esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Fabrication of the NPs
#### 3.3.6 The Influence of the Organic Phase Dropping Rate to the Water Phase  
In these experiments two neutral AABB polymers, **1F6** and **4F4**, and two of eight surfactants were used. The parameters (invariable and variable) of the NPs fabrication process are given in the table below:  
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Parameters of the Process**} \\ \hline  & \multicolumn{1}{c}{**Invariable**} & \multicolumn{1}{c}{**Variable**} \\ \hline \(\bullet\) & solvent--DMI & \(\bullet\) & polymers (two)--**IF6** and **4F4** \\ \(\bullet\) & concentration of the polymer in organic phase--6.0 mg/mL & \(\bullet\) & surfactants (two)--Triton X-100 and Brij 010 \\ \(\bullet\) & concentration of surfactant in the water phase--5.0 mg/mL & \(\bullet\) & Organic phase dropping rate--2, 6 and 12 drops/min \\ \(\bullet\) & or/W ratio--1:10 & \(\bullet\) & stirring rate of the water phase--700 rpm. & \\ \hline \end{tabular}  
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Paractant**} \\ \cline{2-3}
**O:W** & \multicolumn{3}{c}{**Polymer**} \\ \cline{2-3}
**1F6** & \multicolumn{3}{c}{**4F4**} \\ \cline{2-3}
**1:5** & \multicolumn{3}{c}{**MPD(nm) \(\pm\) SD**} & \multicolumn{1}{c}{**[PDI] \(\pm\) SD**} \\ \hline  & \multicolumn{3}{c}{**449 \(\pm\) 23**} & \multicolumn{3}{c}{**483 \(\pm\) 19**} \\  & \multicolumn{3}{c}{(P-I) \(68\%\) \(\bullet  
The conditions of the NPs fabrication were as follows: the solvent: DMI; polymer concentration in the organic phase: 6.0 mg/mL, surfactant concentration in the water phase: 5.0 mg/mL; the organic phase dropping rates: 2, 6, and 12 drops/min, accordingly; the O/W ratio: 1:10; and the water phase stirring rate: 700 rpm. The results obtained for the selected AABB polymers **1F6** and **4F4** using two surfactants in terms of the MPD of the NPs are given in Figure 6.
# Simulation and Catastrophe Detection of Spontaneous Combustion Processes in Suffide Ores
## 2 Materials and Methods  
### Materials  
The ore samples used in the experiment were obtained from the copper mine in Tongshan, Anhui Province. The composition of the ore samples was analyzed using X-ray fluorescence spectroscopy (XRF), a technique widely used for material composition analysis in fields such as mineralogy and metallurgy. XRF determines the elemental composition and concentration of a sample by measuring the fluorescent X-rays emitted from the sample when it is irradiated with X-rays. Before the analysis, the XRF equipment was calibrated using standard samples with known compositions, covering the entire concentration range of the elements to be measured. Additionally, the ore samples were meticulously ground and surface-treated to ensure uniform particle size, reduce errors due to sample heterogeneity, and maintain a clean and flat measurement surface. These steps were taken to ensure the accuracy of the analysis results. Ultimately, the primary elements in the ore samples, from highest to lowest concentration, were iron, sulfur, oxygen, silicon, calcium, barium, magnesium, aluminum, and copper. The main chemical composition of the ore samples is shown in Table 1.  
During the process of crumbling sulfide ore into a heap, large particles tend to roll down to the bottom while fine particles accumulate at the top, forming a cone with a specific slope. For the experiment, the simulated ore heap is designed as a cone with a base diameter of 300 mm, a height of 160 mm, a slope angle of 46.8\({}^{\circ}\), and a mass of approximately 9.1 kg. The particle size composition of the ore heap is detailed in Table 2 below.  
Eight measurement points (labeled A through H) are strategically arranged within the ore heap to simulate spatial distribution and monitor temperature variations in different regions. The schematic diagram of the measurement points is illustrated in Figure 1, the yellow points in the figure indicate the measurement points A to H. The coordinates for each temperature measurement point are detailed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Element & Fe & S & O & Si & Ca & Ba \\ \hline Content (\%) & 31.632 & 27.606 & 24.129 & 9.822 & 2.28 & 4.328 \\ \hline Element & Mg & Al & Ce & Cu & As & Zn \\ \hline Content (\%) & 0.939 & 0.766 & 0.53 & 0.344 & 0.26 & 0.093 \\ \hline Element & K & Mn & Ti & Na & Sr & Mo \\ \hline Content (\%) & 0.082 & 0.072 & 0.059 & 0.032 & 0.021 & 0.006 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main chemical composition and content of the ore samples used.  
Figure 1: Schematic diagram of a simulated sulfide ore heap.
## 3 Results and Discussion
### Compression of Output Pulses after Stimulated Rotational Raman Amplification  
Further experiments were conducted with the aim of exploring the compression capabilities of WLC pulses amplified in the stimulated rotational Raman amplifier. By adjusting the distance between two SF11 glass prisms, the pulses of the first-order rotational Stokes component were compressed to 152 fs, which was approximately eight times shorter than the pump pulse (Figure 7).  
However, in the case of multiple higher-order rotational Stokes combs at the output of the SRRA, the linear compression methods turned out to be insufficient. Here, the proper control of the phases of several molecular modulation sidebands was required. One way to achieve this is to use transparent plates between prisms specific to each rotational Stokes component, with an adjustable thickness and position, thereby providing spectral control over the sideband generation process [34]. Also, the relative phases can be controlled using a spatial light modulator [17] or a spatial phase controller [35]. One example of such a modulator is a programmable liquid crystal spatial light modulator, which has tens of pixels. The refractive index of each pixel can be controlled electronically by applying a specified voltage from a computer [17]. Thus, when using appropriate compression methods, such a multiwavelength SRRS radiation source can potentially be used not only for broadband sensing but also for generating few-cycle optical pulses.
## 3 Machine Learning Techniques for Data Preprocessing
### Data Cluster Analysis
#### 3.2.3 Spectral Clustering via Fusing k-Nearest-Neighbor and Sparse Subspace Representation  
The main disadvantage of k-nearest-neighbor is that it does not use global information, which possibly leads to the less robustness to data noise. As an exceedingly robust method, the sparse subspace representation not only transmits valuable information in the classification task, but also makes use of the overall context information to provide a data adaptive neighborhood [46]. However, a drawback of the sparse subspace representation is that two samples with a large Euclidean distance may be classified into the same cluster. For this propose, this paper provides a spectral clustering fusing the advantages of k-nearest-neighbor and sparse subspace representation, and applies it to the cluster analysis on solar radiation.  
Let \(\mathbf{W}_{\text{k}}\) be the similarity matrix constructed by the k-nearest-neighbor and \(\mathbf{W}_{\text{s}}\) be the similarity matrix obtained by sparse subspace representation. In consideration of their construction principles, both \(\mathbf{W}_{\text{k}}\) and \(\mathbf{W}_{\text{s}}\) are sparse and symmetric. We propose a weighted similarity matrix defined by the convex combination between \(\mathbf{W}_{\text{k}}\) and \(\mathbf{W}_{\text{s}}\):  
\[\mathbf{W}=\gamma\mathbf{W}_{\text{k}}+(1-\gamma)\mathbf{W}_{\text{s}} \tag{7}\]  
where \(\gamma\in\) [0,1] is the trade-off parameter. When \(\gamma=0\), the similarity matrix is constructed by sparse subspace representation. In addition, \(\gamma=1\) means that the similarity matrix is formed by the k-nearest-neighbor.  
Based on the resulting similarity matrix \(\mathbf{W}\), we divide the dataset matrix \(\widetilde{A}\) into \(s\) clusters via the spectral clustering method. The following lists the implementation procedure of spectral clustering. Firstly, a diagonal matrix \(\mathbf{D}\) is calculated, whose diagonal elements are constructed by the sum of each row in \(\mathbf{W}\). Then the Laplacian matrix is computed as \(\mathbf{L}=\mathbf{D}^{-1/2}\mathbf{W}\mathbf{D}^{-1/2}\). Next, the eigen decomposition is performed on \(\mathbf{L}\) and \(s\) mutually orthogonal unit eigenvectors \(\left\{\mathbf{a}_{i}^{\text{v}}\in\Re^{m\times 1}\right\}_{i=1}^{s}\) corresponding to \(s\) largest eigenvalues are acquired. Denote \(\mathbf{A}^{\text{v}}=\left(\mathbf{a}_{1}^{\text{v}},\ldots,\mathbf{a}_{s}^{\text{v}} \right)\in\Re^{m\times s}\). Each row of \(\mathbf{A}^{\text{v}}\) is further transformed into unit vectors in the sense of \(l_{2}\)-norm and the normalized matrix is indicated by \(\widetilde{A}^{\text{v}}\). Finally, each row of \(\widetilde{A}\) is regarded as a sample and \(m\) samples are partitioned into \(s\) clusters by k-means clustering. Compared with only using k-nearest-neighbor or sparse subspace representation, the proposed spectral clustering can maintain a stronger stability and robustness.
# MAISTRO: Towards an Agile Methodology for AI System Development Projects
## 3 Materials and Methods
### Phase III: Data Preparation
#### 3.3.4 Data Enrichment  
Data enrichment is a practice that involves enhancing and expanding existing data with additional information, aiming to increase its usefulness and effectiveness for training models in AI projects. It aims to improve the quality and diversity of data, making it more informative and robust.  
A fundamental approach in data enrichment is the combination of information from diverse sources. By integrating internal data from the organization with external data, such as public or third-party sources, a complete and more enriched dataset is created. This fusion allows adding new dimensions and depth to the analyzed context. This strategy can significantly improve the robustness and performance of predictive models [79].  
In domains such as image recognition, where obtaining annotated data is scarce or costly, data augmentation plays a fundamental role. This technique consists of creating new instances of data from existing ones, applying transformations such as rotation, scaling, adding noise or changing colors. The result is a more diverse and representative set. Data augmentation helps to generalize AI models by making them more robust to variations and noise in the data [80].  
In addition to data combination and augmentation, the generation of synthetic data is an advanced approach. Using algorithms such as Generative Adversarial Networks (GANs), data that is statistically like real data, but not identical, is created. This technique is especially useful when real data are sensitive or difficult to obtain. GANs allow the creation of large datasets without compromising the privacy of individuals [80].  
In data enrichment, it is essential to consider ethical and privacy aspects. Practices such as anonymizing personal data, obtaining appropriate consent, and continually evaluating ethical implications are essential practices. By following these ethical principles, we protect the individuals involved and strengthen trust in the projects and the organizations that conduct them. Maintaining ethical practices in the management of human data is vital in AI projects [81].  
Given the above, the Data Preparation Phase is essential for AI projects, as it lays the foundation for the success of predictive models. The practices of data cleaning, processing, quality assurance, human manipulation of data, and data enrichment are not just technical components, but strategic elements that ensure the relevance, accuracy, and integrity of the data used. Each of these actions ensures that the data are accurate, relevant, and ethical, allowing AI models to function effectively and reliably. Careful attention to these processes not only optimizes the performance of models but also protects the individual rights of individuals and maintains trust in the project.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Technological Considerations
#### 5.1.4 Electro-Optical and Computer Vision-Based Systems  
With the development of machine learning and deep learning, it is now possible to have on-board DAA systems that utilise Electro-Optical (EO) sensors. EO systems work by sensing light waves and utilise software to analyse the incoming scenes and detect any incoming threats to the aircraft [61]. While as recently as 2008, the ability of such systems was questionable because they could not detect small aircraft such as microlights [62], significant progress has been made in recent years to improve their reliability [63; 64; 65] as well as how they can be adopted as part of multi-sensor architectures [66]. EO systems are typically low weight, small, and are often inexpensive [61]. However, they do not perform as well in visibility limiting meteorological conditions or in poor lighting [61]. They also suffer from a limited range, and often, they have a limited field of view, which mean that they are often not a complete solution for DAA [67]. Anecdotally from projects that the authors have worked on, providing a 360-degree field of view can significantly impact upon weight and the power output required, so this may not currently be a viable solution. Additionally, processing power and resolution for images are also challenges with this technology [43]. However, EO sensors provide a significant enhancement when used with other DAA tools, particularly when combined with primary radar [66].
## 5 Case Study and Discussion
### Computation Time Result  
An experiment was then conducted to evaluate the model complexity by varying three key factors, which were the number of parts, printer types, and distribution locations. Matlab software version 8.5 from MathWorks, Natick, MA, USA was used to randomly generate test data by varying the random number of parts scheduled to each AM printer at two levels (i.e., 100 parts and 500 parts). We note  
Figure 3: Screenshots of the decision-support tool: (**a**) Visual Basic development, (**b**) AHP weight synthesis, (**c**) input sheet using the AHP weights, and (**d**) output sheet.  
that multiple items of the same part type or different part types in the same batch were allowed. The number of printers and size of the build chambers for ME, SLA, and SLS were tested at two levels (i.e., 5 printers and 10 printers). The number of distribution locations was also evaluated at two levels (i.e., 5 locations and 10 locations) with random distances and demands. Ten replications of data were randomly generated for each test combination, yielding a total of (2)\({}^{3}\)(10) = 80 experimental runs. The resulting average computation time is shown in Table 6, in which the computation time for each run was capped at 1 h.  
The aggregated average computation time based on the varied printer, part, and destination numbers with two levels for each factor is shown in Table 6. The results show that varying the number of printers, parts, or locations affected the computation time of the model. In particular, the average computation time suggested that minimizing the total cost (i.e., solving \(Z_{1}\)) required the least average computation time at a few seconds (i.e., 2.40 s), whereas minimizing the total lateness or delays (i.e., solving \(Z_{3}\)) required the highest average computation time (i.e., 2449.38 s). Minimizing the load balance or the total number of unassigned parts not printed or delivered (i.e., solving \(Z_{2}\) or \(Z_{4}\)) required an acceptable average computation time of a few minutes (i.e., 50.11 and 155.64 s). Regarding the computation time for the non-preemptive method of the multi-objective solution, the equal-weight consideration required a higher average computation time, regardless of the combination of factors and level of printers, parts, or destinations. This result was not surprising since solving the model with an equal-weight consideration aimed to balance all the objective functions at the same time, requiring a higher complexity and hence computation time.
## 1 Introduction
### Materials and Methods
#### 2.1.3 Analyzing the Link between DOWKI and Yield Losses  
DOWKI values and yield losses were computed for each department over the historical period 2000-2018. This calibration matrix of 1800 values was used to study the statistical relationships between the index value and the yield losses.  
The index values were classified using 50 mm steps. For each class we calculated the number of yield loss values exceeding 0% and the average yield loss value.  
The parameters of the model were:  
* The period over which the annual value of the index was computed. This period corresponded to the vulnerability period of the crop and was different for each crop;
* The extreme event threshold at the departmental scale;
* The minimum cultivation area to be taken into account to rule out small areas in which yields are very volatile.  
These parameters were optimized using an experimental design, which consisted of computing a high number of calibration processes with different values for each parameter. The size of the experimental design was (\(p^{n}\)), with \(p\) being the number of parameters (here, \(p\) = 4) and \(n\) being the number of values for each parameters (here, \(n\) = 10). In our case study, the number of calibration processes was \(p^{n}\) = 10,000. The experimental design was evaluated by analyzing the following parameters:  
* Average error at the national scale over the entire historical period;
* Average error at the departmental scale over the entire historical period.  
This experimental design allowed us to select the best parameters by minimizing both errors. The best parameters are presented in Table 1. A specific experimental design  
Figure 2: Crop yield losses (%) for grassland, soft winter wheat, and winter barley at the national scale computed over the historical period 2000–2018 with the AGRESTE database.  
with the same 4 parameters was run for each crop studied. Thus, a selection of the best parameters for grassland, soft winter wheat, and winter barley was made. The following table gives these parameter values.  
For soft winter wheat, two climatic regions were defined--the North and South of France--to improve the calibration results.
## 2 Related Work
### Resampling Algorithm  
Resampling techniques involve using oversampling or undersampling techniques to balance imbalanced sample distributions. The Synthetic Minority Oversampling Technique (SMOTE) [13] is a classic oversampling method that randomly selects samples and performs linear interpolation between the selected sample and its nearest neighbors. While SMOTE oversampling can partially alleviate data distribution imbalance, it lacks a specific selection criterion before performing linear interpolation on minority class samples, which may result in classifier overfitting. Therefore, [PERSON] proposed the Condensed Nearest Neighbor (CNN) rule in 1968 [14], which iteratively uses the 1-nearest neighbor rule to determine whether a sample should be retained in the majority class. In 1976, [PERSON] defined Tomek Links [15], which are pairs of different-class samples that are nearest neighbors to each other. This method helps identify border samples and noisy samples. [PERSON] proposed the Edited Nearest-Neighbor (ENN) rule [16], which can clean up samples for each class but may delete more samples and lead to data loss. Hybrid sampling is a combination strategy of undersampling and oversampling, which addresses the drawbacks of using oversampling or undersampling individually. [PERSON] et al. [17] proposed two hybrid sampling methods: SMOTE + Tomek and SMOTE + ENN. These methods first apply SMOTE oversampling to the minority class and then use Tomek Links and ENN, respectively, to clean the samples and remove minority class samples that impede the majority class. Overall, SMOTE-ENN hybrid sampling effectively manages imbalanced classification problems by synthesizing minority class samples and removing noisy samples, thereby enhancing model performance and generalization ability.
## 5 Discussion
### Significance of Oil and Gas Exploration  
The fourth member of Tahe Formation is dominated by lithologic and fault lithologic oil and gas accumulation [35]. The significance of oil and gas exploration in this member is primarily associated with three key factors: (1) Superior reservoir architecture, characterized by the north-south channel sand body and east-west long belt sand bar in the western and central-eastern Tahe Oilfield. These sand bodies exhibit distinct advantages: shore-shallow lake sand bodies, particularly coastal sand bars, are repeatedly scoured by lake water, resulting in excellent porosity and permeability despite their low thickness; delta-front channel sand bodies show large single-layer thickness, high sand-mud ratios, and stacked development patterns, with feldspathic lithic sandstone cores dominated by intergranular pores. This dual-type reservoir system provides critical storage capacity and hydrocarbon migration pathways. (2) Fault-controlled migration pathways: NE-SW-trending en-echelon faults with large offsets penetrate Paleozoic source rocks, creating direct conduits for vertical hydrocarbon migration from deep source kitchens to shallower reservoirs. (3) Self-contained trap-seal assemblages: laterally pinched-out sand bodies form lithologic traps that integrate with regional caprocks, completing the essential \"source-migration-reservoir-seal\" petroleum system components [36; 37].  
These factors collectively define the exploration significance: the synergistic combination of high-quality reservoirs, effective fault conduits, and self-sealing traps creates optimal conditions for hydrocarbon accumulation. However, technical challenges persist due to NE-SW-trending secondary faults compartmentalizing sand bodies into isolated blocks with varying oil-water contacts, compounded by widespread muddy interbeds enhancing reservoir heterogeneity [38; 39]. This geological complexity, reflected in poor lateral continuity observed in well correlations, underscores the necessity for advanced 3D geological modeling to optimize drilling strategies and improve recovery efficiency. Future research should prioritize high-resolution characterization of these interdependent factors--reservoir architecture, fault connectivity, and heterogeneity patterns--to fully exploit the member's exploration potential.
## 7 Prototype and Experiment  
The optimized motor has good electromagnetic performance and high mechanical reliability. Thus, a 140 kW, 18,000 rpm high-speed IPM motor is manufactured based on the optimized scheme. The photo of the prototype is shown in Figure 22. In order to better confirm the simulation analysis, the performance of the prototype is verified, including rotor safety, electromagnetic, and temperature tests.  
Rotor stress is the primary factor affecting rotor reliability. In the test experiment of rotor safety, the rotor is run at a 120% rated speed for 30 min. It is found that there is no deformation or damage after removing the rotor. The no-load line back-EMF is measured with the prototype driven by another motor. The power factor of the prototype is obtained by the power analyzer. The test result is 0.969, which is slightly lower than the optimization analysis result. The efficiency of the prototype obtained by the ratio of output power and input power is 96.18%, which is very close to the optimization result. Moreover, the winding temperature is measured by a temperature sensor in the end winding, after the motor has been running in the rated state for several hours.
# Thermal Mapping of Power Semiconductors in H-Bridge Circuit +
## 3 Loss Dissipation and Junction Temperature of Power Devices  
By knowing the current distribution between the IGBT and freewheeling diode within a fundamental period, their loss dissipation can be calculated analytically. According to the thermal impedance of the power devices and cooling method, the junction temperature can be estimated as well.