esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Low-Speed Flight Stage above the Destination  
Figure 8 shows the variation of \(L\)/\(D\) with \(L\)/\(W_{0}\) of VCaCh and VCa airfoil with a trailing-edge deflection angle of 7\({}^{\circ}\), VCh airfoil, and BA airfoil at \(Ma\) = 0.3. There are differences in lift-drag ratio among the three morphing methods under different \(L\)/\(W_{0}\) ranges, as shown in Figure 8. The results indicate that VCh morphing method can sightly improve lift-drag ratio compared with BA airfoil during the designed low-speed flight stage (\(L\)/\(W_{0}\) = 0.835-0.775), while VCaCh and VCa morphing methods have significant improvement. Besides, the VCaCh morphing method is better than VCa under the range of \(L\)/\(W_{0}\) = 0.805-0.835. In Table 4, the drag decomposition and lift-drag ratio of morphing airfoil are compared with that of BA airfoil at \(L\)/\(W_{0}\) = 0.83. Under this state, the lift-drag ratio of VCaCh_7 airfoil is increased by 4.82%, which is 0.22% higher than that of VCa_7 airfoil. From the analysis of drag decomposition, the friction drag of the three morphing airfoils is higher than that of the BA airfoil and the order is VCaCh \(>\) VCa \(>\) VCh \(>\) BA. This demonstrates that the simultaneous variable-camber and variable-chord method will increase friction drag compared to the variable-camber or variable-chord method. Besides, the pressure drags of the three methods are all reduced and the order is BA \(>\) VCh \(>\) VCa \(>\) VCaCh. Observing the pressure coefficient distributions in Figure 9, we find there is no shock wave at low speed, which means there is no shock-wave drag among pressure drag, and therefore all the pressure drag could be considered as form drag. Therefore, it can be concluded that both VCaCh and VCa airfoils obviously improve the lift-drag ratio by reducing the form drag during low-speed stage.  
Figure 8: Baseline and morphing-airfoil comparison: variation of \(L\)/\(D\) with \(L\)/\(W_{0}\) at \(Ma\) = 0.3.  
Figure 9: Pressure coefficient distributions of morphing airfoil at \(Ma\) = 0.3, \(L\)/\(W_{0}\) = 0.83.
# Are There Seven Symbols for the Nucleotide-Based Genetic Code?
## 3 Results
### The Genomic Texts Analysis
#### 3.1.2 K-S Statistic Test of Genomic Texts  
The \(D\) parameter of the Kolmogorov-Smirnov statistic reflects the closeness of the data to the theoretical power-law distribution. We decided on a threshold of 0.05 as the limit below which distributions would be further considered as likely to be subject to power-law distributions. This threshold is presented in Figure 4.  
As expected, none of the analyses involving the triplet tokenization method (SOTTAN, SNTTAN, SOTTAC, SNTTAC) met the criterion of \(D<0.05\). The opposite is true in the case of frame tokenization. Here, almost all of the texts contextually analyzed met the requirement (60/60 of SOTFAN, SOTFAC and 58/60 of SNTFAC), and the majority of them analyzed at the nucleotide level (60/60 of SOTFAN, 33/60 SNTFAN). The most divergent situation was observed with contextual tokenization. Only 19 of 60 genomes translated into the seven-letter alphabet and contextually tokenized (SOTCAC) potentially followed the power law. This poor outcome was even worse for texts shuffled at the nucleotide level (4/60 of SNTCAC). However, this trend was completely reversed for genomes shuffled at the contextual level (60/60 of SCTCAC). It is clear that shuffling the seven-letter sequences at the contextual level improves their fit to the power-law distribution. For more details regarding the data that passed the K-S test, see the Supplementary Materials (Table S2).  
Figure 4: The Kolmogorov–Smirnov statistical test of genomes. The x-axis shows the type of analysis (shortcut explanation in Table 2) and the y-axis the normalized distribution of the \(D\) parameters for all (60) texts. The box shows the quartiles of the dataset, and the whiskers extend for the rest of the distribution.
## 2 Dark Current Noise Correction Method Based on Dark Pixels
### Dark Current Noise Correction Model
#### 2.4.2 The Mechanism of Temporal Noise Generated by Dark Current Fluctuations  
What follows is an analysis of the mechanism of temporal noise generated by temperature fluctuations in the focal plane. Engineering applications will regard the operating temperature of the cooled detector as an ideal constant condition. In practice, when the temperature difference inside and outside the dewar is large and varied, the cooler cannot maintain ideal operating conditions [35]. A cooler temperature with 0.2 K variation is considered a stable state; although even when the cooler operates normally, the temperature of the cold end still fluctuates slightly. Dark current is very sensitive to temperature. In [PERSON]'s research regarding the QWIP's operating temperature (of approximately 50 K), a 0.1 K temperature change resulted in a dark current change of more than 10% [15]. The colder end of cooler temperature changes results in dark current fluctuations that produce temporal noise when the detection system operates.  
Next, the temperature uniformity between the pixels of the focal plane is discussed. The size of this focal plane is microscale, about 1 cm\({}^{2}\). The cold head of the cooler can completely cover the area of the focal plane. The inner wall of the Dewar has a good isolation function. Therefore, the temperature uniformity of the focal plane is considered to be in an acceptable level. That is, the temperature variation of the dark pixels and the imaging pixels are almost the same. In addition, for the focal plane which has a large focal  
Figure 5: Source composition of the radiation received by two pixel types.  
plane size, a large number of pixels, and long line columns, a more precise cooling scheme should be used.  
The premise of correcting the dark current noise of the imaging pixels using the response of dark pixels is as follows. First, when the temperature inside the dewar is below liquid nitrogen temperature, the QWIP's characteristics determine that the photocurrent changes negligibly with temperature, excluding the temperature change photoelectric effect [15]. Second, in the method's conversion to response changes, it is difficult to achieve real-time temperature measurements inside the dewar to substitute into the dark current model to calculate the dark current in engineering applications. As the dark current model needs to fit numerous parameters, any inaccurate parameter will produce errors. Third, in continuous normal power-on imaging, the response characteristics of the pixels remain unchanged and the mapping relationship of the fitted dark pixels' response to the imaging pixels' correction value always holds.
## Discussion  
### Model Validation and Parameter Optimization  
The two free parameters, VPD0 and \(\varepsilon_{\max}\), in the EC-LUE model were selected using the exhaustive method. The exhaustive range is the minimum and maximum values of all PFTs in [53], extended by 10%. The optimal combination of the free parameters is when \(\varepsilon_{\max}\) (2.75 gC MJ\({}^{-1}\)) has a value between DBF (Deciduous broadleaf forest) (2.02 gC MJ\({}^{-1}\)) and EBF (Evergreen broadleaved forest) (3.92 gC MJ\({}^{-1}\)); and the VPD0 (0.34 kPa) is also between that of DBF (0.54 kPa) and EBF (0.29 kPa). Through the numerical analysis of two parameters and two PFTs, the characteristics of the rubber ecosystem are about 60% EBF and 40% DBF. Rubber originated from South America and belongs to EBF [56]. After being introduced to China, rubber showed obvious characteristics of deciduous and leaf-spreading phenology [57]; therefore, it is acceptable that the free parameter of rubber is between EBF and DBF. At the same time, compared with DBF, the deciduous period of rubber in Hainan Island is shorter than two months, and the characteristics of rubber are closer to EBF than DBF, so the values of the free parameters of the rubber ecosystem estimated by this study are very reasonable. On the basis of the optimized free parameters, the EC-CLUE performs well in prediction. Comparing with Observations, through F-test, the estimates by EC-LUE inputting flux tower data can reach the significance level of \(p<0.01\) with the 99% confidence interval for the regression coefficient of [0.85, 0.97]. It should be noted that the EC-LUE in this will slightly overestimate the GPP.
## 2 Materials and Methods
### Experimental
#### 2.2.6 Assessment of Reduction Capacity  
This test was carried out with minor modifications according to the method published in previous papers [6]. At room temperature, this test assessed the ability of the myrrh extract (50, 75, 100, 200, 300, 400, 500, and 600 \(\mu\)g/mL) to reduce ferric ions into ferrous ions. The reaction led to a shift in absorbance measured at a wavelength of 700 nm against phosphate buffer (pH 6.6) for both the control and test samples. All of the tests were repeated three times. The formula described below was used to compute the percentage reducing capacity.  
\[\text{Percentage reduction capacity}=[(\text{Lc}-\text{Ls})/\text{Lc}]\times 100\]  
where, the absorbance of the control is denoted by Lc (extract was absent) and the absorbance of extract-containing solution is indicated by Ls.
## 5 Discussion
### Comparison with Other Datasets  
The WorldPop dataset, LandScan dataset, and Gridded Population of the World (GPW) dataset are widely used global population datasets. The WorldPop dataset is developed  
Figure 18: The SHAP feature importance of random forest model.  
by the University of Florida, based on population census data, nighttime light data, and land use data, and employs the random forest model for population spatialization. The LandScan dataset is developed by Oak Ridge National Laboratory and primarily based on population census data, remote sensing imagery, and geographic information data. This dataset mainly employs the Dasymetric method to estimate population distribution. The GPW dataset is developed by the Center for International Earth Science Information Network (CIESIN) at Columbia University. It spatializes global population statistical data using areal weighting methods. We assess the dataset generated in this study, comparing it with WorldPop dataset, LandScan dataset and GPW dataset at an equivalent resolution. We compared the 2020 Sichuan Province population data generated in this article with the above-mentioned data sets under the same resolution conditions.  
The accuracy evaluation results are presented in Table 7. It can be observed that the RF-BOA model achieves the highest coefficient of determination (R\({}^{2}\)) fitted with the true values, reaching a value of 0.6628. It surpasses the R\({}^{2}\) of the other datasets. Additionally, the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are smaller for RF-BOA compared to LandScan, GPW, and WorldPop, with MAE and RMSE values of 12,459 people per township and 25,037 people per township, respectively.
## 4 Multiple Metrics Implementation
### Implementation Overview and Description
#### 4.1.2 Relevant Link Metric Types  
This refers to the link metric types chosen or assigned to a particular application. A link metric is assigned to application traffic, depending on which link metric provides a better PDR or delay. For example, a source smart meter node may be required to send two application traffic types, application Type 1 and application Type 2. If ETX and MD are the most important metrics that apply to application traffic Type 1, they are chosen as the metric types to support its requirements. On the other hand, for application traffic Type 2, ETX and ML may be the crucial metrics to support its requirements. Therefore, its RLMTs will be ETX and ML.  
Figure 1: Components of the multiple metrics framework.
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 3 Design of a Scheme for Urban Dissemination
### Store-Carry-Forward Mechanism  
A fact that we must take into account is that we will face frequent disconnections in the vehicular network. The many obstacles that are present in cities artificially break the network, and low traffic densities lead to the same outcome. With the current scheme, the message dissemination stops there.  
Depending on the application of the information, this can pose a problem or not. The source may opt to emit the message periodically, and so, vehicles entering the area of interest later on can receive it, as well. In other cases, though, it may be interesting to overcome eventual disconnections. We discuss about how to integrate a store-carry-forward mechanism in our solution for cities in this section.  
Being a totally different scenario, we cannot apply our store-carry-forward approach for roadways to an urban scenario. First of all, vehicles' relative locations are not only behind or in front of another anymore. The message travels omni-directionally, and so do the vehicles. Hence, many of the measures we considered for our scheme for roadways [11] cannot be applied here. However, there are two design principles from the roadway approach that we intend to include in this design, that we proceed to explain.
# Multi-Scale Temporal Convolutional Networks for Effluent COD Prediction in Industrial Wastewater
## 3 Experimental Design
### Optimization  
Considering the complex structures and training consumptions of deep learning models, the Bayesian optimization algorithm with the Gaussian process is used for hyperparameter tuning. It is a classic method for the global optimization of unknown functions, which adjusts the parameter selection for the next iteration within the specified range based on the results of previous iterations and updates the posterior distribution of the objective function until it closely aligns with the true distribution [34].  
In this study, the metric \(RMSE\) is taken as the optimization target, and the global optimal solutions for the following hyperparameters are searched within 100 iterations: kernel size \(k\)s, dilation rate \(r\), batch size, optimizer, and learning rate. To prevent overfitting and reduce the computational resources required, an early stopping mechanism is also performed in this stage. As a result, the MSTCN model with a kernel size of 7 and a dilation rate of 3 is proven to outperform other structures, which is trained at a learning rate of 0.01 and a batch size of 64 using the Adam optimizer.  
Since the kernel size of the convolutional layer in GCN is generally the same size as the adjacency matrix, the critical factors that can significantly influence the final performance of MSTCN are the kernel size of the TCN module and its dilation rate. Thus, the grid search method is applied to further determine their values, and the \(R^{2}\) of MSTCN on the test set for different parameter combinations is plotted in Figure 11. It is obvious that the model provides the most superior ability to fit future trends of the sequence when \(k\)s is 7 and \(r\) is 3, which is consistent with the results of Bayesian optimization. The detailed structure of the optimized MSTCN model is listed in Table 1.  
Figure 11: The results of grid search for MSTCN model.  
Figure 10: The correlations between variables at different time scales: (**a**) period = 20; (**b**) period = 25; and (**c**) period = 41.
## 3 Numerical Methods
### Finite Element Method (FEM)  
The finite element method (FEM) consists of the discretization of the domain of study on simple geometrical elements (or finite elements), where the unknown fields are discretized as linear combinations of shape functions of any order, linear and quadratic being the most common. The shape functions are typically defined at each element depending on local and normalized coordinates (Figure 4b). The local governing equations for each element are then assembled and organized in a matricial system of algebraic equations. Finally, the solution is approximated by minimizing the weighted error associated with each element. Several weighting rules have been proposed, the Galerkin method and its variations being the most widely used [89]. By converging to the solution through the minimization of an error function and not through the exact solution of balance equations, FEM is said to be formulated in a \"weak\" form. However, the weak form is equivalent to the exact solution in the limit of refining the domain discretization. In fact, it has been widely shown that mesh-independent FEM solutions do not show any practical difference from the output of more conservative numerical methods such as FVM [90; 91].  
FEM was developed in the early 1950s to perform structural analysis for the aerospace industry and was soon applied to study the biomechanics of musculoskeletal and cardiovascular tissue [81; 82]. As early as 1968, FEM was used to study the non-linear viscoelastic behavior of arteriole tissue [92]. This technique has been traditionally used for the solution of solid mechanics problems; however, it has also been used to solve the governing equations of other physical phenomena, including fluid mechanics [81]. Regarding the convenience of relying on a single solver engine, many multiphysics simulation software suites have introduced FEM formulations for fluid mechanics, [93] which also facilitates the implementation of FSI simulations [94].
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Overview of Performance Improvement Schemes for BOCDA
#### 4.3.4 Double Modulation Scheme to Elongate Measurement Range  
Another scheme for the elongation of the measurement range is called the \"double modulation scheme\". When the LD FM is done with the two FM frequencies, whose frequencies ratio is just an integer, the lower frequency creates a longer measurement range, and the higher frequency creates a higher spatial resolution. Then, the measurement range can be elongated while keeping the higher spatial resolution. The detail of the scheme is explained in the BOCDR section [55]. Even when the ratio between the two frequencies is not an integer, we can also elongate the total measurement range [56].
# Characterizing Micro-Displacements on Active Faults in the Gobi Desert with Time-Series InSAR
## 3 Methodology  
### Data  
To quantify recent surface displacements, 2.5 years of Sentinel-1 data were accessed via the Alaska Satellite Facility (ASF) online Vertex portal. This dataset includes 70 images from the ascending orbit acquired between 8 October 2017 and 27 February 2020, and 72 images from the descending orbit direction acquired between 11 March 2017 and 31 January 2020.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 6 Regulation of Signaling Pathways by RAC1B
### Mitogen-Activated Protein Kinases (MAPKs)  
RAC1B has been reported to control several MAPKs. In hMSCs, RAC1B negatively regulates MEK-ERK signaling during NT3-stimulated neurogenic differentiation and proliferation of these cells [44]. Interestingly, we also observed negative regulation of ERK1/2 activation in pancreatic cancer cells. RAC1B knockdown by siRNA resulted in strongly increased phospho-ERK1/2 levels and sensitized cells towards further upregulation of phospho-ERK1/2 levels by TGF-\(\beta\)1 independent of the kinase function of the TGF-\(\beta\) type I receptor ALK5 [35]. Moreover, RAC1B knockdown also increased the extent and duration of TGF-\(\beta\)1-induced phosphorylation of p38 MAPK in a SMAD4-independent manner [35]. Upregulated expression of RAC1B in lung cancer is significantly associated with sensitivity to the MEK inhibitor PD-0325901, suggesting that RAC1B expression itself is controlled by MEK/ERK signaling [60]. Moreover, RAC1B utilizes activation of the JNK2 pathway to enhance cell survival [43]. However, it has also been reported that unlike RAC1, activated RAC1B is unable to activate JNK in SW480 cells [7] and NIH3T3 cells [8]. Unfortunately, no information on the JNK isoform analyzed (JNK1 or JNK2) is supplied in these publications.
## 4 Results and Discussion
### Four-Sensor Configurations
#### 4.4.2 Confusion Matrix Analysis for Four-Sensor Non-Overlapping Configuration
TCN's use of dilated convolutions allows it to model both short-term and long-term dependencies, making it ideal for recognizing complex movement patterns in non-overlapping configurations [28]. The TCN model proves to be a strong competitor, performing comparably to LSTM, especially in dynamic classes, with minimal misclassifications. Transformer: The Transformer model, shown in Figure 12i, demonstrates strong classification performance, leveraging self-attention to effectively capture complex, long-range dependencies [29]. Although it ranks just below TCN and LSTM for non-overlapping data, the model's attention mechanism excels at identifying extended sequences. However, subtle distinctions in stationary activities present a challenge, as seen in the 40 instances of _stn_ws_ misclassified as _stn_wos_, likely due to limitations of self-attention in distinguishing closely related activities without temporal continuity [40]. In conclusion, the analysis of the four-sensor non-overlapping configuration underscores **TCN** as the best-performing model overall. The TCN model demonstrates superior accuracy in both dynamic and stationary activities, with minimal misclassifications, thanks to its ability to capture both short-term and long-term dependencies through its dilated convolutional architecture [28]. While the **LSTM** model remains highly competitive and excels in capturing long-term dependencies in stationary activities, TCN's efficiency and robustness across diverse activity types make it the top choice for this configuration. The **Transformer** model, though slightly behind TCN and LSTM, still shows substantial potential by leveraging self-attention to capture long-range dependencies, proving valuable for applications requiring extensive sequential data analysis. Overall, TCN's strength in handling non-overlapping, dynamic activities with high accuracy and LSTM's capability in managing long-duration dependencies highlight the effectiveness of both models for human activity recognition in scenarios where accurate temporal pattern recognition is essential. A detailed comparative analysis of these models across configurations will be presented in Section 4.5, offering a comprehensive view of their relative advantages and trade-offs. Figure 12: Confusion Matrices for models using a four-sensor configuration with Non-overlapping data segments: (**a**) DT, (**b**) KNN, (**c**) RF, (**d**) SVC, (**e**) XGBoost, (**f**) GRU, (**g**) LSTM, (**h**) TCN, (**i**) Transformer.
## 2 Materials and Methods
### Closed Impeller Design  
Since it is intended for space application and considering the compatibility with the working fluid, the material selected for manufacturing the closed impeller by classical machining was stainless steel (AISI316L, also commercially available as UNS S31603 or 1.4404). This grade was chosen due to its good weldability. The closed impeller was designed using classical turbomachinery formulae [22]. Ansys CFX was used by the authors to validate the design by means of CFD [23], and to create the 3D model, Solid Edge 2021 software was used. The outer diameter of the closed impeller was 38 mm, with an axial height of 12 mm (Figure 3). Due to the proprietary nature of the design and to safeguard intellectual property rights, specific dimensions and detailed specifications of the closed impeller cannot be disclosed in this publication.  
Figure 1: AM verification plan [18].  
Figure 2: Post-processing activities to be performed on the AM closed impeller.