esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed DanRETwin Solution
### Technical Approach  
#### 4.2.1 Machine Learning-Based Data-Driven Energy Models  
When it comes to renovating older buildings, particularly those that are 50 years old or older, it is often the case that we have limited knowledge about the building itself. This is mainly due to inadequate documentation from the time it was constructed or because the existing documentation was lost over the years. Consequently, renovation teams are forced to make significant assumptions, some of which may be inaccurate or unreliable. As a result, decision-making in the renovation process becomes suboptimal, leading to a significant disparity between the expected outcomes of the retrofitting project and the actual results. In order to address this challenge, data-driven black-box models emerged as a crucial factor in optimizing energy retrofits and expediting the pace and scale of these endeavors. These models rely on simple yet effective algorithms that leverage the power of advanced artificial intelligence to optimize performance indicators and identify data patterns. As a result, data-driven models gained considerable attention in recent years as the preferred modeling technique for shaping the digital future.  
In recent years, machine learning (ML) emerged as a widely employed technique for constructing and supporting black-box models of building energy performance. ML refers to computer algorithms that learn from existing data, typically utilizing a large dataset and a relatively small set of input features for the learning process. These ML models function as black boxes, establishing the relationship between selected input building features and the  
Figure 6: DanREtwin solution illustration with the main technology components and connections.  
given energy index without requiring extensive information about the building itself. These input features may not necessarily be raw building characteristics or weather data; they can be complex variables derived from basic ones, such as the wall-to-floor ratio or mean daily global radiation. Numerous studies and applications in the field of building energy increasingly utilized machine learning methods in combination with artificial intelligence techniques, particularly for optimizing building energy retrofits, resulting in demonstrated positive impacts. The choice of the ML technique for this work will depend on the specific requirements gathered from end users and feedback on their needs, which will highlight the essential inputs and outputs of the energy model. However, two prominent techniques were recently successfully employed in energy retrofitting and deep energy renovation optimization applications: artificial neural networks and the decision tree algorithm.
###### Abstract  
In this paper, we propose a multichannel active noise control (ANC) system with an optimal reference microphone selector based on the time difference of arrival (TDOA). A multichannel feedforward ANC system using upstream reference signals can reduce various noises such as broadband noise by arranging reference microphones close to noise sources. However, the noise reduction performance of an ANC system degrades when the noise environment changes, such as the arrival direction. This is because some reference microphones do not satisfy the causality constraint that the unwanted noise propagates to the control point faster than the anti-noise used to cancel the unwanted noise. To solve this problem, we propose a multichannel ANC system with an optimal reference microphone selector. This selector chooses the reference microphones that satisfy the causality constraint based on the TDOA. Some experimental results demonstrate that the proposed system can choose the optimal reference microphones and effectively reduce unwanted acoustic noise.  
Keywords: multichannel ANC system; feedforward control; time difference of arrival; causality constraint +
Footnote †: journal: Applied Sciences
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
###### Abstract  
VANET (Vehicular Ad-hoc Network) is an emerging offshoot of MANETs (Mobile Ad-hoc Networks) with highly mobile nodes. It is envisioned to play a vital role in providing safety communications and commercial applications to the on-road public. Establishing an optimal route for vehicles to send packets to their respective destinations in VANETs is challenging because of quick speed of vehicles, dynamic nature of the network, and intermittent connectivity among nodes. This paper presents a novel position based routing technique called Dynamic Multiple Junction Selection based Routing (DMJSR) for the city environment. The novelty of DMJSR as compared to existing approaches comes from its novel dynamic multiple junction selection mechanism and an improved greedy forwarding mechanism based on one-hop neighbors between the junctions. To the best of our knowledge, it is the first ever attempt to study the impact of multiple junction selection mechanism on routing in VANETs. We present a detailed depiction of our protocol and the improvements it brings as compared to existing routing strategies. The simulation study exhibits that our proposed protocol outperforms the existing protocols like Geographic Source Routing Protocol (GSR), Enhanced Greedy Traffic Aware Routing Protocol (E-GyTAR) and Traffic Flow Oriented Routing Protocol (TFOR) in terms of packet delivery ratio, end-to-end delay, and routing overhead.  
intelligent transportation; multiple junctions; position based routing; optimal route +
Footnote †: journal: Applied  
Article
# A New Method to Verify the Measurement Speed and Accuracy of Frequency Modulated Interferometers
## 1 Introduction
It is noteworthy that all types of displacement interferometers determine target displacement by measuring the phase shift. A pure phase modulation at high frequency can be generated using an electro-optic modulator (EOM). This means that the EOM can be used to induce the phase shift rather than mechanical motions, such as PZT or voice coil actuators, to confirm the measurement speed and accuracy of displacement measuring interferometers. By using an EOM, the phase shift can be generated ranging from several kHz to some tens of MHz without distortion, hysteretic, or vibration. Hence, both the measurement accuracy and speed of the interferometer under assessment can be accurately verified. In the primary experiment, an EOM was placed into an arm of a frequency modulated interferometer. The virtual displacement was implemented using the EOM, in which the phase was changed using a modulation voltage at 20 kHz. Using two lock-in amplifiers (LIAs) and a Lissajous diagram, the phase change in the EOM and the corresponding virtual displacement could be measured using the frequency modulated interferometer. In summary, the main contributions of this paper are listed as follows: (1) the calculation and design of a virtual displacement generated using an EOM; (2) the validation and measurement of the virtual displacement using a frequency modulated interferometer; (3) the comparison of the resulting measurement using the interferometer and theoretical result of the motion. Both the measurement speed and accuracy of the frequency modulated interferometer can be verified using the proposed method. This is significant in high-speed measuring applications, such as spindle error measurement, vibration measurement, and machine tool calibration.
## 3 Routing Issues and Challenges in WBSNs
### Security and Privacy  
Like other applications of WSNs, security and privacy are among the basic requirements of WBSNs. It is impossible to apply the conventional techniques of security and privacy because of the low energy availability, limited resources and other constraints [3]. Researchers should take care of the privacy and security of the patient's data while designing routing protocols for WBSNs.
# An Approach to Data Analysis in 5G Networks
## 2 Background
### SELFNET Project  
The SELFNET H2020 Project [22] aims to provide an autonomic network management framework for 5G mobile network infrastructures through the integration of novel technologies such as SDN, NFV, SON, Cloud computing and Artificial Intelligence. SELFNET enables both autonomic corrective and preventive actions to mitigate existing or potential network problems while providing scalability, extensibility and reduce capital expenditure (capex) and operational expenditure (opex). These capabilities are provided through a layered architecture and a use-case driven approach, as is detailed in [34]. The SELFNET architecture addresses major network management problems including self-protection capabilities against distributed cyber-attacks, self-healing capabilities against network failures, and self-optimization to dynamically improve the performance of the network and the QoE of the users. For this purpose, SELFNET defines two kind of advanced network functions: (i) sensors to monitor specific information from the network and (ii) actuators to address or mitigate possible problems. In particular, the network intelligence is provided by SON Autonomic Layer. This layer collects metrics related with the network behaviour and use that information to infer the network status. Then, it decides the actions to be executed to accomplish the system goals. The SON Autonomic Layer is composed by two sublayers: (i) Monitor and Analyzer Sublayer and (ii) Autonomic Management Sublayer. The Monitor and Analyzer Sublayer follows the Endsley Situational Awareness Principles. Monitoring and Discovery, Aggregation and Correlation and Analyzer modules corresponds with the Perception, Comprehension and Projection functions as is shown in Figure 1.  
Regarding the Analyzer Module, its main goal is to infer data from the monitored metrics in order to facilitate proactive responses over the network infrastructure (i.e., enhance diagnosis and decision-making tasks). Therefore the Analyzer Module is the first step to provide intelligence to the system, where complex conclusions should be reached by reasoning about knowledge provided by the Monitoring/Aggregation stages and the definition of each use case. Because of this, the Analyzer Module distinguishes three great information processing activities: Pattern Recognition, Reasoning and Prediction. The achieved conclusions are described in the form of symptoms related with each use case. Bearing this in mind, it is possible to assert that the Analyzer Module provides a symptom-oriented Situational Awareness bounded by the situations defined for each use case.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 1 Introduction
The work ultimately contributes to QoS improvement of video streaming, security, and network resource optimization. Thus, the framework as presented in this paper seeks to monitor, control, manage, and evaluate QoS without compromising network security. The remainder of this paper is organized as follows: Section 2 discusses mechanisms that have been implemented in literature specifically addressing sinkhole attacks in MANETs, Section 3 presents the proposed framework in detail including the sinkhole attack implementation and the counter-attack intrusion detection system.
# Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB
###### Abstract  
Decentralized databases have gained popularity in the last few years in different areas, such as: traceability, supply chains or finance. Leveraging this type of emerging technology will improve knowledge sharing, as well as the transparency and traceability of the data for digital systems. In a similar way, the characteristics are advertised by the centralized ledger technologies, which are manufactured by large cloud service providers such as Amazon. The present study analyzes the performance of two ledger technologies: BigchainDB (i.e., the decentralized blockchain database) and Amazon QLDB (i.e., the centralized ledger database with transparent and immutable characteristics). For the purposes of comparison, we have integrated these technologies into our traceability platform, which is called the Smart Tracking Platform (STP), and performed a series of experiments enabling us to acquire data for different metrics, such as CPU or memory usage for both the reading and writing operations. The findings of the present study show that QLDB has an overall better performance compared to BigchainDB, based on the metrics that have been considered. From the perspective of database ledger implementation, Amazon QLDB proved to be an integrated solution, easier to use, while BigchainDB comprises a more complex system to be implemented and developed, but is more flexible. Although both systems are almost ready to use solutions for local environments, when it comes to configuration and setting up the communication between nodes within a production environment, BigchainDB adds a layer of complexity from a DevOps perspective, while Amazon QLDB completely overcomes it. Depending on the area considered and the identified needs, both BigchainDB and Amazon QLDB can be considered as suitable solutions for a ledger database.  
ledger technology (LT); decentralized ledger technology; centralized ledger technology; performance; BigchainDB; Amazon Quantum Ledger Database (QLDB) Article
###### Abstract  
Vehicular ad hoc networks (VANETs) have the potential to reduce car accidents by facilitating connectivity and warning message exchange between vehicles, both on roads and in parking lots. This research endeavored to accomplish three primary goals: conducting a field measurement in the parking lot of a large shopping mall in Auckland, developing an OPNET-based simulation model to analyze and validate the system performance, and analyzing the compatibility between five selected radio propagation models (Free-space, Shadowing Path-loss, Egli, Hata, and COST231). These models were selected based on their popularity and relevance to our study. We found that the \"Free Space\" model outperforms in the scenario in which measurements were conducted from the Level-1 car park to the Roadside. The received signal strengths in the parking lot ranged from \(-45\) dBm to \(-92\) dBm. This research also examines the coverage distance for the successful transmission of warning messages, achieving up to \(57\) m, \(17.5\) m, \(9.4\) m, and \(68\) m at parking levels \(1\), \(2\), \(3\), and the roadside, respectively. Research findings reveal that a low-cost Wi-Fi-based VANET system can be utilized to prevent car accidents in parking lots. Finally, we provide guidelines for network planners to deploy Wi-Fi-based VANET systems in parking lots.  
Vehicular ad hoc networks (VANETs); radio propagation models; parking lot; low-cost +
Footnote †: journal: Electronics Electronics  
1
# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
## 4 The ASYM-DBT Method and Thermal Noise Performance Analysis  
Although the B1-ADS signal has characteristics similar to that of the BOC signal, it is very difficult to obtain and generate the true subcarrier waveform due to the two components of B1-ADS with unequal power and different modulation schemes. This renders those reception techniques of BOC signals that need to generate local subcarrier waveforms, such as Bump-Jump [15] and DET [16], impossible to apply to the B1-ADS signal. To tackle this problem, we start with the reception model given in Equation (12), extend the previous Dual BPSK Tracking method [17] designed for optimal reception of the AltBOC signal, and propose a novel Asymmetric Dual-Band Tracking technique to achieve the optimal reception processing of B1-ADS signal, which we abbreviate as ASYM-DBT.
# Full-Field Vibration Measurements by Using High-Speed Two-Dimensional Digital Image Correlation
## 3 Experiment and Result
### Out-of-Plane Displacement Sensitivity Assessment  
An additional set of translation tests were carried out to investigate the sensitivity of the out-of-plane displacement measurement. Since the camera's sensor size and the FOV of the imaging system are predetermined, the measurement sensitivity of the out-of-plane displacement measurement is only associated with the working distance, also known as the object distance, of the HS-2D-DIC system. For validation, three different working distances, i.e., 500 mm, 1000 mm, and 1500 mm, as shown in Figure 5, were selected. To keep a consistent FOV, three different imaging lenses with focal lengths of 25 mm, 50 mm, and 75 mm were used. During the translation tests, the flat plate with a speckle pattern was moved from 0 mm to 1 mm with 0.05 mm intervals and controlled by the translation stage. In each position, a speckle image was captured for the 2D-DIC calculation.  
Figure 4: Experimental results of the (**a**) in-plane translation test and (**b**) out-of-plane translation test.  
Figure 5: Schematic of the out-of-plane displacement sensitivity assessment experiment.  
The measured out-of-plane displacements are shown in Figure 6, in which Figure 6a-c correspond to the results of working distances of 500 mm, 1000 mm, and 1500 mm, respectively. First, it can be seen that the lengths of the error bar increased with the working distance, which means that the displacement sensitivity decreased gradually with the working distance. In addition, the root-mean-square error (RMSE) of each experiment can be estimated by comparing it with the prescribed reference values, as shown in the top right corner of each figure. The RMSE values increased from 33.6 \(\upmu\)m to 41.8 \(\upmu\)m when the working distance increased from 500 mm to 1500 mm, which again indicates the displacement sensitivity decreased with the working distance of the imaging system.
# Optimization and Application of Drilling Parameters Based on Gas-Solid Coupling Simulation
## 2 Coupled Gas-Solid Modeling of Gas Extraction  
### Physical Model  
Gas exists in coal seams through multiple occurrence states. The primary form is the adsorbed state, where gas molecules are tightly bound to micropore surfaces of coal matrices via van der Waals forces, forming monolayer or multilayer adsorption. The secondary form is the free state, where gas resides as a free phase within fractures, pores, and other interconnected voids, its distribution governed by pore connectivity and external pressure. A minimal fraction exists as a dissolved state, dissolved in water or organic matter within the coal seam. The internal fracture network and pore structure of coal collectively form the gas storage and migration system [21; 22].  
Gas migration in coal involves multiple coupled mechanisms. Under undisturbed conditions, adsorbed gas remains in equilibrium with the coal matrix. However, mining-induced disturbances, temperature fluctuations, or pressure changes trigger desorption, converting adsorbed gas into free gas. Free gas migrates via seepage through fractures under pressure gradients, while in micropores or low-permeability zones, diffusion dominates due to concentration gradients [22]. Coal matrix swelling caused by stress redistribution or gas desorption dynamically alters fracture apertures and pore connectivity, creating a \"self-regulating\" effect on migration pathways. Additionally, geological structures (e.g., faults, folds) guide fracture development, significantly influencing gas migration routes, with localized stress concentrations potentially forming preferential flow channels. Thus, gas occurrence and migration represent a dynamic equilibrium governed by adsorption-desorption, diffusion-seepage, coal deformation, and geological conditions.  
To simplify the complex gas migration process, the following assumptions are adopted in this study [23]:  
1. The coal body is a continuous, homogeneous medium, ignoring the inhomogeneity of the microscopic pore structure;
2. The deformation of the coal body is much smaller than its geometric size, ignoring its plastic deformation and large deformation;
3. The mechanical properties of the coal rock body are isotropic, ignoring the anisotropic characteristics of joints and fissures;
4. Only gas flow is considered, ignoring the influence of water or other fluids;
5. Gas seepage in the coal body conforms to [PERSON]'s law, and gas diffusion conforms to [PERSON]'s law;
6. The temperature is constant during gas flow.
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 1 Introduction
Finally, the regularized channel inversion technique was used to derive the theoretical formula for the optimized precoding algorithm. Its SINR expression was obtained. The performance of the communication transmission rate was verified in simulation. The SINR simulation expression of LSAS precoding of massive MIMO system was verified by simulation. The simulation compares the precoding communication rate performance before and after improvement. The simulation results show that reducing the number of antennas has no effect on the communication transmission rate. After applying the improved method to downlink transmission precoding, the communication transmission rate can be effectively improved. The remaining of this article is organized as follows. Section 2 describes the system model used in this paper, including the channel model and orthogonal frequency division multiplexing scheme. Section 3 employs the channel inversion to improve the LSFP zero-forcing precoding, optimizes the precoding algorithm and performs the theoretical analysis based on the improved anti-interference precoding matrix, and lastly, obtains the SINR and channel transmission rate. Section 4 sets the infinite number of antennas in the asymptotic field, and performs the progressive analysis of the improved anti-interference precoding to determine the channel anti-interference transcoding technology combined with the anti-jamming precoding that can produce benefits. Section 5 presents the simulations of the channel transmission rate in the limited antenna field and compares the performance of the improved LSFP precoding and conventional LSFP precoding. Section 6 concludes the paper. In this article, ZF-LSFP, A-ZF-LSFP, O-LSFP, AO-LSFP, MMSE-AO-LSFP represent zero-forcing precoding, advanced zero-forcing precoding, optimal LSFP precoding, advanced optimal LSFP precoding, MMSE pre-equalization advanced optimal LSFP precoding, respectively. Throughout the paper, we use \(\mathbf{A}^{T}\), \(\mathbf{A}^{*}\), \(\mathbf{Tr}[\mathbf{A}]\) and \(A_{(\mathbf{m},\mathbf{n})}\) for matrix transposition, Hermitian transposition, the trace of matrix and the \((\text{m},\text{n})\)-th entry of matrix A, respectively.