studies monitoring exposure with the evolution of wireless technology [2, 3] and comparing exposure levels among different countries have been conducted [4], extensive measurement campaigns are essential to ensure that exposure limits are not exceeded [5] and that public health is protected [6]. However, the specialized equipment and highly trained personnel necessary for such measurement campaigns make the latter quite costly.  
Many studies have explored alternative methods for estimating electromagnetic exposure, such as artificial intelligence models using information about base stations [7], utilization of mobile metrics from smartphones for identifying points of interest [8], placement of low-cost sensors near base stations [9] and the development of machine learning models to convert data obtained from mobile applications into electric field strength [10].  
This study aims to provide a method for mapping the electric field strength emitted by the base stations of 4G cellular networks in outdoor urban environments using low-cost tools, such as smartphones equipped with a specific application. It is presumed that a greater level of uncertainty is acceptable in electric field mapping than in compliance testing since the former aims to identify areas of high and low exposure. Moreover, in this work, we aim to identify areas where the estimation is likely to be more accurate based on the variability of received power over different time periods. Specifically, three smartphones with an application that provides Reference Signal Received Power (RSRP) values, alongwith an exposimeter, were used in a drive-test measurement campaign. The exposimeter data were initially used to determine a factor for converting RSRP values to electric field strength and subsequently to validate the proposed method.  
This method offers an immediate estimation of the actual downlink electric field strength from 4G networks without requiring high computational power used in simulations (e.g., with ray tracing) or expensive equipment used in compliance testing (e.g., frequency selective radiation meters). It can serve as a mapping tool to identify points of interest, in terms of exposure, for further measurement with specialized equipment.  
This study is organized in the following sections: Section 2 describes the measurement methodology, equipment used, and data processing procedures. Section 3 presents the analysis results, while Section 4 discusses the findings, highlights the advantages and limitations of the proposed method, and suggests directions for future research. Lastly, Section 5 shows the conclusions of this study.
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
###### Abstract  
High-resolution remote sensing imagery comprises spatial structure features of multispectral bands varying in scale, color, and shape. These heterogeneous geographical features introduce grave challenges to the fine segmentation required for classification applications in remote sensing imagery, where direct application of traditional image classification models fails to deliver optimal results. To overcome these challenges, a multispectral, multi-label model, MMDL-Net, has been developed. This model is integrated with the multi-label BigEarthNet dataset, primarily employed for land cover classification research in remote sensing imagery, with each image composed of 13 spectral bands and spatial resolutions of 10 m, 20 m, and 60 m. To effectively utilize the information across these bands, a multispectral stacking module has been introduced to concatenate this spectral information. To proficiently process three distinct large-scale remote sensing image datasets, a multi-label classification module has been incorporated for training and inference. To better learn and represent the intricate features within the images, a twin-number residual structure has been proposed. The results demonstrate that the MMDL-Net model achieves a top accuracy of 83.52% and an F1 score of 77.97%, surpassing other deep learning models and conventional methods, thereby exhibiting exceptional performance in the task of multispectral multi-label classification of remote sensing imagery.  
Keywords:high-resolution remote sensing images; multiband multispectral; ResNet; multilabel classification; deep learning +
Footnote †: journal: Applied Sciences
## 2 Monte Carlo-Based Channel Parameter Characteristics Analysis
### Detection Efficiency Analysis
**(b)**\(\eta_{r}\) calculated by Monte Carlo simulation versus different receiver FOV under four types of water. Figure 5: Intensity distributions of the light with a beam width of **(a)** 1 mm, **(b)** 2 mm, **(c)** 3 mm at the reception plane after transmitting through a 10 m clear ocean water. Intensity distributions of the light with a maximum beam divergence angle of **(d)** 0 mrad, **(e)** 2 mrad, **(f)** 5 mrad at the reception plane after transmitting through a 10 m clear ocean water. Figure 3: Schematic diagram showing the geometry of the transceiver and the moving trajectory of photons in seawater. \(\theta_{x},\theta_{y},\theta_{z}\) are the angles between the direction vector of the photon and the x, y, and z-axis, respectively. \(\theta_{0,max}\) represents the maximum initial divergence angle of the beam and FOV represents the field of view of the receiver.
## 6 Conclusions  
A model for characterizing rough joint traces was proposed by combining the modeling method of a rough discrete fracture network with the RDFN model. This was conducted by utilizing the Fourier transform principle. The model took the fracture dip angle into consideration and utilized digital image recognition technology to develop a mechanical model of a rough slope, using the particle flow discrete element numerical analysis method. This study's key findings include the following:  
1. According to this study, a rough slope is more stable than a linear slope for a single fracture inclination angle. Although both slope types show a similar overall displacement trend, they differ significantly in their slip surface and through-tension crack distribution.
2. The vectors of displacement for rough-jointed slopes having inclination angles of 0\({}^{\circ}\) and 45\({}^{\circ}\) are concentrated at the center of the side slope surface and are tangential to the slope surface. This leads to the sliding of the entire slope, resulting in local caving on the surface. Inclination angles of 30\({}^{\circ}\) in the joints produce weaknesses in the structural plane, reducing shear strength and causing a breakdown in the bedding slope. For fracture inclination angles of 60\({}^{\circ}\) and 90\({}^{\circ}\), significant displacement of the slope is concentrated in the middle, along with through cracks at the top, which result in local slippage.
3. After comparing simulation results with actual engineering slope failure characteristics and related research, we found that the stability analysis model of rough-jointed rock slopes, based on PFC2D, is closer to the real-world results than the linear-jointed model. This indicates an effective and reliable new method for modeling jointed-rock slopes with complex structures. However, the current modeling method only considers a single fracture distribution mode and a single dip angle, and it needs further optimization to consider multiple dip angles and fracture distributions.  
[PERSON] performed the numerical analysis on slope stability evaluation; [PERSON] established the rough fractured rock slope; [PERSON] performed the numerical modeling and data analysis; [PERSON] revised the manuscript and surprise the whole work content. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (52074020) and the Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities) (FRF-IDRY-21-001).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Authors [PERSON] and [PERSON] were employed by the company Hainan Mining Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Review on the Influence of Complex Stratum on the Drilling Trajectory of the Drilling Robot
## 2 Detection of Complex Formation and Measurement while Drilling Technology  
### Complex Formation Detection  
At present, the equipment and instruments used for complex formation detection mainly include calipers, electric water level gauges, borehole leak detectors (borehole flowmeter) and borehole bending measuring instruments [34].  
The borehole diameter gauge or hole diameter gauge can be used to measure the diameter of the borehole, understand the over-diameter or cavity of the borehole, judge the position of the borehole collapse or leakage layer, and also serve as the basis for calculating the volume of the plugging material. The electric water level gauge, mainly used to measure the dynamic and static water level in the hole, has a simple structure and accurate measurement. The borehole leakage meter (borehole flowmeter) is used to measure the location, thickness and amount of borehole leakage. The measuring instruments for borehole bending are divided into two types: vertex angle measurement and overall measurement (vertex angle and azimuth angle) [35; 36].  
For some complex situations, mainly the leakage layer in drilling, can be detected by mechanical penetration rate, core, rock powder observation, drilling fluid property and consumption observations and water inrush observations [37]. The leakage layer can be determined by water stop measurements and isolation pressure tests [38]. Geophysical methods such as apparent resistivity logging, well fluid resistivity logging, radioactive logging, ultrasonic logging and temperature logging are used to detect complex conditions [39].
## 1 Introduction
### PCM, PCM Composite Materials, and Encapsulated PCM
Encapsulation sizes range from about 1 \(\upmu\)m (called microencapsulation) to several 10 cm (called macroencapsulation), allowing PCMs to be pumped with heat transfer fluids (called slurries) or to be used in variable storage sizes. The shape and capsule wall material also vary, e.g., flat or ball-shaped containers from metals or plastic, or bags and pouches made from polymer foil.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 4 Proposed Scheme
### Data Retrieval Process
#### 4.3.2 Query Request Submission  
When a user wants to request a query service, the user should submit the query request in the form of \(Q=\{prf_{k}(\omega),C,E\}\) to the fog node \(g_{o}\). In the query request \(Q\), \(\omega\in W\) indicates a keyword of interest related to a POI type and \(prf_{k}(\omega)\) is gained by encrypting \(\omega\) with the shared key \(k\) of the pseudo-random function. Moreover, \(C\) is a ciphertext set composed of a series of encrypted data in the form of \(\{c_{d}|1\leq d\leq n\}\), in which \(c_{d}\) represents encrypted data obtained by the public key \(pk\) and an element \(f_{d}\) in the guide set \(F\). Specifically, to obtain \(c_{d}\), the user encrypts the \(f_{d}\) using the Paillier cryptosystem under the public key \(pk=N\) and a random number \(r_{d}\in\mathbb{Z}_{N}^{*}\) as follows:  
\[c_{d}=[(1+N)^{f_{d}}\cdot r_{d}^{N}modN^{2}]\quad. \tag{5}\]  
Moreover, \(E\) represents encrypted data obtained by the public key \(pk\) and encrypted query index \(prf_{k}(\omega)\). Specifically, to obtain \(E\), the user encrypts the \(prf_{k}(\omega)\) using the Paillier cryptosystem under the public key \(pk=N\) and the set \(\{r_{d}|1\leq d\leq n\}\) as follows:  
\[E=[(1+N)^{prf_{k}(\omega)}\cdot(\prod_{d=1}^{n}r_{d})^{N}modN^{2}]\quad. \tag{6}\]
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 9 Results and Discussion
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & \multicolumn{2}{c}{**Related Works**} & \multicolumn{2}{c}{**Proposed Work**} \\ \hline
[24] & 1. & A fuzzy-based optimization was used to meet the demands for a higher data rate and high spectral efficiency. & 1. & A modified-GA-based optimization was proposed for power optimization and high spectral efficiency. & 1. \\
[25] & 2. & Maximized the energy efficiency by optimizing the transmission power. & 2. & Minimized the power at user nodes and increased the spectral efficiency. \\
[24] & 3. & Assumption of infinite battery capacities during flooding, which is impractical. & 3. & Battery capacity was limited and tried to optimize the use of the battery of a cellular user. \\
[25] & 1. & Presented an energy-efficient architecture for power optimization. & 1. & Presented a scheduling and optimization algorithm in a heterogeneous environment. & 2. \\
[25] & 2. & Analyzed the minimization of the transmit power and also met the QoS demands. & 3. & Handoff and coverage problems between adjacent small cells impacted the energy efficiency (EE). \\
[25] & 1. & A fractional power allocation scheme was used along with the minimum channel gain and minimum distance scheduling techniques. & 2. & A centralized transmit pre-coding scheme at the downlink was used to cancel out interference. & 3. & Degradation in network performance with the spatial correlation with both the maximal ration (MR) and local partial minimum-mean-squared error (LPMMSE). \\
[25] & 1. & A cooperative-relay-based technique along with GA-based power optimization. & 2. & Increased spectral efficiency and enhanced the coverage area. & 3. & The optimization algorithm took a longer time to converge when the number of relay nodes increased. \\
[28] & 2. & Increased spectral efficiency and enhanced the coverage area. & 3. & Best Cost for Modified GA = 0.00013698 \\
[3. & 3. & The optimization algorithm took a longer time to converge when the number of relay nodes increased. & 3. & The convergence rate was higher since the scheduling algorithm checked the number of users that can access the channel. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison table. Figure 9: Best Cost vs. Iteration using various optimization techniques.