esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper introduced Representation Consistency Change Detection (RCCD), a transformative framework that fundamentally elevates CD in remote sensing by pioneering a novel paradigm of representation consistency learning. This framework fundamentally advances CD in optical remote sensing by systematically addressing visual style discrepancies. Through innovative representation consistency learning integrating global style transfer and local spatial context constraints, RCCD achieves outperformance on the benchmark (87.68% F1 on LEVIR-CD+, 67.60% on S2 Looking), demonstrating particular efficacy in challenging scenarios with large visual style differences (90.55% and 68.76% F1 on respective Large-VS subsets). The proposed visual style validation method establishes a novel paradigm for quantitative error analysis in CD research, enabling targeted algorithm improvement. In the future, we will delve into the beneficial impact of temporal visual style inconsistencies in remote sensing imagery on foundational models and CD tasks, aiming to advance the technological frontiers and foster innovative applications within this domain.  
Conceptualization, J.W. and K.S.; methodology, J.W.; writing--original draft preparation, J.W.; writing--review and editing, K.S. and W.L. ([PERSON]); validation, S.G.; visualization, S.M.; supervision, K.S.; project administration, W.L. ([PERSON]); software, Y.T.; investigation, W.C.; data curation, Y.D.; funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2022 YFB3902900), and the National Natural Science Foundation of China Projects (No. 42192583). Funded by State Key Laboratory of Geo-Information Engineering, NO. SKLGIE2023-M-3-2.  
No new data were created in this manuscript.  
The authors would like to thank the editor and reviewers for their contributions to this paper.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods
### Data Processing and Analysis Methods  
#### 2.3.1 Spectral Preprocessing  
Spectral preprocessing is essential for improving the robustness and accuracy of classification models by mitigating system noise, baseline drift, and scattering effects caused by sample surface and structural heterogeneity. In this study, five commonly used preprocessing techniques were applied to the raw reflectance spectra: multiplicative scatter  
Figure 2: Schematic diagram of the reflectance spectrum acquisition system for impact-damaged prunes.  
correction (MSC), SG, detrending, standard normal variate (SNV), and CWT. Each method addresses specific distortions and contributes to enhancing signal clarity.  
MSC [25] linearly corrects each spectrum by referencing the mean spectrum of the calibration set, effectively reducing multiplicative and additive scattering effects and improving inter-sample comparability. SG [26] smoothing applies a moving polynomial fit to suppress high-frequency noise while preserving local spectral features. Detrending [27] eliminates sloping baselines by subtracting fitted polynomial trends, thus enhancing subtle spectral variations. SNV [28] standardizes each spectrum by centering and scaling, mitigating the influence of particle size, surface texture, and illumination inconsistencies. CWT [29], a time-frequency analysis technique, decomposes spectra into multi-resolution components using scaled wavelet functions, allowing for simultaneous noise suppression and feature enhancement. Notably, while CWT is formally categorized here under preprocessing, it also serves a dual role by enhancing local spectral features across multiple scales, thus effectively acting as a feature extraction method. Its contribution to model performance lies in this dual functionality.  
Each of these preprocessing techniques was individually applied to the raw spectral data. Subsequently, the preprocessed spectra were used to train classification models using SVM, random forest (RF), and extreme gradient boosting (XGBoost). Model performance was compared in terms of accuracy, precision, recall, and F1-score to determine the most effective preprocessing strategy for cumulative impact damage classification.
# Reduction Potential of Gaseous Emissions in European Ports Using Cold Ironing
## 5 Ports with Cold Ironing in Operation  
The EU target to reduce local and global emissions during the time spent at berths is to provide shore power facilities for ships in all major ports by 2025, which seems very optimistic right now.  
A present picture is also not easy to build because many projects are announced to be ongoing with potential short-term completion [9], but the investigation carried out with reference to the systems in operation in EU countries, Norway, and the United Kingdom highlighted 25 ports with at least one berth equipped to provide onshore electricity (cold ironing) to ships. A schematic map representing the geographic distribution of these ports is reported in Figure 1.  
For these 25 ports, a deeper analysis was finalized to check the availability of data concerning typologies of berths and usability by the various typologies of ships. The results showed that enough data were available to proceed with the study for only 10 of these 25 ports (Table 1). Therefore, this was considered the sampling set of ports for the next methodological steps.  
The sampling set is numerically limited but includes the largest European ports: Rotterdam, Antwerp, Hamburg, Amsterdam, Le Havre, and Genoa are in the top 10 of the EU ports for freight traffic.  
Figure 1: Ports with berths equipped to provide cold ironing services.
## 3 Proposed Method
### Proposed Framework  
Our newly proposed IMTO is an individually guided multi-task optimization based on knowledge sharing as shown in Figure 1. Figure 2 shows the diagram of IMTO when solving two optimization problems. It includes the following novelties in comparison with other MTO algorithms:  
1. We divide the optimization process of MTO into vertical and horizontal evolutions. Each task has its sub-population to execute vertical evolution, and each sub-population can be called a task groups. Traditional single-task optimization methods just contain vertical evolutions that find global optimal solutions by a series of operations, e.g., selection, crossover, and mutation. The distinction between the proposed MTO framework and traditional single-task optimizers lies in horizontal evolution among  
Figure 1: The flowchart of individually guided multi-task optimization (IMTO).  
Figure 2: Individually guided multi-task optimization (IMTO) when solving two optimization problems.  
different task groups. Optimization processes of multiple tasks can be influenced by each other via the information interaction.
2. MTO algorithms are able to perform \(K\) optimization problems simultaneously. Suppose that the dimensionality of the \(j\)th task is \(D_{j}\). We define the unified search space with dimensionality \(D=\max_{j}[D_{j}]\) and each individual is encoded with random variables lying within the fixed range [0, 1].
3. IMTO divides the initial population into \(K\) task groups, and each task group can evolve independently. In order to represent the ability to perform each component task, we introduce the concept of skill membership. A candidate may enter multiple task groups as long as it shows high skill membership values on component tasks.
4. In order to confirm when the optimization information should communicate, we use the convergence rate to guide knowledge transfer. When the convergence rate shows that a task may be trapped into local optima, the knowledge transfer mechanism is triggered.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## Our Proposed Scheme Model
### Security Assumption  
In our PCAC scheme, the following assumptions are built: The network in a smart grid is a local area network with perfect security architecture, so the communication network is safe. PC, CKA, and KSs are always online after the system is initialized, and the determined system parameters do not change while the system is running. PC is the administrator in the system, managing and supervising the operation of the system. All the data stored in the PC comes from the DS, and all Us can obtain the ciphertext of the data in the way defined by the system. Our scheme assumes that CKA is \"honest-and-curious\", which means that CKA will properly execute the task assigned to it, by following the rules. But CKA may be tricked by malicious users from either DS or U, so it is not fully trusted. Therefore, the PC is required to audit the action of DS and U to verify the identification of DS and U. Our PCAC scheme assumes that DS can only encrypt the data with a symmetric encryption algorithm, the data itself is secure, and there is no security risk. During the generation and validation of access structures, CKA is the distributor and collector of tasks and does not modify the data. Therefore, CKA can correctly reflect the operation of U and DS.  
In PCAC model, there is a risk that a malicious user may attack while DSs upload data or Us request data. When DS uploads data, DS should send its public key to show its identification. However, a large number of smart meters are outdoor, and lack strong supervision. These meters' parameters may be modified with other DS's public key which is open to every agency. As a result, CKA will generate incorrect access structure, and the data has a risk of being revealed. Therefore, the attacker can download the data with his attribute sets. On the other hand, there also exists risk on the user side. When [PERSON] requests data, he is asked to send his public key to show his identification. However, an attacker may send another user's public key to forge identification and get access to the data.
## 8 Discussion  
In summary, for three of the five datasets, the HAN and HWAN models performed the best, for the other two the BoW method produced better results. The HAN and HWAN models dominate the other methods in terms of performance due to their attention mechanism. The models use this mechanism to select the most semantically relevant parts of the text and determine the document class based on them, which is similar to what humans do when reading the text. Longformer's results are worse than expected. Intuition suggests that the attention mechanism should improve the quality of text representations and ease classification. Probably due to the longer input sequence, Longformer performs worse than HAN, as shown in Table 1. On the other hand, for the Reuters and Ohsumed collections, simple statistical methods, such as a BoW or TFIDE, perform better. For data with domain-specific vocabulary, the BoW method performs the best, probably due to the absence of some words in the vocabulary of methods based on pre-trained word representations. The LDA model performs significantly worse on bigger datasets with a larger number of classes. Methods grouped under the name doc2 vec achieved the best results for the Ohsumed dataset, excluding the baseline. The doc2 vec-DBoW model, in particular, performs very well for all datasets, slightly worse than baseline. Based on the difference in the results for the HAN and HWAN models in Tables 1 and 5, it can be observed that the deterioration of the results for the Ohsumed and BBC datasets is a result of the fact that the relevant information for the classification problem is included in the initial parts of the documents. Increasing the maximum input processed string results in a decrease of accuracy, which involves processing a longer text, making it more difficult for the model to extract significant information. For the limited length of the processed text string, the HWAN model performed better than HAN for the Reuters, Ohsumed and BBC datasets, as shown in Table 1, due to the inclusion of TFIDF weights in the word representation, which allowed the relevance of individual words to be highlighted to a greater extent. Potentially, the use of statistical features computed from a large external corpus can improve HWAN performance, as shown by the difference in results in Tables 3 and 4 for unlimited and limited processed text lengths for the HAN and HWAN models, where the statistical model had access to full documents.
# A Variable-Order Dynamic Constitutive Model for Clay Based on the Fractional Calculus
## 8 Conclusions  
Based on half-sine wave characteristics, the dynamic stress of clay can be separated into static and alternating stress, and the corresponding accumulative strain consists of creep dominated by static and strain responses ruled by dynamic stress. According to the theories of fractional calculus, rheology, soil dynamics and viscoelastic mechanics, a constitutive model of clay under long-term loading can be established to predict the accumulative strain.  
By analyzing the applicability and effectiveness of the constitutive model, whose ability to predict the accumulative strain of clay under long-term dynamic stress has been verified, by comparison with different models, and by sensitivity analysis of parameters, we can state our main conclusions as follows:  
1. A new dynamic constitutive model suitable for describing the accumulative strain of clay is proposed. Instantaneous strain, viscoelastic strain and viscoplastic strain within total accumulative strain are described by means of hook body, variable-order Abel dashpot and viscoplastic body, respectively.
2. The new constitutive model proposed in this paper was applied to fit the undrained dynamic triaxial data under the action of the amplitude of dynamic stress. According to the fitting results, all correlation coefficients exceed 0.9, which indicates to some extent that the ability of the new constitutive model can characterize the accumulative strain of clay under long-term cyclic loading.
3. By analyzing the accumulative strain of clay under the actions of parameter increments, the sensitivity effect of parameters \(\alpha_{1}\), \(\alpha_{2}\), \(\beta\) and \(\eta\) was revealed, and the interaction mechanisms among parameters, loading duration and accumulative strain was clarified. The influence of parameters \(\alpha_{1}\), \(\alpha_{2}\), \(\beta\) and \(\eta\) upon accumulative strain was clearly demonstrated.
4. In comparison with other constitutive models, the new constitutive model presented in this paper exhibits a significantly higher predictive performance, especially for the accelerating stage, which illustrates that the viscoplastic body method described in this paper can well characterize the deformation characteristics of clay in the accelerating stage.  
B.-L.Z.: writing--review and editing; K.-S.C.: validation; X.H.: validation; K.Z.: validation. All authors have read and agreed to the published version of the manuscript. This work was funded by the Science and Technology Support Program of Guizhou Province (Qian Ke He Support [2020] No. 4Y038). Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Author [PERSON] declares that he has no conflict of interest. Author [PERSON] declares that he has no conflict of interest. Author [PERSON] declares that he has no conflict of interest. Author [PERSON] declares that he has no conflict of interest.
# Short-Run Contexts and Imperfect Testing for Continuous Sampling Plans
## 6 Summary  
CSP-1 was designed under the assumption that the number of items to be inspected was infinitely large, as in a production line assembly context, for example. In addition, an implicit assumption of perfect testing was made. While subsequent research separately relaxed the infinite batch size and perfect testing assumption, to our knowledge our work is the first that simultaneously relaxes these two assumptions. Our research developed a simulation algorithm, and implemented it in the R programming language, for CSP-1 plans in short-run contexts and in the presence of imperfect testing. One of the outputs of the R code is the distribution of the number of failed items in the batch that escape detection, which is a performance measure that has not been studied in previous literature.  
We illustrated the simulation algorithm by comparing two alternative CSP-1 designs that were of interest to the United States Navy for batch sizes of 3200. The two plans differed in the length of the required run under 100% Inspection before switching to reduced inspection (\(n=100\) versus \(n=30\), respectively). If perfect testing is assumed, the trade-off is that Plan 2 reduces the amount of sampling by about 50%, but also approximately doubles the AQO from 0.66% to 1.36%. The decision-maker at the Navy will judge if 1.36% is still an acceptable level of quality, and if so, the benefit of Plan 2 in terms of less testing effort is very compelling.  
Comparing the two plans under 80% test effectiveness shows that Plan 2 offers a similar reduction in the amount of sampling, but the impact of imperfect testing is more noticeable for Plan 1 where the AQO increases by 62% compared to an increase of 12% for Plan 2. The reason for the bigger impact on Plan 1 is because more items are tested and therefore more failed items escape as a result of the imperfect test.  
For future work, an analytical analysis of CSP-1 plans with imperfect testing and in short-run contexts would be interesting. The simulation algorithm could be modified to include other types of CSP plans and a detailed comparison could be carried out, including a comparison with single-stage sampling plans.  
**Acknowledgments:** [PERSON] received a research stipend from the UCR undergraduate program for Research in Science and Engineering (RISE).  
**Author Contributions:** [PERSON] conceived the research problem, communicated with the Navy to understand their design questions, and guided [PERSON] on formulating the simulation algorithm. [PERSON] developed the R code for the simulation algorithm and took primary responsibility for drafting the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Simulation** & **Formulas in Reference [2]** \\ \hline \(A\)\(O\)\(Q\) & 1.07\% & 1.11\% \\ \(APS\) & 58.15\% & 55.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Plan 1 with imperfect testing.
# Adaptive Mutation Dynamic Search Fireworks Algorithm
## 4 Simulation Results and Analysis
### Simulation Results and Analysis
#### 4.2.3 Comparison of AMdynFWA with Other Swarm Intelligence Algorithms  
In order to measure the relative performance of the AMdynFWA, a comparison among the AMdynFWA and the other swarm intelligence algorithms is conducted on the CEC2013 single objective benchmark suite. The algorithms compared here are described as follows.  
1. Artificial bee colony (ABC) [18]: A powerful swarm intelligence algorithm.
2. Standard particle swarm optimization (SPSO2011) [19]: The most recent standard version of the famous swarm intelligence algorithm PSO.
3. Differential evolution (DE) [20]: One of the best evolutionary algorithms for optimization.
4. Covariance matrix adaptation evolution strategy (CMA-ES) [21]: A developed evolutionary algorithm.  
The above four algorithms use the default settings. The comparison results of ABC, DE, CMS-ES, SPSO2011, and AMdynFWA are presented in Table 6, where the 'Mean error' is the mean error of the best fitness value. The best results among the comparisons are shown in bold. ABC beats the other algorithms on 12 functions (some differences are not significant), which is the most, but performs poorly on the other functions. CMA-ES performs extremely well on unimodal functions, but suffers from premature convergence on some complex functions. From Table 7, the AMdynFWA ranked the top three (22/28), which is better than the other algorithms (except the DE), and in terms of average  
Figure 4: The EFWA, AFWA, dynFWA, and AMdynFWA searching curves. (**a**) f1 function; (**b**) f2 function; (**c**) f3 function; (**d**) f4 function; (**e**) f5 function; (**f**) f6 function; (**g**) f7 function; (**h**) f8 function; (**i**) f9 function; (**j**) f10 function; (**k**) f11 function; (**l**) f12 function; (**m**) f13 function; (**n**) f14 function; (**o**) f15 function; (**p**) f16 function; (**q**) f17 function; (**r**) f18 function; (**s**) f19 function; (**t**) f20 function; (**u**) f21 function; (**v**) f22 function; (**w**) f23 function; (**x**) f24 function; (**y**) f25 function; (**z**) f26 function; (**A**) f27 function; (**B**) f28 function.  
ranking, the AMdynFWA performs the best among these five algorithms on this benchmark suite due to its stability. DE and ABC take the second place and the third place, respectively. The performances of CMS-ES and the SPSO2011 are comparable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Functions** & **Mean Error/Rank** & **ABC** & **DE** & **CMS-ES** & **SPSO2011** & **AMdynFWA** \\ \hline f1 & Mean error & **0** & \(1.89\times 10^{-3}\) & **0** & **0** & **0** \\  & Rank & 1 & 2 & 1 & 1 & 1 \\  & Mean error & \(6.20\times 10^{6
# Research on the Application of the Dynamic Assisted Sleep Light to Smart Mobile Devices
## 2 Materials and Methods
### Analytical Method
#### 2.5.1 Brain Wave Analysis  
The Biopac MP-150 equipment was connected with the EEG signal amplifier and brain wave cap, and the cap was worn on the head of the subject, capturing the EEG signal of the subject. By analyzing the collected brain electrical signals, we can distinguish the time and amplitude of different brain electrical signals. The common brain electrical signals can be divided into four types: \(\beta\), \(\alpha\), \(\theta\) and \(\delta\). When you go to sleep, the brain wave \(\beta\) appears first, which represents the clearest focus; then the \(\alpha\) wave, which represents relaxation; and finally the \(\delta\) wave, which represents deep sleep [37]. Sleep can be divided into rapid eye movement (REM) and non-rapid eye movement (NREM). Sleep in non-rapid eye movement can be divided into three stages. Generally, in the absence of insomnia, you will enter the first stage of sleep (NREM1 or N1) within about 10 min, which is a stage between waking and sleeping. When we lie in bed, close our eyes, relax, and get ready to fall asleep, although our consciousness is still clear, our body has entered a relaxed state, and the brain wave pattern presented on EEG is \(\alpha\) wave (8-13 Hz). Second-stage sleep (NREM2 or N2) is really regarded as sleep at the beginning of this stage. At this time, the brain and body are more relaxed and gradually lose their response to the outside world. Brain waves emit two modes: sleep spindle and K-complex, and EEG is mainly \(\theta\) wave (4-7 Hz). The third stage of sleep (NREM3 or N3) is called deep sleep, delta sleep, or slow-wave sleep, SWS), because the brain wave mode at this time belongs to delta wave (0.5-4 Hz), and the respiration, heartbeat, and blood pressure during deep sleep are all the lowest in the day, along with the blood flow and energy consumption of the brain [38].