esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Computational Complexity Analysis
Tables 2-4 show the complexities at the base station of the RBD, MGBD and the proposed scheme, respectively. The parameters in Tables 2-4 are explained in Sections 2.1, 2.3 and 3, respectively. The proposed scheme needs extra operation which requires low number of flops to obtain the power of the channel in (28). However, the complexities of the operations in (33) and (34) are low since the channel matrix which is utilized to form the precoder of the proposed scheme has low dimension. Therefore, the complexity of the proposed scheme is lower than the RBD. Since MGBD requires three SVD operations at (14), (23) and (25), the complexity of MGBD is extremely high. It means that the proposed scheme has lower complexity at the base station compared to MGBD. In terms of time complexity, when the data size is \(N\), time complexities of all schemes can be written as \(N\times\frac{\text{the number of total operations}}{\text{modulation order}\times\text{ code rate}\times L_{k}}=\text{O}\left(N\right)\). In addition, since the modulation order increases with reduced \(L_{k}\), the results  
Since the proposed scheme and RBD have no need of the extra operation at the receiver, the proposed scheme and RBD are efficient schemes in terms of the computational complexity at the receiver. Thus, the computational complexities of RBD and the proposed scheme at the transmitter are compared in order to know more efficient scheme. In addition, the MGBD is also compared with the RBD and the proposed scheme since MGBD has extremely high complexity at the base station. The computational complexities of RBD, MGBD and the proposed scheme are shown in Figure 3. The number of data streams for RBD is \(N_{T}\) and the numbers of data streams for MGBD and proposed scheme are \(\frac{N_{T}}{2}\) which is reduced to obtain diversity gain. The complexities of some operations at the proposed scheme are lower than RBD since the dimension of the channel matrix which is used for operations is small. The proposed  
proposed scheme is lower than the RBD. The MGBD has the highest complexity at the transmitter since the three SVD operations are required for obtaining precoding matrix. The complexities of the RBD and MGBD increase rapidly depending on the number of transmit antennas. Even though the differences in computational complexities from 8 to 12 antennas look like small, the complexity of the proposed scheme is much lower than the complexities of the conventional schemes. In addition, since the number of total operations for the proposed scheme is lower than RBD and MGBD in Figure 3, the time complexity of the proposed scheme is also lower than RBD and MGBD.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 4 Proposed Method
### On the Receivers' Side
#### 4.2.1 Receivers with Restricted Authorization  
For restricted-authorized receivers, they only possess key A that contains parameters to generate the measurement matrix \(A\), and this key behaves as a symmetric decryption key. The details of the data reconstruction process are shown as below.  
Step 1: To begin with, from Equations (11) and (12), we can get the transformed encrypted and concealed signal \(y_{c}^{*}\in\mathbb{R}^{M\times 1}\) as Equation (13).  
\[y_{c}^{*}=\frac{y_{c}-\beta a}{\alpha}=\Phi x+\epsilon. \tag{13}\]  
Step 2: The OMP algorithm could be exploited to obtain the estimate of \(x^{\prime}\), and here \(x^{\prime}\) represents the concealed form of \(x\).  
\[\overset{\wedge}{x^{\prime}}=arg\underset{x}{min}\left\|x\right\|_{1}\quad subject \ to\quad y_{c}^{*}=\Phi x=A\Psi^{\mathsf{T}}x \tag{14}\]  
Step 3: Finally, an inverse transformation of \(x=\Psi s\) is performed to obtain the estimate of the concealed form of \(s^{\prime}\).  
\[\overset{\wedge}{s^{\prime}}=\Psi^{\mathsf{T}}\overset{\wedge}{x^{\prime}} \tag{15}\]
## 4 Passive Anti-Biofouling Nanoc Coatings
### Fouling Release Strategies
#### 4.2.2 Biomimetic Hierarchical Micro- and Nanostructured Superhydrophobic Surfaces
Traditional antibiofouling surfaces are solid materials, meaning that their surface atoms or molecules are fundamentally static in nature, which is a feature that depending on the time scale of the adhesion process, can favour stable attachment and biofouling. Therefore, the creation of a material exhibiting the intrinsic mobility of its surface molecules and structures represents a valuable alternative that might significantly hamper biological adhesion. Keeping that in mind, [PERSON] et al. [147] came up with a completely different strategy for fabricating antibiofouling surfaces: slippery liquid-infused porous surfaces (SLIPS). The fabrication of SLIPS involves four sequential steps as follows: (1) surface nanopatterning of a flat substrate by any of the top-down or bottom-up approaches such as etching and lithographic techniques or sol-gel processes; (2) chemical functionalisation of the rough nanostructured surface of the substrate in order to increase its affinity for the lubricant fluid; (3) complete wetting of the substrate with the lubricant through capillary wicking;(4) removal of excess unattached lubricating fluid. To increase the stability of SLIPS under submerged conditions, three important requirements should be fulfilled: (1) the affinity of the substrate surface for the lubricant should be higher than that for the surrounding fluid; (2) the lubricant and the surrounding fluid should be immiscible; and (3) the surface of the solid substrate should be roughened so as to increase the surface area for the adhesion of the lubricating fluid. The authors used nanoporous Teflon membranes as substrates and the perfluorinated compounds perfluoropolyethers (Dupont(tm) Krytox(r) 100 and 103), perfluorotripentylamine (3M(tm) Fluorinert(tm) FC-70), and perfluorodecalin, as lubricating fluids. The so-prepared SLIPS proved excellent ability to prevent the adhesion of pathogenic bacteria and biofilm formation. Using fluorescence imaging and crystal violet assays, the authors showed that the SLIPS platform prevents 99.6% of _P. aeruginosa_ biofilm attachment over at least a 7-day period, under dynamic low-flow conditions, while other nanostructured superhydrophobic surfaces developed biofilms within hours. Similar high levels of biofilm reduction were also obtained for _S. aureus_ (97.2%) and _E. coli_ (96%) [147].
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
## 3 Results and Discussion
### Effect of CeO\({}_{2}\) on the Catalytic Yield
#### 3.1.1 Upgrading Oil Yield  
Figure 1 shows the yield (Y) change of upgrading oil at 270 \({}^{\circ}\)C for 1 h with different catalysts. Y is higher with the addition of CeO\({}_{2}\) than that without CeO\({}_{2}\), and Y increases first and then decreases with increasing CeO\({}_{2}\) added content. With 15% CeO\({}_{2}\)-Ni-Cu/HZSM-5, Y can reach the highest value, accounting for 47.6 wt%.  
To further explore the effect of reaction temperature of the Ni-based catalysts with 15% CeO\({}_{2}\) for the HDO of bio-oil, we used 15% CeO\({}_{2}\)-Ni-Cu/HZSM-5 at different temperatures. As shown in Figure 2, Y starts to decrease with the increase of reaction temperature, and Y declines rapidly when the temperature is between 270 and 300 \({}^{\circ}\)C.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Soil Moisture  
Soil moisture directly impacts vegetation growth and resistance to drought [35; 36]. The Global Land Evaporation Amsterdam Model (GLEAM) was employed to provide the surface and root-zone soil moisture statistics used in this study [37; 38]. The reliability of GLEAM soil moisture data has been verified by on-site soil moisture measurements in the Loess Plateau [39]. The 2000 to 2020 monthly surface soil moisture (0-10 cm) (SMsurf) and root-zone soil moisture (10-100 cm) (SMroot) data in GLEAM (v3.5a) were selected for analysis. Soil moisture is the primary source of water required for vegetation growth, and it determines the amount of water used by the vegetation [35]. As soils of different textures have varying water-holding capacities and nutrients [40; 41; 42], this study utilized soil moisture and SPEI at different timescales as water availability indicators.
## 3 Proposed System
### Design of Likelihood Functions  
In this study, two likelihood functions are designed to calculate the likelihood of particles to investigate the effect of weight functions considering position coordinates. The likelihood functions can be designed by calculating the weights of particles using a weighting function by distance based on a mixture normal distribution.
# Analytical Study of a Circular Thin Plate Contacting with an Elastic Sphere
## 2 Formulation and Governing Equations  
### Contact Analysis  
As shown in Figure 1, a circular thin plate and an elastic sphere bounded by surfaces \(S_{1}\), \(S_{2}\), and frictionless contact at point \(O_{1}\) were considered. The circular thin plate was clamped peripherally. Taking point \(O_{1}\) as the origin of the coordinate system, we directed axes \(z_{1},z_{2}\), which were perpendicular to the common tangent plane to surfaces \(S_{1}\) and \(S_{2}\) at point \(O_{1}\) into each of the bodies  
or by Equations (1) and (3)  
\[z^{\prime}=\frac{r^{2}}{2r_{b}}+w_{1}+w_{2}-\delta. \tag{7}\]  
For those points \(M_{1}\) and \(M_{2}\), which will be in contact after the deformation, this distance will be zero, whereas this distance is positive for the points in the neighborhood of the contact place. The surface of the contact can therefore be determined as a locus, where  
\[z^{\prime}=0\quad w_{1}+w_{2}=\delta-\frac{r^{2}}{2r_{b}}. \tag{8}\]  
The exact expressions of \(w_{1}\) and \(w_{2}\) are discussed in later sections of the text.  
There is a normal stress \(q(r)\) on the contact surface, while the shear stresses are assumed to be absent. Furthermore, it is assumed that when considering the local effects in the vicinity of the contact, the contacting bodies can be replaced by an elastic half-space pressed against a circular thin plate on the plane surface lying in the tangent plane of surfaces \(S_{1}\) and \(S_{2}\). On this plane, \(z_{1}=z_{2}=0\), and the area of contact is a circle of radius \(b\) (see Figure 2). Whilst the stress to the area of contact between the two bodies can be assumed as [1]  
\[q(r)=\frac{q_{0}\big{(}b^{2}-r^{2}\big{)}^{\frac{1}{2}}}{b}, \tag{9}\]  
where \(q_{0}\) is the maximal contact pressure at the center of the circular contact area determined by  
\[q_{0}=\frac{3P}{2\pi b^{2}}. \tag{10}\]
## 1 Introduction
### Effects on the Neurologic System
#### 1.7.1 Effects on the Neuro-Ocular System  
Prolonged exposure to ionizing radiation is well known to produce secondary cataracts [61; 62]. Most importantly, Spaceflight Associated Neuro-Ocular Syndrome (SANS) is a unique constellation of clinical and imaging findings which occur to astronauts both during and after spaceflight, and is characterized by: hyperopic refractive changes (axial hyperopia), optic disc edema, posterior globe flattening, choroidal folds, and cotton -pool spots [43]. Ophthalmologic screening for SANS, including both clinical and imaging assessments is recommended. (Figure 2) Although the precise etiology and mechanism for SANS remain ill-defined, some proposed risk factors for the development of SANS include microgravity related cephalad fluid shifts [203], rigorous resistive exercise [204], increased body weight [205], and disturbances to one carbon metabolic pathways [206]. Many scientists believe that the cephalad fluid shift secondary to microgravity exposure is the major pathophysiological driver of SANS [203]. Although inflight lumbar puncture has not been attempted, several mildly elevated ICPs have been recorded in astronauts with SANS manifestations upon returning to Earth [43]. Moreover, changes to the pressure gradient between the intraocular pressure (IOP) and ICP (the translaminar gradient) have been proposed as a pathogenic mechanism for SANS [207]. The translaminar gradient may explain the structural changes seen in the posterior globe such as globe flattening and choroidal folds [207]. Alternatively, the microgravity induced cephalad fluid shift may impair venous or cerebrospinal drainage from the cranial cavity and/or the eye/orbit (e.g., choroid or optic nerve sheath). Impairment of the glymphatic system has also been proposed as a contributing mechanism to SANS, but this remains unproven [208,209]. Although permanent visual loss has not been observed in astronauts with SANS, some structural changes (e.g., posterior globe flattening) may persist and have been documented to remain for up to 7 years of long-term follow-up [210]. Further research is required to better understand the mechanism of SANS, and to develop effective countermeasures prior to longer duration space missions.
## 3 Influencing Factors and Interaction Identification of the Machined Surface Error
### Screening of Influence Factors of the Machined Surface Error
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Milling Cutter Number** & \multicolumn{4}{c}{**Cutter Tooth Error (mm)**} \\ \hline Milling & \(\Delta z_{i}\) & 0.046 & 0.007 & 0.017 & 0.036 & 0.028 \\ cutter 1 & \(\Delta r_{i}\) & 0.026 & 0.056 & 0.016 & 0.028 & 0.019 \\ \hline Milling & \(\Delta z_{i}\) & 0.044 & 0.032 & 0.008 & 0.025 & 0.027 \\ cutter 2 & \(\Delta r_{i}\) & 0.005 & 0.012 & 0.009 & 0.028 & 0.036 \\ \hline Milling & \(\Delta z_{i}\) & 0.023 & \(-0.012\) & \(-0.023\) & 0.031 & \(-0.024\) \\ cutter 3 & \(\Delta r_{i}\) & 0.015 & \(-0.003\) & 0.013 & \(-0.007\) & 0.002 \\ \hline Milling & \(\Delta z_{i}\) & 0.012 & 0.022 & 0.034 & \(-0.017\) & 0.026 \\ cutter 4 & \(\Delta r_{i}\) & \(-0.013\) & 0.002 & 0.021 & 0.022 & 0.015 \\ \hline Milling & \(\Delta z_{i}\) & 0.013 & 0.008 & \(-0.023\) & 0.027 & 0.038 \\ cutter 5 & \(\Delta r_{i}\) & 0.001 & 0.012 & \(-0.011\) & \(-0.028\) & 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Design variables for error distribution of milling cutter teeth. The variance values in Table 4 are the variances of each design variable corresponding to the degree of single-factor simulation error correlations, whose values can initially indicate the degree of their influence on milling vibration--the larger the variance, the greater the degree of influence. An F-value greater than F crit indicates an obvious effect; the \(p\)-value is the confidence probability of the corresponding F-value--usually, less than 0.05 is an obvious effect, and less than 0.001 is a highly obvious effect. As can be seen from the table, the F-values for both the positional error and the angular error are greater than their own F-values, and the confidence probabilities for both the angular error and the positional error are greater than 0.05, which is a significant effect. As shown in Table 4, according to the analysis of variance values, the degree of influence of design variables on the machined surface positional errors is as follows: cutter tooth error > feed rate per tooth > milling vibration = rotational speed of the milling cutter. The degree of influence of design variables on the angular errors between the tangent plane and the reference planes is as follows: cutter tooth error > rotational speed of the milling cutter > milling vibration > feed rate per tooth. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Design Variable**} & \multicolumn{3}{c}{**Variance**} \\ \cline{2-4}  & \(\Delta y\) & \(\theta\) \\ \hline \(n\) & 0.00012 & 0.00038 \\ \(f_{2}\) & 0.00021 & 0.00014 \\ Cutter tooth error & 0.00023 & 0.00078 \\ Milling vibration & 0.00012 & 0.00035 \\ F & \(p\)-Value & \(F\) crit \\ \(\Delta y\) & \(\theta\) & \(\Delta y\) & \(\theta\) \\
3.8191 & 4.4814 & 0.0308 & 0.0182 & 3.2388 & 3.2388 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Variance analysis results of machining error. Figure 7: Simulation results of machined surface morphology.
## 3 Verification and Results  
### Image Data Set  
In order to detect cracks through deep learning, various training data are required for the training process to teach the neural network the different characteristics of crack and non-crack data. In this study, the dataset shared by [PERSON] [29] was used for training the proposed network. The dataset contains 458 high-resolution images (4032 \(\times\) 3024 pixels) with RGB channels which are pictures of the Middle East Technical University campus building in Turkey. For the patchwise-training and test, the dataset was divided into two groups, 20,000 non-crack images (Figure 7a) and 20,000 crack images (Figure 7b). Thus, a total of 40,000 images of 227 \(\times\) 227 pixels size were used in this study. In addition, this study prepared crack segmentation data of 2000 separately by manually segmenting crack images (Figure 7c). Note that the shared dataset contains information on the status of cracks, but since the cracks are not segmented. The proposed neural networks were built and verified on the Keras Application Programming Interface of TensorFlow (Google Brain, Mountain View, CA, USA). They were trained on an NVIDIA GTX 1080 Ti GPU, which has 3584 CUDA cores and 11 GB GDDR5X memory. The GPU memory bandwidth is 484 GB/s.  
Figure 6: Concept of profiling algorithm to finding width (or transverse) direction of crack at a certain point
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 5 Simulation Results
### Performance in Sparse Scenario
#### 5.1.3 Efficiency Comparison  
Figure 4 tracks the change in system capacity at each iteration and also shows the efficiency of the steady-state convergence. It is noted that MCBR (Neighborhood Information) and MCBR (Complete Information) converge to steady states after some iterations, while BR (Sequential) and BR (Simultaneous) do not achieve any steady-state solution. Also, the plots for both MCBR (Neighborhood Information) and MCBR (Complete Information) exhibit the convergence to the pure strategy NE, corresponding to maxima at about 78 and 49 iterations, respectively. This validates that MCBR algorithm is an EPG.  
Figure 5 shows the average values of system capacity obtained for 200 random deployments of the sparse scenario. The performance of MCBR (Neighborhood Information) and MCBR (Complete Information) are close to the optimal solution (obtained from an exhaustive search). The results demonstrate the near-optimal performance of the two MCBR schemes. Note that the convergence speeds to steady states for both MCBR schemes are almost similar and exhibit convergence to maxima. The overall average performance of the system capacity is compromised in BR (Sequential) when players opportunistically try to maximize their own capacities, whereas the BR (Simultaneous) results in the worst average performance of all when all links update their strategies simultaneously.  
Figure 4: System capacity comparison. MCBR: marginal contribution-based best-response.  
Figure 3: The evolution of number of links on each of the subchannels.
## 1 Introduction and Literature Review
Usually, the DT is thought to be the same as a model or a theoretical simulation of a given process, but it is, in fact, much more: A digital twin is a high-fidelity representation of the operational dynamics of its physical counterpart, enabled by almost real-time synchronization between the cyberspace and physical space [13]. In a recent work [14], a digital twin-based design platform was validated with a case study of the hollow glass smart manufacturing system. The application to lean production is addressed in [15], comparing the performance of three pull control strategies by simulation. In both cases results prove the efficiency of the approach. However, no references have been found to put the focus on the process itself, which is particularly critical in the case of the grinding industry. The selection of the grinding process is based on the fact that it is one of the most complex machining processes, that involves a large number of variables that, to some extent, cannot be directly controlled. For instance, tool wear in milling or turning is a well-known fact, with good predictive models being currently available. It is also possible to know tool wear in turning or milling by using a pre-setting tool station. However, the mechanisms of wheel wear in grinding are extremely complex because of the composite nature of the grinding wheel. In fact, wheel wear is only indirectly known in grinding, because of its effect on part damage. Also, problems such as grinding burn (because of excessive contact temperatures) do not affect turning or milling. Many other aspects such as part finishing, spark-off operations, etc. can be also cited. In summary, grinding is more heavily based on experience than other machining processes, for which models are available; therefore, it is an optimal technology to test the impact of digital twins in the machining industry. According to this, a number of elements must be included within a DT such as process modelling, the characterization of grinding wheel mechanical behavior, and process monitoring.
## 5 Discussion and Conclusions  
### Monitoring the Adaptability of Models in Different Environments  
In this study, the correlation analysis of accuracy demonstrates that PS-InSAR and SBAS-InSAR are equally effective in monitoring settlement in urban areas of Kunming. However, each method is suitable for different monitoring environments based on its unique advantages.  
PS-InSAR: The PS technique shows better adaptability in areas with high stability and dense scattering points. The technique relies on stable scattering points (e.g., buildings, rocks, etc.) and therefore performs well in built-up areas (e.g., subsidence zones D and F). In these areas, buildings are built stably, and PS points are dense and stable. Hence, PS-InSAR can provide highly accurate settlement monitoring results.  
SBAS-InSAR: For low-frequency deformations and nonlinear deformations, SBAS technology shows a unique advantage. SBAS technology performs better in large engineering and construction areas (e.g., settlement zones B and E). These areas are undergoing large-scale urban construction, the surface changes are more intense, and there are fewer stable scattering points. SBAS technology can better deal with nonlinear deformation and low-frequency deformation, so it is suitable for areas with intense surface changes. In particular, in zone B, due to the existence of large-scale construction activities, SBAS-InSAR can more accurately reflect the subsidence.  
As for zones A and C, where the development rhythm is stable, both PS-InSAR and SABS-InSAR have good performance. Considering that the urbanization process in the future tends to develop towards zone B, while zone A and zone C gradually tend to be stable, the PS method is more suitable for monitoring zone A and zone C.  
The R\({}^{2}\) was as high as 0.98 in settlement zone C and settlement zone F, showing a high degree of consistency between the two techniques. However, the R\({}^{2}\) is only 0.88 in settlement zone B and settlement zone E. Settlement zones B and E are fast-developing urban areas with rapid building growth, which leads to the lack of stabilization of the PS points and some spatial incoherence. Therefore, it further indicates that the SBAS monitoring results are more reliable in these areas.  
Figure 16: Combination of TS-InSAR and GWO-LSTM timing results ((**A**–**F**) are the subsidence zones of A–F, respectively).