esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Benchmarking of ML Methods and Influence of the Design Space Selected
The resulting range of critical frequencies is also shown in Figure 16. For each design space, 500 supporting points were sampled using LHS for the STL models of the infinite plate, correction factor, and MS with and without one-third octave band average. Figure 14: Example of one predicted STL curve for each STL model using the neural network (NN)-based surrogate with physics-guided features: - simulated; - - predicted; (**a**) infinite plate; (**b**) correction factor; (**c**) MS; (**d**) MS (band averaged); (**e**) FEM; (**f**) FEM (band averaged). Figure 15: Mean absolute error (MAE), mean maximum error (MME) per STL design and root mean square error (RMSE) of the NN-based surrogate model with physics-guided features. Figure 17 shows the accuracy obtained with an NN-based surrogate model trained with physics-guided features for each set of interest and the STL method. It is observed that the surrogate error increases with the design space size, especially for the infinite plate and correction factor models. For the MS model without band average, the difference in the surrogates' accuracy is not relevant. Furthermore, it can be observed that a considerable difference in accuracy is obtained for design spaces \(\mathcal{D}_{1}^{+}\) and \(\mathcal{D}_{2}^{+}\), although they have the same size. This is because the samples from \(\mathcal{D}_{2}^{+}\) have, on average, a simpler behavior than the samples from \(\mathcal{D}_{1}^{+}\), once many of them present critical frequency higher than 2500 Hz which is above the frequency range modeled by the surrogates. In conclusion, the developed surrogate models present overall satisfactory performance for most of the studied cases. Even highly discontinuous and rough functions could be properly predicted. Although the ML algorithms' performance is problem-specific, they usually perform well in related tasks; therefore, the benchmarking conducted provides insights into the suitability of the ML methods to model vibroacoustic problems with non-smooth behavior. Moreover, NN-based surrogates are the most accurate for STL models and thus are useful tools to explore the design space of STL simulations, enabling optimal and robust vibroacoustic designs. It has also been observed that the location of the design space can have more of an effect on surrogate accuracy than its size.
## 5 Discussion
### Expandability beyond IoV  
The approach we followed focuses on CAVs and IoV environments. However, our approach can be adopted and applied to other environments as well, such as safety-critical systems and e-banking. The only requirement for such environments is to collect, analyze, and record information related to malicious incidents (e.g., cybersecurity threats) to CTI reports. The approach discussed in the manuscript promotes security situational awareness, real-time defense, and in-depth post-incident threat analysis, and thus, it can be applied to diverse domains effectively and efficiently. However, we should stress that the expandability and adaptability of our approach in other than IoV domains might require the analysis and specification of domain-specific characteristics besides the common ones found in diverse types of malicious activities. For example, in the case of an e-banking system, features such as the number of transactions, transferring amounts, routing numbers, and beneficiaries might be critical for the threat analysis. In such cases, the system should adjust and refine the feature models and extraction processes appropriately to support the efficient matching and correlation of CTI reports in these domains. Therefore, security experts would have the information needed to perform in-depth post-incident forensics analyses and eventually lead to the accurate attribution of the security threat (e.g., incoming attack).
## 4 System Architecture
### Phase Control Validation

Table 4: Calculated and measured phases for \(\theta=45^{\circ}\) and \(\phi=90^{\circ}\). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Uncompensated** & **Compensated** \\ \hline ADC 0 & - & - \\ ADC 1 & \(21.62^{\circ}\) & \(-0.04^{\circ}\) \\ ADC 2 & \(1.32^{\circ}\) & \(0.08^{\circ}\) \\ ADC 3 & \(20.46^{\circ}\) & \(0.16^{\circ}\) \\ ADC 4 & \(-0.49^{\circ}\) & \(0.11^{\circ}\) \\ ADC 5 & \(19.14^{\circ}\) & \(0.14^{\circ}\) \\ ADC 6 & \(1.62^{\circ}\) & \(0.26^{\circ}\) \\ ADC 7 & \(18.85^{\circ}\) & \(0.07^{\circ}\) \\ ADC 8 & \(1.49^{\circ}\) & \(-0.06^{\circ}\) \\ ADC 9 & \(21.79^{\circ}\) & \(0.29^{\circ}\) \\ ADC 10 & \(2.99^{\circ}\) & \(0.18^{\circ}\) \\ ADC 11 & \(22.79^{\circ}\) & \(0.16^{\circ}\) \\ ADC 12 & \(3.75^{\circ}\) & \(-0.53^{\circ}\) \\ ADC 13 & \(21.84^{\circ}\) & \(-0.22^{\circ}\) \\ ADC 14 & \(1.63^{\circ}\) & \(0.13^{\circ}\) \\ ADC 15 & \(22.05^{\circ}\) & \(0.09^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Phase compensation results. In Table 4, the results of beam generation are presented. The \"Calculated\" column contains the outputs of the beam control algorithm when generating a main beam with \(\theta=45^{\circ}\) and \(\phi=90^{\circ}\). When measured, the resulting phases are very close to the calculated counterparts, producing at most a difference of \(-0.36^{\circ}\).
## 2 Materials and Methods
### Functionality Tests
#### 2.5.2 Migration Potential  
For evaluating the migration potential of the three types of MSCs, a number of 2.5 \(\times\) 10\({}^{2}\) cells were aggregated in hanging drop cultures in both experimental media. The spheroid aggregates obtained were harvested at 48 h and transferred into 6-well plates pre-treated with 0.1% gelatine (Sigma-Aldrich) to allow the aggregates to attach and the MSCs to migrate out of the aggregates. The migration area was obtained at 24 h, based on aggregate size measurement and an estimation of the area covered by MSCs after migration. The following formula: migration area = MSC migration area--aggregate size was used.
# Design and Experimental Study of Potato-Soil Separation Device for Sticky Soils Condition
###### Abstract  
The potato-soil separation device comprising five function areas (i.e., shearing, bending, throwing, cleaning, and conveying) was developed to address the mentioned problems (e.g., soil with high moisture content, large viscosity, and poor separation efficacy) in the harvesting process for winter idle paddy fields of Southern China. Given the kinetic analyses for the potato-soil mixture of five function areas, the crucial factors of the effect of potato-soil separation were determined. Subsequently, the orthogonal test of 4 factors and 3 levels was performed, and the percentage of small soil \(\lambda\), the percentage of soil adhering to potato tuber \(\gamma\), and damage rate of potato tuber \(K\) were adopted as assessment indexes. Regression analyses and programming for the orthogonal test result were conducted to optimize the device run parameters, and the optimal run parameters included the rotational speed of the soil-crushing mechanism of 84.18 r/min, linear velocity of second-stage lifting chain of 0.75 m/s, and inclination angle of lifting chain of 15.87\({}^{\circ}\). The field experiments were performed with the modified potato harvester. As indicated from the result, the visible potato rate was 94.31% and damage rate of potato was 0.89%, and the requirement of the national standard was satisfied. This study presented technical feasibility data and equipment for potato-soil separation under sticky soils condition.  
potato harvest; potato-soil separation device; potato-soil mixture; kinetics analysis; orthogonal test
# Augmented Reality Applications in Industry 4.0 Environment
## 4 Results
### Implemented Tracking Methods  
In order to put the right information in the right place within the AR application, it is necessary for the device to know where it is in relation to its surroundings. For this purpose, several tracking methods have been developed that enable the identification of objects. However, all of them can be divided into two large groups: tracking with or without markers. Most of the analyzed manuscripts (66%) use markers in their applications. These markers are located on objects in the work environment and when the device recognizes them, it displays information to the user assigned to the marker. Similarly, in an industrial environment it is possible to recognize 3D objects (16%). In this case, these objects are often predefined in the system. Furthermore, various other methods are used (18%), which are based on location (GPS data and digital compass), context recognition, computer vision, spatial environment recognition, etc. [112].  
An example of a marker-based AR application is described in [113]. With the help of QR markers, the AR module calculates its relative position and orientation and aligns the three dimensions of the virtual objects on the corresponding ones. Object recognition as a tracking method in AR applications is widely used. However, it must be more precise and become faster and, therefore, more reliable, especially with objects that do not possess recognizable texture such as screws, nuts, etc. (characteristic objects in an industrial environment). An innovative approach to how to achieve this is presented in [114].
## 4 Proposed Framework
### Mathematical Formulation
#### 4.1.3 Process Overview  
**Identity Creation**: For each entity \(e\) in the set of entities \(\mathcal{E}\), the function \(\text{Creatledentity}(e)\) generates a unique pair of cryptographic keys \((k_{\text{priv}},k_{\text{pub}})\).  
**Credential Issuance and Signing**: Trusted entities issue specific credentials to entities/users, represented by \(\text{IssueCred}(e,t)\), and subsequently sign them with \(\text{Sign}(c,k_{\text{priv}})\).  
**Credential Verification**: Service providers use \(\text{Verify}(c,s)\) to verify the authenticity of received credentials \(c\) with their associated signatures \(s\).  
**Access Decision**: Based on the verification results, \(\text{GrantAccess}(\text{verification result})\) decides whether to grant or deny access to the service.
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 6 Performance Evaluation and Discussion
### Dynamic Traffic
\begin{table}
\begin{tabular}{c|c c|c c} \hline \multirow{2}{*}{\(\Delta(\%)\)} & \multicolumn{4}{c}{**HT Scenario**} \\ \cline{2-5}  & \multicolumn{2}{c|}{**Without Hidden Nodes**} & \multicolumn{2}{c}{**With Hidden Nodes**} \\ \cline{2-5}  & **EDCA/DCF** & **TAROA** & **EDCA/DCF** & **TAROA** \\ \hline \([-10,10]\) & 0.63673 \(\pm\) 0.00216 & 0.82591 \(\pm\) 0.00135 & 0.51863 \(\pm\) 0.00501 & 0.82441 \(\pm\) 0.00199 \\ \([-20,20]\) & 0.63741 \(\pm\) 0.00265 & 0.83399 \(\pm\) 0.00139 & 0.52047 \(\pm\) 0.00344 & 0.82843 \(\pm\) 0.00065 \\ \([-50,50]\) & 0.64063 \(\pm\) 0.00403 & 0.83163 \(\pm\) 0.00085 & 0.52180 \(\pm\) 0.00398 & 0.82681 \(\pm\) 0.00154 \\ \hline \multirow{2}{*}{\(\Delta(\%)\)} & \multicolumn{4}{c}{**LT Scenario**} \\ \cline{2-5}  & **Without Hidden Nodes** & \multicolumn{2}{c}{**With Hidden Nodes**} \\ \cline{2-5}  & **EDCA/DCF** & **TAROA** & **EDCA/DCF** & **TAROA** \\ \hline \([-10,10]\) & 0.09465 \(\pm\) 0.00004 & 0.09394 \(\pm\) 0.00026 & 0.05648 \(\pm\) 0.00117 & 0.09346 \(\pm\) 0.00031 \\ \([-20,20]\) & 0.09582 \(\pm\) 0.00002 & 0.09486 \(\pm\) 0.00028 & 0.05645 \(\pm\) 0.00090 & 0.09368 \(\pm\) 0.00045 \\ \([-50,50]\) & 0.09383 \(\pm\) 0.00003 & 0.09147 \(\pm\) 0.00018 & 0.05777 \(\pm\) 0.00101 & 0.09150 \(\pm\) 0.00037 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of throughput of EDCA/DCF and TAROA under three different intensities of dynamic station transmission intervals.
## 5 Performance Analysis of Proposed Schemes: MRS-ST-BL and MRS-SD-BL
### Schemes Compared
* Midpoint Relay Selection using Social Trust with Full Battery (MRS-ST-FB): A scheme same as MRS-ST [10]. The relay device always has a full battery level and transmits at a power proportional to the social trust. * Midpoint Relay Selection using Social Trust Without Full Battery (MRS-ST-WFB): A modified version of MRS-ST [10]. It is used to analyze the performance of MRS-ST when a relay has varying battery level. In this scheme, a relay device transmits at a power proportional to the social trust only when the battery level on the device is above certain threshold value (\(B_{\textit{thres}}\)). Otherwise, the relay device transmits at a lowest power (not proportional to the social trust). However, the battery level of devices are not taken into account in the relay selection design. * Midpoint Relay Selection using Social Trust and Battery Level (MRS-ST-BL): Our proposed scheme which considers battery level of devices together with location and social information in the relay selection design. In this scheme, a relay device transmits at a power proportional to the social trust only when battery level on the device is above certain threshold value (\(B_{\textit{thres}}\)). Otherwise, the relay device transmits at a lowest power (not proportional to the social trust). However, the battery level of devices are not taken into account in the relay selection design. * Midpoint Relay Selection using Social Distance Without Full Battery (MRS-SD-WFB): A modified version of MRS-SD [10]. It is used to analyze the performance of MRS-SD when a relay has varying battery level. In this scheme, a relay device transmits at a power proportional to the social trust only when battery level on the device is above certain threshold value (\(B_{\textit{thres}}\)). Otherwise, the relay device transmits at a lowest power (not proportional to the social trust). However, the battery level of devices are not taken into account in the relay selection design. * Midpoint Relay Selection using Social Distance and Battery Level (MRS-SD-BL): Our proposed scheme which considers battery power level of devices together with location and social information in the relay selection design. In this scheme, a relay device transmits at a power proportional to the social trust only when battery level on the device is above certain threshold value (\(B_{\textit{thres}}\)). Otherwise, the relay device transmits at a lowest power (not proportional to the social trust). This scheme is different from MRS-ST-BL in the way it filters devices for selection of a relay.
# Touch, Texture and Haptic Feedback: A Review on How We Feel the World around Us
## 2 The Human Skin and Its Functions in Touch  
The human skin is composed of two main receptor types, the slow-adapting mechanoreceptors and the fast-adapting mechanoreceptors. These mechanoreceptors determine several pieces of information that pertains to humans' sense of touch such as pressure and vibration, both of which play an important role in haptics. Pressure, for example, plays an important role with force sensing and shape discrimination [26; 27] and vibration plays an especially important role in a number of different scenarios such as roughness discrimination and pressure sensing in the human body [28; 29; 30], slip detection [31], and shape perception [32; 33; 34]. Slow-adapting mechanoreceptors such as the slow-adapting type 1 and type 2, which have the Ruffini endings and Merkel corpuscles attached to them, mainly react to continuously applied stimuli such as pressure and skin stretch, whereas fast-adapting mechanoreceptors such as the fast-adapting type 1 and type 2, which have Pacinian and Meissner corpuscles attached to them, react only to stimuli quickly before discharging completely such as vibrations [35] and was believed to only be a supporting function to that of slow-adapting mechanoreceptors. Recent studies, however, have shown that fast-adapting mechanoreceptors play a crucial role in texture sensing than was previously believed [36; 37]. A study in 2013 showed that while the previous statements are only true to a certain extent as both receptor types do react to continuous stimuli, it is only true up to a certain vibrational frequency, as slow-adapting mechanoreceptors showed less response at frequencies lower than 10 Hz, whereas fast-adapting mechanoreceptors continued to exhibit strong responses all throughout [36].  
Beyond pressure and vibration, there are also other factors that make up the whole of tactile stimulation. A person's sense and perception of touch can be described as multidimensional in that it takes into account various properties such as the roughness, hardness, stickiness and other different dimensions of an object that cannot be properly represented or described [29; 37; 38; 39].
## Materials and Methods
### Statistical Analysis  
Data analysis was performed using IBM SPSS statistical package (IBM Corp. Version 21.0. Armonk, NY, USA). Continuous variables are expressed as mean \(\pm\) standard deviation or median and interquartile range (IQR). Categorical variables are presented as frequency and percentages (n, %). A non-parametric Mann-Whitney U test was applied to compare two independent groups. Kruskal-Wallis analysis of variance was used to compare multiple independent groups. To compare categorical variables between groups, [PERSON]'s \(\chi^{2}\) test of independence was used. To assess correlations between continuous variables, [PERSON]'s correlation test was applied. All \(p\)-values were two-sided and statistical significance is indicated at \(p<0.05\).  
Dichotomization of some categorical variables was considered for correlation analysis of some study variables. This dichotomization was based on sample size and was performed in advance of conducting statistical analysis in order to avoid small sample size upon further stratification of data by menopausal status [28]. Therefore, BMI was categorized as non-obese (\(<\)30.00 kg/m\({}^{2}\)) and obese (\(\geq\)30.0 kg/m\({}^{2}\)). The TNM stage of breast cancer was dichotomized as early (I/II) and advanced (III/IV). Histologic grade was further categorized into grades (I/II) and grade (III) tumors. Categories of these tumor variables were selected based on cut points previously published by other researchers [32; 33]. Receptor expression was further categorized as either low or high based on IRS scoring. IRS scores of 7-12 were considered high, and scores below 7 were considered low for expression of target receptors [34; 35].  
Kaplan-Meier survival curves were generated for breast cancer patients based on the expression status of IR and c-MET using GraphPad Prism 8.0.1 software (GraphPad Software, San Diego, CA, USA). Cox proportional hazards models were fitted with overall survival (OS) as the outcome.
## 2 Study Area and Data
### Data and Preprocessing  
The remote sensing data used in this study were obtained from the Google Earth Engine (GEE) ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/)), accessed on 22 March 2023), with a collection period ranging from 2002 to 2022. The MOD09A1 (MOD09A1.006 Terra Surface Reflectance 8-Day Global 500 m) and MOD11A1 (MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1 km) products available in GEE were used to calculate the greenness (NDVI), wetness (WET), and dryness (NDBSI) indices based on the MOD09A1 products, and the heat (LST) index based on the MOD11A1 products. Please refer to Table 1 for more details on the datasets used in this study.  
Figure 2: Deformation and subsidence cause by underground coal mining. (**a**) Building deformation; (**b**) landslide; (**c**) ground cracks; (**d**) ground collapse.  
The data on China's administrative division boundaries were obtained from the National Geomatics Center of China ([[http://www.ngcc.cn/ngcc/](http://www.ngcc.cn/ngcc/)]([http://www.ngcc.cn/ngcc/](http://www.ngcc.cn/ngcc/)), accessed on 22 March 2023). The GGP and urban planning data were obtained from the National Science and Technology Infrastructure ([[http://www.nesdc.org.cn/](http://www.nesdc.org.cn/)]([http://www.nesdc.org.cn/](http://www.nesdc.org.cn/)), accessed on 22 March 2023). The landform data for the study area were obtained from the National Earth System Science Data Center ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)), accessed on 22 March 2023). The precipitation and temperature data for the study area were obtained from the China Meteorological Data Service Centre ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 22 March 2023).  
The annual mining area data of the Dafosi coal mine were obtained through the comparison of working-face images and field measurements. Land-use/land-cover (LULC) data from 2000 to 2021 for the study area were obtained from the China Land Cover Dataset (CLCD) based on Landsat data from the GEE. LULC data for 2022 were obtained through the interpretation of Landsat 8 OLI_TRIS 30 m remote sensing images from the United States Geological Survey ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 22 March 2023). The remote sensing images were analyzed, corrected, fused, and mosaicked using ArcGIS 10.6 and ENVI 5.3, and the land use types were classified using a combination of supervised classification and visual interpretation. Per capita GDP, the area of GGP, and coal production data were obtained from the Statistical Communique of the People's Republic of China on the national economic and social development from 2000 to 2021.