esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Experiment Results
#### 4.2.2 Comparisons with Models Based on Single Time Scale Features  
In this part, we investigate the advantages of the proposed hybrid neural network in combining multiple time scale features. We compared the proposed network with models based on single time scale features in accuracy. The results are reported in Table 1. The models based on \(F_{1}\), \(F_{2}\), and \(F_{3}\) represent the three models in Figure 3. They are all based on single time scale features.  
Table 1 shows that combining the features of multiple time scales can promote accurate prediction. The models based on \(F_{1}\), \(F_{2}\), and \(F_{3}\) are based on the features of a single time scale, while the proposed hybrid neural network combines the features of multiple time scales to predict the trend. The predictive performance of the hybrid neural network is better than other networks. Therefore, combining the features of multiple time scales is helpful.  
In the next group of experiments, taking forecasting price trends one week later as an example, we visualize the trend prediction using test samples, as shown in Figures 6-8. From these graphs, we can intuitively understand the benefits of combining features of multiple time scales.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Forecast Horizon** & **Model** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{One week} & Model based on \(F_{1}\) & 64.40 \\  & Model based on \(F_{2}\) & 63.30 \\  & Model based on \(F_{3}\) & 61.76 \\  & The proposed model & **66.59** \\ \hline \multirow{4}{*}{One month} & Model based on \(F_{1}\) & 71.14 \\  & Model based on \(F_{2}\) & 70.23 \\ \cline{1-1}  & Model based on \(F_{3}\) & 73.64 \\ \cline{1-1}  & The proposed model & **74.55** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons with models based on single time scale features in accuracy.  
Figure 6: Visualization of the trend prediction by different models on test example 1.  
In Figure 6, we can find that due to the influence of short-term fluctuations, in some cases, the model based on \(F_{1}\) fails to predict the price trend in a week accurately. Similarly, in Figure 7, Model based on \(F_{2}\) extracts medium-term features to predict the trend, but ultimately fails due to the neglect of long-term and short-term changes in price series. In Figure 8, the model based on \(F_{3}\) only pays attention to the long-term characteristics in the price series and cannot accurately predict the trend. Therefore, we can conclude that predicting the price trend based on features of a single time scale is not feasible in some cases due to the complexity and variability of price series. It makes sense to combine the features of multiple time scales to predict the future price trend.
## 3 Problem Formulation
### Objective Function  
A probability density model, expressed in Equation (1), is used to evaluate the pathways. Unlike a conventional cost-function model, there is no clear cutoff where damage risk is negligible. As the distance grows, the likelihood of encountering a threat decreases, but it never reaches zero. The symbol \(\|d_{kj}\|\) represents the distance between the \(j\)th step and the \(k\)th threat center, \(\delta=R_{i}/log(20)\) is a parameter that determines the form of the density function, \(R_{i}\) is the radius of the \(i\)th threat element, and \(n_{thr}\) is the number of threats in the battlefield.  
\[Cost_{threats}=\sum\limits_{k=1}^{n_{thr}}exp\left(-\frac{\sum_{l=1}^{D}\|d_{ ij}\|}{\delta}\right) \tag{1}\]  
The fuel consumption of a UCAV with constant flight speed is solely tied to the flight path length, and its cost can be expressed as follows:  
\[Cost_{fuel}=\frac{\sum_{l=1}^{D}d_{ij}}{\|ST\|} \tag{2}\]  
where \(D\) denotes the number of steps, \(\|ST\|\) is the length of the segment \(ST\). Additionally, \(\sum\limits_{l=1}^{D}d_{ij}\) represents the length of the whole generated flight path, and \(d_{ij}\) is the length of the \(j\)th step of the \(i\)th solution.  
Therefore, the objective function of the model taking into account threat cost and fuel cost can be written as:  
\[f_{obj}=\gamma\sum\limits_{k=1}^{n_{thr}}exp\left(-\frac{\sum_{l=1}^{D}\|D_{ kj}\|}{\delta}\right)+(1-\gamma)\frac{\sum_{l=1}^{D}d_{ij}}{\|ST\|} \tag{3}\]  
where \(\gamma\) is the weight coefficient ranging in [0, 1]. In this paper, \(\gamma\) is set to 0.5, meaning that both the threats cost and the fuel cost are of the same importance [29].
# Simplified Calculation Method for Active Anti-Floating of Elliptical Basements by Relief Wells
## 4 IDPR Anti-Floating Design and Case Application  
### Control Parameters for Anti-Floating Design  
In the design of the IDPR anti-floating system, the head value of the bottom plate should be controlled by the following formula in order to ensure the safety of the structure [36]:  
\[H_{\text{max}}\cdot\kappa\leq H_{c}=\frac{G+F_{t}}{A\gamma_{w}} \tag{21}\]  
Among them, \(H_{\text{max}}\) represents the maximum head beneath the bottom plate. The safety coefficient \(\kappa\) is typically equal to or greater than 1. \(H_{c}\) denotes the anti-floating control level. \(G\) accounts for the self-weight of the structure.\(F_{t}\) represents the buoyancy resistance provided by passive anti-floating measures such as anchors. \(A\) corresponds to the area of the structural bottom. \(\gamma_{w}\) signifies the gravitational weight of the groundwater.  
After the simplified calculation of the pit, it is judged whether the maximum head of the bottom plate \(H_{\text{max}}\) meets the design requirements by using the above formula, if not, it must be redesigned. If it meets the requirements, the calculation results are used of the total water flow from the pit \(Q\) and the water from a single well \(Q_{i}\) to design the pump power, head and drainage pipe, and the maximum head of the site \(H_{\text{max}}\) is used to check the reinforcement of the bottom plate and the width of the cracks, etc.
## 4 Analysis of CFD Simulation Results
### Analysis of Simulation Results of Blockage Conditions
#### 4.2.4 Analysis of the Influence of Flow Velocity Under Blockage Conditions  
The effect of flow velocity on pipeline fluid flow was analyzed under three blockage shapes (25%, 50%, and 75% blockage levels).  
From Figure 16, it can be observed that all three blockage shapes lead to a significant increase in flow velocity. Excessively high flow velocities can cause pipe erosion and corrosion, threatening the safe operation of the pipeline. Below the blockage and in the short section downstream, the flow velocity quickly decreases to near zero, forming a stagnation zone. This condition exacerbates the deposition of solids, thereby worsening the blockage. Notably, in the case of blockage B and C, the stagnation zone area is much larger than that of blockage shape A, indicating that different blockage shapes cause varying levels of disturbance in the subsequent section of the pipeline. Therefore, timely removal of blockages is crucial for the maintenance and management of the grout injection pipeline. To avoid the formation of B- and C-blockages, it is advisable to select pipes with smooth inner walls and low roughness during pipeline construction.  
Figure 16: _Cont._  
Figure 15: The curve of blockage degree versus pressure variation.  
As shown in Figure 17, this study analyzes the cross-sections S1, S2, and S3 located at distances of 800 mm, 13,000 mm, and 18,000 mm from the pipeline's inlet and outlet, respectively, as the sections for the analysis.  
Figure 16: Velocity contours of the three blockage patterns.  
As seen in Figure 18, the slurry transport inside the pipeline exhibits distinct laminar flow behavior for all three blockage shapes. During slurry transport, the maximum slurry velocity consistently occurs at the center of the pipeline, forming a \"flow core\" where the velocity remains high. In contrast, the flow velocity at the top and bottom of the pipeline is slower. This phenomenon results from friction between the slurry and the pipeline's top and bottom surfaces, thus creating resistance and causing the slurry's velocity to remain highest in the center of the pipe, while the flow at the bottom and top is relatively slower.  
Figure 17: Schematic diagram of 6 S cross-section.  
Figure 18: Velocity contours of pipeline sections under different morphologies.  
From Figure 19, it can be observed that the flow velocity in the pipeline dramatically increases at the blockage location, reaching its maximum value within a short time. As the blockage degree increases, the velocity in the blocked region rises sharply, with the maximum flow velocity located at the center of the blockage. When the blockage degree exceeds 25%, the flow velocity in the blocked section is several times greater than that in the unblocked portion of the pipeline.  
Figure 19: _Cont._
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 2 Railway Marshaling Yard Propagation Scenario  
We chose the railway marshaling yard located at the National Railway Track Test Center of China Academy Railway Sciences to conduct our measurement campaign. The marshaling yard, which has 8 tracks, is rectangle-shaped with a length and width of 600 m and 40 m, respectively, as shown in Figure 1.  
Three typical propagation scenarios were designed in our measurement campaign, which, to the best knowledge of the authors, cover typical scenarios of the railway marshaling yard:  
The first is an open field scene with Catenary poles distributed with a distance of 31 to 47 m on our testing track, and we chose the middle track as the testing track, as shown in Figure 2.  
As shown in the red circle in Figure 2, the LOS path can be blocked by the horizontal metal beam or the metal net that is used to prevent birds from building nests at some testing points on the selected track; the influence will also be analyze in the following section.  
Figure 1: Railway marshaling yard. (**a**) Sketch map of the railway marshaling yard. (**b**) Picture of the railway marshaling yard.  
Figure 2: Open field scene with Catenary poles.  
The second is a representation of a locomotive on the adjacent track. To build this environment, a 26.6-m-long locomotive was placed on the adjacent track of our testing track, as shown in Figure 3.  
The third is a representation of buildings near our testing track. To collect the data of this scenario, we chose the track next to the building, as shown in Figure 4.  
The parameters of the corresponding scenarios are listed in Table 2.
# MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries
## 5 Performance Evaluation
### Comparison with Existing Works  
In this paper, we have proposed an efficient algorithm for computing the skyband and dominating queries simultaneously by exploiting parallel distributed (MapReduce) computing environment. Therefore, the main focus of the proposed algorithm is to distribute the computation process evenly among distributed computing nodes, so that large data can be processed effectively. On the other hand, both [27, 28] considered distributing the computation based on hash mapping of the domain space. Therefore, the data ratio within each grid cell might not be uniform after hash mapping. That means the distribution of computation process among the computing nodes is not balanced. In contrast, our proposed algorithm dynamically distributes the computation process among the computing nodes based on the worst rank of the objects. Therefore, algorithms proposed in [27, 28] are incomparable with our algorithm.
## 2 Critical Analytical Parameters
### Use of Internal vs. External Standard  
Internal standards (ISTDs) are compounds of similar chemical structure to the target compounds that are not present in the sample matrix. Preferred ISTDs are isotopically labelled analogs of target compounds that contain isotopes such as \({}^{2}\)H (D, deuterium), \({}^{13}\)C, \({}^{15}\)N, and/or \({}^{17}\)O. ISTDs can correct for a multitude of instrumental variances and their use increases the robustness of analytical methods. They are often used to correct for ion suppression in LC/MS applications, where co-eluting compounds may suppress the response of target analytes relative to their response in pure solvent and are very effective when each target has its corresponding ISTD analog. However, in the case of complex mixtures such as OSPW, this is not possible. Figure 7 illustrates that using a single ISTD may only correct for ion suppression within a small portion of a chromatographic run ([PERSON], Vogon Labs, unpublished data). The Total Ion Chromatogram (TIC) is the sum of all MS responses at a given time. The spectrum captured at 4.12 min, the apex of the TIC, is much more complex than the spectrum at 6.85 min, where myristic acid-d27 elutes. As greater ion suppression is assumed at the earlier retention time, the single ISTD does not make a proportional correction for all components. A single ISTD may be more useful as a measure of instrument performance [33] between injections within a batch and over a longer period of time [41].  
Figure 7: Total ion chromatogram (TIC; abundance = \(6\times 10^{7}\)) of OSPW and myristic acid-d27 ISTD (abundance = \(6\times 10^{4}\)) with corresponding spectra (both spectra at the same scale) at apex of peaks [42].  
When comparing external standardization (ESTD) to ISTD within the same set of experiments, [PERSON] et al. showed that ESTD required the use of liquid-liquid extraction (LLE) at high pH to remove the interfering matrix before quantitation [43]. However, this resulted in an underestimation of higher O\({}_{\mathrm{x}}\) compounds that partitioned into DCM. Addition of myristic acid-\({}^{13}\)C\({}_{1}\) as an ISTD avoided the necessity of LLE cleanup prior to analysis and showed that either technique was suitable for cleaner matrices such as groundwater. For ESTD, calibration curves are prepared by analyzing a series of different concentrations of the target compounds, either in pure solvent, an analyte-free matrix (matrix blank) that has been taken through all the method steps, or a simulated matrix when no suitable matrix blank is available. Sample background interferences must be considered closely so that cleanup methods remove as many interferences as possible.
## 2 Research Context
### Interface Management  
In modular design, standard interfaces enable module interchangeability and independence, thereby limiting variety proliferation and promoting module reuse [25]. Reusing interfaces shortens the design time for new modules and updates [27]. Interface management enables companies to sustain their ability to offer a wide variety of products over time through the sharing and reusability of modules [18; 27]. This variety is maintained or enhanced by the ability to adapt to changing customer demands and technological advancements [27]. A focus on the assembly and disassembly of modules through standard interfaces facilitates considerations at various product usage stages. These considerations lead to time savings in adding, upgrading, maintaining, or removing modules throughout the entire product lifecycle [18]. Such time savings facilitate lifecycle upgradability and product sustainability.  
The literature also discusses the level of interface coupling. Coupled interfaces necessitate alterations to multiple components when a single component changes. Favoring decoupled interfaces, which reduce interdependence between modules, diminishes the redesign efforts required for changes. Prioritizing decoupled interfaces simplifies modifications, maintenance, and product upgradability without impacting overall system design [5].  
Effective interface management is a crucial aspect of modular design, enhancing module reuse, enabling adaptation to market demands and technological changes, and directly impacting modular design efficiency. The choice between decoupled interfaces and standard interfaces depends on several considerations that are not clearly outlined in the literature. Decoupled interfaces appear to offer greater flexibility for product evolution and customization over time. On the other hand, standard interfaces seem to facilitate reuse and cost-effectiveness within the confines of the standard interface. An effective strategy could involve a combination of both approaches. This strategy would allow for the selection of the appropriate interface based on the level of innovation, required flexibility, and costs of each module within the product.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 3 Materials and Methods: A Novel Proposed EEG Analog Front-End Circuit System
### EEG Sub-Circuit Modeling the Second Low-Pass Filter  
This sub-circuit is a simple RC low-pass filter. The cut-off frequency is calculated using the equation:  
\[\mathrm{f_{c}}=\frac{1}{2\pi\mathrm{R_{f31}}\mathrm{C_{f31}}} \tag{5}\]  
Utilizing (5) in our sub-circuit design, the 3 dB cut-off frequency can be calculated as follows:  
\[\mathrm{f_{c}}=\frac{1}{2\pi*330\mathrm{~{}k}\Omega*22\mathrm{~{}pF}}\approx 4 8,228.77\mathrm{~{}Hz}=48.22877\mathrm{~{}KHz}\]  
\(\mathrm{f_{c}}\approx 48.23\mathrm{~{}KHz}\)  
This cut-off frequency is in accordance with our design requirements because the input raw EEG signals of interest have maximum frequency of 20 kHz. Indeed, the 20 kHz frequency resides well within the 0 dB gain part of the passband of our high-pass filter with an upper frequency of 48.2 kHz. The settling time to a steady state value from 0% to 99.99% is achieved after 30.39 ms with this design. Figure 14 depicts the EEG sub-circuit modeling the second low-pass filter.  
Figure 14: EEG sub-circuit modeling the second low-pass filter.
## 4 FWA Design Considerations, Challenges, and Solutions
### Envisaged 5G Deployment Spectrum
#### 4.1.4 Penetration Loss due to Vegetation (Tree Foliage Density)
This helps in alleviating the labor-intensive process of the conventional testing approaches in which, each locality has to be physically explored using expensive radio testing equipment [5,69]. In addition, comprehensive schematic site map data can be created by means of different diagrams such as three-dimensional (3D) building maps, overhead satellite maps, and 360\({}^{\circ}\) street-level photography. The establishment of the map on AI technology can facilitate accurate simulation of mm-wave reaction and performance in the environment. Besides, machine learning is another promising scheme that can help in detecting the trees, construction materials, as well as buildings' geometries in the environment. Moreover, for dynamic and kinetic analysis of the radio beam, ray tracing (RT)/beam tracing (BT) techniques can be used for prompt simulation of how it bounces from place to place in the environment before getting through to the potential devices. Consequently, the RT model can be employed as a propagation-prediction engine for performance assessment in several cases. This makes RT a real-time prediction tool that is capable of facilitating BF techniques [5,69,302,303,304,305]. In addition, when there is no adequate LoS between the 5G BS and the closest window or when an ideal beam path is blocked by obstacles (buildings, traffic signs, road signs, street signs, parking signs, or street furniture), leading to inadequate coverage to the user's in-home 5G router, an outdoor 5G router can be employed. For instance, an all-weather and rugged outdoor router can be used to ensure an ideal deployment. The router can be mounted on poles or building roofs to guarantee flexible deployment. This is also applicable to the low-e glass as depicted in Figure 16. As illustrated in Figure 16, an outer router can be employed for a low-e glass while a home router can be used for a normal glass as shown in Figure 16.  
In general, the presented solutions are not only cost-effective but also time-saving for addressing the mm-wave implementation's inherent challenges. Moreover, the schemes can help providers in simplifying the deployment process in reasonable manners for the FWA broadband services [5,69,261].
## 4 Discussion  
### Analyses of Bias  
For the test dataset, the H-LUE model was able to simulate GPP accurately with a low bias of 0.30 gC m\({}^{-2}\) d\({}^{-1}\) (Figure 5a). Both VPM and VPRM underestimated GPP on the test dataset. VPM exhibited a large underestimation of GPP for the test dataset (Bias = \(-1.03\) gC m\({}^{-2}\) d\({}^{-1}\)) (Figure 5b), as did VPRM (Bias = \(-1.18\) gC m\({}^{-2}\) d\({}^{-1}\)) (Figure 5c). However, in the regression analysis between the modeled LUE and the LUE obtained by EnKF, the LUE modeled by VPM had a positive bias on the test dataset (Bias = 0.11 gC MJ\({}^{-1}\)) (Figure 4b), and the LUE modeled by VPRM also had a positive bias on the test dataset (Bias = 0.27 gC MJ\({}^{-1}\)) (Figure 4c). Both VPM and VPRM tended to overestimate the LUE when it was small and to underestimate the LUE when it was large (Figure 4b,c). The MLP can simulate LUE accurately with a low bias of 0.15 gC MJ\({}^{-1}\) for the test dataset (Figure 4a).  
The photosynthesis capacity of plants In the middle of the growing season is much larger than that in the early growing season, while the conventional LUE models do not  
Figure 7: Comparison of performance metrics (**a**) R\({}^{2}\) and (**b**) RMSE among three models for modeling GPP in four extreme environmental conditions. High-P, Low-P, High-T\({}_{\rm a}\), and Low-T\({}_{\rm a}\) on the horizontal axis denote extremely wet, dry, high-temperature, and low-temperature conditions, respectively. The comparisons were carried out on the test dataset.  
account for the change in photosynthesis capacity and run with a constant \(\text{LUE}_{\text{max}}\) value at all times. This is probably the reason why VPM and VPRM overestimate the LUE when is small and underestimate it when it is large. Meanwhile, the \(\text{LUE}_{\text{max}}\) based on machine learning in the H-LUE is likely to be variable. In addition, VPM and VPRM use the land surface water index (LSWI) to account for the impacts of water stress on plant photosynthesis. However, a previous study [48] revealed that although the LSWI was sensitive to soil moisture variability, there was only a weak correlation between LSWI and GPP. This also reduces the accuracy of the GPP estimates of VPM and VPRM. The H-LUE model simulates LUE using multiple input variables (i.e., PFT, T\({}_{\text{a}}\), P, R\({}_{\text{g}}\), R\({}_{\text{L}}\), etc.), without using a constant \(\text{LUE}_{\text{max}}\) or only two or three environmental stresses defined by a single LUE model, effectively avoiding the issues mentioned above.  
VPM and VPRM exhibited a significant underestimation of the GPP, but not of LUE, which may be due to the relatively low FPAR used in these two models. [PERSON] et al. [10] found that among several LUE models, VPM had a relatively low FPAR. The FPAR used in VPRM is calculated using the same method as in the VPM, resulting in a relatively low FPAR as well.