esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Relative Expression of TrHSFB2a  
To determine the relative expression level of _TrHSFB2a_ in white clover under drought, heat and salt stress conditions, the four-week-old white clover plants were subjected to drought or salt stress by irrigating with [PERSON]'s solution containing PEG6000 15% _w/b_ or 200 mM L\({}^{-1}\), respectively, in a controlled growth chamber. High-temperature stress was induced by placing the four-week-old white clover plants in a growth chamber adjusted with a continuous temperature of 35 \({}^{\circ}\)C. The processing times were 0 h, 1.5 h, 3 h, 6 h, 12 h and 24 h. The plant samples were collected, immediately frozen in liquid nitrogen, and stored at \(-80\)\({}^{\circ}\)C for the subsequent process of total RNA extraction. Plant tissues (leaf, stem and roots) were ground in liquid nitrogen and 100 mg of tissue powder was used to extract the total RNA using HiPure Universal RNA Mini Kit (Magen, Beijing) by following the manual's instructions. After confirming the quantity and quality on the NanDrop spectrophotometer and the RNA integrity on 1.5 agarose gel, cDNA was synthesized by reverse transcription of the first strand of RNA using MonScript(tm) RTIII All-in-One Mix with dsDNA (Monad Biotech Co., Ltd., Beijing, China) following kit protocols. Later, the relative gene expression was determined using 2X M5 HiPer SYBR Premix EsTaq (with Tli RNaseH) (Mei5 Biotechnology, Co., Ltd., Beijing, China) and the CFX96 Real-Time PCR detection system (Bio-RAD) according to manual instructions. The qRT-PCR primers are provided in Supplementary Table S1. The TrB-Actin gene was used as an internal control for white clover. The amplification conditions were as follows: initial denaturation at 95 \({}^{\circ}\)C for 5 min, then, 40 cycles at 95 \({}^{\circ}\)C for 10 sec and 60 \({}^{\circ}\)C for 30 s. The relative difference in the expression of the genes was calculated by the 2\({}^{-\Delta\Delta\text{Ct}}\) method [59].  
For determining _TrHSFB2a_ expressions in transgenic _Arabidopsis_, the seeds of 10 overexpression lines were sown on 1/2 MS media supplemented with 3% sucrose and 0.7% agar, followed by vernalization for 2 days. After germination, seedlings were grown for an additional 10 days in the same 1/2 MS media plates placed in a growth chamber set at 21 \({}^{\circ}\)C with 65% relative humidity, 16 h photoperiod, and 150 \(\upmu\)mol m\({}^{-2}\).s\({}^{-1}\) photoactive radiation. Then, the extraction of the total RNA, reverse transcription and qRT-PCR analysis was the same as stated above for white clover.
# A P2P Scheme for Debating and Voting with Unconditional Flexibility
## 4 Analysis
### Security Requirements
#### 4.3.3 Verifiability  
This is the user's ability to verify that their vote has been correctly recorded and accounted for in the final tally. There are two definitions [4]. One is individual verifiability where only the user can verify their vote in the tally. The second is universal verifiability where, after the tally has been published, anyone can verify that all valid votes were included and the tally process was accurate.  
In this scheme every user has a file which fulfills the role of bulletin board with all the contributions. Users can compare their files in order to detect and add missed contributions (Section 3, phase 2, item 3). Further, in the final step everybody can add a signature when they have checked that all their contributions have been included. Thus, at the end of the process, everyone must have the same file, and users can check their contributions (individual verifiability) in it. On the other hand, fake contributions are avoided by the TLS channel security and the alias signature. It is not possible to sign a fake contribution of another user without knowing their private alias key. The owner of an alias is the only one capable of inserting contributions, but they will be associated with the same alias and will not affect the others. Thus, all the users can check every contribution and its signature, relate these contributions with its alias, and check if any alias has submitted more than one vote per proposal or any other irregularity (universal verifiability). The tally is computed by each user. Thus, there is individual and universal verifiability.  
Verifiability is concerned with **accuracy**, which is mentioned in other papers as an additional feature. An e-voting system must be error free, that is, votes must be registered correctly and votes of invalid users must not be counted. The way to achieve this accuracy is by verifiability, such that if an error happens, it can be detected and corrected.
## 4 Results and Discussion
### Impact Mechanism of Urban Spatial Form on Outdoor Environmental Performance
#### 4.2.1 Impact Mechanism of Urban Spatial Form on UTCI-S  
In Model 1, the results of explainable machine learning (Figure 10a) show that the variables influencing UTCI-S include AH, SC, AV, FAR, SV, MA, SA, and others, while shape indicators such as CVH, StdH, OSR, and SCD have little impact on UTCI-S. The SHAP summary plot (Figure 10b) reveals that spatial form indicators such as SC, SA, and SV significantly promote UTCI-S, while AH, AV, MA, and FAR significantly suppress UTCI-S.  
These results show the impact of urban spatial form on summer outdoor thermal comfort. Specifically, the higher the average building height, the larger the building volume, the greater the average building footprint, and the higher the floor-area ratio (FAR), the lower the UTCI value in summer, leading to a more comfortable overall thermal environment. This may be because increasing the building height and volume reduces the sun exposure distance between buildings in the same block, causing the outdoor area to remain in the shadow of buildings, reducing solar radiation on the same unit area, which lowers the temperature of the ground and surrounding air, and improving outdoor thermal comfort for the elderly during the summer.  
On the other hand, the more complex the building shape and the greater the variation in building volume and area within the block, the higher the UTCI value in the summer. This may be due to the fact that complex-shaped blocks hinder natural ventilation, leading to heat accumulation, which raises the surrounding environment temperature and thus exacerbates the discomfort experienced by the elderly.
# Flexural Strength and Stiffness of Donut-Type Voided Slab
## 4 Test Results and Discussion
### Flexural cracking Load of Donut-Type Voided Slab  
Flexural cracking strength, related with a flexural cracking load directly, is important to define minimum tensile reinforcement ratio for slabs. Flexural cracking strength can be obtained from Equation (1) in ACI 318-11 [14], and flexural cracking strength should be higher in 'Solid' than the voided slab specimens theoretically, because these have lower values of gross concrete section moment of inertia (\(I_{g}\)) due to voids.  
\[M_{cr}=\frac{f_{r}I_{g}}{y_{t}} \tag{1}\]  
* \(M_{cr}\) : Cracking moment (MPa)
* \(I_{g}\): Moment of inertia of gross concrete section about centroidal axis
* \(f_{r}\): Modulus of rupture of concrete
* \(y_{t}\): Distance from centroidal axis of gross section to extreme tension fiber, neglecting reinforcement  
However, because the donut-type voided slab has two types of cross-sections which can cause initial flexural crack (the minimum cross-section and the donut-type cross-section, as shown in Figure 13), the cross-section where the initial flexural crack occurs must be defined in order to use Equation (1). Therefore, to define the cross-section for flexural cracking strength of the donut-type voided slab, the test results were compared to the values calculated by Equation (1) in the two types of cross-sections (refer to Table 7). As expected, the initial flexural crack was occurred at a lower load in 'OF-V-S-D' and 'OF-V-S-R' than 'Solid', but contrary to expectations, the flexural cracking load of 'OF-V-M-D' was similar to 'Solid'. In addition, flexural cracking load of voided slabs derived from Equation (1) was higher than that of obtained from the test, and the difference was even greater in the 'Solid' specimen. It is deduced that the gap between these experimental values and calculated values resulted from the fact that the self-weight of specimen is not taken into consideration. Practically, the uniformly distributed load of 7.36 kN/m, 5.41 kN/m and 5.38 kN/m already existed even before loading in the solid, donut and non-donut-type voided slab specimens, respectively, because of the self-weight of specimens. When the self-weight is taken into consideration, the flexural cracking load measured by the test is converted 60.13 kN for 'Solid', 54.11 kN for 'OF-V-M-D', 45.30 kN for 'OF-V-S-D' and 44.73 kN for 'OF-V-S-R'. As a result, it is clear that the flexural cracking load in donut and non-donut-type voided slabs is lower than that in a solid slab.  
Based on these results, it could be deduced that the hole in donut-type void shaper did not influence on flexural cracking strength significantly, but the fixing method of void shaper influence on the flexural cracking strength. In addition, it was verified that minimum cross-section properties should be used to calculate the flexural cracking moment in donut-type voided slabs, which are 81.6%-97.4% of what is obtained from Equation (1).
## 3 Automatically Choosing the Number of Clusters
### Eigengp Heuristic  
This method consists of finding the number of Laplacian eigenvalues such that the first \(k\) eigenvalues \(\lambda_{1},\ldots,\lambda_{k}\) are small, and \(\lambda_{k+1}\) is relatively high [25]. This means that one should sort the eigenvalues in descending order, because it is a trace maximization problem, and find a gap in these values. We define the gap as the maximum difference between two successive eigenvalues as:  
\[gap(k)=|\lambda_{k}-\lambda_{k+1}|,k=1\ldots,K_{max} \tag{45}\]  
where \(K_{max}\) is the maximum number of clusters. The value of the eigengap for subsequent eigenvalues will indicate where the number of clusters should be selected. The number of clusters \(K\) is given by:  
\[K=argmax(gap(k)). \tag{46}\]  
However, if the distribution of the eigenvalues is uniform, the determined \(K\) is not always relevant.
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G-Mec V2X Testbeds
#### 3.3.2 Smart Highway V2X Testbed  
The Smart Highway V2X testbed [49] is a section of the CityLab testbed [50]. The Smart Highway V2X testbed, located in Antwerp (Belgium), is built on top of an open highway and comprises a highway strip with RSUs installed. These RSUs can handle V2X radio connections, such as short-range based on ITS-G5 and C-V2X with a PC5 interface operating at 5.9 GHz and long-range C-V2X Uu, allowing them to communicate with vehicles equipped with these radio access technologies. 4G is used for long-distance connectivity. Experimenters can choose which communication modules to test on the RSUs, which are equipped with commercial and SDR modules. Each RSU is fiber-connected to the virtualized cloud-based backbone. The Smart Highway V2X testbed [51] includes two mobile OBUs, enabling V2X communications. The OBUs are also supported with long-range communications using 4G connectivity from the Mobile Network Operator (MNO). The OBUs are equipped with GNSS modules for localization.  
The Smart Highway V2X testbed provides MEC collocation with the RSU and tests the benefit of the intelligent application in a real-time environment [52]. For the MEC platform deployment and RSU, Kubernetes cluster deployment is performed to monitor services based on Prometheus [53]. The real-time monitoring supports MEC applications and generates the statistics for the containerized MEAO (cMEAO). The cMEAO makes decisions about the MEC host availability and location of the vehicles.
## 4 Conclusions  
Spark duration measurements were applied for identifying the firing TDC in small size SI engines that feature 'wasted' spark ignition systems. Engine speed-based evaluations were taken as the benchmark for validating the proposed approach.  
Implementing the simple condition of above/below a median threshold developed in a previous study, with data analysis of samples acquired at 50 kHz, was found to be compatible only with long term ID, i.e., after acquiring a certain number of cycles. The spark duration method performed better at low load and engine speed, while the rpm-based method ensured slightly better results at high load.  
A new procedure was developed and implemented by running the analysis in odd/even mode to render it compatible with online determinations. One of the main conclusions was that the spark duration method can be applied as a cycle-to-cycle evaluation tool with TDC ID success rates of over 99.8%. While not as accurate as physical sensors (e.g., a camshaft position sensor), this value suggests very high reliability and is directly comparable with that obtained by implementing rpm based evaluations.  
Furthermore, the spark duration procedure was found to be more reliable when performing measurements with much lower sample rates of 10 kHz. In addition to the main goal of correctly identifying the firing TDC, the new method can also be implemented for evaluating cycle-to-cycle variability of the local in-cylinder fluid velocity and thus further enhancing combustion control margins. Future efforts will focus on the development and implementation of a LabVIEW VI that merges the two benefits derived from spark duration measurements.  
Conceptualization, A.I. and S.S.M.; methodology, A.I.; software, A.I.; validation, A.I. and S.S.M.; formal analysis, A.I.; investigation, A.I.; data curation, A.I.; writing--original draft preparation, A.I.; writing--review and editing, A.I. and S.S.M.; visualization, A.I.; supervision, B.M.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData is available upon request.  
The help of [PERSON] with the engine setup and [PERSON] with the electrical setup is gratefully acknowledged.  
The authors declare no conflict of interest.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 6 Conclusions  
Reliable transformer fault detection is crucial for maintaining the stability and efficiency of power systems. This study explored the application of Frequency Response Analysis (FRA) data combined with machine learning models to address challenges in detecting transformer faults such as winding deformation, insulation breakdown, and partial discharges. The research aimed to evaluate the effectiveness of machine learning classifiers, including Decision Trees and Subspace KNN, in fault detection across eight frequency bands (20 kHz to 12 MHz), while identifying performance limitations and opportunities for improvement.  
The study achieved fault classification accuracies between 80% and 100%, demonstrating the significant potential of machine learning in predictive maintenance strategies. However, challenges remain in accurately detecting complex fault types, such as contamination-related defects and lamination deformation. These findings highlight the need for refined feature extraction techniques and higher-resolution data to improve diagnostic performance.  
Integrating machine learning into transformer diagnostics can minimize downtime and enhance asset management. While current results are promising, further research should explore the integration of these models into real-world diagnostic systems, such as online monitoring frameworks. This would allow for continuous assessment of transformer health and early detection of faults before catastrophic failures occur. Additionally, expanding the dataset to include a broader range of transformer types and operational conditions will help improve model generalization and robustness.  
Future research should focus on scaling these models through advanced deep learning techniques, improving generalization across diverse transformer conditions, and addressing data scarcity. Exploring hybrid approaches that combine physics-based modeling with machine learning could further enhance predictive accuracy. Ultimately, these advancements will contribute to the development of more reliable and scalable frameworks, ensuring the longevity and operational efficiency of power transformers globally.  
Conceptualization, N.S.M., B.A.T. and N.M.; methodology, N.S.M., B.A.T. and N.M.; validation, N.S.M., B.A.T. and N.M.; formal analysis, N.S.M., B.A.T. and N.M.; investigation, N.S.M., B.A.T. and N.M.; resources, N.S.M., B.A.T. and N.M.; data curation, N.S.M., B.A.T. and N.M.; writing--original draft preparation, N.S.M.; writing--review and editing, B.A.T. and N.M.; visualization, N.S.M., B.A.T. and N.M.; supervision, B.A.T. and N.M.; project administration, B.A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data will be made available by authors upon request.  
The authors would love to thank the University of Johannesburg for their administrative and technical support.  
The authors declare no conflicts of interest.
## 2 Literature Review
### Financial Data Prediction Using LSTM in Finance
#### 2.1.4 LSTM Applications in Sentiment-Driven and Cryptocurrency Forecasting  
A deep learning-based stock market prediction model incorporating investors' emotional tendencies has been developed by simplifying stock price sequences with EMD and using an LSTM network with an Attention mechanism [6]. Experiments with Apple Inc. (AAPL) stock data showed that the model excels in predicting closing prices, classifying price movements, and reducing time delays. It offers valuable benefits for both governments and individual investors by providing accurate and timely predictions. Future research will investigate the use of semi-supervised models for sentiment analysis.  
LSTM has been employed to predict stock price movements by incorporating investor Attention proxies to improve accuracy [4]. The LSTM model effectively handles nonlinear and nonstationary financial time-series data. Adding new attention proxies, such as Baidu search volume and news counts, enhances predictive performance. The experimental results show that the LSTM model with these proxies achieves the highest prediction accuracy. These findings are beneficial for investors focusing on quantitative investment strategies.  
The impact of aggregate investor Attention on Bitcoin returns has been evaluated using LSTM, incorporating direct proxies such as Google Trends and Tweets alongside Bitcoin trading variables, significantly enhancing prediction accuracy [22]. Specifically, Google Trends and Tweets have been shown to greatly improve forecasting performance. The findings suggest that investor attention provides crucial information for predicting Bitcoin price movements. LSTM is demonstrated as a promising model for these predictions.
# Monitoring a Pre-Normative Multi-Family Housing Case-Study in a Mediterranean Climate
## 4 Results  
Once the monitoring campaign is completed, then the thermal behavior of the dwellings is analyzed, and the way in which the different temperature and relative humidity indoor and outdoor parameters unfold over time is assessed.  
In order to carry out an assessment of indoor comfort conditions, indoor temperature values have been compared to a comfort band in line with the ASHRAE methodology, by applying the adaptive comfort equation [32]. In order to calculate the comfort band, a 90% level of acceptance has been considered, included in a \(\pm\)2.5 \({}^{\circ}\)C interval. According to this equation, 90% of occupants would be satisfied within established conditions of the comfort band.
# Research on the Parametric Design and Application of Ceramic Modeling Based on Python
## 3 Parametric Design Practice
### Python Code Control Manufacturing Generation
The design requirements are divided into three aspects: (1) geometric feasibility (1 mm \(\leq\) t \(\leq\) 10 mm; t represents the thickness of the ceramic bottle) to ensure the plasticity of the 3D printing process; (2) maintaining the integrity of the structure and preventing surface self-intersection; and (3) conforming to ergonomics. Python codes leverage advanced geometric algorithms and mathematical theories--such as interpolation, approximation, and surface subdivision--to compute blended surfaces that preserve the structural integrity and aesthetic appeal of ceramic models. These algorithms ensure smooth, seamless transitions between blended surfaces and adjacent internal or external surfaces, adhering to specified geometric continuity criteria. The process further involves iterative adjustments and optimizations of the blended surfaces to enhance design precision and achieve optimal visual and functional results. Finally, the Python code is responsible for correctly closing the inner and outer surfaces as well as the blended surfaces to form a complete ceramic shape. This includes handling the matching of surface boundaries, adjusting normal vectors, and verifying the overall geometric characteristics. The code must comprehensively verify and inspect the generated ceramic shape to ensure it meets design requirements while also being feasible for actual production, covering multiple aspects such as surface continuity, smoothness, wall thickness uniformity, and overall shape accuracy. In summary, Python code plays a core controlling role in the process of parametric ceramic shape generation. Through the precise calculation of rotation matrices, construction of 3D surfaces, generation of inner surfaces and blended surfaces, and overall verification, Python code ensures that ceramic shape generation is both rapid and accurate, laying a solid technical foundation for subsequent manufacturing and production. For example, NURBS-based modeling methods demonstrate their advantages in constructing vivid ceramic shapes [14], while Python, as an advanced programming language, is widely used for rapid development and prototyping due to its concise syntax and rich libraries, which have been widely applied in the architectural field; however, it has not yet seen reasonable application in traditional ceramics industries. This paper presents the first use of Python programming to achieve simple to moderately complex ceramic shape designs. Its powerful computational capabilities and flexible programming features make parametric design possible, significantly improving the efficiency and quality of ceramic shape design (see Figure 7). Figure 7: Parametric batch production of ceramic shapes.