esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion of the Results
### Research Question Two: How Does Experience Impact the Decision-Making Process for Pilots?  
In response to how experience impacts decision-making, the analysis indicates that there is a modest improvement in decision-making with greater experience. This could reflect that pilots build upon prior experience to inform their decisions [105], so more exposure to various situations enables better decision-making. However, since most regression \(p\)-values in this study were not statistically significant, it would be important to build upon these further before drawing any conclusions. Future research could examine how experience influences decision-making by analysing models, procedures, and pattern recognition across different flight-hour categories.  
It is important to recognise that the sampling was skewed toward experienced pilots. Moreover, whilst this study focused on flight hours, the findings suggest that other factors, such as training, age, gender, aircraft type, private vs. commercial flying experience, and terrain exposure, also influence decision-making and warrant inclusion in future models of this topic. Conducting further analysis on these variables can help determine more about the role of experience during the phases of flight in this research.  
Nevertheless, the discussions did also demonstrate a lack of practise with challenging manoeuvres, such as the go-around from the flare, which highlights how extensive experience may increase the risk of overconfidence. Hence, whilst pilots use previous experience when making decisions, this can also negatively impact the decisions made.  
However, when considering experience, this study focused on hours flown, which isolates experience to within the cockpit. The discussions highlighted that aviation-related activities such as observing other pilots, attending seminars, or informal flying also contribute to a pilot's overall experience.  
To better understand experience and decision-making, future studies could improve pilot participation by offering scheduling flexibility, and incentives may improve participation. Using sampling methods would also ensure a better distribution of pilots participating. Also, including a larger sample of pilots would enable a more representative insight into Canadian GA.  
Research Question Three and Recommendations: What Future Steps Should Be Taken to Help Reduce the Number of Abnormal Runway Contact, Excursion, and Loss of Control on Ground Accidents?  
Moreover, to address future safety improvements that aim to prevent the number of uncontrolled landings and runway excursions, this study makes the following recommendations, summarised in Figure 5 and the list of recommendations below.
## 3 Dynamic Performance Improvement of Solenoid Electromagnetic Actuator
### Dynamic Performance Test  
The force frequency response magnitude plots and phase plots at the start position (1-mm air gap), the middle position (2.3-mm air gap) and the end position (3.6-mm air gap) of the working stroke are illustrated in Figure 14. Moreover, the amplitude of the input signal was rated current (direct current). As seen from Figure 14, the cutoff frequency of the magnitude plots is lower than the phase plots. Therefore, it can be concluded that the dynamic performance of the LPS was determined by the amplitude-frequency characteristics. From the amplitude plot, both the optimal-type LPS and the electrical steel optimal-type LPS showed better dynamic performance than the conventional-type LPS. The improvement of the coil parameter optimization strategy and the armature optimization strategy could be illustrated by Figure 14. In addition, from Figure 14, it also could be confirmed that the amplitude attenuation of the electrical steel optimal-type LPS was less than the optimal-type LPS. Thus, the electrical steel optimal-type LPS was more suitable than the optimized-type LPS for high dynamic performance requirement situation.  
Figure 16: Force–response comparison of the three types LPS. (**a**) 1–Hz-current input signal; (**b**) 20 Hz current input signal; (**c**) 50-Hz-current input signal; (**d**) 70-Hz-current input signal.  
Table 2 illustrates the cutoff frequency of the conventional-type LPS, the optimal-type LPS and the electrical steel optimal-type LPS at start, middle and end position in the working range. The optimal-type LPS increased by 73.4%, 52.2% and 57.5%, respectively. Moreover, the electrical steel optimal type increased by 129.1%, 79.6% and 74.3%, respectively.  
In summary, we can clearly obtain a conclusion from Figure 14 and Table 2 that increasing the coil rated current strategy and the replacing material strategy significantly improved the dynamic performance of the LPS. These two types of the LPS were used in different requirements of the LPS; which one is more suitable depends on what kind of occasion it is used for. The optimal-type LPS showed high static push-force generation and the electrical steel optimal-type LPS showed high push-force frequency response.  
The manufactured LPS is shown in Figure 17.
# Impact of Intersection Left Turn Guide Lines Configuration on Novice Drivers' Behavior
## 2 Material and Methods
### Driving Simulator Experiment Design
#### 2.2.4 Experimental Procedure  
To optimize the experiment's efficiency, participants were grouped, and simulation processes for each scenario were streamlined. Novice drivers sequentially completed simulations for the six scenario categories, each comprising three sub-scenarios with distinct guide line settings. The estimated time per participant was 40-50 min, with an expected total duration of approximately 1 h, accounting for preparation time.  
Before the experiment, participants were asked to sign an informed consent form and complete a questionnaire that included their name, gender, age, and driving experience. The consent form detailed the experiment tasks and precautions. Researchers then checked equipment connections, calibrated instruments, and explained the simulator's operation to participants. Finally, each participant underwent a 5-10 min session in the driving simulator to quickly familiarize themselves with its operation and environment, helping to minimize subjective differences in the perception of the simulator's impact on the results.  
The experiment began with drivers engaging in simulations for the six scenario categories, illustrated in Figure 4. Each category featured three intersections: Intersection 1 without guide lines (N), Intersection 2 with one guide line (O), and Intersection 3 with full guide lines (F). The green dot marks the starting point, the red dot indicates the endpoint (after passing through Intersection 3), and black arrows depict the vehicle's direction. The pink line represents the vehicle's path. Intersections were spaced 300 m apart, and each intersection was controlled by traffic signals. Since the six scenarios were not continuous, after completing each one, the driver was required to immediately evaluate the completed scenario, fill out a questionnaire, and prepare for the next simulation. This process was repeated until the driver completed simulations for all scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Intersection** & \multicolumn{2}{c}{**Small**} & \multicolumn{2}{c}{**Medium**} & \multicolumn{2}{c}{**Large**} \\ \hline Number of left-turn lanes & Single & Single & Dual & Single & Dual & Triple \\ Major scenarios number & 1 & 2 & 3 & 4 & 5 & 6 \\  & 1-A: & 2-A: & 3-A: & 4-A: & 5-A: & 6-A: \\  & N: No guide line & S-Single-N & M-Single-N & M-Dual-N & L-Single-N & L-Dual-N & L-Triple-N \\  & 1-B: & 2-B: & 3-B: & 4-B: & 5-B: & 6-B: \\ Guide line setting & O: One guide line & S-Single-O & M-Single-O & M-Dual-O & L-Single-O & L-Dual-O & L-Triple-O \\  & F: Full guide lines & 1-C: & 2-C: & 3-C: & 4-C: & 5-C: & 6-C: \\  & S-Single-F & M-Single-F & M-Dual-F & L-Single-F & L-Dual-F & L-Triple-F \\ \hline \hline \end{tabular}
\end{table}
Table 2: Designed simulation scenarios.  
Figure 3: Guide line setup at large intersection: (**a**) no guide line; (**b**) one guide line; (**c**) full guide lines.
## 1 Introduction
### Objectives
#### 1.1.1 Object-Based Image Analysis and Change Detection  
Object-based image analysis methods (GEOBIA/OBIA) are frequently being used to map land-cover and land-cover change when the pixel size of the imagery is small relative to the individual extents of common landscape features [35, 38, 39, 40]. In urban and rural landscapes, many features are smaller than the 30-m extent of Landsat pixels and are, thus, better resolved with higher resolution imagery, such as that provided by the previously-mentioned space-based platforms or aerial imaging. OBIA methods group pixels into homogenous units based on local variance criteria [15, 33, 41, 42]. Once the pixels are converted to segments, many classification and change detection techniques, designed for pixel-based analyses, can be applied to the segments [5, 43, 44]. Change detection has two main methods, post-classification change detection and combined change detection [45, 46]. Post-classification change mapping considers the differences between two individual classified maps at different times and inherently combines individual mapping errors [47]. A combined change detection analyzes the two times simultaneously, by assessing the differences or correlations between reflectance values of the two images [16, 48]. In accordance with these two methods, the former often leads to maps of explicit class-to-class changes, while the latter simply maps areas as changed/not-changed.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 3 Proposed IOTA-Based FTN-DFT-s-OFDM Waveform Design
### Transmission Waveform Design
#### 3.1.1 DFT Spreading  
DFT spreading can be regarded as a kind of precoding before OFDM modulation. By deploying DFT spreading on the source symbols, the resulted waveform, termed DFT-s-OFDM, can hold the characteristics of single carrier waveform. For the \(s\)-th OFDM symbol, we denote \(\mathfrak{c}_{s}=\left[c_{s,0},c_{s,1},\ldots,c_{s,m},\ldots,c_{s,M-1}\right]^ {T}\) as the \(M\)-dimensional source symbol vector. Then the transformed signal for the \(s\)-th symbol duration after \(M\)-point DFT is given by  
\[d_{s,l}=\frac{1}{\sqrt{M}}\sum_{m=0}^{M-1}c_{s,m}\cdot e^{-\beta\pi ml/M},\quad (l=0,1,\ldots,M-1). \tag{4}\]Substituting (4) into (1), we have the overall transmit signal for DFT-s-OFDM. The inherent single carrier structure make the above signal a promising enabler of high-frequency communication.
## 2 Materials and Methods
### Determination of the Main Soil Properties and Glomalin Contents in the Soils  
#### 2.2.1 Soil Physicochemical Characteristics  
The pH values of the soils were identified in a 0.01 mol/L CaCl\({}_{2}\) extract (1:10 _w/v_) using a Sentron SI400 device with an ISFET electrode (Sentron, Leek, The Netherlands). Inorganic N forms (N-NH\({}^{4+}\) and N-NO\({}^{3-}\)) and dissolved organic matter (DOC) were identified in the same extracts using the SKALAR San Plus System continuous flow-segmented analyzer (Skalar, Breda, The Netherlands). To determine the total carbon and nitrogen in soils, a CHNS Vario MACRO cube (Elementar Analysensysteme GmbH, Langenselbold, Germany) analyzer was used, and C and N were identified using a thermal conductivity detector. The cation exchange capacity (CEC) was calculated as the sum of Ca, Mg, K, Na, Fe, Mn, and Al extractable in 0.1 mol/L BaCl\({}_{2}\) (1:10 _w/v_ for 2 h; [34]). A hydrometer was used to determine soil texture. To characterize the soil nutrient status, the Mehlich III extraction procedure was performed by shaking the extractant (0.2 mol/L CH\({}_{3}\)COOH, 0.25 mol/L NH\({}_{4}\)NO\({}_{3}\), 0.013 mol/L HNO\({}_{3}\), 0.015 mol/L NH\({}_{4}\)F, and 0.001 mol/L EDTA at a ratio of 1:10 _w/v_) and soil for 10 min [35]. Extracts were filtered, and the filtrates were stored at 6 \({}^{\circ}\)C prior to analysis. Inductively coupled plasma optical emission spectrometry (ICP-OES, Agilent 720, Agilent Technologies Inc., Santa Clara, CA, USA) was used to determine elements in soil extracts.
# Highly Controlled Integration of Graphene Oxide into PAN Nanofiber Membranes
## 2 Materials and Methods  
### Materials  
Graphite flake (Bay carbon Inc. Bay city, MI., USA), sodium nitrate (NaNO\({}_{3}\), \(\geq\)99%, Sigma-Aldrich, St. Louis, MO., USA ), KMnO\({}_{4}\) (99%, Sigma-Aldrich), sulfuric acid (98%, Duksan) and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\), 35%, Samchen Co., Ltd., Seoul, Korea) were used for the synthesis of GO. Materials used to prepare nanofiber membranes were polyacrylonitrile (PAN, Mw 150,000, Sigma-Aldrich) and \(N\),\(N\)-dimethyl formamide (DMF, 99.5%, Duksan, Seoul, Korea ). Cetyltrimethylammonium chloride (CTAC) as a surfactant to modify the surface of GO.
# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
## 3 Methodology
### Unsupervised Weight Map Generative Network  
In the Section 2, we have mentioned the image-level combination strategy CsNet suffers from coarse combination granularity and many details of the images cannot be retained well. To compensate for the lost details of the combined image, [PERSON] et al. designed a boosting procedure that complicates its network structure. For the patch-level combination strategy SPD, it is superior to CsNet in terms of detail retention; however, it produces some artifacts around the edges. To solve the above problem in image combination, we aim to develop a new image combination strategy. Unlike existing coarse-grained image combination strategies that focus on images or patches [28,32], we focus on a finer-grained pixel-level image combination strategy. We argue that with a powerful, adaptive unsupervised learning network, we can set weights for each pixel of the image to be combined. To achieve this, in this section, we will present a model for pixel-level image combination, called the unsupervised weight map generative network (UWMGN). The  
Figure 2: Denoising results of Barbara and Boat with noise level \(\sigma\) = 30.  
pipeline of the proposed UWMGN is shown in Figure 3. It generates a corresponding weight map of the same size for each initial denoised image; using the weight maps, we will combine the initial images and then continuously adjust the network parameters under the guidance of the loss function so that the output weight maps can combine to yield an optimal result. Compared to CsNet, our method only requires one stage to complete the denoising process, without an extra boost.  
First, given a noisy image \(\mathbf{y}\), we obtained a set of initial denoised images processed with different denoising methods. Then, the initial denoised images were matched with the corresponding weight maps of the same size, one by one, and each initial denoised image was weighed and combined to obtain the final combined output. To generate weight maps, we designed a backbone network. It uses random noise as the input to generate weight maps without the need for prior training pairs. As the initial input of the backbone network is random noise, its output weight map also begins with random values. We used a novel loss function that iteratively updates the parameters in the generative network and adjusts the convergence point of our model to ensure that the output combined image is close to optimal. Therefore, for every denoised image \(\{\hat{x}_{1},\hat{x}_{2},\dots,\hat{x}_{K}\}\in\mathbb{R}^{N}\), we constructed the corresponding weight maps \(\{W_{1},W_{2},\dots,W_{K}\}\in\mathbb{R}^{N}\). We will discuss the structure of the backbone network and loss function in detail in the following subsections.
## 2 Methodology
### Goal and Scope
#### 2.1.4 Life-Cycle Impact Assessment Method  
The life-cycle impact assessment (LCIA) included the mandatory phases according to ISO 14044: Impact category selection, classification, and characterization. The LCIA has been carried out by modeling the system with the GaBi 9.2 software [49], which allows transforming the values of the life-cycle inventory into values of the chosen environmental impact categories. GaBi software was selected because it is one of the leading software tools used for LCA, and it is a trusted product  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Scenarios** & **E1** & **E2** & **E3** & **E4** & **E5** & **E6** & **E7** & **E8** \\ \hline Compression (Yes/No) & Y & Y & Y & Y & N & N & N & N \\ Electricity Source (Grid/Photovoltaic) & G & P & G & P & G & P & G & P \\ Methane recovery (Yes/No) & Y & Y & N & N & Y & Y & N & N \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the different scenarios defined.  
sustainability solution with over 10,000 users, including 500 companies, leading industry associations, and innovative SMEs [49].  
To quantify these environmental burdens, the impact evaluation method CML2010 developed by Leiden has been selected [50], from which four categories were analyzed to have a global vision of the impact of the process: Global Warming Potential (GWP 100 years) expressed in kg CO\({}_{2}\) equivalent, Acidification Potential (AP) expressed in kg SO\({}_{2}\) equivalents and Abiotic Depletion Potential both for elements (ADPE) measured in kg Sb equivalent and fossil fuels (ADPF) measured in MJ. LCIA indicators were selected among those considered more relevant for this type of process, mainly related to material energy and material requirements.
## 4 Pressure Characteristics on the Gate Panel
### The Pressure Prediction on the Gate Panel
#### 4.2.2 Prediction and Comparative Analysis  
To better characterize the accuracy and generalization of the model's output, the model was evaluated using the \(\mathrm{E_{MAP}}\) (mean absolute percentage error), \(\mathrm{E_{MS}}\) (mean square error), \(\mathrm{E_{RMS}}\) (root mean square error), \(\mathrm{E_{MA}}\) (mean absolute error), and \(\mathrm{R^{2}}\) (goodness fit) [33]. The calculation formulas for these metrics are as follows:  
\[\mathrm{E_{MAP}}=\frac{1}{N}\sum\limits_{k=1}^{N}\frac{|P_{k}-O_{k}|}{O_{k}} \times 100\% \tag{19}\]  
\[\mathrm{E_{MS}}=\frac{1}{N}\sum\limits_{k=1}^{N}\left(P_{k}{-}O_{k}\right)^{2} \tag{20}\]  
\[\mathrm{E_{RMS}}=\sqrt{\frac{1}{N}\sum\limits_{k=1}^{N}\left(P_{k}{-}O_{k} \right)^{2}} \tag{21}\]  
\[\mathrm{E_{MA}}=\frac{1}{N}\sum\limits_{k=1}^{N}|P_{k}{-}O_{k}| \tag{22}\]  
\[R^{2}=1{-}\frac{\sum\limits_{k=1}^{N}\left(P_{k}-O_{k}\right)^{2}}{\sum\limits _{k=1}^{N}\left(P_{k}-\overline{O}\right)^{2}} \tag{23}\]  
Figure 13: Determination of optimization parameters. (**a**) Fitness curve; (**b**) first hidden node optimization; (**c**) second hidden node optimization; (**d**) iterative optimization; (**e**) learning rate optimization.  
where N represents the total number of samples, P\({}_{k}\) denotes the predicted value of the kth sample, \(O_{k}\) indicates the observed value of the k-th sample, and \(\overline{O}\) represents the average of all observed values for the samples.  
It is generally believed that a higher R\({}^{2}\) value, accompanied by lower values of E\({}_{\text{MAP}}\), E\({}_{\text{RMS}}\), E\({}_{\text{MA}}\), and E\({}_{\text{MS}}\), indicates superior model performance. A comparative analysis of the prediction processes of the three models is presented in Figure 14, while scatter plots comparing the actual measured values with the predicted values are depicted in Figure 15.  
Figure 14: Comparison of the prediction process of the three models.  
Figure 15: Scatter of measured and predicted values for the three models. (**a**) LSTM prediction results; (**b**) ICEEMDAN-LSTM prediction results; (**c**) ICEEMDAN-SSA-LSTM prediction results.  
Figure 14 illustrates that the LSTM model exhibits a notable discrepancy from the actual values, resulting in inferior prediction performance. In contrast, both the ICEEMDAN-SSA-LSTM and ICEEMDAN-LSTM models demonstrate a higher degree of overlap with the measured values, indicating superior predictive effects. However, no discernible differences are observed between the two models. Furthermore, an analysis of the scatter plot in Figure 15 and the radar chart in Figure 16 reveals that the ICEEMDAN-SSA-LSTM model outperformed the ICEEMDAN-LSTM model in terms of peak prediction accuracy during the validation period, while the LSTM model exhibited lower peak prediction accuracy.
## 3 System Architecture, Software Services and Deployment
### Cloud-Based Services
#### 3.4.3 Telemetry  
The telemetry service provides remote access to real-time records and alerts issued directly by the smart water consumption metering devices. The service aggregates all data generated by the affiliated, interconnected smart devices through the nearest gateway and processes them accordingly.
## 4 Performance and Economic Feasibility of a Retaining Wall Displacement Measurement System
### Economic Feasibility of System
#### 4.2.3 Economic Analysis  
According to the cash flow diagram and discount rate, an economic analysis was performed, which included the following: (1) present worth, (2) benefit/cost (B/C) ratio, (3) annual measurement cost-saving effect, (4) rate of return (ROR), and (5) breakeven point analyses (Table 9). The calculation formulas used for the economic analysis are presented in Appendix A.  
According to the economic analysis results (Table 10), the system has a present worth of $56,075.91, a B/C ratio of 3.52, an annual measurement cost-saving effect of 71.59%, an ROR of 61.48%, and a breakeven point of 2.71 years. The present worth was positive, and the B/C ratio was > 1, showing that the automated measurement method is more economically feasible than the conventional measurement method. When the automated measurement method is adopted, the annual measurement cost is reduced by 71.59% compared to the conventional method, and a net profit is generated after 2.71 years by recovering all investments in system construction.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Economic Analysis** & **Definition** \\ \hline Present worth analysis & A method of comparing benefits and costs by converting all the benefits and costs in the cash flow diagram into the present worth. \\ B/C ratio analysis & A method of evaluating the degree to which the benefit exceeds the cost by analyzing the ratio of the benefit to the cost converted into the present worth. \\ Annual measurement cost-saving effect analysis & A method of identifying the annual measurement cost-saving effect of introducing a system by comparing the annual benefit and cost under the assumption that the benefit and cost are identical in different years. \\ ROR analysis & A method of deriving the rate of return that differentiates between the benefit and cost converted into the present worth zero. \\ Breakeven point analysis & A method of deriving the breakeven point between the benefit and cost converted into the present worth. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Economic analysis definitions.  
Figure 16: Inflation and bank deposit interest rates in Korea over the past 10 years (2012–2021).