esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In engineering, data-driven methods are increasingly being used to develop and optimize new materials and related manufacturing processes. Data-driven methods such as data mining (DM) and machine learning are intended to help where existing empirical knowledge reaches its limits. This is the case, for example, in the development and processing of new materials, the increasing complexity of which must be mastered in order to achieve high quality and efficient production. This requires a detailed understanding of the relationships between material, machine, tool, and product properties.  
Another field of application for data-driven methods focuses on increasing efficiency in production. For example, methods for condition monitoring and need-based maintenance are being developed to increase plant availability and ultimately improve productivity.  
The application of data-driven methods in engineering and production technology was made possible by the extended sensory capabilities of machines and tools, powerful networking technology, and the availability of practicable analysis software.  
Data mining is commonly used in many fields (e.g., computer science, financial services) and therefore, several process models are available to lead through such projects. These conventional DM methodologies (e.g., KDD, SEMMA, CRISP-DM [1]) address the knowledge discovery in existing databases while in the engineering domain this is often not the case. Several engineering tools (e.g., LABVIEW [2], XperiDesk [3]) often support data collection but do not have a toolchain to support data mining projects. This problem should be addressed by the process model presented here. While data can be acquired and connected more and more easily, the collaboration of technical competences is still missing [4].  
Therefore, it is the objective of research at the Institute of Mechatronic Engineering of the TU Dresden, especially at the chair, to develop an engineer-suitable procedure for DM. The result is the DM methodology for engineering applications (DMME)--a holistic extension to the CRISP-DM model, which abstracts and summarizes the experiences from numerous research projects. DMME is a general, systematic methodical approach for DM in an engineering context.  
The combination of the networking expertise of computer scientists, the data mining expertise of mathematicians and the engineers' competences on failure mode and effect analysis in technical systems is the major advantage of the methodology.  
This enables DM in a common way as supposed by CRISP-DM with benefits in data quality assessment and the technical interpretation of DM findings.  
The DMME is already in use in current projects and teaching.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 2 Method
### Radar Cross Section  
In order to characterize the features to investigate, the _radar cross section_ (RCS), is employed. It is defined by the radar equation [28]:  
\[\frac{P_{\mathrm{r}}}{P_{\mathrm{t}}}=\underbrace{\frac{G_{\mathrm{t}}}{4\pi R_{ \mathrm{T}}^{2}}}_{\mathrm{T_{X}}}\underbrace{\frac{G_{\mathrm{r}}}{4\pi R_{ \mathrm{T}}^{2}}}_{\mathrm{Rx}}\underbrace{\frac{\lambda^{2}}{4\pi}}_{ \mathrm{RCS}}\underbrace{\frac{\sigma}{\sigma_{\mathrm{RCS}}}}_{\mathrm{antenna\;aperture}} \tag{1}\]  
with subscript \(r\) for terms belonging to the receiver (Rx), subscript \(t\) for terms belonging to the transmitter (Tx), \(P\) for powers (in W), \(G\) for antenna gains (no unit), \(R\) for distances (in m), and \(\lambda\) der wavelength of the transmitted and received electromagnetic wave (in m). Looking at the units of Equation (1), one sees that the unit of \(\sigma\) is m\({}^{2}\): an area (for a more detailed explanation, see Reference [29]). If the sender and receiver are identical, \(\sigma\) is called _monostatic_ RCS. \(\sigma\) is used to characterize and classify radar targets. The monostatic RCS of aircrafts is usually recorded for an azimuth plane cut, which can be referred to in order to identify the aircraft type. However, by just knowing the RCS value, no conclusions can be drawn about the actual shape of the investigated object. For the RCS prediction of regular shapes, several approximations and hybrid numerical approaches can be employed (see Reference [30] for an in-depth treatment). Prediction of the RCS of an object of arbitrary shape, however, is possible only by numerical simulation. In References [31; 32], numerical methods for the computation of the RCS of wind turbines are compared with regard to computational feasibility as well as plausibility and quality of results.
# Compact Sub 6 GHz Dual Band Twelve-Element MIMO Antenna for 5G Metal-Rimmed Smartphone Applications
## 4 Experiment and Measurement Results
In the literature focusing on the integration of metal frame MIMO mobile phone antennas, the radiation efficiency was reported to range from 31% to 65% at 3300 MHz-3800 MHz (with a 50.4 mm spacing between the antennas) [15], and the radiation efficiency was reported to range from 36% to 58% at 4800 MHz-5000 MHz (with a 21 mm spacing between the antennas) [14]. It is important to note that metal frames can have an impact on radiation efficiency due to their ability to reflect, absorb, or scatter RF energy, resulting in radiation loss or non-uniform radiation patterns. Considering these factors, we propose that a radiation efficiency of over 36% is sufficient for the dual-band MIMO mobile phone antenna integrated with a metal frame, especially when the antennas are positioned with a mere 3 mm spacing between them. This level of efficiency is expected to meet the communication requirements and provide a good connection range and stability. Figure 6:2 Measured 3D radiation patterns of the twelve-antenna units at 3450 MHz and 4900 MHz.
## 4 Conclusions
### Potential Use  
The vast research reviewed in this paper indicates the great potential of CML technology in future environmental monitoring, once the availability of measurements is granted. Potential use can be divided between three main families:  
1. Covering blind spots. There are areas where almost no near-ground measurements are available. One such example includes country-wide areas in developing countries, such as Africa [60; 63]. Other examples are local, and include specific challenging landscapes such as slopes and urban areas, where traditional ground weather stations are known to be less reliable. Even with the limited accuracy of CML technology, in cases where there is no alternative, its potential is extremely important.
2. Improving monitoring accuracy. Even in areas where the coverage of conventional weather-monitoring facilities (e.g., gauges and radar) is good, the use of additional ground-level measurements can improve performance. The potential improvement highly depends on the topology of the network (e.g., its density) and on the temporal resolution of the available measurements.
3. Improving models. Complex meteorological and hydrological models, used for forecasting, are continuously improved by comparing their predictions to actual measurements. CML technology offers a new dimension of data to be assimilated in such models.
## 2 Literature Review
### Development of Mixed-Language Datasets  
In response to the need for mixed-language data, several speech recognition datasets have been developed, including SEAME [29], CECOS [30], and OC16-CE80 [31]. However, these datasets remain relatively small compared to the abundance of monolingual data available, which limits the effectiveness of training models for mixed-language speech. [PERSON] et al. [32; 33] pioneered the first large-vocabulary continuous speech recognition (LVCSR) system for a Mandarin-English mixed-language speech. By merging phoneme sets with the International Phonetic Alphabet (IPA), [PERSON]'s distance, and discriminant training, their two-pass system achieved an MER of 36.6% on the SEAME development set. Nevertheless, the scarcity of mixed-language datasets continues to hinder progress, as there is a lack of sufficient training data where speakers naturally switch between languages.
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 4 Applications of DLC Coatings
### Others Applications of DLC  
DLC coatings have found broad industrial applications, particularly in optical and electronic areas. They also have been effectively utilized in various tribological applications [181; 182; 183]. In tribological applications, DLC coatings were used successfully as coatings for ball bearings, where they decreased the friction coefficient between the ball and race; in shaving applications, where they increased the life of razor blades in wet shaving applications; and increasingly in automotive applications, such as racing engines and standard production vehicles. The structural and mechanical behaviors of DLC coatingsare the dependent factors of the deposition method and the incorporation of additional elements such as nitrogen, hydrogen, and silicon. These additional elements control the hardness of the resultant film, the level of residual stress, and the tribological properties. As DLC films increasingly become adopted for use in industry, knowledge of the factors that control their properties, and thus the ultimate performance of coated components in practical tribological applications, are becoming increasingly important.  
[PERSON] et al. [184] developed a coating composition for improving the coating adhesion on high C steel, and progressively the coating morphology. Further, DLC coatings were employed by [PERSON] to enhance the strength of ceramic tools [185]. Recently, [PERSON] et al. [186] demonstrated that a DLC/steel blend gave a smoother running in a measure when contrasted with DLC [187].  
DLC coatings are an amorphous C material that exhibits typical diamond-like hardness and low-friction, characterized by the sp\({}^{3}\)-reinforced C and the structure. The proportion of sp\({}^{2}\) (graphically) and sp\({}^{3}\) (diamond-like) determines the properties of the DLC. The novel fabrication of DLC coatings that incorporated nanoparticles (WO/MoO) and PECVD for automobile applications has shown improvement within the adhesion properties of the DLC coatings [188].  
DLC has been discovered to have great application in biomedical zones, where its biocompatibility and erosion obstruction permit it to be utilized in tribological applications.
# Effects of Refiner Plates with Different Fillings on TMP Properties
## 3 Results and Discussion  
### Comparison of Refining Energy and Stock Throughput from the Refiner Plates with Different Patterns  
Figure 4 presents the effect of increasing refining energy on stock throughput for TMP and CTMP during the refining of wood chips using TP A and TP B with different plate patterns. The results indicate that, initially, there was no significant difference in stock throughput between TP A and TP B regardless of their plate patterns. However, as severe defibration occurred, more pulp stock began to be discharged from TP A with a smaller CEL at the same refining energy compared to TP B. The high-intensity refining of TP A could cause a significant reduction in chip size, which could lead to better fiber separation during the subsequent refining stages. As a result, more fibers could be extracted from a given amount of wood chips, which might increase the overall yield of mechanical pulp. Finally, it contributed to a greater reduction in energy consumption during the subsequent refining stages, as the smaller chip size allowed for more efficient refining.  
These findings are consistent with the previous studies that have demonstrated the effect of plate pattern and refining intensity on pulp quality and energy consumption [20; 21]. High-intensity refining for TMP, as performed by TP A, has been shown to improve pulp productivity and reduce energy consumption by promoting defibration and reducing the refining time consumed up to the final freeness [22; 23].
# Transpilers: A Systematic Mapping Review of Their Usage in Research and Industry
## 4 Industry Applications  
A search was conducted using Google Search and referring to technical forums, searching about transpilers, their synonyms, and another kind of source code transformations. In addition, we considered their usage in applications or frameworks. Then was performed via deep navigation and followed by references to find additional related techniques. The following main references have been found:  
* Haxe cross-compiler, which can transform Haxe language to several target languages ([[https://haxe.org/](https://haxe.org/)]([https://haxe.org/](https://haxe.org/)) (accessed on 11 January 2023)) \begin{table}
\begin{tabular}{c c} \hline
**Term** & **\# Articles** \\ \hline source-to-source & 386 \\ \hline transformation & 231 \\ \hline transpiler & 37 \\ \hline hls & 36 \\ \hline cross-compilation & 32 \\ \hline source to source & 30 \\ \hline transpilation & 24 \\ \hline cross-compiler & 23 \\ \hline transcompiler & 11 \\ \hline cross compiler & 7 \\ \hline cross compilation & 6 \\ \hline transcompilation & 2 \\ \hline \end{tabular}
\end{table} 
Table 16: Most-used transpiler synonyms. \begin{table}
\begin{tabular}{c c} \hline
**Programming Language** & **\# Articles** \\ \hline C++ / C & 135 \\ \hline Java & 63 \\ \hline javascript & 41 \\ \hline Rust & 22 \\ \hline Python & 21 \\ \hline Fortran & 20 \\ \hline matlab & 18 \\ \hline High-level languages & 6 \\ \hline Haxe & 2 \\ \hline Ocaml & 2 \\ \hline \end{tabular}
\end{table} 
Table 15: Top 10 most recurrent programming languages. * Typescript, which can transform the typescript language to javascript (https://www. typescriptlang.org/ (accessed on 11 January 2023))
* Dart is a client-optimized programming language which can compile for several platforms such as Android, iOS, Windows, and linux, and can also generate the code for Javascript. ([[https://dart.dev/overview](https://dart.dev/overview)]([https://dart.dev/overview](https://dart.dev/overview)) (accessed on 11 January 2023))
* React Native is a cross-platform framework that uses the javascript language as a base, and can generate native code for several platforms such as Android, iOS, and the web. ([[https://reactnative.dev/](https://reactnative.dev/)]([https://reactnative.dev/](https://reactnative.dev/)) (accessed on 11 January 2023))
* Angular is a web front-end framework based on Typescript to build small to enterprise-level applications. ([[https://angular.io/](https://angular.io/)]([https://angular.io/](https://angular.io/)) (accessed on 11 January 2023))
* The.Net framework proposes the common intermediate language (CIL), which is a human-readable code. When compatible programming language such as C#, Visual Basic.Net, F#, and others perform the compiling process, they really are performing a transpilation process to CIL.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 3 Case Study: Zhongma Landslide
### Landslide Monitoring Data  
Eight GNSS stations and one rainfall station (Figure 4) were deployed on the landslide. The GNSS stations were installed on the main deformation area of the landslide. The GNSS data were collected every hour, and the rainfall data were collected every 24 h.  
As Figure 5 shows, the deformation of the Zhongma landslide has obvious acceleration steps, temporally coincident with the periods of continuous rainfall or after a large rainfall. So, the landslide deformation is closely related to the amount of rainfall.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 5 Energy-Balancing Techniques
### Topology Control  
Efficient topology control is instrumental in balancing energy consumption among the sensor nodes in WSNs, thereby extending the overall network lifetime. In this section,we discuss topology control techniques with a focus on balancing the residual energies of sensor nodes in the WSN. They are different from the topology control techniques presented in Section 4.1.2 (topology control techniques, which are focused on achieving energy savings at the network level) as their focus is to balance the residual energy of sensor nodes in the WSN [36].  
The primary aim of this type of topology control technique is to improve the overall performance of the WSN by balancing energy consumption, reducing interference, enhancing network connectivity, and ultimately prolonging the lifetime of the sensor nodes. This type of topology control primarily makes use of radio optimization techniques at the node level to achieve its goal [100]. This involves controlling the transmission power and connectivity of individual sensor nodes to create an efficient and energy-aware network structure [113]. The rationale for this type of topology control includes energy efficiency, network connectivity, interference mitigation, and load balancing. By adjusting the transmission power levels, nodes can avoid unnecessary interference, collisions, and packet loss, leading to more reliable communication and reduced energy wastage.  
Topology control can be implemented through strategies such as transmission power control and optimal node placement in order to achieve network-wide energy savings. Through transmission power control, nodes adjust their transmission power to limit the communication range based on the proximity of neighboring nodes and the communication requirements. This results in energy savings for the nodes with higher energy consumption, which in turn leads to energy balancing among sensor nodes and an extended WSN lifetime. Also, topology control may involve dynamically adjusting the positions of sensor nodes based on environmental changes or specific application requirements. Optimizing node placement can lead to more energy-efficient communication by adapting the node density based on the message density to achieve uniform energy dissipation [99].  
As presented in Table 6, the linear topology of LWSNs makes transmission power control and optimal node placement particularly effective. Strategically placing nodes while considering message density and node distribution helps reduce the communication burden on critical nodes (those closest to the sink) and ensures uniform energy dissipation. Transmission power control is especially useful in LWSNs when a node along the linear path fails, as it allows the system to dynamically increase transmission power to bypass the failed sensor node.
## 3 Results and Discussion
### EDS Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Element** & **Wt\%** & **Wt\% Sigma** & **Atomic \%** & **Standard Label** \\ \hline  & C & 8.70 & 0.43 & 15.06 & C Vit \\  & O & 43.63 & 0.33 & 56.67 & SiO\({}_{2}\) \\  & Na & 1.10 & 0.05 & 0.99 & Albite \\ T28S/C28S- & Mg & 3.09 & 0.06 & 2.64 & MgO \\ nanofibers & Al & 1.44 & 0.05 & 1.11 & Al\({}_{2}\)O\({}_{3}\) \\  & Si & 7.62 & 0.09 & 5.64 & SiO\({}_{2}\) \\  & S & 0.92 & 0.05 & 0.60 & FeS\({}_{2}\) \\  & K & 5.05 & 0.08 & 2.68 & KBr \\  & Ca & 27.44 & 0.22 & 14.23 & Wollastonite \\  & Fe & 1.01 & 0.11 & 0.38 & Fe \\ \hline  & C & 12.78 & 0.42 & 20.49 & C Vit \\  & O & 49.00 & 0.33 & 58.98 & SiO\({}_{2}\) \\  & Na & 1.00 & 0.05 & 0.84 & Albite \\  & Mg & 0.62 & 0.04 & 0.49 & MgO \\  & Al & 1.64 & 0.04 & 1.17 & Al\({}_{2}\)O\({}_{3}\) \\ T28 CS/C28 CS & Si & 5.94 & 0.07 & 4.07 & SiO\({}_{2}\) \\  & S & 0.81 & 0.04 & 0.49 & FeS\({}_{2}\) \\  & K & 4.34 & 0.07 & 2.14 & KBr \\  & Ca & 22.75 & 0.18 & 10.93 & Wollastonite \\  & Fe & 1.14 & 0.09 & 0.39 & Fe \\ \hline  & C & 8.92 & 0.41 & 16.42 & C Vit \\  & O & 37.01 & 0.34 & 51.15 & SiO\({}_{2}\) \\  & Na & 1.19 & 0.05 & 1.14 & Albite \\ T28 CS/C28 CS- & Al & 1.58 & 0.05 & 1.29 & Al\({}_{2}\)O\({}_{3}\) \\ nanofibers & Si & 6.83 & 0.08 & 5.38 & SiO\({}_{2}\) \\ spectrum & S & 1.44 & 0.05 & 0.99 & FeS\({}_{2}\) \\  & K & 8.22 & 0.11 & 4.65 & KBr \\  & Ca & 33.30 & 0.25 & 18.37 & Wollastonite \\  & Fe & 1.51 & 0.12 & 0.60 & Fe \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._  
Figure 10: EDS results of plain paste: (**a**) SEM image; (**b**) EDS spectrum. Figure 11: EDS results of cement paste containing TEOS/PVP NFs: (**a**) SEM image; (**b**) EDS spectrum. Figure 12: EDS results of cement paste containing TEOS/PVP NFs: (**a**) SEM image; (**b**) EDS spectrum—focus on nanofibers zone. Figure 13: EDS results of cement paste containing CNT-TEOS/PVP NFs: (**a**) SEM image; (**b**) EDS spectrum.
## 3 Experimental Test Results and Analysis  
### Density  
The density of the pastes containing various replacement percentages is depicted in Table 3. It is apparent that the presence of OWA slightly reduces the density of all samples, but these reductions are not significant (around 2.5%). For instance, the control mix records the highest density of 1938 kg/m\({}^{3}\) at 90 days of curing. Over the same period, the density slightly decreases, reaching 1920, 1916, 1908, and 1892 kg/m\({}^{3}\) for 5, 10, 15, and 20% OWA, respectively. Such behavior may be attributed to the lower density of OWA relative to ordinary cement, suggesting its suitability to produce lighter paving blocks, making them more efficient for handling and transportation, thereby speeding up project completion.
# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method  
This section provides a detailed description of our proposed techniques. Figure 1 presents a schematic illustration of our method, which consists of five phases: (1) preprocessing, (2) data-driven strategy, (3) feature selection, (4) data splitting and oversampling, and (5) stacking ensemble model construction. A detailed explanation of each phase is provided in the following subsections.  
Our technique is specifically designed to predict the various severity levels of COVID-19 in patients. Differentiating the severity levels is essential to provide appropriate interventions, save lives, and efficiently allocate limited medical resources. Therefore, our technique can deliver the final severity prediction results, derived from the input data, in one of three output format types depending on the needs of the user's intervention: Type 1 (low or high severity), Type 2 (mild, severe, and critical), and Type 3 (asymptomatic, mild, moderate, severe, and fatal). The prediction models delivering these results are adaptively constructed based on the availability of clinical variables and built with various forms of internal stacking ensembles. The following steps summarize our technique:  
1. In the first internal phase, we apply data preprocessing techniques to the acquired COVID-19 patient data. This phase includes the removal of irrelevant features, the imputation of missing values, data standardization, and the mean encoding of categorical features.
2. In the data-driven strategy, we create groups of clinical variables for severity classification, considering the availability of a patient's clinical variables. These groups mainly consist of epidemiological variables that can be easily obtained when the patient visits the hospital. To optimize the predictions using the minimum amount of necessary clinical variables, we modify the composition of each group by changing the feature sets (i.e., the clinical variables).  
Figure 1: Overview of the proposed technique.  
3. To identify the feature sets that significantly affect the severity prediction, we propose a feature selection algorithm. This algorithm considers forward selection, backward elimination, and RFECV to identify the optimal feature set.
4. Subsequently, we split the dataset into training and testing subsets. The training data undergo an oversampling process to ensure a balanced data distribution.
5. In the stacking ensemble model construction phase, single models are built and evaluated using the F1 and AUC score metrics. To construct an adaptive stacking ensemble model, we select \(k\) single models that demonstrate excellent performance and ensure diversity, with low correlations among the predictions. The predictions are then performed using adaptive stacking ensemble models, and the results are presented in one of three format types for various severity classes.