esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Electromagnetic Interference (EMI) Shielding Properties  
The EMI shielding effectiveness properties were evaluated using a P9372A Keysight.  
A Streamline Vector Network Analyzer (Keysight, Santa Rosa, CA, USA) and two sets of microwave standard 15 dB gain waveguides were used (WR 187 and WR 147, respectively, obtained from Advanced Technical Materials Inc. (ATM), Patchogue, NY, USA), covering a broad C frequency band in the range of 3.5-7.0 GHz (C-band), which is used for long-distance radio telecommunications, such as satellite communications transmissions, Wi-Fi devices, cordless telephones, weather radar systems, etc. In particular, every sample was placed in the middle of each set of waveguides, and its scattering parameters (S-parameters; S11, S12, S22, S21) were recorded.
# Analysis of Weighted Factors Influencing Submarine Cable Laying Depth Using Random Forest Method
## 3 Results and Discussion
### Discussion  
This study employed the Improved Seagull Optimization Algorithm (ISOA) to optimize the structure of a backpropagation (BP) neural network, enhancing its fit to the dataset and demonstrating superior prediction accuracy compared to traditional machine learning algorithms. Key performance metrics include a root mean square error (RMSE) of 0.0927, mean absolute error (MAE) of 0.0708, and an R\({}^{2}\) value of 0.964, as shown in Table 2 and Figure 5. These figures indicate that the ISOA-BP model not only outperforms existing methods such as decision trees, gradient boosting decision trees, and support vector regression but also provides a close approximation of actual values during submarine cable burial depth predictions.  
Despite these advantages, the model faces several inherent limitations and challenges:  
1. Model Sensitivity and Bias: Performance variance across different datasets highlights the model's sensitivity to data features like outliers and noise. Effective data cleaning strategies, including the removal of missing values and standardization, were essential to ensure robust feature representation.
2. Computational Demands: The substantial computational resources required for training and optimizing neural networks may limit their application to large datasets. GPU-accelerated training was utilized to mitigate this issue.
3. Overfitting Risks: The potential for overfitting, particularly with complex models and small datasets, was addressed using L2 regularization and early training cessation to preserve the model's generalizability.  
Further, the comparison with traditional optimization techniques underscores both the merits and challenges of employing chaos theory and evolutionary algorithms in neural network optimization. While these methods offer a more adaptive parameter search space,  
Figure 5: Comparison of prediction performance between models. (**a**) The effect of the ISOA-BP and traditional machine learning model compared; (**b**) the effect of the ISOA-BP and BP compared.  
their complexity necessitates careful hyperparameter tuning to balance optimization scope and computational efficiency, avoiding the pitfalls of local optima.
## 3 Results
### Comparative Analysis with CCR Model and GDM-DEA Method for JLASTU Experiment  
The CCR model and GDM-DEA method were used for the performance comparison of the data collected from the JLASTU experiment. The results were obtained and are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Group Code** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline CCR efficiency value & 1.000 & 0.917 & 1.000 & 0.944 & 0.963 & 0.995 & 1.000 & 1.000 & 1.000 & 0.973 & 0.997 & 0.815 \\ \hline GDM-DEA efficiency value & 1.045 & 0.917 & 0.961 & 0.878 & 0.956 & 0.952 & 0.938 & 1.064 & 0.852 & 0.852 & 0.799 & 0.792 \\ \hline
**Group Code** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **24** \\ \hline CCR efficiency value & 1.000 & 0.941 & 0.887 & 0.877 & 0.951 & 0.852 & 1.000 & 0.962 & 1.000 & 0.919 & 1.000 & 0.878 \\ \hline GDM-DEA efficiency value & 0.869 & 0.887 & 0.776 & 0.877 & 0.934 & 0.766 & 1.039 & 0.927 & 0.971 & 0.890 & 0.793 & 0.878 \\ \hline
**Group Code** & **25** & **26** & **27** & **28** & **29** & **30** & **31** & **32** & **33** & **34** & **35** & **36** \\ \hline CCR efficiency value & 0.846 & 0.885 & 0.903 & 0.881 & 0.873 & 0.890 & 0.872 & 0.864 & 0.894 & 0.887 & 0.856 & 0.876 \\ \hline GDM-DEA efficiency value & 0.846 & 0.885 & 0.903 & 0.881 & 0.873 & 0.890 & 0.872 & 0.864 & 0.894 & 0.887 & 0.856 & 0.876 \\ \hline \hline \end{tabular}
\end{table}
Table 3: GDM-DEA efficiency value of the JLASTU experiment cultivation measures.  
For the JLASTU experiment, the evaluation results of the CCR model indicated that the CCR model could only distinguish whether the group of cultivation measures was CCR-effective or not (Table 3). The CCR model could only divide the groups into two parts but could not classify or rank all the decision-making units. Roughly, half of the cultivation measures were CCR-effective, but the best cultivation measures could not be determined. In contrast, the results from the GDM-DEA model showed that if a cultivation measure is GDM-DEA-effective, it must be CCR-effective. Otherwise, it may not be true. The GDM-DEA model can classify and rank all the cultivation measures in the JLASTU experiment (Table 3). Therefore, the GDM-DEA model demonstrates better performance than the CCR model in evaluating the effectiveness of cultivation measures.
# An Updated Review of Smac Mimetics, LCL161, Birinapant, and GDC-0152 in Cancer Treatment
## 11 Conclusions and Future Directions  
As dysregulation of IAPs has been found in a variety of tumors, and overexpression of IAPs promotes tumorigenesis and tumor metastasis, the use of Smac mimetics is suggested as a potential therapeutic approach for the treatment of cancer. The anti-cancer potency of Smac mimetics has been demonstrated in different types of malignant tumors in preclinical studies. Smac mimetics induce apoptosis and the non-canonical NF-kB signaling pathway by downregulating the protein expression level of IAPs. However, many unknown anti-cancer molecular mechanisms of action of Smac mimetics are still yet to be discovered. Exploring these signaling pathways is important to provide hints for the modification and combination therapy of further compounds.  
The safety and tolerance of Smac mimetics are currently investigated in many clinical studies. Most studies report that Smac mimetics, such as LCL161, are safe and have good tolerance in patients with cancer. However, other Smac mimetics have shown low anti-cancer potency in the treatment of patients with cancer. The underlying molecular mechanism of action is still unclear. The anti-cancer potency of most Smac mimetics is on the micromolar level. Thus, many studies have combined Smac mimetics with chemotherapeutic drugs, radiotherapy, and immune therapy for cancer treatment. Results showed that the synergistic effect of these combinations can decrease the dosage of Smac mimetics. With the advancement of nanotechnology, delivering chemotherapeutic drugs with various types of nanoparticles can improve anti-cancer potency and cancer-targeting specificity through active or passive cancer targeting. For instance, [PERSON] et al. synthesized a chitosan-based nanoparticle for co-delivering STAT3 siRNA and BV6, a bivalent Smac mimetic, to treat breast cancer cells, colorectal carcinoma cells, and melanoma cells, and their nanoparticles were shown to suppress cancer cell progression through caspase-dependent apoptosis in vitro and in vivo [107]. Therefore, nanotechnology can potentially be applied to improve the anti-cancer potency of Smac mimetics.  
Literature research: Y.-C.C. and C.H.A.C.; Figure and table preparation: Y.-C.C.; Manuscript writing and proofreading: Y.-C.C. and C.H.A.C. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Ministry of Science and Technology, Taiwan (MOST 109-2320-B-006-031).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Data Set  
#### 2.1.1 Training Data Set  
The RSNA and MICCAI developed a data set named the RSNA-MICCAI brain tumour radiogenomic classification for the BraTS 2021 challenge [21], which is available to the public through the Kaggle platform. This data set comprises multi-parametric magnetic resonance imaging (mpMRI) scans acquired from multi-institutional benchmarks, retrospectively collected for the prediction of a specific genetic characteristic of GBM, namely, the MGMT promoter methylation status, fulfilling strict inclusion criteria for image quality and diagnostic confirmation. An mpMRI scan typically includes four imaging modalities, each contributing distinct contrast mechanisms that facilitate tumour characterisation. In this study, only T1w images were selected for analysis (Figure 1), as this sequence provides the most morphologically informative signal [27]. All images were standardised to a resolution of 512 \(\times\) 512 pixels to guarantee uniformity between analyses.  
The BraTS 2021 challenge employs AUC-ROC as its primary evaluation metric, providing a framework for assessing model performance in GBM classification tasks [21]. The ROC curve is a widely utilised tool for evaluating the effectiveness of binary classification models by plotting the true positive rate (sensitivity) against the false positive rate (1--specificity) across various decision thresholds. This graphical representation provides information on the ability of the model to distinguish between classes, independent of a particular threshold. The AUC, derived from the ROC curve, offers a comprehensive summary of the performance of the classifier by quantifying the overall discriminatory power as a single scalar value ranging from 0 to 1. An AUC value of 0.5 indicates random performance, whereas values closer to 1.0 signify superior classification capabilities [28]. The principal benefit of AUC is its validity in the context of unbalanced data sets [29].  
In accordance with the machine learning paradigm of algorithmic evaluation, the BraTS 2021 challenge data is divided into training and testing data sets [21]. The training data set contains complete mpMRI data from 585 patients, with a primary analytical focus on the T1w sequences. In contrast, the testing data set consists of 87 patients and maintains fully blinded diagnostic labels through the Kaggle evaluation platform, ensuring an unbiased assessment of model generalisability. Participants submit predictions for the test set through this private evaluation system, which automatically calculates AUC-ROC metrics as the primary performance benchmark.  
All cases were evaluated by a panel consisting of four board-certified neuroradiologists, with binary classification labels (\"0\" indicating negative and \"1\" indicating positive for GBM) assigned through consensus review [30]. This validation process ensures diagnostic reliability, while the structure of the data set maintains a rigorous separation between training and evaluation data, preventing information leakage during model assessment. The main characteristics of this data set are outlined in Table 1.
## 3 Results and Discussion
### Crack Growth Characteristics  
Figure 15 displays the fundamental geometry and characteristics of DSR parallel plates. The crack length and extension rate calculation methods have been described in detail by  
Figure 14: Fatigue life evaluation of asphalt mastics modified with fibers.  
Figure 13: Effects of fiber incorporation on the \(C\) vs. \(S\) curves of asphalt mastics.  
[PERSON] et al. [32]. Figure 16 summarizes the fracture criteria for all asphalt mastics evaluated in the LAS-5 test. It is evident from the LAS test that all the asphalt mastics exhibit the same patterns of fracture development. Initially, the cracking rate (_da/dN_) increases rapidly, then slowly, eventually becoming unstable until it breaks.  
Figures 17 and 18 illustrate that all the asphalt mastics examined exhibit identical crack initiation phases and different crack propagation characteristics. Under load, cracks initiate within the asphalt material due to internal defects and generate stress concentration at the initial crack location. The micro-cracks propagate rapidly under repeated loading until they reach a critical size, causing structural damage. However, the presence of fibers in the asphalt mastic can create a restraining effect around the cracks, which can prevent further crack propagation. The enhanced performance of bamboo powder fibers in asphalt mastics can be attributed to the mechanical interlocking effect between the rough and uneven surface of the fibers and the asphalt mastic, resulting in a strong bond. This interlocking effect creates a tight connection, and when the material is subjected to external forces, the fibers act as a skeleton within the hybrid system, effectively transferring stress and improving the overall strength of the material. The bending strength of bamboo powder fibers plays a critical role in this mechanism, as their specific length-to-diameter ratio allows them to act as bridges for stress transfer [17]. As a result, the resistance to crack propagation becomes the primary factor influencing the fatigue properties of the asphalt mastic. Furthermore, fiber-modified asphalt mastic exhibited lower _da/dN_ levels as \(G\) increased and longer crack lengths as loading cycles increased, indicating better crack resistance.  
Figure 16: This pertains to the correlation between the rate of cracking (_da/dN_) and the energy release rate (\(G\)).  
Figure 15: A diagrammatic representation for the calculation of asphalt binder crack length [32].  
The fracture length of fiber-modified asphalt mastic is also considerably longer than that of regular asphalt mastic under the same number of cyclic loadings, as illustrated in Figure 17. This suggests that fiber integration has a detrimental effect on the asphalt mastic's fatigue performance. The observed effect can be explained by the strong mechanical properties of bamboo fibers, which tend to cause stress concentration when exposed to outside forces, making asphalt mastic samples more likely to crack at room temperature.
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 3 Methodology
### Stacked Hourglass Module  
The stacked hourglass module extracts key information from the input image based on the feature map extracted by the feature extractor and represents the probability of keypoints existing at those locations as heatmaps. This study adopts a structure similar to that of the original hourglass network, which consists of the encoder and decoder (Figure 2). We applied various lightweight techniques to improve the internal structure of the encoder and decoder, thereby maintaining the pose estimation accuracy during inference while reducing resource use.  
In the encoder section, a shuffle-gated block and max pooling are used to extract spatial feature maps of the input image at each resolution. The feature map size of the first encoder is \(144\times\frac{h}{4}\times\frac{w}{4}\), and the height and width are reduced by half through max pooling for each encoder. The feature map size of Encoder block 5 is reduced to \(144\times\frac{h}{32}\times\frac{w}{32}\). In the decoder section, we combined the features extracted by the encoder and applied the multi-dilation block and up-sampling to progressively restore the original resolution and generate keypoint heatmaps. These heatmaps are the same size as those produced by the encoder and eventually match the size of the original input feature map, \(144\times\frac{h}{4}\times\frac{w}{4}\).  
Finally, by adjusting the number of hourglass networks, we can balance model accuracy and computational efficiency. Increasing the number of modules improves accuracy; however, fewer modules reduce inference time and complexity.
# A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes
## 3 The Proposed HCD Algorithm
### Protocol Performance  
After deploying nodes, a layering stage is performed. Each of the nodes computes its distance from the BS. For calculating this distance, the BS broadcast a signal which will be heard by all nodes and each node approximates its own calculated distance to the BS using the RSSI. Consequently, the node informs its position to the BS. The BS then starts layering the network. In the beginning, the BS calculates the difference between the closest and the furthest nodes to itself and experimentally defines four layers for networks. The BS sends messages within the network and assigns layer ID for each node.  
All HCD operations are involved in cluster initialization stage and data transmission stage. Cluster initialization stage is composed of four phases including the delay time calculation phase, CH selection phase, cluster formation phase, and route construction phase. The data transmission stage is divided into a number of major slots. Each major slot is formed by several rounds and two substages which are called CH rotation and adjustment route. Each round involves in intercluster transmission phase and intracluster transmission phase. In a CH rotation, the role of CH is to turn between member nodes to prevent discharging of the energy in CH. The operation of HCD is displayed in Figure 2. Figure 3 also shows the flowchart of the proposed algorithm.  
Figure 3: Flowchart of the HCD protocol.  
Figure 2: Hybrid unequal multihop clustering based on density (HCD) operation.
# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 2 Literature Review
### Cox Proportional Hazards and Hazard-Based Models in Transport Research  
Cox models fall under the broader category of survival analysis models and are commonly used in healthcare research. Some applications can be found in the field of transportation, although one may argue that their adoption still remains limited.  
Their flexibility makes them suitable for analyzing both short-term and long-term behaviors related to travel activity. These durations may refer to travel times, as in the study by [PERSON] et al. [27] in Shenzhen, China, where they estimated the duration of trips made by public transport users based on selected variables. Similarly, [PERSON] et al. [28] focused on the estimation of both trip start times and travel durations across different modes of transport in eight European cities. In Toronto, Canada, [PERSON] and [PERSON] [29] applied deep survival analysis using a Cox model to estimate pedestrians' available waiting time, based on traffic-related characteristics. Another application is found in the work of [PERSON] [30] in Washington, D.C., where the model was used to examine the duration of post-work activities and their relationship with socioeconomic and spatiotemporal factors. In Kochi, Japan, [PERSON] et al. [31] applied Cox modeling to smart card data to predict the likelihood of discontinuing public transport usage.  
Beyond the transport domain, more advanced applications of Cox models have emerged in healthcare research. For example, [PERSON] et al. [32] used a latent class extension of the Cox model to analyze survival patterns among patients with chronic heart failure, demonstrating the value of incorporating unobserved heterogeneity in model structure. Likewise, [PERSON] et al. [33] discussed methodological practices for latent class analysis in clinical research, reinforcing the importance of probabilistic modeling in uncovering hidden population subgroups with distinct risk trajectories.
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 3 Model
### Inflated (2+1)D Convolution  
In generally, 2D convolutions are applied to capture spatial features. In addition to spatial features, it is essential to consider the temporal relationship between frames. Conceptually, 3D convolution is achieved by convolving a 3D kernel to a cube that is formed by stacking multiple contiguous frames together [24]. Comparatively, 3D convolution is superior to 2D convolution in that 3D convolution can extract both spatial and temporal information, whereas 2D convolution can only extract spatial information. We use 3D ResNet50 to capture spatial-temporal features from videos.  
3D ConvNets, like C3D [25], have significantly more parameters than the corresponding 2D ConvNets by reason of the new convolution kernel of the time dimension. With the large number of parameters, they are hard to train [14]. We attempt to work around this issue by inflating 2D convolution weights from ImageNet pre-training to 3D. We initialize all 3D convolution kernels with 2D convolution kernels, which are pre-trained on ImageNet. For instance, if a 3D convolution kernel size is \(t\times k\times k\), we initialize them by repeating the pre-trained 2D \(k\times k\) kernel weights along the temporal dimension \(t\) times and then rescale by \(1/t\).  
The 2D convolution contains directions \(x\) and \(y\). Roughly speaking, the step sizes in these two directions are the same (kernel size and stride). However, in 3D convolution, the time dimension cannot be reduced too fast or too slow because it relies on image dimensions and frame rates. If it changes too quickly relative to space in time, it may confuse the edges of different objects, thus undermining the early feature detection. If the time dimension changes too slowly, it may not be able to effectively capture the scene dynamics. With this tradeoff in mind, we set the kernel of the first convolution layer as \(5\times 7\times 7\); the first Maxpooling layer's kernel as \(1\times 3\times 3\), and the second Maxpooling layer's kernel as \(2\times 1\times 1\).  
Due to the asymmetry between time and space domains, the convolutions of these two domains are carried out separately [29; 30]. The new spatial-temporal convolutional block is called (2+1)D Convolution. 3D convolution is solved with two consecutive operations: 1. a 1D temporal operation; 2. a 2D spatial operation. In other words, the spatial-temporal convolution consists of one temporal convolution and one spatial convolution. An illustration of this process is given in Figure 2. The full 3D convolution kernel size is \(t\times k\times k\), where \(t\) is the temporal width and \(k\) denotes the spatial width and height.  
Based on the aforementioned idea, continuous \(1\times 1\) and \(3\times 3\) convolutional kernels in 2D ResNet50 are changed into \(3\times 1\times 1\) and \(1\times 3\times 3\) in 3D. Table 1 shows the structural differences across the 2D ResNet, the R3D50, and the R(2+1)D50 network structures.