esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Retrieval Method
#### 2.3.2 Integrated Retrieval Technique Using Multimode Doppler Spectra
As a simple solution, reflectivity of the traced spectral bin (similar to Z), \(\mathrm{Z_{traced}}\) (in dBZ), can be calculated to document the qualitative reliability and bias of the retrieval. The value of \(\mathrm{Z_{traced}}\) is related to the size of the traced target, which can be used to judge whether the terminal speed could be neglected. A small value of \(\mathrm{Z_{traced}}\) can illustrate that the traced target is small-sized, with a negligible terminal speed, and the retrieved \(\omega\) from Doppler spectra will be reliable and accurate. On the contrary, a large value of \(\mathrm{Z_{traced}}\) can indicate that the traced target is relatively large, with a non-negligible terminal speed; as a result, the retrieved \(\omega\) will have a certain bias. After the above processing, a simple procedure is performed to merge the results from three modes. The procedure uses the deviation between the CI and BL/PR to shift the profile under 2.01 km, since CI (corresponding to the upper profile with a range from 2.01 to 15.3 km) yields the best sensitivity, which is useful for maintaining retrieval accuracy. Finally, an entire profile with a range from 0.12 to 15.3 km is obtained.
# Application of EDEM Simulation for Calculating and Optimizing a Closed Coal Fly Ash Screw Conveyor
## 2 Materials and Methods
### Specification of Coal Fly Asht
Additionally, moisture content exerted an impact, as fly ash with higher moisture content exhibited lower bulk density compared to counterparts with lower moisture content and the bulk density of fly ash in Vietnam, which is approximately 880 kg/m\({}^{3}\). The bulk density of fly ash from thermal power plants in developed and developing countries worldwide was determined using the Archimedes method. The findings indicated that fly ash from developed countries exhibits a higher bulk density compared to that from developing countries. This discrepancy arises from the fact that developed nations commonly employ coal with higher ash content and employ more advanced coal combustion technologies [25; 26]. The difference in moisture content and combustion technology is the cause of the variation between the two materials in the EDEM library and the actual conditions in Vietnam, as illustrated in Table 2. Furthermore, a separate investigation outlined in study [21] explored the solid density of fly ash originating from coal-fired power plants, revealing a value of approximately 2200 kg/m\({}^{3}\). The coefficient of static friction of fly ash is subject to variation based on specific properties such as particle size distribution, moisture content, and compaction. It is crucial to measure this coefficient for the fly ash to be utilized to ensure safe and efficient handling. A study conducted by [PERSON] al. reported a coefficient of static friction of 0.56 for fly ash [27]. For the coefficient of rolling friction, which denotes the ratio of force necessary to sustain rolling motion between two surfaces to the normal force between them, values typically range between 0.05 and 0.1 for fly ash from coal-fired power plants. [PERSON] et al. observed a coefficient of rolling friction of 0.08 for fly ash [27]. Regarding the coefficient of restitution, which represents the ratio of the velocity of separation after a collision to the velocity of approach before the collision, values for fly ash generally fall between 0.3 and 0.6. [PERSON] et al. found a coefficient of restitution of 0.5 for fly ash [28]. Finally, the set of material property parameters is established for the purposes of calculation, optimization, and simulation, as outlined in Table 2.  
The two material properties are quite similar, differing primarily in bulk density. In this section, the adjustments were made to align with the characteristics of fly ash in Vietnam. This adjustment is a critical factor influencing the accuracy of the screw conveyor calculation and simulation.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 13
#### 2.1.6 Coverage Enhancement Method  
NB-IoT is designed to enhance coverage for the applications that are in hard-to-reach areas such as deep indoors and basements. In this regard, NB-IoT delivers an additional coverage of 20 dB as compared to the legacy LTE system. This corresponds to 164 dB of MCL. To enhance its coverage, NB-IoT uses up to 128 and 2048 retransmissions in uplink and downlink, respectively. Hence, this makes NB-IoT suitable for use cases that are latency insensitive as it can tolerate up to 10 seconds transmission delay.
# Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
## 4 Experiments and Discussion
### Parameters Study  
We studied the model performance with different values of two important hyper-parameters: the embedding dimension \(d\) in the time-evolving graph generation module and the hidden size \(h\) of GRU units. The latter is also the output dimension of the graph convolution module. For each experiment, we only change the selected hyper-parameter while fixing other settings. The average MAE loss of different settings on METR-LA and PEMS-BAY are shown in Figures 6 and 7, respectively. The optimal values of both embedding dimension and hidden size on PEMS-BAY are larger because of the larger amount of traffic nodes. For METR-LA, we set the embedding dimension at 30 and the hidden size at 40. The counterparts of PEMS-BAY are 40 and 50. The performance on METR-LA is more sensitive to the values of hyper-parameters.  
Figure 5: Heatmaps of predefined distance-based graph and the learned time-evolving graphs on METR-LA. (**a**) Distance-based graph. (**b**) Learned time-evolving graph at 08:00. (**c**) Learned time-evolving graph at 17:30.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Overall MAE** & **Overall RMSE** & **Overall MAPE** \\ \hline TECCRN & 2.908 & 5.705 & 7.866\% \\ Static Adpative & 2.969 & 5.792 & 8.072\% \\ Static Predefined & 3.015 & 5.887 & 8.258\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of ablation study on METR-LA.
## 2 Materials and Methods  
### The Background to Deblurring Algorithms  
#### 2.1.1 The Lp-Pseudo-Norm  
The Lp-pseudo-norm represents the distance in the vector space and is a generalized concept of \"distance.\" The Lp norm of the matrix \(\mathbf{X}\) is defined as \(\left\|\mathbf{X}\right\|_{p}=\left(\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N} \left|\mathbf{X}_{ij}\right|^{p}\right)^{\frac{1}{p}}\), when \(p\) is 1 and 2, and corresponds to the L1 and L2 norms, respectively. The Lp-pseudo-norm is defined as \(\left\|\mathbf{X}\right\|_{p}^{p}=\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N} \left|\mathbf{X}_{ij}\right|^{p}\). This study focused on the case where \(0<p<1\). The contour maps of different norms are presented in Figure 1. The Lp-pseudo-norm has one more degree of freedom than the L1 and L2 norms, and the contour of the Lp-pseudo-norm was closest to the coordinate axis. Hence, the solution has a higher probability of being a point that is on or close to the axis. The Lp-pseudo-norm has a stronger sparse representation ability based on these reasons. Therefore, the Lp-pseudo-norm was introduced into the model for salt-and-pepper denoising in this study.
## 6 Conclusions  
This paper considers a satellite communication network made up by a GEO satellite and multiple EH-STs. The EH-STs transmit data packets to the satellite randomly over a shared wireless collision channel. Packet replicas and a successive interference cancellation mechanism are adopted to improve the network throughput. Considering the EH process and energy constraints of EH-STs, we have designed a distributed RA scheme aiming to maximize the average long-term network throughput. Firstly, we developed an analytical model of the average long-term throughput with constraints  
Figure 8: Packet delay cumulative distribution for the proposed scheme and DFSA with number of EH-STs \(U=200\) under different EH rates \(\tilde{\beta}\in\{1,2,3\}\), \(e_{max}=10\). For access schemes, number of time slots in each frame \(N=200\) (only for the proposed scheme. For DFSA, the packet delay in frames is normalized, since the frame length of DFSA is dynamic), QPSK modulation, \(E_{s}/N_{0}=10\) dB.  
of packet loss ratio and energy and adopted a game theoretic method to approximate the solution of the nonconvex optimization problem. Then, we characterized the symmetric NE of the game and proved its existence and uniqueness, which indicated it is a locally optimal solution of the original optimization problem. A policy iteration algorithm combined with a bisection method was used to compute the symmetric NE. Finally, we evaluated the proposed RA scheme by simulations. Simulation results showed that the proposed RA scheme is more suitable for the large network size and high EH rate scenario, which is applicable to EH devices in the future low-cost interactive satellite communication system.  
This paper provides a common but effective train of thought of designing a distributed RA policy for energy harvesting devices in satellite communication networks, but it is based on a simplified version of CRDSA. In order to further improve the policy, we will concentrate on researching the following works in the future. First, we will extent the policy to an asynchronous version, since it is more suitable to the energy constrained system (time synchronization will cost extra signalings and energy). Second, we will investigate the performance of using irregular replicas based on the analysis method in this paper. In addition to the number of packet replicas, the effect of power imbalance and transmission power selection are also important factors that need to be taken into consideration in future work.  
[PERSON] proposed the basic framework of the research scenario. [PERSON] was in charge of modeling the problem and researching the optimization algorithm. In addition, [PERSON] did the numerical simulations and wrote the paper. [PERSON] gave some suggestions on the mathematical model and formula derivation.  
This research was supported by the National Nature Science Foundation of China (NSFC) under the Grant No. 61601045 and 61372111.  
The authors declare no conflict of interest.
## 5 Discussion
### Epidemiology Implementation Subgroup  
ML and AI technologies are also applied to monitor and forecast epidemics around the world, based on data collected from satellites, historical information on the web, and real-time updates on social networks. SVM and ANN have been used, for example, to predict malaria epidemics, taking into account data such as temperature, average monthly precipitation, and total number of positive cases [85; 86].  
As a result of processing the corpus of documents corresponding to this topic in [87], about 20 machine learning technologies used in modern pharmaco-epidemiology were identified: deep learning of neural networks of various topologies (mainly CNN), RF algorithms, K-clustering and K-random neighbors, classification, regression and decision trees, Bayesian methods, SVM, discriminant analysis, hierarchical clustering, etc.  
Machine learning technologies and methods were used to solve problems predicting clinical reactions and responses to the applied pharmacotherapy, determining the required dosage for pharmacotherapy for a particular patient, predicting drug consumption, predicting the degree of adverse reactions to drug use, predicting the length of stay of patients receiving clinical therapy, etc.  
It was shown that in more than 50% of comparisons, AI showed better results than traditional pharmacoepidemiological methods. However, there is a high heterogeneity of efficiency among various AI methods. Many methods have hardly been investigated. Ultimately, these results suggest that further research is needed, focusing on head-to-head comparisons of traditional pharmacoepidemiological methods with machine learning methods in different research scenarios and with different data sources.
## 5 Experiments
### Scalability  
The PTC dataset was used for the proposed algorithm's scalability tests. PTC is a dataset composed of four graph collections: PTC-FM, PTC-FR, PTC-MM, and PTC-MR. These collections are described in Table 9. The four PTC collections were combined into a single collection containing 1380 graphs with an average of 14.24 nodes and 14.63 edges per graph.  
Multiple tests were conducted using portions of the collections described above based on their size. Each test was performed using dissimilarity thresholds of 1 and 2. Regarding the support threshold, a constant support threshold was set to 70% because a higher threshold did not produce any frequent subgraphs. In each test, runtime and maximum RAM usage were measured to evaluate the resources required to execute our algorithm.  
Initially, we ran our algorithm on 100 graphs from both collections, using the parameters described above. Subsequent tests were performed by adding 100 graphs per experiment until all the graphs in the collection were included.  
The measured runtimes are presented in Figure (a)a, and the maximum memory usage is shown in Figure (b)b. We evaluated all 1380 graphs in the collection with both dissimilarities in less than 1 h using a maximum of 120 GB of RAM.  
These tests exhibit growth in terms of runtime and memory usage; however, the runtime increased at a faster rate than the memory usage. The results reveal that increasing the dissimilarity threshold significantly affected the runtime, leading to longer runtimes. However, this effect does not extend to the memory usage.
# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 1 Introduction
Moreover, future traffic information forecasting based on CFVD has not been investigated. Therefore, this study proposes analytic models and traffic information estimation methods based on CFVD to obtain a low-cost solution for ITS. The proposed analytic models are used to analyze the relationship between traffic information (e.g., traffic flow, traffic density and vehicle speed) and the amount of cellular network signals (e.g., HO, NLU, CA and PLU). Then, traffic information can be estimated based on the proposed analytic models and CFVD. Furthermore, a vehicle speed forecasting method based on a back-propagation neural network (BPNN) algorithm is proposed to analyze the estimated vehicle speeds from CFVD and to predict the future vehicle speed for road users. The rest of this study is organized as follows. Section 2 discusses the related studies and techniques of traffic information estimation and forecasting methods. Section 3 purposes vehicle speed estimation and forecasting methods based on CFVD. A case study of a highway in Taiwan and experimental results are illustrated and analyzed in Section 4. Finally, Section 5 summarizes the contributions of this study and presents future work.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 4 Problem Solving
### Formulate the Problem as the MDP
#### 4.1.1 State Space  
In this enemy avoidance problem, the design of the state space mainly considers the positions and attitudes of the UAVs, as well as the position of the goal point \(G_{f}\). However, these raw data are not used directly as the state variables. Instead, higher-level variables based on these data are defined. To be specific, the state space contains three sets of variables:  
1. [label=()]
2. Variables about the status of the decision making UAV \(f\): \[\begin{array}{l}\bullet\quad\quad v_{f}\text{: The velocity of the decision making UAV }f.\\ \bullet\quad\quad\psi_{f}\text{: The heading angle of the decision making UAV }f.\\ \end{array}\]
3. Variables about the goal point \(G_{f}\): \[\begin{array}{l}\bullet\quad d_{fg}\text{: The distance from the decision making UAV }f\text{ to the goal point }G_{f}.\\ \bullet\quad\omega_{g}^{\prime}\text{: The angle between the north direction and the line from the decision making UAV }f\text{ to the goal point }G_{f}.\\ \end{array}\]
4. Variables about the status of the enemy UAV \(e\): \[\begin{array}{l}\bullet\quad v_{e}\text{: The velocity of the enemy UAV }e.\\ \bullet\quad\psi_{e}\text{: The heading angle of the enemy UAV }e.\\ \bullet\quad d_{fe}\text{: The distance from the decision making UAV }f\text{ to the enemy UAV }e.\\ \bullet\quad\omega_{e}\text{: The angle between the north direction and the line from the decision making UAV }f\text{ to the enemy UAV }e.\\ \end{array}\]  
As we can see, there are 8 state variables in total. Figure 3 illustrates these state variables. The value ranges for all the velocity variables are \([10,20]\), while the value ranges for all the angle variablesset in this way: the \(X\) axis points to the North, and the \(Y\) axis points to the East. Besides, the system neglects the rotation and acceleration of the earth, and the earth is assumed to be flat.
## 5 Discussion
[23] used the ARMA model to predict the longwave radiation values of the 8 August 2017 Jiuzhaigou _M_s7.0 earthquake and found OLR anomalies at the epicenter on 27 July and 5 August; [PERSON] et al. [61] used the sliding quartile to detect ionospheric TEC anomalies for the same earthquake and found TEC anomalies on 28 July, 4 August, and 6 August; the timing of OLR anomalies and of ionospheric anomalies were close. [PERSON] et al. [62] used Chinese seismo-electromagnetic satellite data to detect electron density anomalies of the Yangbi _M_s6.4 earthquake in Yunnan on 21 May 2021 and found anomalies on 5 May and 8 May; [PERSON] et al. [63] used ionospheric TEC data to study the same earthquake case and detected the strongest anomaly near the epicenter on 5 May. We found strong and concentrated OLR anomalies from 5 May to 6 May before the Yangbi earthquake. The timing of the OLR anomalies is also very close to that of the ionospheric anomalies detected before the Yangbi _M_s6.4 earthquake. Therefore, we believe that there is a specific link exists between the OLR anomaly and the ionospheric anomaly that existed before the earthquake. In this paper, we only selected the Yangbi _M_s6.4 earthquake of 21 May 2021 and the Luding _M_s6.8 earthquake of 5 September 2022 for study. The number of earthquake cases was too low to form a statistical analysis of the method. We detected significant anomalies within a \(5^{\circ}\times 5^{\circ}\) area of the epicenter and a month before the earthquake with the parameters of the earthquake case study determined with reference to the summary of previous works. It was not possible to determine whether such anomalies existed beyond this range and time period. The distribution and timing of pre-earthquake anomalies varied between different-magnitude earthquakes, and specific study areas and times need to be delineated based on specific earthquake examples. The rapid development of deep learning has led to more and better time series prediction models, such as the Transformer algorithm [64; 65], a complex time series algorithm that can better predict OLR values and reduce errors. In addition, the parameter setting of the model was also an important factor affecting good and bad errors, and different parameters give different results. The experiment needed to establish the optimal set of model parameters. In future research, we need to improve and refine the above-mentioned drawbacks.
###### Abstract  
It is important to accurately evaluate ecosystem respiration (\(RE\)) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (\(LST\)), enhanced vegetation index (\(EVI\)), and land surface water index (\(LSWI\)) to build a satellite-based model to estimate \(RE\) at a regional scale. First, the dependencies of both spatial and temporal variations of \(RE\) on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of \(RE\) in northern China's grasslands; while temperature plays a major role in regulating the temporal variability of \(RE\) in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on \(RE\) and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based \(RE\) models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of \(RE\), and then, we evaluated the model performance. Our results showed that the model well explained the observed \(RE\) in both the alpine (\(R^{2}\) = 0.79, RMSE = 0.77 g C m\({}^{-2}\) day\({}^{-1}\)) and temperate grasslands (\(R^{2}\) = 0.75, RMSE = 0.60 g C m\({}^{-2}\) day\({}^{-1}\)). The inclusion of the \(LSWI\) as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of \(RE\). Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of \(RE\), and it avoided overestimating \(RE\) in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of \(RE\) for two typical grasslands and a new alternative up-scaling method for large-scale \(RE\) evaluation in grassland ecosystems.  
Keywords: ecosystem respiration; MODIS; moisture effect; ChinaFLUX; alpine grasslands; temperate grasslands +
Footnote †: journal: Remote Sens.