esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### M\({}^{2}\)-Factor
Figure 7: Normalized M\({}^{2}\) factors of the partially coherent flat-topped beam rectangular arrays during the (**a**) forward and (**b**) reverse transmission with the different beam orders (H = P = 3). advantage is attributed to the fact that laser beams with larger beam widths tend to have smaller divergent angles, which helps in maintaining beam quality over longer distances. Specifically, in the far field, partially coherent flat-topped beam rectangular arrays with smaller beam widths exhibit significantly larger normalized M\({}^{2}\) factors. Figures 10 and 11 present the normalized M\({}^{2}\) factors of partially coherent flat-topped beam rectangular arrays, highlighting the influence of both the number of beamlets and the beam order on these factors. As depicted, the normalized M\({}^{2}\) factors decrease as the beam order increases until a certain point is reached, where further increments in beam order result in negligible changes to the normalized M\({}^{2}\) factors. This suggests an optimal range for beam order beyond which improvements in beam quality, as measured by the M\({}^{2}\) factor, become minimal. It is evident that, within a specific range, variations in the number of beamlets do not significantly alter the normalized M\({}^{2}\) factors of these beams. Moreover, under identical conditions, the normalized M\({}^{2}\) factors displayed during reverse transmission are consistently higher compared to those during forward transmission. From Figures 7-11, the results indicate that while both the turbulent atmosphere and plasma influence the beam quality, the impact of the turbulent atmosphere is relatively minor compared to that of the plasma. By selecting appropriate beam parameters, the robustness of the beam against disturbances can be significantly enhanced. Figure 10: Normalized M\({}^{2}\) factors of the partially coherent flat-topped beam rectangular arrays during the (**a**) forward and (**b**) reverse transmission with the different beam orders and propagation distances (H = P = 3). Figure 9: Normalized M\({}^{2}\) factors of the partially coherent flat-topped beam rectangular arrays during the (**a**) forward and (**b**) reverse transmission with the different beam widths (H = P = 3).
# A Review of Flywheel Energy Storage System Technologies and Their Applications
## 2 Description of Flywheel Energy Storage System
### Structure and Components of FESS  
FESS consist of a spinning rotor, MG, bearings, a power electronics interface, and containment or housing, which are discussed in detail in the following subsections. A typical flywheel system suitable for ground-based power is schematically shown in Figure 1.
## 6 Evaluation and Discussion
### Security Analysis
#### 6.1.4 Denial of Service Attack  
In NDN, Denial of Service (DoS) attacks are mainly associated with an _Interest_ Flooding Attack (IFA) [41]. Such an attack involves sending a large number of _Interest_ packets to a target to overwhelm it, making it unable to accept legitimate requests. NDN-BDA, and specifically the Management Server, is also susceptible to DoS attacks. The attack is characterized by the adversary sending an excessive number of join and sign_Interest_ packets with random CID and VID values to overwhelm the MS. The aim is to fill the MS's PIT with these fake requests, making it impossible to add legitimate requests. However, the MS can limit the impact of the attack by discarding successive _Interest_ packets with the same signature. The adversary, _adv_, is also capable of launching an IFA using _BC_ request_. _Interest_ packets. Nevertheless, the impact of this attack is considered less severe since the blockchain could be obtained from network caches, thereby satisfying the _Interest_ packets before they reach the \(MS\). Despite this, the design of NDN-BDA does not prevent DoS attacks, and the MS should use an IFA mechanism such as the one proposed in [42] to mitigate the attack.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 7 Future Work  
While during the experiments of the evaluation it was observed that the number of iterations the heuristic needed to converge was consistently below five (i.e., \(c\) in Section 3.5) and had no impact on its execution time, necessary future work will be the calculation of the exact upper bound of this number of iterations, to fully understand the limits of the heuristic. Furthermore, future work will be focused on a fully distributed implementation of the heuristic in the 6 TiSCH stack with the low-power routing protocol RPL. Additionally, to further improve the performance of the heuristic in a completely distributed implementation, the trade-off between the current state of the node (e.g., amount of traffic, battery state and free schedule space) and the different characteristics of each possible PHY (e.g., achievable throughput, energy consumption and resource duration) should be carefully considered. Furthermore, while combining different PHYs with different transmission lengths, future implementation should also closely monitor and manage the frequency band duty cycle regulations, which is a requirement that is often overlooked.  
Conceptualization, G.D., C.D. and J.F.; Formal analysis, G.D., C.D. and J.F.; Investigation, G.D., D.V.L., C.D., E.D.P. and J.F.; Methodology, G.D. and D.V.L.; Software, G.D. and D.V.L.; Supervision, C.D., E.D.P., S.L. and J.F.; Writing--original draft, G.D.; Writing--review and editing, G.D. All authors have read and agreed to the published version of the manuscript.  
Part of this research was funded by the Internet-of-Shipping (IoS) project, and by the CERCA Programme of the Generalitat de Catalunya. IoS was co-financed by imec and received support from Flanders Innovation and Entrepreneurship.  
Not applicable.  
The reliability values monitored at the imec Wireless OfficeLab (Ghent, Belgium), used in the evaluation section of this article, are publicly available at [[https://github.com/imec-idlab/officelab-relabilities](https://github.com/imec-idlab/officelab-relabilities)]([https://github.com/imec-idlab/officelab-relabilities](https://github.com/imec-idlab/officelab-relabilities)), accessed on 9 May 2021.  
The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by FWO and the Flemish Government, department EWI.  
The authors declare no conflict of interest.
However, only few Korean spontaneous speech corpora are available. The major languages, such as English, Chinese, and Japanese, have representative conversational and spontaneous speech corpus such as FISHER [14], TED-LIUM2/3 [15; 16], HKUST [17], and CS] [18]. In addition, these datasets are widely used as benchmark datasets for evaluation of ASR models. Meanwhile, Korean is a low-resource language [19], and only a few small-scale or goal-oriented speech corpora have been released, such as the Zeroth project [20] and ClovaCall [21].  
In this paper, we introduce a large-scale Korean spontaneous speech corpus on AIHub [22]. AIHub is an open data hub site of the Korea government. As one of the various available open datasets, we are releasing KsponSpeech, an open-domain spontaneous speech corpus in cooperation with the National Information Society Agency (NIA) [22]. This corpus includes 969 h containing 622,545 utterances recorded from 2000 Korean native speakers. Its evaluation data consist of two evaluation datasets of 2.6 and 3.8 h spoken by 60 speakers. In this paper, we describe the recording and transcription method of the Korean spontaneous speech corpus and define the composition of datasets for a speech recognition experiment.  
We demonstrate the baseline performance of an end-to-end model trained with KsponSpeech. The experiment was performed with two end-to-end models used as the standard in the ESPnet toolkit [23]: (1) attention-based recurrent neural network (RNN) [24; 25] and (2) Transformer [26; 27]. Since each model is jointly trained with a connectionist temporal classification (CTC) [24; 28] objective function, they are also known as Hybrid CTC/Attention [24] and Hybrid CTC/Transformer models, respectively [26]. However, we abbreviate these to RNN and Transformer, respectively, following previously-used notations [27]. For the speech recognition experiment, we present the baseline performance of two standard end-to-end models. We examined the effectiveness of a data augmentation method known as SpecAugment [29] and then investigated the number of sub-word units [30] suitable for the Korean language. Finally, we compared the performance of the large Transformer architecture.  
The rest of the paper is organized as follows: Section 2 describes the KsponSpeech corpus. Section 3 presents the baseline performance of the end-to-end models trained with this corpus. Finally, conclusions are presented in Section 4.
## 3 Proposed Method
### Precoding Matrix Design  
To improve the SE performance, UL precoding can be implemented at the UE side in the scenario with multi-antenna UEs. Inspired by CF M-MIMO, the UL precoding scheme can be based on the correlation feature \(\mathrm{U}_{mk,t}\) at the UE side as  
\[\mathrm{F}_{k}=\sqrt{p_{k}}\|\sum_{m=1}^{M}\ \mathrm{U}_{mk,t}\ \|_{\mathrm{F}}^{-1} \left(\sum_{m=1}^{M}\ \mathrm{U}_{mk,t}\right) \tag{18}\]  
The correlation feature \(\mathrm{U}_{mk,t}\) represents the correlation between the channel vectors at the UE side and can be used to design efficient precoding schemes that take advantage of the spatial diversity of the wireless channel.  
To improve computational efficiency and numerical stability, QR decomposition is applied to the PA-EW-MMSE weight matrix. Specifically, it is performed on the diagonal blocks of the covariance matrix, which simplifies the matrix inversion process into a more manageable triangular back substitution. At the user equipment (UE) side, a precoding matrix is applied to enable direct beamforming, enhancing the signal quality and reducing interference. In this configuration, the PA-EW-MMSE estimator not only provides improved estimation accuracy but also significantly lowers computational complexity compared to traditional approaches.
## 4 Case Study Investigation
### Platform Application Results  
In this research study, an asphalt pavement construction quality monitoring platform was developed and applied to a highway construction project. The results are summarized as follows:  
1. Centralized supervision of raw materials for construction projects was realized for the digital storage of 9380 pieces of data, thereby providing data support for the traceability of construction engineering quality.  
2. The early warning management system for the asphalt concrete pavement construction process was developed based on real-time monitoring. The system accumulated early warning compaction substandard data (N = 954), unqualified asphalt parameter data (N = 102), and unqualified concrete parameter data (N = 39,896). Consequently, the digital monitoring system significantly improved the quality outcomes for asphalt pavement construction.  
3. The asphalt pavement construction quality monitoring platform was used for centralized online supervision of the project. The system reduced the difficulty for engineers in checking whether the metrology procedures were complete, and the whole cycle was 30% shorter.  
4. The asphalt pavement construction quality monitoring platform for asphalt mixture construction process provided real-time dynamic monitoring. The monitoring data feedback efficiency was high, which ensured that the construction quality was up to standard as well as 100% data compliant, directly saving labor and thereby reducing management costs by RMB 2.6 million. The use of electronic archiving to store a large amount of data also saved paper waste. The system integrated the four stages of mixing, transportation, paving, and rolling in asphalt pavement construction, unified personnel and machinery management, and allocated personnel according to real-time road construction status monitoring, thereby saving RMB 400,000 in material costs and RMB 800,000 in labor and machinery fees.
## 3 Results
### Technical Analysis
#### 3.2.7 Assessing Efficiency and Improvements
This is the reason why many researchers studied and proposed the use of enhanced hybrid fuzzy-based controllers to obtain superior results, for example, the combination of an FLC and a PID controller, where the FLC is used to tune the three constants of the PID controller [68; 75; 133], or the use of evolutionary algorithms, like particle swarm optimization [74; 134], to improve the parameters of the control system. Figure 10 presents the results (number of papers per year) of the analysis of the final set in relation to the efficiency-improving methodologies used by the researchers in their studies. Another aspect that we considered in the analysis was the FLC's efficiency testing and improvement concerning the fuzzy controller's features (Sections 3.2.2 and 3.2.3), as in [132], where the authors present the difference between the Mamdani and T-S strategies when controlling an SVC for power factor reduction. From the 135 papers of the final set, as underlined in Figure 10, only 12 papers contained this type of FLC efficiency-improving methodology. Table 10 describes 10 studies that focused on the efficiency of the fuzzy control assessment and improvement. The information in the table gives a general description of the  
Figure 10: Yearly distribution of published papers concerning methods of efficiency improvement. A—no improvement; B—improvement using PI/PID/hysteresis; C—improvement using optimization algorithms; D—improving the features of FLC; E—improvement using other methods (adaptive, unified). Figure 9: Yearly distribution of efficiency testing strategies in published papers. I—no control; II—PI/PID; III—other fuzzy; IV—literature methods; V—experimental. selected papers, information on the efficacy testing technique, and a detailed presentation of the efficiency testing and improvement method for each proposed FLC.
## 1 Introduction
[22] adopted the idea of transfer learning, combining the UNet basic architecture with ResNet101 and ResNet152 weights. By applying the feature parameters trained on large datasets to new classification tasks, the efficiency and accuracy of small data classification problems improved, reducing data requirements and improving the building extraction capabilities of basic models on remote sensing images. [PERSON] et al. [23] designed context collaboration networks using self-attention mechanisms to capture long-range dependency between buildings, providing complementary semantic and positional information. The above studies show that introducing attention and other feature learning modules can effectively reduce category confusion in semantic segmentation networks and enhance the recognition capability of complex building types. Although improved deep learning methods have achieved good performance in urban building recognition, their applications in rural scenes are still limited [24]. Difficulties exist in recognizing complex and diverse rural building types. Additionally, the high cloud cover in rural areas affects satellite image quality [6]. To solve this problem, low-altitude remote sensing technologies have advantages, such as low cost and high-quality data acquisition, which can effectively improve the accuracy of rural building recognition. For example, [PERSON] et al. [25] used UAV oblique photography to construct rural building datasets and generate digital surface models to realize building contour extraction and area measurement. [PERSON] et al. [26] designed a fully convolutional network based on attention mechanisms to extract multiscale features of rural buildings. However, the above studies mainly focus on local regions and have limited generalization ability. In summary, to solve the problems of insufficient rural building samples and large regional differences, this study constructs a dataset of building images from typical rural areas across China to train deep learning models and improve their generalization ability. Considering the limitations of existing methods, which tend to confuse buildings with objects such as vegetation and roads that have similar spectral features when extracting complex architectural features of rural buildings [27], this paper proposes an improvedremote sensing rural building extraction network called AGSC-Net. Experiments show that the network can effectively extract semantic information from different building types and significantly improve the identification between buildings and backgrounds. The main sections of this paper are arranged as follows. Section 2 introduces the study area and dataset construction and processing. Section 3 presents the main experimental methods, including the improvements and implementation of the AGSC-Net model. Section 4 shows the results of rural building recognition experiments and provides an analysis. Section 5 discusses the effects of different feature combinations on the training results. Finally, Section 6 summarizes the study.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 5 Challenges and Future Directions
### Environmental Factors and Dynamic Obstacles
#### 5.3.2 Future Directions  
Future research could explore advanced control techniques to adapt to environmental factors and dynamic obstacles [163]. Additionally, the use of AI, advanced navigation systems, and modern obstacle detection sensors could make quadcopters more robust and more effective in collision avoidance [164; 165]. For example, the authors in [166] developed a data-driven dynamic obstacle avoidance system for a quadcopter equipped with a liquid tank for pesticide spraying. The system uses data from one long-range wide-angle sensor and four single-point detection sensors to accurately detect and avoid obstacles. By pro cessing sensor data in real-time, the quadcopter achieves precise obstacle avoidance, while maximizing spray coverage and minimizing mission time. This technology allows UAVs to perform agricultural missions efficiently while eliminating concerns about potential damage from collisions.
## 2 Results and Discussion
### Morphological Parameters of Pelargonium graveolus
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{1}{c}{**Shoot**} & \multicolumn{1}{c}{**Root**} & \multicolumn{1}{c}{**Leaves**} & \multicolumn{1}{c}{**Branches**} & \multicolumn{1}{c}{**Fresh Weigh**} & \multicolumn{1}{c}{**Dry Weigh**} \\ \cline{2-9}
**Treatment** & **Length** & **Length** & **Number** & **Number** & **Shoot** & **Root** & **Shoot** & **Root** \\ \cline{2-9}
**(g/L)** & **(cm)** & **(cm)** & **NO.** & **NO.** & **(g)** & **(g)** & **(g)** & **(g)** \\ \hline
**Control** & \(18.00\pm 1.26\) & \(16.67\pm 1.65\) & \(28.08\pm 6.30\) & \(3.33\pm 0.33\) & \(19.63\pm 1.35\) & \(1.42\pm 0.19\) & \(5.27\pm 0.43\) & \(0.53\pm 0.10\) \\  & (a) & (a) & (a) & (a) & (a) & (a) & (a) & (ab) \\ \hline
**Mg** & \(23.67\pm 0.80\) & \(26.33\pm 0.49\) & \(52.50\pm 5.76\) & \(4.17\pm 0.31\) & \(19.12\pm 4.35\) & \(1.28\pm 0.18\) & \(4.86\pm 1.15\) & \(0.45\pm 0.06\) \\
**NP/GW 0.5** & (c) & (b) & (c) & (ab) & (a) & (a) & (a) & (a) \\ \hline
**Mg** & \(20.67\pm 0.42\) & \(24.50\pm 1.28\) & \(44.83\pm 2.34\) & \(3.00\pm 0.26\) & \(21.79\pm 2.96\) & \(2.09\pm 0.35\) & \(5.93\pm 0.39\) & \(0.77\pm 0.07\) \\
**NP/GW 1** & (abc) & (b) & (bc) & (a) & (a) & (a) & (a) & (ab) \\ \hline
**Mg** & \(19.50\pm 0.72\) & \(20.33\pm 1.82\) & \(31.50\pm 2.94\) & \(3.00\pm 0.45\) & \(25.21\pm 0.77\) & \(1.99\pm 0.21\) & \(6.07\pm 0.56\) & \(0.70\pm 0.08\) \\
**NP/GW 5** & (ab) & (a) & (ab) & (a) & (a) & (a) & (a) & (b) \\ \hline
**GW** & \(30.83\pm 1.30\) & \(25.00\pm 1.06\) & \(53.83\pm 4.27\) & \(4.67\pm 0.33\) & \(23.34\pm 1.66\) & \(1.55\pm 0.05\) & \(6.49\pm 0.31\) & \(0.71\pm 0.05\) \\
**0.5** & (d) & (b) & (c) & (b) & (a) & (a) & (a) & (a) \\ \hline
**GW** & \(22.67\pm 2.08\) & \(20.33\pm 1.15\) & \(36.17\pm 5.59\) & \(3.33\pm 0.33\) & \(22.61\pm 0.47\) & \(1.87\pm 0.42\) & \(5.47\pm 0.34\) & \(0.65\pm 0.12\) \\
**1** & (bc) & (a) & (ab) & (a) & (a) & (a) & (a) & (ab) \\ \hline
**GW** & \(23.00\pm 0.63\) & \(25.67\pm 1.33\) & \(34.83\pm 4.63\) & \(3.83\pm 0.48\) & \(22.93\pm 0.37\) & \(1.30\pm 0.42\) & \(5.13\pm 0.54\) & \(0.63\pm 0.10\) \\
**5** & (bc) & (b) & (ab) & (ab) & (a) & (a) & (a) & (ab) \\ \hline \hline \end{tabular} \({}^{\star}\) Different letters (a–d) denote significant differences between treatments at \(p<0.05\).  
\end{table} 
Table 3: Effects of different concentrations of Mg NP/GW and GW nanoparticles on shoot length, root length, number of branches, number of leaves, and fresh and dry weight of _P. gravelens_ plant \({}^{\star}\). Figure 7: Effect of Mg NP/GW at concentrations of 0.5, 1, and 5, and GW at concentrations of 0.5, 1, and 5 on plant growth and biomass: (**a**) shoot length and root length; (**b**) number of leaves and branches; (**c**) shoot fresh weight and dry weight; (**d**) root fresh weight and dry weight of _P. gravelens_.
# Consensus Big Data Clustering for Bayesian Mixture Models
## Appendix A  
In this section, we try to marginalise \(\mu\) and \(\sigma_{2}\) for the full conditional of \(\rho\).  
\[p(\rho|\mathbf{y}) \propto\prod_{j=1}^{k_{n}}\Bigg{\{}c(S_{j})\int\int\prod_{i\in S_{ j}}p(y_{i}|\mu_{j},\sigma_{j}^{2})p(\mu_{j})p(\sigma_{j}^{2})d\mu_{j}d\sigma_{j}^{2} \Bigg{\}},\] \[\propto\prod_{j=1}^{k_{n}}\Bigg{\{}(|S_{j}|-1)!\Bigg{\}}\times\] \[\propto\prod_{j=1}^{k_{n}}\int\int(\sigma^{j})^{-n_{j}/2}\exp \Bigg{\{}-\frac{1}{2\sigma_{j}^{2}}\sum_{i\in S_{j}}(y_{i}-\mu_{j})^{2}\Bigg{\}} \exp\Bigg{\{}-\frac{1}{2\sigma_{0}^{2}}\sum_{i\in S_{j}}(\mu_{j}-\mu_{0})^{2} \Bigg{\}}d\mu_{j}d\sigma_{j}^{2},\] \[\propto\prod_{j=1}^{k_{n}}\Bigg{\{}(|S_{j}|-1)!\Bigg{\}}\times\] \[\propto\prod_{j=1}^{k_{n}}\int\int(\sigma_{j}^{2})^{-n_{j}/2}\exp \Bigg{\{}-\frac{1}{2}\bigg{(}\mu_{j}^{2}\bigg{[}\frac{|S_{j}|}{\sigma_{j}^{2} }+\frac{1}{\sigma_{0}^{2}}\bigg{]}-2\mu_{j}\bigg{[}\frac{\sum_{i\in S_{j}}y_{i }}{\sigma_{j}^{2}}+\frac{\mu_{0}}{\sigma_{0}^{2}}\bigg{]}\Bigg{)}\Bigg{\}}d\mu /d\sigma_{j}^{2}\] \[\propto\prod_{j=1}^{k_{n}}\Bigg{\{}(|S_{j}|-1)!\Bigg{\}}\times\] \[\propto\prod_{j=1}^{k_{n}}\int(\sigma_{j}^{2})^{-n_{j}/2}\Big{(}n _{j}/\sigma_{j}^{2}+1/\sigma_{0}^{2}\Big{)}^{-1/2}\exp\Bigg{\{}-\frac{1}{2 \sigma_{j}^{2}}\sum_{i\in S_{j}}y_{i}^{2}+\frac{\Big{(}\sigma_{j}^{-2}\sum_{i \in S_{j}}y_{i}+\mu_{0}/\sigma_{0}^{2}\Big{)}^{2}}{n_{j}/\sigma_{j}^{2}+1/ \sigma_{0}^{2}}\Bigg{\}}d\sigma_{j}^{2}\]  
The expression for \(p(\rho|\mathbf{y})\) is a product of integrals, where each integral is over the variables \(\mu_{j}\) and \(\sigma_{j}^{2}\). The integral of a Gaussian distribution is a Gaussian distribution with a modified mean and variance. Hence, we can simplify the expression as follows:  
\[p(\rho|\mathbf{y})\propto\prod_{j=1}^{k_{n}}(|S_{j}|-1)!\times\prod_{j=1}^{k_ {n}}\!\!\left(\frac{n_{j}}{\sigma^{2}j}+\frac{1}{\sigma^{2}0}\right)^{-1/2}\int (\sigma_{j}^{2})^{-\frac{n_{j}}{2}}\exp\left(-\frac{1}{2\sigma^{2}j}\sum_{i \in S_{j}}y_{i}^{2}+\frac{\Big{(}\frac{\sigma^{-2}j\sum_{i\in S_{j}}y_{i}+\mu_ {0}}{\sigma^{2}0}\Big{)}^{2}}{\frac{nj}{\sigma^{2}j}+\frac{1}{\sigma^{2}0}} \right)d\sigma_{j}^{2}. \tag{1}\]