esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Spatial Distribution of Dynamic Response Indicators by FEM Analysis  
#### 4.4.1 Vertical Distribution  
When vehicle load was applied to its maximum value of 0.7 MPa, vertical distributions of dynamic response indicators along structural depths of asphalt pavement at three different vehicle speeds were analyzed for the testing profile \(A\). Variation trends of dynamic responses along structural depths with vehicle speeds in dry and saturated conditions are shown in Figure 14. It could be clearly found that vertical distributions of stress and strains in compressive regions inside dry asphalt pavement decreased with the increase in vehicle speed. However, vertical distributions of transverse strain increased with vehicle speed in tensile regions in dry conditions. Similarly, stress and strains exhibited similar  
Figure 13: Variations of dynamic response magnitudes with vehicle speed obtained from field tests (controlling vehicle load at 0.7 MPa): (**a**) Pore water pressure; (**b**) Vertical stress; (**c**) Vertical strain; (**d**) Transverse strain.  
variation trends in both compressive and tensile regions inside dry and saturated asphalt pavement, while vertical distributions of pore water pressure increased with the growth of vehicle speed.  
In Figure 14a, vertical distributions of stress in saturated conditions do not show obvious declining trends compared with those in dry conditions. Moreover, stress in the upper layer of saturated asphalt pavement inversely increased first, which might be induced by the rapid decrease in pore water pressure that was quite sensitive to vehicle speed. In Figure 14b, vertical distributions of vertical strain in the middle and lower layers of saturated asphalt pavement became complicated, which repeatedly increased and decreased in compressive regions that were opposite to those in the corresponding positions in dry conditions. In Figure 14c, vertical distributions of transverse strain showed clear variation trends along structural depths in dry and saturated conditions. Tensile regions mainly concentrated in the middle and lower layers inside dry asphalt pavement, which moved to the lower layer inside the saturated structure. The distributions of transverse strain in tensile regions increased first and then decreased in dry conditions, but tensile regions did not show decreasing trends in saturated conditions. As shown in Figure 14d,  
Figure 14: Vertical distributions of dynamic responses along depth in asphalt pavement: (**a**) Vertical stress; (**b**) Vertical strain; (**c**) Transverse strain; (**d**) Pore water pressure.  
pore water pressure inside saturated asphalt pavement gradually decreased with pavement depths. The attenuation degree between the range from the surface layer to the bottom of the upper layer was relatively larger than those inside the middle and lower layers. Pore water pressure slightly increased within the lower layer of asphalt pavement, which might be induced by water accumulation at the bottom of the lower layer and caused dynamic hydraulically scouring with positive and negative alternations on the top of the base layer.
###### Abstract
A detailed design was conducted after the one-dimensional preliminary design to further refine the meridional configuration and to generate detailed blade profiles, the core of which is the throughflow design method, generally regarded as a simplified theory proposed by [PERSON] [9] in the 1950s. Afterward, many studies had been devoted to develop throughflow design methods, which were mainly implemented with the streamline curvature method [10; 11; 12; 13]. The throughflow design method plays a crucial role in the design process of compressors [14; 15; 16; 17; 18] due to the reasonable compromise between design accuracy and computational cost. The higher accuracy is attainable by the application of a high-fidelity but time-consuming CFD method [19]. In recent years, some multi-fidelity design systems have been developed for axial-flow compressors. In 2012, [PERSON] et al. [20] developed a compressor design software made up of 3 modules: a one-dimensional meanline module, a throughflow solver, and a throughflow grid generator. In 2017, [PERSON] [19] opened his multi-fidelity compressor design codes that consist of a one-dimensional meanline code (Mangen) and a three-dimensional CFD solver (Multall). The main drawback of the above aero-engine compressor design systems is the parametric analysis method employed during the preliminary design phase, which limits the automatic and extensive exploration of design space. As noted by [PERSON] and [PERSON] [21], the preliminary optimization would greatly exploit the aerodynamic performance potential of compressor. [PERSON] and [PERSON] [22] indicated that optimization of annulus geometry and stagewise distribution of pressure ratio at the one-dimensional design phase can obviously increase efficiency and margin potentials. The objective of the present work is three-fold. The primary objective is to develop a preliminary optimal design method for industrial compressors by virtue of the engine core compressor preliminary design strategy. The secondary objective is to combine the developed preliminary optimal design method with the throughflow-based detailed design method and CFD method to develop a multi-fidelity design system. The third objective is to apply the system to the optimization of axial-flow compressors with a varying number of stages but with the same total performance requirements in large-scale air separation applications. The remainder of this article is organized as follows. Section 2 demonstrates the aerodynamic design system framework. Section 3 illustrates the preliminary optimal design method. The CFD model and detailed design method are described in Section 4. Application of the developed system to 4-stage, 5-stage, 6-stage, and 7-stage industrial compressors is demonstrated in Section 5. Conclusions are summarized in Section 6.
## 4 Architecture of Software Receiver
### Software Architecture
#### 4.1.5 Adaptive MVDR Beamforming  
The adaptive MVDR beamforming function starts from computing the covariance matrix. The covariance matrix in Equation (3) is a Hermitian matrix, so only the elements of the upper triangular part need to be computed and the remaining elements can be obtained by the conjugation operation. The computational complexity of Equation (3) is significant. Indeed, in the four antennas and five time taps case, there are 210 elements to be computed. The computational load for each element is \(N_{s}\) complex 16-bit multiply-and-add operations. With the sampling rate of 4 MHz, 4,000 multiply-and-add operations per element need to be completed within 1 ms. Hence, two approaches are adopted to expedite the computation. First, multithreaded programming approach is used to assign operations to multiple threads for exploiting the resources of multi-core CPU. Second, SIMD assembly programming is leveraged to accelerate the complex multiply-and-add operations. The procedure to compute covariance matrix initializes with the loading of the complex IF samples from both \(a^{\text{th}}\) and \(b^{\text{th}}\) antenna element. Then, the multiply-and-add and accumulation operations, as the 3 rd and 4 th steps in Table 2, are performed. After computing the covariance matrix, the adaptation step size is decided by Equation (9). Lastly, the adaptive procedure in Equation (8) for each channel is performed to update its weight using calculated covariance matrix, steering vector, and adaptation step size.
# Scheduling Non-Preemptible Jobs to Minimize Peak Demand
## 3 Algorithms
### An Optimal Dynamic Programming Algorithm
#### 3.1.1 Configuration Lists  
Jobs are sorted first by increasing deadline and then by increasing arrival time. For each job \(j\), a list of preceding jobs \(\mathcal{L}_{j}\)--called the _configuration list_--is defined as the set of jobs whose execution windows either overlap \(j\)'s execution window, or overlap the execution window of a job following \(j\). Figure 1 illustrates a sample set of jobs along with their configuration lists. We observe that each job configuration list consists of a consecutive interval in the sorted list of jobs; i.e., \(\mathcal{L}_{j}=[s(j),j]\), where the head \(s(j)\) of each list has the earliest deadline (and arrival in case of ties).  
**Lemma 1**.: _The list heads are non-decreasing; i.e., \(s(1)\leq s(2)\leq\ldots\leq s(n)\)._  
Proof.: Suppose job \(j<k\). Since \(s(k)\in\mathcal{L}_{k}\), \(\exists\;l\geq k\;s.t.\;a_{l}<d_{s(k)}\). If \(j\in\mathcal{L}_{k}\), then \(s(k)\in\mathcal{L}_{j}\) since \(a_{l}<d_{s(k)}\leq d_{j}\) and \(l\geq k>j\). Thus \(s(j)\leq s(k)\). If \(j\
otin\mathcal{L}_{k}\), then \(d_{s(j)}\leq d_{j}<d_{s(k)}\) and so \(s(j)<s(k)\).
# Synthesis of Oligomeric Silicone Surfactant and its Interfacial Properties
## 3 Results and Discussion
### Foaming and Defoaming Property
#### 3.8.2 Defoaming Property  
As seen in Figure 16, OSSF first accelerates the foam coalescence and collapse on the measuring cylinder wall but cannot reduce the defoaming time of the final thin layer on the surface. The defoaming process is clearly divided into the induction, acceleration, and attenuation periods. Figure 17 shows that the defoaming rate of OSSF first decreases sharply and then decreases to a constant when the weight percentage increases from 5.00 to 25.00 wt %. On the other hand, a decrease in defoaming time is observed with increasing weight percentage of OSSF. When the mass concentration of OSSF is greater than the critical micelle mass concentration, the formed micelles can adsorb and spread on the foam film and finally dissolve in the foam film. As the surface tension of OSSF is much lower than SDBS, the surface tension of OSSF adsorption points is less than the surrounding region, which can destroy the \"self-healing\" effect of the foam, and then can significantly accelerate merger and breakdown [29]. In the process of developing low permeability gas-condensate reservoirs, the defoaming effect of OSSF not only reduces the amount of defoamer, but also maintains the stability of drilling fluid column pressure, drilling fluid rheological properties, and pump efficiency.  
Figure 16: Relative foam volume of SDBS versus time at 5.00 wt % OSSF (T = 25 \({}^{\circ}\)C, free pH).  
Figure 15: Foam half-lives of OSSF versus weight percentage in water and NaCl solution (T = 25 \({}^{\circ}\)C, free pH).
# Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds
## 3 Method
### Fine-Grained Detail Matting  
As shown in Figure 2, detail matting is usually preserved in high-resolution feature maps. However, existing encoder-decoder segmentation networks with downsample  
Figure 2: MORLIPO architecture. The framework consists of the backbone network, a background recovery module (BRM), and fine-grained matting (FGMatting).  
upsample structures cannot efficiently maintain high-resolution representations. This work proposes a detail matting module (FGMatting) to obtain fine-grained details. Precisely, FGMatting consists of three residual blocks and a convolutional layer. The initial input to FGMatting combines two intermediate features with corresponding upsampling scales, which contain low-level texture and high-level abstraction information. A semantic context is obtained from the semantic module using a bootstrap flow between the two blocks. The output of FGMatting is a detailed map focusing on the precise representation of the transition regions, which is then fused with the semantic map in the semantic module to generate the final alpha matte.
## 3 Design of a General Relay-Node Selection on Intersection
### Procedure of the Relay-Node Selection on General Intersection  
The objective of our relay-node selection is to deliver the message from Br\({}_{k}\) to other branches as far and fast as possible. The proposal consists of two phases. The first phase is to find a node closest to C, aiming to cover the largest range in all branches with the selected relay node, referred to as Intersection Phase. In the second phase, referred to as Branch Phase, the farthest nodes in each branch except Br\({}_{k}\) are selected as relay nodes.
## 6 Experimental Results
### Calibration  
For the calibration, we used six transmitters and seven receivers although only four transmitters would have been required. The additional ones are used for verification and cross checking. In addition, one of the seven receivers was mounted on a linear belt where two positions have been used during the calibration process. These positions will be used as reference positions later on, as they have a known uncertainty. In total, a measurement at each position delivers 48 ToF values. As the measurements including the belt movement require some time, the assumption of a constant temperature is not sound anymore and a compensation approach has to be used. Applying the compensation algorithm of Equation (10) where the sum of ToF is kept constant a compensation curve shown in Figure 14a is obtained. An example for two compensated ToF measurements is shown in Figure 14b.  
The post processed averaged data is used as input for the calibration algorithm. Another input is an initial guess of transmitters and receivers positions, the speed of sound and the acoustical delay due to the cones, which are required for broad coverage. The final output of the calibration algorithm is then scaled to the length of the reference path. The three dimensional calibration results are shown in Figure 15 and Table 1. The transmitters are marked with their x/y/z coordinates, the green symbols are the receivers used within the calibration process and the red symbols are located but unused receivers within the localization process. The reference path between the receivers is shown as a line between the two receivers. Comparing the result manually with the real transmitter and receiver positions shows feasible results. However judging the calibration result is not possible in this way with sufficient  
Figure 13: Receiver unit mounted on moving belt.
## 5 Disturbance Rejection and Tracking Reference  
The control strategy for disturbance rejection and tracking reference is analyzed in this section. Note that once an adequate estimation is achieved, there can be proposed any control strategy for the delay-free system (1) (Theorem 3) or with a part of the time-delay (Lemma 4 and Corollary 2). To make the controlled system follow references and reject disturbances, both step types, the following PID control is used,  
\[C(s)=k_{p}+\frac{k_{i}}{s}+k_{d}s, \tag{22}\]  
where \(k_{p}\), \(k_{i}\), and \(k_{d}\) are the tuning parameters of the controller.  
The control parameters in (22) are obtained considering the system \(G(s)\) (Theorem 3), using a root locus diagram for \(G(s)C(s)\). It is recommended to locate the controller's zeros  
Figure 3: Proposed observer–predictor scheme for longer delays.  
in the left half-plane to ensure the existence of a region of stability. The control parameters can be obtained by using the root locus analysis and considering a damping factor of \(\zeta\geq 1\) as a specification performance of the delay-free process transfer function.
## 4 Results and Discussion
### Results
#### 4.3.4 Dynamic Topology  
The dynamic topology offered by the model is through the variable associated with the ENS (\(\delta_{i,t}^{p}\)), which takes values greater than zero when the DERs cannot provide sufficient energy to meet the demand. These values are recorded for every period at each bus. Table 4 shows simplified results of these values, where the values greater than the maximum DSR flexibility have been replaced by one, and when these values are within the range allowed by the DSR or the load is satisfied, the values are zero for the sake of clarity. Specifically, Table 4 represents the results for the time comprised between period 40 h to 48 h corresponding to the second scenario. This scenario has been selected because it presents many buses with load curtailment, which is the case at hour 46. Taking as a reference the results in Table 4, hour 46 has been depicted in Figure 6 to observe how the VMs, represented by binary numbers in the previous table, take form in the ADN. The red points indicate the nodes where the load has been decreased to zero, and the dashed red lines to visualize the isolated system which is bounded by the red points. For those nodes where the load has been suppressed but there is power generation, the energy injected in these red points is used to meet the demand in the closest points of the ADN for which the load is not zero. On the other hand, when the DSR is included in the formulation, the load curtailment is reduced, as shown on the right side of Table 4, leaving only buses 21 and 22 without energy supplied and formed the VM represented in Figure 7.  
\begin{table}
\begin{tabular}{r r r r r r r r r r r r r r r r r r} \hline \hline  & \multicolumn{10}{c}{Without DSR} & \multicolumn{10}{c}{With DSR} & \multicolumn{10}{c}{**DERs**} & \multicolumn{10}{c}{**DERs Installed**} \\ \hline  & \multicolumn{4}{c}{ENS [MWh]} & \multicolumn{4}{c}{ENS \%} & \multicolumn{4}{c}{ENS [MWh]} & \multicolumn{4}{c}{ENS \%} & \multicolumn{4}{c}{**DERs Installed**} \\ \hline
**Bus** & **Sc 1** & **Sc 2** & **Sc 3** & **Sc 4** & **Sc 1** & **Sc 2** & **Sc 3** & **Sc 4** & **Sc 1** & **Sc 2** & **Sc 3** & **Sc 4** & **Sc 1** & **Sc 2** & **Sc 3** & **Sc 4** & \\ \hline
**1** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** & **0** \\  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & & & & \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: DN topology under the second scenario.  
Figure 6: PG&E 33-bus radial DN partitioning for the second scenario at 46 h. Red balls indicate the loads reduced to zero, without considering DSR in the model.
## 3 Proposed Architecture
### Yang Models for Network Controllers  
As described until now, the three control elements: SDTN controller, Network Slicing Controller and Network Domain controllers, has standard SBI and NBI interfaces to communicate between them and against the network or the OSS/BSS systems. The standard interfaces are composed of selecting a protocol to transfer the data and YANG data models to define how the message is formed. For that sense, the YANG modelling activities have acquired significant relevance across the standardization entities. This is to such an extent that by 2019, the number of correctly extracted YANG models from IETF drafts was 283, in the Broadband forum was 214, and in Openconfig was 137 [27]; similarly, other organizations like the MEF, 3 GPP or ONF produced YANG data models to describe technologies, protocols or connectivity services as well. Thus, navigating through the massive set of YANGs available and selecting the suitable pack of data models to define each functional block's interfaces becomes an essential task from the architectural definitions point of view.  
To request and after instantiate the network slices a set of these data models are described as the experimental base of this work are described in the Table 2.
# Development of Multi-Channel Seismic-Electrical Combined Rolling Coverage Measurement System
## 3 Instrument Testing and Result Analysis
### Measurement Result and Analysis  
The survey results of the \(L1\) survey line are shown in Figure 22. The three subplots from top to bottom display the measured apparent resistivity pseudo-section, the calculated  
Figure 21: Geographical location of survey line \(L1\).  
Figure 20: Field test photo.  
apparent resistivity pseudo-section, and the inverted resistivity model. A least-squares inversion method was applied to derive the subsurface resistivity model from the measured data. The inverted resistivity model reveals a region of lower resistivity at the deeper part of the \(L1\) line, around the 400 m coordinate.  
Figure 23 shows the core sampling analysis at the 400 m point of the \(L1\) survey line. The 'w' column represents soil moisture content, while the '\(\rho\)' column indicates soil density. The surface layer, up to 50 cm in depth, consists of artificial fill soil with gravel particles typically ranging from 4 to 6 cm in diameter and approximately 20% clay content. The layer extending from the surface to a depth of 4.3 m consists of moderately dense variegated gravel mixed with clay. The layer between 4.3 and 5.5 m in depth consists of yellow-brown, moist subclay containing mica and coarse particles. The section from 5.5 to 12.3 m in depth is composed of moist variegated gravel mixed with clay, with gravel particle diameters typically ranging from 2 to 3 cm, approximately 30% clay, and locally distributed fine sand and subclay layers. The layer between 12.3 and 17.4 m in depth consists of variegated cobbles, with diameters typically ranging from 6 to 8 cm, and containing small amounts of medium sand, clay, and gravel. The section from 17.4 to 32.3 m in depth is composed of yellow-brown, moist subclay containing mica, iron oxide, and a small amount of gingerstone, with a pebble interlayer present between 20.6 and 20.9 m. The section from 32.3 to 37 m in depth consists of red-brown, moist clay soil mixed with gravel, containing approximately 30% weathered gravel, with gravel particle diameters typically ranging from 2 to 3 cm. The section from 37 to 49.5 m in depth is composed of gray, strongly weathered limestone, with drill core samples predominantly broken and blocky, and occasionally cylindrical. The section from 49.5 to 53 m in depth consists of gray, moderately weathered limestone, with drill core samples in columnar form, each approximately 20 cm in length. The core sampling results align with the low resistivity observed in the electrical inversion of the underground structure. Specifically, the section from 32.3 to 37 m, with a density of 1.99 g/cm\({}^{3}\) and 24.9% moisture content, matches the low resistivity feature in the inverted model (red arrow in Figure 22). This demonstrates the system's effectiveness and efficiency in surveying underground structures.  
Figure 22: High-density electrical measurement of apparent resistivity of pseudo-section, calculated apparent resistivity of pseudo-section, and inverted resistivity model section.