esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spectral Analysis of Field Data  
Discrete-time data, which are usually in sequence and contain a series of data points, are indexed in time order. Recorded GPR signal data that follow this trend are classified as time series data. They are considered to be taken at successive equally spaced points with the change of time. Thus, their representations are frequently plotted on curves in the time domain such that details culminating in the materials that gave rise to such signals are not known. Although a series of processing such as filtering, correlation, and enhancement may improve the analysis of the data, details about the inherent information may still be concealed. Hence, the concept of spectral analysis may provide insight into the variations in properties of what make the recorded data [38].  
Spectral analysis involves several methodologies that evaluate time series data in terms of frequency contents. It commences with the breaking up of the data often referred to as'spectral decomposition of time series data', which is the partitioning of the variance of the time series in terms of frequency [38]. There are various methods used to decompose time series data that are computed on the waveform of the data and, in the case of GPR, on the traces [39]. The most commonly used include Fourier transform, Wavelet transform, Wigner-Ville distribution, and Empirical Mode decomposition. Following the decomposition of the signal, the analysis of the frequency components is made, which may reveal details about the signal. Wavelet transform (WT) was used for the decomposition of the processed field GPR signal data in this study. This was informed by the better performance of such transform in non-stationary signals such as the GPR signal [40].
# Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology
###### Abstract  
This paper presents a study that analyses the effect of financing costs on grid parity in photovoltaic (PV) installations by applying a probabilistic methodology. Three different case studies, located in Spain, have been considered, with 500 kW, 50 kW and 5 kW grid-connected PV generators. The technical and economic calculations were performed, considering the interest rate, yield across the Spanish geography, and PV module cost as parameters. The Monte Carlo method was applied to consider the full probabilistic range of values given to the different variables. The goal of this study was to determine, for the studied cases, the levelised cost of energy (LCOE) and the internal rate of return by considering realistic values of the variables. A success rate parameter was calculated, which determined the likelihood of the number of times that the LCOE was below the retail cost of electricity. All the cases were evaluated by applying 10,000 iterations, considering the standard deviations and means defined.  
Keywords: photovoltaic (PV) tariffs; remuneration policies; grid parity +
Footnote †: journal: Applied Sciences
# Multi-Tier Heterogeneous Beam Management for Future Indoor FSO Networks
## 2 Background and Motivation
### Background and Existing Literature  
Fiber optic and FSO communications are two proven high-data-rate technologies, each with successful commercial adoption; however, both are typically used with persistent point-to-point links. Indoor optical wireless communications technologies (e.g., visible light communications or LiFi) are also beginning to see commercial acceptance; however, these systems are typically static emission systems without beam steering due to lighting requirements in the common dual-use paradigm (i.e., systems that provide both data communications and indoor illumination). Furthermore, these systems do not approach the extreme directionality of pencil-beam FSO links. Thus, novel architectures and protocols are needed at the system level in order to address the challenge of connecting mobile  
Figure 2: Generalized evaluation of link technology characteristics (**left**) and a context-of-use based analysis of preferred link technologies (**right**). We consider VLC as LED-based systems for both lighting and communications. FSO is broadly categorized as fixed building-to-building FSO or short range steerable FSO for indoor communications. _Range_ directly related to potential coverage. _Resolution_ is inversely related to instantaneous coverage. Absence of \(+\) or \(-\) indicates that the technology is typically non-steerable.  
and quasi-static users in dynamic environments using highly directional optical beams. At the scale of cellular networks, heterogeneous beam management techniques have been introduced in the literature in order to provide a method of coordination between macrocell and microcell base stations [27; 28; 29; 30; 31]. However, this heterogeneity is typically considered in the context of scale rather than wireless technology. In other words, the work referenced in [27; 28; 29; 30; 31] primarily considers spatial tiers of similar RF mmWave technologies that are collectively managed to avoid interference. Our vision considers the heterogeneous integration of microwave, mmWave, and Indoor FSO technologies at the scale of indoor wireless networks. Considering optimal tier alignment, mobility-aware networks have also been presented at the cellular level in order to assign resources based on mobility context [32; 33]; however the scale of indoor FSO networks implies the need for novel methods that account for translational and rotational motion at higher resolution. Work in the area of heterogeneous radio and optical wireless networks has similarly introduced the idea of mobility aware or context aware resource allocation [26; 34; 35]; however, this has typically applied to the distribution of traffic across static RF small cells and static directional optical cells.
## 3 Results and Discussion  
### Hygroscopicity  
Significant differences were found among the modified and unmodified specimens. Specimens with 60% air humidity were compared, modified, and referenced during this process. The multiple comparison was determined using Scheffe test. The decrease in EMC between the groups was statistically significant. The specimens that were modified at a temperature of 180 \({}^{\circ}\)C showed an approximately 36% lower EMC than the unmodified specimens. The specimens that were modified at 200 \({}^{\circ}\)C showed an approximately 42% decrease in water content. The results are shown in Figure 4. The EMC reduction is related to the time and temperature of the process in correspondence to [4; 24; 25]. [PERSON] et al. [24] found reduction of EMC by 25% at 180 \({}^{\circ}\)C and 41% at 230 \({}^{\circ}\)C for black pine wood (_Pinus nigra_). [PERSON] et al. [25] found 18% reduction of EMC at 8-h/180 \({}^{\circ}\)C modification and 51% reduction of EMC at 8-h/230 \({}^{\circ}\)C modification of calabrian pine wood (_Pinus bratia_ Ten.). The EMC evaluated after modification at 180 \({}^{\circ}\)C are slightly higher in comparison with above mentioned results; however, higher temperature of modification brought results comparable to other researchers. The slight differences may reflect the wood species individuality (e.g., higher number of extractives in pine wood) as well as the differences in duration and temperature of treatment processes.  
Figure 4: EMC of specimens: group no.1—unmodified specimens; group no.2—specimens modified at 180 \({}^{\circ}\)C; group no.3—specimens modified at 200 \({}^{\circ}\)C. EMC at 60% air humidity. \({}^{\circ}\)—outlier values.
## 2 Route Network Construction
### Route Network Model  
The aviation network topology model \(G(V,E,W)\) is a network consisting of military and civil airports, navigation points, civil air routes, and temporary routes for military aviation. The node \(\mathrm{v}\) in the topological model denotes the airports and navigation stations, identified as the set \(V=\{v_{1},v_{2},\cdots,v_{i}\}\), whose number is \(i\); the edge \(\mathrm{e}\) denotes the transportation relationship between airports and airports, between airports and navigation points, and between navigation points and navigation points, i.e., the airway routes. If the actual operating airway route in the airspace passes through the airport or navigation point, it is considered that there is a connecting edge between the nodes; otherwise, there is no connecting edge; the set \(E=\{e_{1},e_{2},\cdots e_{j}\}\) is the set of edges, and its number is \(j\). The weights on the connected edges \(w_{j}\) are set as the combined weights of four indicators, namely, route saturation, meteorological conditions, flight delays, and military aviation activities, which reflect the operation situation of the aviation network and are determined as the set \(W=\{w_{1},w_{2},\cdots,w_{j}\}\).  
East China includes many large cities such as Shanghai, Nanjing, Suzhou, Ningbo, etc., with rich economic and trade activities and tourist attractions, while the route network in this region is more complicated. Further, 104 real airports and navigation points in East China were screened as target nodes on 28 April 2024 from the 2023 China Civil Aviation Route Route Data from Civil Aviation Network ([[https://www.ccaonline.cn/](https://www.ccaonline.cn/)]([https://www.ccaonline.cn/](https://www.ccaonline.cn/)), 21 October 2024). The complex network analysis tool Gephi (a powerful open-source network analysis and visualization tool for processing and displaying various types of complex networks) was applied. It is able to map the relevant network through data import and calculate various attributes of the network, including the degree of nodes, centrality, PageRank, clustering coefficient, etc. Input the geographic coordinates of each node and map the route network of East China, as shown in Figure 1.  
Based on the connectivity relationship between airports and navigation point nodes, the East China route network contains a total of 104 nodes and 195 edges. Nodes denote airports/navigation points/mandatory reporting points, and connecting edges are the connection relationship between two nodes.  
Figure 1: Eastern China Route Network Schematic.
###### Abstract  
Hybrid composite pile foundations face critical challenges in terms of optimizing load transfer mechanisms across variable soil densities, particularly in regions like Kano, Nigeria, characterized by loose to dense sandy deposits and fluctuating groundwater levels. This study addresses the need for sustainable, high-performance foundation systems that are adaptable to diverse geotechnical conditions. The research evaluates the mechanical behavior of steel-concrete and timber-concrete hybrid piles, quantifying skin friction dynamics, combining eight (8) classical ultimate bearing capacity (UBC) methods (Vesic, Hansen, Coyle and Castello, etc.) with numerical simulations, and assessing load distribution across sand relative densities (10%, 35%, 50%, 75%, 95%). Laboratory investigations included the geotechnical characterization of Wudil River well-graded sand (SW), direct shear tests, and interface shear tests on composite materials. Relative densities were calibrated using electro-pneumatic compaction. Increasing Dr from 10% to 95% reduced void ratios (0.886-0.476) and permeability (0.01-0.0001 cm/s) while elevating dry unit weight (14.1-18.0 kN/m\({}^{3}\)). Skin friction angles rose from 12.8\({}^{\circ}\) (steel-concrete) to 37.4\({}^{\circ}\) (timber-concrete) at Dr = 95%, with timber interfaces outperforming steel by 7.4\({}^{\circ}\) at Dr = 10%. UBC for steel-concrete piles spanned from 353.1 kN ([PERSON], Dr = 10%) to 14,379 kN ([PERSON], Dr = 95%), while timber-concrete systems achieved 9537.5 kN ([PERSON], Dr = 95%). PLAXIS simulations aligned closely with Vesic's predictions (14,202 vs. 14,379 kN). The study underscores the significance of soil density, material interfaces, and method selection in foundation design.  
hybrid composite piles; ultimate bearing capacity; numerical simulation (PLAXIS 3D); skin friction angle; interface shear test; relative density effects +
Footnote †: journal: Applied sciences
# Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network
## 2 System Model  
### Block Diagram  
The conceptual block diagram of the secured cooperative UAV-based CP-less GFDM system is shown in Figure 2. In its ground segment section, initial significant signal processing operations are executed on the ground BS. Ground BS is set to be comprised of \(N_{T}\) (=16) transmitting antennas, which should serve eight ground mMTC users via one main UAV and two cooperative UAVs, and all the UAVs are \(N_{T}\times N_{T}\) multi-antenna configured. The synthetically generated binary data assigned for individual mMTC users are processed with concatenated channel-coding schemes based on (3,2) single parity check (SPC), low density parity check (LDPC), and repeat and accumulate (RA) [22; 23] and subsequently encrypted with improved 3D controlled Lorenz mapping-based encryption technique [10].  
Initially, complex symbols are produced via the digital modulation of the encrypted binary data [24]. After that, the generated complex symbols undergo T-transformation with a combined effect of the WHT and DFT [25] and zero padd  
Figure 2: Simplified block diagram of secured cooperative UAV-aided CP-less GFDM system.  
of the GFDM modulation. In the GFDM modulation, the zero padded complex symbols are circularly convolved with the BTRC filter, followed by frequency up conversion [16; 26]. The GFDM modulated symbols are power scaled and subsequently precoded with channel-dependent multi-user transmit GP schemes [21]. The transmission of the spatially multiplexed precoded signals from the ground BS to the main UAV with ending signal processing operations in the ground segment section of this proposed system.  
In the UAV segment, the main UAV decodes the transmitted signal with the ZF linear signal detection technique [27], and the power of the detected signal is boosted up to make it compatible with the power of the ground BS. In the first time phase, the main UAV transmits the detected signal simultaneously to the cooperative UAVs working in half-duplex mode and the eight ground mMTC users, each of which is equipped with \(N_{R}\) (=2) receiving antennas. In the second time phase, the cooperative UAVs simply adopt the amplify-and-forward (AF) relaying strategy [28] and forwards the signal to the ground mMTC users with identical transmission power of the main UAV. On the basis of the estimated effective channel between the UAVs and the individual mMTC user in two consecutive time phases and their respective beamforming weight, mMTC user's own transmitted signal is detected at the receiving end of each mMTC user [29]. The detected signals are power scaled again to be restored at the original power. The GFDM demodulation process is then initiated by the removal of the padded zeros, and the multiplication with inverse T-transformation process yields despreaded signals. Despreaded signals are then passed through the phases of digital demodulation, channel decoding, and improved 3D controlled Lorenz mapping-based decryption to eventually recover the transmitted data.
## 2 Theory
### Energy Factor  
Some scholars have proposed the ratio of the Lamb wave energy after and before the stiffener as the transmission coefficient to characterize the effect of the stiffener on the Lamb wave energy, but they ignored the influence of the propagation distance [15]. This paper proposes a new factor--energy factor--which represents the ratio of the Lamb wave energy at the same distance after propagation in the stiffened panel and the flat panel. The energy factor can be expressed as Equation (3).  
\[R(f)=\frac{E_{S}(f)}{E_{P}(f)}, \tag{3}\]  
where \(R(f)\) is the energy factor, \(E_{S}(f)\) is the energy of the Lamb wave of frequency \(f\) after passing through the stiffener in the stiffened panel, \(E_{P}(f)\) is the energy of the Lamb wave of frequency \(f\) after propagating the same distance in the flat panel.  
The total energy of the discrete signal in the frequency domain is equal to the sum of the squares of the spectra at each frequency. The energy of the discrete signal at frequency \(f\) is the square of the spectrum at frequency \(f\), which is calculated by Equation (4).  
\[E(f)=\left|A(f)\right|^{2}, \tag{4}\]  
where \(E(f)\) is the energy of the Lamb wave of frequency \(f\), \(A(f)\) is the spectrum of the signal at frequency \(f\).  
Since the effect of the stiffeners on different frequency Lamb waves is different, the energy factor curve fluctuates in the frequency domain. Selecting the appropriate frequency band for positioning can improve the accuracy and stability of the algorithm.  
Figure 2: Dispersion curves for 3 mm-thick 5A06 aluminum plate: (**a**) phase velocity; (**b**) group velocity.
## 5 Discussion
### Infographic Solutions for Procedural Overview  
To showcase the procedural complexity of integrating AI, IoT, and digital twins in predictive maintenance, we have designed a step-by-step infographic (Figure 9). This visual representation underscores how NLP-driven literature analysis--combined with expert evaluations--can iteratively refine the identification of high-impact strategies for industrial deployment.  
As shown in Figure 9, the cycle begins with _NLP-based Literature Survey_, extracting key themes and emerging methodologies from a large corpus of studies. Next, domain experts refine these findings (_Expert Feedback_), aligning proposed solutions with real-world constraints, such as network latency or sensor durability [40; 67]. The _IA + IoT Integration_ stage focuses on deploying sensor networks and AI inference modules--often at the network edge--to reduce latency [68]. Real-time data streams enable dynamic reconfiguration (_Real-time Monitoring_) and augment _Digital Twin Simulation_ for predictive scenarios, eventually culminating in robust _Fault Diagnosis_ that guides maintenance operations in harsh mining conditions [68; 84]. By closing the loop with ongoing data collection, the system iteratively refines fault detection accuracy and operational efficiency.  
**Relevance of Infographic Strategy.** These step-by-step visual representations highlight the complexities and interdependencies inherent in modern predictive maintenance, from data ingestion to final fault detection. Researchers and practitioners benefit from a streamlined, high-level roadmap that articulates each major phase, clarifying how individual components (e.g., NLP-based knowledge extraction, digital twins, human expertise) interlock to deliver a cohesive predictive maintenance solution.  
References and Use Cases  
* IoT Integration and Real-time Monitoring: [PERSON] et al. [40] and [PERSON] et al. [67] emphasize the importance of network architecture design for capturing continuous equipment data with minimal latency.  
Figure 9: Infographic demonstrating the iterative process of integrating AI, IoT, and digital twins in predictive maintenance workflows, informed by NLP-derived literature analyses and expert domain reviews.  
* Digital Twin Simulation: [PERSON] et al. [2], [PERSON] et al. [84], and [PERSON] [68] illustrate multi-agent digital twin frameworks that dynamically adjust model parameters based on sensor streams, improving both reliability and cost-effectiveness.
* Expert-driven Validation: [PERSON] [3] and [PERSON] et al. [4] demonstrate that expert input can rectify misclassifications from purely data-driven models, especially in edge cases (e.g., rare fault types, novel sensor setups).  
In summary, harnessing tables, charts, and infographics provides multiple dimensions for understanding predictive maintenance strategies. Summarized tables enable quick comparisons of AI performance metrics, while comparative charts illustrate which techniques excel in particular operational contexts. Finally, infographics clarify the procedural integration of IA, IoT, and digital twins, culminating in robust end-to-end solutions.
###### Abstract  
The Internet of Things (IoT) is transforming industries by integrating sensors and connectivity into everyday objects, enabling enhanced monitoring, management, and automation through Machine-to-Machine (M2M) communication. Despite these advancements, the IoT faces limitations in accurately predicting environmental conditions and power consumption. This study proposes an advanced IoT platform that combines real-time data collection with secure transmission and forecasting using a hybrid Long Short-Term Memory (LSTM)-Gated Recurrent Unit (GRU) model. The hybrid architecture addresses the computational inefficiencies of LSTM and the short-term dependency challenges of GRU, achieving improved accuracy and efficiency in time-series forecasting. For all prediction use cases, the model achieves a Maximum Mean Absolute Error (MAE) of 3.78%, Root Mean Square Error (RMSE) of 8.15%, and a minimum \(R^{2}\) score of 82.04%, the showing proposed model's superiority for real-life use cases. Furthermore, a comparative analysis also shows the performance of the proposed model outperforms standalone LSTM and GRU models, enhancing the IoT's reliability in real-time environmental and power forecasting.  
Internet of Things (IoT); power forecasting; hybrid LSTM-GRU; secure data transmission; environmental monitoring +
Footnote †: journal: applied sciences  
Article
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 3 Methods
### Neighbor Discovery Mechanism  
#### 3.2.1 Low-Duty Cycle  
In a WSN, nodes must be in a listening state to receive the data from the neighbor node. However, nodes are in idle listening state for most of the time, and the time used for data transmission is often very short, while idle listening causes a lot of invalid energy consumption. Therefore, in many practical applications, nodes adopt low-power communication chips with alternating listening and hibernation modes. These can reduce the unnecessary energy consumption of nodes by replacing idle listening through dormancy.  
Considering the application in practice, the low-duty-cycle mechanism is introduced into WSNs, where duty cycle is the ratio of periodic dormancy to activity time, and low-duty cycle means that the duty cycle is not greater than 10%. When the node works in the duty cycle mode, the time will be divided into the time slot of the same size. At a certain number of time slots, the RF will enter the active state that can send and receive data, and the RF will be turned off and will enter the dormant state after the time slot ends. The low-duty-cycle mechanism greatly extends the life cycle of the network and reduces the energy consumption caused by idle listening.  
Under the low-duty-cycle mechanism, nodes are in sleep state for most of the time, but nodes are required to open RF as much as possible to communicate with other nodes in order to quickly complete the mutual discovery between nodes. Therefore, saving energy consumption and reducing the detection delay are contradictory, and it is very necessary to study the neighbor discovery problem in the low-duty-cycle wireless sensor networks.