esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Previous studies have shown that thermally modified wood (TMW) performs well in outdoor, above-ground conditions in terms of resistance to wood-decaying fungi. Yet, little is known about the development of defects such as checks and the corresponding mechanical properties of TMW in this condition. This experiment focused on the effect of 30 months outdoor above-ground exposure (weathering) on the degree of checking, dynamic stiffness and static bending properties of thermally modified timber (TMT) of Norway spruce. Two board pairs per log were cut from 190 logs; one board of each pair was thermally modified and the other used as control. Then, 90 board pairs were exposed to the weather in south Sweden. Surface checking and axial stiffness were monitored at six-month intervals by using digital photography and non-destructive tests (time-of-flight and resonance method) to monitor changes in the material upon weathering. Finally, all boards were tested destructively in a 4-point static bending test following EN 408 standard. Results showed that weathering had no significance influence on static bending properties of TMT even though the degree of checking was considerably higher in TMT than unmodified timber after weathering. In particular, checks along growth rings were deeper, longer and more common in TMT after weathering, especially on the pith side of boards. The maximum depth of these checks did not depend on board orientation (i.e., which side was exposed) and exceeded limits given in strength grading standards for 7% of the modified boards included. Axial dynamic stiffness determined at 6-month intervals was less influenced by fluctuations in moisture content for TMT compared to unmodified timber, but did not confirm the increase in the degree of checking of TMT. The presence of checks from weathering did influence failure modes in TMT; horizontal shear failure became more frequent and some boards failed in compression.  
cracks; MOE; MOR; ThermoWood(r); time-of-flight; resonance method; weathering +
Footnote †: journal: Applied Sciences
## 2 Background
### Weather Data
#### 2.1.3 Weather Information Extraction  
While the versatility of formatting on weather websites offers users a wide array of options, it simultaneously introduces complexities when attempting to extract weather information. Noteworthy challenges in weather information extraction include the incon  
Figure 1: Examples of weather information in websites: **(a)** Weatherspark.com [47]; **(b)** Weather.com [48]; **(c)** Tiempo3.com [49]; **(d)** Windfinder.com [50].  
sistency in presenting weather data, making the development of a universal extraction algorithm a challenging task. Additionally, weather data undergo frequent updates, with varying update frequencies across different parameters, necessitating synchronization with each website's update schedule. Another significant challenge arises from the constant updates and redesigns that weather websites undergo, potentially disrupting existing data extraction scripts. To address these complexities, developers often need to create customized web scraping scripts or data extraction tools tailored to each website's unique format. It is important to note that the primary obstacle with these scripts lies in their intricate programming requirements, demanding advanced programming expertise even for small changes in the weather website. To address this challenge, the following section presents a more intuitive and user-friendly solution for automating the extraction of weather data from specialized websites through the use of an RPA protocol. Furthermore, this proposed protocol is also integrated into an automated BIM-based alarm system.  
The use of web sources has been gaining relevance, as shown in the work of [PERSON] et al. [55]. This research shows the use of the X social media [56] platform to provide a spatiotemporal analysis of the textual content from millions of tweets shared during Hurricane Harvey (2017) across several affected counties in southeast Texas. The study introduces a new Hazard Risk Awareness (HRA) Index that incorporates multiple factors, such as the volume of tweets, population data, internet usage rates, and the characteristics of natural hazards by geographic location. However, as this work also highlights, one of the main challenges is providing a real-time platform to prevent hazardous events based on these web sources.
## 2 Materials and Methods
### Statistical Analysis  
Multivariate and univariate analysis was performed using MetaboAnalyst 5.0 web-based tool suite ([[http://www.metaboanalyst.ca](http://www.metaboanalyst.ca)]([http://www.metaboanalyst.ca](http://www.metaboanalyst.ca)), accessed on 1 August 2022) [30; 31] and The Unscrambler X (CAMO Software AS, Oslo, Norway) software. Non-parametric one-way analysis of variance (Kruskal Wallis test) was used to test for significant differences (\(p<0.05\)) on the relative content of individual compounds and chemical categories between the cultivars and geographical regions independently. The multivariate analysis was carried out as follows. First, the original full dataset was split into two subsets using the Kennard-Stone method. One subset (approximately 75% of all samples) was used for developing (calibration) and testing the multivariate models by the full cross-validation approach, whereas the other subset (approximately 25% of all samples) was used exclusively for the external validation of the models. The peak area values were used as variable X (predictors) after log transformation and autoscaling. A PLS-DA model was built to discriminate the olive oils from Koroneiki and Kolovi cultivars (Adramytini was left out due to the low number of samples). A second PLS-DA model was built using the Koroneiki samples as variable Y (responses) to discriminate them according to geographical origin. Due to partial overlap of the groups, OPLS-DA was applied because it maximizes the between-class separation by removing the uncorrelated variation. Volatile compounds with variable importance in projection (VIP) score \(>\) 1 were selected as important variables for class separation and further used in multivariate ROC curve analysis for the discovery of a combination subset with the minimum number of potential biomarkers. For this purpose, the module \"Biomarker Analysis\" of MetaboAnalyst was used. Multiple classification models were built using the Monte Carlo cross-validation approach with balanced sub-sampling. The classification and feature ranking method were based on the linear support vector machine algorithm. The optimal model was selected by comparison of the AUC (area under the curve) values as well as the predictive accuracies and further validated by two approaches (cross-validation and external validation).
# Deep Neural Networks Classification via Binary Error-Detecting Output Codes
## 3 Results and Discussion
### Effect of CRC Output Coding  
First, we experimented with different linear block codes represented by CRC codes (see Table 1). The results for CNN1 architecture with linear normalization of outputs and the Zadeh decision are reported in the accuracy-reliability characteristic in Figure 2. The accuracy is computed according to Equation (12). To measure reliability (Equation (13)), we use the previously mentioned error detection ability of binary output codes. Here, we use the whole codeword space, exploiting 1024 codewords instead of 10. If the output code is outside of the 10 codewords assigned to the classes, the sample is rejected as untrustworthy. One point in the graph shows accuracy vs. reliability for one threshold value applied during the classification decision. Different points can be obtained using different threshold values in the decision process described in Section 2.2. If no samples are rejected as untrustworthy, the accuracy is equal to the reliability. This case corresponds to the dashed line in Figure 2. As the CRC codes have the detection ability to reject untrustworthy patterns, the reliability outperforms the accuracy even with a confidence threshold equal to zero. In all performed experiments, the classification reliability can be improved by trading off reliability for accuracy using higher thresholds.  
We run three experiments for each CRC output code (CRC1-9) and report the accuracy vs. reliability characteristic for the highest maximal accuracy. We can see the dependency of the results on the output coding, which suggests further possibilities for finding better output coding. However, finding the best binary output coding is not our goal, and for the rest of the paper, we use the winning CRC7 as a representative of CRC codes. CRC7 will detect all errors \(\mathbf{e}\) with non-zero syndrome \(\mathbf{e}\mathbf{H}^{T}\
eq\mathbf{0}\), where \(\mathbf{H}^{T}\) is the check matrix [15].
## 5 Conclusions  
In this paper we proposed a cooperative routing algorithm that allows visible satellites to transfer data among themselves by using the ISLs when some feeder links are congested, such that high packet loss rates caused by long queuing delay can be avoided and to maximize the data downloading throughput. A Maximum-Flow-Minimum-Cost (MCMF) routing algorithm is presented to compute maximum-flow routings for data download in CDD scheme. By using STK, OPNET modeler, and MATLAB, extensive simulations have been executed to test our proposed approach. The simulation results demonstrate that our proposed CDD scheme is superior to the method that does not use the ISL data transfer in terms of throughput and delay.  
Figure 14: Distribution of delay of heavy data load.  
Figure 13: Distribution of delay of light data load.  
**Author Contributions:** Conceptualization, Z.Y. and F.T.; methodology, Z.Y. and F.T.; software, Z.Y. and J.J.; validation, F.T. and H.L.; formal analysis, Z.Y.; investigation, Z.Y.; resources, F.T. and H.L.; data curation, Z.Y.; writing--original draft preparation, Z.Y.; writing--review and editing, F.T.; visualization, Z.Y.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. and F.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Special Support Plan for High-level Talents (grant number WRJH19 DH01), Youth Innovation Promotion Association of Chinese Academy of Sciences (grant number 2021291) and Shanghai Sailing Program (grant number 19 YF144640).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** Portions of this work were presented at the IEEE 21 st International Conference on Communication Technology (ICCT) in 2021, A Cooperative Routing Algorithm for Data Downloading in LEO Satellite Network.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Encrypted SFC Architecture
### The Service Function Forwarders  
In order to support the new NSH packet header formats [3], we created a new customised virtual switch, based on P4. The programming language P4 enables programmers to customise packet forwarding rules in switches. We made a very simple switch that constitutes an SFF, with VXLAN-GPE and NSH forwarding support. The SFF has the following functionality:  
* It can parse the new encryption attributes in the NSH header (MD-type = 3, E-SPI, E-SI [3]).
* It classifies IPv4 traffic in order to apply NSH headers.
* The SFF can act as a forwarder for NSH packet destined to other SFFs.
* It can act as an NSH packet forwarder inside an SF in order to make the SF NSH-aware.
* It can provide Layer 2 mac-address resolution based NSH packets instead of using IP and ARP (see Section 4.4).
* It can provide VXLAN-GPE support.
## 2 Overall Methodology
### Limiting Low Shear Measurements and Pressure-Viscosity Calculations  
The limiting-low-shear viscosities of the [choline] [L-proline] IL were measured up to 350 MPa with a falling cylinder viscometer using a sinker which applies shear stress of 5.7 Pa. The measurements were conducted at the Center for High-Pressure Rheology at Georgia Institute of Technology following the procedure described in [23]. Because of the low shear stress used in these stress-controlled measurements, the viscosities may be considered to be the limiting-low-shear viscosities. Temperatures of 25, 50 and 90 \({}^{\circ}\)C were employed. The estimated uncertainties are 3% for viscosity, 0.5 \({}^{\circ}\)C for temperature and the greater of 1 MPa and 0.5% for pressure. Before the measurements, the viscometer cartridge was heated up to 150 \({}^{\circ}\)C and vacuum was applied to remove bubbles. This process also reduced the water content of the sample.  
The pressure-viscosity coefficients based on the measured limiting-low-shear viscosities of the [choline] [L-proline] IL were calculated with the use of the McEwen model [24; 25] for the following pressure-viscosity definitions: The conventional pressure-viscosity coefficient, \(\alpha_{0}\) the reciprocal asymptotic isoviscous pressure coefficient, \(\alpha^{*}\) employed in Hamrock & Dowson film thickness formulas [26], and finally the general film-forming pressure-viscosity coefficient \(\alpha_{filln}\). A more complete description of the different definitions of pressure-viscosity coefficients can be found elsewhere [27].  
The Hamrock and Dowson [26] film thickness formula employs the reciprocal asymptotic isoviscous pressure coefficient, \(\alpha^{*}=1/p_{ia}\), as the pressure-viscosity coefficient.  
\[p_{ia}=\int_{0}^{\infty}\frac{\mu(p=0)dp}{\mu(p)} \tag{1}\]  
The 1952 [PERSON] model [24] can be written in terms of \(\alpha^{*}\):  
\[\mu=\mu_{0}\left(1+\frac{\alpha^{*}}{q-1}p\right)^{q},q>1 \tag{2}\]  
This model is widely used by laboratories which actually measure viscosity under pressure. See for example [28]. The [PERSON] model can also be used to obtain the conventional pressure-viscosity coefficient:  
\[\alpha_{0}=\left[\frac{d\text{ln}\mu}{dp}\right]_{p=0}=\frac{\alpha^{*}}{ \left(1-\frac{1}{q}\right)},q>1 \tag{3}\]  
The proper pressure-viscosity coefficient for film thickness [27] is:  
\[\alpha_{filln}=\frac{1-exp(-3)}{p_{i}(3/a^{*})} \tag{4}\]  
with  
\[p_{i}(p)=\int_{0}^{p}\frac{\mu(p=0)d\dot{p}}{\mu(\dot{p})} \tag{5}\]  
when [PERSON]'s integral above converges. In terms of the McEwen parameters it is:\[\alpha_{fill}=\frac{a\times(1-exp(-3))}{1-\left[1+\frac{3}{q-1}\right]^{1-q}} \tag{6}\]  
Here, \(p\) is the pressure, \(p_{ia}\) is the asymptotic isoviscous pressure, \(q\) an exponent, \(\mu\) is the limiting low-shear viscosity at local pressure and \(\mu_{0}\) is the low shear viscosity at \(p=0\).
# A Dual-Band Flexible MIMO Array Antenna for Sub-6 GHz 5G Communications
## 5 SAR Analysis of the MIMO Array Antenna  
One of the major applications of the flexible antenna is as a wearable application, where the antennas are positioned near the human body. The essential parameter to be evaluated is the specific absorption rate (SAR) value that accounts for the maximum level of radiation absorbed by the human body. According to international norms, this is restricted to 1.6 W/kg and 2 W/kg for every 1/10 g of human tissue, respectively. To analyze the SAR value of the proposed antenna, a three-layer tissue model is formed using the CST studio suite. The model comprises muscle, fat, and skin layers that have different dielectric properties at different frequencies. The SAR value at both the resonances for 1/10 g is estimated, as given in Figure 26. The tissue dielectric properties at these frequencies are used from the ITIS database [22]. The antenna provides an SAR of 0.0702/0.25 W/kg and 0.175/0.507 W/kg for 1/10 g at 3.75 GHz and 5.9 GHz, respectively. The suggested antenna is ideally suited for on-body applications, as seen by the low SAR values.  
Figure 25: MIMO diversity parameters: (**a**) ECC and DG, (**b**) MEG, and (**c**) TARC.  
The scattering properties of the proposed antenna over the phantom are displayed in Figure 27. As depicted in the figure, there is a slight shift in the resonant frequency of the antenna towards the first band due to the dielectric properties of the tissues. However, the antenna still maintains the operational bandwidth, as indicated in the figure. The E-field at 3.77 GHz and the SAR value accompanied by the internal field along the depth region are given in Figure 28.  
Figure 27: Reflection coefficient comparison in free space and over the phantom.  
Figure 26: SAR values at (**a**) 3.75 GHz and (**b**) 5.9 GHz.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 3 System Model
### Multipath Channel Model  
Due to the impedance mismatch between the inherent impedance of low-voltage power lines and the impedance of a large number of connected electrical devices, signal multipath propagation occurs at certain nodes. In this paper, we adopt the multipath channel transmission model proposed in [23] to establish the PLC channel model, and the frequency response is given by:  
\[H(f)=\sum_{i=1}^{K}g_{i}\cdot A(f,d_{i})\cdot e^{-j2 nf_{i}} \tag{11}\]  
where \(K\) is the number of paths; \(g_{i}\) is the weighting coefficient representing the product of the reflection and transmission factors along the \(i\) path, \(|g_{i}|\leq 1\), \(d_{i}\) is the propagation distance of the \(i\) path, \(\tau_{i}\) is the multipath delay of the \(i\) path, represented as \(\tau_{i}=\frac{d_{i}\cdot\sqrt{\varepsilon}}{\varepsilon_{0}}\), where \(\xi_{r}\) is the dielectric constant and \(\varepsilon_{0}\) is the speed of light. The attenuation factor of the \(i\) path that varies with frequency \(f\) and path propagation distance is calculated by:  
\[A(f,d_{i})=e^{-\alpha(f)\cdot d_{i}}=e^{-(\alpha_{0}+\alpha_{1}\cdot f^{k})d_{ i}} \tag{12}\]  
where \(\alpha_{0}\) and \(\alpha_{1}\) are two attenuation constants, and \(k\) is the exponent of the attenuation factor.
## 3 Methodology
### Network Architectures
#### 3.2.5 Model Structure Overview  
The structure of this model consists of multiple modules, each processing the input multi-view features through different operations to achieve the final goal of classification. Table 1 summarizes the layer-wise input and output dimensions of the proposed network architecture.  
Our model adopts a hierarchical structure, progressively processing the input feature maps through multiple modules to accomplish the multi-view object classification task. The first layer is the FEM, which transforms the input multi-view feature maps into more compact feature representations. The following layers employ the Spatial Self-Attention (SSSA) and Temporal Self-Attention (TSSA) modules, capturing spatial and temporal features, respectively, while gradually reducing the spatial resolution of the feature maps and retaining their important information. Finally, the Classification Head uses the extracted features for object classification.  
The input feature maps in the model undergo a transformation from \(\mathrm{S}\times 3\times\mathrm{H}\times\mathrm{W}\) to \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}\times\mathrm{N}\) in the first layer. After multiple SSSA and TSSA layers, the spatial resolution of the feature maps progressively decreases from \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}\times\mathrm{N}\) to \(\mathrm{S}\times\mathrm{C}\times\mathrm{N}/4\times\mathrm{N}/4\). The Classification Head then maps these features to the specific target classes to perform the classification task.  
The hierarchical design of the model not only improves computational efficiency but also effectively captures the spatial-temporal feature relationships, enabling the model to perform well when processing multi-view inputs.
## 3 Results  
### Genetic Diversity and Polymorphism of SSR Loci  
Table 3 shows the results of genetic diversity analysis using the 20 SSR markers for the 172 soybean varieties developed in Korea. A total of 344 alleles were detected. The number of alleles ranged from 7 (Satt164 and Satt179) to 29 (Sat_076) per locus, with an average of 17.2. The size of alleles ranged from 107 to 361 bp, with Sat_417 and Sat_190 at the widest range of 139-229 bp and 125-215 bp, respectively; whereas Sat_164 had the narrowest range of 222-246 bp (Table S2). PIC values ranged from 0.6799 to 0.9318, with Sat_076 having the highest (0.9318) and Satt179 the lowest (0.6799). The average PIC value was 0.8675.  
The total varieties per cluster were as follows: 4 (2.3%) in cluster I, 52 (30.2%) in cluster II, 61 (35.5%) in cluster III, 46 (26.7%) in cluster IV, and 9 (5.2%) in cluster V. More than 92% of the total varieties were found in three clusters (II, III, and VI; Figure 1, Table S1 and Table 4). Calculation of genetic distance for pairwise combinations of all soybean varieties supported this conclusion (Table 5). Although the results were obtained with only a limited number of genotypes, clusters III and IV showed the lowest genetic distance (Table 5).  
Figure 1: Unweighted pair group mean arithmetic (UPGMA) phylogenetic tree using 20 SSR markers in 172 Korean soybean varieties.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Marker** & **Size (bp)** & **N\({}_{\mathbf{A}}\)\({}^{\ddagger}\)** & **N\({}_{\mathbf{AF}}\)\({}^{\ddagger}\)** & **PIC \$ \\ \hline Sat\_374 & 273–343 & 21 & 0.1919 & 0.9060 \\ AW132402 & 137–183 & 15 & 0.1977 & 0.8630 \\ Sat197 & 107–182 & 16 & 0.1395 & 0.9126 \\ Sat\_342 & 176–246 & 19 & 0.2035 & 0.9113 \\ Sat164 & 222–246 & 7 & 0.3256 & 0.7529 \\ Sat\_076 & 122–208 & 29 & 0.1337 & 0.9318 \\ Sat179 & 130–184 & 7 & 0.4244 & 0.6799 \\ Sat216 & 164–248 & 15 & 0.3430 & 0.7850 \\ Sat\_022 & 203–255 & 18 & 0.1919 & 0.9114 \\ Sat185 & 210–270 & 16 & 0.1919 & 0.8819 \\ Sat\_417 & 139–229 & 22 & 0.1105 & 0.9267 \\ Sat\_210 & 217–287 & 18 & 0.2151 & 0.8741 \\ Sat\_434 & 301–361 & 18 & 0.2326 & 0.8875 \\ Sat\_105 & 228–274 & 18 & 0.1628 & 0.9067 \\ Sat\_151 & 210–272 & 17 & 0.2849 & 0.8241 \\ Sat\_043 & 242–294 & 18 & 0.1453 & 0.9167 \\ Sat\_245 & 119–207 & 19 & 0.1395 & 0.9107 \\ Sat\_391 & 214–282 & 16 & 0.1628 & 0.8903 \\ Sat\_339 & 203–245 & 14 & 0.2849 & 0.8041 \\ Sat\_190 & 125–215 & 21 & 0.2616 & 0.8737 \\ Total & & 344 & & \\ Mean & & 17.2 & 0.2172 & 0.8675 \\ \hline \hline \end{tabular} \({}^{\ddagger}\) number of alleles; \({}^{\ddagger}\) major allele frequency; \({}^{\lx@sectionsign}\) polymorphic information content.  
\end{table}
Table 3: Linkage group analysis of 20 simple sequence repeat (SSR) markers used for genotyping in 172 Korean soybean varieties.
# Recent Advances in Photodetectors Based on Two-Dimensional Material/Si Heterojunctions
## 3 Photodetectors Based on 2D Material-Si Heterojunctions
### Graphene/Si Heterojunctions
(**g,h**) Reproduced with permission from Ref. [95]. Copyright 2018, WILEY-VCH. Figure 3: Broadband graphene-Si heterojunction photodetectors. (**a**) Schematic cross-section of the resonant cavity enhanced graphene/Si Schottky photodetector. (**b**) Spectral response of the device without backside Au mirror. (**c**) Responsivity spectra of the device with and without backside Au mirror. (**d**) Responsivity spectra under different reverse bias voltages. (**a–d**) Reproduced with permission from Ref. [86]. Copyright 2017, American Chemical Society. (**e**) Extinction spectra of single layer graphene/Si and nMAG/Si heterojunctions. (**f**) Current density vs. bias voltage in dark and under laser illumination at different wavelengths with light intensity of 40 mW/mm\({}^{2}\). (**g**) Photocurrent vs. time under pulsed light illumination. (**h**) Energy band diagrams for two operation mechanisms at different wavelengths. (**e–h**) Reproduced with permission from Ref. [48]. Copyright 2022, WILEY-VCH.