esa-sceva/satcom-chunk-collection huggingface dataset:
# Micromechanical Characterization of Diatom Frustules of Multiple Origin
## 2 State-of-the-Art
### Diatom Frustules  
Diatom frustules originate from a biomineralization process in an ordered, porous silica medium (nanospheres deposited in geometric and dimensional patterns). The frustules help diatoms in functional activities like mechanical stability, virus and nutrient filtration, rate of sinking, and regulation of light entering the cell [2; 44].  
The structure of the fossils makes it self-supporting, with the minimum content of silica. Frustules must be able to withstand mechanical loads, not only external (predators) but also internal (turgor and reproductive stages) [9].  
The liquid-solid phase surrounding the structure and pores of the frustules is of special interest. The diatoms' double external porous layers reduce friction between them and the outer water and increase resistance to liquid pressure. Depending on their morphology, frustules tend to act as self-repairing and self-lubricating media [10].  
The size, presence of pores and stiffness are characteristics of the frustules that differentiate them from other inorganic or soil particles with typical diameters between 30 and 100 \(\upmu\)m. Diatom-rich sediments exhibit physical properties different from those reported by typical organic and inorganic soils. Conventional geotechnical methods are insufficient to complete their characterization, which is necessary to establish a new type of fossiliferous soil, given their unique behavior [12; 13]. Creating a new diatomite classification based simultaneously on their engineering properties and the biological structure of the frustules is suggested by Ref. [45].  
Some frustules extracted from Jawornik (Poland) report cylindrical and centric shapes, variable radii between 50 and 60 \(\upmu\)m and porous surfaces. The pore sizes range from 100 to 200 nm. There is no evidence of dissolved silica inside the pores [1]. The content of fossil fragments affects both the fabric and microstructure of the soil matrix containing them and their water retention properties [46].  
Diatomite is composed almost exclusively of diatom fossils. It records two types of pores: skeletal pores in the frustules and interparticle pores. Diatomite samples from Oita (Japan) report skeletal pores with diameters generally less than one \(\upmu\)m. Centric diatoms of _Cyclotella_ species have variable diameters between 20 \(\upmu\)m and 40 \(\upmu\)m. Centric diatoms present in the clay soils of Osaka in Japan exhibit variable skeletal pore diameters between 0.02 and 1 \(\upmu\)m [47].
## 4 Results and Discussion
### RQ2: What Are the Key Search Spaces Explored in NAS-GAN?
#### 4.2.2 Cell-Based Space
Moving forward, researchers should strive for more transparent and comprehensive reporting of search space sizes and implementation details. There is also an apparent imbalance in the focus of current research, with many approaches prioritizing the optimization of Generator architectures over Discriminators. While the Generator plays a crucial role in GAN performance, the Discriminator is equally important. The diversity of search strategies employed in the field suggests that the optimal approach may vary depending on the specific problem or dataset. As search spaces continue to grow in size and complexity, computational efficiency becomes increasingly critical. Innovative approaches that balance the trade-off between search space size and flexibility, such as DEGAS [40] combining fixed and searchable architectural components, could prove valuable. Lastly, the potential for transferability of discovered architectures across different tasks or datasets is an area that warrants further investigation. While cell-based approaches might inherently offer better transferability, this aspect isn't explicitly addressed in much of the current literature. Table 3 presents a summary of reviewed literature.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 3 Simulation Analysis of Factors Affecting Detection Effect
### Crack Width  
Due to the difference in LR processes, materials and service conditions, the size and distribution of the surface cracks generated in the repair layer are different, which results in a distinct detection difficulty of laser thermography [29,33]. Therefore, the temperature and temperature gradient value \(T_{G}\) of different crack widths under laser excitation are studied, so as to reflect the sensitivity of crack width to laser point excitation. A nickel-based alloy  
Figure 4: Geometry and meshing of the simulation model.  
K348 is used as the repair layer material. The simulation model is shown in Figure 4. The effective power of the heat source is 1200 mW, the scanning speed of the heat source is 10 mm/s, the frequency of thermal image acquisition is 50 Hz, and the scanning time is 2 s. The crack widths \(L\) are set as 40 \(\upmu\)m, 80 \(\upmu\)m, 160 \(\upmu\)m, and 320 \(\upmu\)m. Figure 6 shows the distribution of the temperature change field \(\Delta T\) and temperature gradient field \(T_{G}\) under the given crack width.  
Figure 6 shows the simulation results which indicate that the temperature gradient at the crack increases rapidly with the increase in the crack width. When the crack width is larger than 80 \(\upmu\)m, the location and geometry of the crack can be clearly observed from the temperature change field without image enhancement, as shown in Figure 6a, which makes it easier to detect the crack. When the crack width is larger than 160 \(\upmu\)m, the two sides of the crack boundary gradually separate. However, when the width of the crack is 40 \(\upmu\)m, the temperature rise at the crack is difficult to be identified from the temperature change field, and the crack characteristics need to be enhanced. A good identification result can be obtained by gradient calculation, as shown in Figure 6b.
## 3 Results
### Model Performance in Evaluation Site-years
#### 3.3.3 Model Performance at the 8-day Scale  
When data in all 58 validation site-years was lumped together, TL-LUEn performed similarly with TL-LUE at the 8-day scale (Figure 4e,f). The differences between the two models were significant (\(p<0.05\)) in average RMSE value but was not significant (\(p=0.33\)) in average R\({}^{2}\) value (Table 4). TL-LUEn outperformed MOD17 with significant differences (\(p<0.05\)) in their average R\({}^{2}\) value but not  
Figure 5: Average RMSE and R\({}^{2}\) of GPP simulated using calibrated TL-LUEn, TL-LUE and MOD17 in the validation site-years at half-hourly (the first and second columns), daily (the third and fourth columns), and 8-day (the last two columns) scales for individual vegetation types. Note: Broadleaf forest (BF); Mixed forest (MF); Needleleaf forest (NF); Crop (CROP); Grass (GRASS); Shrub (SHRUB). Solid black circles are the means and horizontal error bars denote standard deviations.  
significant differences in their RMSE value (_p_ = 0.18), while TL-LUE outperformed MOD17 with significant differences (_p_ \(<\) 0.0001) in their average RMSE value but not significant differences in their average R\({}^{2}\) value (_p_ = 0.33) (Figure 4e,f, Table 4). However, the improvement of both TL-LUE and TL-LUEn over MOD17 was smaller in comparison with the improvement at half-hourly and daily temporal scales. MOD17 produced an average RMSE value of 13.0 g C m\({}^{-2}\) (8 days)\({}^{-1}\) and an average R\({}^{2}\) value of 0.755. The average RMSE and R\({}^{2}\) of TL-LUE were 12.0 g C m\({}^{-2}\) (8 days)\({}^{-1}\) and 0.775, respectively.  
The RMSE of GPP simulated by TL-LUEn, TL-LUE and MOD17 were smaller than 12 g C m\({}^{-2}\) (8 days)\({}^{-1}\) at 35, 34 and 27 sites. The three models had similar numbers of sites in each R\({}^{2}\) class. TL-LUEn performed poorer than TL-LUE at 32 sites, while MOD17 outperformed TL-LUEn and TL-LUE at 20 and 13 sites, respectively, which were mainly non-forest and NF sites (Table A3 in the Appendix).  
TL-LUEn only performed better than TL-LUE for BF. As to non-forest types (CROP, GRASS and SHRUB), TL-LUEn performed similarly with MOD17. The average RMSE and R\({}^{2}\) of the former were larger than those of the latter by 1.0 g C m\({}^{-2}\) (8 days)\({}^{-1}\) and 0.012, respectively. TL-LUE outperformed MOD17 in all the three non-forest types. As to forests, both TL-LUEn and TL-LUE showed a better performance than MOD17 with an average RMSE smaller than that of MOD17 by 1.5 g C m\({}^{-2}\) (8 days)\({}^{-1}\) and 1.6 g C m\({}^{-2}\) (8 days)\({}^{-1}\) and corresponding average R\({}^{2}\) larger than that of MOD17 by 0.030 and 0.022, respectively.
# Collaborative Networks: A Pillar of Digital Transformation
## 5 Relevant Collaboration Aspects in Industry 4.0
### Collaboration in Through-Engineering  
Under this dimension, at least three relevant collaboration \"spaces\" can be envisaged: (i) internal collaboration, (ii) external collaboration, and (iii) collaboration along the product lifecycle (Figure 7).  
Internal collaboration, a notion that can be traced back to the earlier ideas of concurrent engineering, fosters collaboration among different departments of an organization that have responsibility over the design, manufacturing, support and distribution of products.  
External collaboration includes collaboration with suppliers and business partners, but also with the customer. \"Service-enhanced products\", i.e., adding business services to products, requires the establishment of collaborative networks of designers, manufacturers, and service providers. This need is particularly evident when the aim is to deliver integrated/value-added service packages. It is also the case when differentiation of products/services according to the geographical area of each market is needed. The growing importance of involving the customer in the process of designing new products and/or services is reflected in the \"co-design\"/\"co-creation\"/\"co-innovation\" concepts. This customer's involvement in co-design, together with the needed interactions among engineers belonging to different organizations of the value chain (a kind of co-engineering), demand effective support for collaboration,  
Figure 7: Collaboration in through-engineering.  
e.g., collaboration platforms, shared spaces, shared ontologies and knowledge assets. etc. Exploitation of social networks and trend analysis in media are other forms of capturing customer preferences.  
When engineering considers the full lifecycle of the products and the perspective of circular economy, there is a clear need for collaboration, at different stages, among the involved stakeholders and a new perspective on engineering activities.  
What Can CNs Contribute?  
The adoption of collaboration platforms, often cloud-based, and CSCW (Computer Supported Collaborative Work) methods have been persistent areas of attention in the last decades. Some examples include:  
* The topics of co-design, co-innovation, and establishment and leveraging of customer communities, have been addressed in multiple works in the CNs area, e.g., [86; 87].
* The interactions between the lifecycles of the product/service and the interactions/ interdependencies among multiple collaborative networks were studied, namely in the GloNet project [76; 88].
* The role and importance of collaborative networks in supporting service-enhanced products/product-service systems have attracted considerable research attention in recent years [77; 89; 90; 91; 92].
* Tools to exploit user community networks and social media; innovation networks [93; 94].
## 4 Experiments
### Experimental Results  
The experimental results in Table 3 demonstrate that our model, MHAKE-GCN, outperformed the other compared models on all four benchmark datasets. As for the  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Positive**} & \multicolumn{2}{c}{**Neural**} & \multicolumn{2}{c}{**Negative**} \\ \cline{2-7}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline Restaurant14 & 2164 & 728 & 637 & 196 & 807 & 196 \\ Laptop14 & 996 & 341 & 464 & 169 & 870 & 128 \\ Restaurant15 & 1178 & 439 & 50 & 35 & 382 & 328 \\ Restaurant16 & 1620 & 597 & 88 & 38 & 709 & 190 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics on the distribution of sentiments in the four datasets.  
syntactic and attention-based approaches, the GNN-based model outperformed them across the board according to two evaluation metrics on the four datasets. The best graph neural network-based approach, R-GAT, outperformed the best syntactic and attention-based approach, AOA, by 3.42%, 1.12%, 2.13%, and 1.42% in accuracy and 6.91%, 8.04%, 7.15%, and 4.68% in F1 for the four datasets, respectively. When compared with syntactic and attention-based approaches, MHAKE-GCN significantly outperformed them on all four datasets, indicating that our model, which uses a GCN based on a dependency tree, is better at capturing the dependencies between the context and the aspects. Compared with the graph neural network-based approach, MHAKE-GCN also showed superior performance in most metrics. There were only slight performance differences on the F1 metric for Restaurant14 and Restaurant16 datasets compared to the R-GAT model, suggesting that our model, which incorporates external sentiment knowledge and uses multi-head attention to fuse sentiment information, is better at extracting aspect-specific sentiment information, whereas R-GAT can construct aspect-related dependencies well due to its use of aspects as root nodes. Our model, which is constructed using traditional dependencies, is 2.4% and 1.57% behind R-GAT in terms of F1 score for Restaurant14 and Restaurant16, respectively. In addition, compared with the best BERT-based results, MHAKE-GCN-BERT, our BERT-based model achieved accuracies 1.05%, 1.37%, 0.78%, and 2.19% higher, and the F1 metrics were 1.20%, 2.88%, 1.21%, and 2.38% higher for the four datasets, respectively. Our proposed model outperformed the other BERT-based models used in the comparison, indicating that our model can achieve comparative performance even when encoded using a powerful pre-trained language model.
# Valorisation of Star Wastewater by Anaerobic Fermentation
## 4 Other Anaerobic Fermentations  
### Acetone-Butanol-Ethanol (ABE) Fermentation  
The fermentation products acetone, butanol and ethanol can be used as bulk chemicals in industry. In addition, ethanol and butanol can be applied as biofuels. [PERSON] et al. investigated wheat starch wastewater for ABE fermentation [28]. They utilised a wastewater termed \"suspensions after hydroxyclone centrifugation\" as a main carbon source, which contained 68.6 g L\({}^{-1}\) of starch and 48.1 g L\({}^{-1}\) crude fibre. Corn steep liquor was used as nitrogen source. In the fermentation, they obtained 11.6 g L\({}^{-1}\) of butanol and total solvent of 18.3 g L\({}^{-1}\). In the literature, ABE fermentation trials on tapioca starch wash wastewater can also be found, where the highest butanol concentrations were achieved of 1.8 g L\({}^{-1}\) at pH 5.5 with _C. butyricum_[29].  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline \multirow{2}{*}{**Feedstock**} & \multirow{2}{*}{**Details on Experiments**} & **Biohydrogen Yield** & **Biohydrogen Productivity** & \multirow{2}{*}{**Literature**} \\  & & **(L H\({}_{2}\) kg COD\({}^{-1}\))** & **(mL H\({}_{2}\) L\({}^{-1}\) d\({}^{-1}\))** & \\ \hline Corn gluten water, corn & & & & \\ steep liquor & Two isolated strains & 186.3 & 3981 & [4] \\ \hline Cassava starch & & & & \\ processing wastewater & Thermophilic fermentation & 260 & 2163 & [7] \\ \hline Cassava starch & & & & \\ wastewater & With base-treated bacteria & 37.4 & 3348 & [24] \\ \hline Cassava starch & & & & \\ wastewater & With acid-treated bacteria & 22.6 & 914.4 & [24] \\ \hline Cassava starch & & & & \\ wastewater & Co-digestion with buffalo dung & 16.9 & 839 & [25] \\ \hline Synthetic cassava & & & & \\ starch wastewater & Themethane production & 81.5 & - & [26] \\ \hline Corn starch wastewater & Thermophilic hydrogen & & & \\ fermentation (mixed culture) and & 141.9 & 3450 & [27] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Biohydrogen yields and productivity from different starch wastewaters and under varying experimental conditions.
## 3 Basic System Operation
### SBD Routing
Figure 6:1

For simplicity, consider that the data first travels toward a corresponding track and then sector. So, the longest distance the data travels up or down is: \begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Description** \\ \hline C\({}_{\mathrm{ta}}\) & Cost of local update \\ \(\alpha\) & Local update rate \\ C\({}_{\mathrm{ru}}\) & Cost of remote update \\ \(\lambda\) & Remote update rate \\ C\({}_{\mathrm{qr}}\) & Cost of query request and response \\ \(\eta\) & Query rate \\ S\({}_{\mathrm{i}}\) & _i_th sector number \\ t\({}_{\mathrm{i}}\) & _i_th track number (row) \\ s\({}_{\mathrm{i}}\) & _i_th sector number (column) \\ C\({}_{\mathrm{ru,to,so,n}}\) & Cost of remote update from _S0_ to _S1_, _S2_,..., _Sn_ \\ _C\({}_{\mathrm{rc,5H}}\)_ & Transmission cost between two _SH_ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Notations used in analysis. affecting seeds to provide results that included average and 95% confidence interval. In Section 5.1, performance of SBD was tested in terms of Energy Consumption and Latency. For the experiments presented in Sections 5.1.1 and 5.1.2, the routing efficiency of SBD is evaluated against Low Energy Adaptive Clustering Hierarchy (LEACH) [18], Greedy Perimeter Stateless Routing (GPSR) [19], Directed Diffusion (DD) [20] and Car Pooling [7]. The querying performance of DCSMSS is evaluated in Section 5.2 in terms of Point Query, Range Query, KNN Query, Similarity Searching and Scalability. For the experiments presented in Sections 5.2.1-5.2.5 the querying performance of DCSMSS is evaluated against SDS, GHT [21] and DD. Weight Matrix, and thus level of significance, is set using the configuration file that is used to initialize the network during the deployment of the network. In addition, an XML file is used that can be dynamically loaded any time from any SH and thus any change of the behavior of the environment or network can be disseminated throughout the network. The frequency of this dynamic dissemination technique is 1/round, where round = 1, 2, 3,..., and this frequency is set based on how quickly the monitored network changes its behavior over time. The aggregation schemes are loaded at initialization of the network and can be changed on-demand during run-time. However, on-demand update during run time doesn't effect on previously collected data. It is obvious that LSH is a very powerful tool. However, LSH is good for data with high dimension. In WSN, dimension is usually limited and fixed at the time of deployment because total number of dimension depends on the number of sensor attached to a node. Thus a similarity searching based on the events, which are categorized in terms of attributes, is not scalable. In this paper, multi-dimensional data has been normalized into a one-dimensional domain. The domain is segmented into n intervals, where n is the total number of sectors. Each sector is responsible for storing data that falls in that interval. Hence, we could say, this hash function is more suitable than LSH for WSN.
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 3 Results
### Measurement of the ARC
#### 3.3.2 System Transfer Function  
To evaluate the system transfer function, a measurement system consisting of a signal generator and a spectrum analyzer was set up [12]. The test signal was sent to the ARC by the signal generator and then to the spectrum analyzer. The amplitude of the signal spanned a range from \(-60\) dBm to \(-27\) dBm for the L-band, \(-60\) dBm to \(-17\) dBm for the C-band, \(-60\) dBm to \(-16\) dBm for the X-band, and \(-70\) dBm to \(-34\) dBm for the Ka-band, according to various parameters including the effective isotropic radiated power (EIRP), the incidence angle, and the orbit of the spaceborne SAR. Figure 12 gives the transfer functions for the four bands. From Figure 12, we calculated the linearity, which is better than \(0.1\) dB for the L-band, \(0.15\) dB for the C-band, \(0.2\) dB for the X-band, and \(0.5\) dB for the Ka-band, and the linearity of the ARC is rather good.  
Figure 11: Gain during the two week period: (**a**) L-band; (**b**) C-band; (**c**) X-band; (**d**) Ka-band.  
Figure 12: Transfer function: (**a**) L-band; (**b**) C-band; (**c**) X-band; (**d**) Ka-band.
## 3 Basic Assumptions and Network Communication Model  
### Basic Assumptions  
The work presented in this research article is based on certain assumptions so that the simulation model can be set up suitably. The assumptions and network communication model incorporated in this article are derived from [16; 17]. Similar assumptions are made in [28].  
* The sensing nodes are distributed in random order within the sensing region.
* The nodes have fixed coordinate positions; i.e., sensing nodes and BS are stationary.
* Each node initially has two Joules of energy. Generally, energy is utilized for sensing, communicating, and data transmission. A node is said to be dead if its energy is completely depleted and is considered non-operable.
* The BS has sufficient energy so that it can never run out of energy.
* Nodes are involved in sensing, transmission, and reception of data with surrounding nodes. A node has a fixed transmission range, beyond which it cannot establish direct communication.
# A Thermodynamic Model for Cryogenic Liquid Hydrogen Fuel Tanks
## 3 Validation of Thermodynamic Model
### Validation of Fuel Emission Model  
Validation of the proposed model for fuel emission was performed with the tank model and experimental results of [PERSON] et al. [33]. The tank had an outer diameter of 329 mm, an inner diameter of 290 mm, a length of 920 mm, and a volume of 40 L. The initial pressure of the filled tank was 70 MPa. The average mass flow rate discharged to the outside of tank was 1.8 g/s. The gas temperature was measured at six points inside the tank. The internal pressure was measured with one pressure sensor.  
To verify the prediction accuracy of the proposed model for fuel emission, the temperature and pressure analysis results for the gas area were compared. When gas was discharged from the pressurized tank, the pressure was naturally reduced, and the temperature was decreased because the gas expanded by the volume at which it was released. This phenomenon was also observed in the temperature and pressure prediction results, as shown in Figure 3. In the figure, the black solid line is the present analysis result, and the red open circle represents the experimental data of the reference. Figure 2(a) shows that the temperature decreased as the gas was discharged. The gas temperature of the present model was lower than that of the reference data, and the relative error was within 7.75%. The pressure of the present analysis result represented a higher value, as shown in Figure 2(b). The relative error was calculated as being within 9.78%. The error of pressure was relatively large compared to the error of temperature. This is thought to be due to the use of the ideal gas equation of state when calculating the pressure over a wide range of pressures. In order to increase the accuracy of the analysis result, it is necessary to modify this part to include a realistic formula. Despite the assumption of an ideal gas, it can be said that the present analysis model produced very good results.  
Figure 3: Comparison of gaseous temperature and tank pressure for defueling model validation.