esa-sceva/satcom-chunk-collection huggingface dataset:
# Topological Navigation and Localization in
## 4 Development and Testing Methodology
### Validation in Test-Fields  
The LHD automation navigation was installed in a GHH's LF11H LHD (GHH Fahrzeuge). The installation required mechanical and electrical modifications of the equipment to place the system's sensors, processing units, and wireless communication equipment. All the automation software runs in an industrial fan-less computer equipped with an Intel i7 processor and with 4 logical cores. The interface between the automation and the machine was implemented on the machine controller (IFM mobile mini controller), based on GHH's factory program (implemented in Codesys).  
After the system was installed and all basic control, communications, and safety functions were thoroughly tested, the equipment was moved to a test-field nearby the OEM (Original Equipment Manufacturer) facilities in Santiago, Chile. The test-field emulated an underground tunnel by using light material to mimic the walls of the mine (Figure 13), which was enough to trick the system. It also had a dummy loading point, a dumping point, and a truck loading point. These tests were used to calibrate the system controller's parameters and the kinematic characteristics of the vehicle's model, such as the acceleration, steering, and breaking response to different operation inputs.  
Because the test-field was located just outside the city where our development team is based (Santiago, Chile), it was possible to make a short trip to test new versions of the software, which contained bug fixes or improvements to the system. Usually a team of developers would go to the test-field two or three times a week to try out different modifications in the algorithms of the automation system.  
The last milestone of this stage was to validate the reactive navigation algorithm. In order to do this in a safe manner, a hybrid operation mode was used, in which the speed of the LHD was remotely controlled by an operator while the autonomous navigation system handled the steering of the vehicle in an assisted tele-operation mode. To ensure safety  
Figure 12: 1:5 scaled LHD built for testing and validation.  
Figure 13: Test-field close to GHH facilities in Santiago de Chile.  
precautions, an onboard operator who could shut down or override the automation system commands was on the vehicle for every test.
## 4 Strain Results and Analysis
### Dimensional Analysis
#### 4.1.1 Load Acting on the Pipe  
In these experiments, the explosion source was simplified to a point source. An explosion was assumed to be instantaneous, and the reaction process was ignored here. The main point of concern was the stress wave propagating outside of the shock wave zone in the soil. Prior studies have found that the minimum stress on the shock front propagating in high saturation is about 2 MPa. The stress acting on the pipeline in this experiment was far less than this threshold; therefore, the compression wave velocity \(c\) was considered to be a constant, which represented the compression effects of the soil. It was assumed that the earth was semi-infinite, homogeneous, and isotropic. The soil density \(\rho_{s}\) represents the inertia of the soil. The parameters on both sides of the wave front should satisfy the law of conservation of mass and momentum. The relationship between the vibration velocity \(v_{p}\) and the pressure \(p_{w}\) on the wave front is as follows [28]:  
\[\rho_{s}^{\prime}(c-v_{p})=\rho_{s}c \tag{2}\]  
\[\rho_{s}^{\prime}(c-v_{p})^{2}+p_{w}=\rho_{s}c^{2} \tag{3}\]  
where \(\rho_{s}^{\prime}\) is the density behind the wave front. Therefore, the peak pressure \(p_{\max}\) acting on the pipe is a function of soil density, wave velocity, and peak particle velocity (PPV) \(v_{\max}\) as follows:  
\[p_{\max}=\rho_{s}c\cdot v_{\max} \tag{4}\]  
where \(c\) and \(\rho_{s}\) are constants. Here, \(v_{\max}\) was used to represent the load level and was calculated by the Sadowski formula:  
\[v_{\max}=k(R/Q^{1/3})^{-\alpha}=k\overline{R}^{-\alpha} \tag{5}\]  
where \(k\) and \(\alpha\) are mainly related to factors such as field character and charge conditions, \(k\) is the field coefficient, \(\alpha\) is the attenuation index. The scaled distance definition is:  
\[\overline{R}=R/Q^{1/3} \tag{6}\]  
The variable was derived from dimensionless parameters and considered the comprehensive effect of \(Q\) and \(R\).
Similar results have been described in prostate cancer; quercetin activates caspase-3 and caspase-9 triggering apoptotic mechanisms via the mitochondrial pathway [32].The NPs used as carriers for the delivery of quercetin into target cells have been evaluated in different diseases [33; 34]. On the one hand, NPs alone offers a system that causes damage to specific cells by mechanisms such as oxidative stress, lipids and proteins oxidation, and high toxicity due to their physicochemical characteristic as the superficial charge [35]. In some cases, like cancer, central nervous degenerative, and cardiovascular diseases damage is generated by oxidative stress [36]. Two strategies to respond to oxidative stress have been described: (i) endogenous detoxifying systems including the enzymatic activities such as superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) and (ii) nonenzymatic activity of exogenous molecules such as quercetin, vitamin C, and lycopene which also show potent antioxidant activities [34]. However, the exogenous antioxidant molecules are light, temperature and pH sensible, and enzymatic degradation susceptible [36]. An efficient form to inhibit this degradation is the encapsulation or transportation of these molecules by organic or metallic nanoparticles [36]. Gold NPs have been used as quercetin carriers, the NPs inhibit epithelial-mesenchymal transition, angiogenesis, and metastasis in breast cancer cell lines [37]. Quercetin has been encapsulated in a self-assembled chitosan system. The system proved to be efficient in the inhibition of breast cancer cell proliferation [38]. Metallics and organic nanoparticles coupled to quercetin have been also proved to be useful in the inhibition of oxidative stress in degenerative diseases [37; 39]. Ultrasmall bimetallic Cu2-xSe-based bimetallic nanoparticles modified with quercetin promoted the microglia polarization and downregulate IL-6 and upregulate IL-10 playing anti-inflammatory activity [34]. However, as was describe before, PdNPs play an important role in nanomedicine and nanobiology due to their activity against a broad spectrum of organisms, and their chemical properties make the PdNPs a desirable candidate for the functionalization with quercetin. Previously, we have developed PdNPs and tested their effects as a fungicide, showing that treatments induce cell wall damage and oxidative stress in Aspergillus niger and Candida albicans [18]. Here, we extend these initial findings in fungus; we functionalized PdNPs with PVP (PdNPs-PVP) and with quercetin (PdNPs-PVP-Q). The physicochemical characteristics were evaluated, and their biological activity was tested in colorectal adenocarcinoma HCT-15 cells, as quercetin is a well-known antitumor naturally occurring agent. Although several studies have reported the anti-tumor effects of quercetin in HCT-15 cancer cells, no previous approaches using palladium nanoparticles coupled with quercetin to deliver cargo into colon cancer cells have been reported.
# Towards the Internet of Smart Trains:
###### Abstract  
Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11 ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges.  
IoT; IIoT; internet of trains; railway safety; rail planning and scheduling; predictive maintenance; WSN; railway enhanced services; freight transportation; cyber security +
Footnote †: journal: Sensors
## 2 Applications of Nanotechnology in the Petroleum Industry
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Area** & **Nano Type** & **Purpose** & **References** \\ \hline \multirow{4}{*}{Exploration} & Hyperpolarized silicon NPs & Imaging sensors for oil in a hydrocarbon reservoir & [20] \\ \cline{2-4}  & Nano-optical fiber & Detect oil-microbe, estimate reservoir pressure and temperature & [21] \\ \cline{2-4}  & Nano-robots & Well logging and borehole measurement (patent) & [22] \\ \cline{2-4}  & Coated carbon-nano structure & Real-time oil reservoir evaluation with two-dimensional detection technology & [23] \\ \cline{2-4}  & Magnetic NP & Detect flood front, fluid contact, hydrocarbon bypass and fracture & [24] \\ \cline{2-4}  & Superparamagnetic NP & Cross well magnetic sensor for tracking flood front & [25] \\ \hline \multirow{4}{*}{Drilling \& \multirow{2}{*}{Nanodiamond}} & Reduce or stop water invasion in shale & [26; 27] \\  & Silica NPs & by plugging shale pore & [26; 27] \\ \cline{2-4}  & \multirow{2}{*}{Nanodiamond} & Improve drilling process in harsh and demanding environment & [28] \\ \cline{2-4}  & Silica and alumina NPs & Cement accelerator & [29] \\ \cline{2-4}  & MgO and ZnO NPs & Improve thermal stability of drilling fluid & [30] \\ \cline{2-4}  & \multirow{2}{*}{Nanoclay} & Reduce permeability and porosity of cement and enhanced compressive strength & [31] \\ \cline{2-4}  & Carbon nanotubes & Improve compressive strength in HPHT & [32] \\ \cline{2-4}  & Cellulose nanofibers \& \multirow{2}{*}{} & Increase yield stresses, degree of hydration, flexural and compressive strengths & [33] \\ \cline{2-4}  & graphene nano-platelets & & \\ \hline \multirow{4}{*}{Stimulation \& \multirow{2}{*}{Pyroelectric NP}} & Additive for fracturing + viscoelastic surfactant to increase efficiency in fracturing & [34] \\ \cline{2-4}  & \multirow{2}{*}{Ni-Fe NPs} & Hydrate mitigation in the well & [35] \\ \cline{2-4}  & \multirow{2}{*}{ZnO NPs} & Increase low shear rate viscosity on Threadlike micelle fluids and more stable & [36] \\ \cline{2-4}  & \multirow{2}{*}{Metal oxides based} & Improve fracturing fluid stability and viscosity at high temperature (300 F). & [37] \\ \cline{2-4}  & \multirow{2}{*}{Non-ferrous NPS} & Combine with surfactant for IFT reduction & [38] \\ \hline \multirow{4}{*}{Refinery} & Nano-supported HDS & Patent on nano-supported HDS catalyst & [39] \\ \cline{2-4}  & MoS\({}_{2}\) nano-catalyst & Observe atomic-scale edge structures of MoS\({}_{2}\) & [40] \\ \cline{2-4}  & TiO\({}_{2}\) NPs & Improve water treatment by reducing fouling effect & [41] \\ \cline{2-4}  & TiO\({}_{2}\), ZrO\({}_{2}\), and SiO\({}_{2}\) NPs & Additive for stabilizing asphaltene in oil under acidic conditions & [42] \\ \cline{2-4}  & Magnetic NPs & Accelerate oil removal in water-oil emulsion & [43] \\ \cline{2-4}  & Nickel oxides and Al\({}_{2}\)O\({}_{3}\) NPs & Patent on nanocatalyst for hydrocracking & [44] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the literature studies.
# Laser Processing of Intraocular Lenses
## 2 Materials and Methods
### CW Diode Laser Micromachining Setup  
A low-cost CW diode laser was employed (4 PICO, HSLU) with a wavelength of 405 nm, producing a collimated laser beam with maximum power at 36 mW. The optical power of the laser was measured with a photodiode power sensor (Thorlabs, S120C). The laser operated either with an external trigger mode for multi-shot experiments or a continuous mode for line ablation. The beam was focused onto the IOL surface using a high-magnification objective lens with \(NA\) = 0.90 (Corning Tropel, Glendale, Arizona) and the beam spot size was approximately 300 nm. The IOL samples were placed vertically on the \(X\)-\(Y\) motorized linear stages (Smaract, CLS-92152, Oldenburg, Germany) and a CMOS monochrome camera (Genie Nano M2020, Teledyne Dalsa, Waterloo, ON, Canada) was used for maintaining precise focusing during the experiments. Due to the specific optical arrangement, the sample in the diode writing setup was moving only in one axial direction in this setup.  
Figure 1: Schematic representation of laser ablation on IOLs and associated patterning regions.
## 2 Materials and Methods
### Conventional Ultrasound and Elastography Examination  
A Hitachi Preirus machine with a 5-15 multi-frequency linear probe was used to perform Conventional B-mode ultrasound and strain elastography (real-time elastography--RTE) of the thyroid. All subjects were clinically evaluated (including thyroid palpation and height and weight measurements), and then grey-scale thyroid ultrasonography was performed. The optimal examination position is in the supine position, with the head tilted back to expose the neck. The child is then asked not to talk for a few seconds and not to swallow. Transverse diameters (two dimensions) and a longitudinal diameter (one dimension) were measured, the thyroid volume being calculated by the device and expressed in milliliters (mL).Using the probe perpendicular to the skin, slight, repetitive compressions were applied. All images were obtained in the longitudinal plane. The color blue signifiesno strain (high stiffness), green suggestsintermediate stiffness, and red indicatessoft tissue on a blue-green-red color map. Two regions of interest (ROI) were positioned forstrain ratio (SR) calculation. The thyroid tissue was represented by ROI A, while ROI B represented the sternocleidomastoid muscle in front of the ipsilateral thyroid parenchyma.Avoiding areas of thick calcification is advised because these regions tend to be stiffer and may not be an accurate representation of the surrounding soft tissue. Similar to this, regions of interest should not be positioned posterior to cystic areas because of the probable artifactual increased stiffness.Each lobe had five successive measurements, and the mean value was considered for analysis. For each lobe, the SR was calculated and automatically presented (Figures 1 and 2).  
Figure 1: Strain elastography (**left**) and conventional US (**right**) of a girl, age 17, diagnosed with CAT; The yellow circle A is the local ROI on the thyroid tissue; the yellow circle B is the local ROI on the adjacent sternocleidomastoid muscle. SR = 1.27.
## 5 Experiments and Analysis
The comparison chart after normalizing the flapping angles of the carbon fiber rods obtained from actual filming and simulation using the multi-plane model is shown in Figure 25. The analysis indicates that the multi-plane model fits well with the simulated angle of rod2, and is consistent with the actual filming results. However, in terms of fitting the flapping angle of rod3, while the fitting is good in the part overlapping with rod2, it  
Figure 23: **(a)** Diagram of the variation of the carbon fiber rod angle over time and **(b)** time-varying plot of the phase angle difference of the carbon fiber rods. Figure 24: Plot of the angle of attack of the three planes over time. fails to adequately capture the increased flapping range of rod3. The increased flapping range of rod3 reflects the delayed response of plane3 to flipping during the flapping cycle. The analysis suggests that the low linear velocity and small aerodynamic force of plane3, when combined with its distance from the flapping drive source which directly drives rod1, results in relatively small aerodynamic and wing stretching forces, leading to a slow response to deformation. Through the real shooting and analysis of wing flapping shapes in combination with actual measurement data, the multi-plane method can effectively fit the deformation of wings during the flapping cycle, providing strong support for morphological studies. Additionally, the description of wing deformation contributes to more accurate study of the aerodynamics of flapping-wing aircraft during flight.
# Vibration Velocity Prediction with Regression and Forecasting Techniques for Axial Piston Pump
## 3 Results and Discussion
### Complexity
#### 3.1.2 Ensemble Learning  
The complexity of the ensembles of the models should be assessed not only in terms of complexity of the underlying models, but also in terms of complexity that is implied by implementing the ensemble itself. Furthermore, the ensembles can only be compared with each other because, as described in Section 2.3.2, ensembles and neural networks use separate pieces of a dataset to train and evaluate them. Using the stacking strategy implies the need for training a separate model that will mix (blend) outputs of the ensembles into single one. The outputs of the best dense, RNN and LSTM models in terms of test MAPE metrics presented in Tables 6-8 were taken into consideration. In the analyzed case, linear regression was taken as a blender model. Therefore, it allows for the assessment of which output of which model impacts the final output the most. The higher the coefficient of the linear model, the more impact it has on the final result. The trained model coefficients sorted in descending absolute values are as follows:  
* Dense model: 1.1849
* RNN: \(-\)0.2081
* LSTM: \(-\)0.0610  
Figure 13: Quality metrics comparison for top 10 LSTM models for each selected set of model input variables with respect to network architecture. \begin{table}
\begin{tabular}{l l} \hline \hline
**Property** & **Value** \\ \hline Number of hidden layers & 3 \\ Number of neurons in hidden layers & 10 \\ Length of time window & 4 \\ Variable set & [Output pressure, outlet flow, bleed pressure] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of the best model in terms of complexity. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Num Epochs** & **MAPE** & **MAE** & **Test MAPE** & **Test MAE** & **Loss** & **Num Layers** & **Num Neurons** & **Num of Input Timepoints** & **Input Cols** \\ \hline
358 & 6.65848 & 0.1077 & 6.353 & 0.0792 & 0.0363 & 4 & 10 & 8 & [Outlet pressure/, “Outlet flow/, “Bled pressure”] \\
453 & 6.9464 & 0.1078 & 6.3551 & 0.0766 & 0.0356 & 3 & 10 & 4 & [Outlet pressure/, “Outlet flow/, “Bled pressure”] \\
339 & 6.841 & 0.059 & 6.3657 & 0.0778 & 0.014 & 3 & 10 & 32 & [Outlet pressure/, “Outlet flow/, “Bled pressure”] \\
500 & 10.672 & 0.147 & 0.15487 & 0.1185 & 0.0413 & 5 & 10 & 8 & [Outlet pressure/, “Outlet flow”] \\
500 & 11.5593 & 0.1558 & 11.2289 & 0.1301 & 0.0418 & 5 & 10 & 16 & [Outlet pressure/, “Outlet flow”] \\
500 & 11.4723 & 0.1536 & 11.2484 & 0.1272 & 0.0416 & 5 & 10 & 4 & [Outlet pressure/, “Outlet flow”] \\
500 & 12.4225 & 0.1452 & 12.2398 & 0.14 & 0.0232 & 4 & 10 & 32 & [Outlet pressure/, “Outlet flow”] \\
441 & 13.3095 & 0.1745 & 12.9897 & 0.147 & 0.0463 & 4 & 10 & 16 & [Outlet pressure/] \\
337 & 13.4765 & 0.1751 & 13.0594 & 0.147 & 0.0459 & 5 & 10 & 4 & [Outlet pressure/] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of metrics evaluated during training for three best dense models for each of input variables set.
## 3 Machine Learning Approaches for Slope Deformation Prediction
### Employed Statistical and Machine Learning Approaches
#### 3.4.2 Traditional Machine Learning Approaches  
(1) SVR  
The SVR involves the application of a support vector machine (SVM) [36] to a regression problem. As is shown in Figure 5b, the principle of the SVR is to find a hyperplane so that the distance between the sample point farthest from the hyperplane and the plane is the shortest. In the nonlinear regression problem, the SVR can achieve a better regression effect by adding a kernel function. The choice of the kernel function is critical for performance. The main optional kernel functions include the linear function, polynomial function, and radial basis function (RBF).  
(2) XGBoost  
As shown in Figure 5c, XGBoost is a tree-based model. In this paper, the sliding window representation was used to transform the time-series dataset of Huanglianshu landslide into supervised learning, and the _k_-fold cross validation method was exploited to evaluate the performance of the model. When the input is a time series and the output is a predicted value, each leaf node in the model is no longer a category but instead is a value, i.e., the prediction score. The calculation principle is shown in Equation (3).  
\[\hat{y}_{i}=\sum_{k=1}^{K}f_{k}(x_{i}) \tag{3}\]  
where \(\hat{y}_{i}\) is the predicted value, \(x_{i}\) is the feature vector, \(f_{k}(x_{i})\) is the calculated value for each tree, and \(K\) is the total number of trees; in this paper, \(K\) is 1000.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 5 Experiment
### Evaluation Index
#### 5.3.2 Jaccard Coefficient  
The Jaccard coefficient [29] is also known as the Jaccard similarity or Jaccard index. It is a statistical indicator used to measure the similarity between two sets. It is defined as the ratio of the size of the intersection of two sets to the size of the union. The value ranges from 0 to 1, where:  
* A Jaccard coefficient of 0 indicates that the two sets have no common elements, meaning they are completely dissimilar;
* A Jaccard coefficient of 1 indicates that the two sets are exactly the same, meaning they are completely similar. The formula is shown in Equation (3).
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 5 Security Analysis
### Descriptive Security Analysis
#### 5.2.2 ESL Attack Resistance  
Resistance to ESL attacks means \(\mathcal{A}\) is unable to figure out the session key in spite of knowing ephemeral secrets \(x_{s}\) and \(x_{sp}\). For \(SSK_{s}=H(ID_{s}||ID_{sp}||B_{s}||C_{s})\), where \(C_{s}=(x_{s}k_{s}\text{ mod }q)\cdot A_{sp}=(x_{sp}k_{sp}\text{ mod }q)\cdot A_{s}\), even if \(x_{s}\) and \(x_{sp}\) are revealed, \(\mathcal{A}\) cannot figure out \(SSK_{s}\) because they do not know the long-term secrets \(k_{s}\) and \(k_{sp}\). Similarly, if \(\mathcal{A}\) knows the short-term secrets \(x_{s}\) and \(x_{sp}\), then he/she cannot calculate \(SSK_{sp}\).