esa-sceva/satcom-chunk-collection huggingface dataset:
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 3 Results
### Multiple Methods to Build Models Based on Vegetation Index
#### 3.3.1 Multiple Linear Regression Method  
The selected vegetation indices with Pearson correlation coefficients exceeding 0.5 were utilized as independent variables, and yield was used as the dependent variable in the SPSS software. The Multiple Linear Regression method was employed to construct the wheat yield estimation model. The resulting model achieved a correlation coefficient of 0.835, and the significance (F) was 0.03, meeting the requirements for conducting regression analysis. This indicates a strong relationship between the selected vegetation indices and wheat yield, suggesting the model's effectiveness in estimating wheat production.
# Safe and Sustainable Design of Composite Smart Poles for Wireless Technologies
## 5 Conclusions  
Several potential designs of 5 GPs have been proposed for the physical device frame and service platforms within the current industry of wireless communication technologies. This study focuses on the physical structure of a 5G smart light pole and its multidisciplinary design process. The work includes an interacting research of a GFRP composite pole structure with finite element (FE) analysis and experimental verification, signal attenuation measurements of the latest low-attenuation materials and metal 3D printing combined with high-fidelity CFD computation to understand the heat management inside the densely device-integrated 5G pole. Based on the results, the work revealed the following specific novelties related to the next-era wireless application platform:  
* A full-composite glass fibre reinforced 5G pole was FE-modelled and analysed against standard wind and thermal load. The findings showed that a mechanically safe and functional (stiff) GFRP shaft results in significant weight savings (37-80%) compared to traditional steel shafts;
* RF signal attenuation at a GHz-regime (2.45 GHz) was found to increase significantly (90-175%) due to any paint layer while long-term UV degradation in the polymer structure led only to a nominal decrease of attenuation in terms of dielectric loss;
* Entirely integral one-piece heat exchangers were designed with CFD analysis of the fluid-solid interaction for heat transfer, and printed. It was found that a parallel liquid cooling of four radio units is rather insensitive to the flow rate (range 2...6 L/min) and as high as \(\approx\)60 \({}^{\circ}\)C inlet temperature can be allowed to keep the device surfaces at or below a critical 65 \({}^{\circ}\)C.  
Conceptualization, M.K. and V.V.; methodology, T.P. and K.S. and H.H.; software, D.D.V. and A.L. and M.K.; validation, D.D.V., A.L. and T.P.; investigation, J.J. and K.K.; writing--original draft preparation, M.K. and D.D.V. and T.P.; writing--review and editing, V.V. and H.H.; visualization, M.K.; project administration, M.K. and H.H. and V.V. All authors have read and agreed to the published version of the manuscript.  
This work was done as part of the LuxTurrim5G ecosystem funded by the participating companies and Business Finland.  
Researchers [PERSON] and [PERSON] are acknowledged for their help and support. Nokia Bell Labs ([PERSON] and [PERSON]) are acknowledged for the collaboration and design process development. Exel Composites ([PERSON] and [PERSON]) is acknowledged for the material and manufacture support.  
The authors declare no conflict of interest. The funding agency had no role in the interpretation of data.
## 5 Conclusions and Future Work  
Accurate wind power prediction is of great significance for energy management and scheduling in wind farms. To achieve high-precision wind power forecasting, this paper proposes improvements from the perspectives of the dynamic and heterogeneous relationships between wind power and its covariates, and proposes the dynamic heterogeneous graph attention recurrent network, including the following enhancements:  
1. In addition to weather characteristics such as wind speed, wind direction, and temperature, the proposed model incorporates conditions related to the generator, including generator speed, rotor speed, and current. This provides the prediction model with comprehensive and sufficient information.
2. To fully utilize multi-variate data, a heterogeneous graph attention mechanism is proposed. This method not only effectively learns the interrelationships among different features, but also introduces heterogeneous functions to capture diverse features from different sources.
3. A novel flow-aware dynamic graph learning method is proposed, which connects the generated graph with real-time data.
4. The proposed model adopts GRU as an external predictor and replaces its linear layer with a dynamic heterogeneous graph attention mechanism. This allows the model to effectively utilize the rich information embedded within the features.
5. Experimental results demonstrate that the proposed model can effectively exploit the heterogeneity and dynamics of the wind power dataset, leading to significantly improved performance compared to existing SOTA baselines.  
Although the proposed model has achieved promising prediction results, it still has some limitations. In the future, we will further improve it from the following perspectives:  
1. In addition to heterogeneous variables, the wind power data from different monitoring stations also influence each other. In the future, we will further consider establishing a spatiotemporal wind power prediction model.
2. Due to the combination of graph neural networks and recurrent neural networks, the computational complexity of DHGAR is O(N\({}^{2}\)L\({}^{2}\)). In the future, we will focus on optimizing the model's efficiency.  
Conceptualization, C.Y. and M.X.; methodology, M.X.; software, R.Z. and M.X.; validation, R.Z. and C.Y.; formal analysis, M.X.; investigation, M.X.; resources, R.Z.; data curation, R.Z.; writing--original draft preparation, M.X.; writing--review and editing, R.Z.; visualization, R.Z. and X.M.; supervision, X.M.; project administration, X.M.; funding acquisition, X.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset used in this study has been released on GitHub at [[https://github.com/ChengqingYu/MTS_dataset](https://github.com/ChengqingYu/MTS_dataset)]([https://github.com/ChengqingYu/MTS_dataset](https://github.com/ChengqingYu/MTS_dataset)), accessed on 28 October 2024.  
The authors declare no conflicts of interest.
# A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
## 1 Introduction
To assess the performance of our DRL-DM model, we conduct comprehensive experiments on both synthetically generated datasets and real-world benchmarks, spanning various problem sizes and node distributions. Empirical results demonstrate that DRL-DM achieves substantial performance gains, reducing tour length by up to 8.75% compared to heuristic and learning-based baselines. Moreover, our approach exhibits superior generalization capabilities relative to existing DRL models, underscoring its robustness and scalability across diverse routing scenarios. In summary, our key contributions are as follows:  
* We propose a DRL-based decision-making approach designed to address routing problems across varying problem sizes and node distributions. * We develop a dynamic-aware context embedding that explicitly captures state transitions and graph variations during route construction, enhancing the model's ability to adapt to changing environments. * We conduct extensive experiments demonstrating that our approach achieves substantial improvements in both solution quality and computational efficiency, establishing a new benchmark for learning-based routing optimization. The remainder of this paper is structured as follows. Section 2 reviews the related work on both traditional and DRL-based methods for routing problems. Section 3 introduces the overall framework of the proposed approach, detailing the encoder, decoder, and training algorithm. Section 4 presents the experimental setup and results. Finally, Section 5 concludes the paper and outlines potential directions for future research.
# Steering Dynamic and Hybrid Steering Control of a Novel Micro-Autonomous Railway Inspection Car
## 3 Lateral Steering Dynamic Simulation
### Running on a Right-Corner Track  
The vehicle's self-steering ability during cornering was simulated with reference to the MRT track in Taipei, Taiwan, with a 200 m radius right turn. The turn radius of the track changed gradually and then increased to facilitate smooth passage. The straight track (STR) refers to the section of the track leading into the curve. The clothoid transition (CLO1)  
Figure 6: Lateral displacement of the vehicle center running on a straight rail track in SIMPACK and SIMULINK.  
Figure 7: Lateral forces in (**a**) front wheelset and (**b**) rear wheelset running on a straight track in SIMPACK and SIMULINK.  
connected the straight track to the curve, gradually reducing the turn radius to the smallest value of 200 m. The vehicle then navigated the circular curve (CIR), where the radius remained constant at 200 m. Afterward, the turn radius increased gradually along the second clothoid transition (CLO2), eventually returning to another straight-track segment.  
Figure 8 illustrates the lateral displacement of the vehicle center when turning right. SIMULINK and SIMPACK simulation results indicated that the distance between the horizontal position of the vehicle center and the center of the track increased when the vehicle was running on the CLO1 segment and peaked at 4.5 mm when the radius of the track was decreasing. These figures remained unchanged when the vehicle was operated on a CIR segment with a constant track radius. When the vehicle was running on the CLO2 stretch, the lateral position of the vehicle center substantially reduced before returning to a stable position after cornering.  
Figure 9 illustrates the lateral forces acting on the front and rear wheels while navigating a right-turning track in SIMPACK and SIMULINK simulations. In Figure 9a, the right wheel of the front wheelset experiences a peak lateral force of approximately 125 N, while the left wheel remains near zero. In Figure 9b, the left wheel of the rear wheelset shows a peak force of about \(-\)175 N, indicating significant asymmetry in the lateral force distribution during cornering. Simulation results indicated that the vehicle center did not maintain a balanced position during cornering; this can affect the test results of inspection systems. Thus, the proposed steering control system was deemed necessary to improve the inspection capabilities of the micro-autonomous vehicle.  
Simulation results for the straight and right-corner track indicated that the lateral position of the vehicle center and the lateral forces acting on the left and right wheels differed by less than 10%. Thus, the horizontal dynamics model created using SIMULINK was considered appropriate for further simulations of the vehicle's steering system control.  
Figure 8: Lateral displacement of the vehicle center running on a right-cornering track in SIMPACK and SIMULINK.
## 5 Test-Result Analysis
### Prediction-and-Evaluation System of Mechanical Properties of Steel Anchor Box Based on PSO-BP
#### 6.2.2 PSO-BP Prediction Model
The prediction results of three cases were selected for comparison with the calculated results to demonstrate the workability of the model. The comparison results are shown in Table 11. The analysis of the results in Figure 21 and Table 11 showed that the correlation coefficients for the predicted mechanical properties of the main transmission welds in the three stages of training, testing, and prediction were all greater than 0.99, and that the _MSEs_ were all less than 0.13, indicating that the model can be used to explore the intrinsic relationship between the design load, the plate-geometry parameters, and the mechanical properties of the main transmission welds in the process of iterative training, and generate more reasonable predictions of the mechanical properties of the main transmission welds. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Set** & **Validation Set** & **Test Set** \\ \hline _MSE_ & 0.12119 & 0.12250 & 0.07065 \\ _NS_ & 0.99970 & 0.99972 & 0.99984 \\ \hline \hline \end{tabular} Note: the closer the Nash efficiency coefficient (_NS_) is to 1, the higher the likelihood that the model has a high degree of confidence.  
\end{table} 
Table 10: PSO-BP algorithm’s performance-evaluation results. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{**Dataset**} & **Sample** & **Statistical** & \multirow{2}{*}{\(\mu_{1}\)} & \multirow{2}{*}{\(p_{1}\)} & \multirow{2}{*}{\(\tau_{\text{max1}}\)} & \multirow{2}{*}{\(\mu_{2}\)} & \multirow{2}{*}{\(p_{2}\)} & \multirow{2}{*}{\(\tau_{\text{max2}}\)} & \multirow{2}{*}{\(p_{3}\)} & \multirow{2}{*}{\(p_{3}\)} & \multirow{2}{*}{\(\tau_{\text{max3}}\)} & \multirow{2}{*}{\(p_{4}\)} & \multirow{2}{*}{\(p_{4}\)} & \multirow{2}{*}{\(p_{4}\)} & \multirow{2}{*}{\(\tau_{\text{max4}}\)} & \multirow{2}{*}{\(P\)} \\  & **Size** & & & & & & & & & & & & & & & \\ \hline \multirow{4}{*}{\ \begin{tabular}{c} Training \\ set \\ \end{tabular}  } & \multirow{4}{*}{263} & Max & 2.008 & 24.84\% & 60.002 & 2.286 & 23.16\% & 61.934 & 2.183 & 24.76\% & 62.298 & 2.103 & 24.11\% & 58.313 & 96.49\% \\  & & Min & 1.781 & 23.01\% & 27.738 & 1.954 & 21.69\% & 28.974 & 1.852 & 22.63\% & 29.681 & 1.783 & 21.82\% & 26.709 & 89.42\% \\  & & Average & 1.892 & 0.240 & 40.957 & 2.078 & 0.226 & 42.380 & 2.025 & 0.235 & 43.032 & 1.934 & 0.229 & 40.147 & 92.89\% \\  & & Stdevs & 0.065 & 0.005 & 8.555 & 0.090 & 0.004 & 8.807 & 0.088 & 0.004 & 8.947 & 0.081 & 0.003 & 8.452 & 1.42\% \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Validation \\ set \\ \end{tabular}  } & \multirow{4}{*}{56} & Max & 1.991 & 24.82\% & 58.313 & 2.283 & 23.16\% & 60.067 & 2.171 & 24.21\% & 61.040 & 2.089 & 23.26\% & 57.194 & 95.33\% \\  & & Min & 1.782 & 23.04\% & 27.853 & 1.969 & 21.78\% & 29.103 & 1.861 & 22.66\% & 29 Figure 21: Correlation of predicted output data with actual output data for training, validation, and testing phases.
## 3 Identification and Quantification of Volatile and Semi-Volatile Compounds in Sparkling Wines
### Sample Preparation
#### 3.1.4 Stir Bar Sorptive Extraction (SBSE)  
Alternative configurations to traditional SPME have emerged over the years to enhance extraction efficiency and sensitivity in wine analysis. One example is stir bar sorptive extraction (SBSE), which employs a magnetic stir bar, known as a twister, encapsulated in glass and coated with a sorptive phase, typically PDMS or ethylene glycol (EG) [76; 79]. In the same way as SPME, this technique relies on the partitioning of analytes between the sample and the sorbent phase. However, it offers significantly improved sensitivity (50-250 times greater) because of the larger capacity of sorptive material in the device (24-126 uL versus 0.6 uL in SPME), which enables greater analyte uptake. This is particularly relevant for trace analysis in complex matrices such as musts and sparkling wines [67; 91; 92; 93].  
In addition to its higher sensitivity, SBSE allows for the extraction of analytes from larger sample volumes, increasing its versatility. It has been applied successfully in the quantification of varietal and fermentative volatiles in sparkling wines using approaches such as SBSE coupled with liquid desorption and large volume injection GC-qMS [94], as well as SBSE followed by thermal desorption in the GC-MS system [95; 96].  
Despite these advantages, SBSE requires thermal desorption units (TDUs), as the device is not compatible with conventional GC split/splitless injectors (only if retro extraction is performed to a suitable solvent) and the range of commercially available coatings is limited to PDMS and EG, which can restrict selectivity and application [79; 97].
# Power Stability Analysis and Evaluation Criteria of Dual-Infeed HVDC with LCC-HVDC and VSC-HVDC
## 4 Evaluation Criteria for the Power Stability of Hybrid Dual-Infeed HVDC Systems
### Evaluation Criteria for the Stable Operation of Hybrid Dual-Infeed HVDC Systems  
The evaluation criteria of power stability for the hybrid dual-infeed HVDC system can be divided into two cases: constant AC voltage control mode and constant reactive power control mode. The expert in [22] proposed that when PSI > 0.4, the HVDC system has a high stability margin.  
Based on the above analysis, it can be seen that if the VSC-HVDC is in constant AC voltage control mode, the variable range of CESCR is 1.56\(\sim\)1.65, and when PSI = 0.4, the variable range of ESCR is 2.48\(\sim\)2.63. Therefore, the evaluation criteria of the single-infeed LCC-HVDC system can still be used.  
If the VSC-HVDC is in constant reactive power control mode, the variable range of CESCR is 1.52\(\sim\)2.0, and when PSI = 0.4, the variable range of ESCR is 2.45\(\sim\)2.9. In order to ensure a sufficient stability margin, the evaluation criteria of power stability can be divided as follows:  
1. When ESCR < 2.0, the HVDC system is difficult to operate stably.
2. When 2.0 < ESCR < 3.0, the HVDC system can operate stably, but the stability margin is low.
3. When ESCR > 3.0, the HVDC system can operate stably with high stability margin.
## 4 Results
### Statistical Analysis
#### 4.7.1 Normality and Homogeneity of Variance Tests  
The Shapiro-Wilk test was used to assess whether the validation accuracy values for each model follow a normal distribution. The results are summarized below:  
* **CLDNN:**\(W=0.895\), \(p=0.193\to\text{Normal}\);
* **SCRNN:**\(W=0.972\), \(p=0.908\to\text{Normal}\);
* **ResNet:**\(W=0.913\), \(p=0.299\to\text{Normal}\);
* **CNN:**\(W=0.916\), \(p=0.322\to\text{Normal}\);
* **CV-CNN-TCN:**\(W=0.914\), \(p=0.310\to\text{Normal}\);
* **CV-CNN-TCN-DCC:**\(W=0.941\), \(p=0.570\to\text{Normal}\).  
Although all groups passed the normality assumption, [PERSON]'s test indicated that variances among groups are not equal:  
* **[PERSON]'s test**: statistic = 25.776, \(p=0.000\to\text{Variances}\) are not homogeneous.  
Given the violation of the homogeneity of variance assumption, a non-parametric method was adopted for comparing model performance.
## 4 Algorithms for Core System Functionality
### Bottleneck Identification and Order Transferring Methods  
The practical application of the G-DSS showed that second-level dispatching is usually followed by alleviation of the resulting bottlenecks. Due to the accumulation of late orders and violated due dates by the subcontractors, the planned orders by the PPC system may still surpass the capacity of a work center for a full PPP. Bottlenecks are pinpointed using the bottleneck identification method. Using data regarding capacity, order quantities, shifts per day, hours per shift, and efficiency, the G-DSS calculates the workload of a work center via the following function: \(WLm_{i}=\sum_{i=1}^{i=n}{{\it tim}_{i}}\), where \(\tau_{im_{i}}=N_{i}/(am_{i}\times pCapim_{i})\), with \(am_{i}\) representing the efficiency of the work center \(m_{i}\) and \(pCapim_{i}\) representing the capacity of machine \(m_{i}\) required to produce a unit of SKU \(i\) (\(am_{i}\) and \(pCapim_{i}\) are taken from the PPC database). If \(WLm_{i}>PPPLm_{i}\), where \(PPPLm_{i}=daysppp\times no\_shiftsm_{i}\times hrs\_shift\), then \(m_{i}\) is flagged as overloaded and the planner is prompted for action. There are two types of action the planner may take: Either transfer orders to earlier or later PPPs or alternate work centers. In the case of alternate centers, the G-DSS functions in the following manner:  
1. Calculate the capacity margin of alternate work centers \(m_{k}\), i.e., \(CMm_{k}=PPPLm_{k}-WLm_{k}\).
2. If \(CMm_{k}>0\) do: 1. Calculate \(\tau_{im_{k}}=N_{i}/(a\times pCapim_{k})\). 2. Add \(\tau_{im_{k}}\) to \(WLm_{k}\). 3. Repeat until \(CMm_{k}=0\).  
Instead of making the above moves automatically, the G-DSS informs the planner through a form of possible production orders that may be moved to alternate work centers and leaves the end decision up to them.  
Except for the typical manual transferring function, the planner may also utilize the \"freeze mode\". In \"freeze mode\", the planner may transfer production order \(i\) to any given point in the sequences \(Sm_{i}\lor Sm_{i}\lor\ldots\lor Sm_{k}\), with \([ml,\ldots,mk]\) representing the set of alternate machines, by concurrently inserting machine idle time, which is prohibited during the first and second dispatching levels. This function was employed in the G-DSS mainly to cater for dynamic shop floor events such as machine breakdowns, maintenance, rush orders, etc. For example, if \(m_{i}\) breaks down, then the planner would activate \"freeze mode\" to insert the necessary idle time in \(Sm_{i}\) to accommodate the repair time.
## 1 Introduction
### Contributions  
#### 1.1.1 Parallel ARIA Implementation on ARMv8 Processor  
We are the first to implement a parallel implementation of an ARIA block cipher on an ARMv8 processor. In order to efficiently use TBL instructions in an ARIA substitution process that uses an Sbox where each byte is different, 4 or 16 plaintext blocks are implemented in parallel. The LD4 instruction provided by ARMv8 optimizes the process of sorting plaintext blocks for parallel implementation.
# A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments
## 3 Proposed Work  
As presented in Figure 1, the traffic classification scheme consisting of four phases, including data preparation, data sampling, feature selection, and traffic classification, is proposed. In particular, the objective of data preparation was to enhance data quality, data integration, data cleaning, data transformation, and data normalization. After preparing the data, an under-sampling process was conducted to reduce the irregularity of the benign and malicious traffic samples, as the amount of specific data transmission traffic was much larger than that of regular transmission. The selection of features was done in such a way as to eliminate the unnecessary characteristics of attack behavior, improving the effectiveness of the data training and testing. Finally, the purpose of the GBM-based ensemble machine learning model was to identify malicious attacks.  
To train a model, it is necessary to have a well-organized intrusion detection dataset. The network-based dataset should include modern network traffic scenarios, large varieties of low footprint intrusions, and deeply structured information on network traffic. Selecting such a dataset proved to be a significant challenge. Before finalizing the dataset for our research, we conducted a comparison-based study between the latest available intrusion detection datasets: UNSW-NB15-v2, TON-IoT-V2, BOT-IoT-V2, and CSE-CIC-IDS2018-V2, as shown in Table 3 and Figure 2.  
The proportion of classes, i.e., benign, DoS, and DDoS, varies in each dataset. The CSE-CIC-IDS2018 dataset has incredibly vast coverage and a very high benign-to-attack ratio, which drew the attention of this research to the CSE-CIC-IDS2018-V2 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **UNSW-NB15** & **ToN-IoT** & **BoT-IoT** & **CSE-CIC-IDS2018** \\ \hline Benign & 1,550,712 & 270,279 & 13,859 & 7,373,198 \\ DoS & 5051 & 17,717 & 56,833 & 269,361 \\ DDoS & 0 & 326,345 & 56,844 & 380,096 \\ Web Attacks & 0 & 0 & 0 & 4394 \\ Infiltration & 0 & 0 & 0 & 62,072 \\ Brute Force & 0 & 0 & 0 & 287,597 \\ Bot & 0 & 0 & 0 & 15,683 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of datasets on coverage of latest attacks [29; 30; 31].  
Figure 1: Proposed methodology.