esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of the Obtained YSZ-rGO Ceramics  
X-ray diffraction method (SHIMADZU XRD-6000, Cu-K\({}_{\alpha}\) = 1.5406 A) was used for the phase composition analysis. Phase identification was carried out via Powder Diffraction File database (PDF-2, 2021) [26]. The ratio of crystalline and amorphous phases was calculated based on obtained XRD data using the standard diffractometer software. Crystalline sizes were estimated via Scherrer equation:  
\[d_{cryst}=\frac{K\lambda}{\beta\cos\theta} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Brutto-Composition** & **Sintering Conditions** \\ \hline Z0 & 91 ZrO2-8Y2O3 (8 YSZ, mol.\%) & 1550 \({}^{\circ}\)C; \\ Z0.25 & YSZ + 0.25 wt.\% rGO & 3 h; \\ Z1 & YSZ + 1 wt.\% rGO & 3-layer graphite/SiC/graphite \\ Z2 & YSZ + 2 wt.\% rGO & powder bed \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sintered samples: powder compositions and sample designation.  
Figure 1: Schematic cross-section image of the alumina crucible with sintered ceramics in 3-layer graphite/SiC/graphite powder bed. Ten specimens are placed in two lines.  
where \(d_{cryst}\)--is the crystallite size in nm, \(K\)--Scherrer constant (\(\sim\)0.9 for spherical particles), \(\lambda\)--X-ray wavelength, \(\beta\)--line broadening at half of the maximum intensity, and \(\theta\)--Bragg angle.  
Raman spectroscopy (SENTERRA (Bruker), laser irradiation wavelength 532 nm and 785 nm, irradiation power of 2 mW and 10 mW, respectively) was used for the detailed investigation of the rGO state. Densities of the sintered specimens were measured using the Archimedes method in isopropyl alcohol (scales RADWAG 220 c/xc with a density measurement kit, Poland). The theoretical density was calculated using the rule of mixture with the density of pure YSZ equal to 5.96 g/cm\({}^{3}\) (82-1244. JCPDS-International Centre for Diffraction Data) and the density of rGO equated to graphite density 2.26 g/cm\({}^{3}\)[27], respectively. Obtained ceramic samples were subjected to microstructure investigations with the following surface preparation procedure: filling of the specimen with an epoxy resin compound, aging until the complete resin solidification and polishing by the series of abrasives up to 1 \(\upmu\)m polycrystalline diamond suspension (MetaDi Supreme, Buehler, Lake Bluff, Illinois, USA). No additional etching was used. Prepared samples of each composition were studied by high-resolution scanning electron microscopy (Zeiss Merlin with GEMINI-II optical system and accelerating voltage 21 kV). For elemental mapping, X-ray energy-dispersive spectroscopy (microanalysis spectrometer Oxford Instruments INCAx-act X-ray) was used for elemental mapping. Microhardness of sintered ceramics was measured via micro Vickers hardness method (Tester Shimadzu HMV-G21 DT with CCD camera microscope) with applied force of 2.9 N (HV0.3) 50 times for each specimen. Surfaces of the samples were prepared by the same polishing procedure as for microstructure investigations.
# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 4 Experiments and Results
### Channel Attention
#### 4.3.1 The Attention Distribution on the Feature Maps  
Because interpretability is a notorious disadvantage of the DNN, it is not possible to clearly disclose how the CNN learns the importance of the feature maps. Thus, we illustrate an attention distribution example in Figure 10, and hope this helps explain its positive effects on the performance. As can be seen from Figure 10b, the features with some useful textures to discriminate the singing voice from the spectrogram were extracted by \"observing\" the upper layer feature maps. In the conventional CNNs, these feature maps are fed to the next layer with the same weight; that is, the attentions of the feature maps are imposed equally, and the corresponding gray level in Figure 10c is the same, i.e., all white. By comparison, after the attentions were measured by the scaled dot-product attention module, the importance of the feature maps was adjusted, and the next layer imposed specific attentions on those feature maps according to the attention distribution described in Figure 10c. The adjusted feature maps are shown in Figure 10d. It can be seen that some feature maps in the first column of Figure 10b are restrained because they may play a relatively unimportant role in detecting the singing voice.
## 2 Fisher-Snedecor \(\mathcal{F}\) Composite Fading Model
### Probability Density Function (\(p_{\gamma}\))  
The probability density function (\(p_{\gamma}\)) of the considered FSO RIS-ACs system over F-S \(\mathcal{F}\) TI fading channels, after substituting (10) in (9), can be written as:  
\[p_{\gamma}(\gamma) = \frac{a_{1}a_{2}}{}^{a_{1}}(b_{1}-1)^{b_{1}}a_{2}{}^{a_{2}}(b_{2} -1)^{b_{2}}\bar{\gamma}^{(b_{1}+b_{2})/2}\gamma^{a_{1}/2-1} \tag{17}\] \[\times\ \int_{0}^{\infty}\frac{\tau^{a_{2}/2-a_{1}/2-1}dr}{(a_{1} \gamma^{1/2}r^{-1/2}+(b_{1}-1)\bar{\gamma}^{1/2})a_{1}+b_{1}}(a_{2}r^{1/2}+(b _{2}-1)\bar{\gamma}^{1/2})^{a_{2}+b_{2}}\]  
where \(\bar{\gamma}=\bar{\gamma_{1}}=\bar{\gamma_{2}}\). Using ([39], Equation (3.259.3)) and some additional mathematical manipulations, the C-F expression for \(p_{\gamma}(\gamma)\) of FSO RIS-ACs in terms of the Beta and Gaussian hyper-geometric function \({}_{2}F_{1}(\cdot,\cdot;\cdot;\cdot)\) ([39], Equation (9.10)) is derived as:  
\[p_{\gamma}(\gamma) = \frac{a_{1}a_{2}a_{2}}{}^{a_{2}}\gamma^{a_{2}/2-1}{2\bar{\beta}(a _{1},b_{1})\beta(a_{2},b_{2})(b_{1}-1)^{a_{2}}(b_{2}-1)^{a_{2}}\bar{\gamma}^{a _{2}}}\beta(a_{2}+b_{1},a_{1}+b_{2}) \tag{18}\] \[\times\ _{2}F_{1}\bigg{(}a_{2}+b_{2},a_{2}+b_{1};a_{1}+b_{1}+a_{2}+b _{2};1-\frac{a_{1}a_{2}\gamma^{1/2}}{(b_{1}-1)(b_{2}-1)\bar{\gamma}}\bigg{)}\]
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
## 5 Field Monitoring and Data Analysis  
### Site Monitoring Scheme  
The underground shield tunnel crosses through the original bridge piles foundation area, due to safety reasons it is necessary to monitoring the structure internal state parameters such as vertical settlement on the underpinning bearing platform and stress changes of the underpinning concrete and the bridge pier structure during the shield tunnel excavation.  
Figure 10: Stress result of the numerical model.  
As it in Figure 11 presented, the layout of monitoring points for bridge pier and underpinning bearing platform. To investigate the effects of shield tunneling under the bridge, in this case, 6 settlement gauging points and 12 stress gauging points are set up.  
Settlement sensors no.1 and no.2 are placed on the bridge pier and sensors no.3 to no.6 are placed around the underpinning bearing platform to test the structure settlement changes during the shield tunneling. Due to the complexity of this foundation underpinning construction a new settlements collection system named intelligent hydrostatic level are adopted in this project to monitoring the settlement changes on the underpinning bearing platform and the pier 24 h.  
Stress sensors no.1 to no.4 are set up around the bridge pier to observe the variation of stresses especially in the foundation actively underpinning and replacement stages. The other stress sensors are placed inside the underpinning bearing platform named Sections 1-4 which can track the stress changes during the whole construction.
## 3 Data and Methods  
### Data Collection  
To validate the effectiveness of the proposed methodology, a case study was conducted in this paper. California, US was selected as the study area due to data availability. Hourly PM2.5 concentrations data of California from 2016 to 2017 were collected from the United States Environmental Protection Agency (EPA). Data of 30 monitoring stations were covered. A brief summary of the data is shown in Table 1. The distribution of these 30 stations is presented in Figure 2. It can be seen that the distribution is imbalanced. It is dense in the northern and southeastern areas of California but sparse in the middle areas. As mentioned in Section 0, spatial and temporal correlation of the air pollutant concentrations between two neighboring stations could be different due to the spatial distribution of stations. Take station 11 as an example. It may have higher correlation with station 12 but lower correlation with station 5 since station 5 is geospatially farther. The data from station 5 may have smaller impact on predicting the air quality in station 11 but increase the risk of noisy data contamination and lower computation speed. Therefore, it is important to consider the spatial-temporal correlation among stations when building the forecasting model. Regarding this, this paper developed a correlation filtered spatial-temporal LSTM (CFST-LSTM) model, which can automatically determine the highly correlated data segments and simultaneously optimize the model from both temporal and spatial aspects. Detailed modeling process of CFST-LSTM will be introduced later.
# List-Based Threshold Accepting Algorithm with Improved Neighbor Operator for 0-1 Knapsack Problem
## 2 Related Work
### List-Based Threshold Algorithm
#### 2.1.4 Pseudocode of LBTA  
The pseudocode of LBTA is shown in Algorithm 1. We assume that the problem solved here is a maximization optimization problem that is consistent with the 0-1 KP. The algorithm is composed of two parts: initialization and iteration. In the initialization phase, a preliminary list of threshold values is established through a specific local search method, where \(L\) represents the length of the list. In the iteration phase, the algorithm generates a new solution by performing a local search move and updates the current solution and threshold list based on an acceptance criterion. The iteration continues until the maximum number of iterations \(G\) is achieved.  
```
1 Initialize a feasible solution \(\mathbf{s}\), a best solution \(\mathbf{s_{best}}\), \(T_{max}=+\infty\), and an empty priority threshold list;
2 whilethe length of list is less than \(L\)do
3 Generate a neighbor solution \(\mathbf{s}\);
4 Use Equation (2) to generate threshold value \(T\);
5 Insert \(T\) into the threshold list;
6
7 end while
8 forg=1 to Gdo
9 Generate a neighbor solution \(\mathbf{s}^{\prime}\);
10 if\(f(\mathbf{s}^{\prime})>f(\mathbf{s})\)then
11 Update \(\mathbf{s}\) with \(\mathbf{s}^{\prime}\);
12 if\(f(\mathbf{s})>f(\mathbf{s_{best}})\)then
13 Update \(\mathbf{s_{best}}\) with \(\mathbf{s}\);
14
15 end if
16
17 else
18 Generate \(T\) with Equation (2);
19 if\(T<T_{max}\)then
20 Replace \(T_{max}\) with \(T\) ;
21 Update \(\mathbf{s}\) with \(\mathbf{s}^{\prime}\);
22
23 end if
24
25 end for
```  
**Algorithm 1**List-based threshold accepting algorithm
## 2 Materials and Methods
### Data
#### 2.2.4 Vegetation Category  
The Vegetation Map of China (vector format, 1:1,000,000) from the Institute of Botany of the Chinese Academy of Sciences [37], as well as the MODIS landcover product MCD12Q1, were used for extracting and categorizing different forest types. The Vegetation Map of China is derived from multi-source information, including forest inventory data, plot-level measurements, and remote sensing data. The detailed base data help to bring the Vegetation Map of China closer to the actual vegetation distribution. Unfortunately, this map is generated with data over many years and thus may not reflect land cover change. Meanwhile, the MCD12Q1 is a dynamic dataset recording the landcover type for each year from 2001 to 2019 at a 500-m spatial resolution. Thus, we converted the Vegetation Map of China into 500-m resolution raster data and compared them with the MCD12Q1 for each year and pixel. We then chose the pixels which were constant for all years in the MCD12Q1 and consistent between the Vegetation Map of China and the MCD12Q1 for our study. As a result, most of the constant forest cover from 2001 to 2018 was deciduous broadleaf  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Standardized Precipitation-Evapotranspiration** & **Condition** \\
**Index (SPEI) Value** & **Extremely wet** \\ \hline \((1.5,2]\) & Severely wet \\ \((1,1.5]\) & Moderately wet \\ \((0,1]\) & Normally wet \\ \([-1,0]\) & Normal drought \\ \([-1.5,-1)\) & Moderate drought \\ \([-2,-1.5)\) & Severe drought \\ \(<\)\(-2\) & Extreme drought \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thresholds of Standardized Precipitation-Evapotranspiration Index (SPEI) values for water conditions.  
forests (DBFs, 50,737 pixels) or deciduous needleleaf forests (DNFs, 26,425 pixels). Only 822 pixels were categorized as constant evergreen needleleaf forests, and neither constant nor inconstant evergreen broadleaf forest pixels were found in the study area. Hence, our research would focus on the DNF and DBF biomes in North China.
## 2 Related Work
### The GA  
This section presents the related works concerning the GA, which demonstrate its effectiveness in finding the optimal multi-hop transmission paths, thereby saving energy, extending the network lifetime, and decreasing the transmission delay. [PERSON] et al. proposed an improved GA to optimize the routing paths, which encodes the paths as chromosomes and presents special crossover and mutation operations for realizing the optimal topology [45]. Moreover, they developed the fitness function by considering power consumption, time delay, and throughput of the network. The GA they proposed possesses the merits of fast convergence and robustness. [PERSON] et al. presented an improved GA to optimize the multicast routing by using a simplified encoding operation, a special crossover operation, and a modified mutation operation [46]. In addition, they defined the fitness function based on the energy cost and time delay, which can decrease the energy consumption and extend the life expectancy of the network. [PERSON] et al. put forward a routing protocol based on GAs that are used to look for suitable routing paths to satisfy the requirements of anycast sessions, which can improve the efficiency of the delay tolerant network [47]. An optimal multi-hop path finding method (OMPFM) was proposed in [48],where an enhanced GA is adopted to find the optimal paths through the proposal of a fitness function. Furthermore, the performance of the GA is improved in the execution time and the chromosome quality. Results show that the OMPFM can find an optimal multi-hop path, thereby saving energy and prolonging the network lifetime. In [49], [PERSON] et al. introduced a compact GA to select the optimal path for mobile ad-hoc networks, which can reduce the path cost, improve the packet delivery rate and decrease the energy consumption. [PERSON] et al. presented a modified GA through the increase in the number of offspring and the conduction of the second fitness assessment that can remove the undesirable offspring and keep the dominant individuals [50]. Moreover, this enhanced GA was used in the navigation, as well as the control system of unmanned surface vehicles, and simulation results indicate the GA performs well in the convergence speed, the robustness, and the optimal path searching.  
Therefore, combining the clustering routing protocol, the data fusion technique, and the GA in UWSNs could greatly reduce the energy dissipation and prolong the network lifetime. In our proposed underwater clustering routing protocol, the data fusion technique is used by CHNs to eliminate the data redundancy and the GA is employed to find the optimal multi-hop transmission paths when CHNs transmit the fused data to the SN.
## 4 Presentation and Discussion of the Results
### Performance, Costs, and Security  
Despite the advantages of the blockchain-based approaches in reputation systems, some challenges are posed in terms of performance, operation costs, and security.  
In a blockchain network, all history is preserved from the \"genesis block\" until the last transaction in the last block. Despite the transparency enabled by this approach, it is very challenging in terms of performance, because in real world e-commerce scenarios, such as Amazon and eBay, there is a huge number of daily transactions, each one requiring stored data and operation costs in the blockchain (the public ones). Note that feedback from a product sale may imply more than one transaction in the blockchain and these costs are variable due to the fluctuation of the cryptocurrency price in the market.  
In the studied literature, we found four approaches for dealing with these two issues: (1) use of external storage systems [18, 40, 44], such as IPFS, in order to reduce the amount of data in the blockchain, as well as to reduce the costs of operation in the public blockchain; (2) cache mechanisms [38, 39]; (3) modifications in the blockchain in order to reduce its size, and generation of a daily genesis block [36]; and (4) link the blocks of the same seller [17]. According to the authors, these approaches mitigate the performance issues and costs, but the impact on a real-world global blockchain network that interconnects platforms such as the ones referred to above is unclear.  
A blockchain network is a public ledger to which all participants have read permissions over the data in the blocks. This requires additional mechanisms of cryptography, typically based on tokens, bling signatures, and asymmetric encryption, in order to protect users' privacy, for instance, avoiding retaliation to the user rating a product or seller, making users and their feedback unlikable. In the literature, there is much attention to this issue [43].  
The blockchain was initially proposed using a POW consensus algorithm, which is a secure algorithm, despite its financial costs in the miner nodes [24]. In order to solve this problem, other consensus algorithms have been proposed and adopted, such as the PoS in the public Ethereum blockchain. Independent of the consensus algorithm, the blockchain is compromised only when the attacker controls 51% of the network.
## 3 Results
### IMOSs Are Hydrolyzed in the Extracellular Medium  
Given the diversity of DP in the IMOS mix, the consumption of mono-(DP1), di-(DP2) and trisaccharide (DP3) was monitored by HPLC every 2 h in the culture medium of _B. subtilis_ CU1 during its growth on IMOS. We noted that, among those three saccharides, a major proportion of isomaltose (DP2) was found in the mix before adding the bacteria to the medium, while a weak amount of glucose was measured (Figure 2C). We observed that the glucose monomer was the first metabolized carbohydrate, whatever its initial concentration in the culture medium (Figure 2C), while higher DP were metabolized after the consumption of glucose. We also observed that glucose residues were totally consumed after 8 h of growth. However, the glucose level increased between 12 h and 14 h of growth to reach its initial concentration, while higher DP oligosaccharides were consumed. Moreover, increasing concentrations of DP2 and DP3 before their consumption by the strain was also observed. These results suppose that high DP IMOSs are hydrolyzed in shorter oligosaccharides in the culture medium, suggesting that a glucosidase is released by the strain during its growth.  
IMOS Induces \(\alpha\)-Glucosidase Activity in B. subtilis CU1 and Reduces Lactic and Acetic Acid Production  
Given the ability of the strain to use IMOS as a unique carbon source, we monitored the possible release of glucosidase in the culture medium. The activities of \(\alpha\)- and \(\beta\)-glucosidasees were determined in the culture medium and in the cytosol of _B. subtilis_ at the end-log phase of growth with IMOS or glucose as unique carbon sources. No difference in the \(\beta\)-glucosidase activity was observed between the two tested conditions (Figure 2D). Moreover, the results show that only a basal level of \(\alpha\)-glucosidase activity is detected in both the culture medium and the cytosol of _B. subtilis_ CU1 after growth on glucose (Figure 2D). However, high \(\alpha\)-glucosidase activity was detected in the supernatant and the cytosol of the strain in the culture medium with IMOS. These results suggest that the synthesis and release of \(\alpha\)-glucosidase is specifically induced by IMOS when it is used as a carbon source. In order to explore the metabolism of IMOS, we determined the production of lactic acid and short-chain fatty acids (SCFAs) in the culture medium by HPLC. If no production of butyric and propionic acid was observed, we measured a drastic reduction in lactic acid and acetic production with IMOS when compared to glucose (Figure 2E,F, respectively).
# Fatigue Resistance Characterization of Warm Asphalt Rubber by Multiple Approaches
## 3 Results
### Mixtures  
#### 3.3.1 Iftft  
A linear relationship between log (cycles to failure) and log (microstrain, \(\xi\)) can be obtained using the data of the ITFT results, as illustrated in Figure 6. According to previous research, the tensile strains generated under a standard axle load at the bottom of the asphalt layer always ranges from 0 and 200 microstrains [13]. To analyze the ITFT results, two microstrain values (100 and 200) were considered in this research, representing the normal and high strain levels, respectively. Table 2 provides the \"cycles at 100/200 microstrains\" of each mixture (labeled as N\({}_{\rm H100}\) and N\({}_{\rm Z200}\)). A larger number of cycles at the same microstrain indicates a longer fatigue life. Based on Table 2, at both strain levels the fatigue performance of rubberized mixtures was superior to that of the Pen60/70 mixture. Among the WAR mixtures, only ARE mixture had a longer fatigue life than AR mixture. ARA and ARS mixtures had similar fatigue performance, and ARS mixture had the worst resistance to fatigue at both microstrains.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mixture** & \multicolumn{3}{c}{**N\({}_{\rm f}=\) k\({}_{\rm t}(\xi)^{\rm k_{2}}\) or logN\({}_{\rm f}=\) logk\({}_{\rm t}+\) k\({}_{\rm z}\)log\(\xi\)**} & \multicolumn{3}{c}{**Failure Cycles**} \\ \cline{2-7}
**Types** & **logk\({}_{\rm t}\)** & **k\({}_{\rm t}\)** & **k\({}_{\rm t}\)** & **k\({}_{\rm z}\)** & **R\({}^{2}\)** & **Microstrain = 100** & **Microstrain = 200** \\ \hline Pen60/70 & 7.7886 & \(6.14611\times 10^{7}\) & \(-1.8937\) & \(0.97460\) & \(1.03\times 10^{4}\) & \(2.70\times 10^{3}\) \\ AR & 15.00943 & \(1.02195\times 10^{15}\) & \(-4.41648\) & \(0.84440\) & \(1.50\times 10^{6}\) & \(7.03\times 10^{4}\) \\ ARE & 13.05896 & \(1.14541\times 10^{13}\) & \(-3.37453\) & \(0.95901\) & \(2.04\times 10^{6}\) & \(1.97\times 10^{5}\) \\ ARS & 11.90274 & \(7.99356\times 10^{11}\) & \(-3.18268\) & \(0.96721\) & \(3.45\times 10^{5}\) & \(3.80\times 10^{4}\) \\ ARA & 11.46023 & \(2.88556\times 10^{11}\) & \(-2.90851\) & \(0.80283\) & \(4.40\times 10^{5}\) & \(5.86\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the indirect tensile fatigue test (ITFT).  
Figure 5: Shear fatigue test result for asphalt mortars.