esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Program
### Experimental Setup and Instrumentation  
The tested continuously supported beams comprised two equal spans with two horizontally moveable end supports and one hinged support at the middle of the beam. Two hydraulic presses with a capacity of 200 kN were used to load the beams. Strain gauges were used to measure actual strains in the critical sections of the beams. Electrical strain gauges were attached to areas of tension and compression longitudinal reinforcement in both bottom and upper zones of the beams (Figure 3). Six strain gauges were attached on the concrete compression zone of the continuous beams in critical sections. Three linear variable differential transducers (LVDT) were placed on each span to measure deflections at three different locations of each beam span. Crack widths were measured in critical sections by a Zeiss microscopic magnifier. Two load cells with a capacity of 100 kN were used to measure the end support reactions. The experimental beams were loaded monotonically in appropriate increments to the failure, with a speed for each increment of approximately 5 kN/min. All data were electronically recorded in the computer using a data logger. The equipped continuous beam before testing is shown in Figure 3.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Diameter**} & **Tensile Strength** & **Modulus of Elasticity** & **Ultimate Strain** \\  & \(f_{u}\) **(MPa)** & \(E_{f}\) **(MPa)** & \(\varepsilon_{u}\) **(\%\(\infty\))** \\ \hline G1—\(\mathcal{O}\)9 & 1170.4 & 50,235 & 23.3 \\ G1—\(\mathcal{O}\)10 & 1059.3 & 43,734 & 24.2 \\ G1—\(\mathcal{O}\)12 & 1060.4 & 48,182 & 22.0 \\ G2—\(\mathcal{O}\)8 & 714.8 & 42,640 & 16.8 \\ G2—\(\mathcal{O}\)10 & 703.1 & 41,300 & 17.0 \\ G2—\(\mathcal{O}\)12 & 865.9 & 45,832 & 18.9 \\ G2—\(\mathcal{O}\)14 & 813.5 & 44,324 & 18.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical and deformation properties of GFRP bars.  
Figure 3: Equipped continuous beam before testing (dimensions in mm).
## 3 HELIOS Concept in the Real Estate Market--Methodology and Implementation
### Methodological Overview
#### 3.1.1 Research Paradigms  
The HELIOS concept is grounded in several fundamental research paradigms.  
* Cognitive Paradigm: Using language models to understand and generate text akin to human cognition enables more advanced and precise real estate market analyses.
* Systemic Paradigm: A systemic approach to data analysis that accounts for the complexity and diversity of data sourced from various origins and their interconnections.
* Iterative Paradigm: An iterative process of model refinement through early validation and incorporation of expert feedback, ensuring continuous improvement and adaptation of the system to the specific needs of the real estate market.
# Vehicular Localization Enhancement via Consensus
###### Abstract  
This paper presents a strategy to cooperatively enhance the vehicular localization in vehicle-to-everything (V2X) networks by exchanges and updates of local data in a consensus-based manner. Where each vehicle in the network can obtain its location estimate despite its possible inaccuracy, the proposed strategy takes advantage of the abundance of the local estimates to improve the overall accuracy. During the execution of the strategy, vehicles exchange each other's inter-vehicular relationship pertaining to measured distances and angles in order to update their own estimates. The iteration of the update rules leads to averaging out the measurement errors within the network, resulting in all vehicles' localization error to retain similar magnitudes and orientations with respect to the ground truth locations. Furthermore, the estimate error of the anchor--the vehicle with the most reliable localization performance--is temporarily aggravated through the iteration. Such circumstances are exploited to simultaneously counteract the estimate errors and effectively improve the localization performance. Simulated experiments are conducted in order to observe the nature and its effects of the operations. The outcomes of the experiments and analysis of the protocol suggest that the presented technique successfully enhances the localization performances, while making additional insights regarding performance according to environmental changes and different implementation techniques.  
Keywords:autonomous vehicular networks; cooperative localization; vehicle-to-everything (V2X); consensus algorithm +
Footnote †: journal: Sensors
## 2 Description of USTARFM
### Fused Image Generation  
The two unmixed images from MODIS on date \(t_{k}\) and \(t_{0}\), and one fine-resolution image on date \(t_{k}\) are applied to predict the fine-resolution image on date \(t_{0}\). The critical step to implement the USTARFM algorithm, as well as the STARFM, is weighting the spatial information from neighboring pixels which are used to estimate reflectance of the central pixel and allow the weight function to be flexibly adjusted according to land surface complexity and heterogeneity [25]. To ensure that suitable information is acquired from neighboring pixels, similar spectral pixels from fine-resolution images within the moving window are involved to compute the reflectance of the central pixel. Then these similar pixels are weighed and used to calculate the reflectance of the central pixel as shown in Equation (3).  
\[W_{ijk}=\left(\frac{1}{S_{ijk}\times T_{ijk}\times D_{ijk}}\right)\Bigg{/} \sum\limits_{i=1}^{w}\sum\limits_{j=1}^{w}\sum\limits_{k=1}^{n}\left(\frac{1} {S_{ijk}\times T_{ijk}\times D_{ijk}}\right) \tag{2}\]  
\[L\left(x_{w/2},y_{w/2},t_{0}\right)=\sum\limits_{i=1}^{w}\sum\limits_{j=1}^{w} \sum\limits_{k=1}^{n}W_{ijk}\times\left(M\left(x_{i},y_{j},t_{0}\right)+L\left( x_{i},y_{j},t_{k}\right)-M\left(x_{i},y_{j},t_{k}\right)\right) \tag{3}\]  
where \(L(x_{w/2},y_{w/2},t_{0})\) represents the central pixel estimated reflectance of the moving window \(w\) at Landsat-like scale, \(L(x_{i};y_{j},t_{k})\) and \(M(x_{i},y_{j},t_{k})\) are the given location ( \(x_{i}\),\(y_{j}\) ) pixel surface reflectance of the fine resolution image and unmixing data at the base date \(t_{k}\) and \(M(x_{i},y_{j},t_{0})\) is the value of the unmixing data of MODIS on the prediction date \(t_{0}\). The \(W_{ijk}\) refers to the weight of similar pixel \((x_{i},y_{j},t_{k})\), which is determined by three indices: spectral difference (\(S_{ijk}\)), temporal difference (\(T_{ijk}\)) and spatial distance between the central pixel and the similar pixel (\(D_{ijk}\)).  
A smaller value of \(S_{ijk}\) implies that the fine-resolution pixel has spectral features that are similar to the coarse pixel, and thus, pixel reflectance should be assigned a higher weight in Equation (2). A smaller \(T_{ijk}\) means there is less change in the surface reflectance between \(t_{k}\) and \(t_{0}\); thus, the pixel should be assigned with a higher weight. A smaller \(D_{ijk}\) means there is a closer distance to the central pixel, and therefore a higher weight for the pixel should be assigned.  
For a more detailed description of the above algorithm readers may refer to [25].
###### Abstract  
Cement stabilized macadam semi-rigid base materials are widely used in road construction in China. However, a traditional static molding method and heavy compaction method cannot guide the design and engineering application of cement stabilized macadam mixture because it often appears that the compaction degree exceeds 100% in the practical engineering. In view of this, this paper carried out the research of an indoor vibration compaction method of cement stabilized macadam mixture, and compared the mechanical properties and void distribution characteristics of two kinds of compositions of mixtures under the vibration compaction method as well as static pressure molding and heavy compaction method and on-site sample after 7 days curing period, which was combined with the physical engineering project of Yu-Song Expressway in Jilin Province, China. The research results show that the maximum dry density of mixture under vibration compaction is larger and the best moisture content is smaller, which has a heavy incomparable advantage on the simulation of on-site compaction. And the compressive strength and splitting strength indexes of vibration compacted specimens are close to those of an on-site sample, which are all larger than static pressure specimen. Moreover, the void distribution characteristics of vibration compacted specimens is much closer to those of the on-site sample and more universal, while static pressure specimens lack in uniformity. In addition, different results caused by the two gradation are compared. All of the above research results can verify that the vibration compaction method has more reliability and accuracy to simulate the actual properties of base material. This study provides a reference for the application of vibration compaction method in road engineering design.  
**Keywords: cement stabilized macadam; vibration compaction; static pressure molding and heavy compaction; mechanical properties; void distribution**
## 3 Results
### Environmental Toxicity Quotient (ETQ)  
According to the Environmental Toxicity Quotient (_ETQ_) (Figure 6), the analyzed samples are mostly in the second class (60 samples). Only one sample falls in class 3 (high _ETQ_), and three samples in class 4 (very high _ETQ_). These are the same samples from Pristol, which have very high potential ecological risk and are seriously polluted, according to \(PI_{\textit{Nemenew}}\).  
The threshold value 0.081 mg/kg, computed for Hg, although close to the natural abundance or Clark of this element, seems too low, influencing both \(PI_{\textit{Nemenew}}\) and \(RI\), leading to the idea that a great number of samples show some pollution or a moderate potential ecological risk. By removing mercury from the computations, the new class statistics (Figure 7) dramatically changed, with the number of samples in the \"precaution domain\" increasing from 29 to 43, the low \(RI\) class increasing to 73 instead of 49 samples, and the moderate \(RI\) class decreasing to only 2 instead of 22 samples.  
Nevertheless, a comparison of Figures 6 and 7 reveals that the Hg threshold is not the sole factor determining the number of samples reported as polluted or having a moderate potential ecological risk. Furthermore, the ecological risk may be attributed to outlier values (e.g., Cd and Cu), as illustrated in Figure 4, which depicts the box-plots for the trace elements.  
Figure 6: Results of complex indices: \(PI_{\textit{Nemenew}}\), \(RI\), and \(ETQ\).
## 5 Experimental Results
### Experiments with Multitasking Cores (MTP)  
The next group of experiments concerned multitasking architectures (MTP) with thread-interleaving mechanism [2]. During the experiments, the same (as previously) set of WCET benchmarks was used. The most representative results were selected and are presented in Table 2. Again, the first experiments were conducted with the original multitasking architecture [2]--the first five rows of the table contain symbols of the form: MTP \(K\) p \(i\times n\). \(K\) denotes the number of pipeline stages; \(i\) stands for the number of cores; and \(n\) is the number of tasks processed by a single core. The second part of Table 2 (rows 6-10) gathers results for the structure built of multitasking cores with the memory operations  
Figure 8: The average power savings per task for the architecture with a clock-gating mechanism compared with the appropriate STP structure for different frequencies.  
Figure 9: The average power savings per task for the architecture with clock-gating mechanism compared with the appropriate STP structure for the number of tasks (cores).  
blocking mechanism (Figure 7). These architectures are denoted by symbols of the form: MTP RG \(K\) p \(i\times n\). The meaning of symbols is the same as for the MTP architectures.  
The analysis of the results shows that, similar to the previous case (STP), the number of resources used in both solutions, i.e., MTP and MTP RG, are comparable. Figure 10 presents the averaged results of the power savings achieved for selected structures with RAM gating compared with appropriate MTP structures for different frequencies, while Figure 9 shows the percentage power savings by structure configuration (the number of tasks per core). As in the STP case, the results presented in all diagrams shows the average power per task. In Section 4, another coefficient, \(AP\), was defined as the average power necessary to execute a single task. Figure 11 presents some results concerning the dependence of relative \(AP\) savings on frequency for MTP RG architectures compared with the basic MTP scenario. These results showed that, in the range of tested frequencies, the differences for individual configurations (scenarios) did not exceed 2.5%. The characteristics had some local extrema, which were related to the matching with the FPGA chip resources during the high-level synthesis. After a certain threshold, all configurations showed a monotonic increase of the consumed power ratio with frequency.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{\
\begin{tabular}{c} **Frequency** \\ **(F\({}_{\text{sys}}\) [MHz])** \\ \end{tabular} } & \multirow{2}{*}{_Min\({}_{\text{indistance}}\)_} & \multirow{2}{*}{
\begin{tabular}{c} **Cores’ Power** \\ **(P\({}_{\text{CPU}}\) [mW])** \\ \end{tabular} } & \multicolumn{4}{c}{**FPGA Utilization**} \\ \cline{5-8}  & & & & **Slice LUT** & **Slice Reg** & **F7 Murcs** & **F8 Muxes** \\ \hline MTP 12p 1 \(\times\) 30 & 150 & 30 & 1028 & 25,208 & 18,494 & 6850 & 3030 \\ MTP 5p 2 \(\times\) 15 & 75 & 15 & 646 & 26,039 & 16,888 & 6712 &
# Influence of Blade Wrap Angle on the Hydrodynamic Radial Force of Single Blade Centrifugal Pump
## 2 Pump Model and Simulation Method
### Simulation Method  
The simulation method is the same as the author's early paper, which is validated by experiment [22]. The ICEM code was used to mesh the model, and tetrahedral mesh was used for the impeller. To improve the grid quality, we refined the wall boundary layer. Meshing results of the impellers are illustrated in Figure 2. The other calculation domains adopted the hexahedral structure mesh the same as the reference [22].  
As the flow in the centrifugal pump has a high Reynolds number, the numerical simulation was adapted from the standard \(k\)-\(\epsilon\) turbulence model [23; 24; 25; 26]. ANSYS CFX 14.5 is used for numerical calculation, the coupling of velocity and pressure adopts the SIMPLEC algorithm, and the convection term adopts high-order format [27]. Using 25 \({}^{\circ}\)C water as the calculation medium, the solid wall is selected as a nonslip wall surface. Since the impeller and solute are castings, the surface roughness is set as 50 \(\upmu\)m. The inlet is set to the total pressure inlet, and the outlet is set to the mass outlet. By setting different mass flows, the external characteristic curve of the pump can be obtained. A multi-coordinate reference system is adopted, the impeller is set to the rotating domain, the static domain is adopted for the others, and the interface between the dynamic and static domains is set to the frozen rotor. Considering the calculation time and accuracy, the convergence accuracy is set to \(10^{-4}\).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Main Parameters** & **Value** \\ \hline Inlet diameter of impeller, \(D_{1}\) (mm) & 45 \\ Outlet diameter of impeller, \(D_{2}\) (mm) & 125 \\ Outlet width of impeller, \(b_{2}\) (mm) & 30 \\ Blade wrap angle, \(\varphi\) (\({}^{\circ}\)) & 360 \\ Inlet diameter of volume, \(D_{3}\) (mm) & 135 \\ Inlet width of volume, \(b_{3}\) (mm) & 46 \\ Blade outlet angle, \(\beta_{2}\) (\({}^{\circ}\)) & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main geometric parameters of the pump.  
Figure 1: Design process of single blade centrifugal impeller by Bladegen.
## 4 Evaluation Model Based on AH-Entropy and Normal Cloud Model
### Cloud Model-Related Theories
#### 4.1.3 Positive Cloud Generator  
The generator is a specific algorithm employed to convert qualitative concepts and quantitative data in cloud models, with two types: positive cloud generator and backward cloud generator. The positive cloud generator facilitates the transformation from qualitative concepts to quantitative values. The cloud is generated from the cloud parameters (\(Ex\), \(En\), and \(He\)), as shown in Figure 3.  
Utilizing the cloud drops generated by the positive generator of the cloud model, a normal cloud model for pavement condition evaluation can be established through the following main steps:  
Step 1: Obtain the digital feature entropy \(Ex\) and hyper entropy \(He\) of the cloud model based on the measured data of pavement condition evaluation indicators.  
Step 2: Generate a normal random number \(En^{\prime}\) using statistical methods based on the obtained digital features of the cloud model, \(En^{\prime}\sim\)N(\(Ex\), \(He^{2}\)).  
Step 3: Calculate the uncertainty value \(\mu(x)=\exp\left\{-\frac{(x-Ex)^{2}}{2 Ex^{2}}\right\}\) based on the generated normal random number \(En^{\prime}\) to generate a cloud drop \((x,\mu)\).  
Step 4: Repeat the aforementioned three steps until \(n\) cloud drops are generated, and the normal cloud model is depicted by the cloud drops.
## 4 Conclusions  
It has been shown that with their longer codes modernized signals take longer to acquire. In order to minimize that impact, a preliminary solution based on GPS L1 C/A signals can be leveraged to reduce the search span of other signals from tracked satellites. This transition from old to new signals of any given satellite, emphasizes the need for a universal channel approach, avoiding many idle dedicated-channels. This power reduction strategy is especially critical for portable devices whose battery optimization is the challenge of the century. Moreover, a commercial application based on such a universal channel could easily introduce a pricing scheme based on available constellation and signal types. Its future compatibility would thus make it a great option for expandable design based on SW only upgrades, easily deployable into already released products.  
In this paper, the GNSS signals characteristics have been identified and addressed by a universal acquisition and tracking channel, while maintaining power consumption as low as possible (by avoiding idle channels and sharing resources) and maintaining a high level of robust tracking and flexibility. As shown, the proposed architecture allows sequential acquisition and tracking of any chipping rate, carrier frequency, FDMA channel, modulation--_i.e._, BPSK (\(q\)), QPSK (\(q\)), \(\sin\)/\(\cos\) BOC (\(p,q\)), CBOC (\(r\), \(p\), \(P_{r}\), \(\pm\)) and TMBOC (\(r\), \(p\), \(w_{r}\))--or constellation, and is totally configurable (any integration time, discriminator, _etc._) within a fast turn-around time.  
Also, its dual-component architecture allows for two sequential acquisition options: (1) dual secondary chip estimation and (2) dual primary code delay (twice as fast) estimation for a single-component acquisition. Moreover, its upgradable memory codes and sub-carriers configurability (co/sine phase and \(\alpha\) and \(\beta\) weights in time) make it future-compliant for any variation that could be proposed for GLONASS modernization and BeiDou phase III signals, the final description of which are yet to come.  
These benefits came at the cost of increasing the tracking channel correlators number from six (NEML reference BPSK architecture) to eight for a single-component (or equivalently 16 for a dual-component) GNSS channel, thanks to the proposed resources reduction. All these GNSS signal tracking features result in a 66% power consumption increase compared to those used in a reference BPSK channel. Nevertheless, a dual-component channel requires twice the resources of a reference BPSK channel, but with twice the throughput, thus achieving an equivalent resource per channel ratio. Also, the proposed TMBOC combined with the 2 phase-controlled sub-carriers approach could be reused to implement the AltBOC sub-carrier weights with a periodicity of eight sub-chips, provided the RF front-end and sampling frequency could process a bandwidth of least 51 MHz.
## 2 Literature Review
### LSTM Models for Traffic Prediction  
The prediction and forecasting of short-term traffic conditions (up to 60 min into the future) play a vital role in the success of ITS, such as adaptive traffic management systems and travel information systems. LSTM networks have been widely used for traffic prediction, leveraging real-world data from inductive loop detectors, CCTV, and probe vehicles [3]. LSTMs are designed to capture long-term dependencies in sequential data, making them highly effective in predicting future traffic speeds, flows, and travel times [53; 55]. For example, Ref. [3] applied LSTM models for future speed prediction, demonstrating higher accuracy than classical methods, while [54] showed that LSTM models are particularly promising for irregular travel time prediction, with a relatively small prediction error for 1-step-ahead forecasts. Additionally, LSTM models were applied to traffic flow prediction, achieving high accuracy for various prediction horizons, in [56], which demonstrated that LSTM models outperformed other models in predicting speed and flow across different road hierarchies, including arterial roads, where the variability of traffic is higher. LSTM models have also been applied in studies on car-following behaviour, predicting vehicle acceleration and deceleration on different road networks. These models have proven to be particularly useful in urban environments where traffic conditions fluctuate due to external factors such as weather or incidents [3; 57; 58; 59].