esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 1 Introduction
### Traditional Approaches for SITS Classification  
The state-of-the-art classification algorithms used to produce maps are currently Support Vector Machines (SVMs) and Random Forests (RFs) [12]. These algorithms are generally applied at pixel-level on the stack of multi-spectral images contained in the SITS. These algorithms are oblivious to the temporal dimension that structures SITS. The temporal order in which the images are presented has thus no influence on the results: counter-intuitively, a shuffle of the images in the series would result in the same model and thus in same accuracy. It induces a loss of the temporal behaviour for classes with evolution over time, such as the numerous forms of vegetation that are subject to seasonal change.  
One solution to mitigate this problem has been to pre-calculate temporal features extracted from vegetation index time series, and then to feed them to a classification algorithm [13; 14; 15]. Temporal features that have been used include some statistical values, such as the maximum vegetation index, and the approximation of key dates in the phenological stages of the targeted vegetation classes, e.g., the time of peak vegetation index. However, the addition of such temporal features has shown little effect on classification performance [16].  
To make the most of the temporal domain, other works have applied Nearest Neighbor (NN) -type approaches combined with temporal similarity measures [17]. Such measures aim at capturing the temporal trends present in the series by measuring a similarity independent of some temporal  
Figure 1: An example land cover map.  
shifts between two time series [18]. Although promising, these methods require a complete scan of the training set for classifying each test instance--leading to prohibitive computational costs when using more than a few thousand profiles [19]--and only provide little abstraction of the training data.
## 4 Discussion
### Limitations and Future Directions  
The results from the current study contribute to our understanding of the risk factors associated with problematic smartphone use among Canadian adolescents during the COVID-19 pandemic. Despite the contributions of this research to the extant literature, some limitations should be noted. First, our study was cross-sectional, limiting our ability to investigate the direction of effects between constructs and make reliable causal inferences. For example, previous studies have found that early problematic smartphone use could predict later internalizing problems [71; 72; 73]. As a result, future research should use a longitudinal design to investigate these effects over time. Moreover, our study only assessed screen time and did not measure smartphone checking, which is another important aspect of smartphone usage. In fact, recent research indicates that smartphone screen time and checking may have distinct associations, with checking to have more negative consequences [10]. Therefore, future studies should investigate the relationship between smartphone checking and problematic smartphone use. Furthermore, while we only used \"shrinkage\" methods, extreme gradient boosting, and random forest machine learning algorithms in the current study to train the statistical model of predictive variables in predicting problematic smartphone use, future research should compare other popular deep learning algorithms, such as neural network and Support Vector Machine.  
Additionally, screen time was measured by asking participants to report their daily average screen time from their smartphone screen time application. Thus, it is possible that there is some error in the reporting of this data. However, our large sample size likely helped reduce some of the risks around the influence of this measurement error. Future work, including objective measures of screen time, is needed. Furthermore, our participants were recruited from a specific region and age range and were not randomly selected. Therefore, the predictors of problematic smartphone use among young children remain unknown. At last, there might be other risk factors [74], such as parenting neglect [75] or low academic performance [76; 77; 78], that could also predict problematic smartphone use. Measuring these factors and including them in our regression models may have yielded different results. Thus, future studies should further investigate other potential risk factors of problematic smartphone use.
# Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue
## 2 Intelligent Ship Action Planning Method for Ocean Targets
### Experimental Strategy
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories of Constraints** & **Constraints** & **Constraint Meaning** \\ \hline \multirow{4}{*}{Time constraint} & Sailing path start time & The ship’s departure time cannot be later than the start time of the mission \\  & Sailing path duration & The sailing time cannot exceed the mission duration \\  & Starting point of the navigation path & The starting point of the A* route planning algorithm \\ \hline \multirow{2}{*}{Spatial constraint} & Sailing path target point & The end point of the A* route planning algorithm \\  & Navigable area & The planned route can only pass through navigable areas \\ \hline \multirow{2}{*}{Task conflict constraint} & Task time window & Task time window conflict solution \\  & Task priority & Tasks considered priorities are executed first \\ \hline \multirow{2}{*}{Ship performance constraint} & Ship speed constraint & The estimated sailing time refers to the maximum speed \\  & Ship range constraints & The distance of the planned navigation route shall not \\  & & exceed the maximum sailing mileage of the ship \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Constraints of navigation route planning.
## 2 System Model  
The time-varying UWA channel impulse response (CIR) can be demonstrated as the superposition of propagation multipaths with the Doppler effect. The time-domain CIR with \(N_{p}\) discrete multipaths is given as [8]:  
\[h(t,\tau)=\sum_{i=1}^{N_{p}}A_{i}(t)\delta(\tau-(\tau_{i}-\alpha_{i}(t)t)) \tag{1}\]  
where \(A_{i}(t)\) and \(\tau_{i}\) are the time-varying amplitude and initial time delay of the \(i\)-th path, \(\alpha_{i}\) is the Doppler factor of the \(i\)-th path. For the shallow-water UWA communication scenario, where the horizontal transmission distance is much further than the vertical direction, the Doppler factor of multipaths is approximately the same [35; 36], i.e., \(\{\alpha_{i}(t)\}_{i=1}^{N_{p}}=\alpha(t)\). Furthermore, assuming that the channel is slow time-varying, we can suppose that \(A_{i}\gets A_{i}(t)\) and \(\alpha_{i}\leftarrow\alpha_{i}(t)\) are invariant during observation. Considering a single-carrier UWA communication system, the passband received signal of hydrophone is given as [30]:  
\[y(t)=\sum_{i=1}^{N_{p}}A_{i}\tilde{x}((1+\alpha)t-\tau_{i})+v(t) \tag{2}\]  
where \(\tilde{x}(t)=\sum_{n=1}^{N}\tilde{x}_{n}(t)\) is the transmitted signal of source, \(N\) is the length of baseband sequence, \(v(t)\sim\mathcal{N}(0,\sigma_{v}^{2})\) is the additive white Gaussian noise (AWGN).
## 3 Results and Discussion
### Electrical Properties  
The influence of Al doping on the electrical resistivity of the as-grown Al:CuO thin films is shown in Figure 5. It is clear that resistivity value decreases gradually with Al doping. This can be attributed to two main factors: (i) when the Al\({}^{+3}\) ions occupy the Cu\({}^{+2}\) ion sites, an additional number of free electrons are created; (ii) the significant reduction in size of the grains generates a huge number of frontiers, and hence an improvement in transport time and/or electron collisions.  
The electrical resistivity data obtained in this study are comparable to those obtained by [PERSON] et al. [20].
## 2 Impacts on the UK EV Market
### Challenges and Potential Drawbacks for the UK EV Sector  
While global EV growth offers substantial opportunities, it also presents a series of economic and strategic challenges that could hinder the development of a robust domestic EV industry in the UK:  
* Loss of Domestic Market Share: With Chinese and Japanese EV brands entering the UK market, domestic manufacturers such as Jaguar and other UK-based firms face mounting competition. These brands may lose market share to lower-cost, high-spec international offerings, reducing profits and deterring further investment in local EV development and production.
* Battery Supply Constraints: As global demand for lithium-ion batteries rises--especially from major players in China and Japan--UK automakers may face challenges in securing sufficient battery supplies. Limited access to these critical components could restrict production capacity and delay vehicle deliveries.
* Import Dependence: An influx of competitively priced imported EVs could undermine the growth of domestic manufacturing. If the UK becomes primarily an EV importer rather than a producer, many of the economic benefits of the electrification transition--such as employment, innovation, and capital investment--could shift abroad.
* Geopolitical Tensions and Trade Risks: Growing economic rivalry in the EV sector, particularly with China, raises the risk of future trade disputes. Tariffs, export restrictions, or diplomatic strains could disrupt supply chains and negatively impact the pricing and availability of EVs and components in the UK market.
* Consumer Price Pressure: The cost advantage of Chinese EVs--produced at up to 40% lower cost than European models--may drive down price expectations across the UK market. While this benefits consumers in the short term, it may pressure domestic manufacturers to cut costs in ways that are unsustainable or damaging to quality and labor conditions.
* Brand Perception and Trust: Despite growing sophistication, some consumers may remain hesitant to adopt vehicles from relatively new or unfamiliar brands, particularly those with a legacy of perceived lower reliability. Overcoming these perceptions will be essential for the widespread adoption of imported EVs.
* Policy and Regulatory Risks: Domestic policy shifts--such as changes to tax incentives, vehicle grants, or emissions regulations--could alter the competitive balance between foreign and domestic EVs. Likewise, if the UK must align with new international standards driven by dominant global players, this could impose additional adaptation costs on UK manufacturers.
# Evaluating DL Model Scaling Trade-Offs During Inference via an Empirical Benchmark Analysis
## 4 DL Use Cases and Validation Datasets
### Natural Language Understanding  
This use case involves natural language understanding, where the DL application setup is responsible for processing a series of text-based inference tasks using a language model to semantically interpret the provided text. The chosen model architecture for this use case is the well-known Bidirectional Encoder Representations from Transformers (BERT) model. BERT, as a model structure, was first introduced in 2019 by Google and is considered one of the most novel model structures for NLP tasks, language reasoning, and conversational AI. In brief, BERT is structured with stacked quantized transformer encoder layers, where each layer comprises self-attention mechanisms and feed-forward neural networks [3]. BERT was intentionally designed to pre-train deep bidirectional representations, adopting a large English text corpus (i.e., Wikipedia), by jointly conditioning on both left and right contexts in all layers [28]. With this, BERT models can capture intricate patterns and dependencies in text-based datasets, making them powerful for understanding the nuances of language.  
For the empirical study, we utilize 20 BERT model variants that have been introduced by Google Research ([[https://github.com/google-research/bert/](https://github.com/google-research/bert/)]([https://github.com/google-research/bert/](https://github.com/google-research/bert/)) accessed on 7 November 2024) and referenced as suitable for edge computing. These models vary in network depth (D = {2, 4, 6, 8, 10}) and hidden embedding sizes (H = {128, 256, 512, 768}). For the workload of inference tasks, we use the widely recognized GLUE-MRPC dataset released by Microsoft Research ([[https://www.microsoft.com/en-us/download/details.aspx?id=52398](https://www.microsoft.com/en-us/download/details.aspx?id=52398)]([https://www.microsoft.com/en-us/download/details.aspx?id=52398](https://www.microsoft.com/en-us/download/details.aspx?id=52398)) accessed on 7 November 2024), with the validation set containing 1125 sentence pairs automatically extracted from online news sources, along with human annotations indicating whether the sentences in each pair are semantically equivalent (paraphrase).
## 4 Numerical Model
### Influence of Water-Cement Ratio  
The grouting and solid strength of surrounding rock were related to the uniaxial compressive strength of the rock mass before grouting and the water-cement ratio \(\eta\). After the grouting rock mass was determined, the slurry water-cement ratio \(\eta\) was the only influencing factor.
# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 4 Results
### Weighted Optimization  
In our ABC ensemble model, the probability values from the five blocks are combined through soft voting to obtain the final classification. We assign double weight to two generalization blocks, assuming general image features are most crucial, while ABC-related blocks provide additional information. Table 4 presents the results of an experiment conducted to validate this assumption and identify the optimal weight combination for the ensemble model. In this table, the order of the model weights in parentheses in the column 'weight' is as follows: [ViT-B/16 (Block 1), DenseNet121 (Block 2), EfficientNet-B0 (Block 3), MobileNetV2 (Block 4), and ResNet50 (Block 5)]. Excluding the title row, the first row represents the case where no weight is applied, meaning all weights are set to 1. The second to sixth rows represent cases where a weight of 2 is assigned to only one model. The seventh row shows the case where a weight of 2 is assigned to the three ABC-related blocks, while the last row shows the case where a weight of 2 is given to the two generalization blocks. The best performance is highlighted in bold, and the second-best performance is underlined.  
The best-performing configuration is the last row, where weights of 2 are assigned to the two generalization blocks (Block 1 and Block 2), resulting in an accuracy of 0.9326, a recall of 0.9316, a precision of 0.9326, and an F1-score of 0.9308. The second-best model, which assigned a weight of 2 to Block 1 (ViT-B/16), also achieved strong performance, with an accuracy of 0.9320, a recall of 0.9311, a precision of 0.9320, and an F1-score of 0.9302, followed by the third-best model, which assigned a weight of 2 to Block 2 (DenseNet121). These results suggest that placing greater emphasis on generalization blocks tends to yield better results. They also indicate that boosting general image feature analysis through additional weighting on the generalization blocks leads to stronger overall performance across the ensemble.
# An Efficient Dropout for Robust Deep Neural Networks
## 2 Literature Review  
Regularization is a vital component in deep learning, as it mitigates overfitting and increases the generalization capability of models. Among regularization strategies, dropout remains one of the most famous methods. Since its introduction by [PERSON] et al. [1], various versions of dropout have been designed to solve its shortcomings and increase performance across diverse architectures and tasks.  
Dropout works by randomly deactivating a subset of neurons during training, eliminating excessive co-adaptations and effectively training an ensemble of subnetworks [1; 2]. Since its release, various changes have been proposed to overcome its limits and boost adaptability [3]. These include DropConnect [4], which applies dropout to weights instead of activations, and Adaptive Dropout [5], which modifies drop probabilities based on cell activity. Other notable strategies include Maxout [6], Shakeout [7], Soft Dropout [8], and DropBlock [9], which applies structured dropout to continuous spatial regions, especially increasing regularization in convolutional layers.  
More recent developments focus on adaptive dropout mechanisms, including Bayesian optimization [10], trainable gradient dropout [11], evolutionary algorithms [12], and biologically inspired models [13]. Methods like Multi-sample Dropout [14], and Guided Dropout [15] improve generalization while minimally adding inference-time complexity. Furthermore, task-specific dropout modifications, such as Clustering-Based Dropout [16], PLACE Dropout [17], and State Dropout for reinforcement learning [18], indicate promise efficacy in some applications.  
Despite these developments, traditional dropout techniques still suffer from key constraints. Binary masking causes instability, and uniform dropout probability disregardsneuron significance. More recent systems, such as Y-Drop [19], Variational Dropout [20], and Stochastic Delta Rule Dropout [21], aim to address these concerns by applying better control algorithms based on neuron importance, weight distributions, or learned priors.  
Table 1 outlines numerous dropout and dropout-inspired regularization techniques, comparing their underlying principles, adaptivity, neuron targeting strategies, implementation complexity, and their applicability for dynamic or pre-trained neural network designs.  
While existing dropout strategies have improved regularization, many continue to confront issues such as the need for specialized topologies, large processing costs, or poor adaptation to neuron importance.  
To address these concerns, this work introduces a novel dropout strategy that promotes adaptive behavior, neuron recovery, and model compatibility. A major strength is that it works directly with dynamically defined, pre-trained, or saved models without needing architectural changes. Unlike other advanced methods that require special layers or retraining, the proposed method can be easily added to most deep learning workflows. Details of the method are explained in Section 3.
## 6 Conclusions  
The experimental research on the axial compression bearing capacity of 25 grid structure members with different sizes, initial stress ratios, and cross-sectional forms was presented. The reinforcement process of grid members was simulated, which verified the operability of the reinforcement method proposed in this paper. The following conclusions can be drawn:  
(1) The method of strengthening compressive members in a grid structure by infilling RPC effectively enhances the bearing capacity. RPC and initial stress in the steel tubes did not change the final failure mode of specimens. With the decrease of the confinement coefficient \(\zeta\) or the initial pressure, the contribution of the reinforcement material can reach up to 218.34%, fully proving the superiority of the reinforcement method in this paper.  
(2) For length-to-diameter ratios of 6.60-17.5, all members show elastic-plastic instability failure. In the descending section of the bearing capacity, oblique shear failure appeared at the bottom of the reinforced specimens with \(\zeta\leq 0.69\), while the specimens with a larger hoop coefficient exhibited only instability failure. There is no local buckling around the grouting hole of reinforced specimens, and it is suggested that the necessary measures in this paper, including using steel glue, should be used to repair the grouting hole.  
(3) The variation tendency of longitudinal and hoop strains in the middle of the steel tube throughout the loading process is consistent with the bending direction of the steel tube. At the beginning of the loading, the whole section of the steel tube is compressed while the longitudinal strain of steel tubes gradually decreases and changes from compressive to tensile strain as the load increases. At the end of the loading, half of the steel tube section is compressed, and half is tensioned, which presents a typical instability failure.  
Figure 17: Comparison of specific values and standard deviation. (**a**) stub columns; (**b**) medium long and long columns.  
(4) Regarding the test results in this paper and 242 from past research, a comparison of calculated bearing capacity from six domestic and foreign codes was carried out. The results indicate that the axial compression calculation method of composite members given by those national codes cannot accurately estimate the bearing capacity of RPC-filled steel tubular members. The nominal bearing capacity \(N_{nom}=\varphi_{i}\eta_{c}(A_{sf}f+A_{c}f_{c})\) should be adopted as the ultimate capacity of compressed members reinforced by RPC in grid structures.  
Conceptualization, Y.D. and H.L.; methodology, Y.D.; software, Y.D.; validation, Y.D., Y.Z. and S.J.; resources, S.J.; data curation, Y.D. and Y.Z.; writing--original draft preparation, Y.D.; writing--review and editing, Y.Z.; visualization, Y.D.; supervision, H.L. and S.J.; project administration, Y.D. and S.J.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Nature Science Foundation of China grant number No. 51578357.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### AE Sensor Modeling  
For the modeling results to be of practical importance, it is necessary to consider the features of AE wave detection--i.e., the transfer function of the AE sensor. During propagation, Lamb waves because displacements oriented in both the transverse and longitudinal directions. The AE sensor, as a rule, is oriented in such a way that it detects only the normal components of displacement. The proportion of the longitudinal and transverse components in the displacement vector has a complicated frequency dependence. The dependences of the amplitudes of the longitudinal and transverse components of the displacements on frequency are given in [18]. Based on these dependences, the transverse fractions vs. frequency for the modes \(A_{0}(\delta_{A0})\) and \(S_{0}(\delta_{S0})\) were calculated.  
As can be seen in Figure 2a, the transverse component for the mode \(A_{0}\) prevails in the region of the small values of \(f\cdot h\). The fraction of transverse component \(\delta_{S0}\) for the mode \(S_{0}\) in the region of small \(f\cdot h\) values is close to zero. Then, it gradually increases and approaches unity in the region of maximum dispersion of \(f\cdot h\sim 1.25\) MHz\(\cdot\)mm.  
A resonant sensor GT200 (Global Test LLC) with a resonant frequency of 180 kHz, and 16 mm in diameter and 15 mm in high is considered to be an example of an AE sensor. This sensor is characterized by relatively high sensitivity and is widely used for industrial control. The frequency response of the sensor is shown in Figure 2b.