esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 3 Materials and Methods
### Methods
#### 3.2.3 Feature Selection
Figure 5: Feature Selection Steps and Techniques flowchart. Finally, the XGBoost embedded method was used. This feature-selection technique leverages a second-order Taylor expansion to approximate the loss function, enhancing model accuracy, and incorporates a regular term to control model complexity, thereby preventing overfitting. It employs feature importance selection, an embedded method, wherein gain is used to identify optimal segmentation nodes during training. The importance of each feature is gauged by calculating the average gain (total gain of all trees divided by each feature's division count), which indicates the feature's contribution to the model's construction and training. Therefore, XGBoost is an optimal choice for wind feature selection since its integrated regularization can prevent overfitting and demonstrates its efficient cross-validation implementation for accurate assessment [56; 57; 58]. For feature selection, the mRMR method employed the Mutual Information Quotient (MIQ) from the pymrmr library, while the GA relied on the mean absolute percentage error (MAPE). The RFECV, as defined by the default values in scikit-learn, utilized the R\({}^{2}\) score for evaluation. XGBoost determined feature importance through gain during its training process. To prevent overfitting, scikit-learn's regression tree and XGBoost's specific default parameters were used. For scikit-learn's regression tree, nodes are expanded until all leaves contain fewer than the default value of minimum samples split, which is set to 2. In XGBoost, the subsample parameter defaults to 1, indicating that the model uses the entire dataset, and the learning rate (or eta) is set to 0.3. These inherent parameter settings mitigate the risk of overfitting, thus helping the models generalize effectively to unseen data.
# Pile Base and Shaft Capacity under Various Types of Loading
## 2 Pile Bearing Capacity
### Shaft Bearing Capacity  
The physical behaviour of the pile shaft is different from its base. The unit resistances on the shaft surface result from the shear friction at pile-soil contact. Its values depend mainly on the stress around the pile and after the pile installation [16]. The shaft friction value is also influenced by the friction between the pile and the soil, usually determined by the angle \(\delta\). The values of the angle \(\delta\) can be determined from shear tests for various materials. [PERSON] and [PERSON] examined \(\delta\) for various surface roughness and they determined tan \(\delta\) in the range of 0.75-1.00 [17]. The determination of the \(\delta\) parameter was also discussed in several other works, e.g., [18] and [19].  
Figure 1: The behaviour of the pile shaft and base depends on the load applied to the pile: (**a**) compression loading, (**b**) uplift loading, (**c**) bidirectional loading.  
The shaft bearing capacity for an uplift pile is slightly lower than for a compression pile, and it is estimated by most design guidelines to be around 70%-90% [4]. [PERSON] and [PERSON] in their work [20] conducted a parametric study for piles in an elastic and perfectly plastic Mohr-Coulomb model. On the basis of the research, they proposed the following relationship  
\[\frac{Q_{t}}{Q_{c}}\approx\left[1-0.2\cdot\log_{10}\left(\frac{100}{L/D}\right) \right]\left(1-8\eta+25\eta^{2}\right) \tag{1}\]  
\[\eta=\
u_{p}\left(\frac{L}{D}\right)\left(\frac{G_{ave}}{E_{p}}\right)\tan\delta \tag{2}\]  
where \(\eta\)-compressibility parameter, \(Q_{c}\)-compressive shaft capacity of a pile, \(Q_{t}\)-tensile shaft capacity of a pile, \(G_{ave}\), \(E_{p}\), \(\
u_{p}\)-average shear modulus of soil, [PERSON]'s modulus of the pile, and [PERSON]'s ratio of the pile, respectively. [PERSON] and [PERSON] observed that even for a large variety of \(G_{ave}/E_{p}\)--despite not taking into account other effects (e.g., local changes in stresses caused by dilatation)--the ratio \(Q_{t}/Q_{c}\) remains within the range of 0.7-0.85.
## Materials and Methods
### Data Preparation
#### 2.3.2 Meteorological Datasets and Flux Measurement  
The input meteorological datasets included daily maximum, minimum and average temperature (\({}^{\circ}\)C), median dew point (\({}^{\circ}\)C), total precipitation (mm), wind speed (m/s) and total short-wave radiation (W/m\({}^{2}\)). These datasets were derived from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) at the Arou Superstation, which is located in the southeastern part of the study area (100.46\({}^{\circ}\) E, 38.05\({}^{\circ}\) N) [36; 37]. Except for the input datasets above, the energy and carbon fluxes and soil moisture data were also prepared for model validation. Simulated volumetric soil water content (VSWC) were compared with a 0-40 cm average measurement with cosmic-ray probes buried at the Arou station, with a footprint scale of 700 m in diameter [38]. The eddy covariance (EC) technique was used in the Arou footprint tower for continuous and long term monitoring of energy and CO\({}_{2}\) fluxes. GPP was derived from the fluxes of net ecosystem exchange (NEE) measured at the EC tower. Based on the Lloyd-Taylar model [39], the relationship between temperature and respiration during the night was used to estimate the daylight respiration rate, thereafter GPP flux could be detected from monitored NEE. The evapotranspiration was converted from measured latent heat fluxes (LE, W/m\({}^{2}\)) as shown below, where \(\lambda\) is \(2.501\times 10^{6}\) J/kg from [35].  
\[\text{ET}=\text{LE}/\lambda. \tag{6}\]
# Review of the Research Progress in Soft Robots
## 2 Driven Methods and Materials for Soft Robots
### Smart Materials and Their Driven Methods
#### 2.2.7. Liquid Crystal Elastomer (LCE)  
Liquid crystal elastomer, as a most representative smart material, has both liquid crystal anisotropy and rubber elasticity. Under external stimulation (heat, light, electricity, magnetism, pH, humidity, etc. ), its phase state or molecular structure will change, which in turn will change the arrangement order of liquid crystal elements, resulting in macroscopic deformation of the material itself, and when the external stimulation is removed, liquid crystal elastomer can return to its original shape. The advantages of liquid crystal elastomers are unique bi-directional shape memory, large deformation, fast response time, various response mechanisms, and scalability, but the disadvantages are lower mechanical properties and higher preparation cost. [PERSON] et al. [70] prepared a thermally driven liquid crystal elastomeric soft robot by the 3D printing technique. They first polymerized the liquid crystal monomer RM82 and chain extender to obtain liquid crystal oligomers with acrylate end groups as printing inks, then edited and printed them into the designed shape by the 3D printer, and used UV light to trigger a cross-linking reaction to fix the complex 3D structure of the liquid crystal elastomer to obtain a cylindrical hollow liquid crystal elastomer soft robot, which can achieve radial contraction as well as axial expansion under external temperature stimulation. [PERSON] et al. [71] successfully prepared a rolling soft robot based on azobenzene liquid crystal elastomer and demonstrated its rolling behavior under continuous UV light irradiation. When the robot was exposed to UV light, the stress generated by the asymmetric strain between the top and bottom overcame the rolling resistance to achieve a rolling motion. In addition, when the robot starts to roll, the film originally at the lower part is displaced to the upper part of the robot, re-generating asymmetric stress to achieve continuous rolling. [PERSON] et al. [72] reported an ultrathin sensing and electronically controlled soft-body robot capable of sensing the environment and performing adaptive soft-body crawling, mimicking an inchworm. These soft-bodied robots consist of ultrathin deformable heated actuators, sensors with single-crystal silicon photodetectors, and liquid crystal elastomer (LCE-CB) nanocomposites doped with carbon black, which can achieve an adaptive crawling motion through a combination of sensing and actuation, where the sensors sense the environment and the actuators respond accordingly and control the motion autonomously by adjusting the deformation of the LCE-CB. Based on the above description of smart materials and their drivers, the performance of these smart materials is compared in Table 1.
## 3 Results
### Characterization of Natural Extracts
#### 3.1.3 Antioxidant Activity  
The antioxidant activity of all extracts was measured by four methods, and two of them showed the chelating activity percentages against ABTS (2,2'-azino-bis(3-ethylbenzothazoline-6-sulphonic acid) and DPPH (2,2-diphenyl-1-picrylhydrazyl) radical cation, in a hydrophilic and lipophilic system, respectively (Table 3). The other two methods showed the efficiency to reduce Fe\({}^{3+}\) to Fe\({}^{2+}\) (FRAP) and the hydrophilic antioxidant capacity obtained by measuring the oxygen radical absorbance (ORAC), both expressed in \(\upmu\)M Trolox equivalents (TE) 100 g\({}^{-1}\) (Table 3).  
Firstly, considering the results presented in Table 3, it can be reported that beet, acerola, and rosemary showed the highest chelating activity against DPPH and ABTS radical cations. Garlic powder obtained a 51.5% scavenging activity against DPPH, while the lowest value was presented by Ct with 8.45% of quelation power.  
On the other hand, the scavenging activity against the hydrophilic radical, ABTS, is generally lower than against DPPH. For this reason, the scavenging activity against ABTS followed the next hierarchy: W, garlic, A, paprika, S, Ch, oregano, Ct, Le, and Ce, with values from 33.4% to 12%, after B, R, and Ac, which presented values of 85.7%, 70.2%, and 46.5%, respectively.  
Secondly, in Table 3, we can also observe a similar behaviour regarding the efficiency of each extract to reduce Fe\({}^{3+}\) to Fe\({}^{2+}\) by comparing the hydrophilic antioxidant capacity measured by their oxygen radical absorbance. In this case, applying the FRAP method, natural extracts rich in phenolic compounds are the first on the list: R from rosemary, oregano, Ct from citrics, B, W, and paprika followed by Ch, A, L, S, Ac, garlic, and Ce, the last one with 804.7 \(\upmu\)M TE 100 g\({}^{-1}\), 50% less than garlic with 1915.7 or 95% less than R with 17,790 \(\upmu\)M TE 100 g\({}^{-1}\).  
Otherwise, the obtained results from the analysis of the oxygen radical absorbance showed the next hierarchy, with similar values published by USDA [23]: R \(>\) oregano \(>\) paprika \(>\) Ct \(>\) B \(>\) A \(\geq\) Ch \(\geq\) garlic \(\geq\) L \(\geq\) Ac \(\geq\) spinach \(\geq\) watercress \(>\) Ce.
###### Abstract  
The present short communication described a new procedure for the reconstruction of the horizontal severely resorbed edentulous maxilla with custom-made deproteinized bovine bone block, fabricated using three-dimensional imaging of the patient and computer-aided design/computer-aided manufacturing (CAD/CAM) technology. The protocol consisted of three phases. In the diagnosis and treatment planning, cone-beam computed tomographic scans of the patient were saved in DICOM (digital imaging and communication in medicine) format, anatomic and prosthetic data were imported into a dedicated diagnostic and medical imaging software, the prosthetic-driven position of the implants, and the graft blocks perfectly adapted to the residual bone structure were virtually planned. In the manufacturing of customized graft blocks, the CAD-CAM technology and the bovine-derived xenohybrid composite bone (SmartBone(r) on Demand - IBI SA - Industrie Biomediche Insubri SA Switzerland) were used to fabricate the grafts in the exact shape of the 3D planning virtual model. In the surgical and prosthetic procedure, the maxillary ridge augmentation with custom-made blocks and implant-supported full-arch screw-retained rehabilitation were performed. The described protocol offered some advantages when compared to conventional augmentation techniques. The use of deproteinized bovine bone did not require additional surgery for bone harvesting, avoided the risk of donor site morbidity, and provided unlimited biomaterial availability. The customization of the graft blocks reduced the surgical invasiveness, shorting operating times because the manual shaping of the blocks and its adaptation at recipient sites are not necessary and less dependent on the clinician's skill and experience.  
Keywords:bone tissue regeneration; xenografts; bone substitutes; computer-aided design/computer-aided manufacturing; deproteinized bovine bone +
Footnote †: journal: Applied Soft 2020, 10, 2659; doi:10.3390/app10082659
## 5 Conclusions  
In this study, we proposed a DPGA, which is a novel framework to predict the RUL and fault-types. It is a self-adaptive method to select the close-to-optimal set of data-preprocessing algorithms and optimize the involved parameters in each subtask of data rebalancing, feature extraction, feature reduction, and learning. Although DPGA used four machine-learning methods such as the multi-layer perceptron network, \(k\)-nearest neighbor, support vector machine, and random forest in this study, it can be easily extended to combine other kinds of machine-learning methods. In addition, our method seems robust because it can generate an ensemble of prediction models. Through the performance comparison of DPGA with the traditional grid-search framework over three benchmark datasets, the former showed significantly better accuracies than the latter in a comparable running time. This implies that our genetic search was efficient in solving the large-scaled diagnostics and prognostics problems. It was interesting that the best solutions found by DPGA involve many filtering- or reduction-based feature extraction algorithms to generate various feature variables. As shown in the results, it is advantageous that DPGA can be applicable to other machinery systems without a priori knowledge about the most proper machine-learning method or a feature processing algorithm. In a future study, a parallel and distributed version of the DPGA method can be developed to reduce the execution time. It is also promising to further validate the usefulness of our approach by employing other kinds of the machine-learning models such as the recurrent neural network. Finally, it will be another interesting future study to design a more robust ensemble approach than what was employed in DPGA.  
Conceptualization, Y.-K.K.; Formal analysis, H.-C.T.; Funding acquisition, Y.-K.K.; Methodology, H.-C.T.; Project administration, Y.-K.K.; Writing--original draft, H.-C.T.; Writing--review & editing, Y.-K.K. All authors have read and agreed to the published version of the manuscript. This work was supported by National IT Industry Promotion Agency (NIPA) grant funded by the Korea government (MSIP) (S1106-16-1002, Development of smart RMS software for ship maintenance-based fault predictive diagnostics).  
Figure 6: Comparison of running time between DPGA and exhaustive grid-search (EGS)-E. (**a**) RUL prediction datasets. (**b**) Fault-types classification datasets.  
**Acknowledgments:** This work was supported by National IT Industry Promotion Agency (NIPA) grant funded by the Korea government (MSIP) (S1106-16-1002, Development of smart RMS software for ship maintenance based fault predictive diagnostics).  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Industry 4.0 towards Forestry 4.0: Fire Detection Use Case +
## 5 Methodology
### Data Stream Processing Pipeline
#### 5.2.3 Evaluating the Impact of Changes in IoT Data Streams Rate over Query Window Configurations  
In this section, we overviewed the evaluation of the impact of any changes in stream rate and window configurations over the performance of a stream processing engine which was addressed in our previous work [5]. In particular, we implemented an IoT data stream pipeline over Fink to consume data from Kafka, ingest it into Fink, execute inner joins over Fink query engine, and then send back the joined streaming result to Kafka be consumed by other data sinks.  
We conducted our experiments using two input data streams which are streamed using Kafka and processed by Fink. Query results are stored in MongoDB as data sinks. We evaluated three types of windows supported by Fink, namely tumbling, sliding, and session. For the first input stream, we varied it to 10 different stream rates i.e., 60, 120, 180, 240, 300, 360, 420, 480, 540, and 600 tuples per minute. For the second input stream, we only produced one tuple per minute at a fixed rate. We assess the latency (time consumed between the input arrival and output generation) using two window sizes; (1) the small window size which is one minute denoted by 1-min, (2) the large window size is five minutes denoted by 5-min. To consider the queue delay for Kafka, we calculated the latency of window-based join queries executed on Fink as (1) Fink with Kafka (i.e., the time consumed between the event observed until the output is generated), denoted by [PERSON] w/[PERSON] and (2) Flink without [PERSON] (i.e., the time consumed between the event is already ingested into Fink till the output is generated), denoted by [PERSON] w/[PERSON].  
Figure 9: Example of deployed join stream query on Apache Flink web dashboard including data source and window operator using tumbling window for 1 min, i.e., 60,000 milliseconds.  
As shown in Figures 10 and 11, Flink w/[PERSON] and Flink wo/Kafka latency of queries increases linearly with the increase in stream rate for both 1-min and 5-min window sizes for three window types. In particular, the Flink w/Kafka grows fast due to Kafka's delay in launching Flink Kafka consumers to consume the input streams and Flink Kafka producers to send back the output stream to Kafka. However, the average latency of Flink w/Kafka over 5-min window is longer than the 1-min because the ingested tuples (i.e., from the first minute to the fifth minute) wait until the Flink window triggers its output. The larger window size can result in large data size ingested into Flink, and when this large data is processed, it incurs higher processing time/latency. Our evaluation confirms the impact of dynamic stream rate or other factors over the query engine's performance. However, there is no one window size and/or type fitting all input stream rates. Our evaluation strengthens the case for adaptive window type and size recommendation based on the input stream rate variation.
## 3 Results and Discussion
### Gold Adsorption from Cyanide Media
#### 3.2.5 Separation of Gold (I) from Metal-Cyanide Complexes  
In order to investigate the effect of other metal-cyanide complexes accompanying Au(CN)\({}_{2}\)\({}^{-}\), a study was carried out about their interference with the overall transport of gold(I). The metal-cyanide complexes studied, Cu(CN)\({}_{4}\)\({}^{3-}\), Fe(CN)\({}_{6}\)\({}^{3-}\) and Fe(CN)\({}_{6}\)\({}^{4-}\), were investigated in the form of binary mixtures with Au(CN)\({}_{2}\)- on a 1:1 molar basis. Thus, the feed phase contained 5.1 \(\times\) 10\({}^{-5}\) M of each metal in aqueous solutions of pH 13 \(\pm\) 0.1 and the carbon dosage was of 2 g L\({}^{-1}\).  
The results obtained indicated very low Cu(I), Fe(II) and Fe(III) adsorption. From the D values defined as in Equation (4), the values of the separation factors Au(I)/M, defined as  
\[\beta_{\frac{\rm Au}{\rm M}}=\frac{\rm D_{\rm Au}}{\rm D_{\rm M}}, \tag{5}\]  
are summarized in Table 5 and show the excellent possibilities, \(\beta\) values greater than 1, of these nanomaterials to separate gold(I) from these others accompanying metals in the solution.
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 2 System Model  
We consider uplink in a mmWave based multi-user single-input-multiple-output (MU-SIMO) wireless access, with \(K\) single antenna transmitters (users) and \(B\) receivers (Base Stations, BS). The set of all users is denoted as \(\mathcal{K}~{}=~{}\{1,2,...,K\}\) and \(\mathcal{B}~{}=~{}\{1,2,...,B\}\) is the set of receiving BSs. Each BS is assumed to be equipped with a uniform-linear-array (ULA) with \(N_{R}\) isotropic receive antennas, each antenna with the gain of 0 dBi and the spacing between two adjacent elements is set of \(\lambda/2\), where \(\lambda\) is the wavelength corresponding to the operating frequency.  
The problem formulation and proposed method, presented in this paper, are general and can easily be applied to any receive beamforming architecture. However, in this paper, we restrict our analysis to the case when each BS is equipped with an array of antennas and connected to the single baseband processing circuit. However, the analog front-end at each BS supports both antenna specific amplitude scaling and phase-shifting due to antenna specific amplifier and phase-shifter. Thus, one active analog beam in a specific spatial direction can be generated for a given time by appropriate phase and amplitude scaling to each antenna element at each BS. We consider coordinated multi-point reception, whereby all coordinating BSs concurrently receive and coherently combine a time synchronous signal from each active user during baseband signal processing. All BSs are connected to a common baseband-processing-unit (BBU), in the edge cloud by using zero-delay and finite-capacity fronthaul links, as illustrated in Figure 1. BBU performs all the digital signal processing functions, such as resource allocations and joint decoder design for the coordinated reception, over all connected BSs assuming full-CSI of the equivalent channels at the output of analog beamformers (concatenation of analog beamformers and channel vectors).  
The two-dimensional (2D) location of \(b\)th BS is denoted by \(\mathbf{q}_{b}=[d_{x^{\prime}}^{b},d_{y}^{b}]^{\mathrm{T}}~{}\forall b\in \mathcal{B}\) and location of \(k\)th user is give by \(\mathbf{p}_{k}=[p_{x}^{k},d_{y}^{k}]^{\mathrm{T}}~{}\forall k\in\mathcal{K}\). It is assumed that the location of all BSs \(\mathbf{q}_{b}~{}\forall b\) is fixed and known, while location of all users, \(\mathbf{p}_{k}~{}\forall k\) is not known.