esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization  
Viscoelasticity was accomplished by small-amplitude oscillatory shear (SAOS) at 265 \({}^{\circ}\)C, using a rotational rheometer model Physica MCR 501 ([PERSON], Graz, Austria) with 25 mm diameter plates and a 1 mm gap. Dynamic frequency sweep tests were achieved between 0.01 and 100 Hz and 0.1% strain, i.e., linear viscoelastic regime. Each measurement was repeated at least three times to minimize the experimental error.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Sample** & **PPE (wt\%)** & **HIPS (wt\%)** & **SBS (wt\%)** \\ \hline PPE/HIPS-22-IM & 77.8 & 22.2 & 0 \\ PPE/HIPS/SBSX-6-IM & 73.1 & 20.9 & 6 \\ PPE/HIPS/SBSX-12-IM & 68.5 & 19.5 & 12 \\ PPE/HIPS/SBSX-18-IM & 63.8 & 18.2 & 18 \\ PPE/HIPS/SBSX-24-IM & 60 & 16 & 24 \\ PPE/HIPS-40-IM & 60 & 40 & 0 \\ PPE/SBS416-40-IM & 60 & 0 & 40 \\ PPE/HIPS-22-TSE & 77.8 & 22.2 & 0 \\ PPE/HIPS/SBSX-6-TSE & 73.1 & 20.9 & 6 \\ PPE/HIPS/SBSX-12-TSE & 68.5 & 19.5 & 12 \\ PPE/HIPS/SBSX-18-TSE & 63.8 & 18.2 & 18 \\ PPE/HIPS/SBSX-24-TSE & 60 & 16 & 24 \\ PPE/HIPS/SBS411R-TSE (1:1:1) & 33.33 & 33.33 & 33.33 \\ PPE/HIPS/SBS416R-IM (1:1:1) & 33.33 & 33.33 & 33.33 \\ PPE/HIPS/SBS416R-IM (1:1) & 33.33 & 33.33 & 33.33 \\ \hline \hline \end{tabular} \(\text{X}\) = 411R, 416R, or 4301L; IM = Internal mixing:TSE = twin-screw extruder.  
\end{table}
Table 1: Formulations of the poly(2,6-dimethyl-1,4-phenylene ether) (PPE)/high-impact polystyrene (HIPS)/styrene-butadiene-styrene (SBS) blends.  
The thermomechanical stability and T\({}_{\text{g}}\) of the blends were performed employing a dynamical mechanical analyzer (RSA III, TA Instruments, New Castle, DE, USA) in the temperature sweep mode at a frequency of 1 Hz, 0.1% strain, a heating rate of 5 \({}^{\circ}\)C/min, and three-point bending geometry. The elastic modulus (E\({}^{\prime}\)) was taken at 25 \({}^{\circ}\)C, and the peak of the viscous modulus (or loss modulus) (E\({}^{\prime\prime}\)) curve was referred to as the T\({}_{\text{g}}\) of the blends.  
Notched Izod impact strength was measured according to ASTM D256 [17] using a pendulum machine (XJF MultiImpact, Physical Test Solutions, Culver, CA, USA). In addition, the pendulum capacity and velocity were 5.5 J and 3.5 m/s, respectively. Five samples were tested at 25 \({}^{\circ}\)C for each blend, and the average values were reported.  
For data analysis, the software Statgraphics Centurion XV version 15.1.02 was employed, where a Pearson correlation matrix was performed to identify the correlation coefficients or association forces of storage modulus and Izod impact resistance.
# Experimental Study of 3D Concrete Printing Configurations Based on the Buildability Evaluation
## 4 Results and Discussion  
### Basic Properties  
The initial setting times for mixes OSS1, OSS2, and OSS3 were 2, 8, and 13 min, respectively. As expected, the initial setting times decreased with the increasing content of SAC. The average flow diameters of the corresponding samples were 138 mm, 143 mm, and 160 mm, in an acceptable range for mixtures having a smooth surface and high buildability, as concluded by [PERSON] et al. [27]. It can be seen in Figure 9a that the printed layers or circumferential lines are of high consistency, without obvious fractures, cracks, or settlement. Moreover, a regular width of the printed filament can be seen in Figure 9b, indicating high printing quality [7,16]. Therefore, it is believed that the material proportion and the  
Figure 8: The leaning angle tests. (**a**) The definition of the leaning angle and (**b**) sketch designs for multi-angle tests.  
Figure 7: A collapse occurred during a U-shaped component printing process, and the extruded filaments fell onto the platform, as the arrows show.  
printer achieved acceptable printability, with accurate mechanical positioning, controllable extruding operation, and stable material properties, based on which the buildability could then be further studied.  
The printed cylinder D200V40 OSS3 consisted of 30 layers and took about 8 min to print. The height of each printed layer and the accumulative height measured by ImageJ are shown in Figure 10. The average height was 4.96 mm, with a standard deviation of 0.42. It should be pointed out that the first layer and the last layer were excluded from the measurements because of the spiral-up printing method used. The layer height results show that the layer stacking operation was successful and stable, as the printer could extrude the material evenly at a stable printing speed. Therefore, the printer and the material proportion were considered suitable for further evaluation.
## 6 Simulation Analyses
### The Relationship Between Orbit Determination Accuracy and Measurement Accuracy
#### 6.4.1 The Effect of Angle Measurement Accuracy on Orbit Determination Accuracy  
When the range measurement accuracy is 30 m, the angle measurement accuracy is set to 0.01\({}^{\circ}\), 0.05\({}^{\circ}\), and 0.1\({}^{\circ}\). The relationship between the thrust parameter pre-identification accuracy and the time span is shown in the Figure 23.  
As the angular measurement accuracy decreases, the average relative error of the thrust parameter pre-identification also decreases. When the angular measurement accuracy is 0.01\({}^{\circ}\), a 5% solution error can be achieved within 4 h; when the angular measurement accuracy is 0.05\({}^{\circ}\), a 5% solution error can be achieved within 8 h; when the angular measurement accuracy is 0.1\({}^{\circ}\), a 5% solution error can be achieved within 12 h.
## 1 Introduction
Literature [16] investigates the power control and sensing time optimization problem in a cognitive small cell network, where the mitigation of cross-tier interference, imperfect hybrid spectrum sensing, and energy efficiency are considered. As one of the most popular and efficient energy saving schemes [17; 18], BS sleeping has been proposed and widely studied to realize substantial energy saving in cellular networks [19; 20; 21; 22]. However, integrating MEC with BSs significantly complicates the energy saving issue due to the fact that BSs now provide not only radio access services but also caching services. Furthermore, since caching resources on MECS are limited, downloading some content from the CN is inevitable. As a result, energy consumption couples the caching capacity and MECSs' sleeping decisions over time. It has been observed that the content popularity and caching capacity are two main factors affecting the MECSs' sleeping decisions. Literature [23] discussed the caching deployment problem with a given wireless transmission rate, and it also made an assumption that three factors of the backhaul transmission rate, MECSs' storage capacity and system energy consumption are fixed. However, in practical mobile networks, base stations should consider different wireless channel states and conditions as well as different types of backhaul links and system power. Thus, it is necessary and crucial for caching deployment and active MECS to consider the above three factors [7; 11]. As a consequence, how to design an optimal solution to minimize energy cost while guaranteeing high user's quality of experience (QoE) is a challenging issue. In this paper, we study the joint optimization of average download latency and average energy consumption in cellular networks with MEC integration in order to maximize the QoE for users while keeping the energy consumption of the system as low as possible. The main technical contributions of this work can be summarized as follows:  
* We make a trade-off between system average download latency (SADL) and system average energy consumption (SAEC) by developing an effective caching placement strategy. Our algorithm achieves a close-to-minimum delay cost to SADL compared to the delay optimal algorithm, while minimize SAEC. * We indicate the influence of the content popularity distribution satisfying the required number of active MECS, as well as the influence of the backhaul capacities on the required cost of system. Numerical results show that the proposed joint cost optimal (JCO) algorithm outperforms the conventional caching placement strategies achieve a significant performance improvement and effectively reduces system energy consumption. The remainder of this paper is organized as follows. In Section 2, we first describe the system model and problem optimization. Then, we derive and propose the MECS allocation algorithm for joint cost delay and power. Simulation results are shown and discussed in Section 3. Finally, conclusion is drawn in Section 4.
# An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network
## 3 Model Framework
### Causal Gated Linear Unit (C-GLU)  
Although RNN-based models are widely used in time series analysis, recurrent networks used for flow prediction have disadvantages including long iteration time, complex gate mechanisms, and slow response to dynamic changes. In contrast, CNNs show such advantages as fast training, simple structure, and independence from the previous step [37]. We performed convolution on the time axis to capture the feature relationships of traffic flow in the time dimension. Causal convolution can simply and effectively capture long-term dependencies, so we used causal convolution to capture the feature connections of the time dimension. Meanwhile, in order to alleviate the problem of gradient disappearance, we used a residual connection gating mechanism of to enable the causal convolution to learn the features relationship for longer.  
As shown in Figure 3, the first step of the causal gated convolution block is to perform one-dimensional causal convolutions of data. Each convolution without filling explores the \(k_{t}\) order neighborhood of each node on the traffic network graph \(G\), and each convolution shortens the time series of \(k_{t}-1\). So, the \(i\) node data can be considered as the data \(Y\in\mathbb{R}^{N\times\tau\times C_{i}}\) with a slice time series length of \(\tau\) and the number of channels \(C_{i}\), and the convolution kernel can be defined as \(\Gamma\in\mathbb{R}^{1\times C_{i}\times 2C_{0}}\). In the first convolution, one-dimensional causal convolution is performed; in the second convolution, nonlinear change is performed after the one-dimensional causal convolution is completed. The data of the two parts are the two gates of the causal gated linear unit, and the two data outputs are recorded as \([P,Q]\in\mathbb{R}^{N\times(M-k_{t}+1)\times 2C_{0}}\). Finally, the data of the two parts are recorded with the Hadamard product. Therefore, the convolution of the causal gated linear unit can be defined as:  
\[\Gamma*_{f}Y=P\odot sigmoid(Q)\in\mathbb{R}^{N\times(\tau-k_{t}+1)\times C_{0}} \tag{10}\]
# The Influence of Hard Protection Structures on Shoreline Evolution in Riohacha, Colombia
## 2 Study Area and Geographical Data
This section includes a small cliff (\(\sim\)6.5 m high) with narrow beaches at its base. Figure 1: Location of the study area and division into three work zones. Satellite image taken from Google Earth Pro, corresponding to the year 2024. * Zone 3 (1600 m) runs from a coastal rocky outcrop to Las Delicias. Here, the cliffs are slightly higher (-9.1 m) with no beach at the base, except in the southern area.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Range-Based Localization Techniques
#### 2.2.2. Hyperbolic  
The hyperbolic localization technique utilizes the TDoA measurements. Each TDoA provides a hyperbolic curve in the localization space, as shown in Figure 5, on which the position of the unknown receiving node lies, and their intersection provides a unique position.  
Figure 4: Trilateration localization technique. (**a**) The receiving node can be located anywhere on the circle circumference; (**b**) the receiving node can only be located on any of the two intersection points; (**c**) the receiving node can only be located on the intersection of the three circles.  
Specifically, the known transmitting nodes represent the foci of the hyperbola, while the different hyperbolic curves formed represent the different transmission times. The unknown receiving node is assumed to lie on the hyperbola, whose foci includes the known anchor nodes 1 and 2, with the focal length corresponding to their distance difference \(\Delta d_{1,2}\). By adding another transmitting node, an additional hyperbola is formed, whose foci includes the known anchor nodes 1 and 3, and the focal length is their distance difference \(\Delta d_{1,3}\). The uncertainty is reduced, and the unknown receiving node position is estimated at the intersection of all the hyperbolae. For the 3D space, the difference in distance \(\Delta d\) is estimated by using the Euclidean distance, as follows:  
\[\Delta d_{2,1}=\sqrt{(x-x_{2})^{2}+(y-y_{2})^{2}+(z-z_{2})^{2}}-\sqrt{(x-x_{1} )^{2}+(y-y_{1})^{2}+(z-z_{1})^{2}} \tag{13}\]  
\[\Delta d_{3,1}=\sqrt{(x-x_{3})^{2}+(y-y_{3})^{2}+(z-z_{3})^{2}}-\sqrt{(x-x_{1} )^{2}+(y-y_{1})^{2}+(z-z_{1})^{2}} \tag{14}\]  
which can be re-written in the following general form:  
\[\Delta d_{i,1}=\sqrt{(x-x_{i})^{2}+(y-y_{i})^{2}+(z-z_{i})^{2}}-\sqrt{(x-x_{1} )^{2}+(y-y_{1})^{2}+(z-z_{1})^{2}} \tag{15}\]  
where \((x_{i},y_{i},z_{i})\) are the known \(i\)-th transmitting node's coordinates.  
Solving these nonlinear hyperbolic equations can be challenging, and numerous methods involving a Taylor series expansion and transformation into a linear set of equations are commonly used. Alternatively, the [PERSON]'s, [PERSON]'s, [PERSON]'s, and [PERSON]'s methods can be used, which were evaluated in [43]. It was concluded that the [PERSON]'s method is the best available option for solving the hyperbolic equations, at the expense of a priori knowledge of the approximate locations and distance between the various transmitting nodes and the first transmitting node.
## 3 Database of Water Content and Hyperspectral Information
### Comparison of Measured Water Content and Hyperspectral Information  
The final database was composed of an Excel sheet, as shown in Figure 12. Column A is the surface water content, column B is the reflectance extracted at a wavelength of 400 nm, and columns C to EU are the reflectance extracted in 4 nm increments at a wavelength of 404-1000 nm.  
Figure 11: Verification of atmospheric correction: (**a**) Orthophoto of flight path from departure to turning point; (**b**) Orthophoto of flight path from turning to arrival point; (**c**) Comparison of hyperspectral information on flight path from departure to turning point before correction; (**d**) Comparison of hyperspectral information with flight paths from turning to arrival point after correction.  
Simple linear regression was performed to determine whether the predicted equation of water content could be obtained by reflectance with a single wavelength. The reflectance for every single wavelength was set as the independent variable 'x', and the actual water content was set as the dependent variable 'y'. Simple linear regression is a model with a single independent variable and can define the dependency of the variable [37]. The basic model is the same as Equation (4): \(a\) and \(b\) are coefficients, and the reliability of the final predicted equation of water content was judged to be the coefficient of determination (R\({}^{2}\)).  
\[y=ax+b \tag{4}\]  
Figure 13 shows R\({}^{2}\) with each wavelength from the results of simple linear regression. The R\({}^{2}\) has low values, below 0.5 in most cases, and when using wavelengths of 600-900 nm, the R\({}^{2}\) tended to converge to 0. Accordingly, it was confirmed that it was difficult to calculate the predicted equation of water content through simple linear regression using reflectance for each single wavelength.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 7 Challenges/Future Directions of CR-LPWAN
### PHY Layer Challenges
#### 7.1.2 Spectrum Sensing  
Spectrum sensing (SS) is a fundamental function of any CR system including CR-LPWAN systems. SS determines the presence/absence of PU signals in any band of interest. This is achieved by comparing the estimated energy value in a channel with a threshold value. If the energy value surpasses the threshold, then a PU is declared present, and if the energy value is below the threshold, the band is declared free (white space). There are several methods proposed for SS in CR including: energy detection (ED) method [20; 29; 114; 115; 116; 117; 118], matched filter, cyclostationary detection, eigenvalue method, covariance method, and prediction-based approaches [72]. Other recent applications of evolutionary algorithms to improve SS performance in CR have been identified [119; 120; 121; 122].  
There are several challenges in developing effective and efficient SS techniques for CR-LPWANs. For example, the ED is a fast, simple and cost effective method for SS, however, it performs poorly in low SNR regimes [117; 123; 124]. This poor performance occurs because the ED depends on predefined threshold values for detection. Many CR-LPWAN systems may be deployed in critical application domains such as military, medical, and business domains. These application areas require greater accuracy levels, which the ED may be unable to render. For example, in low SNR conditions, the threshold value of the ED drops too close to the noise floor resulting in increased noise samples crossing the threshold value, leading to increased false alarm rates. There are several options to address this challenge, for example, using effective adaptive threshold techniques such as the forward consecutive mean excision (FCME) algorithm [125; 126], double threshold methods [110; 117; 118; 127; 128; 129; 130], Otsu [114; 124], recursive methods [124; 131], and several other methods [132; 133; 134; 135; 136; 137]. These methods would compute new threshold values without prior knowledge of the noise floor in order to prevent excessive false alarms, and to maximize the detection rate of CR-LPWANs. Furthermore, it is essentially to reduce the computational requirements and the energy consumption rates of ED methods deployed in CR-LPWAN systems.
## 1 Introduction
Other methods have also been developed in recent years; for example, 3D printing can artificially create 3D shapes [37; 38; 39], using a computer-controlled arrangement of heterogeneous cells with a gel-like culture medium as the filler. Although these methods exhibit excellent features, such as dimensional control of the spheroid, reduction in damage and irritation to the cells, simplicity of the procedure, high success rate, mass production, high throughput, and low cost, none of them satisfy all these requirements simultaneously. A new type of classification is microfluidic devices. Microfluidic devices are devices that have microscopic grooves or tubes within. These are used for various applications, such as forming small bioreactors [32; 40] or circulatory systems [31] to recapitulate in vivo, using flow paths. Some of these microfluidic methods are included in the micropatterned forced flotation method. Fluoropolymers have a low surface free energy and are generally regarded as materials that do not allow cell adhesion. They have a high biocompatibility and inertness, due to these properties. Therefore, fluoropolymers are used in artificial blood vessels and other components that are implanted in the body. In general environments, these polymers are very stable, due to the high binding energy of C-F bonds, and they are, thus, a safe and non-toxic material for use in the living body. However, their high inertness prevents cell adhesion, which is a problem for biomaterials. As a result, various surface modifications have been investigated to take advantage of their high biocompatibility and inert properties [41; 42; 43; 44; 45]. To improve adhesion, plasma treatment [47; 48; 49; 50; 51], UV irradiation [52; 53; 54; 55], \(\gamma\)-radiation [56; 57], ion introduction [58; 59; 60; 61], and polydopamine treatment [62; 63; 64; 65; 66] have been employed for surface modification; however, most of these methods require high energy to break the strong C-F bonds and add functional groups to improve adhesion. We are working on the control of cell adhesion by manipulating the surface properties of fluoropolymers for 3D cell patterning and have found that extracellular matrix (ECM) substrates deposited on fluoropolymers show weak cell adhesion in this study. In this study, we took advantage of this property and developed a new spheroid fabrication method that simultaneously satisfies all of the above requirements for spheroid production by forming an ECM dot pattern on the fluoropolymer, which allows cells to adhere on it, retain it during cell growth, and detach from the fluoropolymer without employing damaging enzymatic treatments.
# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 1 Introduction
By studying the prediction of OD traffic flow, the population flow patterns between different ODs can be predicted, revealing the impact of population factors on cross regional traffic OD distribution, and providing data support for intelligent management and control of highways [5]. The model adopts a hard parameter shared multi-task learning network structure, which is divided into sub-task learning modules for inflow trend learner, sub-task learning modules for outflow trend learner, and main task learning module for OD traffic flow prediction. At the same time, we use population density data from yearbooks to construct an attraction intensity matrix between stations as the external feature of the sub-task module for outflow trend learner, and introduce stronger constraints between tasks to achieve better fitting results. Finally, an OD traffic flow prediction case experiment was conducted between stations on highways in Sichuan Province to evaluate and analyze the performance of the proposed model. The experimental results showed that the proposed model not only had higher accuracy in predicting results than other baseline models, but also had better effectiveness and robustness. Our main contributions are listed as follows:  
1. We will design a hybrid deep learning model with a multi-task learning framework for OD traffic prediction. 2. We innovatively considered the impact of population factors in the prediction algorithm. We evaluated the performance of the proposed framework using actual traffic flow data. The results demonstrated the advantages of our framework compared to the four baseline methods.