esa-sceva/satcom-chunk-collection huggingface dataset:
# A Crowd Cooperative Spectrum Sensing Algorithm Using a Non-Ideal Channel  
[PERSON] and [PERSON]  
1 Correspondence: [EMAIL_ADDRESS]; Tel.: +86-025-8349-2617  
influence the performance of spectrum sensing is analyzed. However, because the SUs will not take part in the sensing without some rewards, a certain mechanism is needed to stimulate the SUs.  
Crowd sensing is a method of collecting samples, and the main way is to collect samples by using intelligent equipment. Crowd sensing incentive mechanism is a mechanism for recruiting and stimulating the people involved in sensing. This mechanism can be divided into monetary incentive mechanism and non-monetary incentive mechanism [17]. At present, most of the research is monetary incentive mechanism. In [18], the game theory is applied to crowd sensing incentive mechanism, and the economic model of crowd sensing application is studied to improve the quality of sensing data. In [19], the task assignment problem of sensitive service quality is studied, the rewards of participants are related to the quality of sensing data. To ensure that the tasks are completed, each task can be completed by more than one participant. In order to reduce the social cost, a task participant cooperation mechanism is designed [20]. Participants in this mechanism are interrelated, so it is easy for participants to cooperate, and the sensing cost can be reduced. However, the above literatures do not concretely translate the crowd sensing into some applications.  
In cognitive radio network, the SUs need to sense the spectrum to judge whether the primary user (PU) occupies a specific frequency spectrum resource. In order to stimulate the SUs to take part in the cooperative spectrum sensing, we need to combine the crowd sensing incentive mechanism with the cooperative spectrum sensing. The contributions of this paper are summarized as follows:  
1. Under non-ideal channel, we propose a system model which combines crowd sensing incentive mechanism with cooperative spectrum sensing, and define SU's utility expectation function which considers the SUs' sensing time and transmission power at the same time.
2. We construct an optimization problem about the SU's utility expectation, and prove that the optimization problem is a convex optimization problem. We obtained the optimal solution by using KKT conditions.  
This paper is organized as follows. In Section 2, we introduce the system model. In Section 3, we propose a cooperative spectrum-sensing algorithm based on crowd sensing incentive mechanism under non-ideal channel with the optimal SUs' utilities. The numerical simulation results and comparisons are given in Section 4, and finally, we draw the conclusions in Section 5.
# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 5 Discussion
Cross-dataset evaluation protocols could also be adopted to assess the transferability of the system under varying recording conditions, cultural expression norms, and emotional labeling schemas. Second, while the current system leverages handcrafted expert rules to resolve emotion confusion, future work could explore adaptive rule generation via data-driven methods. For example, integrating symbolic learning or reinforcement learning to refine or suggest rule updates based on misclassifications could reduce the reliance on manual tuning and enhance scalability. Additionally, the prompt optimization process, while effective, remains semi-manual and may benefit from automated techniques. Employing prompt tuning or instruction optimization frameworks could help identify optimal prompt structures for different datasets or emotion subsets, thereby improving consistency and reducing model variability. Further experiments are also needed to quantify the system's robustness to environmental variations. Dedicated testing under conditions such as occlusions, rapid head movements, extreme lighting changes, and varied camera angles would provide a clearer understanding of the system's applicability in real-world, unconstrained environments. Moreover, expanding the continuous learning component into a fully automated feedback loop--potentially through unsupervised clustering of new emotion data--would allow the system to evolve over time without human intervention, enhancing its long-term adaptability. Finally, future work could investigate the integration of audio or physiological signals (e.g., heartbeat, skin conductance) to enrich the emotion recognition process. Multimodal fusion, especially when aligned with the modular architecture of the current system, could substantially improve the recognition of complex or ambiguous affective states. Enhancing dataset diversity, automating rule and prompt adaptation, expanding multimodal capabilities, and validating real-world robustness stand as key directions to strengthen and extend the impact of the proposed system.
# Off-Cloud Anchor Sharing Framework for Multi-User and Multi-Platform Mixed Reality Applications
## 3 Design and Implementation of the System  
### Main Features of the Proposed System  
The solution described in this article has been designed to provide the following main features:  
* It provides smooth user interaction within the virtual environment both for desktop and MR devices.
* The system allows clients to either connect to an external server or to host the server themselves (i.e., the server can be executed on the device of a specific client).
* It allows multiple users to connect to a server and to experience the same scenario in real time.
* The system is able to keep the status of all the virtual elements synchronized across all the connected devices.
* Specifically for MR devices:
* Spatial anchors are used to align virtual objects between the different devices and to keep them synchronized.
* The system has been designed to share spatial anchors as fast as possible among MR devices.
* An efficient local storage system is utilized for saving spatial anchors in local memory.
* To reduce communications overhead, the anchor exchange protocol has been designed for minimizing the number of times a spatial anchor needs to be
exchanged between devices via LAN. Thus, loading times at scene launching can also be decreased.
## 2 Materials and Methods
### Data Collection  
#### 2.2.1 Spectral Measurements  
All spectral measurements were taken from a height of 50 cm above the canopy, and spectra were obtained under a clear sky with minimal or no wind between 12:00 and 16:00 (Beijing local time) using a Field Spec HandHeld spectrometer. The spectral measurement range was from 325 to 1075 nm with a resolution of 1.4 nm, and the canopy spectra were averaged over ten scans. Before and after each sample measurement, the sensor was calibrated for baseline reflectance using a white polytetrafluoroethylene panel. To reduce the impact of environmental conditions, sample points with 4 m\({}^{2}\) (2 m \(\times\) 2 m) were randomly used for measurement, and the average value was used as the representative spectral reflectance of the sample point. In this study, a total of 61 field measured samples were obtained and used to verify the effectiveness of the proposed method. The samples were randomly split into the training (75%) and testing (25%) datasets.
## 3 Results and Discussion  
### Characterization of the Prepared SPME  
#### 3.1.1 Structure of Coating  
SPME is a technique that separates and preconcentrates the target analytes on polymer coatings from the matrix in one step [23]. The effectiveness of extraction using SPME can be ascribed to its physico-chemical characteristics and structure. It is the ability of the fiber coating that strongly affects the sample preconcentrating effect. In this study, we report a simple, one-pot synthesis of a hybrid coating preparation consisting of PDMS, DVB and carbon particles. The high efficiency and stable extraction ability of the coating is attributed to the uniformity of the coating. It should be emphasized that DVB and carbon particles are highly dispersed in PDMS during the synthesis process.  
FTIR was used to determine the functional groups present in the mixed material [24]. In Figure 1, the peaks at 2960 cm\({}^{-1}\) are attributed to the C-H stretching vibration peak of Si-CH\({}_{3}\), and the peaks at 1409 cm\({}^{-1}\) demonstrate the C-H deformation vibrational peak of Si-CH\({}_{3}\) in PDMS. There is no stretching vibration absorption peak of alkene C=C in the region of 1680-1620 cm\({}^{-1}\), indicating that the polymerization is basically complete. The peaks at 1260 and 842 cm\({}^{-1}\) are assigned to Si-CH\({}_{3}\) deformation, while the wide and strong absorption bands at peaks at 1100-1000 cm\({}^{-1}\) are related to the Si-O-Si asymmetric stretching vibration. In addition, the peaks at 787 and 740 cm\({}^{-1}\) are assigned to Si-CH\({}_{3}\) oscillation and Si-C stretching vibration, which are characteristic in PDMS. Based on the FTIR spectral analysis, it was confirmed that the SPME was successfully synthesized. Compared with the FTIR diagram of commercial SPME fibers (Figure S1), both diagrams show peaks at nearly the same position, indicating that both fibers bear the same functional groups as shown in IR. This observation can be further confirmed in that the only difference in composition between commercial fibers and synthesized fibers is the difference between CAR and Carbon, both of which show no characteristic peaks in the IR spectrum.  
Figure 1: FTIR spectra of prepared DVB/Carbon/PDMS SPME coating.
## 5 Simulations and Experimental Results
### Comparative Simulations
It can be seen that the errors of the proposed controller in the two cases are both negligible, and the errors converge to zero. To verify both the parameter uncertainties and the external load force rejection performance, which due to the actual shifting force is difficult to define, different step load force is applied on the actuator at \(t=25\) ms and removed at \(t=30\) ms. As shown in Figure 11, the position curve of the proposed controller can return to the original balance state when the load force is increased to 200 N,  
Figure 8: Simulation results of the proposed controller with the different parameter \(\omega_{n}\), when the parameter \(\omega_{c}=100\): (**a**) Position; and (**b**) Position error. Figure 7: Simulation results of the proposed controller with the different parameter \(\omega_{c}\), when the parameter \(\omega_{n}=300\): (**a**) Position; and (**b**) Position error.
## 2 Seafarer Fatigue, Sleep and Ship Noise  
### Seafarer Fatigue  
The concept of crew fatigue is categorized by the International Maritime Organization (IMO) in 2001 into four major divisions: Crew-specific Factors, Management Factors (both ashore and aboard ship), Ship-specific Factors, and Environmental Factors. Within these broad categories, the Crew-specific Factors related to fatigue comprise ten elements: Sleep and Rest, Biological Clock/Circadian Rhythms, Psychological and Emotional Factors (including stress), Health, Stress, Ingested Chemicals, Age, Shiftwork and Work Schedules, Workload (mental and physical), and Jet Lag [17].  
[PERSON] analyzed data from maritime accidents and concluded that human error was a contributing factor in 75% of the incidents [18]. [PERSON] investigated the influence of crew fatigue on ship accidents and found that fatigued crew members were more likely to make errors and have slower reaction times, increasing the risk of accidents [19]. One paper examined highlighting the significant role of human element, such as lack of communication and inadequate training, in ship accidents [20]. It is observed that sleep deprivation and prolonged working hours negatively affected cognitive functions and decision-making abilities, leading to a higher likelihood of accidents. Similarly, a meta-analysis conducted reviewed multiple studies and found that fatigue-related impairment significantly increased the risk of maritime accidents. Additionally, survey-based research investigated the subjective experiences of seafarers and revealed a strong association between fatigue and accidents, with fatigued crew members reporting more near misses and collisions. Mitigating the impact of human errors and crew fatigue is crucial for enhancing ship safety. Strategies such as adequate rest periods, effective fatigue management programs, and crew resource management training have proven effective in reducing the occurrence of accidents [21]. It assessed the effectiveness of fatigue management interventions and highlighted their positive impact on crew performance and safety [22]. Moreover, it is examined various interventions targeting human factors in maritime settings and emphasized the importance of implementing comprehensive safety culture and crew well-being programs [23]. By understanding and addressing these issues on the correlation between these factors and the need for effective mitigation strategies, stakeholders in the maritime community can work towards creating safer environments and reducing the occurrence of accidents at sea [24].
# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 4 Experiments and Results
### Experimental Details  
2D Detector. We set the batch size for training to 8. The improved RetinaNet was trained using stochastic gradient descent (SGD) with a momentum factor of 0.9, a learning rate of 0.001, and a weight decay coefficient of 0.0001. To enhance the overlap between anchor boxes and target boxes, we configured three aspect ratios (1:2, 1:1, 2:1) for each pyramid level, employing anchor boxes with scales \(\left\{2^{0},2^{1/3},2^{2/3}\right\}\). Each anchor box was assigned a 3-dimensional 1-hot vector as a classification target (corresponding to car, pedestrian, and cyclist), along with a 4-dimensional vector as a regression target. Anchor boxes with an IoU greater than 0.7 with the ground truth were considered positive samples, while those with an IoU less than 0.3 were deemed negative samples. Matching was performed using an IoU threshold of 0.5, with anchor boxes having IoU in the range (0, 0.4) allocated as background, and unallocated anchor boxes ignored during training. The regression targets were calculated based on the offsets between the anchor boxes and their corresponding target boxes.  
3D Detector. In training the 3D detector, we utilized the AdamW optimizer with a cyclic learning rate strategy for 40 epochs, applying a weight decay of 0.001 on a single GPU. The Cyclic Learning Rate (CLR) adjusts the learning rate periodically, increasing it from 1 \(\times\) 10\({}^{-4}\) to 1 \(\times\) 10\({}^{-3}\) over each cycle (10 epochs), then gradually decreasing it back to 1 \(\times\) 10\({}^{-4}\). The momentum factor varies inversely with the learning rate, ranging from (0.85, 0.99). This contrasting change mechanism aids the model in broader exploration at high learning rates and fine-tuning at low learning rates.
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 1 Introduction
Our primary goal in choosing semantic segmentation over instance segmentation was to accurately identify and segment liver tumors in CT images for medical purposes. Semantic segmentation usually employs simpler architectures and requires fewer computational resources than instance segmentation methods. Given the complexity of medical image analysis and limited computational resources in clinical settings, we prioritized a method that could provide more efficient and dependable results without excessive computational burden. However, instance segmentation requires more detailed and precise annotation of individual objects in the training data. Therefore, our decision to use semantic segmentation aligns with our goal of accurately delineating liver tumors in CT images while considering factors like simplicity, efficiency, data availability, annotation complexity, and clinical interpretability. In this paper, we implemented the ResUNet model architecture to detect liver and tumor segmentation in CT images with Monai version 0.6. The process of preparing images for analysis by a deep neural network involves several steps. It consists of preprocessing techniques such as data augmentation to increase the number of images and variations, a Hounsfield windowing unit to adjust the brightness of images and image normalization. The ResUNet network's performance is measured using the DC metric Dice coefficient, and the proposed methodology provides better results for liver detection and tumor segmentation. In this work, we analyzed other related state-of-the-art techniques related to liver tumor segmentation. We present the materials, methods, and practical results with a discussion and comparisons with other methods. In conclusion, our work contributes to advancing the field of liver tumor segmentation by introducing a novel approach that overcomes existing limitations and demonstratespromising performance. We suggest future research directions to refine further and extend our methodology for broader applications in medical imaging.
## 3 Performance Characterization and Reliability Assessment
### Performance Summary and Comparisons  
Table 1 provides a performance summary in the context of other state-of-the-art wide-band RF MEMS filters. It is important to note that a direct comparison of absolute metrics like insertion loss is challenging due to the wide range of operating frequencies across the reported works. Therefore, the comparison primarily serves to benchmark our filter's fractional bandwidth, which is a more scalable figure of merit. The proposed design's 25% fractional bandwidth represents a significant advancement, substantially exceeding the other designs, which range from 4.3% to 10%. Despite the inherent differences in filter structure (e.g., Interdigital vs. Lumped), substrate material, and operating frequency, which make a direct 'apples-to-apples' comparison difficult, the table provides valuable context for our work's primary contribution. The proposed design's 25% fractional bandwidth represents a significant advancement for wideband filter technology, offering more than double the bandwidth of the closest competing design while maintaining reasonable performance. This exceptional bandwidth-to-insertion-loss ratio positions the filter advantageously for ultra-wideband applications where wide frequency coverage is paramount.  
Figure 14: Temperature and humidity test results showing insertion loss measurements before and after environmental reliability testing according to MIL-STD-810E standards, confirming the stability of all three resonator width configurations under extreme environmental conditions.
# Dynamic Analysis of Modified Duffing System via Intermittent External Force and Its Application
## 3 Design and Dynamic Characteristic Analysis of MD System
### Dynamics Analysis of MD System with Respect to Parameter A  
Chaotic systems are highly sensitive to their parameters. Different parameters lead the dynamic system to different states, such as periodic motion and chaotic motion. In order to know about the dynamic characteristics of an MD system, one needs to fix all parameters except for magnitude \(A\) of external force, i.e.,  
\[\begin{cases}\beta=0.2,\ k_{1}=0.6,\ k_{2}=0.1,\ \omega=1.1,\ \tau=\dfrac{2\pi}{3 \omega},\ A\in[0,\ 10],\\ \left[T_{i},\ T_{i+1}\right]=\left[\dfrac{i\cdot 2\pi}{\omega},\ \dfrac{(i+1) \cdot 2\pi}{\omega}\right],\ t_{i}=\dfrac{i\cdot 2\pi+2\pi}{3\omega},\ \ (i=0,1,2, \cdots).\end{cases} \tag{12}\]  
Then, the bifurcation diagram spectrum with respect to parameter \(A\) is given in Figure 4a, and the corresponding Lyapunov exponent spectrum with respect to parameter \(A\) is shown in Figure 4b.  
Figure 3: (**a**) Chaotic attractor of MD system (10). (**b**) Time-domain waveform of variables \(x(t)\) and \(y(t)\) when initial conditions are given in Equation (11).  
Figure 2: Intermittent external force \(F\) (red) of modified Duffing (MD) System (10) and continuous function \(A\cos\omega t\) (dotted line).  
Obviously, MD System (10) has a positive Lyapunov exponent in some parameter intervals, such as [2.1, 2.7], [6.5, 7.1] and [7.8, 8.5].  
Furthermore, there are no positive Lyapunov exponents in interval [3.5, 6], i.e., one is equal to zero, and the others are negative; so, MD System (10) behaves periodically and there exists a limit cycle. For example, the phase diagram of MD System (10) is shown in Figure 5a when parameters \(A=3.74\), and there exists a limit cycle when \(t>500\). Similarly, MD System (10) behaves periodically too when \(A=6\), and there exists a limit cycle in Figure 5b when \(t>500\).
## 3 Results  
### Fit Quality of Simulated Spectra  
The experimental and simulated spectra of the \({}^{1}\)H and \({}^{19}\)F regions of the JCS for the fluorobenzene molecules are shown in Figures 3 and 4, respectively. Included also in these figures are the residuals for each fit.  
The accuracy of the simulation, compared to the experimental data, was contextualized by the \(R\)-factor (Table 1), where a lower \(R\)-factor indicates better agreement. Visually, each simulated spectrum was well matched with the number of peaks and the peak spacing. The simulation package, ANATOLIA, simulates high field NMR spectra and suggests that fits with \(R\)-factors of \(<15\%\) are \"satisfactory\" [44]. While four of the seven spectra simulated here (Table 2) had \(R\)-factors \(\leq 15\%\) and fell under the \"satisfactory\" criteria, two of the other three had \(R\)-factors = 19, and one (1,2,4-trifluorobenzene) had an \(R\)-factor = 24. Consequently, our results nearly met this criterion for all spectra, even though JCS typically have a much lower SNR than high field NMR, and this increases the \(R\)-factor. Furthermore, if the scaling factor for the spectra is increased, the \(R\)-factor is improved. For example, 1,2,4,5-tetrafluorobenzene presented an \(R\)-factor = 14% if the scaling factor for \({}^{19}\)F was changed from 1.52 to 1.10. This suggests that the simulated parameters, which included \(J\)-couplings, Larmor frequencies, chemical shifts, and relaxation rates, were representative of the molecule, but the response of the tuned circuit may not have been constant for each sample. Understanding the spectral  
Figure 3: The \({}^{1}\)H region of the JCS for benzene (**a**), monofluuorobenzene (**b**), 1,2-difluorobenzene (**c**), 1,3-difluorobenzene (**d**), 1,4-difluorobenzene (**e**), 1,2,4-trifluorobenzene (**f**), 1,2,4,5-tetrafluorobenzene (**g**), and pentafluorobenzene (**h**). The black lines are the experimentally measured spectra, the red lines are the simulated spectra, and the blue lines are the residuals between the experimental and simulated spectra. Each spectrum also has a scaling factor, which is denoted below the compound name, indicating the amount the experimental, simulated, and residuals that were multiplied such that the maximum peak intensities of the experimental spectra (**a**–**h**) were equivalent.  
scaling effects of the tuned circuit on the JCS is under continuing investigation in our laboratory but does not alter our conclusions. From the data presented here, we suggest that an \(R\)-factor \(\leq\) 25% is a satisfactory fit for LF JCS.