esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Phase Stabilization Algorithm of Femtosecond Timing System
## 3 Phase Correction Module Design
### Performance Verification of Phase Correction Devices and Phase Detection Modules  
Upon completing the design of the FPGA-based interface conversion module, testing of the phase correction device and the phase detection module can commence. The real-time detection accuracy of the high-frequency reference signal's phase is central to this experiment, while precise phase correction equipment is crucial for ensuring the reliability of the experiment, as its accuracy directly impacts the validity of the results. During the functional testing of the phase correction device, the correctness of the phase detection platform can also be validated, facilitating mutual verification between the two components. This process is vital for ensuring the overall system's accuracy and provides a solid technical foundation and reliable basis for subsequent experiments.  
The functional test algorithm for the optical delay line is set to begin from an initial phase of 0, with a delay increase of 1 picosecond every 2 s until a total delay of 28 picoseconds is reached. For the fiber stretcher's functional test, the algorithm starts with the phase of the high-frequency reference signal at 0, increasing the delay by 100 femtoseconds every 5 s until it reaches 900 femtoseconds. The test results, as shown in Figure 6, are consistent with expectations.  
Figure 4: Workflow diagram of interface conversion module.  
Figure 5: Simulation waveform diagram of overall interface conversion scheme.  
The testing outcomes demonstrate that the phase correction device can achieve precise control, and the results from both the phase correction and phase detection modules align with the anticipated outcomes. This achievement lays a strong foundation for subsequent experiments, ensuring the accuracy and stability of the system.
## 2 Materials and Methods
The material demand weight (\(D\)) is calculated for each year (\(i\)) and each critical raw material (\(j\)), as follows (see Equation (1)): the projected number of vehicles per year (\(V_{i}\)) is multiplied by the material weight per vehicle for each raw material (\(M_{j}\)), depending on the vehicle type. Additionally, the demand for testing and certification (\(T\)) is also multiplied by the material weight per vehicle (\(M_{j}\)). The sum of both parts will result in the material demand per year and per raw material:  
\[D_{i,j}=V_{i}\times M_{j}+T\times M_{j}. \tag{1}\]  
The available secondary raw material, i.e., the mass of material recovered from recycling (\(W\)), is calculated as shown in Equation (2). This includes the amount of material recovered from used vehicles--after the battery lifetime (\(LT\))--and the amount of material recovered after the 2 nd life. The material recovered from used vehicles is calculated using the battery demand (\(D\)) of the corresponding year to determine the number of LIBs that return from the market (\(B\)). From that, we subtract the number entering 2 nd life (S) and  
Figure 2: Four scenarios for cell-chemistry rollouts in the premium segment (data based on published study [19]). the amount of production waste (\(P\)). The material recovered after 2 nd life is calculated by multiplying the repurposed LIB quantities of the corresponding year (\(S_{i-LT-10}\)) with the amount of material used per vehicle type (\(M_{j}\)). The resulting amount is then multiplied by the efficiency of the recycling process of each material (\(E\)):  
\[W_{i,j}=(V_{i-LT}\times M_{j}\times(B-S-P)+T\times M_{j}+S_{i-LT-10}\times M_{ j})\times E_{j}. \tag{2}\]  
Additionally, in order to validate the robustness of the input data and verify the results, a sensitivity analysis is conducted. For that, we adjust selected input parameters, which can affect the number of batteries entering recycling and thereby potentially reduce the cumulated available secondary materials. These parameters include battery lifetime, 2 nd life rate, batteries entering recycling and the scrap rate at battery-production stage.
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 3 Methods
### Prediction  
The prediction module shown in Figure 2 consists of four branches: heatmap, offset, box parameters, and orientation, which share the same feature extraction network for training, and predict the class and bounding box for each object. The location of the object center is inferred from the heatmap and offset branch, and the box boundary-aware vectors are obtained according to the box parameters and orientation branch. Thus, the prediction module achieves oriented object detection.
## 2 Bluetooth Low Energy Protocol Stack
### BLE Protocol Stack Overview  
Like in classic Bluetooth [7], the BLE protocol stack is composed of two main parts: the Controller and the Host. The Controller comprises the Physical Layer and the Link Layer, and is typically implemented as a small System-on-Chip (SOC) with an integrated radio. The Host runs on an application processor and includes upper layer functionality, _i.e._, the Logical Link Control and Adaptation Protocol (L2 CAP), the Attribute Protocol (ATT), the Generic Attribute Profile (GATT), the Security Manager Protocol (SMP) and the Generic Access Profile (GAP). Communication between the Host and the Controller is standardized as the Host Controller Interface (HCI). Finally, non-core profiles (_i.e._, application layer functionality not defined by the Bluetooth specification) can be used on top of the Host. Figure 1(a) illustrates the BLE protocol stack. Figure 1(b) depicts the structure and size of the different fields contributed by each layer to a Physical Layer data unit when application data are transmitted. Subsections 2.2 to 2.8 focus on each layer of the BLE protocol stack.  
Although some of the BLE Controller features are inherited from the classic Bluetooth Controller, both types of Controller are currently incompatible. Hence, a device that only implements BLE (which is referred to as a _single-mode_ device) cannot communicate with a device that only implements classic Bluetooth. It is expected that many devices will implement both the classic Bluetooth and the BLE protocol stacks. These devices are called _dual-mode_ devices.
# Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making
## 3 Materials and Methods
### Logarithmic Normalization: An Adaptive Approach for TOPSIS
To further clarify the proposed method, Figure 1 provides a step-by-step flowchart illustrating the transformation of raw data into fuzzy values, the application of fuzzy clustering, and the final logarithmic normalization. The proposed method introduces several methodological innovations that enhance the flexibility, interpretability, and robustness of the TOPSIS framework. First, instead of relying on data-driven clustering techniques, the approach uses expert-defined ideal cluster profiles (step 4), represented through fuzzy numbers, allowing for context-aware classification of alternatives. Second, alternatives are assigned to clusters based on their Euclidean distance to these ideal profiles, enabling a deterministic and transparent grouping process (steps 7 and 8). Third, logarithmic normalization is applied within each cluster to reduce the influence of outliers and large variances, improving the stability and fairness of the rankings (step 9). Finally, the method derives the weights of criteria directly from the ideal cluster centroids, eliminating the need for subjective or complex weighting procedures (step 10). Together, these innovations offer a practical and scalable solution for multi-criteria decision-making in real-world scenarios involving uncertainty and heterogeneous data.
# Optimization Design of Space Camera Enclosure Based on Bionics
## 2 Biomimetic Structural Design
### Biomimetic Optimization Design Process  
To enhance optimization efficiency, this paper introduces a biomimetic design process for space camera housings, utilizing response surface methodology, as illustrated in Figure 1.  
This process primarily consists of three parts. Firstly, the design object of the biomimetic structure is determined, where the optimization objectives of the space camera box structure are comprehensively compared with the structural advantages of the biomimetic object. A similarity evaluation is established between the two to theoretically demonstrate the feasibility of biomimetic design. Subsequently, a preliminary 3D model of the space camera is created. Secondly, finite element analysis is conducted to calculate the maximum deformation, first-order natural frequency, and weight of the space camera enclosure. These are then used as constraints based on actual conditions. Thirdly, structural parameters of the space camera enclosure are extracted as design variables, and response surface analysis configurations are constructed through DOE (design of experiments) experiments. The sensitivity between design variables and optimization objectives is analyzed to determine the priority of optimization. Finally, using a genetic algorithm, the optimal solution is obtained through multiple iterations, completing the optimization design. Natu  
Figure 1: Biomimetic Optimization Design Flowchart.  
rally, the given optimization results must undergo necessary experimental verification at the end.
# Fatigue Resistance Characterization of Warm Asphalt Rubber by Multiple Approaches
## 5 Conclusions  
This study presents a fatigue behavior characterization of warm asphalt rubbers by five different evaluating approaches, including the G*sin\(\delta\) and LAS tests for binders, shear fatigue test for mortars, and ITFT and 4 PB tests for mixtures. Based on the experimental results and analysis, the following findings were obtained:  
Crumb rubber modifier significantly enhances the fatigue resistance. All five used parameters consistently verified the fatigue performance enhancement brought by crumb rubber. WAR specimens have shorter fatigue lives compared to AR, except those with the Sasobit additive.  
The LAS, mortar shear fatigue test, and 4 PB test provided the same prediction of fatigue resistance, while the results of the G*sin\(\delta\) test and ITFT were inconsistent with the others. It is recommended to use LAS, mortar shear fatigue test, and 4 PB test for the fatigue evaluation of rubberized binders and mixtures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Binder Type**} & \multicolumn{5}{c}{**Ranking by Different Evaluating Approaches**} \\ \cline{2-6}  & **G*sin\(\delta\)** & **LAS** & **Mortar** & **ITFT** & **4 PB** \\ \hline Pen60/70 & 5 & 5 & 5 & 5 & 5 \\ AR & 1 & 2 & 2 & 2 & 2 \\ ARE & 4 & 3 & 3 & 1 & 3 \\ ARS & 2 & 1 & 1 & 4 & 1 \\ ARA & 3 & 4 & 4 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fatigue performance ranking of test binders obtained from different tests.  
It is worth mentioning that the above findings are based on laboratory evaluation only. Future research on field validation is strongly suggested. In addition, the parameters applied in this study can only be used to characterize the fatigue property of specific asphalt binders or mixtures. The fatigue life of asphalt pavement is determined by both the internal factors (material properties) and external factors (traffic and environmental conditions).  
Experiments design, J.Y. and H.Y.; Performing Experiments, X.Y.; Formal Analysis, X.Y., F.G., D.W.; Paper Writing, H.Y. and Z.G.  
This research was funded by the National Natural Science Foundation of China, grant number NSFC 51678251, NSFC 51808228.  
The authors would like to thank the technicians in the Road laboratories of South China University of Technology, [PERSON], [PERSON], [PERSON], for their technical support and assistance in experimental activities.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Validation of Drifting Buoy Data for Ocean Wave Observation
## 4 Discussion
### Data Processing Difference  
The raw data of the drifting and GPS buoys are unavailable. The comparison between \(H_{0}\) and \(H_{1/3}\) by the numerical simulation for various wave spectra was summarized in Reference [29]. The ratio \(\langle H_{1/3}\rangle/\langle H_{0}\rangle\) ranged from 0.94 to 0.99, which was consistent with that shown in Figure 1 in Appendix A (blue lines). However, the ratio of the wave heights was not consistent with that in Figure 2c because of the spatial variability of the wave heights near the coast. The ratio \(\langle H_{b}\rangle/\langle H_{g}\rangle\) was smaller than 1 (Table 2). In contrast, the simulation result showed that the \(H_{1/3}\) by the drifting buoy (512 s observation at 0.5 s interval) was higher than that by the GPS buoy (1024 s observation at 1 s interval) because of the spatial variability of the wave heights.  
The comparison between \(T_{0}\) and \(T_{1/3}\) by the numerical simulation was conducted by Reference [30], who showed that the ratio of \(\langle T_{1/3}\rangle/\langle T_{0}\rangle\) was close to 1, especially for the narrower spectrum. The value \(\langle T_{1/3}\rangle/\langle T_{0}\rangle\) was also close to 1 (Figure 1, green lines).  
The order of the magnitudes of the wave periods is \(\langle T_{b}\rangle>\langle T_{e}\rangle>\langle T_{g}\rangle\) (Table 2), while \(\langle T_{1/3}\rangle\)(GPS buoy) \(>\langle T_{1/3}\rangle\)(drifting buoy) \(>T_{0}\) (Figure 1). The effect of different processing on the difference of the wave periods was small compared with the other effects, such as spatial variability and Doppler shift.
# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 5 Problems and Solutions
### Small Sample Problem  
As a result of continuous optimization of industrial processes, the number of defective samples has decreased. This makes it difficult to use deep learning methods to detect surface defects in industrial products, since there are fewer and fewer defect images available for deep learning. Overfitting problems in training can easily occur with small samples [142]. Transfer learning applies knowledge gained from one task to a different but related task when there is insufficient data to complete the target task. Consequently, transfer learning is also a critical method for solving the small sample problem. For surface defect detection, literature [143; 144] used VGG networks and transfer learning to detect emulsion pump bodies, printed circuit boards, transmission line components, steel plates, and wood surfaces. Fabric surface defect detection using DenseNet and transfer learning was described in [145]. The combination of transfer learning and AlexNet was used to detect surface defects on solar panels and fabrics in [146; 147]. Solving the small sample problem can also be achieved by optimizing the network structure. For the first time, GAN was used for image anomaly detection with the AnoGAN model [148] in 2017. A continuous iterative optimization process is used to find an image that matches the test image closest in the latent space, and then DCGAN is used to detect anomalies in that image. The f-AnoGAN model was introduced in [149]. This model proposes a method of encoding an image so that latent points can be quickly mapped, and then using WGAN to detect anomalies. As a result of the introduction of an encoder, the AnoGAN's iterative optimization process is much faster and less time-consuming. Additionally, the GANomaly model was proposed in [150] in 2018. It detects abnormal samples by comparing latent variables obtained by coding with latent variables obtained by reconstructing. There is no requirement for training with negative samples in any of the above models. It is also possible to obtain many sample images by enlarging the data. Using synthetic defects [151], the decorated plastic part dataset is expanded by adding synthetic defects to the defect-freeimage. Literature [152] described a technique for generating defect representations that combine hand-made and unsupervised learning features.
## 5 Conclusions
The following abbreviations are used in this paper:  
MODIS  
Moderate Resolution Imaging Spectroradiometer  
OLI  
Operational Land Imager  
BPNN  
back-propagation neural network  
VEG  
VEGETATION  
AVHIRR  
Advanced Very High Resolution Radiometer  
ASTER  
Advanced Spaceborne Thermal Emission and Reflection Radiometer  
VIIRS  
Visible Infrared Imaging Radiometer Suite  
TM  
Thematic Mapper  
ETM+  
Enhanced Thematic Mapper plus  
SAR  
Synthetic Aperture Radar  
GPP  
Gross Primary Production  
LAADS  
Level-1 and Atmosphere Archive & Distribution System  
DAAC  
Distributed Active Archive Center  
USGS  
United States Geological Survey  
InciWeb  
Incident Information System  
GeoMAC  
Geospatial Multi-Agency Coordination Group  
MTBS  
Monitoring Trends in Burn Severity
# A Doppler Frequency-Offset Estimation Method Based on the Beam Pointing of LEO Satellites
## 2 Model Establishment  
In satellite communications, the orbital motion of a satellite is typically described within the Earth-centered inertial (ECI) coordinate system, where the satellite's position and velocity vectors are characterized by six orbital elements. To analyze the Doppler frequency effect and the resulting Doppler frequency offset, models of the relative position and relative velocity between the satellite and the ground terminal are commonly established. However, such models require real-time knowledge of the position, velocity, and other parameters of both communicating entities, and the associated computations are often complex and resource-intensive.
## 4 Methodology
### Procedure  
#### 4.1.1 Phase One  
In phase one, which serves as our baseline, players participated in an FPS game with a manually set difficulty curve. We established two distinct sets of manually implemented difficulty levels, with finer granularity than in our previous study [10]. In the first set, called Group A, the enemy volume progresses from low to high, ranging from 50% to 100%, across five levels, with equal increments: 0.5, 0.625, 0.75, 0.875, and 1. Conversely, the second set, called Group B, involves a reverse progression in which the enemy volume decreases: 0.5, 0.375, 0.25, 0.125, and 0. The primary rationale for having two groups is based on the following considerations:  
* Firstly, due to our restriction on players participating in only five rounds of gameplay, it is deemed impractical to have a linear progression from 0 to 100% across five levels with a uniform increment. Such a dramatic variation in enemy volume between two consecutive levels would likely hinder the understanding of our collected data.
* Secondly, dividing into two groups allows us to examine player experiences when encountering scenarios where the enemy volume increases from low to high and the opposite. Although this is not the primary focus of our experiment, it has the potential to uncover discrepancies in player experiences under different volume trends. Therefore, this may yield additional insights, enhancing the reliability of the audio cue volume design.  
During phase one, players will be randomly assigned to one of the groups and will not be informed of their group assignment in advance. Players are expected to discern changes in audio cue volume on their own. This approach is implemented to avoid creating any psychological expectations in players prior to gameplay, as such expectations could compromise the reliability of the experimental data.
## 5 Discussion
These included a general large language model that had not undergone any fine-tuning, models that were fine-tuned using LoRA, and systems that incorporated the RAG technique in addition to fine-tuning. Comprehensive evaluations were conducted using a combination of expert reviews and automated evaluation metrics. The experimental results demonstrated that the model optimized with both LoRA and RAG integration exhibited significant improvements in answer accuracy, domain professionalism, and overall interpretability. Specifically, when addressing questions related to agricultural pest control, crop growth regulation, and the promotion of regional agricultural technologies, the optimized system was able to better capture key information within the queries. By effectively integrating real-time retrieved external professional data, the system generated responses that were both accurate and practically informative. This not only validates the effectiveness of parameter-efficient fine-tuning in specialized applications but also highlights the positive impact of real-time information retrieval on reducing \"hallucination\" problem frequently encountered by large-scale models. In several instances, the performance of individual question-answer pairs was particularly noteworthy, suggesting that the proposed method has substantial potential to enhance system robustness and domain adaptability. Despite these promising outcomes, there remain several shortcomings in the present research. First, although the constructed plant protection dataset covers a substantial amount of foundational and professional knowledge, its breadth and depth are still limited. Certain specialized issues in peripheral subdomains have not been fully captured, which may result in suboptimal model performance when such edge-case queries arise. Second, the effectiveness of the RAG technique is, to a considerable extent, contingent upon the quality and update frequency of the external literature databases it relies upon. If the external knowledge sources are not updated promptly or lack sufficient authority, the accuracy and reliability of the generated answers could be adversely affected. Third, the current research has primarily focused on processing textual data, without exploring the integration of multimodal information. This limitation means that the system's applicability is restricted when it comes to handling queries that involve multiple forms of data, such as images, videos, or audio. Given that multimodal question-answer systems may play an increasingly important role in future agricultural information services, the challenge of effectively incorporating multimodal data into such systems represents a crucial direction for future research. Finally, although this study has achieved notable advancements in the areas of large language model fine-tuning and retrieval-augmented generation, there remains significant room for further optimization in terms of injecting domain-specific knowledge and implementing robust real-time information update mechanisms. These aspects warrant continuous improvement and further exploration in subsequent research endeavors.