esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 7 Results
### Forecasting Results
#### 7.2.2 Azure Trace Forecasting  
The Azure dataset considered in this work contained traces for 28,858 VMs. We categorized these VMs into three groups:  
* Incomplete traces (5): Only traces that included data for all the time intervals of the complete 30-day period were selected. Incomplete traces were excluded.
* Low CPU usage traces (25,218): Traces with low CPU usage (averaging below 10%) were excluded from the application of forecasting models. For these traces, we assumed the expected CPU usage for the next 24 h period could be computed as the average CPU usage from the historical dataset.
* Predictable traces (3635): These traces did not fall into either of the two previous groups. In the Azure dataset, there were a total of 3635 predictable traces. The different forecasting models were exclusively applied to this group of traces.  
We ran and compared five forecasting models (Naive seasonal, Bayesian regression, FB prophet, Auto-ARIMA, and RNN-LSTM) with the same parameters as in the Bitbrains case. The N-BEATS model was not considered for forecasting Azure traces due to its extended execution time for all 3635 VM traces, exceeding three days.  
Prediction results for this dataset are summarized in Figure 6, which shows the prediction accuracy (RMSE) achieved by the different models when forecasting CPU usage for the VM traces considered in this dataset. Table 2 displays the average RMSE and execution time (including fit and predict functions for all selected VMs) for each prediction model. As observed, the Bayesian regression model once again demonstrated the highest average accuracy. Regarding execution times, models based on neural networks (RNN-LSTM) required significantly more computational time for model fitting but they did not enhance the accuracy compared to the Bayesian regression. In addition to the five tested forecasting models, we also implemented the two previously explained adaptive methods (oracle and realistic). In this scenario, it can be observed that the realistic adaptive model marginally improved on the average RMSE of the predictions obtained by the Bayesian regression model.  
Based on these results, we can conclude that the Bayesian regression model was sufficiently effective in generating accurate CPU usage forecasts, while also requiring a reasonably low execution time. Therefore, the outcomes generated by this model were selected as inputs for the optimization algorithms.
## 4 Results and Discussion  
### Similarity Score Distribution Comparison  
The keyword matching method and the sentence similarity method show a significant difference in performance with regard to measuring similarity between two groups of documents. For relative comparison, the scores obtained from both methods were scaled together into a 0-100 range. The distribution of the similarity scores is shown in Table 6.  
Table 6 is the comparison of the similarity score distributions that we obtained from the keyword matching method and the similarity method. The table clearly shows that there is a serious problem when using the keyword matching method to analyze thematic structure. The scores of the keyword matching method are too densely packed between 20 and 21, with an average of 20.72 and a standard deviation of 0.03. The difference between scores is too small to induce any meaningful comparisons in the analyses that follow after the measurement. On the other hand, the scores of the sentence similarity method averaged 43.15, and the standard deviation was 10.42, which is widely distributed between 4 and 89.  
Since the purpose of this analysis is to identify the theme structure in the sustainability reports, a significant variation in calculated scores is needed for subsequent analysis after measurement. Therefore, we present only the results derived by the sentence similarity method in the next section.
# Research on Driving Scenario Knowledge Graphs
## 3 Methodology: DSKG Construction
### Knowledge Inference Models Based on Representation Learning
#### 3.4.2 Model Training and Analysis  
Multiple evaluation metrics were employed in this study to assess the performance of different models in link prediction tasks, including Mean Rank (MR), Mean Reciprocal Rank (MRR), Hits@1, Hits@3, and Hits@10. MR represents the average rank of correct triples in the test set. Hits@10 indicates the ratio of correct triples among the top 10 rankings to the total number of triples in the test set. When the average rank is lower, the MRR, Hits@1, Hits@3, and Hits@10 are higher, indicating better inference performance. The optimal hyperparameters for different models are shown in Table 5.  
For a deeper understanding of the model training process, we provide Figure 9, which details the training process. Additionally, Table 6 showcases the entity prediction results in the Traffic Genome dataset. These results offer insights into the accuracy, precision, recall, and other metrics of each model in capturing entities within driving scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Learning Rate** & **Hidden Layer** & **Margin** & **BatchNorm_Size** \\ \hline TransE & 0.001 & 50/256/512 & 0.9 & 20,000 \\ Complex & 0.001 & 50/256/512 & None & 20,000 \\ Distmult & 0.001 & 50/256/512 & 0.9 & 20,000 \\ Rotate & 0.001 & 50/256/512 & 1.0 & 20,000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameter values of the different models.  
Based on Figure 8 and Table 6, we conducted a comprehensive analysis of four different models. The Rotate model performs best in MR, with an average rank of 4.99, significantly lower than other models. The Complex model excels in MRR, reaching 42.01%, slightly higher than the Rotate model's 45.68%. However, under stricter evaluation metrics such as Hits@10, Hits@3, and Hits@1, the Rotate model demonstrates outstanding performance, ranking first in Hits@10, Hits@3, and Hits@1 with 76.54%, 52.17%, and 32.51%, respectively. The Distmult model also performs well, ranking second in Hits@10 with 65.91%, and second in Hits@3 and Hits@1 with 46.92% and 36.29%, respectively. In contrast, the Transe and Complex models show relatively average performance across all metrics. Therefore,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model (Optimal Hidden Layer)** & **MR** & **MRR** & **Hits@10** & **Hits@3** & **Hits@1** \\ \hline Transe (256) & 9.18 & 41.67\% & 59.79\% & 42.82\% & 32.47\% \\ Complex (50) & 8.51 & 42.01\% & 56.43\% & 42.98\% & 33.77\% \\ Distmult (256) & 7.01 & 46.09\% & 65.91\% & 46.92\% & 36.29\% \\ Rotate (50) & 4.99 & 45.68\% & 76.54\% & 52.17\% & 32.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of predicting the entities.  
Figure 9: Details of the model training process.  
considering the overall performance, the Rotate model emerges as a preferred choice among these evaluation metrics and could serve as one of the primary models in research.
## 1 Introduction
(2) The TFR obtained from the smooth pseudo-Wigner-Ville distribution (SPWVD) is used as the first input branch, and the HOS is introduced as the second input branch to enhance the extraction of signal phase features and make up for the defect that a single feature is affected by channel interference. (3) Experiments on the Danjiangkou measured dataset show that the model improves accuracy by 6.67% compared with existing CNN-Transformer models in long-distance ranges, providing new insights for UWA communication signal processing.
## 5 Simulation and Verification of SDC Inhibition Effect
### Simulation and Verification of the Suppression Effect of SDCs Added to the Control System
The combined inhibition effect was the best at 85.99% when \(\mathrm{K_{g}}=-64\), \(\mathrm{T_{g}}=0.95\), \(\mathrm{K_{r}}=-100\), and \(\mathrm{T_{r}}=0.95\), as shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Kg** & **Tg** & **Kr** & **Tr** & **\(\beta\)** \\ \hline
1 & \(-64\) & \(0.99\) & \(-110\) & \(0.99\) & \(84.70\%\) \\ \hline
2 & \(-68\) & \(0.99\) & \(-120\) & \(0.99\) & \(85.15\%\) \\ \hline
3 & \(-10\) & \(0.2\) & \(-30\) & \(0.2\) & \(37.14\%\) \\ \hline
4 & \(-25\) & \(0.5\) & \(-60\) & \(0.5\) & \(65.58\%\) \\ \hline
5 & \(-50\) & \(0.8\) & \(-70\) & \(0.8\) & \(78.22\%\) \\ \hline
6 & \(-64\) & \(0.95\) & \(-100\) & \(0.95\) & \(85.99\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: GSC + RSC-SDC parameters and results. Figure 6: Comparison of \(\mathrm{I_{aa}}\) output waveforms of the SDC added only in the GSC. Figure 7: Comparison of \(\mathrm{I_{aa}}\) output waveforms with the joint addition of the SDC in the GSC and RSC.
## 7 Evaluation Results
### Maximum Required Buffer Size  
We measured the maximum required buffer size of each node in the network. The required buffer size of a node varies over time as shown in Figure 26.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Routing** & **Average Number of Hops** & **Maximum Number of Hops** \\ \hline Proposed routing with the circular & 5.5 & 16 \\ covered area model & 5.5 & 16 \\ Proposed routing with the & 5.5 & 16 \\ vectorial covered area model & 12.8 & 35 \\ Spray and wait & 9.6 & 23 \\ Location-based routing & 32 & 77 \\ Motion vector & 5.6 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison in terms of the number of hops.  
Figure 26: How the required buffer size of a node varies over time.  
Figure 25: How to measure the number of hops.  
The distribution of the number of nodes for each maximum buffer size is shown for each routing method in Figure 27. The proposed routing with the circular covered area model and that with the vectorial covered area model had more or less the same result, with 90% of nodes requiring no buffer. This was followed by \"location-based routing\" and \"motion vector routing\", both of which resulted in 60% to 70% of nodes requiring no buffer. In contrast, with \"spray and wait routing\", only 40% of nodes required no buffer. With \"epidemic routing\", almost all nodes required a buffer size of 1.2 MBytes.  
The reason why the proposed traveling route information-based routing had 90% of nodes requiring no buffer was that only traveling nodes, which are dedicated to relaying bundles, relayed bundles. Therefore, bundles were not relayed to 90% of mobile terminals that were outside the destination area concerned. These mobile terminals were not involved in the relaying of bundles. On the other hand, since relay nodes are solely responsible for relaying bundles, their maximum required buffer sizes were very large: 800 KBytes for the circular covered area model and 600 KBytes for the vectorial covered area model.
### Paper Contributions and Organization  
The main contributions of this paper can be summarized as follows:  
* We propose a novel scheme that utilizes RISs to simultaneously address the energy harvesting limitations, coverage area expansion, and achievable rate enhancement in batteryless IoT networks, improving both the energy efficiency and communication performance in IoT devices at the same time.
* To achieve these goals, we propose a novel DRL framework augmented by the 6 DChOA, which fine-tunes hyper-parameters to enable more efficient and adaptive training. The 6 DChOA introduces two innovative roles: the leader, which enhances exploitation and accelerates convergence by guiding the search process toward optimal solutions, and the ranger, which boosts exploration, helping the model escape local minima and ensuring a more robust search across the solution space, improving the training efficiency and overall performance of the optimization approach.
* We then formulate the optimal RIS phase-shift design using the proposed 6 DChOA-DRL approach to increase the received power at the IoT device while simultaneously suppressing interference from both the direct source-to-receiver link and the cascaded source-to-RIS-to-receiver link.
* We conduct extensive simulations under various experimental settings. The results show that 6 DChOA-DRL exhibits superior performance in various metrics, including the root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation (SD), run time, and convergence trend. Additionally, the results demonstrate that optimizing RIS elements with the proposed 6 DChOA-DRL approach significantly improves energy harvesting and the achievable rate compared to other optimization techniques and the conventional batteryless IoT networks without RISs.  
The rest of this paper is organized as follows: In Section 2, we introduce the system model and formulate the problem, outlining the key parameters and constraints for optimizing the RIS-aided IoT network. Section 3 details materials and methods including the backgrounds and the proposed optimization scheme, where we present our novel DRL-based approach combined with the 6 DChOA to solve the non-convex optimization problem. Section 4 provides the simulation results and analysis. Section 5 presents a comprehensive discussion on the results, implications, and future research directions, and finally, Section 6 concludes this paper by summarizing the main achievements.
###### Abstract  
Data aggregation is a widely adopted method to effectively reduce the data transmission volume and improve the lifetime of wireless sensor networks (WSNs). In the data aggregation networks, some parameters directly determine the delay of aggregation. In industrial applications, the data generated by different sensors have different requirements for delay or other QoS performance. In the previous study, a common strategy is that all kinds of data is aggregated into one frame when the condition is satisfied with a QoS requirement, which causes excessive energy consumption and severely impairs the lifetime of network. A Differentiated Data Aggregation Routing (DDAR) scheme is proposed to reduce energy consumption and guarantee that the delay could be controlled within the corresponding QoS requirement constraint. The primary contributions of the DDAR scheme are the following: (a) The DDAR scheme makes data with different QoS requirement route to the sink along the different paths. The parameters of the aggregators in each path, such as aggregation deadline (\(T_{t}\)) and the aggregation threshold (\(N_{t}\)), are configured according to the QoS requirements. Accordingly, energy consumption can be reduced without degrading the performance of data transmission. (b) Based on DDAR scheme, an improved DDAR scheme is proposed to further improve performance through fully utilize the residual energy in the nodes which are far from the sink. The frequency of aggregation of these nodes increases by reducing the value of \(T_{t}\) and \(N_{t}\) so as to further improve the energy efficiency and reduce delay. Simulation results demonstrate that compared with the previous scheme, this scheme reduces the delay by 25.01%, improves the lifetime by 55.45%, and increases energy efficiency by 83.99%. The improved DDAR scheme improves the energy efficiency by 33.97% and service guarantee rate by 10.11%.  
differentiated data aggregation routing; wireless sensor networks; delay sensitive; energy efficiency 2018 18 2349; doi:10.3390/s18072349
## 3 Methods  
In the present study, we conducted not only a detailed field survey but also microstructural image analysis in thin sections of sandstones with deformation bands under an optical microscope. In addition, we conducted microchemical analyses using a scanning electron microscope (SEM) energy dispersive X-ray spectrometry (EDS) and electron probe micro analyzer (EPMA). The SEM-EDS analysis was conducted using an SEM (JEOL JSM-IT200, made by Japan Electron Optics Laboratory (JEOL) Ltd., Tokyo, Japan) with an EDS system operating in the backscattered electron (BSE) mode with 20 kV installed at Hokkaido University. The EPMA analysis was conducted using a wavelength dispersive electron-probe microanalyzer (JEOL JXA-8800R, made by JEOL Ltd., Tokyo, Japan) installed at Hokkaido University. The analysis was performed at an acceleration voltage of 15 kV and at a specimen current of 20 nA for point analyses with ZAF correction. Natural and synthetic silicates and oxides are used as standards. Furthermore, we measured the vitrinite reflectance of coal layer samples from the Shakubetsu Formation to infer the maximum temperatures which they experienced. The measurement was conducted at Japan Petroleum Exploration Co., Ltd. (Tokyo, Japan).  
Below, we briefly described the method of image analysis, which we used for microstructural analyses in sandstones with deformation bands. First, images of thin sections were captured by GNU Image Manipulation Program [42], and outlines of detrital grains were traced. Manual tracing of grain boundaries was also used. Then, the microstructures of detrital grains were analyzed by ImageJ [43]. The followings were analyzed by ImageJ: area (\(A\)), perimeter (\(P\)), length of long (\(a\)) and short (\(b\)) axes of grains and angle (\(q\)) between the long axis and the reference orientation. Here, the long (\(a\)) and short (\(b\)) axes of grains were those of a fitted ellipse matching the grain shape by ImageJ. The data for each analyzed area was copied and pasted on an Excel spreadsheet, and then the equivalent diameter of a circle (\(D\), grain size), aspect ratio (\(R\)), and circularity (C) (e.g., [44]) were calculated using the following equations.  
\[D=2\surd(A/\pi) \tag{1}\]  
\[R=a/b \tag{2}\]  
\[C=4\mathrm{p}A/\mathrm{P}^{2} \tag{3}\]
## 3 The Proposed Method  
This study proposes a novel few-shot learning framework based on self-training with noise and pixel-wise knowledge distillation (FSLNKD) for depth completion (Figure 2). This framework is built upon a self-training paradigm that leverages a teacher-student architecture. By incorporating noise samples and pixel-level distillation techniques, our approach effectively enhances the student model's training process.  
First, we introduce the teacher-student model, a conventional machine learning method that effectively handles few-shot datasets, particularly in few-shot learning models. The teacher model, characterized by its expressiveness and superior performance, is typically trained on a larger or more complex dataset. It often possesses a greater number of parameters and a deeper structure. The teacher model identifies and transfers the intricate data structures and patterns to the student model. We choose a high-precision depth completion network as our teacher model, which is trained using labeled data--such as the data containing ground-truth annotations. After training, the pre-trained teacher  
Figure 2: Configuration of the proposed few-shot learning framework for depth completion with noisy-student training and KD integration.  
model is utilized to generate pseudo-labels for the unlabeled dataset. The student model, typically less complex with fewer parameters or a shallower architecture, is designed for data-constrained environments. To enhance its performance and generalization capabilities, it leverages knowledge distilled from a more complex teacher model, even with minimal training data. By combining labeled, pseudo-labeled, and noisy samples, the student model is effectively trained.  
Second, to enable the student model to derive granular insights from the teacher model, we implement pixel-level distillation to ensure precise knowledge transfer at the pixel scale. This approach proves particularly advantageous in image segmentation and synthesis tasks, where preserving intricate details and spatial consistency is crucial. Within this framework, we adapt KD techniques for depth completion, enabling the student model to generate outputs with fine-grained details comparable to those of the teacher model. This approach allows for the deployment of more compact and computationally efficient student models while maintaining high performance.  
Finally, the student model is reconfigured to function as a teacher, enabling an iterative training process, where the teacher model guides the student in re-labeling previously unlabeled data, which are then used to train a new student model. This iterative teacher-student framework improves both the model efficiency and generalization by facilitating effective knowledge transfer. Moreover, it mitigates the challenge of limited data, facilitates model compression, and lays a solid groundwork for future learning tasks.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 4 MC-BDP Prototype Evaluation
### Experimental Results  
The experimental results for the evaluation of the MC-BDP reference architecture are described in this section. Three categories of experiments were run, where each metric was monitored for different velocities, windowing rates, and cluster sizes. The velocity for all experiments was measured as two records per time unit and represents the speed of emission for: (1) total energy consumption of the data centre and (2) energy consumption by the IT equipment in the data centre. The windowing rate represents the period (how often a window of processing starts) divided by the duration (how long each window lasts). For example, the scalability, fault tolerance, and technology agnosticism experiments used a sliding window of five seconds, starting every second, for data processing. Non-parametric Mann-Whitney U tests (two independent samples) were employed for hypothesis testing due to the very small sample sizes [97]. The significance level chosen for all tests was \(\alpha=0.05\). The null hypothesis (Ho) was that the means of the two samples were not significantly different, while the alternative hypothesis, Ha, stated otherwise.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 3 Proposed Method
### Depth Position Mapping and Transformer Integration
#### 3.3.1 Transformer Encoder  
The Transformer Encoder in the Depth Position Mapping (DPM) module plays a critical role in capturing global dependencies and refining feature representations by leveraging self-attention mechanisms. It enables the model to incorporate contextual information across the entire sequence, facilitating a deeper understanding of spatial and depth relationships. The encoder refines input features by employing a multi-head self-attention mechanism and a feed-forward neural network (FFN) [17,28]. Given the input feature tensor \(X\in R^{N\times L\times C}\), where \(N\) is the batch size, L is the sequence length, and C is the feature dimensionality, the encoder projects X into query \(Q_{f}\), key \(K_{f}\), and value \(V_{f}\) matrices, where \(Q_{t}\), \(K_{t}\), \(V_{t}\in\mathcal{R}^{N\times L\times C}\). These are further divided into \(H\) attention heads, with each head having a dimensionality of \(D_{k}=C/H\). The attention weights are computed as follows:  
\[Attention\Big{(}Q_{f},K_{f}\Big{)}=Softmax\Bigg{(}\frac{Q_{f}\times{K_{f}}^{T} }{\sqrt{D_{k}}}\Bigg{)}. \tag{8}\]  
enabling the encoder to capture long-range dependencies across the sequence. A linear attention mechanism enhances computational efficiency by transforming [29]\(Q_{f}\) and \(K_{t}\), using an ELU-based feature map. The output of the attention layer, \(Z\in\mathbb{R}^{N\times L\times C}\), is combined with the input via a residual connection and normalized using layer normalization, \(X_{1}=Norm(X+Z)\). This is followed by a two-layer FFN, \(FFN(X)=ReLU(X\times W_{1})\times W_{2}\), where \(W_{1}\) and \(W_{2}\) are learnable weight matrices. The FFN output is also added back to the input with a residual connection and normalized, \(X_{ot}=Norm(X_{1}+FFN(X_{1}))\). The final output, \(X_{ot}\in R^{N\times L\times C}\), integrates depth-sensitive global context and long-range dependencies, providing refined features for downstream tasks.