esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Simulation Performance Across Different Temporal Scales  
Figure 11 presents a Taylor diagram summarizing the model's simulation skill for ET and GPP across different temporal scales (half-hourly, monthly, annual) based on the Pearson correlation coefficient (R) and standardized deviation (SD). The R values for water and carbon fluxes mainly range from 0.6 to 1 (ET) and 0.5 to 1 (GPP), while SD values range from 0.5 to 1.3 and 0.5 to 1.5, respectively.  
The model simulations for both ET and GPP show high consistency with observations at the half-hourly scale, with R values typically between 0.8 and 1. The ET simulations also maintain good consistency at the monthly scale, with R values mostly between 0.95 and 1. At the annual scale, the GPP simulations show correlations above 0.5 for most vegetation types, except for DNF, DBF, MF, and OSH, which did not pass the significance tests. Figure 12 also indicates that annual scale simulation biases (RMSE and MAE) are relatively small in magnitude compared to the total annual flux, despite lower correlations.  
Figure 11: Taylor diagram showing the comparison of simulation performance for ET and GPP at half-hourly, monthly, and annual temporal scales for different vegetation types (1: ENF; 2: EBF; 3: DNF; 4: DBF; 5: MF; 6: CSH; 7: OSH; 8: WSA; 9: SAV; 10: GRA; 11: WET; 12: CRO).  
Figure 12: Model performance in simulating ET and GPP on temporal scales of half-hourly (HH), monthly (MM), and annual (YY) measured using the RMSE and MAE for different vegetation types.  
Overall, RMSE and MAE values (Figure 12) are smallest at the half-hourly scale and largest at the annual scale. This reflects that the model has the smallest simulation bias at the half-hourly scale, performing better than at monthly or annual scales in terms of absolute error magnitude relative to the variability at that scale. Comparing ET and GPP (Figure 11), the model generally performs better for ET than for GPP. At half-hourly and monthly scales, the ET simulations show higher R values and SD values closer to the reference point (REF = 1) compared to GPP. Furthermore, Figure 12 shows that the average RMSE and MAE values for ET across all three temporal scales are smaller than those for GPP.  
Noah-MP 5.0's simulation accuracy for ET and GPP is significantly higher at the half-hourly scale compared to the annual scale (ET: R = 0.93 vs. 0.66; GPP: R = 0.89 vs. 0.67). This aligns with the model's ability to finely capture responses to transient drivers like radiation and temperature at short temporal scales. However, the substantial increase in cumulative error at the annual scale highlights the deficiencies in simulating long-term cumulative effects of vegetation physiology, such as carbon allocation and soil moisture memory. [PERSON] et al. (2017) observed similar phenomena in tropical rainforest evaluations, suggesting that the parameterization of phenological lag responses in Noah-MP's dynamic vegetation module requires further optimization.
# FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN
### Our Contributions  
This research is constrained by the challenge of sample imbalance, impacting the model's feature learning, and lacks a quantitative analysis method for fastener conditions. Our research advances railway maintenance with innovative methodologies for SFC fastener inspection and assessment. The integration of Mask-FRCN with the FIQ method will significantly improve the accuracy and efficiency of rail fastener inspection compared to traditional methods.  
(1) We have developed a novel enhancement to the Mask FRCN model, integrating the Fastener Convolutional Block Attention Module (FCRM), Fully Refined Convolutional Network (FRCN), and Channel-WiseKnowledge Distillation (CWD). This integrated approach significantly improves the segmentation and feature extraction of SFC fasteners, enabling the more accurate identification of fastener conditions. The FCRM module prioritizes key features within fastener imagery, whereas the FRCN refines segmentation boundaries for precise geometric capture. CWD distills a larger network's knowledge into a smaller, more efficient model.  
(2) We have developed an enhanced Mask FRCN model that integrates the Fastener Convolutional Block Attention Module (FCRM), the Fully Refined Convolutional Network (FRCN) module, and Channel-WiseKnowledge Distillation (CWD). This integrated approach has led to significant improvements in the segmentation and feature extraction of SFC fasteners. The FCRM module, with its attention mechanism, prioritizes the most relevant features within fastener images, whereas the FRCN module refines segmentation boundaries to capture the complex geometries with high precision. The CWD technique efficiently distills knowledge from a larger network, resulting in a compact model that maintains high performance with reduced computational resources.  
(3) We introduced the FIQ method, which utilizes a template-matching approach with random forests for fastener status assessment. This method capitalizes on the enhanced segmentation from the Mask FRCN model to quantify the condition of fasteners based on image features. The FIQ method provides a systematic evaluation of fasteners, including damage and displacement, through a clustering calculation method. It has improved the fault detection accuracy of SFC-type fasteners, demonstrating the method's efficacy and efficiency in automated fastener assessment.
# Data Privacy and Ethical Considerations in Database Management
## 5 Results and Analysis
### Data Hashing  
To implement data hashing, we used the SHA-256 algorithm provided by PostgreSQL's pgcrypto extension. We applied hashing to the patient's name and medical condition to verify data integrity.  
Here is an example of how we implemented hashing:  
UPDATE PatientRecord  
SET NameHash = encode(digest(Name,'sha256'), 'hex'),  
MedicalConditionHash = encode(digest(MedicalCondition,'sha256'), 'hex');  
This query creates hash values for the Name and MedicalCondition fields, storing them in the new columns NameHash and MedicalConditionHash, respectively.  
To verify data integrity, we can compare the stored hash with a newly generated hash:  
SELECT Name, NameHash,  
encode(digest(Name,'sha256'), 'hex') AS ComputedHash,  
NameHash = encode(digest(Name,'sha256'), 'hex') AS HashMatch  
FROM PatientRecord;  
This query computes a new hash for each Name and compares it with the stored hash, indicating whether they match.  
Ethical Considerations in Data Hashing  
While hashing enhances data integrity, it also raises ethical considerations:  
* Privacy Implications: Although hashes are one-way functions, they could potentially be used for tracking or linking records across databases, raising privacy concerns [46].
* Transparency: It is important to be transparent about the use of hashing techniques to maintain trust with data subjects [77].
* Data Utility: Hashed data may limit certain types of data analysis or use, potentially impacting research or healthcare outcomes [51].  
* Collision Risks: While rare with modern algorithms like SHA-256, hash collisions could potentially lead to data integrity issues, raising ethical concerns about data accuracy [50].  
The implementation of data hashing provides an additional layer of security and data integrity verification, complementing our other data protection measures. However, it is crucial to balance these benefits against the ethical considerations and potential impacts on data utility.
# Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures
Moreover, EMD and ensemble empirical mode decomposition (EEMD) was tested with the standard deviation ratio in a slew bearing at 1 rpm [31]. [PERSON] et al. used ensemble empirical mode decomposition (EEMD) combined with spatial indicator to diagnosis a bearing at 4 rpm [32]. [PERSON] et al. discriminated the state of a bearing at 20 rpm combining the complete EEMD method and Teager energy operator [33]. An important number of publications found in the literature is focused on identifying and evaluating the bearing state combining several methods, to obtain the current state of bearings. Nevertheless, indicators directly applied to the vibration signal to obtain information about the state of low-speed bearings are hard to track [14]. Entropy-based indicators provide a different perspective in classical signal analysis. Entropy-based indicators have been effectively applied in several fields, such as biomedical engineering [34], economics [35], electronics [36], health monitoring [37], and even image and ground-penetrating radar signal processing [38; 39]. Recent studies have shown the use of entropy-based indicators in vibration analysis [40; 41; 42; 43; 44; 45; 46; 47], but mainly on general-purpose bearings at high speed. For the case of low-speed bearings, few articles can be found where entropy-based indicators are used. In this scenario, entropy-based indicators are used with several techniques mentioned previously,such as wavelet packet sample entropy with wavelet decomposition and EMD [48], or energy entropy in addition to wavelet and EEMD [49]. Frequency band entropy indicator with the short-time Fourier transform is also found in the literature [50]. The use of wavelet energy entropy as part of a set of indicators to determine the state of a bearing at 4 rpm is found in the work of [PERSON] et al. [51]. To establish a different perspective in vibration signal analysis for very low-speed bearings analysis, this paper proposes the use of entropy-based indicators with no combination of other methods based on time or frequency domain. The rest of the paper is structured as follows. Section 2 briefly describes the theory on which the entropy indicators are based, while Section 3 provides a description of the experimental set-up. The methodology is explained in Section 4, and the results are given in Section 5. The conclusions are presented in Section 6.
Several studies have explored the effect of adding hydrogen on flame stabilization, the underlying mechanisms, and the blow-off behavior near the lean limit. However, most of the research in the literature is limited to the use of low hydrogen ratios or 2D simulations, neglecting the three-dimensional (3D) flow stress. For instance, vortex stretching and the processing vortex core (PVC) cannot be illustrated in a 2D combustion field. Overall, turbulence is a well-known 3D phenomenon, and 2D simulations cannot capture the turbulence flow features completely. Moreover, most simulations are coupled with a simplified chemical mechanism that may not explain the interaction mechanism between the flame and the 3D flow at high hydrogen ratios. The best approach seemingly involves the use of 3D simulations with detailed chemistry, which capture the real 3D interaction processes. Furthermore, enstrophy analyses can also be carried out to investigate flame stability and the dynamics of the H\({}_{2}\) swirl flames, as enstrophy is a scalar field that inherently measures the strength of the vorticity field without implying directionality and can be partially attributed to the intensity of vortical structures [26]. The primary aim of this study was to investigate the mechanism of flame stabilization and the blow-off process by applying hydrogen ratios of up to 90% to a bluff-body burner and using the large eddy simulation (LES) technique with a dynamic thickened flame (DTF) model. A bluff-body burner with a high blockage ratio, developed by [PERSON] et al., was used [27]. Turbulence was created and enhanced through slotted plates upstream of the burner exit. The results obtained from our numerical simulation were validated with the experimental data, which included OH-PLIF images and velocity profiles.
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## 4 Evaluation
### Evaluation of the Mathematical Model
#### 4.1.2 Tail latency  
As said before, the IEEE 802.11 network family has become a common wireless solution for data networks. However, most of the services running on top of these networks (particularly VANET, WSN and IoT) are delay sensitive applications, being very sensitive to tail latency.  
Tail latency, defined as the delay between consecutive frames, has been studied recently by [6]. Because the proposed DEDCA mechanism is focused on providing response to dynamic QoS requirements, even in delay-sensitive environments, it is important to test the behavior of this parameter when DEDCA is applied.  
Here, we consider 10 stations competing for the channel. The default \(CW_{min}\) is set to 31 for all of them. However, in each simulation, station one decrements its \(CW_{min}\) value by two units from 29 to 19 (concretely \(CW_{min}=\{29,27,25,23,21,19\}\)). Figure 7 shows the _Cumulative Distribution Function_ (CDF) of the tail latency at station one. As can be seen, the use of the proposed DEDCA mechanism enables to achieve a noticeable reduction of the tail latency. For example, the probability that the tail latency is below 25 slots is around 0.8 when \(CW_{min}=19\), but only above 0.6 when \(CW_{min}=31\).  
Figure 6: Obtained access rate under saturated conditions. Stations 2 and 3 are fixed as _requesting stations_. 4, 6 and 8 terminals are chosen as _giving terminals_ to compensate the gain.  
Although the solution proposed in [6] is a solution specifically designed to be used in scenarios where real-time services are dominant, the proposed mechanism DEDCA is able to reach similar performances and, as it has been pointed out, it can be used -at least temporarily- in a wireless network with a mixture of services in terms of QoS.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 3 ConvLSTM-DSConvGRU Hybrid Prediction Model
### Depthwise Separable Convolution  
Depthwise separable convolution decomposes the conventional convolution operation in two processes: depthwise convolution and pointwise convolution. This greatly reduces the number of parameters in the convolutional layer while maintaining the model's ability to extract input features [29]. The schematic diagram is shown in Figure 7.  
Depthwise convolution splits the convolution kernel into single-channel form and convolves each input channel separately. The output feature map has the same number of channels as the input channels. Then, a \(1\times 1\) convolution kernel is used for the pointwise  
Figure 7: Schematic diagram of depthwise separable convolution.  
convolution operation, which combines the feature maps generated by the depthwise convolution in the depth direction. The comparison of computational complexities between the depthwise separable convolution and conventional convolution is shown in Equations (10)-(12).  
\[Z_{DSC}=K\times K\times H\times W\times C+Q\times H\times W\times C \tag{10}\]  
\[Z_{CONV}=K\times K\times H\times W\times C\times Q \tag{11}\]  
\[\frac{Z_{DSC}}{Z_{CONV}}=\frac{1}{Q}+\frac{1}{K\times K} \tag{12}\]  
where \(K\) and \(Q\) represent the size and number of convolution kernels, respectively; \(H\), \(W\), and \(C\) represent the height, width, and number of channels of the input features, respectively; \(Z_{DSC}\) represents the computational complexity of depthwise separable convolution; and \(Z_{CONV}\) represents the computational complexity of conventional convolution. Taking the convolution kernel size \(K=3\) used in this study as an example, Equation (12) shows that depthwise separable convolution reduces the computational complexity by approximately 88%.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 3 Data acquisition  
### Data Collection and Preparation  
The historical data on rock-fall events, in addition to meteorological data, was gathered between January 2015 and August 2021. Different sources, such as KSA Civil Defense and the Geological Hazards Research Center, were used as data sources. This period was divided into 2040 samples, which included 415 rock-fall events. During the initial filtering of the data, three non-dependent variables (slope angle, rainfall, and temperature variation) and one dependent variable (rock-fall event) were selected. The dataset from the rock-fall inventory was split into a training dataset of 70% (1428 samples) and a test dataset of 30% (612 samples) in order to train and test the model.
## 1 Introduction
Precise assessment of the severity of various FMs is a significant challenge that engineers often misunderstand in a real industrial scenario. Faulty assessments lead to the incorrect prioritization of the enterprise according to its risk levels, reducing the reliability and availability of the system and increasing the costs. Incorrect assessment of the criticality of the components may result in the assignment of improper maintenance tasks, increasing the maintenance cost. Keeping in mind the importance of accurate component criticality assessment, we proposed a new integrated, multi-attribute Expert Judgment based Preference Section Index (Ex-PSI) model to rank failure modes and identify critical components based on their risk levels. The proposed model allows a number of experts to collect accurate risk factor data. In the conventional FMEA method, failure modes are assessed by a small number of analysts. Most industrial systems are complex and contain a wide number of subsystems. Therefore, it is important to involve several experts from various fields of expertise and from different organizations to achieve a more accurate assessment and evaluation. This paper proposes a new risk priority ExJ-PSI model, based on conflict judgment and the opinions given by a large number of experts, to identify failure modes and critical components. The model presented here enables data collection on numerous failure modes and their risk variables. This approach is useful for achieving unbiased group assessments, which can easily be accepted and minimized by decision-makers on the final impact of their decisions. A mathematical framework based on scale normalization is then developed to deal with the uncertainty and vagueness of the opinions of various experts in the risk assessment process. The normalized RF values are used to form a decision matrix, and finally, the PSI method is used to estimate the criticality values. Finally, it is used for an effective evaluation of failure modes and critical components of a boiler system to validate the effectiveness and applicability of the developed model. This paper is structured as follows: the detailed procedure of the developed model is introduced in Section 2. A particular case analysis of the boiler system is then presented in Section 3. It includes detailed failure causes and their effects on the performance, including FMECA of the boiler system. Section 4 discusses the effects of the developed model and its comparison with the conventional FMECA. Finally, Section 5 summarizes the concluding remarks on the proposed model.
## 6 Conclusions  
In this article, we proposed a novel enhanced OFDM (eOFDM) scheme that was established on the linkage of guard interval sequences to user data. We introduced a variable PN sequence (guard sequence), which was selected according to data symbols carried by a subset of subscribers and has the advantage of eliminating the need for pilot symbol insertion while also possesing channel estimation capabilities for different subscribers. Our simulation results, which were based on a fair comparison, showed a reduction in the bit error rate (BER) and an increased capacity when compared to both TDS-OFDM and CP-OFDM. Additionally, our eOFDM showed a better performance in  
Figure 6: BER comparison of CP-OFDM, TDS-OFDM and eOFDM in multipath Rayleigh fading channel.  
Figure 7: Capacity of CP-OFDM, TDS-OFDM and eOFDM in multipath Rayleigh fading channel.  
spectral efficiency and energy efficiency in relation to CP-OFDM and demonstrated an improved spectral efficiency and energy efficiency compared to TDS-OFDM depending on the guard interval selection criterion. Thus, our scheme outperforms TDS-OFDM and CP-OFDM based on capacity and BER performance for a wide range of SNR.  
Conceptualization, O.O., S.W., R.A.S. and M.M.N.; methodology, O.O., and S.B.A.K.; software, O.O.; validation, S.W., R.A.S. and M.M.N.; formal analysis, O.O., R.A.S., S.B.A.K. and M.M.N.; investigation, O.O., S.W. and M.M.N.; resources, S.W.; data curation, O.O., R.A.S. and S.B.A.K.; writing--original draft, O.O.; visualization, O.O. and R.A.S.; supervision, S.W., R.A.S. and M.M.N.; funding acquisition, S.W. All authors have read and agreed to the published version of this manuscript.  
This research was funded by the National Natural Science Foundation of China under grants 62271167, 62171163, and 61831002.  
Not applicable.  
The authors would like to acknowledge Prince Sultan University and the Smart Systems Engineering Lab for their valuable support. In addition, we extend our utmost appreciation to our colleagues of the Intelligent Signal Processing Lab of the School of Electronics and Information Engineering at Harbin Institute of Technology for their immeasurable contribution.  
The authors declare no conflict of interest.
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
###### Abstract  
Physical layer network coding (PNC) is a promising technique to improve the network throughput in a two-way relay (TWR) channel for two users to exchange messages across a wireless network. The PNC technique incorporating a TWR channel is embraced by a free space optical (FSO) communication link for full utilization of network resources, namely TWR-FSO PNC. In this paper, bit interleaved coded modulation with iterative decoding (BICM-ID) is adopted to combat the deleterious effect of the turbulence channel by saving the message being transmitted to increase the reliability of the system. Moreover, based on this technique, comparative studies between end-to-end BICM-ID code, non-iterative convolutional coded and uncoded systems are carried out. Furthermore, this paper presents the extrinsic information transfer (ExIT) charts to evaluate the performance of BICM-ID code combined with the TWR-FSO PNC system. The simulation results show that the proposed scheme can achieve a significant bit error rate (BER) performance improvement through the introduction of an iterative process between a soft demapper and decoder. Similarly, Monte Carlo simulation results are provided to support the findings. Subsequently, the ExIT functions of the two receiver components are thoroughly analysed for a variety of parameters under the influence of a turbulence-induced channel fading, demonstrating the convergence behaviour of BICM-ID to enable the TWR-FSO PNC system, effectively mitigating the impact of the fading turbulence channel.  
Keywords:physical layer network coding; free space optical communication; bit interleaved coded modulation with iterative decoding; turbulence-induced channel; Monte Carlo simulation; ExIT charts +
Footnote †: This paper is an extended version of our paper published in the 8 th Computer Science and Electronic Engineering (CEEC), Colchester, UK, 28–30 September 2016.