esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the increasing applications of UWB indoor positioning technologies in industrial areas, to further enhance the positioning precision, the UWB/IMU combination method (UICM) has been considered as one of the most effective solutions to reduce non-line-of-sight (NLOS) errors. However, most conversational UICMs suffer from a high probability of positioning failure due to uncontrollable and cumulative errors from inertial measuring units (IMU). Hence, to address this issue, we improved the extended Kalman filter (EKF) algorithm of an indoor positioning model based on UWB/IMU tight combination with a double-loop error self-correction. Compared with conventional UICMs, this improved model consists of new modules for fixing time desynchronization, optimizing the threshold setting for UWB ranging, data fusion in NLOS, and double-loop error estimation, sequentially. Further, systematic error controllability analysis proved that the proposed model could satisfy the controllability of UWB indoor positioning systems. To validate this improved UICM, inevitable obstacles and atmospheric interferences were regarded as Gaussian white noises to verify its environmental adaptability. Finally, the experimental results showed that this proposed model outperformed the state-of-the-art UWB-based positioning models with a maximum deviation of 0.232 m (reduced by 83.93% compared to a pure UWB model and 43.14% compared to the conventional UWB/IMU model) and standard deviation of 0.09981 m (reduced by 88.35% compared to a pure UWB model and 22.21% compared to the conventional UWB-IMU model).  
U 2023 13, 10046. [[https://doi.org/10.3390/app131810046](https://doi.org/10.3390/app131810046)]([https://doi.org/10.3390/app131810046](https://doi.org/10.3390/app131810046))
## 4 Discussion  
The presented results clearly indicate the importance of meteorological parameters during blasting in clay. It is important to emphasize that adding more input features to an ANN does not necessarily yield better prediction results. Adding additional input data also adds to complexity of the model, which consequently leads to more complicated training procedure of the ANN. This is visible, where adding more input features required increased training duration (more epochs). Interestingly, adding more meteorological data improved the stemming length predictions, but somewhat lowered the explosive charge mass prediction quality, yielding more than satisfactory results, nonetheless.  
Figure 16: Model_1—Stem length–leverage vs. residuals.  
Figure 17: Model_2—Stem length–leverage vs. residuals.  
Figure 15: Model_2—Explosive charge mass–leverage vs. residuals.  
The weak results of stemming length prediction have to be attributed to the input data, since most of the blasting experiments were done with 0.5 m or 1 m stemming length. Such results are somewhat expected and reflected by the statistical metrics, especially in high values of RSE during training phase for stem lengths. The error decreases during validation and testing phases due to larger range of different stem lengths used during those two phases. For a classical data interpretation, this can be favourable; however, in case of predicting this exact parameter using ANN-s this is not the best possible input parameter. The coefficient of determination values are not particular high; therefore, the results of stem length predictions should be taken with care. Additionally, in situ experiments with different stem lengths will be considered in possible future research. Nonetheless, the network managed to produce results of some value. Such results can be a guideline for new blasting, in terms of which stemming lengths could be used as experiments to acquire valuable data for improving such models.  
On the other hand, the predictions result of the explosive charge mass are very satisfactory and prove that such a modelling approach using ANN-s should be considered before field experiments, in order to avoid the high cost of experimental borehole drilling and blasting. The coefficient of determination results of >0.8 indicate that the predictions of charge mass are more than satisfactory. The bias values of model_1 indicate slight underprediction of values but are fixed with the enhanced model_2. The RSE values are overall low, for both models, so even a model with just one meteorological station with just precipitation and mean air temperatures can be considered sufficient for charge mass predictions. It is also proved that weather, and meteorological data in particular, has significant effects on blasting. This study should serve as an idea for similar studies in this field due to its cost effectiveness and simple data requirements.
## 4 Simulation Results Analysis
Table 2 shows the spatial non-uniformity data of the gas at different cross-sections in the eight different mixer structures with spiral blades added at the forefront of the mixing zone. As shown in the table, the spatial non-uniformity decreased as the length increased,  
Figure 10: Pressure loss at inlet and outlet and non-uniformity of outlet gas space. Figure 5:0 Diagram of spatial non-uniformity variation along the cross section of the mixer. with the a1 structure showing a decrease of up to 0.3061, far exceeding the other structures. Comparing the cross-sectional data at 0.5 m, the b1 structure had a spatial non-uniformity as low as 3.5%, with an improvement of more than 10% compared to the other structures. When the length reached 1.25 m, the spatial non-uniformity of all structures decreased to less than 15%, showing a significant improvement in performance compared to the mixer structures without helical blades at the front end of the mixing zone.
## 4 Experiment
### Comparison of the Two Test Results and the Simulation  
It can be seen from the comparison of the two test results that the results based on the acceleration sensor were in good agreement with that of the imaging test in regards to the amplitude of pixel offset, distribution of peak frequency, and harmonic order characteristics. The effect of microvibrations on image motion were mainly concentrated at 350-450 Hz, which was similar to the simulation prediction. However, analysis was performed with critical damping (nominal 0.1%) set as a constant value, which was different from actual damping in the ground test. Therefore, the amplitude of simulation results has a difference to the test results.  
Figure 12: The pixel offset results of the test based on acceleration sensors in the frequency domain.  
Figure 13: The pixel offset results of the imaging test in the frequency domain.  
To better verify the correctness of the experimental scheme, three peaks in different speeds were extracted to compare the corresponding results of the imaging test. According to the comparison, the frequency and amplitude of the three peaks in the two tests were nearly identical. In light of the difference of inherent sampling frequencies set by the signal acquisition equipment, a slight error of 0.5 Hz was found after FFT. The maximum tested amplitude was 0.071 pixels, with a relative error of 8.4% in the imaging test results, indicating that influence ratio analyses for each optical component were effective. It can be inferred that the error could be reduced with a complete input signal from every optical component.
## 3 Results and Discussion
### High-Temperature Property Analysis
#### 3.2.3 Tg  
The TG test is regarded as an effective way to evaluate the thermal stability and to subsequently assess the high-temperature property of a material. In this study, the TG test results for all bitumens are illustrated in Figure 7. According to Figure 7, no matter if it is virgin bitumen or modified bitumen, the mass gradually decreased along in correlation to the rising temperature. Two parameters were utilized when quantitatively comparing the thermal stability between these bitumens, namely the mass loss at 160 \({}^{\circ}\)C (which is the highest construction temperature among the bitumens in Figure 5), and the final residual mass, the results shown in Figure 8. Regarding mass loss at 160 \({}^{\circ}\)C, the ranking was: Virgin bitumen \(>\) MMT-modified bitumen \(>\) REC-modified bitumen \(>\) OMMT-modified bitumen \(>\) OREC-modified bitumen. Obviously, the existence of the layered silicates gives an improvement in thermal stability due to the reduction of mass loss. Among these layered silicates, the OREC restricts the volatilization of some of the bitumen components most effectively, because of its largest specific surface area. Although the REC-modified bitumen exhibited a relatively large mass loss at 160 \({}^{\circ}\)C with respect to OMMT-modified bitumen, the increment was slight. This can suggest that the REC-modified bitumen has a similar thermal stability to the OMMT-modified bitumen. It can be noted that, in Figure 8a, the organic treatment to MMT caused a greater decrement in mass loss, the reason of which can be explained by the cation exchange capacity difference. In summary, the mass loss results further verify the effects of layered silicates on the high-temperature properties of bitumen.  
On the other hand, in terms of final residual mass, the REC- and OREC-modified bitumens were dramatically greater than MMT and OMMT bitumens, indicating that the larger aspect ratios and interlayer spaces of REC and OREC are able to hinder the bitumen mass loss more obviously at 600 \({}^{\circ}\)C. Therefore, when considering the selection of pristine layered silicates, REC is probably more conductive to bitumen high-temperature properties than MMT.  
Figure 6: The G*/sin\(\delta\) result at 64 \({}^{\circ}\)C for bitumen specimens.
## 4 Role of Ap4A: Functions in Normal and Cancer Cells
### Interaction with Purinergic Receptors
#### 4.5.1 P2X Receptors  
P2X receptors are a family of ligand-gated ion channels that are activated by extracellular ATP. These receptors are integral membrane proteins composed of three subunits, which can form either homo- or heterotrimers. Upon binding ATP, these channels open, allowing the flow of cations such as sodium (Na+), potassium (K+), and calcium (Ca2+), leading to depolarization of the cell membrane and subsequent intracellular signaling events. P2X receptors are known to be involved in various physiological and pathological processes, including synaptic transmission, inflammation, pain perception, and immune responses [31]. For instance, P2 XRs are expressed by multiple malignant tumors and are increasingly recognized as prognostic indicators and potential therapeutic targets [41]. Additionally, P2X4, P2X5, and P2X7 receptors exist on the membrane of multiple tumor cells, such as non-small cell lung cancer, colorectal cancer, bladder cancer, renal cancer, as well as hematological malignancies, to promote the proliferation and metastatic potential of the tumor [42]. Ap4A acts as an agonist at several P2X receptors; therefore, its specific interactions with key P2X receptor subtypes and their roles in cancer will be discussed below.
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 4 Applications of RL in FANET
### Flight Trajectory Selection
#### 4.2.2 Decentralized Trajectory Design  
A scenario is considered in [56], where multiple UAVs are performing real-time sensing and sending tasks. The main motive is to determine the decentralized flight trajectories using the opponent modeling \(Q\)-learning algorithm to transmit data efficiently using the sense-and-send protocol. The opponent modeling \(Q\)-learning algorithm is a  
Figure 12: UBS flight trajectory in FANET.  
multiagent \(Q\)-learning algorithm, in which explicit models of the other agents are learned as stationary distributions. Using these distributions, agents take action from joint state-action pairs in each cycle [56]. Moreover, two performance-enhancing methods, action set reduction, and model-based rewarding, are introduced in the opponent modeling \(Q\)-learning algorithm to achieve a high convergence speed.  
Similarly, in [57], a decentralized DRL algorithm was used to navigate multiple UBSs to provide data services to a set of ground users. [PERSON] et al. formulated an optimization problem using POMDP and used actor-critic-based distributed control method to fly the UBSs energy-efficiently.
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 3 Method  
### Search Strategies  
This paper searched for journal articles related to online learning through VR and VC platforms. To ensure comprehensive coverage, two prominent academic databases, Web of Science and Scopus, were consulted. The search terms used to identify literature on VR were (\"virtual reality\" OR \"metaverse\" OR \"virtual world\") AND (\"online learning\" OR \"online teaching\" OR \"online course\" OR \"distance learning\"). For literature on VC platforms, the search terms were (\"video conferencing\" OR \"Zoom\" OR \"Tencent meeting\") AND (\"online learning\" OR \"online teaching\" OR \"online course\" OR \"distance learning\"). The search was limited to peer-reviewed journal articles published in English between 1 January 2003 and 31 December 2023. In Web of Science, searches were limited to the core collection and the research area of Education Educational Research, yielding 969 articles. In Scopus, 1352 articles were retrieved without restricting the search to subject areas, as Scopus does not include a distinct \"Education\" classification. Initially, a total of 2321 articles were identified for potential inclusion (including duplicates). Because there are few articles comparing VR with VC platforms, this paper conducted two independent meta-analyses of the literature related to the two topics. This approach was necessary to present accurate and tangible educational application outcomes.
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 3 Results
### Performance 2D CNN  
We tested the performance of the 2D CNN in our dataset. The confusion matrix for the 2D architecture applied to the MFCCs is shown in Table 6, and the confusion matrix achieved using the Mel spectrogram is shown in Table 7. The average recognition rates of events using this architecture were 69.3% and 75.3% with false positive rates of 5.6% and 3.52% for the 2D CNN using MFCCs and Mel spectrogram as inputs, respectively. These results show that this architecture presents a clear improvement over the 1D CNN, especially when the Mel spectrogram is used as an input.
## 3 Results and Analysis
### Validation of the C/N0 Based Weighting Model
The observations with C/N0 below 30 dB-Hz are not considered in this study. According to the MINQUE method, the estimated coefficients and of the proposed C/N0-based weighting model are \(663.826\) and \(-664.125\), respectively. The dashed line in the figure shows the \(95\%\) confidence level of the observation noise, which agrees well with the data. It should be mentioned that the coefficients are the same for GPS and BDS because they are estimated with both GPS and BDS L1 measurements. Considering that the C/N0 characteristics may vary in different constellations and signals, but we are limited by the length of this paper, so in the next step it is worth conducting further comprehensive research on different models for GPS and BDS, respectively, even for each satellite signal.
# Analyzing GPU Performance in Virtualized Environments: A Case Study
## 2 Background
### Sr-Iov  
SR-IOV addresses the resource-sharing limitation of PCI passthrough. Partitioning a single physical device's hardware functions into physical functions (PFs) and virtual functions (VFs) allows multiple VMs to share a single device. Supported by major hypervisors such as KVM and Xen [19; 20], its implementations include NVIDIA GRID [5] and AMD MxGPU [6]. SR-IOV offers balanced performance, equitable pGPU distribution, and enhanced security via vGPU isolation.
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 6 Simulation Results
The results are shown in Figure 4.  
It can be seen from the results in Figure 4 that no matter which optimization scheme is adopted, the residual energy decreases with the increase of the task size, but the proposed joint optimization scheme is still superior to other benchmark schemes and has better stability than the benchmark scheme. Specifically, when the task size is bigger, the decline speed of the LOC curve is faster, while the decline speed of MMM, OFF and FTO is very slow. However, the residual energy of MMM is always greater than OFF, and the FTO curve decreases faster when the task is large, which shows that MMM has a good solution combination and better ability to adapt to larger tasks. MMM jointly optimizes task-offloading size, energy harvesting time and task-offloading time to find the best combination, while LOC and OFF can only execute all tasks in the local and MEC servers, respectively. FEH optimizes the task-offloading size, fixes the energy harvesting time and the total offloading time of all equipment tasks. Therefore, the residual energy of FEH is relatively stable as the task increases. In addition, when the task is relatively small, the residual energy of the other four methods except FEH tends to be equal, and the residual energy of FEH is only half of them, which shows that fixing energy harvesting time is unreasonable. Figure 4: Task size vs. residual energy. The third simulation shows the relationship between the residual energy (J) and the length of the time block T(s). By changing the length of the time block, we observe the residual energy changes when the five schemes are executed separately. The results are shown in Figure 5.  
From Figure 5, we have several observations. First, when the time block is small relatively, the residual energy of the LOC is the smallest. The energy harvesting time at this time is relatively short, and the energy consumed by the local execution task is relatively large. At the same time, the residual energy of MMM and FTO are very close to some extent, which indicates that the combination of FTO and MMM solutions is similar. Secondly, as the length of the time block increases, the residual energy of the LOC quickly approaches that of OFF, which indicates that the combination of OFF method solutions is no close to the MMM optimal combination, and the rapid growth of LOC does not quickly approach MMM. Finally, we found that the curves of MMM and OFF and FTO are closer to a straight line, but the linear rise of MMM and FTO is larger, which shows that the better performance and adaptability of MMM and FTO schemes. In addition, as the time block becomes increases, the growth rate of the FET is the smallest. From the results, compared with other solutions, the residual energy is only approximately half compared with other solutions.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
###### Abstract  
Understanding the impact of water availability on vegetation growth in the context of climate change is crucial for assessing the resilience of vegetation to environmental shifts. In this study, the relationship between vegetation growth and water availability was studied using a variety of indicators. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Solar-Induced Chlorophyll Fluorescence (SIF) were utilized as vegetation growth indicators, while the standardized precipitation evapotranspiration index (SPEI) and soil moisture indicators served as water use indices. To investigate the vegetation response to water deficit in the Loess Plateau during the growing season from 2000 to 2020, Spearman's rank correlation coefficients were calculated using a 5-year sliding window approach. The spatial and temporal heterogeneity of vegetation response to water deficit during the growing seasons were also explored. The results showed that: (1) with the improvement of moisture conditions, vegetation growth recovered significantly, and there was no expansion trend for vegetation water deficit. (2) The most sensitive timescale of vegetation to water deficit was 6-8 months; the response degree and sensitivity of vegetation to water surplus and deficit were the highest from June to August; and broadleaved forest was the vegetation type most sensitive to water deficit in the early growing season, while grass was the vegetation type most sensitive to water deficit during the mid and late growing seasons. (3) Soil moisture emerged as the dominant factor influencing vegetation growth in the Loess Plateau, followed by precipitation, albeit to a lesser extent. These findings contribute to understanding the mechanism and characteristics of the response of vegetation to climate fluctuations induced by global climate change.  
Loess Plateau; NDVI; SPEI; water deficit; multiple timescales; water availability Article
# Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application
## 2 Design Theory  
### Introduction to Microstrip Antennas  
A microstrip antenna is a resonant radiator, whose radiation field is produced by the electromagnetic field of mutual-motivation between the upper radiation patch edges of microstrip antenna and the grounding plate, and constantly radiates out electromagnetic waves through the gap between them [16; 17]. The parameters of the antenna are the measure of the quality of the antenna. The microstrip antenna designed in this paper will mainly study the bandwidth and the gain. The bandwidth is the frequency range of the antenna when the antenna is off the center of the operating frequency; meanwhile, the antenna's performance parameter is reduced to the allowable value [18; 19]. Generally, the bandwidth of the microstrip antenna is in 0.7%-7%. The gain of the antenna is the ratio of the radiation power intensity generated by the actual designed antenna to the ideal radiation unit at the same point in space as the same input power is equal.