esa-sceva/satcom-chunk-collection huggingface dataset:
# Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment
## 4 Statistical Model Analysis  
We built some statistical models of multipath characteristics by analyzing the experimental data. The characteristics included multipath time delay, multipath power attenuation and multipath fading frequency. The validity of these models was proven by the chi-square test. The raw multipath data of all collection spots were mixed in the following analysis to achieve reasonable statistical models. According to the experiment results of all three satellite orbits, the statistical characteristics of time delay and power attenuation are the similar, while the characteristics of fading frequency have significant differences. Thus, the model comparison of three satellite orbits were analyzed only for the multipath fading frequency.
# Mechanical-Empirical Pavement Design Guide Applied to Portuguese Pavement Structures
## 2 The MEPDG Design Methodology
### Climate  
The MEPDG software is linked to the Enhanced Integrated Climatic Model (EICM) to include the effects of environmental conditions on pavement performance [3; 25; 26; 27]. The EICM uses data from climatic files containing hourly ambient temperature and relative humidity, precipitation, sunshine percentage, and wind speed. The EICM is used to predict the temperature profile throughout the pavement depth together with the moisture content and freezing conditions in the unbound layers. The predicted temperature in flexible pavements is directly related to the stiffness of the asphalt concrete layers and, therefore, is indispensable for predicting thermal cracking and rutting.  
Figure 2: Traffic classes considered by MEPDG [23; 24].  
There are 850 weather stations in the USA that collect all the necessary data in the format used by the MEPDG software [17]. The Transportation Association of Canada has also implemented the MEPDG using accurate climatic data from 220 weather stations across Canada, which provided different values of pavement performance indicators for each province [10].  
In Portugal, the weather stations do not collect the required data in the format used by the MEPDG software. Therefore, it was necessary to collect and organize all the climate data provided by the Portuguese Institute of the Sea and Atmosphere.  
The climatic ICM file consists of one line for each day (Table 1) containing information related to month, day, year, hour of sunrise, hour of sunset, and daily solar radiation. Below this line, there are 24 lines, each one corresponding to one hour of the same day. These lines have the information indicated in the following order: hour; temperature in Fahrenheit; precipitation in inches; wind speed in miles per hour; the percentage of the sun or cloudiness level; and the hourly groundwater depth in feet. A climate ICM file was prepared for the Coimbra region with two years of climate data.
## 7 Conclusions  
In this paper, we have designed an energy-efficient adaptive bidirectional transmission strategy in the SWIPT-enabled CRN. To design the transmission strategy, we first select an energy-constrained best relay node with the consideration of SNR and global channel gain. At the same time, we let the energy-constrained best relay node transmit a signal with the SWIPT-PS protocol. Then, with the selected energy-constrained best relay node, we further design a TTPT determining algorithm and design the adaptive bidirectional transmission strategy. With the designed transmission strategy, the network can achieve an adaptive transmission between the BRT and BDT modes to obtain a higher network SE. Furthermore, to further achieve energy-efficient transmission of the designed transmission strategy, we optimize the nodes' power under the requirement of the PUs' interference threshold and obtain the analytical expressions of the optimal power. Simulation results have shown that our designed transmission strategy can obtain higher SE and EE, and a lower outage probability, which verifies the effectiveness of our transmission strategy. In addition, considering the intelligent reflective surface can effectively improve the network's EE and SE, it will be explored in our future work.  
Figure 11: The EE vs. the PS factor.  
Figure 10: The EE vs. the total transmit power.  
**Author Contributions:** The main contributions of [PERSON] and [PERSON] were to create the main ideas, perform theoretical analysis, and execute performance evaluation by extensive simulation while [PERSON] and [PERSON] worked as the advisors to discuss and advise the main ideas and performance evaluations together. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported in part by the National Nature Science Foundation of China Project under Grant 62102466, Grant 52371331, Grant 52271321, and Grant 52101362.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data are unavailable due to privacy or ethical restrictions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Hypothesis Testing Fusion for Nonlinearity Detection in Hedge Fund Price Returns
###### Abstract
For both approaches, the detection of nonlinearity in HF price returns is a challenge since the return TS contain few samples, HF prices being generally updated once a month. Thus, a ten year record provides only 120 samples. In fact, the bispectrum (and its normalized version) is known to have a high variance and its estimate needs a fairly large number of samples to provide robust results. On the other hand, parametric tests are generally slightly more efficient for small TS length but they imply some underlying hypothesis on the model driving the TS under the null hypothesis. For instance, the models used in the parametric tests (AR-STAR) are infinite memory processes (i.e., the AutoCorrelation Function, ACF, has an infinite support), while the TS of asset returns, and in particular, HF returns, are finite memory. Moreover, the robustness of this last class of tests decreases when the model parameter number increases (as seen again below). Thus, a straightforward idea is to merge several HT to improve the detection. In order to expose the process of TS nonlinearity detection, the fusion algorithm and its application to HFs, we develop two points:  
* The first is to expose the design of this HT fusion algorithm. Hypotheses have to be carefully defined and the fusion process carefully designed to avoid false conclusions on HT and fusion algorithm robustness, as previously stated. In particular, several points are generally overlooked in operational decisions, such as the pdf under the null hypothesis, key features in the decision process, or the information shared by several HT. * The second aim of the paper is the application of the decision rule to detect nonlinearity in HF price returns. We first inspect whether there is a relationship with the a priori classification and the nonlinearity detection in two databases. These databases, respectively, provided by TASS and Standard and Poors (S&P), contain several styles of HFs and we seek to inspect whether some styles exhibit nonlinear feature, unlike the others. All the fusion methods need a \"learning\" base on which some parameters of the fusion process have to be estimated (see [25]). A \"validation\" base is used to verify the efficiency of these estimated parameters over data not involved in their estimates. An underlying assumption made thorough this paper is that HF returns are stationary up to the third order (since all the HT need this assumption). Obviously, this assumption is questionable, but due to the dependence of each HT on a management style, as well as the low rate of price updating, it seems realistic (we discuss this point again in Section 4.2). To expose our results, the paper is organized as follows. In Section 2, we present the hypothesis definition, the robustness criterion and the 12 statistical indices used in the fusion process step, while the topology and fusion approaches are presented in Section 3. In Section 4, this decision rule is applied to detect nonlinearities in simulated TS and HF returns.
## 4 Results
### LIDAR and UWB Comparison
Additionally, it has been illustrated that this error is amplified by multipath interference under NLoS conditions. Methods for AoA mitigation, such as Kalman filtering (KF) or inertial measurement unit (IMU) fixation, can be applied to minimize errors. Subsequent research explores the potential of leveraging AoA and AoD data provided by BLE.1 to ascertain the device's position. In the empirical study, an error below 0.85 m was achieved for more than 95% of the locations, indicating that sub-meter AoA precision can be attained. Nevertheless, considerable efforts are still required to reach centimeter-level precision. The integration of these various technologies may allow for greater location accuracy in conditions with limited visibility. Moreover, in the context of the development of UWB technology, more advanced and competitive solutions based on beacons compatible with Apple and Android devices may be available in the future. It is worth noting that currently, there is only a small number of competitive hardware products compatible with UWB beacons on the market, which limits the possibility of result comparison. In the future, the development of UWB technology can lead to an increase in the availability of competitive hardware, enabling a more comprehensive assessment of the accuracy and effectiveness of this technology.
# Mechanism and Characterization of
## 2 Results
### Molecular Dynamics
#### 2.5.3 Binding Energy (Section S2.3)  
The binding energy is used to characterize the strength of interactions between substances. It is calculated using the formula:  
\[E_{int}=E_{A+B}-(E_{A}+E_{B}) \tag{3}\]  
where \(E_{int}\) is the total interaction energy and \(E_{A}\) and \(E_{B}\) are the energies of individual components. When \(E_{int}<0\), the interaction between materials is stronger [37]. Figure 6c shows the binding energy of MNR, SNR, and MSNR composites. The results indicate that MSNR exhibits the highest negative binding energy, suggesting improved interactions between the fillers and the polymer, leading to better dispersion within the NR matrix [37].  
The results of the experimental data show that MSNR has significant advantages over other composites in terms of mechanical properties, energy storage modulus, loss modulus, and dispersion of filler in a rubber matrix. Furthermore, by comparing the molecular dynamics simulations, the volume fraction (FFV) and mean square displacement (MSD) of MSNR reached the minimum values, and the binding energy showed the maximum; these results demonstrate the strong interaction and high binding efficiency of MWCNT and SiO\({}_{2}\) with the rubber matrix at the microscopic scale. This agreement between experimental and simulation results not only proves the reliability of the simulation method, but also highlights the remarkable potential of MWCNT and SiO\({}_{2}\) bicomponent fillers to enhance the properties of natural rubber, which provides an important theoretical and experimental basis for the design and development of high-performance rubber matrix composites.
# Study on the Bearing Characteristics of Composite Foundations with Permeable Concrete Piles
## 4 Results and Discussions
### Influence of the Parameters of Pile
The figure indicates that as the load increases, the pile-soil stress ratio initially increases and then decreases for all pile diameters. Specifically, the ratio increases when the load is less than 150 kPa and decreases when the load exceeds 150 kPa. Additionally, the pile-soil stress ratio gradually decreases with increasing pile diameter. This is because  
Figure 16: Influence of loads on settlement under different pile diameters. a larger pile diameter increases the load-bearing area of the pile, allowing it to share more of the load and reducing stress concentration at the pile head. Moreover, increasing the pile diameter enhances the drainage capacity of permeable concrete piles, accelerating the consolidation of the surrounding soil and improving its bearing capacity. This process further reduces the pile-soil stress ratio. The pile modulus was adjusted to 5 GPa, 10 GPa, 15 GPa, 20 GPa, and 25 GPa, with other parameters kept constant. The simulation results are shown in Figures 18 and 19. Figure 19: Influence of loads on pile–soil stress ratio under different pile moduli. Figure 17: Influence of loads on pile–soil stress ratio under different pile diameters. Figure 18: Influence of loads on settlement under different pile moduli. Figure 18 shows the influence of load on the settlement of composite foundations with different pile moduli. The figure indicates that under the same load conditions, the settlement of the foundation decreases as the pile modulus increases. When the pile modulus reaches 15 GPa, further increases in the pile modulus do not significantly change the settlement. A further analysis of the settlement at a load of 300 kPa shows that as the pile modulus increases from 5 GPa to 15 GPa, the settlement decreases from 13.98 mm to 12.46 mm, a reduction of 10.87%. However, when the pile modulus increases from 15 GPa to 25 GPa, the settlement decreases from 12.46 mm to 12.19 mm, a reduction of only 2.17%. This result indicates that the influence of pile modulus on foundation settlement is relatively limited and that there is a critical modulus value. Beyond this value, further increases in the pile modulus have little effect on settlement. Figure 19 shows the influence of load on the pile-soil stress ratio for different pile moduli. The figure indicates that the pile-soil stress ratio increases under the same load conditions with an increasing pile modulus. This means that increasing the stiffness of the pile helps it to more effectively share the load, thereby enhancing the stability of the foundation. However, when the pile modulus exceeds 15 GPa, the difference in the pile-soil stress ratio decreases as the pile modulus increases. This suggests that, under high-modulus conditions, the pile's ability to share the load is close to saturation, and further increases in pile modulus have a limited effect on the pile-soil stress ratio.
## 2 Materials and Methods
### Data Processing  
In last few years, electronic noses have been applied to detect different compounds in the air [33]. However, the main problems with this type of device are accuracy and precision. It is necessary to develop algorithms that detect specific compounds based on the data transmitted by the sensors integrated into the electronic nose. In this sense, pattern recognition techniques, based on machine learning methods, can be of great help. In the present work, machine learning techniques were applied to detect two different types of hazardous compounds. The first compound considered was ethanol, which is used in the processing of food and alcoholic beverages and in the production of fuel and personal and household products. The gas emitted by ethanol is not only highly flammable but also poses serious health risks to people [34]. The second compound considered was hydrogen peroxide because it can become a toxic and asphyxiating gas with a significant implicit risk of asphyxiation. Hydrogen peroxide is antiseptic due to its oxidizing power. It has antibacterial, antiviral, and antifungal properties, and exposure to this substance can cause shortness of breath, edema, and bronchitis [35].  
To identify both compounds, a perceptron neural network was developed. The input layer was composed of 4 neurons, corresponding to the 4 sensors integrated into the electronic nose; an internal layer of 3 neurons; and an output layer with 2 neurons, in which each one represented one of the two substances to be distinguished. Backpropagation algorithms were applied as the learning technique, developing the following stages: feed-forward stage, in which sensor data were introduced into the input nodes of the neural network, propagating the results by the entire neural network. The second stage consisted of adjusting the weights of neurons based on the differences between the actual and the desired output. The dataset was obtained by performing laboratory tests and applying solutions of ethanol, hydrogen peroxide as pollutants, and clean air to the electronic nose. Clean air acted as the reference or zero gas to train the neural network. In these tests, the data were divided into two different sets: a learning set, with the data calibrating the neural network; and the validation set, used to verify the values resulting from processing the data with the neural network. Finally, a success rate of 98.072% was achieved in the classification process.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 2 Materials and Methods
### Attribution to Climate Change and Climate Variability  
Climate variability and climate change are two related but distinct concepts in the field of climate science. Climate variability refers to the natural fluctuations in climate parameters such as temperature, precipitation, and wind patterns that occur over relatively short periods of time (typically months to years). These variations can be driven by various factors such as natural cycles in the Earth's climate system, including El Nino and La Nina events, solar variability, and volcanic activity. Climate change, on the other hand, refers to long-term shifts in climate patterns, typically over decades to centuries, that result from changes in the Earth's energy balance due to human activities, particularly the emission of greenhouse gases such as carbon dioxide. Climate change is often associated with rising global temperatures, shifts in precipitation patterns, and changes in the frequency and intensity of extreme weather events. In summary, climate variability refers to the natural, short-term fluctuations in climate, while climate change refers to long-term, human-induced changes in climate patterns. The climate effects are estimated from NDVI\({}_{a}\) through TSS-RESTREND. Additionally, we need to separate the climate effects to climate change (CC, change with anthropogenic activities) and climate variability (CV, change without anthropogenic activities). We apply a 20-year leading edge moving window on observed per-pixel precipitation and temperature datasets to remove the interannual CV. So, it is necessary to use the dataset that dates back to 1960. The long-term trend calculated by the Theil-Sen slope estimate is CC. NDVI change due to CV is the detrend climatology. Other factors that cannot be attributed are defined as OF = Obs - (CO\({}_{2}\) + CC + CV + LU). The flowchart of the methods is shown in Figure 3.
# Golgi Metal Ion Homeostasis in Human Health and Diseases
## 2 Ca\({}^{2+}\)/Mn\({}^{2+}\) Transporters and Channels
### Ca\({}^{2+}\)/Mn\({}^{2+}\) Transporters in the Golgi
Additionally, investigation using Ca\({}^{2+}\) probes targeted to the _cis_- and _trans_-Golgi showed that the Ca\({}^{2+}\) accumulation in these compartments is mainly facilitated by SERCA and SPCA1, respectively [39]. As Golgi-specific Ca\({}^{2+}\) pumps, SPCAs have been shown to be the major Ca\({}^{2+}\) pumps that facilitate the Ca\({}^{2+}\) uptake in the _trans_-Golgi network (TGN). TMEM165 was first identified in congenital disorders of glycosylation (CDG) [17] and localize to the Golgi [42]. Loss of TMEM165 function interrupts Ca\({}^{2+}\), pH, and Mn\({}^{2+}\) homeostasis and causes glycosylation abnormalities, which are restored by Mn\({}^{2+}\) supplementation. Therefore, TMEM165 is proposed to be a Golgi-localized Ca\({}^{2+}\)/Mn\({}^{2+}\) antiporter involved in glycosylation regulation [43; 44]. Interestingly, TMEM165 is relocated to lysosomes and subjected to lysosomal degradation upon high Mn\({}^{2+}\) exposure in a Rab7 and Rab5 independent manner, indicating a Mn\({}^{2+}\)-dependent TMEM165 quality control mechanism involving direct targeting of the protein to the lysosomal degradation pathway [45]. The Mn\({}^{2+}\)-induced translocation and lysosomal degradation of TMEM165 was later associated to the function of SPCA1, since the phenotype was rescued by a gain-of-function mutation of SPCA1. This suggests a possible functional link between the two proteins in response to the cytosolic Mn\({}^{2+}\) change, however the underlying mechanism remains unclear [46]. One of the unanswered questions is why TMEM165 is degraded upon high Mn\({}^{2+}\) exposure. One proposal is that TMEM165 works as a Mn\({}^{2+}\) importer using the gradient of Golgi Ca\({}^{2+}\). TMEM165 degradation would increase cytosolic Mn\({}^{2+}\) and engage the major Mn\({}^{2+}\) importer SPCA1 for detoxification. Another hypothesis is that TMEM165 imports Ca\({}^{2+}\) into the Golgi using the Golgi Mn\({}^{2+}\) gradient. Then TMEM165 degradation would restrain the Mn\({}^{2+}\) in the Golgi to avoid further toxication. Therefore, TMEM165 is proposed to function in both directions depending on the combined effects of Ca\({}^{2+}\) and Mn\({}^{2+}\)[45].