esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
The CNN-STK model is a hybrid model combining a CNN model with the spatiotemporal Kriging method, which is similar in essence to regression Kriging [37], i.e., decomposing the non-stationary regionalized variables into two components: a deterministic trend and stochastic residuals [38] (p. 8), as expressed in Equation (1). To the best of our knowledge, this integrated method is new and has never been reported. Implementing the CNN-STK method involves three main steps. Firstly, a CNN model is constructed based on XCO2 observations and geographic covariates to extract the deterministic spatial trend. Secondly, the spatiotemporal Kriging method is employed to handle the stochastic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Latitude** & **Longitude** & **Start Date** & **End Date** \\ \hline Hefei & 31.9\({}^{\circ}\)N & 117.17\({}^{\circ}\)E & 2015-11-02 & 2020-12-31 \\ Xianghe & 39.8\({}^{\circ}\)N & 116.96\({}^{\circ}\)E & 2018-06-14 & 2022-05-31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of TCCON sites selected in this study.  
residuals, involving the computation of its empirical semi-variogram, construction of the covariance functions, and prediction of unsampled points. Thirdly, the final interpolated results of the CNN-STK model are obtained by combining the trend values extracted by the CNN model with the residual terms estimated by the spatiotemporal Kriging. The flowchart of this study is shown in Figure 1.  
\[\hat{Z}(s,t)\ =\ \hat{M}(s,t)+\hat{R}(s,t) \tag{1}\]  
where \(\hat{Z}(s,t)\) denotes the XCO2 estimation of the unknown point at position \(s\) and time \(t\); \(\hat{M}(s,t)\) represents the trend component simulated by the CNN model at the same unknown point; \(\hat{R}(s,t)\) is the residual term at the same unknown point obtained by the spatiotemporal Kriging interpolation.
# A Massively Parallel SMC Sampler for Decision Trees +
###### Abstract  
Bayesian approaches to decision trees (DTs) using Markov Chain Monte Carlo (MCMC) samplers have recently demonstrated state-of-the-art accuracy performance when it comes to training DTs to solve classification problems. Despite the competitive classification accuracy, MCMC requires a potentially long runtime to converge. A widely used approach to reducing an algorithm's runtime is to employ modern multi-core computer architectures, either with shared memory (SM) or distributed memory (DM), and use parallel computing to accelerate the algorithm. However, the inherent sequential nature of MCMC makes it unsuitable for parallel implementation unless the accuracy is sacrificed. This issue is particularly evident in DM architectures, which normally provide access to larger numbers of cores than SM. Sequential Monte Carlo (SMC) samplers are a parallel alternative to MCMC, which do not trade off accuracy for parallelism. However, the performance of SMC samplers in the context of DTs is underexplored, and the parallelization is complicated by the challenges in parallelizing its bottleneck, namely redistribution, especially on variable-size data types such as DTs. In this work, we study the problem of parallelizing SMC in the context of DTs both on SM and DM. On both memory architectures, we show that the proposed parallelization strategies achieve asymptotically optimal \(O(\log_{2}N)\) time complexity. Numerical results are presented for a 32-core SM machine and a 256-core DM cluster. For both computer architectures, the experimental results show that our approach has comparable or better accuracy than MCMC but runs up to 51 times faster on SM and 640 times faster on DM. In this paper, we share the GitHub link to the source code.  
parallel algorithms; machine learning; Bayesian decision trees; sequential Monte Carlo samplers; Markov Chain Monte Carlo; shared memory; distributed memory +
Footnote †: This paper is an extended version of our paper published in the 2023 IEEE 35 th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Porto Alegre, Brazil, 20 October 2023.
## 2 Simultaneous Segmentation and Detection Network (SSADNet)
### Detection Branch  
A detection branch in SSADNet for detection training and inference has an FCN structure, including a bridge connecting the segmentation branch and the detection branch, a feature extractor, and bounding box regressor as shown in Figure 4. The bridge stacks the activation map obtained from the decoder of a segmentation branch in the direction of the input and channel. We expected this to suggest areas where the object being detected was located within the input image. The feature extractor performed extraction through convolution and pooling layers. The feature maps obtained from the feature extractor were placed in four bounding box regressors for predicting the x and y coordinates of a center point as well as the width and height of a bounding box. Each feature map was trained by updating parameters. The object class can be found from the class that corresponds to the coordinates of the center point of the bounding box in a segmentation map.  
In SSADNet, the detection branch was connected to the segmentation branch to perform segmentation and detection simultaneously. The detection branch consists of the feature extractor and the bounding box regressor. Figure 6 shows (a) the feature extractor block (F.E. Block) for the feature extractor, and (b) the bounding box regressor block (B.B.R. Block) for the bounding box regressor, and Figure 7 shows the detailed structure of the detection branch. The feature extractor consists of five F.E. blocks. An F.E. block has a convolution filter layer which abstracts from simple features such as color, texture, edge, etc., to complex features which represent each class well, an activation layer, which gives non-linearity to the feature map in order to represent a complex model, and a pooling layer to reduce the feature map size or to prevent overfitting. In the detection branch, there are five F.E. Blocks (Feature Extractor) consisting of two \(3\times 3\) convolution filters with stride 1, batch normalization, ELU activation function, and \(2\times 2\) max pooling with stride 2, in order, and after each F.E. block except the first and last F.E. blocks, an additional \(3\times 3\) convolution filter with stride 1 is applied to extract the features.  
After feature extraction from the feature extractor, the bounding box regressor branches out into four small branches, each re-regulating the width, height, and center point x and y coordinates, because the scales of the bounding box's width, height, and center point x and y coordinate values are different. The bounding box regressor in the detection branch consists of four B.B.R. Blocks connected to the last F.E. Block. Each B.B.R. Block repeats the dropout and ELU to prevent over-fitting on the \(1\times 1\) convolution filter, after applying the \(1\times 1\) convolution filter and the ReLU activation and global average pooling [29].
## 2 Materials and Methods
### Curve Fitting Algorithms  
Let \(\mathbf{x}\) be the vector of model parameters, \(P\) its length, and \(M\) the number of frequency points where the measures are taken. We define the data vector as follows (\({}^{\top}\) stands for transposition):  
\[\mathbf{y}=\left[y(\omega_{1}),\ldots y(\omega_{m}),\ldots y(\omega_{M})\right]^{\top} \tag{5}\]  
in which the \(m\)th component of the vector \(\mathbf{y}\) is the observed value \(y(\omega_{m})\). Let the following:  
\[\varepsilon_{\mathrm{r}}(\mathbf{x})=\left[\varepsilon_{\mathrm{r}}(\omega_{1}; \mathbf{x}),\ldots\varepsilon_{\mathrm{r}}(\omega_{m};\mathbf{x}),\ldots\varepsilon_{ \mathrm{r}}(\omega_{M};\mathbf{x})\right]^{\top} \tag{6}\]  
be the model vector, here given by Equation (4), with \(\varepsilon_{\mathrm{r}}(\omega_{m};\mathbf{x})\) being its estimation at \(\omega_{m}\).  
Solving the least squares problem means finding \(\mathbf{\hat{x}}\) such that the following is the case:  
\[\mathbf{\hat{x}}=\arg\min_{\mathbf{x}\in\mathbb{R}^{P}}\biggl{\{}\frac{1}{2}\|\varepsilon _{\mathrm{r}}(\mathbf{x})-\mathbf{y}\|_{2}^{2}\biggr{\}} \tag{7}\]  
in which the function to minimize, \(\mathbf{\Psi}=\frac{1}{2}\|\varepsilon_{\mathrm{r}}(\mathbf{x})-\mathbf{y}\|_{2}^{2}\), is the \(\ell_{2}\) quadratic norm of the misfit \(\mathbf{r}=\varepsilon_{\mathrm{r}}(\mathbf{x})-\mathbf{y}\), which is a non-linear function such that \(\mathbf{r}:\mathbb{R}^{P}\mapsto\mathbb{C}^{M}\) with \(P\ll M\).  
Many studies in the literature have proposed metaheuristic algorithms for curve fitting, such as genetic algorithm, simulated annealing algorithm, particle swarm algorithm, hybrid variants, etc. [11; 16; 30], because of their ability to deal with very large search spaces. However, they have non-negligible disadvantages: longer execution time, stochastic nature, and poor mathematical background. For these reasons, we preferred to focus on gradient-like algorithms by evaluating their performance in terms of what is considered their weakness: the choice of the starting point.  
Thus, we address the non-linear fitting problem with two methods: the Levenberg-Marquardt Algorithm (LMA) and the Variable Projection Algorithm (VPA). The first is a classic algorithm already used in this context [13; 14]; the other, to the best of our knowledge, is used for the first time for this problem.
## 3 Improvements in WPAN MAC
### Payload Adjustment Mechanism (PAM)  
In this paper the channel bit error rate (BER), denoted as \(p_{e}\), (0 \(<\)\(p_{e}\)\(<\) 1) can be calculated via previous frames or some other mechanism. How to obtain \(p_{e}\) is beyond the scope of this paper. At higher values of BER the throughput performance of WPAN MAC decreases to a very low value. One way to counteract this problem is to reduce the size of payload with increasing BER value. So we apply a simple payload adjustment mechanism technique to WPAN MAC. This mechanism can control the size of the MAC protocol data unit (MPDU) based on the calculated value of BER. PAM is very simple to implement on the top of WPAN MAC without many changes to the existing protocol. From the results (see Figure 18) we can see the improvement in throughput performance with PAM over existing protocol.
## 4 Experiment Validation
### Case 1: Bearing Fault Diagnosis Based on CWRU Bearing Datasets
#### 4.1.3 Comparison between Different Number of Sensors  
To further test the performance of MSDWF, the signals obtained by different numbers of sensors were input into DWSC to compare the fault diagnosis accuracy. Each sensor data have two states: weighted and unweighted. That is, a total of seven different curves are shown in Figure 9. It can clearly be seen that the weighted single sensor data have higher accuracy and faster convergence rate than the unweighted data and the weighted data fusion of all sensors has the highest accuracy, fastest convergence rate, and best stability, which indicates the superior performance and advantages of the method. In addition, the t-SNE visualization results of different kinds of input data are shown in Figure 10. Figure 10a-c are the visualization results of the weighted data of the base sensor, FE sensor,  
Figure 7: Time domain graph of outer race fault. (**a**\(-\)**c**) are the unweighted data obtained by the sensors located at DE, FE, and base, respectively, (**d**\(-\)**f**) are the corresponding weighted data.  
and DE sensor, respectively, which show a high degree of separation for each fault type, but a small part of the different fault types is still mixed; Figure 10d-f are the visualization results of the unweighted data, and it can be observed that the most of all the fault types are mixed. Compared with the weighted data, there is no good recognition between various types of faults of unweighted data; Figure 10g is the t-SNE visualization result of the weighted data of all sensors, and there is the highest degree of separation for each type of bearing fault.
## 2 Related Works
### Aerial IoT Communications  
Under the IoT, several wireless technologies have been developed to cater for sensor-based communication, also popularly known as machine-to-machine (M2M) communications. The idea is to assign a dedicated network platform independent of the typical mobile/wireless broadband used to cater to human-based communications (such as streaming, data transfer, and voice communications). As a result, there are several variations of wireless IoT technologies and they are summarized in Table 1.  
Recently, drones have been deployed as flying cellular base stations to provide reliable and energy-efficient IoT communications [39; 40; 41]. Studies show that such efficient utilization of drones can significantly improve the communications link between sensor nodes and drones, by enhancing the probability of line of sight communications [42] and reducing shadowing and blockage effects [43]. Moreover, the limited battery of the sensors will need considerably lower transmission power for transferring their data to the receiver sides [44]. However, most recent studies have focused on cellular-connected drones and less attention has been paid to studying the application of LPWAN technologies to drones.  
On the other hand, the purpose of wireless propagation studies is to perform analysis in two crucial areas:  
1. Link budget: represents how much fade margin is available between a transmitter and a receiver to ensure a reliable wireless connection, and
2. Coverage prediction: to estimate the maximum area covered based on the hardware configuration of a particular wireless technology [45]. Therefore, in the LoRa wireless deployment, a suitable wireless propagation model must be identified for the drone operator to ensure reliable connectivity and optimum wireless coverage.  
Table 2 presents a summary of previous works on investigating LoRa performance versus the Doppler effect. From the table, it can be observed that the previous research works proved that LoRa is robust to Doppler shift, and five of these research works discovered that 80% of the packet is received under LoRa spreading factor 12 (SF12) via an experimental test on a moving car and human. Meanwhile, based on a simulation with a drone done by [46], drone speed at a maximum of 50 km/h does not affect the delay, jitter, packet loss, and output of LoRa. As the speed of VTOL drones is much faster than rotary-wing drones, there is a need to investigate the Doppler effects at a higher speed.
# Survey of Blockchain-Based Applications for IoT
## 7 Smart Contracts and Automation
### Smart Contracts in Resource-Constrained IoT Devices  
Deploying smart contracts on IoT devices presents significant challenges due to their resource-constrained nature. Unlike traditional computing environments, IoT devices often have limited processing power, memory, and storage, making running complex smart contract operations difficult. To deploy smart contracts efficiently in IoT networks, it is essential to optimize the smart contract code to reduce computational overhead and gas costs. Additionally, offloading the processing of smart contracts or parts of their execution to other nodes in the network, such as edge or cloud nodes, can significantly improve performance and resource efficiency.  
Updating smart contracts frequently is essential due to code bugs and evolving application requirements. However, traditional methods are inefficient, as they require recompilation and redeployment on the blockchain. This process involves proposing a deployment transaction, waiting for consensus, and then executing the redeployed bytecode through an invocation transaction, which is time-consuming and resource intensive. Exist  
Figure 11: Blockchain oracle.  
ing blockchain architectures, like the Ethereum Virtual Machine (EVM), lack direct support for modifying deployed bytecode. As a result, even minor changes require complete recompilation and redeployment, leading to significant latency and high computational overhead. [PERSON] et al. (2022) introduced an optimization mechanism called ATOM for executing smart contracts in IoT systems [56]. ATOM optimizes smart contract updates and execution in blockchain-based IoT by eliminating costly redeployments. It uses prebuilt bytecode templates to replace recompilation, reducing update latency by 62.7%. Its Application-Oriented Instructions (Aols) minimize storage needs by 70% and gas usage by 90%. Instead of full redeployment, ATOM modifies bytecode at the instruction level, reducing ledger size and costs.  
Another approach suggested by [PERSON] et al. is delaying smart contract instantiation in IoT-based blockchain networks to conserve resources [57]. Traditional preinstantiated smart contracts consume memory and processing power even when idle, which is inefficient for resource-constrained IoT devices. To address this, they propose three methods: Instant instantiation, where contracts are created only when first invoked and terminated when out of scope; Postinstantiation (Lazy Instantiation), which further delays instantiation until a critical function explicitly requires it, caching non-essential requests; and the Wrapping Variable Mechanism, which hides instantiation details for optimized execution.
## 4 Discussion
### Differences between the Results from the Spectral Method Algorithm and Baik [26]
It can be seen from Figure 10 that the maximum amplitude of the axial displacement component \(u_{z}\) of these four modes in the water was larger than that in the tube wall, and the energy carried in the water was higher than that in the tube wall. These phenomena are similar to a Scholte wave propagating at the solid-fluid interface. [PERSON] [26] only obtained the \((0,1)\) mode, which is labeled as the \((0,0)\) mode in Figure 10.  
The modes \((1,2)\), \((2,2)\), and \((3,2)\) in Figure 8 are flexural waves propagating along the axial direction. Figure 10: The radial distribution of the displacement components of the modes at \(f=8\) kHz. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Circumferential Order** & **Spectral Method Results** & **Results from [PERSON] [26]** \\ \hline \(n=0\) & \((0,1)\), \((0,3)\), \((0,4)\), \((0,5)\) & \((0,0)\), \((0,1)\), \((0,2)\), \((0,3)\) \\ \(n=1\) & \((1,3)\), \((1,4)\), \((1,5)\) & \(  
In conclusion, dispersion curves of the water-filled PMMA tube provided by [PERSON] [26] missed the torsional wave mode \((0,2)\), Scholte wave modes \((1,1)\), \((2,1)\), and \((3,1)\), and flexural wave modes \((1,2)\), \((2,2)\), and \((3,2)\). In contrast, the spectral method algorithm can find all the axial propagation modes precisely without missing any low-order modes.
## 6 Reviewed Papers on Machine-Learning-Based Path Loss Models
### Evaluation of High-Band Machine Learning Path Loss Models in Urban Environments  
Several machine learning models were tested for predicting of path loss in urban environments at high-band frequencies in different scenarios. At the same time, some disagreed with the empirical models. For example, the NN model outperformed the empirical models, including the FI, CI, and 3 GPP models in [93], which had a minimum RMSE value of 4.5 dB, as shown in Figure 7.  
Figure 6: Comparative analysis of models in the mid-band frequency spectrum for outdoor urban environments.  
Meanwhile, the independent machine learning models performed excellently, like the CNN model with an RMSE value of 8.59 dB at 28 GHz, the RF model with an RMSE value of 6.1 dB at 28 GHz, and the RNN model with an RMSE value of 2.4 dB at 26.4 GHz.
## 5 Conclusions  
Fuzzy neural network (FNN)-fractional differential simulation (FDS) hybrid model is proposed to make an adaptive prediction of enhanced oil recovery (EOR) performance by N2 huff-n-puff in naturally fractured-cavity carbonate reservoirs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Well No.** & **Annual Production (m\({}^{3}\))** & **Well No.** & **Annual Production (m\({}^{3}\))** \\ \hline LG01 & 5121 & LG12 & 2637 \\ LG02 & 2031 & LG13 & 6824 \\ LG03 & 3526 & LG14 & 1865 \\ LG04 & 4542 & LG15 & 1754 \\ LG05 & 2019 & LG16 & 3951 \\ LG06 & 2028 & LG17 & 1468 \\ LG07 & 5965 & LG18 & 896 \\ LG08 & 1506 & LG19 & 2087 \\ LG09 & 3017 & LG20 & 1065 \\ LG10 & 2027 & LG21 & 2614 \\ LG11 & 5684 & LG22 & 3687 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Predicted annual oil production for the 22 producers with decline rate of 15%.  
Figure 7: Absolute percentage error of annual oil production prediction.  
The performance appraisal of N\({}_{2}\) huff-n-puff in a typical carbonate reservoir shows that the prediction ability of the FNN-FDS hybrid model is satisfactory, indicating that the proposed model is efficient for EOR prediction by N\({}_{2}\) huff-n-puff.  
The proposed model can guide optimal design of development scheme when using N\({}_{2}\) huff-n-puff to tap more remaining oil in similar carbonate reservoirs.  
Conceptualization, Q.W. and J.L.; methodology, Q.W.; software, D.W.; validation, H.J. and J.H.; formal analysis, D.W.; investigation, Q.W.; resources, J.L.; data curation, D.W.; writing--original draft preparation, Q.W.; writing--review and editing, J.L.; visualization, D.W.; supervision, J.L.; project administration, Q.W.; funding acquisition, Q.W. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (Grant No. 52074344) and Science Foundation of China University of Petroleum, Beijing (No.2462020 BJRC006).  
The authors sincerely appreciate the Research Institute of Petroleum Exploration and Development (RIPED) and PetroChina for the permission to publish this paper. We also thank all anonymous reviewers for their constructive comments to improve the quality of this paper.  
The authors declare no conflict of interest.