esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 3 Methodology
### Long Short-Term Memory (LSTM)  
Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) particularly adept at learning from sequences of data. LSTMs are designed to overcome the limitations of traditional RNNs, especially issues related to long-term dependencies in data sequences. Figure 2 shows the structure of a single LSTM unit.  
An LSTM unit is composed of a cell, an input gate, an output gate, and a forget gate. These components work together to regulate the flow of information into and out of the cell, and to decide which information to store and which to discard.  
The key equations governing the operations within an LSTM unit are as follows:  
\[f_{t} =\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}), \tag{14}\] \[i_{t} =\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}),\] (15) \[\tilde{C}_{t} =\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}),\] (16) \[C_{t} =f_{t}*C_{t-1}+i_{t}*\tilde{C}_{t},\] (17) \[o_{t} =\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}),\] (18) \[h_{t} =o_{t}*\tanh(C_{t}). \tag{19}\]  
Figure 2: Structure of a single unit of classical LSTM.  
Here, \(f_{t}\), \(i_{t}\), and \(o_{t}\) represent the activations of the forget, input, and output gates, respectively, at time \(t\). \(\sigma\) denotes the sigmoid function, and \(*\) represents element-wise multiplication. \(C_{t}\) is the cell state at time \(t\), and \(h_{t}\) is the hidden state. \(W\) and \(b\) are the weights and biases associated with the respective gates and cell state updates.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications  
Globally, the use of smart devices and data-hungry services has increased in recent years. This is especially true now that the Internet of Things is putting a lot more strain on telecom networks. Despite the fact that traditional telecom operators still want to increase their income and steak, they are unable to easily find ways to expand their networks and valued services [176]. While 5G provides answers to various IoT requirements [70; 177], there are additional opportunities in QoS-controlled end-to-end slicing; in addition to controlling the performance of the radio network, these operators should also be able to control the performance of the cloud. The authors of [178] provide a thorough survey of 5G network slicing, including a taxonomy of related terms, architecture comparisons, and a list of future challenges.  
To meet new types of customer needs, telecom operators are establishing virtualized cloud platforms capable of ensuring upkeep for services with high QoS demands. This so-called cloudification or Telco Cloud approach allows telecom operators to adopt a new perspective, shifting away from the traditional categories of wireline, wireless, voice, text, and web servicing toward cloud-based infrastructures and slice-defined servicing [179; 180; 181]. With the Telco Cloud design, telecom operators can intelligently manage both IT foundations and network assets in order to adapt to dynamic application requests and build an open platform for growing new services. According to [182], the Telco Cloud aims to meet the needs of telco-grade networked services through a distributed, flexibly configurable, virtualized cloud approach.  
There were several reasons for the shift away from hardware-oriented traditional telecommunications networks and toward all software-oriented IP networks. One of the primary motivators is the possibility of using commercial IT hardware as a platform, owing to the cost efficiency and combined experience required to operate and maintain the hardware devices. The growing interest in decoupling hardware and application software necessitates a greater use of virtualization. Aside from IT-grade hardware, the telecom world has been evolving towards new environments that now include 5G New Radio and evolving packet core, as well as private and public clouds [183; 184].  
This chapter will present an architectural view of Telco Cloud, as well as highlight related technologies. The main differences between the Telco Cloud and IT cloud will be highlighted, and some Telco Cloud applications will be showcased.
## 5 Feasibility Study Through System Simulations
### Simulation Process
#### 5.6.2 Generation of the Same Datasets of Fingerprints for the Different Pattern-Matching Methods  
When comparing the results from the different pattern-matching methods, we want to avoid possible differences in the results introduced by the statistics of different datasets of fingerprints. One step toward minimizing these differences would be to increase the number of samples. However, even when enough samples are considered in the Monte Carlo methods in our simulations, we want to remove any source of variations in the results introduced by the statistics. So, it was decided that each pattern-matching method must receive the same datasets of fingerprints as input for each combination of the parameters \(S_{S}\) and \(N_{SS}\) considered. In this way, we will compare differences in the results attributed only to the performance of each pattern-matching method.  
We propose two options for how to generate the same input datasets of fingerprints for the different pattern-matching methods in the simulator. One option is to execute steps 1 to 7 of the main execution flow of the simulator with the same seeds in the pseudo-random number generators for each pattern-matching method. Another option is to simply execute steps 1 to 7 only once and store the datasets for posterior use with each one of the pattern-matching methods.  
It is noted that in the case of the pattern-matching method without side information, 1-SingFingIn-PosOut, not all of the fingerprints from the tile are required for pointwise estimation of the position, only the set of fingerprints corresponding to the selected reference sub-tile. In our implementation, for simplicity, we always generate the fingerprints  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Variable** & **Description** & **Value** \\ \hline \(S_{T}\) & Side length of tile (side length of the 2D sensor array) & \(N_{SS}\times S_{S}\) \\ \(N_{ST}\) & Total number of sub-tiles in a tile & \(N_{SS}2\) \\ \(N_{T}\) & Total number of tiles to cover the positioning area & Returned by function \(FTilecCenters()\) \\  & (for method 4, it is equivalent to the number of classes) & (Pseudocode 1), listed in Table: The arrangements. \\ \(N_{FTT}\) & Number of fixed tiles for training & 50 fixed tile batches of \(N_{T}\) tiles each \(=N_{TTB}\times N_{T}\) tiles \\ \(N_{FTV}\) & Number of fixed tiles for validation & 50 fixed tile batches of \(NT\) tiles each \(=N_{TTB}\times N_{T}\) tiles \\ \hline \hline \end{tabular}
\end{table}
Table 10: Main variables for simulation.  
for all of the sub-tiles in all of the tiles. The purpose of this is so that the same datasets of fingerprints can be used for all the methods. Then, for the pointwise-based method, we just use only the necessary fingerprints.
## 2 Materials and Methods
### Analysis and Selection of Genes
\begin{table}
\begin{tabular}{c c c c c} \hline
**Maturity** & **Days to Maturity** & **Varieties** & **No. of** & **Maturity** \\
**Group** & **(From Sowing)** & & **Varieties** & **Ecotype** \\ \hline
0 & \(\leq\)79 & OT89-06 & 1 & \\ \hline \multirow{4}{*}{I} & \multirow{4}{*}{80–84} & Dongnong13-6522, Dongnong44, & \multirow{4}{*}{10} & \\  & & Dongnong59, Dongnong66, & & \\  & & Dongnongdou117, Dongnongdou118, & & \\  & & Geomjeongol, OT89-05, OT93-37, OT94-51 & Early \\ \hline \multirow{4}{*}{II} & \multirow{4}{*}{85–89} & Dongdou641, Dongnong13-6010, & \multirow{4}{*}{19} & \\  & & Dongnong13-6271, Dongnong13-6334, & & (EM) \\  & & Dongnong13-6590, Dongnong13-6622, & \multirow{4}{*}{(EM)} & \\  & & Dongnong55, Dongnong67, Heinong48, & & \\  & & Jilioudu4, Jiyu201, Jiyu401, Liaodou21, & & \\  & & Liaodou32, OT93-26, OT93-28, OT94-39, & & \\  & & Yandou2, Yandou4 & & \\ \hline \multirow{4}{*}{III} & \multirow{4}{*}{90–94} & 211B, Dongnong13-6004, Dongnong56, & \multirow{4}{*}{17} & \\  & & Heihe43, Heinong44, Heinong52, IT213198, & & \\ \cline{1-1}  & & IT263300, IT252691, Kenfeng20, Kenfeng22, & & \\ \cline{1-1}  & & L62-667, Liaodou46, Nongqingdou1, & \multirow{4}{*}{(MM)} & \\ \cline{1-1}  & & Yandou1, Yandou3, Zaoshu1 & & \\ \hline \multirow{4}{*}{IV} & \multirow{4}{*}{95–99} & Dandou12, Dongdou339, Dongdou9, & \multirow{4}{*}{(MM)} & \\  & & IT142772, IT273835, IT284565, Liaodou14, & & \\ \cline{1-1}  & & Liaowanshu, Liaozhongshu, Ludou10, & & \\ \cline{1-1}  & & [PERSON], Shanning16, Xudou9, & & \\ \cline{1-1}  & & [PERSON], [PERSON], [PERSON], [PERSON], & & \\ \cline{1-1}  & & [PERSON], Zhonghuang35 & & \\ \hline \multirow{4}{*}{V} & \multirow{4}{*}{100–104} & [PERSON], [PERSON], [PERSON], & \multirow{4}{*}{(LM)} & \\  & & [PERSON], [PERSON], [PERSON], [PERSON], & & \\ \cline{1-1}  & & [PERSON], IT142770, IT212835, IT213177, & & \\ \cline{1-1}  & & [PERSON], [PERSON], [PERSON], & & \\ \cline{1-1}  & & [PERSON], [PERSON], [PERSON], [PERSON], & & \\ \cline{1-1}  & & Zhonghuang37, Zhonghuang57 & & \\ \hline \multirow{2}{*}{VI} & \multirow{2}{*}{105–109} & Daechan, IT213175, Jinpung, Pungwon, & \multirow{2}{*}{6} & \\ \cline{1-1}  & & [PERSON], Taekwangkong & & \\ \hline \multirow{2}{*}{VII} & \multirow{2}{*}{\(\geq\)110} & Daepung, Daepung2, Daewonkong, & \multirow{2}{*}{8} & \\ \cline{1-1}  & & Haepum, IT213173, Pungsannamul, & & \\ \cline{1-1}  & & Seonpung, Uram & & \\ \hline \multicolumn{2}{c}{Total} & 100 varieties & 100 \\ \hline \end{tabular} The least significance difference (LSD) for days to maturity (DTM) of varieties is 2.51 days (\(p<0.05\)).  
\end{table} 
Table 1: Classification of 100 soybean varieties by maturity group and ecotype according to their days to maturity.
###### Abstract  
Domain adaptation can handle data distribution in different domains and has been successfully applied to bearing fault diagnosis under variable working conditions. However, most of these methods ignore the influences of noise and data distribution discrepancy on marking pseudo labels. Additionally, most domain adaptive methods require a large amount of data and training time. To overcome the aforementioned challenges, firstly, sample rejection and pseudo label correction using K-means (SRPLC-K-means) were developed and explored to filter the noisy samples and correct the pseudo labels to obtain pseudo labels with higher confidence. Furthermore, a bearing fault diagnosis method based on the improved transfer component analysis and deep belief network is proposed, which can achieve subdomain adaptation and improve the compactness of the samples, leading to a complete bearing fault diagnosis under variable working conditions that is faster and more accurate. Finally, the results of the comparative tests confirmed that the proposed method could boost the average accuracy of 0.73%, 0.99%, and 5.55% in the three tests than the state-of-the-art methods, respectively. Moreover, the comparison of the time required for a fault diagnosis using different methods shows that compared to the end-to-end models, the proposed method reduces the time required by 594.9 s and 1431.6 s, respectively.  
variable working conditions; transfer component analysis; SRPLC-K-means; deep belief network; bearing fault diagnosis
## 3 Experimental Setup and Testing
### Dynamic Setup for High Strain Rate Tests  
The first use of long and thin rods to measure the pressure waves produced by impacts was reported by [PERSON] [40] in 1914, and later improved through the work of [PERSON] [41]. The Hopkinson bar technique, initially introduced for compression tests, was later extended to tensile and torsion tests as well [42], and is typically used to carry out tests at high strain rates, ranging from \(10^{2}\) to \(10^{4}\) s\({}^{-1}\).  
In this work, a direct tension Hopkinson bar (SHTB) is used, consisting of three aligned bars, named the pre-stressed, input and output bars. A tensile input wave \((\sigma_{i},\varepsilon_{i})\) is generated by pre-loading the pre-stressed bar with compression, and by the subsequent sudden release of this pre-load (Figure 1a). The release of the pre-load is accomplished by the failure of a brittle sacrificial element; the strength of this element governs the input wave amplitude.  
The input wave \((\sigma_{i},\varepsilon_{i})\) travels at the speed of sound along the input bar; when it reaches the input bar-sample interface, part of the wave is reflected back into the input bar as a _reflected wave_\((\sigma_{r},\varepsilon_{r})\), and the other part propagates through the sample and travels along the output bar as a _transmitted wave_\((\sigma_{t},\varepsilon_{t})\). While the bars remain within their elastic limit, the sample deforms plastically. The reflected and transmitted strain waves are acquired by means of properly placed strain gauge rosettes; assuming uniaxial wave propagation in the bars and dynamic equilibrium in the sample, the engineering strain rate, strain and flow stress of the sample material can be obtained from the following formulas:  
\[\sigma(t)=\frac{A_{b}E_{b}}{A_{s}}\varepsilon_{t}(t) \tag{13}\]  
\[\dot{\varepsilon}(t)=-\frac{2C_{0}}{L_{s}}\varepsilon_{r}(t) \tag{14}\]  
\[\varepsilon(t)=-\frac{2C_{0}}{L_{s}}\int_{0}^{t}\varepsilon_{r}(t)dt \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Delivery State** & **Density (kg/m\({}^{3}\))** & **Specific Heat (J/(kg \({}^{\circ}\)C)** & **Yield Stress (MPa)** & **Ultimate Stress (MPa)** & **Elongation at Fracture (\%)** \\ \hline API X65 & Quenched and tempered & 7800 & 486 & 464 & 530 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: API X65 mechanical properties taken from [31].  
Figure 1b shows the direct Split Hopkinson bar facility used in this work [33,34].
# Performance Improvement by FRFT-OFDM for Visible Light Communication and Positioning Systems
## 4 Conclusions  
This paper addresses the challenges of indoor VLC and VLP, which are affected by LED nonlinearities and limited modulation bandwidth. We introduce an OFDM scheme based on the FRFT. By discretizing the real and imaginary parts of the modulated signal, the system is applied to both optical communication and positioning. The performance of this system is then compared to that of the FT-OFDM scheme. In the FRFT-OFDM scheme, it is necessary to recalculate the optimal FRFT order \(p\) under the current environmental conditions, which is used as the basis for subsequent communication or positioning. Our results demonstrate that the FRFT-OFDM scheme, when using the optimal order \(p\), achieves superior performance in both communication and positioning compared to the FT-OFDM scheme. This is reflected in improvements in both the BER and positioning accuracy. Specifically, for communication, the FRFT-OFDM scheme provides over a 6-dB \(E_{b}/N_{0}\) gain at a BER of \(3\times 10^{-4}\) compared to the FT-OFDM scheme. For positioning, the FRFT-OFDM scheme significantly outperforms the FT-OFDM scheme in most positions throughout the room, maintaining a low localization error. Simulation results indicate that the FRFT-OFDM scheme offers promising applications and valuable research potential. Currently, research on VLC and VLP systems has reached a relatively mature stage. However, the integration of the FRFT with VLC or VLP systems remains underexplored. This may be attributed to the need for further research and optimization of discrete FRFT algorithms and their supporting techniques within these systems. In the future, key research directions include exploring different coding methods in FRFT-OFDM schemes, designing channel equalization techniques in the fractional domain, and selecting appropriate positioning methods tailored to various environments.  
Conceptualization, W.L. and Z.W.; software, W.L.; validation, W.L., Z.W. and J.Y.; writing--original draft preparation, W.L.; writing--review and editing, Z.W. and J.Y.; supervision, Z.W. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under Grant 61835003 and Grant 62005194.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# A Multiple and Multi-Level Substructure Method for the Dynamics of Complex Structures
## 4 Analysis of Dynamic Characteristics Based on Substructure
### Modal Experiment of Combustion Rotor and Verification of Substructure Method
#### 4.3.3 Data Acquisition  
Data collection is divided into two steps: preparation for collection and formal collection. On the one hand, preparation for collection is to determine reasonable parameters, such as sampling frequency, acquisition range, sampling duration, excitation signal, reference point, etc. On the other hand, data need to be checked, including a linearity check, FRF, coherence check, reciprocity check and so on. Collections begin after calibration. Once a set of data have been formally collected, the consistency of the measured data should be checked in both the time and frequency domains.  
The connected data acquisition instrument and the computer, as well as the hammer and sensors, were as shown in Figure 14. Before measurement, relevant parameters of the software were set, and the highest frequency of the first three modes of concern was about 4 kHz after preliminary simulation. Therefore, the analysis frequency in the setting was 5 kHz, and the sampling frequency was 12.8 kHz.  
In the test process, attention should be paid to the time domain diagram and the corresponding spectrum diagram of the excitation signal. This experiment uses the hammering method, and the signal should be as smooth as possible to extend to the specified frequency. In addition, in the modal experiment, the coherence function is used to describe the linear relationship of multiple measured data. Its value range is 0-1. The coherence function is equal to 1, which means the data has a good linear relationship. If the coherence value is low, the data may be affected by noise or leakage, resulting in large differences between the data. The coherence value is generally required to be greater than 0.8 in the experiment.
## 1 Introduction
### Facility Management  
Facility management is a multidisciplinary topic that requires the collaboration and coordination of different people [6]. ISO 41011:2017 defines FM as an \"organisational function which integrates people, place and process within the built environment with the purpose of improving the quality of life of people and the productivity of the core business\" [12].  
According to International Facility Management Association (IFMA), there are 11 core competencies in FM [13]: Occupancy and Human Factors, Operations and Maintenance (O&M), Sustainability, Facility Information and Technology Management, Risk Management, Communication, Performance and Quality, Leadership and Strategy, Real Estate, Project Management, Finance and Business.  
Currently, not all buildings have optimal management [14; 15] due to outdated procedures that cause a lack of data and information. In other cases, despite the use of sensors/automatic devices and databases, the information collected is not entirely exploited [16]. An example is given by FM information systems, e.g., Computerised Maintenance Management Systems (CMMS), Energy Management Systems (EMS) and Building Automation Systems (BAS), where data are often fragmented and manually entered after the handover of the building. Fragmentation and data poorness could generate laborious and inefficient processes [7]. Furthermore, FM operators often rely on paper documents in their daily activities. This increases both the time needed and the difficulties of getting accurate information [17]. For these reasons, the improvement of both FM tools and processes is a crucial issue in FM companies [18]. Hence, with increasing industry interest, a review of the current status and a description of a future research agenda on FM is needed.
# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 3 Results  
From 623 total eligible papers, 355 publications were screened, and they were later sorted, first by title and abstract, and then by suitability for the review's topic.  
Studies examining whether RR time series could be used as physiological indicators for the early identification of cardiac fatigue in an adult population (see Figure 1's green arrows) were selected.  
In order to describe the most recent (in a 20-year span) literature information on cardiac fatigue in adults (>18 years of age), the data were classified according to the methods applied (and in order of year of publication) and to the healthy status of the participants; the obtained information is summarized in Table 3, and these selected papers are described in the following 10 paragraphs.  
Regarding model performance (see the last column of Table 3), the most common evaluation metrics, accuracy, precision, recall and F1, are defined in Appendix B.  
We report the full descriptive statistics: Seven of the ten papers (70%) selected were published in the last 5 years; the studies included from 8 to 139 subjects. All of these studies were conducted on a limited number of subjects, with the exception of one involving hypertensive patients. Of the 435 subjects included, 223 (51%) were healthy, 139 were hypertensive (32%) and 99 were spinal cord injury (SCI) (17%) subjects, respectively.  
Of the 10 studies included, 40% used ECG-based RR interval acquisition, 40% used a chest belt sensor and 20% used PPG. In two studies, multi-sensor wearable devices were used. All studies reported using HRV features for fatigue detection, with nine studies applying ML methods and reporting accuracies > 80.5%. Specifically, four studies reported High Model Performance (\(\geq\)90% accuracy or consistently strong across multiple metrics) and five studies reported Moderate to Good Model Performance (80-89% accuracy or F1/precision/recall in the -0.80 range). Of the 10 studies included, 2 (20%) studies were conducted in Australia, 2 (20%) in China, 2 (20%) in USA and 4 (40%) in EU.  
Figure 3: Annual publications for “cardiovascular fatigue”. Older adults (>65 years) in gray, children and adolescents (birth-18 years) in green; other filters applied: humans, exclude preprints, PubMed from 2005–2025.  
A word frequency analysis of the article titles revealed the key thematic focuses within the research field (Figure 4).  
The most frequently occurring terms were \"fatigue\" (8/61), \"heart rate\"(6/61), \"variability\" (4/61) and \"analysis\" (3/61), highlighting the centrality of physiological monitoring, particularly HRV, in detecting fatigue. The repeated presence in the titles of terms like \"wearable sensors,\" \"machine learning,\" and \"monitoring\" confirm the strong interest in technological and AI-based approaches for real-time health assessment.