esa-sceva/satcom-chunk-collection huggingface dataset:
# FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications
###### Abstract  
Visible Light Communication (VLC) represents an emerging technology where a short-range data connection is obtained by modulating the energy radiated by Light Emitting Diodes (LEDs) at frequencies from a few kHz up to hundreds of MHz. The bandwidth/distance performance of such links is a compromise related to the available Signal-to-Noise ratio (SNR). At present, VLC links with bandwidth beyond the Gb/s and distance limited to a few cm or distances up to 100 m but data rates of a few kb/s have been demonstrated. Chirp coding with pulse compression is a well-known technique capable of recovering useful data from low SNR signals, widely employed, for example, in radar. In spite of the possible advantages, its application in VLC has never been investigated. Unfortunately, the pulse compressor is quite calculation-intensive, and only devices like Field-Programmable-Gate-Arrays (FPGAs) can support a low-latency real-time implementation. In this paper we demonstrate a real-time VLC link based on chirp coding and pulse compression coded in FPGA. For example, a chirp with bandwidth and length of 1.7 MHz and 17.92 us, respectively, is demonstrated to support a link at 1.56 Mb/s over 2.8 m distance and a latency below 40 us. Moreover, the communication-distance increase achievable by chirps of increasing temporal length is demonstrated and compared to the theoretical background.  
Visible Light Communication (VLC); pulse compression; FPGA; ultra low-latency communications +
Footnote †: journal: Advances in Neural Information Processing Systems
# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 3 Methods
### Overall Framework  
Figure 3 illustrates the systematic architecture of our DBLCANet-HCIE network for hydraulic concrete image enhancement, which features a dual-path structure integrating four principal components: (a) a color space transformation block (CSTB), (b) a Luminance Adjustment Branch (LAB), (c) a Chroma Restoration Branch (CRB), and (d) a feature fusion block (FFB). The input image \(I_{RGB}^{Lou}\) is converted into the \(YCbCr\) color space using the CSTB, resulting in three components: \(Y\), \(Cb\), and \(Cr\). The \(Y\) component is processed in the LAB via the BPAB, which extracts both the global and local luminance features \(F_{Y}\). These features are then directed to the LSB for local supervised enhancement guided by luminance attention features. Meanwhile, the \(Cb\) and \(Cr\) components enter the CDB, where channel denoising is performed. After denoising, the \(Cb\) and \(Cr\) components are merged and passed to the FDDEB for texture detail enhancement, following local supervision in the LSB. The outputs from both branches are fused through a feature fusion block (FFB) to achieve overall image enhancement. The enhanced image [\(\hat{I}_{Y}^{E}\), \(\hat{I}_{CbCr}^{E}\)] remains in the \(YCbCr\) space. The final enhanced RGB image \(\hat{I}_{RGB}^{E}\) is obtained by performing a color space conversion.  
Figure 3: Overall framework of DBLCANet-HCIE.
# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 3 Results
### Experimental Setup
(3) Comparison with state-of-the-art multimodal algorithms: To assess the classification effectiveness of the IFF-Net network proposed in this study, we compare it quantitatively and qualitatively with several advanced algorithms for multichannel remote sensing data classification. These algorithms include support vector machine (SVM) [39], CapsNet [40], S2 FL [25], LeMA [21], and Two-branch CNN [26].
## 5 Conclusions  
The proposed decentralized control using aerodynamic forces allows the satellites to achieve the required relative satellite configuration, as demonstrated for a particular case in this paper. The performance numerical study showed that in the case of 2 \(\times\) 2 m\({}^{2}\) sunlight reflectors, used as aerodynamic wings in LEO, the satellites converge to the reference trajectories in about 30 h by changing their attitude relative to the incoming airflow at the initial orbit height of 350 km. On the one hand, the change in the orbit height during the maneuvering leads to accelerated orbit decay, and the lifetime of a mission using only aerodynamic control is limited by about a month. On the other hand, this can be considered as an opportunity to extend a regular mission lifetime after some of its satellites have run out of fuel. As a continuation of the presented study, the controlled motion performance should be investigated, taking into account disturbances caused by the uncertainties in atmospheric density, control implementation errors, and relative navigation features.  
Numerical study, K.C.; algorithm development, U.M.; results validation, Y.M.; formal analysis, S.B.; supervision, D.P.; project administration and writing, D.I. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Russian Science Foundation, grant No 22-21-00845, [[https://rsc.ru/en/project/22-21-00845/](https://rsc.ru/en/project/22-21-00845/)]([https://rsc.ru/en/project/22-21-00845/](https://rsc.ru/en/project/22-21-00845/)) (accessed on 8 September 2023).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Adaptive Thermal-Aware Routing Protocol for Wireless Body Area Network
## 4 Performance Evaluation
### Simulation Results
#### 4.1.2 Analysis of Average Temperature Rise in Node  
The comparisons between ATAR, SHR [26], TARA [25] and MRRP [47] are shown in Figure 16. The simulation results indicate that initially the temperature of all nodes was the same, i.e., 35 \({}^{\circ}\)C/0.035 \({}^{\circ}\)C. The simulations are performed for the time period of 1000-6000 s. Initially, the ATAR started with the reading of 0.037 \({}^{\circ}\)C at 1000 second and later gave a maximum reading of 0.039 \({}^{\circ}\)C at 6000 s. The MRRP initially started from 0.048 \({}^{\circ}\)C and at 6000 s it gave a maximum reading of 0.102 \({}^{\circ}\)C. TARA initially started from 0.030 \({}^{\circ}\)C and at 6000 s it gave a maximum reading of 0.080 \({}^{\circ}\)C. Similarly, SHR initially started from 0.040 \({}^{\circ}\)C and at 6000 s it gave a maximum reading of 0.11 \({}^{\circ}\)C. The high-temperature reading coming from MRRP, TARA and SHR protocol indicates that it is also not suitable for implanted nodes. Finally, MRRP, TARA, SHR provides temperature peaks while transmitting data of sensor nodes thus ignoring the temperature factor in its working; whereas the ATAR takes temperature factor in its routing in the implanted nodes.  
Figure 14: Temperature rise versus simulation time = 3000 s.  
Figure 15: Temperature variation versus simulation time.  
The low-temperature peaks for ATAR in the simulation is due to the reason that ATAR always selects the forward with minimum available temperature. In case of temperature rise of the forwarder, ATAR selects another forwarder within the next ring level to forward obtained sensor readings.  
Figure 17 represents the comparison of temperature rise of ATAR, SHR [26], TARA [25] and MRRP [47]. It can be seen that even if we increase the node density, the performance in terms of average rise in temperature of the ATAR remains far better as compared to the MRRP. If we scrutinize the MRRP graph, the increase in nodes also causes the temperature to rise at 39.002 \({}^{\circ}\)C and it was dangerously high at 48.727 \({}^{\circ}\)C at 1000 s that is dangerous for a normal human body. On the other hand, the ATAR has a consistent temperature even when the node density is increased at 1000 s. Therefore, we can conclude that the ATAR is far better in terms of rise of temperature for node density increase.  
Figure 16: Average temperature rise versus simulation time.  
Figure 17: Average temperature with different nodes density at simulation time = 1000 s.
# An IoT System for Remote Monitoring of Patients at Home
## 8 Conclusions  
In this paper, a remote monitoring system for patients at home in IoT environments is proposed, constructed, and evaluated through several experiments. To prepare for the massive number of PHDs needed, the system was designed as multilayered. To use PHDs as objects in an IoT system based on the oneM2M protocol, a protocol conversion process between ISO/IEEE 11073 protocol and oneM2M protocol was proposed and implemented. In addition, a multiclass data communication scheme was proposed and implemented based on the urgency of delivery to medical staff. For the protection of a patient's privacy, two security schemes are proposed in this study. First, a patient's biomedical data obtained by a PHD are not stored as a single unit, but stored in parts in the IoT server. Furthermore, the separated information is stored in the IoT authentication server. In this way, an attacker would need to successfully breach both servers simultaneously to access the data. Second, an authentication scheme called the Buddy-ACK authorization scheme is proposed in this study. In the Buddy-ACK authorization scheme, a specific piece of biomedical data can be accessed only after both a patient and the related medical staff are authorized.  
The experiments on the constructed system showed that the system worked well; the protocol conversion process worked efficiently for IoT environments. The experiments also showed the MQL scheduling algorithm proposed in this study transmits urgent messages immediately, and transmits non-urgent messages later, when they will not interfere with urgent message transmissions. In addition, the MQL scheduling algorithm performs better than the MBDP scheduling algorithm. We also found that the throughputs of the MQL algorithm increase almost linearly as the measurement time increases, whereas the throughputs of the MBDP algorithm increase with decreases in the increasing ratio. This is partially because the MQL algorithm performs significantly better than the MBDP algorithm, especially when the number of messages waiting to be scheduled becomes quite large. In addition, despite the heavy traffic, the alarm signals of the highest class were immediately pushed to medical staffs due to the data classification scheme.  
This research was supported by the Basic Science Research Programs grant through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (No. NRF-2015R1D1A3A03019278).  
[PERSON] conceived the ideas proposed in this paper and designed the Buddy-ACK authentication scheme. [PERSON] designed the MQL algorithm and performed the related experiments. [PERSON] designed the protocol conversion process and performed the related experiments. Finally, [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 19: Captured screens of registration (Conversion of an ISO/IEEE 11073 Present (Store Sensing Data) Request message into a oneM2M Update primitive Request message).
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
### Research Questions  
To improve RPL security against internal attacks, this work poses the following research questions (RQs) based on the research gaps found and discussed in Section 2.1:  
* How can security in RPL be improved to mitigate internal attacks while maintaining the mobility of the network?
* How can we ensure that data are gathered securely to protect RPL-based networks against internal attacks?
* In support of RQ1, can we minimize network congestion (overhead due to the number of messages exchanged) to improve network lifetime and minimize energy, memory, and computational overheads at the device level in RPL-based infrastructures?
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 4 The Immersive Video ME App
### The i-EVS ME App  
The i-EVS ME app consists of three VNF components (VNFCs), namely the \"VTU\" Media Engine (ME), the \"eSTORE\" repository, and the \"dB\" database.
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 1 Introduction
Consequently, the possibilities of the operational framework have also been extended, and simple measurement and reporting have given way to advanced AI-based designs for various advanced features, such as tampering detection, non-intrusive load monitoring, peak shaving, grid estimation, etc. Thus, the architecture and design of the smart meter have become varied because of the application needs that vary over the functional domains. In a similar vein, the evolution of computational hardware brings with it the flexibility and the resultant complexity in the configuration and software. To review the computational infrastructure in the smart meter, it is again pertinent to compare it with the evolution of computational devices, making it possible to point out the obvious gaps and the missed opportunities. Communication systems have evolved to the point of a paradigm shift. With the introduction of 5G, hitherto un-explored concepts have made their way into cellular communications, opening new paradigms, especially in configurability and programmability, which can be optimized on the fly according to the underlying situation and application. Integrating such communication facilities into smart meters can open new vistas in applications. Finally, the complexity of the underlying hardware has led to the introduction of software stacks to simplify the development and operation of the sensing node. These include operating systems, development environments, cross-chain development setups, programming languages and runtimes, special purpose libraries, and stacks for communication protocols, which make the increasingly complex tasks of developing an IoT-based node simpler. A review of such tools in the field of smart meters is warranted owing to the increasing complexity and broad application spectrum. The nature of the smart meter and its functionality in smart energy are presented in Figure 2.  
While the smart meter has to evolve from a simple reading and reporting design and there are numerous application scenarios which rely on the integration of the sensing of different energy variables, computation, and communication technologies at the smart metering level, there is a lack of a review that presents an integrated and holistic state of  
Figure 2: Functionality and nature of smart meter: (a) smart meter as combination of sensing, hardware, and software components; (b) functionality of smart meter according to the location in IoT stack; (c) functionality of smart meter according to the location in the grid. the art in the smart metering paradigm. Table 1 lists the different reviews on smart meters and the applications. Though the list is not exhaustive, it is indeed a representative one, and in general, the reviews report either a single technology or an application.
# Error Probability of a Coherent \(M\)-ary PSK FSO System Influenced by Phase Noise
## 1 Introduction
### Structure  
Section 2 presents the system, channel, and receiver model. Section 3 contains four subsections. In the first part of Section 3, we derive novel expressions for Fourier coefficients in representing the PDF of the received signal phase. These coefficients are further incorporated into the formula for SEP presented in the second part of Section 3. The third part of Section 3 presents convergence analysis of the formula for SEP. In the fourth part of Section 3, we illustrate that our approach is convenient for asymptotic analysis and derives a simple formula for estimating the SEP floor. Numerical results with appropriate comments are presented in Section 4, while some concluding remarks are given in Section 5.
## 2 Acquisition and Characterization of Pore Structure
### Nuclear Magnetic Resonance  
Nuclear magnetic resonance (NMR) is a physical process in which atomic nuclei with non-zero magnetic moments undergo spin energy level Zeeman splitting under the influence of an external magnetic field and resonate by absorbing radiofrequency radiation at a specific frequency [33]. In 1956, [PERSON] and [PERSON] discovered that the NMR relaxation times of fluids in a free state and those confined in rock pores differ significantly, with the former being much longer than the latter. This finding indicated that the pore size in which the fluid is present has a significant impact on its relaxation time. For rock samples, since the solid components of the sample generally do not contain hydrogen atoms, and the magnetic sensitivity of other common elements in the pores is negligible compared to that of hydrogen nuclei, the hydrogen nuclei in the pore fluids of rock cores are the main subjects in NMR testing [34].  
Currently, the nuclear magnetic resonance (NMR) technique used for pore structure characterization generally refers to low-field NMR analysis conducted under magnetic field strengths below 1T. It provides parameters that reflect the structural characteristics of a sample by measuring different relaxation times. For rock materials, a large amount of experimental and theoretical research has shown that when the surface characteristics of the solid and the properties of the fluid are similar, the differences in relaxation time T2 reflect the differences in the pore structure of the rock sample (pore size) [35]. In general, the larger the pore size, the greater the number of hydrogen nuclei present, resulting in slower decay of the detected NMR signal, which manifests as longer relaxation times on the T2 spectrum.  
This method allows for the precise characterization of the full-size pore structure in a non-destructive manner. However, NMR signals are easily interfered with, and the accuracy of the measurement results is greatly influenced by the analysis of the NMR signals and relaxation times.
## 6 Conclusions  
This paper proposes the CoMP scheme with the pre-coding scheme in order to improve reliability and mitigate the interference in the HetNet system based on OFDM. The proposed CoMP scheme  
Figure 10: BER performance of the proposed scheme and conventional system according to the mobile’s location.  
improves diversity gain, and mitigates the interference signal by adaptively using the constructive and destructive SPC scheme according to the mobile's location. Therefore, the SNR of the desired signal is increased, and the SNR of the interference signal is reduced, thereby the reception performance of the mobile is improved. In addition, because the proposed CoMP scheme does not use a relay, interference can be mitigated without additional transmission rate reduction, security problems, or error propagations. However, in order to mitigate interference, the proposed scheme requires pre-coding vectors according to the channel relationship between mobile and base station.  
Several topics serve as background research for the HetNet system. Such research includes energy efficiency and optimization issues. Energy-efficiency for real-time vehicular cloud services, allocation of virtual elements (VEs) for minimizing the energy consumption and routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks are proposed in [21; 22; 23] respectively. The analysis of robust optimization under multi-band uncertainty is included in [24]. In addition, [25] proposes a hybrid system based on genetic neural computing (GNC). Because the GNC is a hot topic, it can be considered as a research issue of the HetNet system. Therefore, we plan to study more efficient interference mitigation schemes through the joint transmission (JT) scheme--which is one of the CoMP types--, beamforming scheme, and power allocation for energy efficiency. The simulation results show that the BER performance of the proposed scheme is better than that of conventional schemes. As a result, the proposed CoMP scheme can improve the reliability of the HetNet system.  
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00217, Development of Immersive Signage Based on Variable Transparency and Multiple Layers).  
[PERSON] proposed an adaptively CoMP scheme with spatial phase coding for interference mitigation in the HetNet system and performed computational simulations; [PERSON] and [PERSON] performed simulation of the conventional system and schemes; [PERSON] gave feedbacks about a modified algorithm and all simulation results. Also, [PERSON] provided the experimental materials for better computational simulations and revised critical errors of the manuscript.  
The authors declare no conflict of interest.
# Studies on a Piezoelectric Cylindrical Transducer for Borehole Dipole Acoustic Measurements
## 2 Principles and Numerical Analyses
### Input Electrical Conductance in Air  
The admittance can be calculated by using the following formula from ref [18]:  
\[Y=\frac{dQ/dt}{V}=j\omega\frac{Q}{V}=j2\pi f\frac{Q}{V}, \tag{8}\]  
where \(Q\) is the electric charge; \(V\) is the excited voltage; \(\omega\) is the angular frequency; and \(f\) is the frequency. The conductance \(G\) and susceptance \(B\) are the real and imaginary parts of \(Y\), respectively.  
The numerical simulation result of the conductance curve of the transducer in air is shown in Figure 5. There are four resonance peaks in the curve. Their corresponding frequencies are 504 Hz, 1012 Hz, 3700 Hz and 5016 Hz, respectively. The conductance values at the four resonant frequencies are 54, 691, 987 and 975 uS, respectively. The conductance amplitudes at 1012 Hz, 3700 Hz and 5016 Hz are much larger than that at 504 Hz, which explains that the latter three modes are more easily excited than the first one.
# Energy Optimisation of Industrial Limestone Grinding Using ANN
### Evolution of Intelligent Control in Mineral Processing  
The first wave of \"intelligent\" controllers relied on expert-system shells that encoded heuristics from senior metallurgists. With the rise of inexpensive computing power, researchers began training shallow artificial neural networks (ANN) to emulate plant responses. Early studies by [PERSON] & [PERSON] [17] and [PERSON] et al. [18] demonstrated that multilayer perceptrons could predict grind size and flotation recovery with errors lower than linear or polynomial regressions. More recent investigations applied deep LSTM architectures to capture longer-horizon dynamics and incorporate machine-vision signals, achieving superior set-point tracking under variable ore blends [19]. Nevertheless, these data-driven approaches are often criticised for their \"black-box\" nature and the difficulty of embedding hard process constraints--issues that hybrid models, evolutionary multi-objective optimisation (EMOO) and physics-informed networks attempt to address. Recent studies have further demonstrated the effectiveness of neural networks in modelling and optimising grinding processes. For instance, ref. [20] applied a hybrid LSTM-MLP model with NSGA-II to optimise robotic grinding, achieving superior trade-offs between energy use and surface quality. The ANN-based prediction of particle size distribution was shown to be accurate across varying conditions in [21]. A comprehensive framework for the modelling, control, and optimisation of an industrial grinding circuit using neural networks was introduced in [22]. These works highlight the adaptability of deep and hybrid architectures to nonlinear, delayed, and multivariable systems.