esa-sceva/satcom-chunk-collection huggingface dataset:
# Pulse-Shape Discrimination of SiPM Array-Coupled CLYC Detector Using Convolutional Neural Network
## 5 Conclusions  
In this study, a CNN was used to discriminate the pulse shape of the signal above 325 keV of the CLYC detector. When comparing the input of different formats, we found that the input signal of the two-dimensional matrix format was better than the one-dimensional vector format in terms of training time and effect. After further analysis of the feature extraction and transmission processes of the CNN, it was found that the CNN could effectively extract the features of the input signal and amplify the feature variance of different types of signals during the transmission process. After using the CNN and charge comparison method to classify the same unknown signal, the charge comparison method's _FoM_ value was \(1.37\), whereas the CNN's _FoM_ value was \(37.20\), showing that the CNN had better \(n/\gamma\) discrimination ability. Although the output of the high-energy \(\gamma\) signal is slightly higher than that of the low-energy \(\gamma\), the accuracy rate of the CNN in the thermal neutron energy region was \(99.95\%\), and the neutron F1 measure was \(0.99988\), indicating  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Number of Neutrons** & **Number of Gamma Rays** & **Total Number** \\ \hline Charge comparison & 4535 & 38 & \\ CNN & 4536 & 37 & 4573 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of charge comparison method and CNN discrimination results.  
Figure 12: CNN output 2-D histogram of neutron and \(\gamma\) ray.  
Figure 13: One-dimensional projection of CNN output parameters.  
that the results were highly reliable. Future work will focus on conducting experiments to collect more high-energy gamma signals for training and perfecting the CNN method. We concluded that CNNs provided a new method for CLYC detectors to discriminate neutrons and \(\gamma\)-rays.  
Conceptualization, X.T. and Q.W.; Data curation, J.L. and H.L.; Formal analysis, H.Y. and H.L.; Funding acquisition, X.T.; Investigation, Y.L.; Methodology, X.T., H.Y., C.D. and Q.W.; Project administration, Q.W.; Software, J.L., Y.L. and C.D.; Writing--original draft, J.L.; Writing--review & editing, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 41904162 and 42004151), the Sichuan Science and Technology Program (Grant Nos. 2021 JDRC0011 and 2021 YSY0058), the Nuclear Energy Development Research Program (Research on High Energy X-ray Imaging of Nuclear Fuel), Open Foundation of Artificial Intelligence Key Laboratory of Sichuan Province (Grants Nos. 2020 RZJ01).  
Not applicable.  
All the data generated and analyzed during this study are included in this paper or are available from the corresponding authors on reasonable request.  
The authors declare no conflict of interest.
## 1 Introduction
A numerical study on the flow in the return device of a multistage centrifugal pump is presented in Ref. [17]. The standard \(\mathrm{k}\)-\(\varepsilon\) turbulence model is adopted as the turbulence model, and impeller rotation is simulated using the multiple reference frames (MRF) method. The results obtained concern the effects of the return channel blade curvature on reducing the recirculation of flow inside the return channel vanes of a multistage centrifugal pump. It is demonstrated that return channels with exit blade angles of 90 and 100\({}^{\circ}\) ensure the best performance in the head and efficiency of the pump among the considered return channels. In addition, these channels are used to remove the flow recirculation inside the return channel; hence, the efficiency of the pump can be increased by approximately 2%. The impact of channel-diffuser blades on the energy performance of a three-stage centrifugal pump is studied in Ref. [18]. The research is numerical, and for accomplishing this study, ANSYS CFX is used. In this case, a \(\mathrm{k}\)-\(\varepsilon\) turbulence model is applied. The analysis of the results obtained indicates that under the design condition, the flow loss in three-stage diffusers accounts for 60.23% of the total flow loss. The pump efficiency is maximum when the blade inlet angle is set to 12 degrees. Through research conducted in the field of multistage centrifugal pumps, particularly in relation to the assessment of the impact of RGV on pump performance, it is evident that enhancing vane geometry remains an unresolved research issue. The advancement of computational techniques and software tools for the numerical modeling of fluid flows has made these studies significantly more cost-effective and accessible compared to experimental approaches. The reviewed studies have demonstrated enhancements in RVG geometry, primarily applicable to systems operating with a specific radial vane diffuser. As mentioned in Ref. [5], the diffuser effect on pump losses is considerable. A study comparing two different vane diffusers in combination with various RGVs in a multistage pump is presented in Ref. [2]. The study demonstrates that the head produced by the pump is lower than what is stated in the manufacturer catalog, despite the pump being manufactured with smaller clearances in the front impeller seal. This study aims to validate a two-stage CFD model of an 11 MT32 pump using experimental data provided in Ref. [2]. A new geometry for an RGV is designed, and a geometric model of the studied pump stages with this new geometry is created. A computational mesh is generated and numerical experiments are conducted using ANSYS CFX 14.0. Performance indicators for the studied pump stages with the new RGV geometry are obtained as a result. After conducting a comparative analysis of the results, it is clear that when using the modified vane, the studied stage achieves a higher head and operates with a higher hydraulic coefficient of efficiency compared to the original (standard) vane.
## 4 Discussion
### Adaptability of the Model  
At the regional scale, the Biome-BGC model is widely used to study the variations in water and carbon fluxes within individual vegetation ecosystems [17; 45; 46]. Most studies usually conducted research on the carbon flux after optimizing the model parameters using data of a single EC site. And their research object most only included one kind of vegetation. Therefore, there was a lack of exploration into the following two questions: What is the model adaptability of optimized parameters for the same typical vegetation type in different environments? What are the differences in model adaptability among different typical vegetation types?  
The GPP product data, as the observation data for this study, provided great conditions for the adaptability test. The average R\({}^{2}\) values for temperate grasses, alpine meadows, shrubs, evergreen needle leaf forests, deciduous broad leaf forests, one crop a year, and two crops a year were all above 0.8 (two crops a year was 0.75). The average RMSE values ranged from 26.57 to 66.24 g C m\({}^{-2}\). Moreover, there were relatively few outliers among the simulation of all typical vegetation types (R\({}^{2}\): Percentile < 10%, RMSE: Percentile > 90%). The overall average R\({}^{2}\) value for the Yellow River Basin was 0.85, with an average RMSE value of 50.92 g C m\({}^{-2}\). From the results of the adaptability test it was clear that the Biome-BGC model could achieve stable and accurate simulation of the carbon flux in complex environments across large regions after great calibration and validation. And, surely, Biome-BGC had an excellent adaptability test.  
The model adaptability varied significantly between different typical vegetation types, and the reasons are as follows: (1) To carry out regional simulation, this study divided the YRB into grids with a resolution of 0.25\({}^{\circ}\). This would unavoidably produce errors due to the scaling-up of vegetation coverage data. As a result, this led to the confusion of vegetation coverage information in areas with complex vegetation type distributions, whereas regions with uniform vegetation distribution were less affected. Alpine meadows are primarily distributed in the eastern part of the Tibetan Plateau, characterized by a single type of vegetation. Therefore, the adaptability test of the alpine meadow ecosystem presented the best results. On the other hand, other vegetation types are affected to some extent by errors in upscaling the data. (2) Some temperate grass and crop areas need human management, such as moving grass, crop rotation, and so on,.  
The Biome-BGC model can only reflect the influence of climatic factors and cannot account for human activities. Despite achieving good simulations for crop rotation in this study, there will still be some errors in simulating temperate grass and crops. In summary, following great parameter calibration and validation, the Biome-BGC model can be used for long-term carbon flux simulations in large-scale study areas.
## 5 Evaluation Results: Influence of Each Layer to the Overall Performance  
This section presents the results of the evaluations of the ZigBee stack configurations presented in Section 3.2, which have been carried out within the basic topology scenarios described in Section 4.3. At the MAC layer, the impact of the maximum number of MAC layer retries is evaluated. The effects on the performance of using different routing metrics at the NWK layer are also analyzed in this section. Finally, the influence of the APL layer RTO algorithm is studied. Each set of measurements for a specific layer is carried out, while the rest of the communication protocol stack is configured with the default settings (see Section 3.2).  
The effect of changing the parameters and mechanisms of the communication protocol stack, as well as introducing new mechanisms is measured in terms of three main performance metrics: the end-to-end PDR, the end-to-end delay and energy efficiency. While the goodput is mainly considered to be an interesting performance indicator for the transmission of large files, it may not have the necessary expressiveness in a WHAN, where the traffic is sporadic and of relatively low volume. The PDR is calculated by counting the total number of successfully delivered packets at the APL layer and dividing it by the number of packets created by the source APL layer entities. The PDR is an important indicator, since it describes how successful the communication protocol stack is in transferring packets through the network. On the other hand, the end-to-end delay is the total time between the transmission of an APL layer packet and its reception at the destination node. The end-to-end delay indicates the time it takes for the network to react to events, such as the push of a button or the triggering of a motion sensor. To measure the end-to-end delay, we use timestamps, taking advantage of the fact that all nodes in the network are synchronized (see Section 4.3). The energy efficiency indicates the average energy that is consumed by the RFDs to deliver one bit of useful application payload to corresponding destinations. The energy consumption is calculated by observing the behavior of the end devices, in particular by measuring the durations of different states and applying the energy consumption values listed in [45] for each state. Since we employ a different transmit power level, we measured the energy consumption of our nodes at the transmit state (\(I_{tx}\)).
# Control-Flow Integrity: Attacks and Protections
## 4 Classification of CFI Techniques
### Coarse-Grained CFI  
Coarse-grained CFI is referred to as loose CFI [48]. Coarse CFI distinguishes indirect branch instructions by their classification and imposes policies on each type. Loose CFI checks when a control-flow transfer commences with a return instruction so that the destination can be targeted right after the call instruction. This CFI approach is more relaxed and secures desirable performance, but proven to be dangerous [47]. Though it does not experience high-performance overhead; however, coarse-cfi has a limitation in obtaining details of application [49].  
Figure 2 shows a coarse-grained CFI approach where the execution flow consists of direct and indirect control-flow transitions. Both direct and indirect call/ret instructions from 1-4 are valid and verified by coarse CFI. However, the indirect call, call *aax, is a pointer; therefore, besides going toits valid destination function f2(), attackers are also able to divert the flow by illegal jumps to function f1() and initiate a control-flow attack.  
Coarse-grained focuses on if many unessential edges exist, whereas a fine-grained CFI intensifies a fully precise static CFI. A recent investigation reveals that coarse-grained CFI is not fully defensive against control-flow attacks, whereas fine-grained CFI is presumed to be more secure. [PERSON] et al. [45] demonstrate that coarse-grained CFI is not protective enough to defend against small or even simple applications. However, [PERSON] et al. [48] and [PERSON] et al. [50] argue that compare to fine-grained CFI approach; the coarse-grained enforcement method is enough to protect against CRA.  
Figure 1: Fine-grained CFI approach bypass example. The attacker can successfully redirect (arrow in red) the control flow from the ret instruction in step 4 to the mov instruction next to call f1().  
Figure 2: Coarse-grained CFI approach bypass example. The attacker is able to redirect the control flow from multiple call()s and ret points. Arrows in red show the valid destinations where attackers can redirect the flow.
###### Abstract  
The detection of Gamma-Ray Bursts (GRBs) using spaceborne X/gamma-ray photon detectors depends on a reliable background count rate estimate. This study focuses on evaluating a data-driven background estimator based on a neural network designed to adapt to various X/gamma-ray space telescopes. Three trials were conducted to assess the effectiveness and limitations of the proposed estimator. Firstly, quantile regression was employed to obtain an estimation with a confidence range prediction. Secondly, we assessed the performance of the neural network, emphasizing that a dataset of four months is sufficient for training. We tested its adaptability across various temporal contexts, identified its limitations and recommended re-training for each specific period. Thirdly, utilizing Explainable Artificial Intelligence (XAI) techniques, we delved into the neural network output, determining distinctions between a network trained during solar maxima and one trained during solar minima. This entails conducting a thorough analysis of the neural network behavior under varying solar conditions.  
GRB; neural network; XAI Article
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 2 Materials and Methods
### Simulation Scenario
A Brownian motion simulation is used for jumps to subsequent positions. The dwell time at each position depends on the velocity. The velocity of the UE is defined as a linear distribution within an interval \([v_{min},v_{max}]\) that is defined as desired. Figure 3 shows the random locations of APs and the paths followed by 40 UEs. All the results presented are formed over 100 network snapshots for different random realizations of the AP/UE locations. Figure 3: A random realization of the proposed scenarios. APs are located randomly within the circle of radius R, and the paths followed by each UE are shown with different colors. The simulation scenario has been designed to identify the advantages and disadvantages of the proposed method compared to other methods commonly in wireless networks. The main points to be evaluated in the proposed scenario are as follows. * To evaluate the impact of using DMD on the accuracy and adaptability of CSI in CF-MMMO systems, especially in highly dynamic scenarios. * To evaluate the performance in terms of the accuracy and adaptability of DMD/PiDMD-based Channel Estimation compared to the most common methods used in wireless networks, such as MR, MMS, and other recently proposed methods, i.e., PZF, FZF, and PZFZF. * To evaluate the overall system performance. To achieve the stated objectives, the scenario used was formulated as follows. * Randomly deploy the APs across the simulation area using an independent uniform distribution, employing a square grid within the circular simulation region. * Sequentially introduce the UEs in a random fashion and simulate their movement using Brownian motion. * Compute the distance between the chosen UE and each AP. * Calculate the channel gain from the selected UE to each AP using Equation (3). * Construct spatial correlation matrices \(R_{kl}\) and estimation error correlation matrices \(C_{kl}\). * Generate the estimated channels \(h_{kl}\), and utilize them to compute sample averages that approximate all the expectations in the SE expressions. * Compute the CSI, uplink, and downlink capacity using the DMD algorithms.
# MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data
## 4 Experiment
### Computational Complexity Analysis  
In our research, the MM-iTransformer model integrates textual data with historical price data to improve the accuracy of the economic time series forecast. This integration introduces additional computational complexity primarily due to the use of pre-trained neural networks to generate text embeddings. Given the minimal computational demands of traditional models like ARIMA and the Random Walk Model, which do not use deep learning frameworks, our analysis focuses exclusively on comparing the MM-iTransformer with the original iTransformer.  
Table 5 illustrates that MM-iTransformer exhibits slower training speeds and higher memory requirements than iTransformer. Specifically, on the Forex dataset, the MM-iTransformer processes at 232.89 milliseconds per iteration compared to 49.91 milliseconds for the iTransformer. Furthermore, the memory usage for the MM-iTransformer is 1.02 GB versus 0.59 GB for the iTransformer.  
These findings underscore the increased computational costs associated with the MM-iTransformer. However, the significant improvement in prediction accuracy by effectively leveraging multimodal data justifies these costs. This capability to utilize diverse data types is crucial for accurately capturing complex market dynamics. Future efforts will aim to optimize the model's architecture to reduce computational demands without compromising  
Figure 7: Comparison of predictions on the Forex dataset with a historical window of 96 and a forecast horizon of 12; the vertical axis represents series values, (**a**,**c**) showcase predictions from the MM-iTransformer, integrating textual data alongside historical price inputs, while (**b**,**d**) illustrate results from the iTransformer, which utilizes only historical price data.  
performance. Enhancing the efficiency of the model will broaden its applicability, making it more suitable for real-time trading systems and large-scale financial applications.
# Multi-Focus Image Fusion Based on Decision Map and Sparse Representation
## 3 Experiment and Analyses
### Objective Evaluation Metrics  
To evaluate the fusion quality of different fusion methods, three fusion quality metrics were utilized in our experiment. The large value of the fusion quality metric indicates better fusion quality.  
1. Normalized mutual information (MI), \(Q_{MI}\)[36]: \(Q_{MI}\) is used to overcome the deficit of MI [37]. \(Q_{MI}\) is defined as \[Q_{MI}=2\Bigg{[}\frac{MI(A,F)}{H(A)+H(F)}+\frac{MI(B,F)}{H(B)+H(F)}\Bigg{]}\] (10) where \(H(X)\) is the entropy of image \(X\), and \(MI(X,Y)\) is the mutual information between image \(X\) and \(Y\). The \(Q_{MI}\) measures the amount of information in the fused image inherited from the source images.
2. [PERSON]'s metric, \(Q^{AB/F}\)[38]: \(Q^{AB/F}\) evaluates the fusion performance by measuring the amount of gradient information transferred from source images into the fused image. It is calculated by \[Q^{AB/F}=\frac{\sum_{i,j}\Big{(}Q^{AF}(i,j)W^{A}(i,j)+Q^{BF}(i,j)W^{B}(i,j) \Big{)}}{\sum_{i,j}(W^{A}(i,j)+W^{B}(i,j))}\] (11) where \(Q^{AF}(i,j)=Q^{AF}_{g}(i,j)\cdot Q^{AF}_{o}(i,j)\). \(Q^{AF}_{g}(i,j)\) and \(Q^{AF}_{o}(i,j)\) are the grad magnitude and orientation at pixel location \((i,j)\), respectively. \(Q^{BF}\) is computed similarly to \(Q^{AF}\). \(W^{A}(i,j)\) and \(W^{B}(i,j)\) are the weights of \(Q^{AF}(i,j)\) and \(Q^{BF}(i,j)\), respectively.
3. The quality index, visual information fidelity for fusion (_VIFF_) [39]: This is a multiresolution image fusion metric based on visual information fidelity. To calculate the _VIFF_, the images are divided into blocks in each sub-band, and visual information in each block is measured using  
Figure 7: Twenty pairs from the Lytro image dataset.  
different models, including the Gaussian scale mixture (GSM) model, the HVS model, and the distortion model. The _VIFF_ of each sub-band is then calculated, and an overall quality measure is determined by weighting.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 4 Distributed Machine Learning Algorithms  
With the advances of the 5G wireless communication standard, a large set of heterogeneous devices with sensors, storage, and processing capabilities are being integrated into the networking infrastructures. These devices can collect tons of data that can be utilized to analyze and solve various networking challenges. Various ML algorithms can be useful for these operations [22]. In the traditional centralized ML approaches, the data collected by the devices are required to be transmitted towards a centralized node equipped with more powerful processing capabilities. However, wireless devices can have limited resources, and such an approach can be challenging, especially in the case of latency-critical applications and services. Additionally, network users are becoming more aware of privacy and are reluctant to share their sensitive data with the outside world to avoid potential data theft. With recent progress in hardware and software technologies, devices can process a limited set of data onboard, allowing them to create the local ML models based on their data. However, such models can have limited accuracies, since most wireless environment users can impact each other's decisions. By integrating the device's local computing capabilities and centralized processor coverage capabilities, various distributed and federated learning (FL) techniques can be implemented. Additionally, the cloud computing-based network settings are increasingly being replaced by processing at the network's edge, i.e., edge computing, which allows for the implementation of ML algorithms to make the best use of the data these devices produce. The concept of centralized ML is no longer dominant [23]. This trend reversal has as its cornerstone the introduction of distributed learning algo  
Figure 2: Reinforcement learning framework.  
rithms [3]. In the following we will focus on the most important DL paradigms, i.e., FL and multi-agent RL (MARL).