esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation of the Relationship between
## 3 Results  
### Optimization of Anastomosis Angle  
#### 3.1.1 Pressure Drop  
The human vascular system, just like any other fluid flow system, evolves to reduce flow resistance and induce fluid flow [32]. It is therefore expected for blood to flow with low resistance, thus reducing the pumping power required of the heart to circulate the fluid throughout the body. The amount of resistance is indicated by the total pressure drop between the inlet and the outlet. It represents the amount of friction, static, and acceleration pressure drops experienced by the fluid between two points [33]. Small pressure drops indicate lesser fluid flow resistance, so it must be sought when designing a bypass graft.  
Looking at the pressure contour on the longitudinal section of the geometric model shown in Figure 5, it can easily be seen that the anastomosis angle heavily influenced the pressure distribution of the flow. Much of the difference between the four models can be observed in the entry length to the graft. A greater pressure reduction in this area was observed with the larger anastomosis angles. In essence, this indicates that larger anastomosis angles result in higher inflow resistance in bypass grafts.  
Figure 4: Pulsatile velocity waveform of the femoral artery for transient CFD analysis.  
Figure 5: Pressure contours across the longitudinal section of the models.  
In addition to this, another notable difference is the area around the anastomosis toe. The anastomoses with larger angles had higher pressure gradients within these regions as evidenced by lower pressure (bluer) areas on the anastomosis toe. This could be explained if we consider the anastomosis to be a pipe bend configuration. At the bend (anastomosis), the flow direction changed. At the bend exit, due to inertia, centrifugal force caused the fluid to migrate toward the outer curve or extrados (host artery bed) and away from the intrados (anastomosis toe), explaining the relatively lower pressure values at the upper region of the host artery compared to the lower region within the anastomosis region [34; 35].  
The results presented in Table 1 show a positive relationship between the anastomosis angle and pressure drop. The pressure drop increased by about 5-6% per an increase of 15\({}^{\circ}\) in the anastomosis angle. This is congruent with findings in the literature, indicating the benefit of smaller anastomosis angles in bypass graft performance through the reduction of the pressure drop [8; 36; 37].
## 4 Results and Discussion  
### Developed Models  
#### 4.1.1 Gaussian Process Regression  
As shown in Table 6, the optimum GPR model that produced the MSE of 0.0903 and the highest \(R\)-value of 0.9986 for the testing dataset, had a matem 3/2 covariance function with a noise variance of 0.06434, a length scale of 3.6019, and a signal variance of 7.0339. This indicates that the GPR-matem 3/2 can generalize well with unseen datasets relative to the other GPR models. Hence, GPR-matem 3/2 model was selected as the best GPR model in this study.
# Optimization of Xylose Recovery in Oil Palm Empty Fruit Bunches for Xylitol Production
## 3 Results
### The Surface Plot of Xylose Recovery vs. Temperature and SL  
This section describes the effect of temperature and SL at a pretreatment time of 37.5 min on xylose recovery as shown in Figure 2a,b. The surface plots for the effect of temperature and SL at 15 min and 60 min are not shown because they were similar.  
As described above, Figure 2a,b showed a curved shape in the response surface plot, which suggested that there was an optimum value. The surface plot justified the main effect plot in Figure 1. There was a slight increase in xylose recovery in solid pretreated OPEFB and a slight decrease in liquid pretreated OPEFB, along with an increase in SL up to 10%. As for temperature, the figure asserted that there would be a maximum temperature at about \(165\pm 5\)\({}^{\circ}\)C, corresponding to a maximum xylose recovery. The higher SL in combination with temperature gave a higher xylose recovery and decreased after 165 \({}^{\circ}\)C. The peak on the plot corresponds to the highest xylose recovery in solid pretreated OPEFB hydrolysate at an approximate temperature of \(165\pm 5\)\({}^{\circ}\)C and SL of 15%. This combination of temperature and SL showed that there was a need for an optimization procedure.  
Figure 2: Effects of temperature and solid loading on xylose recovery (**a**) in solid pretreated OPEFB hydrolysate, and (**b**) in liquid pretreated OPEFB hydrolysate at pretreatment time of 37.5 min.
# Underwater Object Detection Method Based on Improved Faster RCNN
## 4 Experiments
### Mosaic Data Enhancement and Related Preprocessing  
#### 4.1.1 Dataset Preprocessing  
There were 2372 underwater environment image samples (for example, holdhurian, echinus, scallop, starfish and waterweeds) collected initially in the experiments. For deep learning network model training, the number of samples in the experimental dataset is not sufficient. In order to overcome the problem of an insufficient number of dataset samples, this paper expands and optimizes the dataset samples by performing data enhancement techniques such as flipping, local trimming, color adjustment, Gaussian noise and salt and pepper noise on the images in the original dataset. The image flipping process is to flip the original image of the experimental data sample horizontally or vertically; the cropping operation is to locally cut the image, select the object position, and remove the irrelevant or disturbing parts. In order to enhance the robustness of the underwater object detection model and prevent overfitting, this paper actively introduces Gaussian noise and salt and pepper noise to some images during the data enhancement processing.  
Since the color components of underwater imaging are different from those of land-based imaging, the underwater images are prone to imbalance in the main three colors. Therefore, color compensation is performed on the underwater images to adjust the image tones and to achieve better underwater image detail features. Since the color is attenuated when the light enters underwater, the red component is most obviously attenuated; therefore, the red component in the collected underwater environment images is small, while the information in the blue and green channels is relatively well preserved. Therefore, we mainly compensate the red color in the image to make the image closer to the real color. The data enhancement effect is shown in Figure 3. After the pre-processing and expansion of the dataset samples, the experimental dataset sample images are expanded from the original 2372 to 3500 and the resolution of the images is unified to 500 \(\times\) 400.  
Figure 3: The effect of dataset preprocessing.
## 4 Experimental Data and Simulation Results
### Comparison between Experiment and Simulation  
The measured value of no-load excitation current without DC bias current was compared with simulation results, as shown in Figure 15. Figure 16 shows the comparison between the simulated and measured values of winding current with rated operation under various DC bias currents.  
As can be seen from Figures 15 and 16, the measured data showed a good accuracy with the calculated by field-circuit coupling FEM.  
It should be noted that two errors can be noticed easily in Figures 15 and 16, which are mainly caused by: neglecting the hysteresis effects, which caused the error when excitation current was close to 0, as shown in Figure 15.  
Due to the actual data provided only to 1.9 T, linear curve fitting was utilized for the deep saturation situation of BH curve, therefore the actual saturation condition was more severe than the simulated one, which caused the calculation error about peak value, as shown in Figures 15 and 16.  
However, the relative deviations of peak value and effective value of excitation current without DC bias were 2.2% and 4.2% respectively. The relative deviations of peak excitation current and effective  
Figure 14: The flux distribution with and without DC bias current. (**a**) without DC bias current. (**b**) with 5 A DC bias current.  
Figure 13: The measured losses and active power under various DC bias current. (**a**) no load loss. (**b**) active power of each side at rated operation.  
**Author Contributions:** Conceptualization, B.L.; methodology, S.G.; software, M.L.; writing--review and editing, B.L.; supervision, Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key R&D Program of China, grant number 2016 YFC0800100; And this work was also supported by the China Scholarship Council (CSC), grant number 201906730078.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# High-Precision Combined Tidal Forecasting Model
## 4 Conclusions  
This study analyzes predictions of tide level by common analysis techniques and those of a proposed method that combines simple harmonic analysis with autoregressive moving-average-support vector regression (ARIMA-SVR). To better capture the tidal dynamic properties and improve the prediction accuracy, the original tidal wave data were de-noised by wavelet threshold analysis. The method fully utilizes the strong time-series processing ability of ARIMA and the outstanding nonlinear regression predictions of the SVR. Harmonic analysis and ARIMA-SVR in the combined model predict the astronomical tide level and the non-astronomical tidal level, respectively. In the non-astronomical component of the data, the data were normalized and the ARIMA-SVR parameters were optimized by the PSO algorithm, which improved the overall prediction accuracy of this component. In experimental tests, the combined model achieved a higher prediction accuracy than any of its composite models used on their own, as well as the BP neural network. Therefore, the proposed model ameliorates the low precision associated with single prediction models. Accordingly, it promises broad applications in future tidal prediction. However, as mentioned in the introduction, harmonic analysis as a part of the combined model has the problem of the high cost of obtaining large amounts of data from on-site monitoring equipment. This problem has not been solved in the model proposed in this paper and is the next problem that needs to be solved.  
Software, Writing-Original Draft Preparation, Writing-Review & Editing, J.L.; Supervision, G.S.; Validation, K.Z.  
This research was funded by the National Natural Science Foundation of China, grant number 51579025; the Natural Science Foundation of Liaoning Province, grant number 20170540090; and the Fundamental Research Funds for the Central Universities, grant number 3132018306.  
The authors declare no conflict of interest.
# Evaluation of Effective Cognition for the QGIS Processing Modeler
## 4 Evaluation of Processing Modeler
### Evaluation of Effective Cognition by Physics of Notations Method
#### 4.1.8 Principle of Cognitive Interaction  
The principle recommends increasing the options for navigating in the model. The connector lines affect navigation. In the Processing Modeler, round connector lines join symbols. The lines are rendered automatically. Symbols very often overlap lines when symbols are manually moved (Figure 6). Lines also sometimes cross each other, and they are not parallel. The user must manually attempt to find the best position for symbols in order to prevent overlapping and perplexing criss-crossing of curved lines. Previous research recommended that the number of edge crossings in drawings should be minimised [33]. For these reasons, curved connector lines do not appear to be the proper solution. It is often difficult to trace the connector's direction. A suggested change is to replace curved lines with straight lines (Figure 14). Straight lines ensure good continuity for reading. Good continuity means minimizing the angular deviation from the straight line of two followed edges connecting two nodes [34]. In this new suggestion for the Processing Modeler, straight lines could be optionally angled at an oblique or right angle when it is necessary to avoid a symbol. An acute angle is not suitable because of its smooth line tracking. If curved connectors remain in notation, there is necessary to add the user control over shaping these connectors to prevent crossing and overlapping.  
Figure 14: Replacing curved connector lines (**a**) with straight connector lines (**b**).  
Figure 13: (**a**) Groups of existing models that can be added to other models; (**b**) suggested blue colour fill for the sub-model.  
Operation symbols linked with lines and a black dot offset from the edge of the symbol unnecessarily occupy space in the model's area. It would be possible to terminate the lines directly on an edge or at the plus sign of the symbol to save space.  
Finally, the ability to display a model's thumbnail in the separate preview window helps to navigate the model. The preview window has not yet been implemented. In terms of cognitive interaction, version 3 was supplemented by a zooming function. The functions zoom in and zoom out in the model was absent in version 2.  
Aligning the symbols makes reading the model quicker and easier. No automatic function for aligning the model to the grid is implemented. Symbols usually snap to a grid in other graphical software. No snapping function is given in the Processing Modeler. Post alignment to the vertical or horizontal line of the model could, therefore, be beneficial for design. All arrangement of symbol positions depends on user diligence, which is entirely manual work in the Processing Modeler. Manually aligning is a time-consuming activity.
# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
## 3 Methods
### Implementation Details  
* Architecture details. We use the first four convolution blocks of the adjusted VGG16 [45] as the encoder. We remove the third max-pooling layer, and adjust the convolution operator in the fourth convolution block to our single linear bottleneck operator. The \"SP\"  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Input Feature Map Size: 14 \(\times\) 14 \(\times\) 512**} \\ \hline
**Module Settings** & **Baseline** & **Bottleneck [37]** & **Ivert LB [24]** & **Our** \\ \hline FLOPs & \(1.4\times 10^{9}\) & \(1.9\times 10^{8}\) & \(1.6\times 10^{8}\) & \(\mathbf{6.5\times 10^{7}}\) \\ Params & \(7.1\times 10^{6}\) & \(2.6\times 10^{6}\) & \(9.9\times 10^{5}\) & \(\mathbf{4.0\times 10^{5}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparing the computation and parameters of different bottlenecks. The best scores are marked in bold.  
Figure 3: Pooling loss detail enhancement processing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Input Feature Map Size: 112 \(\times\) 112 \(\times\) 3**} \\ \hline
**Method** & **MDNet-LBPC** & **FgSegNet\_v2**[9] & **FgSegNet\_S**[15] & **FgSegNet [10]** & **MU-Net2 [13]** \\ \hline FLOPs & \(2.4\times 10^{9}\) & \(7.2\times 10^{9}\) & \(7.2\times 10^{9}\) & \(7.2\times 10^{9}\) & \(\mathbf{1.9\times 10^{9}}\) \\ Params & \(\mathbf{2.1\times 10^{6}}\) & \(8.1\times 10^{6}\) & \(8.2\times 10^{6}\) & \(8.2\times 10^{6}\) & \(1.2\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational comparison with state-of-the-art methods. The best scores are marked in bold.  
in Figure 1 is consistent with [PERSON] et al. [46]. For the decoder part, before the upsampling operation, the input is concatenated with the output of the pooling compensation, which is then upsampling with bi-linear interpolation.
* Training details. The pixel-level cross-entropy is used to calculate the loss. We consider that the number of background pixels in some video frames in the CDNet2014 [47] benchmark is much larger than the number of foreground pixels, which may lead to sample imbalance. This paper penalizes the background pixels in the loss function, using weighted cross entropy loss function, as shown in the Equation (3). \[L=-\sum_{(r,c)}[T(r,c)log(S(r,c))+\lambda(1-T(r,c))log(S(r,c))]\] (3) Among them, \(T(r,c)\) represents the pixel value of the target mask with the position of row \(r\) and column \(c\). \(S(r,c)\) represents the pixel value at row \(r\) and column \(c\) of the prediction result. \(\lambda\) represents the weight coefficient.  
Furthermore, the weights of the first three convolutional blocks of VGG16 [45] pretrained on Imagenet [48] are used as the initial weights of the first three convolutional blocks of the encoder module and remain unchanged. The RMSProp optimizer is used as the training optimizer. The batch-size is set to 1, the number of maximum epochs is set to 100, and the initial learning rate is set to 1 \(\times 10^{-4}\) and constantly adjusted during training.
## 3 Results  
The literature search revealed the presence of a total of 504 published papers focused on the effect of the caryophyllane sesquiterpenes \(\beta\)-caryophyllene, \(\beta\)-caryophyllene oxide, \(\alpha\)-humulene, and isocaryophyllene on the immune system (Figure 1). Among them, 49 records were removed, being publications other than journal articles, while 224 were replicates in the searched databases.  
Out of the 231 records screened, none investigated the effects of isocaryophyllene on the immune system, while 135 papers were focused on essential oils or herbal extracts  
Figure 1: Study selection by PRISMA flow diagram for the immunomodulatory activity of caryophyllane sesquiterpenes.  
containing \(\beta\)-caryophyllene, \(\beta\)-caryophyllene oxide and/or \(\alpha\)-humulene and 28 were on other substances: these latter papers were excluded as the selection criteria were not accomplished. Among the 67 eligible papers, 19 reports were excluded as other bioactivities of caryophyllane sesquiterpenes were evaluated or their immunomodulating effects were hypothesized (\(n=2\)); moreover, 5 studies were excluded for lacking data, and 23 were excluded as they used caryophyllane sesquiterpenes of low purity (<90% purity) or unspecified purity. Based on the PRISMA analysis, a total of 20 studies was included in this review.  
The selected studies showed that the immunomodulatory properties of caryophyllane sesquiterpenes have been investigated in different immune cells (i.e., microglia, macrophages, lymphocytes, monocytes, and neutrophils) and in some animal models of disease; \(\beta\)-caryophyllene was the most studied compound, followed by \(\alpha\)-humulene and \(\beta\)-caryophyllene oxide (Figure 2). For instance, the modulation of microglia by \(\beta\)-caryophyllene in neurodegenerative diseases has been associated with its neuroprotective properties, while attenuation of lymphocyte autoreactivity has been approached as a possible strategy against autoimmune diseases. In line with this evidence, the ability of caryophyllane sesquiterpenes to modulate the immune cell function has been described, and possible future developments have been highlighted.
## 7 Conclusions and Future Work  
In this paper, we proposed two concepts for trust adaptation and privacy-preserving user identity using PBE. In trust adaptation, we moved the trust relationship and identity propagation out of the application implementation and modelled them using TOSCA. We proved that we gain three benefits: First, whenever a cloud service migrates from one cloud provider to another one, the complexity of the security infrastructure does not move with the application infrastructure. We enforced a single security infrastructure that is independent of multiple cloud providers. Second, when the business comes up with a new e-commerce scenario, we proved that IDaaS generates and adapts a platform-specific security infrastructure for the given scenario. Finally, we proved that developers do not need to have specialised security knowledge to understand various protocols, to relearn and implement repetitive tasks using proprietary APIs of any vendors.  
We also presented a novel architecture design for privacy-preserving user identity in FIDM. Unlike previous approaches, our novel access control model does not rely on the execution of plaintext code but on the cryptographic computation of time and purpose. We proved that access could be revoked when the ciphertext or the tokens were expired. We showed that existing IDM systems and applications could easily adapt our solution by extending the standard protocols SAML and OAuth. While protecting identity propagation over intermediaries in the backend, the performance is fast for the security levels of 112 and 128 bits. In future work, we may improve the performance of the security levels of 192 and 256 bits.  
Our thesis has advanced the field of IDM for cloud computing. However, a number of areas for future work can be built upon the results achieved, especially in edge computing and Internet of Things as follows. In edge computing, sensor devices may need to collect sensitive information from data producer. These devices need to cooperate with each other for some sensitive jobs without the involvement of the users and the data center. When user devices produce data, they automatically encrypt the data with PBE. As a result, only sensor devices from specific domains could process a portion of user data in the given time if they have the right purposes. Notice that we do not define an explicit sensor device that can process the user data in the disclosure policies. Therefore, our concept of PBE protects the confidentiality of user data from unknown devices and from the centralized data storage, of which users may not be aware. If the sensor device or the central data storage is a malicious host and tries to alter the disclosure policies or the tokens, it cannot reconstruct the decryption key (with more capabilities) to access user data.  
Director of studies: [PERSON]; conceptualization, methodology, implementation, validation, writing--original draft preparation: [PERSON]; supervision, writing--review and editing: [PERSON], [PERSON]-P.F.-H., S.F.  
This research received no external funding.  
The authors declare no conflict of interest.