esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Experimental Results and Analysis
#### 5.2.2 TAPP Algorithm Evaluation  
(1) Experiment with different computing power  
The experiment used two different computing nodes to perform the prediction network training process to calculate the time consumption of prediction network training. SingleCPU used the No. 1 node CPU and SingleGPU used the No. 2 node GPU, for which the time unit is the hour. The experimental results are shown in Table 2.  
It can be seen from Table 2 that, due to the powerful computing performance of GPU, the convergence speed of the SingleGPU method model was generally faster than that of SingleCPU. For the VGG-16 model, the GPU accelerated model had the most obvious convergence effect, with the training time shortened by 2.17 times. The AlexNet and Inception-V3 models are smaller than VGG-16, so the training was faster. However, the training time was not significantly improved, being 1.67 times and 1.6 times, respectively. Figure 6 shows the training-loss curve of the pipeline parallel task allocation scheme generated by the prediction network for the VGG-16 model. The SingleGPU method can complete the convergence in about 40 min. Therefore, it can be concluded that the use of GPU nodes can effectively improve the training speed of the prediction network and reduce the time overhead.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **AlexNet** & **VGG-16** & **Inception-V3** \\ \hline
**SingleCPU** & 0.5 & 1.6 & 0.8 \\
**SingleGPU** & 0.3 & 0.6 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Predicted network training convergence time (hours).  
Figure 5: Experimental results of training accuracy and time.  
Experiment with different batch sizes  
The experiment put the No. 1 node CPU into train the AlexNet model, as an example, to explore the convergence of the pipeline parallel task allocation scheme generated by the network under different batch size conditions. The experimental results are shown in Figure 7. It can be seen from the Figure 7 that the batch size affects the convergence speed of the prediction network with the smaller batch size having the faster convergence speed. The prediction network was trained based on the word vector constructed by the runtime parameters of the DNN model as input. The batch size affects the parameter amount, calculation time, activation value, and other information of each layer of the model to be trained, thereby affecting the training complexity of the prediction network.  
It can be seen from the experimental results that the prediction network had different convergence times due to different batch sizes, but the overall convergence time was within 1 h. Combined with the analysis of the pipeline parallel training experiment, it can be shown that the sum of the predicted network training time and the pipeline parallel training time is always less than the BSP running time, which proves the effectiveness of the task allocation scheme based on deep reinforcement learning proposed in this paper.
## 2 Study Area and Methods
### Data Sources
#### 2.2.3 Vegetation Type Data  
The international geo-biosphere program (IGBP) classification method is used to classify plant type data in the MCD12Q1(NASA, USA) data package, which has a 500 m spatial resolution. In order to reduce the classification error and the possible impact of land cover changes, in this paper, we only retained the pixels in which the land cover types remained unchanged from 2001 to 2020. The main vegetation types were as follows: Deciduous Needleleaf Forests (0.66%), Deciduous Broadleaf Forests (1.30%), Mixed Forests (0.26%), Woody Savannas (6.08%), Savannas (1.34%), Grasslands (49.99%), Croplands (4.65%), Other (25.43%), Change (10.25%).  
Figure 1: The geographical distribution of the study area. (**a**) Digital elevation model, (**b**) vegetation types.
## 1 Introduction
Furthermore, models which do not require LUE maybe easier to implement across habitats and are worth exploring [16]. Vegetation indices (VIs) are frequently used in GPP models. [PERSON] et al. [19] demonstrated that a simple model based on the Enhanced Vegetation Index (EVI) alone, could provide estimates of GPP. Their methods yielded results that were as good as or better than MOD17A2 for many sites. [PERSON] et al. [16] developed another model, the temperature and Greenness (TG) model, which was based on MODIS EVI and the Land Surface Temperature (LST) products. Further, [PERSON] et al. [20] proposed a new vegetation index GPP model that demonstrated the usefulness of VIs as reliable proxies of both LUE and fAPAR [19; 21; 22; 23]. The vegetation index approach for estimating GPP might also be useful within coastal marshes, provided that we can overcome the distortion of conventional vegetation indices by periodic tidal flooding. Such an approach needs to begin with daily surface reflectance information, because composited MODIS products used for calculating MOD17A2, such as fAPAR and leaf area index (LAI), are derived from multi-day composites of MODIS NDVI or EVI products. As such, observations that make up multi-day composites may contain noise from observations collected during high tide and cannot be removed post hoc [24]. Thus, we need a procedure for filtering daily MODIS surface reflectance for tidal observations, calculating a vegetation index related to GPP, and then creating tide-free composites from that filtered time series. This procedure should be a step forward for estimating GPP from MODIS within tidal wetlands. Others have studied productivity within coastal wetlands through remote sensing data [1; 14; 25; 26]. However, these studies have either not accounted for tidal influence [14; 25; 26], focused on biomass or net ecosystem exchange (NEE) rather than GPP [14; 26], or evaluated canopies that are not submerged by tides, (e.g., mangroves; [PERSON] et al., 2013 [1]). Indeed [PERSON] et al. (2010) [26] observed a loss of phenological pattern in pixels that were most tidally influenced, which might be caused by the use of composite MODIS products without tidal filtering [24]. Thus, additional work to solve the problem of tidal influence on the spectral reflectance of coastal marshes, and particularly to apply this effort to GPP estimation, is still needed. In this paper, we present a method to calibrate MODIS GPP in tidal wetlands. The objective of this study was to build a tide-robust model to predict GPP within a _Spartina alterniflora_ salt marsh on Sapelo Island, GA. The specific objectives of this research were to (1) evaluate and compare the potential of using different MODIS vegetation indices to characterize flux tower GPP, (2) to establish the statistical relationship between vegetation indices from MODIS 500 m surface reflectance products and GPP flux, and (3) calibrate a MODIS GPP algorithm within a tidal wetland.
# Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World
## 3 Ultra-Low Power MCU Architectures
### Memory Hierarchy for Multi-Core Domain  
Multi-core systems introduced a new problem: parallel memory hierarchy and management. Since CPU computational power started increasing, memory always represented a bottleneck for both speed and energy consumption. Indeed, CPUs can process data at a higher rate than memories. As aforementioned, a multi-core domain requires smart memory management in order to avoid cache invalidation. A schematic view of a typical multi-core system is shown in Figure 10. This type of coherency represents a limit to the number of cores.  
IoT is a heterogeneous environment, so it is not possible to refer to a specific architecture. The device architecture is based on the type of application and data dependency. Thus, the memory hierarchy must be designed taking into account the final application and dataflow.  
The common multi-core memory hierarchy is composed of two levels, the first one is private and is placed inside the single core, while the second layer is common to all the cores. The first one is smaller and is very energy efficient, leading to a lower hit latency, but it presents coherency problems. Moreover, when the cores are working on the same dataset, there is the need for data replication that translates to energy wasting. The second level is bigger and stores data in a fixed position in order to maintain integrity. Due to this characteristic, the hitting process is slower, but no data replication is required.  
In order to remove coherency problems due to local storage, different types of L2 caches have been developed. [PERSON] et al. (2015) present a model of L2 cache shared among different cores that behaves as a final memory stage. The final implementation of the innovative architecture on a 28 nm technology showed a very high bandwidth and low power consumption.  
Researchers conducted many studies on memory hierarchy performance for multi-core platforms, presenting some innovative solutions. [PERSON] et al. (2016) proposed a different approach in which Non-Volatile Memories (NVM) are used instead of conventional static or dynamic RAM for producing a low power hierarchy storage. They exploited the NVM property of consuming less static power while presenting a higher density. Based on the fact that NVM presents a limited lifetime, they proposed a memory management-aware method able to allocate data based on the access patterns defined at compilation time. This approach reduces power consumption but presents low adaptability to the final application and a lower throughput with respect to RAM technology.  
[PERSON] et al. (2017) compared different architectures in their work, presenting a new tightly coupled shared data storage with respect to each core cluster. The result shows an improvement of roughly 20% respect to other solutions, tested on 10 different applications.  
Figure 10: Memory management of single-core and multi-core systems.
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 1 Introduction
### Model Verification  
An appropriate experimental validation phase is commonly required to assess the model's accuracy in describing the battery behavior. Experimental data evaluate the results of battery life prediction and degradation prediction. The prediction accuracy of the LSTM is first verified, followed by an analysis of the feasibility of the HI degradation model prediction and, finally, the capacity estimation model is evaluated. The root mean square error (RMSE) and the mean absolute error (MAE) [33] are used as measures of the evaluation accuracy of the proposed method.  
Figure 5a shows the ability of the LSTM to predict health indicators. The LSTM can mine past input information and better learn the time\(-\)domain characteristics of the data. Experimental results show that MAE and RMSE are less than 10%, which is accurate for predicting health indicators. The statistical results for the battery input of three HIs and input of two HIs, and a single HI, are shown in Figure 5b. The goal of capacity estimation with a single HI is to evaluate the performance of that HI. The results indicate that MAE and RMSE gradually decrease with increased health indicators. The combination of multiple HI can compensate for the shortcomings of a single HI, resulting in a narrower error distribution. Overall, RMSE and MAE are less than 5%, indicating that they strongly correlate with capacity and can be used for capacity estimation. Combined with the HI degradation prediction model and capacity estimation model, the test set is used for verification. Figure 5c shows the statistical results of partial dispersion points utilizing this strategy, indicating that the capacity prediction strategy is effective. Through confirmation, we can learn that the output prediction capacity error is within the acceptable range and can guarantee a good accuracy to predict the capacity by the trained model.  
Figure 5: (**a**) Accuracy of LSTM prediction of HI; (**b**) error of predicted capacity for different quantities of HI; (**c**) LSTM+GPR prediction accuracy scatter plot.
## 5 Case Study: An Actual Deployment at the City Campus of the University of Auckland
### Visualizable Interactions and Results
Additionally, we created a heat map of the equivalent A-weighted SPL over the entire 15-minute period to compare results from other sensor nodes. This heat map visualizes the spatial distribution of different sound categories and highlights areas with higher noise levels. Figure 15 displays environmental data collected from a single sensor node, including (a) air quality (quantified by eCO\({}_{2}\) levels), (b) light intensity, (c) temperature, (d) humidity, and (e) atmospheric pressure. According to online meteorological records [41], at 7:30 PM on 30 August 2024, NZST, the ambient temperature, relative humidity, and atmospheric pressure were 13 \({}^{\circ}\)C, 77%, and 1017 hPa, respectively. In comparison, our sensor measurements were 14.22 \({}^{\circ}\)C, 80.2%, and 1014.38 hPa, showing deviations of 1.22 \({}^{\circ}\)C, 3.2%, and 2.62 hPa from the official data. Considering inherent sensor inaccuracies and spatial differences in sampling locations, our results closely align with the official records, confirming the reliability of our measurements. The air quality and light intensity are highly dependent on the location, and due to the equipment limitations, we cannot verify their sampling accuracy. Overall, the additional parameter collection validates the system's ability to monitor environmental parameters, extending its functionality beyond acoustic surveillance to support more complex applications. In summary, the successful deployment and operation of the HWASN, coupled with the high-accuracy sound classification achieved by the fine-tuned CCBA, demonstrate the practical applicability of our system in urban acoustic and environmental monitoring. The  
Figure 15: Other monitored environmental parameters: (**a**) Air quality quantified by eCO\({}_{2}\) levels; (**b**) ambient light intensity; (**c**) temperature; (**d**) humidity; (**e**) atmospheric pressure. HWASN preserved data integrity without packet loss, even when managing indirect communication pathways. The CCBA model exhibited robust adaptability through fine-tuning, suggesting minimal additional training suffices when deploying in diverse environments. Incorporating additional environmental parameters enhances the system's utility, enabling users to gain a more comprehensive understanding of the urban environment. The results indicate that our system can be effectively scaled for extensive urban deployments, offering valuable data for various applications.
## 3 Deep-Learning-Based Channel Estimation Methods
### Overview of Neural Networks
#### 3.1.4 Gan  
Figure 10 represents the general GAN structure, which consists of a generator and a discriminator. The generator receives a latent vector (usually random noise) as input and generates samples analogous to the training data. On the other hand, the discriminator receives samples (which can be real or generated by the generator) as input and predicts their veracity. Notably, the generator and the discriminator train against each other in a competitive and collaborative dynamic. Specifically, the generator's objective is to trick the discriminator by bringing the generated samples closer and closer to the real samples to the point where they cannot be distinguished accurately. Concurrently, the discriminator is tasked with accurately classifying the samples as possible, thereby heightening the distinction between the real and generated samples. During the iterative adversarial training process, the generator and the discriminator keep adjusting their parameters until they reach an equilibrium point. That is to say, the generator can generate realistic samples, while the discriminator cannot distinguish between real and generated samples.  
Sometimes, GAN is \"too free\", and thus, we cannot control the generator to generate the required data. For this reason, researchers have proposed a variant of GAN, conditional GAN (CGAN) [50], where conditions are introduced into the modeling of both the generator and the discriminator to guide the data generation process.  
Figure 10: Basic structure of GAN.  
Figure 9: Basic structure of RNN.
# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 3 TLLFD for NR IQA
### TllFD Flow Chart and Feature Comparison  
In order to further explain the TLLFD method, Figure 4 shows the flow chart.  
In this paper, an NR IQA method is proposed. First, the distorted image is downscaled to obtain the scale reduced image; second, the distorted image and the scale down image are locally normalized to obtain the normalized image, and the texture features is extracted from the normalized image. The gradient features are statistically analyzed by Weibull distribution, and two statistical parameters are obtained: \(\gamma=0.2796\) and \(\beta=0.9625\). (\(\gamma\) is the parameter of shape, and \(\beta\) is the parameter of ratio. In Figure 4, \(\gamma\) and \(\beta\) only represent). Finally, the extracted features are pooled by SVR to obtain the quality score.  
Figure 3: Weibull parameter scatter plots of different distorted image.  
In order to further compare the features, this method is analyzed with the four classical methods (NIQE [22], BIQI [30], DIIVINE [31], BRISQUE [21]), as shown in Table 1.
# A Density-Based Random Forest for Imbalanced Data Classification
## 3 Methods
### Definition
#### 3.1.2 Direct Density-Reachability and Density-Reachability  
Before introducing the density domain, we introduce two core concepts defined in DBSCAN: direct density-reachability and density-reachability. These two concepts are used when defining the density domain:  
* **Direct density-reachability:** If \(dist(x_{i},x_{j})\leqslant\eta\), and \(\|D_{\eta}(x_{i})\|\geqslant\omega\), then sample \(x_{j}\) is directly-reachable by the density of \(x_{i}\), where \(x_{i}\in BoundaryDomain\), \(x_{j}\in(D^{\prime\prime}\cup Boundary)\). Here, \(D^{\prime\prime}\) represents the set of majority class samples, \(dist(x_{i},x_{j})\) represents the distance between samples \(x_{i}\) and \(x_{j}\), \(\eta\) represents the maximum distance that \(x_{i}\) can reach, \(D_{\eta}(x_{i})\) represents a set of majority samples whose distance from sample \(x_{i}\) is less than or equal to \(\eta\), and \(\|D_{\eta}(x_{i})\|\) represents the total number of sets. \(\omega\) represents the minimum number of sets whose distances between the samples in the set and sample \(x_{i}\) are less than or equal to \(\eta\);
* **Density-reachability:** For samples \(x_{j}\) and \(x_{i}\), if there are sample sequences \(p_{1},p_{2},\ldots,p_{n}\) where \(p_{1}=x_{j}\), \(p_{n}=x_{i}\), and \(p_{i+1}\) is directly density-reachable by \(p_{i}\), then it is said that \(x_{i}\) is density-reachable by \(x_{j}\).
# Analytical and Measured Effects of Short and Heavy Rail Cars on Railway Bridges in the USA
## 3 Results of Case Studies
### Riveted DPG Spans  
#### 3.2.1 FEM Modeling and Analysis of DPG Bridge  
The analytical investigation of the riveted steel DPG span included a simple beam model and an FEM model. To evaluate the load effects, a 3D model was developed in LUSASTM software [17]. The bridge model was created using shell elements appropriate for members in which the primary stresses are due to bending. Stresses were calculated under moving loads for the entire span using standard 16.2-m (53-ft) railcars and short 12.8-m (42-ft) railcars. Figure 18 presents examples of bending stress histories at mid-span and the quarter-span location due to a moving set of railcars.  
The results show that the 12.8-m (42-ft) railcars produced higher stresses than 16.2-m (53-ft) railcars. In addition, it was visible that the quarter span location experienced higher stress ranges than the mid-span location, particularly under the 16.2-m (53-ft) common length railcars. This means that on the long girder span, the mid-span is not the most critical location for fatigue for common railcars ([PERSON] et al. 2002). Figure 19 shows the stress ranges that were calculated for three common unit train cars (12.8-m (42-ft) sand or cement  
Figure 16: Typical peak stress calculated from measured strains, comparisons for stringers.  
Figure 17: Typical peak stress calculated from measured strains, comparisons for stringers.  
hoppers, 16.2-m (53-ft) coal cars, and 18.3-m (60-ft) grain cars) using gross section and zero impact. As the stress range envelopes showed, the mid-span location had an amplitude of stress cycles close to zero for the coal and grain cars, while other locations, specifically cover plate terminations, showed higher cyclic stress ranges. Therefore, cover plate terminations are critical areas for fatigue evaluation, particularly on longer bridge spans.  
Figure 19: Calculated cyclic stress range envelopes.  
Figure 18: FEM stress histories for 35-m (115-ft) girder of DPG span.
# Optimization of Stop Plan for Skip-Stop Operation on Suburban Railway Line
## 1 Introduction
[16] propose a new collaborative optimization method for both train stop planning and train scheduling problems that consider the minimization of the total dwelling time and total delay between the real and expected departure times from the origin station. [PERSON] et al. [17] proposed a mixed-integer nonlinear programming (MINLP) model to formulate the integrated planning of stop planning, train scheduling and rolling stock circulation, explicitly considering time-dependent origin-destination demand. Passenger demand distribution has a significant impact on the optimization plan under E/L mode. [PERSON] and [PERSON] [18], and [PERSON] et al. [19] calculated optimal stop plans for E/L mode to accommodate different passenger flow distributions. In addition to researches with static passenger demand, [PERSON] et al. [20], [PERSON] et al. [21], [PERSON] et al. [22; 23] and [PERSON] et al. [24] developed models for optimizing the stop plan in E/L mode that are applicable to dynamic passenger flow patterns. Furthermore, [PERSON] et al. [25], and [PERSON] et al. [26] proposed the mixed-integer linear programming models under express/local model consider the capacity constraints ([PERSON] et al. [27]) and \"overtaking\" condition. In this study, a multi-objective optimization model is established for optimizing the stop plan and departure frequency of suburban railway under E/L mode. The model takes the upper and lower limits of train departure frequency as the constraints and aims to minimize the total passenger travel time and the total number of operating trains. Meanwhile, a solution approach based on a genetic algorithm (GA) is proposed to solve the model. Different from previous studies, this paper mainly focuses on the schedule adjustment and impacts on solution results under the overtaking condition. Finally, a case is taken to verify the effectiveness of the proposed model. The rest of this paper is organized as follows. Section 2 describes the proposed problem and assumptions. Section 3 describes the overtaking judgment and adjustment; the multi-objective programming model is also established in this section. Furthermore, a solution algorithm based on GA is designed to search for the optimal solution in Section 4. Section 5 uses a case to demonstrate the effectiveness and superiority of the proposed model and algorithm. Finally, the conclusions and future work are presented in the last section.