esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Whilst these super-harmonics are also impacted upon by other rotor faults, analytical and experimental studies demonstrated that 2X and 3X components are good indicators for the presence of fatigue cracks [2]. Geometry-based approaches [12; 23] utilise breathing models developed using symmetric bending principles and therefore the neutral axis of bending remains horizontal. From Euler-Bernoulli bending theory, symmetric bending occurs when a principal centroidal axis is coincident with the bending moment vector. If no such coincidence occurs then the neutral axis has an angle of inclination relative to the bending moment vector [24], this phenomenon is known as non-symmetric bending. When a crack is open or partially open, the non-cracked area tends to be not symmetric relative to the bending moment vector and so non-symmetric bending principles may be applied. As such, a breathing model based on non-symmetric bending principles was developed herein. In this study, the aim was to develop a new crack breathing model which can consider the shifting of the neutral axis for cracked rotors. Starting to confirm this phenomenon, in Section 2, a case study of a cracked rotor using three-dimensional (3-D) finite element analysis demonstrates that non-symmetrical bending principles should be used when modelling the breathing responses of a crack. Section 3 provides the analytic development of the new crack breathing model with the application of non-symmetrical bending principles. In Section 4, this new analytical model is further implemented into a cracked rotor with two disks and additional eccentric mass for vibration analysis. In both Sections 3 and 4, the proposed analytical and finite element models are both validated by comparing the obtained theoretical results with those using an existing horizontal neutral axis (HNA) model where the neutral axis is assumed to remain horizontal.
# A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things
## 2 Related Works
### Low-Cost Location Scheme  
To propel the location application on a large scale, research on the low-cost RFID-based scheme has been one of the hot topics. Although GNSS and the ultra-wide band (UWB) can obtain a higher accuracy for indoor and outdoor scenarios, the primary disadvantage is the high cost [20; 21]. The most famous low-cost solution is reference tags deployed at fixed positions to replacement the expensive RFID reader [22; 23]. Target tag location uses three or more signals between the target tag and the readers or reference tags, while dense reference tags and a good connectivity RFID network is necessary [8; 24; 25], which requires more investment for a larger number of RFID readers or tags. Two methods have been developed to reduce the investment cost. The first is the tag matrix schema with a number of RFID readers, in which the column and row indexes of tags denotes the tags' locations [26]. However, proximity coordinates are not suitable for infrastructure management. The second approach is to design virtual references replacing the physical reference tags. For example, with a specified number of virtual reference tags between two real adjacent reference tags every 30 cm, an interpolated RSSI reading sequence was generated [8]. Depending on the communication among adjacent tags and virtually dense deployment, higher location accuracy is achieved. However, the position of the neighboring physical reference tags and the number of virtual reference tags need to be carefully designed [27].  
Because the deployment of readers and reference tags and the location accuracy are fixed, the conventional RFID-based location is widely used in small areas or closed spaces such as museums [2], and digital manufacturing workshops [28]. For application in a large area, an easily deployable and low-cost scheme is needed.
# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
###### Abstract
High-resolution reconstruction of the target is achieved with the aid of the phase recovery algorithm. The main contributions of this paper are as follows:  
1. Highly generalizable speckle recovery with better interpretability was achieved by combining basic elements with autocorrelation. 2. Designing neural network structures suitable for stochastic scales and complex targets to enhance the reconstruction performance of the model. Using a two-stage approach (i.e., enhancement and reconstruction) to develop a robust method under different scattering media and interference terms.
## 6 Feasibility Study  
The feasibility of the Smart Money system was evaluated using real-life scenarios where the transactions involve buyers who purchase goods from sellers and where HMRC utilises the Smart Money system for enforcing tax collection and compliance. There are seven classes of goods sold in these transactions, and each class incurs a VAT rate, i.e., adult clothing: 20%, alcohol: 20%, books: 0%, children's clothing: 0%, electrical: 20%, energy: 5%, groceires: 0%. A transaction involves the following entities:  
* A seller named MegaCompany, which is a retailer that sells all the available classes of goods. This seller takes part in MTS through their account, which is held in a Corda node named _SellerCWP_. This node hosts Corda accounts for sellers.
* A buyer named Alice, which is a consumer that may purchase any of the available classes of goods. This buyer takes part in MTS through their account, which is held in a Corda node named _BuyerCWP_. This node hosts Corda accounts for buyers.
* HMRC, which collects VAT for all VAT-incurring transactions that take place through MTS. HMRC takes part in MTS through an account named VATPayments, which is held in a Corda node named _HMRCCWP_.
* The _Notary_ node which, by design, participates in every transaction which takes place in R3 Corda for validation purposes.  
Each core functionality of Smart Money is implemented using Corda flows (see Section 4). The feasibility is evaluated in the form of assertion tests [113] which check the correctness of the flows, i.e., whether the flows produce the correct results. The assertion tests presented in the remainder of this section verify that it is feasible to implement the MTS core functionality with distributed ledger technology.
# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 2 Related Work
### Age of Information in IoT-Based Real-Time Monitoring
There is a need for further research to develop more adaptable algorithms that can address both energy and delay challenges while ensuring robust, real-time data transmission in underground IoT applications.
## 3 Post-Injury Scenario--A Short Overview
(b1) shows nano Bioglass/gelatin conduits prior to the implantation and (b2), (b3), and (b4) represent the conduits at 30, 60, and 90 days post-implantation. **(C)** Histological longitudinal section of the implanted conduits at 30 (a), 60 (b), and 90 (c) days post-surgery, confirming the nerve outgrowth from proximal (P, right) to distal (d, left) (\(\times\)10). Reproduced with permission from ref [20]. More recently, electrospun films made of polypyrrole/collagen polymers and strontium-substituted nano-sized BGs (PPY/Coll/n-Sr@BG) were evaluated for the promotion of sciatic nerve rejuvenation in vivo [68]. These constructs demonstrated a native-mimicking morphology, better porosity, and higher specific surface area than customary nerve channels, thus allowing appropriate transportation of nerve development factor and glucose, as well as hindering the section of lymphatic tissue and fibroblasts. Furthermore, PPY/Coll/n-Sr@BG provided a suitable substrate for the growth and expansion of neurilemma cells. PPY/Coll/n-Sr@BG resulted in viable recovery of sciatic nerve wounds at 24 weeks post-implantation in rats which was comparable to the results of autotransplants and superior to those of PPY/Coll groups. Electrospun composites based on poly(glycolic acid) (PGA)/collagen/nano-sized BG (NBG) were fabricated as potential nerve conduits by [PERSON] et al. [69]. The obtained data proved the superior features of NBG-containing nanofibrous conduits in comparison to glass-free counterparts (PGA or PGA/collagen groups) in terms of mechanical/chemical properties, biocompatibility, and biodegradability. Silicate-based BGs have also been applied in other shapes, including aligned microfibers. [PERSON] et al. fabricated double-layered conduits by the incorporation of BG microfibers (SiO\({}_{2}\)-Na\({}_{2}\)O-K\({}_{2}\)O-MgO-CaO-P\({}_{2}\)O\({}_{5}\) system) in nanofibrous poly(\(\varepsilon\)-caprolactone) (PCL) membranes [70]. The PCL polymer was electrospun upon BG microfibers (\(20\pm 2.3\)\(\upmu\)m) to make a two-layer bio-composite. The fabricated samples were permeable to water vapor, thus facilitating the exchange of cell metabolites between the inner portion of the nerve guide and the surrounding environment. In addition, a significant decrease in contact angle and an increase in mechanical properties were recorded as a result of the incorporation of BG fibers into the electrospun polymeric scaffolds.
## 4 Experimental Section
### Baseline Methods  
In the first part of the experiment, we choose the following baseline models to compare with our model.  
* SPLITQA [20]: This model decomposes complex questions into a set of simple questions using a pointer network. It builds the encoder and decoder of the pointer network using a one-layer BiGRU network. First, it vectorizes the words using GloVe. Next, it creates a simple question sequence by copying the words from the original input question. Finally, it uses cross-entropy for training.
* DECOMPRC [22]: The pre-trained model Bert is used as the encoder in the question decomposition part of this model, and simply a linear layer is utilized in the decoding phase, ultimately the softmax mechanism is used to label the simple question locations from the input sequence.
* BiLSTM_CRF [40]: BiLSTM is used as the encoder and conditional random field (CRF) is used as the decoder to find the optimal path.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Real Positive Value** & **Real Negative Value** \\ \hline
**Predicted Positive Value** & \(TP\) & \(FP\) \\
**Predicted Negative Value** & \(FN\) & \(TN\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
BiLSTM_CNN_CRF [41]: This model uses BiLSTM as an encoder, CNN as an reasoning layer, and CRF as a decoder to find the best path.
* Bert_BiLSTM_CRF: This model employs Bert as the encoder, followed by BiLSTM as the intermediate layer for reasoning and CRF as the decoder to determine the optimum path. In the second and third part of the experiments, we selected the following baseline models.
* IRN [8]: This model is an interpretable hop-by-hop reasoning network that reasons the entire path from the topic entity in the question to each candidate response node in the subgraph. The model uses a bag-of-words model for encoding and hence has a loss of semantic representation of the question.
* SRN [35]: This is a reinforcement learning-based hop-by-hop reasoning model that includes a perceptron to handle question encoding during each hop. However, it limits the model's scalability.
* UHop [28]: This model is built on information extraction and focuses on solving multi-hop questions. It is a multi-hop relation detection framework that detects numerous relation structures but does not handle constraint information individually.
* PullNet [29]: This model uses a fusion idea to fuse data from other knowledge sources to assist in knowledge reasoning early in the model.
* TransferNet [42]: This model uses entity recognition, entity linking, and relation extraction to generate text-formed triples, which are then used to supplement the incomplete KG, with answers inferred by transferring entity scores along relation scores.
* MRP-QA [43]: This model can use information across multiple reasoning paths and simply needs labeled answers as supervision. To train the model, a marginalization probability objective function is used.
* HTL [44]: This model categorizes natural language questions into answer templates. To aid a Tree-LSTM in identifying the most significant information, an attention mechanism is built.
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 3 The Proposed Method  
This section introduces the input features of the proposed method for the DOA task. As shown before, three different covariance matrices are considered as the input features for the neural network. The first covariance matrix is the covariance matrix of the received signal of the microphone array in the time-frequency domain (TFD). The second one is the covariance matrix of the signal in the spherical harmonic domain (SHD). The third one is the covariance matrix of the signal in the azimuth-elevation domain (AED). All the covariance matrices are to be introduced in detail.
## 1 Introduction
As a conclusion, they state that the better results were found for the PSO and the GWO algorithms. Indeed, the two algorithms are inspired by natural competence to reach high speed and precision. Based on these previous works, in this paper we evaluate their relative feasibility and performance of employing the swarm algorithms to configure the power converter of the PV panels in order to cope with different shading conditions. These two algorithms have already been applied and evaluated separately in PV systems. Particle Swarm Optimization algorithm can help to calculate the duty cycle of the power converter in the PV connection dynamically. Several works tested this solution for this application, as in [35] where the authors proved that this solution could be efficient if it is running offline. On the other side, the Grey Wolf Optimization (GWO) algorithm appeared as a useful solution for extracting energy from the PV system with maximum efficiency [36]. However, the two algorithms have several parameters and constants, which must be fixed initially to start the algorithms correctly. The contribution of the paper is to apply and evaluate these two optimization algorithms for the same PV system, considering different partial shading conditions. The PVsystem model considered in this work is based on a commercial PV panel. So we can compare both performances to study their suitability in realistic implementations. The evaluation of each of these algorithms is based on the precision and the speed for tracking the global MPP with different partial shading conditions. Specifically, we have studied the two swarm-intelligence based algorithms for four shading conditions in a four-module PV system. This research paper is organized to initially present an introduction section, which describes the objective of the paper and explains the state-of-the-art in this application field. In the second section, the PV system is modeled with the necessary equations that regroup all the parameters and constants that define this physical system. In the third section, the two optimization algorithms are explained. Their flowcharts are exposed and the principle running of each one is described for the MPPT algorithm. In the next section, the simulation conditions and the obtained results are shown for each irradiance case. In the end, a conclusion section is formatted for resuming the paper and giving some perspectives of this work.
# Cost-Benefit Assessment of 5G Rollout: Insights from Brazil
## 4 Case Study in Southern Brazil
of 5G networks in the southern region of Brazil. This could help enhance coverage and efficiency in both urban and rural settings, though further analysis is required to confirm this applicability. Although the NHN and network slicing models are primarily analyzed in international contexts such as India and Europe, their applicability to Brazil stems from structural similarities in rural deployment challenges. In Brazil, the vast rural areas, low population densities, and high infrastructure costs mirror the conditions found in countries where the NHN has shown positive outcomes. Furthermore, Brazil's increasing push for infrastructure sharing and public-private collaboration suggests a favorable environment for implementing such models. Therefore, these international frameworks serve as valuable references for policy design and technical strategy in the Brazilian scenario. Furthermore, a study from India [13] explores in detail the implementation of an NHN to improve connectivity in rural areas through the cutting of the network. This approach, in which a single physical network infrastructure is divided into multiple virtual networks, each dedicated to different tenants or services, can be considered in the Brazilian context. Implementing such a model could significantly reduce capital and operational expenditures, making the deployment of 5G in rural areas economically viable. A previous study [12] suggested using clusters for network deployment, where geographic and demographic characteristics dictate the infrastructure requirements. This approach segmented areas based on population density and geographic characteristics, allowing for targeted and efficient network deployment. For Brazil, adopting a similar clustering approach could help us understand the unique cost elements associated with different regions, thus refining financial models to accurately reflect local conditions. While the cost structure comparison with Belgium highlights significant differences, it is important to recognize that such differences are not solely due to infrastructure or equipment costs. Socioeconomic and geographic conditions--such as labor costs, terrain complexity, and population distribution--play a major role in shaping deployment expenses. Furthermore, regulatory policies, including spectrum allocation mechanisms, taxation on telecommunications equipment, and incentives for infrastructure sharing, differ substantially between Brazil and Belgium. These factors should be considered when evaluating the applicability of European models to the Brazilian context.
## 3 Results and Discussion
### HLD\({}_{\rm N}\) Equation of SDHS-AAE(6 EO4 PO) Systems  
After dividing by the K coefficient, the HLD\({}_{\rm N}\) equations of SDHS and AAE(6 EO4 PO) are presented in Equations (10) and (11), respectively. The k\({}_{\rm ln}\)(S) coefficient of SDHS was 7.75 and the ionic surfactant contributing parameter (SCP\({}_{1}\) : \(\frac{\rm S}{\rm R}_{\rm n}^{2}\)) was \(-\)8.77. The salinity coefficient (k\({}_{\rm s}\)) of AAE(6 EO4 PO) was 0.78, temperature constant (k\({}_{\rm T}\)) was 0.13, and the nonionic surfactant contributing parameter (SCP\({}_{1}\) : \(\frac{\rm S}{\rm R}_{\rm n}\)) was \(-\)2.99.  
\[\rm HLD_{N,\ SDHS}=7.75\ln(S)-\ EACN\ -8.77 \tag{10}\]  
\[\rm HLD_{N,\ AAE(6 EO4 PO)}=0.78S\ -\ EACN\ +0.13(T-T_{\rm ref})-2.99 \tag{11}\]  
At the optimal condition, the HLD\({}_{\rm N}\) of the SDHS-AAE(6 EO4 PO) mixture was assumed to be zero. The interaction terms were determined by Equation (7), with the result shown in Figure 7. The interaction-term results were fitted with the polynomial trendlines (2 nd order) with R\({}^{2}\) = 0.67-0.91. Surprisingly, it shows that the interaction terms had a trend close to the \(\frac{G_{\rm grav}}{\rm RT}\) results of HLD in Figure 6. The average interaction term was 1.82 \(\pm\) 0.86. The HLD\({}_{\rm N}\) equation of SDHS-AAE(6 EO4 PO) would be as follows:  
\[\rm HLD_{N,\ SDHS-AAE(6 EO4 PO)}=X_{\rm SDHS}\{7.75\ln(S)-\ EACN\ -8.77\}+\ X_{\rm AAE(6 EO4 PO)} \{(0.78S\ -\ EACN\ +0.13(T-T_{\rm ref})-2.99\}+1.82 \tag{12}\]  
Figure 6: The \(\frac{G_{\rm grav}}{\rm RT}\) values of SDHS-AAE(6 EO4 PO) systems.  
The interaction terms of each anionic-nonionic mixing ratio showed a strong linear relationship with R\({}^{2}\) = 0.98, as presented in Figure 8. An increase of AAE(6 EO4 PO) in anionic-rich conditions resulted in an increase of the term of interaction. Therefore, it should be remarked that the interaction terms of the SDHS-AAE(6 EO4 PO) system could be represented in the function of the mole fraction of nonionic surfactant. The HLD\({}_{\text{N}}\) equation of SDHS-AAE(6 EO4 PO) in another form is shown in Equation (13).  
\[= X_{\text{SDHS}}(7.75\ln(\text{S})-\text{ EACN }-8.77)+\text{ X}_{\text{AAE(6 EO4 PO)}}0.78\text{S}-\text{ EACN }+0.13(\text{T}-\text{T}_{\text{ref}})\] \[-2.99)+\left\{4.33\text{X}_{\text{AAE(6 EO4 PO)}}+1.02\right\}\]
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 2 Theoretical Considerations
### Wave Surface Dynamics  
In the literature there exist many wave theories to describe the wave's surface dynamics such as Cnoidal wave theory, second or higher-order Stokes theory and Airy linear theory [17; 18]. For this work, monochromatic unidirectional wave has been considered to be input to the developed numerical wave-to-wire model of the OWC plant.  
The Airy wave theory is the simplest depiction of waves, it describes the waves as sinusoidal waveforms by neglecting turbulences, friction losses and other energy losses [18].  
The waveform of a regular wave is depicted in Figure 1, where \(SWL\) denotes the Still Water Level, \(h\) marks the sea depth between the seabed and the \(SWL\). \(H\) represents the wave which is between the wave trough and the wave crest. \(A\) marks the wave amplitude which is between \(SWL\) and the wave crest and \(\lambda\) marks the wavelength which measures the distance between successive crests [18; 19].  
Therefore, the elevation of a wave's surface can be expressed by [19; 20]:  
\[z(x,t)=A\sin\left(\omega t-kx\theta\right)=H/2\sin\left(\omega t-kx\theta\right) \tag{1}\]  
here \(x\) represents the horizontal coordinate with the direction of wave propagation as positive direction, \(\theta\) represents the angle between the \(x-\)axis and the wave direction, and \(k\) represents the wave number defined by (2) given in [21].  
\[k\tanh(kh)=\omega^{2}/g \tag{2}\]  
where \(\omega\) is the wave frequency and \(g\) is the acceleration gravity.
# Real-Time State Evaluation System of Antenna Structures in Radio Telescopes Based on a Digital Twin
###### Abstract  
To enhance the intelligence and digital management level of radio telescopes and ensure the safe and stable operation of antennas, this paper proposes a real-time state evaluation method for the antenna structure of radio telescopes based on digital twin (DT) technology. Firstly, based on the five-dimensional model of DT, a digital twin system (DTs) framework for radio telescopes is designed. Secondly, the quadric error metrics (QEM) mesh-simplification algorithm and mesh-reconstruction technology are employed to obtain a lightweight twin model of the antenna. Furthermore, a random forest (RF) regression surrogate model is established using finite element point cloud data samples. The K-nearest neighbor (KNN) algorithm and radial basis function (RBF) interpolation algorithm are utilized to construct the virtual-physical mapping model of the antenna, enabling rapid prediction and evaluation of the antenna structure state. Finally, a DT for real-time antenna structure state evaluation is developed using the Unity3D engine, with an experimental prototype of a reflector antenna as the object. Experimental results show that the average prediction accuracy of the physical field surrogate model of the system is 0.98, and the average computation time is 0.4 s. The system meets the precision and computational efficiency requirements for the real-time and accurate evaluation of the antenna structure state.  
radio telescope; digital twin; lightweight model; surrogate model; random forest; virtual and real mapping 2020