esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### HRSCD Results  
The proposed method was comprehensively compared with other approaches in terms of precision (Pre), recall (Rec), F1-score, and Intersection over Union (IoU). Table 1 presents the quantitative results of various methods on the HRSCD dataset, with the bold part representing that this model has achieved relatively optimal performance. Specifically, the FC-EF and FC-Siam-conc exhibited relatively lower performance, with F1-scores and IoU values of 59.48%, 42.33% and 57.34%, 40.19%, respectively. The DTCDSCN showed slightly better performance, with an F1-score and IoU reaching 59.39% and 42.24%. BiT further enhanced these metrics, reaching an F1-score of 60.30% and an IoU of 43.16%. In contrast, MFPNet and DSIFN demonstrated superior performance, with F1-scores of 63.95% and 63.66% and IoU scores of 47.01% and 46.70%, respectively. Although MSCANet achieved an F1-score of 64.67% and an IoU of 47.79%, the proposed method attained a recall rate of 69.16%, significantly surpassing all other methods. This highlights the superior capability of the proposed method in capturing actual changes, effectively reducing the risk of missed detections. In farmland change detection tasks, higher recall is particularly critical, as missed detections can delay the timely identification of changes, thereby affecting agricultural management and decision-making. Furthermore, the proposed method achieved an F1-score of 65.48% and an IoU of 48.68%, demonstrating well-balanced performance between accuracy and spatial coverage, enabling more precise alignment with actual change regions.  
The Figure 4 illustrates the experimental results of the different methods on the HRSCD dataset under various scenarios. As shown in the second row, for the lake excavation changes, most methods exhibit limited detection results due to the relatively small number of relevant samples. In such cases, the proposed model is still able to completely identify these changes. Similarly, for scenarios with fewer buildings (fourth row) and more buildings (fifth row), the proposed method effectively reduces missed detections in low-building scenarios and demonstrates strong robustness in detecting farmland changes in high-building scenarios.  
Figure 4: Visualization of experimental results on HRSCD dataset: (**a**) ImageNet; (**b**) ImageNet; (**c**) Label; (**d**) FC-EF; (**e**) FC-Siam-conc; (**f**) DTCDSCN; (**g**) BiT; (**h**) MFPNet; (**i**) DSIFN; (**j**) MSCANet; (**k**) the proposed method.
###### Abstract
The designed lightweight deep feature extractor (DWFE) employs multi-scale depthwise separable convolution modules, which not only strengthen the capability to capture fine-grained features but also effectively reduce computational complexity through modular design. For the first time, two-dimensional rotary position encoding (2D-RoPE) [42] is introduced to preserve temporal dependencies during feature extraction, compensating for the shortcomings of traditional transformer models in modeling local dependencies, and a unified feature extraction RoPE-DWTrans model is constructed to address the insufficient cross-dataset adaptability at the overall architecture level. Compared with methods based on InceptionV3 and SE channel attention mechanisms [11], this work combines a lightweight design with multi-scale feature enhancement techniques, significantly improving classification accuracy while effectively reducing computational resource consumption. Compared to methods modeled directly in the frequency domain [21; 43], the proposed approach demonstrates stronger adaptability to complex working conditions and better generalization of data distributions through a unified feature extraction and two-dimensional image conversion process. By introducing 2D-RoPE, a close integration of time and frequency domains is achieved, showing higher efficiency and robustness in local feature modeling and global information fusion. Experimental results show that the proposed method exhibits excellent robustness and generalization capability on the combined gearbox datasets of MCC5 and HUST, validating its effectiveness and reliability in practical industrial scenarios. The main contributions of this paper are as follows:  
1. It explores the impact of different time segment extraction strategies on data feature extraction, revealing the balance between sampling intervals and segment lengths in feature information redundancy and model training effectiveness and providing a reference for selecting sampling strategies in practical engineering. 2. A lightweight front-end encoder, the DWFE, is designed, which enhances the fine-grained representation and global pattern description of the data at the feature level by incorporating multi-scale depthwise separable convolution modules, providing richer features for input into the ViT model. 3. An optimized self-attention module is proposed, which embeds relative position encoding into the query and key vectors through 2D-RoPE, allowing for more effective capture of relative positional relationships. At the same time, the ReZero mechanism is incorporated, enhancing the stability and convergence speed of training and thus improving the performance of the self-attention mechanism in visual tasks. 4. A general model is constructed that, through a unified feature extraction and processing workflow, is able to adapt to feature differences across different datasets, enabling cross-dataset fault diagnosis and prediction.
## 3 Results and Discussion  
### Cleavage Activity and Amount of Released RNP  
We examined whether the Cas9-sgRNA RNPs could be released from the NNA (Figure 1B). RNPs were adsorbed on the hydrophobized NNA and the unadsorbed fraction was collected. The RNPs adsorbed on the NNA were then released by oscillating the NNA in cytosolic simulation buffer. The solution was collected as the released fraction (Figure 1C). The substrate plasmids were added to both unadsorbed and released fractions, and were allowed to react for 60 min. The cleavage efficiency of the unadsorbed fraction was considered as saturated when the amount of applied Cas9 exceeded 1 pmol (Figure 1D; right). Moreover, most of the DNAs were not cut when a Cas9 threshold of 0.1 pmol was used, indicating that almost all of the RNPs were adsorbed. The number of Cas9 packed on the NNA, as estimated by dividing the NNA area by the area occupied by a single Cas9 molecule, was approximately 1 pmol. Therefore, the actual surface capacity of NNA to adsorb RNP would be between 0.1 and 1 pmol.  
On the other hand, the cleavage activities of the released fractions were clearly evident (Figure 1D; left). The results indicated that cleavage activity of a Cas9-sgRNA RNP was retained even after adsorption onto the silicon surface. The cleavage efficiencies were almost similar, ranging between 30-35%, under all conditions. When 0.1 and 1.0 pmol of intact RNP were directly mixed into the plasmid solution, the cleavage efficiencies were 34% and 100%, respectively (data not shown). These results indicated that the maximum level of RNP adsorption onto NNA was approximately 0.1 pmol and that almost all RNPs could be released from the NNA, where oscillation had a slight effect on RNP release. Consequently, the amount of RNPs released from a single nanoneedle is estimated to be 0.1 amol. In an RNP delivery test, we used 10 pmol of RNP or more, and performed oscillation expecting assured adsorption and release of RNPs.
## 7 Conclusions  
A household automation system monitors and controls smart home devices remotely using a wireless sensor network (WSN). A WSN consists of a set of sensor technology, computer technology, communication technology, etc. In this paper, we focus on the packet detection, which is a significant step for wireless network communication. We first introduce four packet detection algorithms, which are based on energy power and preamble structure. Then, we analyze the factors that affect the accuracy of the algorithm and provide suggestions for improvement. Further, we conduct a detailed comparison and provide an in-depth discussion of these algorithms from three dimensions. Finally, we analyze the current challenges and future directions in this field. By comparison, we find that the above four algorithms have their own advantages and usage scenarios, among which the auto-correlation algorithm has the best overall performance and is the most commonly used method in existing OFDM systems. Although these algorithms have been widely used in today's active systems, they have not yet achieved good performance in passive wireless transmission systems. More new algorithms need to be explored and designed to improve the efficiency of packet detection to improve the communication performance of household wireless sensors, and further improve the service quality of the smart home.  
**Author Contributions:** Conceptualization, W.G.; Formal analysis, Y.H. and L.Y.; Investigation, W.G.; Methodology, Y.H. and L.Y.; Project administration, W.G.; Resources, L.Y.; Software, Y.H.; Supervision, W.G.; Visualization, Y.H.; Writing--original draft, Y.H.; Writing--review & editing, L.Y.; Funding acquisition, W.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by NSFC Grant No. 61971390.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the authors upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 4 Experiment
### Results
#### 4.3.2 San Francisco Bay Data Set  
Like the Flevoland data set, firstly, as shown in Figure 7a, the city and water area in red ellipses show large variation. Secondly, in Figure 7b, though morphological features alone show a smoother map, they may also lead to an incorrect classification map, e.g., the two vegetation areas in white ellipses that are classified as water areas. Moreover, feature fusion via vector stacking also has some flaws; e.g., in the upper right (luminous yellow ellipses) of Figure 7c, there are areas and variations that are obviously incorrectly classified. However, when combining those two features via composite kernels, the performance of the PolSAR image improves, and the map of the composite kernel method (in Figure 7d) is smoother and has more correctly classified areas than those of other methods. And Figure 7e is the reference map of San Francisco Bay Data Set.  
From Table 6, the overall accuracy of the four methods (only POL, only MP, vector stacking, and composite kernels) is 86.9%, 89.6%, 92.6, and 94.4%, respectively. Compared with the other three methods, the overall accuracy of the new method we proposed is increased by 7%, 4.8%, and 1.8%. The kappa coefficient also exhibits marked growth.  
Figure 7: Classification maps of the San Francisco Bay data set with different methods. (**a**) Only polarimetric features. (**b**) Only morphological features. (**c**) Feature fusion via vector stacking. (**d**) Feature fusion via composite kernels. (**e**) Reference map.  
Our results confirm the validation of the composite kernel method. The performance of classification can be boosted when we fuse two types of information. The composite kernel method can tune the contribution of different content, yielding better accuracy than the \"stacked\" method. However, we have not considered other possible kernel distances. The classification performance may be improved if we take other kernels into account.
## 2 Materials and Methods
### Constitutive Model  
Numerical methods are commonly used in slope stability analyses to address complex geometries, material anisotropy, and nonlinear behavior [46]. This study will use the Mohr-Coulomb constitutive model with the Fully Coupled Flow Deformation anal ysis [47] in Plaxis 2D software, 2024 version. The Mohr-Coulomb model is selected to adequately represent the soil's elastic-plastic behavior, considering key parameters such as cohesion, friction angle, and deformation modulus. Additionally, the Fully Coupled Flow Deformation analysis enables precise simulation of the interactions between water flow and soil deformation, which is crucial for evaluating slope stability under intense rainfall conditions. This comprehensive methodology accurately represents the combined effects of water infiltration and the soil's mechanical properties, providing more precise and reliable results for slope design and risk mitigation.
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 2 ChG Glasses and Fibers
### Overview of ChG Fibers
#### 2.2.2 MOFs  
MOFs have become a popular topic in recent years because of their unique optical properties, such as strong light confinement [71; 72; 33], adjustable dispersion [74; 75; 76], and enhanced optical nonlinearity [2; 77; 78] when the core diameter and air holes are arranged along the transverse section of the fiber. As optical media with great fiber structure flexibility are designed to operate in the MIR, MOFs based on ChGs have already been reported extensively in the literature. Several typical geometrical profiles of ChG MOFs are shown in Figure 2.  
The three research institutes mainly involved in the fabrication of ChG MOFs are the University of Rennes I, the University of Bourgogne, and Perfos Company. Apart from the extrusion technique, the other approaches that have been developed to fabricate ChG MOFs include stack and draw [81; 82], casting [72; 83; 84], and mechanical drilling [33; 85; 86]. The fabrication process of ChG MOFs is complicated and technically difficult. It has been shown that when the MOF core size is decreased, the fiber loss is increased due to microdeformations [87; 88; 89]. Nevertheless, a low loss of \(<\)0.8 dB/m in As-S MOF with a small core diameter of 3 \(\upmu\)m has been achieved [72].  
Despite the long ZMDW of ChGs, large values of anomalous waveguide dispersion can be used to engineer the total dispersion into zero dispersion even in the anomalous regime [20]. MOFs have great advantages in terms of dispersion control because of their flexible design. ChG MOFs with ZDWs shorter than 2 \(\upmu\)m have been reported from experiments and simulations [33; 90; 91], and a very large \(\gamma\) up to 4.9 \(\times\) 10\({}^{4}\) W\({}^{-1}\)km\({}^{-1}\) was obtained from a AsSe\({}_{2}\)-As\({}_{2}\)S\({}_{5}\) hybrid MOF with a core diameter of 3.7 \(\upmu\)m [80].
## 4 Method
### DSM Model of Mechatronic Products Based on Core Parts
#### 4.2.1 Correlation Between the Core Parts  
The calculation of the correlation between parts is the basis of the establishment of a complex product structure model. In the process of analyzing the correlation between parts, the multi-source correlation information (structure, function, flow, etc.) is taken into account to assess the strength of correlation between the parts. The multi-source correlation information is also considered by many researchers to be the module partition driver factors. Different module partition driver factors will generate different module structures. In the modular design process of mechatronic products, it is not only the influence of structure, function, and flows that should be considered, but also the influence of life span and recovery methods. Therefore, we present the key driver factors of module partition in this paper, which include structural correlation, functional correlation, flow correlation, life span correlation, and recovery method correlation.  
* Structural correlation: The connection form and fitting type are the main factors determining the strength of the structural correlation between parts. The acquisition of these data relies mainly on engineers' design knowledge and relevant engineering information.
* Functional correlation: The functions to be realized by mechatronic products are generally complex, so the total functions are often decomposed into simple sub-functions in the design process. It is beneficial for product design and subsequent function upgrading to categorize parts that have the same function into one module.
* Flow correlation: Flow correlation is analyzed by determining whether energy flow, material flow, or signal flow exists between parts. The flow correlation strength between assembled parts is also evaluated according to energy flow, material flow, and signal flow.
* Life span correlation: Mechatronic products usually comprise a large number of parts, while the service times and maintenance periods vary. Parts that have similar service times and maintenance periods can be grouped into the same module to reduce the use costs of a mechatronic product.
* Recovery method correlation: Increasingly serious environmental issues have led government agencies to focus more on the recycling of renewable resources. Mechatronic products have a large number of parts with high recycling values. However, the complexity of mechatronic product structures results in higher costs for recycling. Therefore, it is beneficial to reduce the recycling cost by grouping parts that can be recycled using the same method into the same module.  
Based on this, the engineers can use these factors as an assessment index of correlations between product parts, and then estimate the correlation strengths between parts based on the corresponding evaluation criteria. The correlation strengths of linguistic variables can be divided into four levels: strong, medium, weak, and none; the corresponding interval values are shown in Table 2.
# A Visual Object Tracking Algorithm Based on Improved TLD
## 4 Experiments and Analysis  
To evaluate the proposed algorithm, experiments were conducted on object tracking benchmark (OTB)50, which were manually annotated and widely used in previous works [23]. Firstly, the tracking accuracy of the improved algorithm was evaluated. On the one hand, the overlap rate (OR) was used to compare the tracking accuracy changes before and after the TLD algorithm was improved. On the other hand, the improved TLD was put to the full benchmark and evaluated by the precision curve and success rate curve. The performance of the improved algorithm was compared to other algorithms and their performance was examined in special scenarios. Finally, the tracking speed of TLD and the improved TLD was compared on the same computer and platform. Hereinafter, we refer to the improved TLD as \"ours\".
## 1 Introduction
Moreover, the current levelized cost of electricity generation (LCOE) of an ISCC was lower than that of a gas-fired combined cycle (NGCC), when fuel price reached 13.5 \(\upxi\)/MMBtu. Similar studies were carried out to evaluate the performance of a hybrid integrated solar combined cycle (ISCCs) [16; 27; 28]. Due to the reduction of the energy efficiency of gas and combined cycles caused by high ambient temperature, resolving this problem is essential especially in countries such as Iran. Moreover, Iran has a very high potential for solar energy utilization and the average sunshine in Iran is about 2800 hours per year [29]. Using a heliostat solar receiver (HSR) to increase the efficiency of the gas and combined cycle, which carried out in this research is very crucial and has not been performed in any previous research. So, application HSR can be used to preheat the air before entering the combustion chamber to reduce fuel consumption and efficiency improvement. In this regard, using an HSR system in the gas cycle (GC) and combined cycle (CC; the combination of gas turbine cycle integrated by heat recovery steam generator (HRSG)) is investigated by energy, exergy, and economic analyses. To achieve this target, GC and CC are modeled with/without an HSR system to reveal the benefit of this application. So, the quantify application of HSR in GC and CC cycles are one of the novelties of this research. In this regard, several important parameters such as energy and exergy efficiencies, net present value (NPV), payback period (PP), simple payback period (SPP), and internal rate of return (IRR) are calculated and compared to answer this question that using HSR systems is beneficial or not from energy, exergy, and economic point of views. In summary, the innovation of this paper is as follows:  
* Investigation of the integration of HSR via energy, exergy, and economic point of views;
* Calculation of the energy and exergy efficiencies of gas and combined cycles with and without HSR;Calculation of the economic key parameters such as NPV, IRR, PP, and SPP.
## 4 Performance Analysis  
In this study, we introduced a unique elliptic curve cryptography-based energy-efficient routing protocol (ECC-EERP) approach to improve the security and energy efficiency of IoMT healthcare 5.0 systems. The efficacy of the recommended ECC-EERP method in boosting data security and energy savings for healthcare services was assessed. The suggested system offers advantages in terms of security, encryption throughput, energy efficiency, network lifetime, communication overload, and computation time. The existing approaches that we used for comparison included the optimized genetic algorithm (OptiGeA), rank-based energy-efficient key management (RBE-EKM), routing protocol with low-power and random-phase multiple access (RPL-RPMA), and ant colony optimization and integrated glowworm swarm optimization (ACI-GSO).