esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Methodology and Autoencoder Formulation
#### 3.3.2 Hidden Layers
Overfitting affects our model resulting in deficient performance when the model is evaluated on new data. Dropout layers can help us to prevent overfitting. The term \"Dropout\" refers to leaving out some nodes in the neural network. Using Dropout in a neural network makes the training process more turbulent, which compels nodes in a layer to randomly accept or reject responsibility for the input data [28]. In other words, the Dropout layer refers to ignoring a set of nodes during the training phase which are randomly selected. Therefore, the Dropout layer forces a neural network to learn more about the key features and on top of that, the training time of each epoch is shorter. When we have features with values in the range 0-1 and some others in the range 1-100, we suggest normalising these values, so the training process of our model becomes faster. If this technique benefits the input layer, why do we not do the same for the values inside the dense layers of our convolutional autoencoder that constantly change? Batch normalisation layers reduce the overfitting effect and, similar to the Dropout layer, add a little noise to the activations of each hidden layer. Therefore, if we use batch normalisation layers, we will use fewer Dropout layers, which is good because we lose much information. However, we should not rely solely on the Batch normalisation layers as using a combination of Dropout layers is more efficient. The convolutional autoencoder consists of the encoder and the decoder. In the decoding part, the model learns how to reconstruct data from the compressed encoder representation by having the same layers the encoder has but mirrored. As we explained before, the MaxPooling layer helps us compress the input image (Downsampling), so now it makes sense that we must restore the compressed image to its original dimensions. Here is where the Upsampling layer takes over action. The upsampling layer is a simple version of Unpooling (the opposite of the pooling layer), where it repeats the input's rows and columns. Finally, the need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a standard convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. Hence, choosing a convolutional autoencoder would be a good idea in the decoding part of our model to use the Conv2 Transpose layer. There are convolutional transpose layers for two and three dimensions; we chose the ones for two because our images have two dimensions. The Conv2 DTranspose layers learn many filters, similar to the superficial Convolutional layer. We used multiple times the Conv2 DTranspose layer in the decoder of our proposed model, reflecting the Convolutional layers of the encoder. We could use the superficial Convolutional layer in the Decode part as well, but the performance of our model was significantly lower.
# An Initial Load-Based Green Software Defined Network +
## 6 Performance Analysis
### The effect on the delay caused by ICWGA
#### 6.3.2 The Effect of Some Factors on the Performance of ICWGA  
**(1). The effect on the performance of ICWGA caused by link density**  
In order to evaluate the effect of link density on ICWGA, three sets of random topologies with same number of nodes and different link densities generated by Brite are used. Each set includes five random topologies. Link density is denoted as \(D\) in the results. In the simulation, the number of the nodes \(|V|=30\), and \(D\in\{4,6,8\}\).  
Figure 5: The results for the increased propagation delay of different SND-Lib topologies. (**a**) Abilene. (**b**) India. (**c**) Germany.  
Figure 6: The results for the increased number of hops of different SND-Lib topologies. (**a**) Abilene. (**b**) India. (**c**) Germany.  
Figure 7 shows the energy saving ratio for the random topology sets. The curves present the averages of the results for the five topologies with the same link density. The error bars show the maximum and minimum values of the results for same cases in a same topology set. It is observed that the difference among the results for the five topologies in a same topology set presented by the height of the error bars are very small and can be ignored. The trend of curves is similar to that of the results for the SND-lib topologies. As \(\lambda\) increases, the energy saving ratio decreases in all cases, and the decrease rate of ICWGA is slower than that of Dijkstra. Comparing the results for the three topology sets with different link densities, the energy saving ratio increases as the link density increases, and the topology set with \(D=8\) achieves the highest energy saving ratio.  
Looking at the average bandwidth utilization of each case in Figure 8, similar to the results in Figure 7, the differences among the results for the five topologies in a same topology set can also be ignored. It is observed that the curves of ICWGA almost overlap. That is to say, no matter how rich the connection of the network is, the number of used links in ICWGA are almost the same under the same traffic load. But for Dijkstra algorithm, the average bandwidth utilization decreases with increasing link density.  
Figures 9 and 10 show the effect on delay caused by ICWGA in the topology set with \(D=4\). The height of the bars show the averages of the results for the five topologies with \(D=4\). They show similar trends to the results for the SND-lib topologies. Since the trend of results for the other topology sets are similar to those for the topology set with \(D=4\), they are omitted. To compare the effect on the delay caused by link density, the cumulative distribution of the increased delay and the increased  
Figure 8: Average bandwidth utilization of different link densities.  
Figure 7: Energy saving of different link densities.
## 4 Experiments
### Chime-4
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**CHIME-4**} & \multicolumn{8}{c}{**LM Weight = 0**} \\ \cline{2-10}  & \multicolumn{4}{c}{**CER**} & \multicolumn{4}{c}{**WER**} \\ \cline{2-10}  & **et05 simu** & **et05 real** & **dt05 simu** & **dt05 real** & **et05 simu** & **et05 real** & **dt05 simu** & **dt05 real** \\ \hline Baseline & 12.2 & 14.5 & **8.7** & 8.6 & 29.5 & 33.1 & **21.5** & 21.7 \\ LA CTC/Attention & 12.7 & 15.1 & 9.5 & 9.2 & 30.4 & 34.2 & 23.4 & 22.9 \\ SA CTC/Attention & **11.9** & **14.0** & 8.8 & **8.5** & **28.4** & **32.2** & 21.7 & **21.7** \\ \hline \hline \multirow{2}{*}{**CHIME-4**} & \multicolumn{8}{c}{**LM Weight = 1**} \\ \cline{2-10}  & \multicolumn{4}{c}{**CER**} & \multicolumn{4}{c}{**WER**} \\ \cline{2-10}  & \multicolumn{4}{c}{**et05 simu**} & **et05 real** & **dt05 simu** & **dt05 real** & **et05 simu** & **et05 real** & **dt05 simu** & **dt05 real** \\ \hline Baseline & 6.9 & 9.2 & 4.1 & 4.5 & 13.4 & 16.9 & 8.0 & 9.2 \\ LA CTC/Attention & 6.9 & 8.9 & 4.5 & 4.6 & 13.0 & 16.2 & 8.8 & 9.1 \\ SA CTC/Attention & **6.2** & **8.3** & **4.1** & **4.1** & **11.9** & **15.6** & **8.0** & **8.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performances of the SA CTC/Attention for CHIME-4 compared with the LA CTC/Attention and baseline architectures. Figure 3: The multichannel time-restricted attention CTC/Attention architecture. Evaluation of Speech Quality) of the enhanced speech. Table 5 shows the PESQ results of the three systems. It can be observed that the SA CTC/Attention behaves almost the best in all conditions. This indicates that, while improving the performance of ASR components, the SA CTC/Attention is also conducive to the optimization of the neural beamformer. In order to further make a comparison between the LA CTC/Attention and SA CTC/Attention, we analyze the losses during training. Figure 4 shows that the SA CTC/Attention framework arrives at the lowest main loss, CTC loss, and attention loss among the three systems. These results indicate that our proposed SA CTC/Attention algorithm works efficiently and improves ASR performance.
# Plasmonic and Dielectric Metasurfaces: Design, Fabrication and Applications
## 5 Conclusions  
In summary, we have introduced basic concepts of metasurfaces and reviewed our recent works in metasurface-based devices and their design, fabrication and applications: (1) metasurfaces consisting of multiple concentric rings in a gold film are designed to generate broadband OAM-carrying vector beams in the regime from the near-infrared to mid-infrared band; (2) a metal V-shaped antenna array is designed to realize multicasting from a single Gaussian beam to four OAM beams assisted by the PSI algorithm, achieving a low-crosstalk of near \(-15\) dB; (3) a kind of gold metasurface is designed and fabricated on the facet of a G.652 SMF to generate the LP\({}_{11}\) mode at the wavelength of 632.8 nm; (4) a kind of split-ring graphene is designed to realize CPA in the THz regime, achieving a tunable CPA covering from 99.7% to less than \(2.1\times 10^{-4}\)%; (5) a kind of dielectric metasurface is designed and fabricated on the SOI platform, realizing OAM beam generation; (6) a kind of dielectric metasurface is designed on the SOI platform at a wavelength of 1550 nm, achieving Bessel beam generation with high purity and multicasting from a single Gaussian beam to six OAM beams with a relatively low crosstalk of less than \(-14\) dB. The designed and fabricated metasurfaces in these works have nano-scaled dimensions, and the generated beams carrying OAM have favorable qualities, which are promising for a compact and effective beam-steering system and space-division multiplexing (SDM)-assisted optical communication systems. In the future, the metasurface devices to generate optical beams carrying OAM can be combined with tunable and reconfigurable functions reported in [137; 138; 139; 140; 141; 142; 143], which makes robust spatial light manipulation possible and facilitates more interesting applications.  
This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 11574001, 11274131 and 61222502, the National Basic Research Program of China (973 Program) under Grant 2014 CB340004, the National Program for Support of Top-notch Young Professionals and the Program for New Century Excellent Talents in University (NCET-11-0182). The authors would like to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for technical support and helpful suggestions.  
[PERSON] and [PERSON] contributed to writing and finalizing the paper. [PERSON] supervised the project.  
The authors declare no conflict of interest.
## 2 Coded Aperture Compressive Temporal Imaging (CACTI)
### Bayesian Model for CACTI Super-Resolution (SR) Reconstruction
The SR reconstruction accuracy of image sequence is highly dependent on the estimation accuracy of the inter-frame motions between the LR observations. To address error propagation, we adopt a coarse-to-fine scheme that significantly improves the performance. The basic idea is to roughly estimate the HR image, then gradually update motion estimation and reconstruct the HR image from coarse to fine using the multi-regularization algorithm and BP algorithm. Considering the computational complexity, we only estimate the two-scale resolution image sequence and update the motion estimation once time.
# Feature Augmentation Based on Pixel-Wise Attention for Rail Defect Detection
## 3 Materials and Methods
### Model Architecture  
The overall detection model is illustrated in Figure 2. Our model is based on the one-stage object detection architecture to ensure high detection speed. The whole model consists of three parts: (a) a feature extractor as backbone network that builds multi-level semantic feature maps at all scales, (b) the proposed feature augmentation framework based on pixel-wise attention that enables the model to better distinguish ambiguous defect patterns, and (c) multi-level detect heads for both object classification and bounding box regression. To be more specific, the pretrained backbone network is fine-tuned to extract input and support features from an input batch. A novel pixel-level similarity metric is developed to evaluate dense spatial relations that enables pixel-level match of input and support features. The final refined feature are activated by support features for subsequent detector heads.  
To establish feature augmentation, \(N\) numbers of representative defect images are manually selected and masked as support samples. Every single image of the rest of the images is grouped with the \(N\) masked images to form a batch of input images. The feature extractor will extract features of the input batch for subsequent feature augmentation.
## 4 Experimental Results
### Ablation Study  
Comprehensive ablation experiments were conducted to meticulously verify the efficacy of the proposed image processing and enhancement modules in facilitating subsequent object detection, especially in various adverse weather conditions. The IDP-Improved YOLOV9 method was comprehensively compared with several baseline methods, including Improved YOLOV9, Enhancement + Improved YOLOV9, and MultiRemoval + Improved YOLOV9, across three independent test datasets. Table 4 presents the mAP evaluation results for the three adverse weather conditions: fog, rain, and snow.  
In diverse adverse weather environments, the combination of Enhancement + Improved YOLOV9 and MultiRemoval + Improved YOLOV9 demonstrated a significant advantage in improving detection performance compared to independently using Improved YOLOV9. The IDP-Improved YOLOV9 algorithm notably enhanced visibility in various adverse weather images, effectively improving the effects of defogging, deraining, desnowing, and enhancement while significantly increasing the accuracy of object detection. Specifically, in rainy conditions, IDP-Improved YOLOV9 achieved a 6.2% increase in detection accuracy (mAP) compared to Enhancement + Improved YOLOV9 and a 5.5% increase compared to MultiRemoval + Improved YOLOV9.  
This series of ablation studies fully validated the synergistic effect of the parallel architecture for the image defogging, deraining, and desnowing modules, as well as the image enhancement module, on object detection algorithms. The experiments demonstrated that the IDP-Improved YOLOV9 method not only enhances visibility but also significantly improves the efficiency of image defogging, deraining, and desnowing and other image processing tasks, leading to an enhancement in object detection performance.
# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 2 Related Work
### Per-Frame Semantic Video Segmentation  
Section 1 has introduced the ways that per-frame methods reduce the computational cost. In this section, we review the per-frame methods from the aspect of architecture design to better compare them with our work. Technically, most per-frame methods adopt either the encoder-decoder architecture or the two-pathway architecture. The encoder-decoder architecture features repeated down-sampling in the encoder, which reduces the computational cost significantly [19; 27; 40; 41; 42]. Although our method is a warping-based method instead of a per-frame method, we also adopt the encoder-decoder architecture. The decoder of the encoder-decoder architecture is responsible for restoring the spatial information but the down-sampling makes it difficult. To deal with the problem, the two-pathway architecture has a deep pathway to extract high-level features and a shallow pathway to extract low-level features. The combination of features from different levels improves the accuracy [10; 43; 44; 45; 46]. Feature fusion in our proposed non-key-frame CNN (NKFC) is similar to those in [35; 47; 48], where lateral connections are used to fuse the low-level (spatial) and high-level (context) features. In comparison, our NKFC only retains a few layers of the encoder to extract low-level features and obtains high-level features by feature warping. Thus, NKFC saves the heavy computations of context feature extraction.
## 3 Energy-Efficient Architectures
### Spatial Architectures: Fpgas and Asics
* Row Stationary (RS): this dataflow jointly maximizes the reuse of all data, i.e., inputs, weights, and partial sums. In this dataflow, the operations of a row of the convolution are mapped to the same PE. The weights are kept stationary in the PEs. For instance, Eyeriss [63] is an RS accelerator. * No Local Reuse (NLR): This dataflow reduces the accelerator area by eliminating the RFs from the PEs and reading data only from GBs. There is no data reuse. DianNao [64] is an NLR accelerator. In recent years, to further improve the performance and energy efficiency of accelerators, attention has been focused on new strategies. In this survey, those that have led to better results and on which the research has invested more will be discussed, namely sparsity exploitation, variable bitwidth accelerators, and reconfigurable accelerators.
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 6 Conclusions  
In this paper, we showed a novel Variable Delayed Dual-Core Lockstep (VDCLS) technique for error detection. In contrast to the classic DCLS approach, where slave core delay is fixed, we allow the checker core to run freely behind the main core within the constrained boundaries of clock cycles. This increases the temporal diversity needed for CMF mitigation. One of the consequences of the proposed method was to increase the SoR area with the instruction and data cache memories, which reduces SPOF risk. Each core fetches instructions and data to their cache memories using separate requests on the system bus. This additional layer over typical ECC codes significantly hinders side channel manipulation as the attacker would need to intervene in two bus transactions taking place in two hard-to-predict time points. The VDCLS technique was implemented using the CCRV32 ST processor core utilizing RISC-V standard \"GC\" instruction set. The results show a low 4% overhead in system level FPGA resources of VDCLS implementation versus similar dual-core processor. The additional hardware results in a flexible split/lock system where the split mode features high dual-core performance, whereas the lockstep mode ensures fast error detection and makes security attacks more difficult. The performance drawback of the proposed technique comes from the necessity to inject stall cycles to maintain core synchronization. We showed that this negative impact is low and depends on the executing software. In return we are able to flatten the power consumption correlation between the running cores, essential for a wide range of attacks. The design of VDCLS Controller Modules allows random dummy cycles to be injected into each core independently. The presented results show that this step further decreases the correlation peak. The introduced power disruption caused by the variable delay between the processing cores and randomly injected dummy cycles make it more difficult for an attacker to infer about executed code and inject precisely timed faults. Despite the fact that VDCLS is mainly an error detection technique, it can be further enhanced and used jointly with other techniques in order to create safe and secure microprocessor systems.  
**Author Contributions:** Methodology, investigation, validation, visualization, writing--original draft preparation, K.M.; writing--review and editing, funding acquisition, W.A.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
This study investigated the spatiotemporal evolution of vegetation CUE and WUE, utilizing remote sensing data to identify potential influencing variables. The results revealed a significant decline in both CUE and WUE over the period from 2001 to 2023, with rates of change of 0.0014/a and 0.0022/a, respectively. Meanwhile, this pattern was found to be statistically significant (\(p<0.05\)). The average CUE and WUE values in the NMR during this time were 0.47 and 0.82 g C\(\cdot\)m\({}^{-2}\cdot\)mm\({}^{-1}\), respectively, exhibiting clear geographic variation. Specifically, a noticeable geographic trend was identified, with smaller values in the northwest and larger values in the southeast. Moreover, the trends in CUE and WUE exhibited notable variation across different vegetation types. Sen trend analysis revealed that CUE showed widespread degradation, with 90.03% of the area experiencing a negative rate of change. WUE also exhibited a marked degradation trend across 73.60% of the region. The Mann-Kendall test confirmed significant declining trends in 64.14% of the CUE values and 39.88% of WUE values, with some regions, such as Zhaoqing, Ganzhou, and Guilin, showing improvements. The Hurst exponent analysis showed sustainability in the trendsacross most of the areas, with 95.95% for CUE and 88.87% for WUE. However, future projections suggest that the overall trajectory for both CUE and WUE in the NMR is likely to continue declining. The decline in CUE and WUE was linked to climate factors, with precipitation strongly influencing WUE and temperature having a greater impact on CUE. Despite some regions showing potential for recovery, projections suggest continued decline in both CUE and WUE across most of the region, influenced by environmental shifts and anthropogenic factors.  
Conceptualization, S.L. and P.Z.; methodology, J.L.; software, Z.T.; validation, S.L., P.Z. and Z.T.; formal analysis, J.H.; investigation, P.Y.; resources, H.C.; data management, S.L.; writing--original draft preparation, S.L.; writing--review and editing, P.Z.; visualization, S.L.; supervision, P.Z.; project administration, J.H.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by GDAS' Project of Science and Technology Development (2022 GDASZH-2022010201-01), GDAS' Project of Science and Technology Development (2022 GDASZH-2022010106), the Science and Technology Program of Guangdong (No. 2024B1212080005), and the Science and Technology Planning Project of Guangdong Forestry Bureau (LC-2021124).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 1 Introduction
This is particularly important for scalability and deployment in real-world environments, where labeled depth data are scarce. * The integration of these components results in a powerful depth estimation framework that achieves state-of-the-art performance on challenging datasets such as SUN RGB-D and KITTI. Our method outperforms previous approaches, particularly in dynamic scenes, where moving objects often cause significant depth prediction errors. The DI-MDE framework achieves better depth consistency between static and dynamic regions by aligning their scales while refining depth estimates iteratively. The rest of the paper is organized as follows: Section 2 reviews related work in monocular depth estimation and self-supervised learning. Section 3 details the methodology of the proposed DI-MDE framework, including the depth refinement process and the scale-alignment module. Section 4 presents our experimental results, demonstrating the superiority of our approach over existing methods. Finally, Section 5 concludes the paper and suggests future directions for research.
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 6 Conclusions  
This section presents a summary of the results of this work and the conclusions from the application of the proposed routing protocol in DTNs. Finally, some possible future extensions of the research work are suggested.