esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We investigated the structural and magnetic properties of 20 nm-sized nanoparticles of the half-doped manganite Ho\({}_{0.5}\)Ca\({}_{0.5}\)MnO\({}_{3}\) prepared by sol-gel approach. Neutron powder diffraction patterns show _Pbnm_ orthorhombic symmetry for 10 K \(<\) T \(<\) 290 K, with lattice parameters a, b, and c in the relationship \(c/\sqrt{2}<\) a \(<\) b, indicating a cooperative Jahn-Teller effect, i.e., orbital ordering OO, from below room temperature. In contrast with the bulk samples, in the interval 250 \(<\) T \(<\) 300 K, the fingerprint of charge ordering (CO) does not manifest itself in the temperature dependence of lattice parameters. However, there are signs of CO in the temperature dependence of magnetization. Accordingly, below 100 K superlattice magnetic Bragg reflections arise, which are consistent with an antiferromagnetic phase strictly related to the bulk Mn ordering of a charge exchange-type (CE-type), but characterized by an increased fraction of ferromagnetic couplings between manganese species themselves. Our results show that in this narrow band half-doped manganite, size reduction only modifies the balance between the Anderson superexchange and Zener double exchange interactions, without destabilizing an overall very robust antiferromagnetic state.  
Keywords:nanocrystalline half-doped manganites; charge ordering; SQUID magnetometry; neutron diffraction +
Footnote †: journal: Physics Letters B
## 4 Results and Discussion
### ANN Structure and Model Validation  
The predicted results for each group of fuel cells are shown in Figure 5, and the grouping strategy is shown in Table 2. The number of cells in each group decreases from D1 (6-8-5-5) to D5 (6-8-5-100): D1 (6-8-5-5) has five groups of data that have an average voltage of twenty cells; D2 (6-8-5-10) has ten groups of data and ten cells in each group; D3 (6-8-5-20) has twenty groups of data with the average voltage of five cells; D4 (6-8-5-50) has fifty groups of data with two cells in each; D5 (6-8-5-100) has a hundred groups of data with one cell in each group. Due to the fact that there are more groups in D5 (6-8-5-100), the corresponding regression line has more data points and seems thicker. All of these predictions pass the test with high accuracies of \(R^{2}\geq 0.9999\).  
Meanwhile, more data groups require a longer time to train the ANN model with relatively higher _MSEs_. The training D5 (6-8-5-100) consumes much more time than D4 (6-8-5-50). It is found that the time needed grows exponentially. Compared with building a model such as D5 (6-8-5-100), a model such as D4 (6-8-5-50) requires a much shorter time and lower _MSE_. This is because the model could not predict well under a smaller current, and the more data points the model has, the lower its accuracy would be.  
Admittedly, the gap in _training time_ would be narrowed if the hardware configuration was upgraded, and the result could have higher accuracy with more data groups. However, a good grouping strategy contributes to a higher processing efficiency, especially when a considerable amount of experiment data needs to be processed online.  
Figure 4: The load current and cell voltage curves under steady-state tests at assembling torques of 12 N.m, 14 N.m, and 15 N.m, respectively.
# A Data-Driven Approach for University Public Opinion Analysis and Its Applications
## 3 Model Construction
### Deep Learning Model  
The deep learning model is the key part of the proposed university public opinion analysis model, and it is introduced in this subsection. Because the structure of convolutional neural networks is based on local perception and weight sharing, high-dimensional semantic features can be extracted through the deep neural network to better understand the sentiment tendency. The architecture of the deep learning model is illustrated in Figure 4. The input layer uses a pretrained Word2 Vec model to map the segmented Chinese text (containing \(n\) words) into the word vector space (word vector dimension, \(d\)), and these word vectors are merged to form a two-dimensional matrix as the input of the convolutional layer. The convolutional kernel is the core component of the convolutional neural network. By performing convolution operations in the input matrix, corresponding high-level feature maps can be established. In order to obtain more content features, three convolution kernels with dimensions of \(3\times d\), \(4\times d\) and \(5\times d\) (where \(d\) is the word vector dimension) are used in the model. Given the large size of the feature map, a pooling operation is used to realize the downsampling of feature maps to reduce the dimensionality and decrease the computational effort. Maximum pooling is selected in the model, and the pooling window size is set to \(2\times 2\), whereas the slide step is set to \(2\). Then, the size of feature maps is effectively reduced, and the three feature maps are concatenated and flattened to form a one-dimensional vector. Two fully connected layers are connected after the flattened layer to map the semantic features acquired by the convolutional network to sentiment labels. A Softmax classifier calculates the probability that the feature vector belongs to positive or negative comments and determines the sentiment tendency of the text data based on the magnitude of the probability value. In the deep learning model, the Relu function is used as the activation function to accomplish gradient backpropagation.  
Figure 4: Architecture of the deep learning model.
## 3 Consolidation Properties
### Effects of Line-Cement Stabilization on the Compressibility Modulus of the Clay Soil  
Figure 11 demonstrates that the compressibility modulus, representing soil resistance to volumetric deformation under stress, increases progressively from 7048 kPa at 0% additives to 10,474.28 kPa at 6% cement and 30% lime content through complex physicochemical mechanisms. This 48.6% enhancement results from pozzolanic reactions between cement, lime, and soil particles that generate calcium silicate hydrate and calcium aluminate hydrate gels, creating an interconnected microstructural network that significantly increases soil skeleton stiffness and constrains particle mobility. The progressive modulus improvement reflects the gradual formation of these secondary cementitious products, which develop robust inter-particle bonds and establish a more rigid crystalline structure that effectively resists compression. Higher compressibility modulus values indicate reduced compressibility and enhanced material stiffness, making the stabilized soil more suitable for foundation design, embankment construction, and structural applications where volumetric stability is critical. The synergistic effects of cement and lime demonstrate how chemical bonding fundamentally modifies soil mechanical properties by transforming loose particle arrangements into cohesive, compression-resistant matrices through controlled cation exchange and gel formation processes.  
The compressibility modulus values achieved using additives such as marble dust, bagasse ash, PVA, and others exhibit varying degrees of improvement compared to lime-cement stabilization. For instance, marble dust enhances the shear modulus by up to 9% relative to cement-only mixes in Toubal Seghir, Mellas (1988), while partial replacement of cement with 10% marble powder increases strength by 28 days (1988). Similarly, silica fume, a pozzolanic material, contributes to strength improvement (1988), though specific modulus values for most additives (e.g., bagasse ash, PVA, Butane Tetra Carboxyl Acid (BTCA)) remain unquantified in the literature (1988). In contrast, lime-cement stabilization demonstrates a quantifiable increase in compressibility modulus from 7048 kPa (unstabilized) to 10,474.28 kPa with 6% cement and 30% lime, surpassing the reported incremental gains from additives like marble dust. While additives improve specific mechanical properties  
Figure 11: Compressibility modulus trends with cement content (0-8%) for various lime percentages (0-30%).  
(e.g., cohesion, plasticity), lime-cement stabilization provides a more pronounced and directly measurable enhancement in compressibility modulus.
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
###### Abstract  
In recent years, C-ITS services have been extensively specified, tested, and deployed, leading to their first commercial applications. While technical advancements are progressing, the human factor remains crucial for widespread system implementation. The paper presents results of two field studies on user acceptance evaluations focusing on six use cases. Eighteen drivers participated in highway tests, while over 70 individuals responded to an online survey. The empirical results are discussed considering related literature. A structured literature review was conducted, starting with 426 papers, of which 32 were deeply analysed. The key findings of the activities are that the compliance rate is extremely high for safety-related services like hazard warning. However, compliance rates differ depending on the use case. People trust information coming from road operators compared to other sources of traffic information. In-vehicle information does not distract drivers from driving and must be clear and easy to understand. While user acceptance is high, particularly for safety-related services, there remains a need for clearer communication about C-ITS benefits to enhance transparency and trust.  
C-ITS; user acceptance; field study; motorway; structured literature review Article
# Detection and Localization of Failures in Hybrid Fiber-Coaxial Network Using Big Data Platform
## 5 Failure Detection and Localization Based on Big Data
### Comparison
PNM is the only solution dedicated to soft failures, but on the other hand, PNM is the only solution that targets HFC networks besides FDLBD. FDLBD and log-based solutions are the most adaptive because they rely on SNMP and logs that are commonly used in most of the networking technologies and devices. Of course, in both solutions some adaptations would have to be made since metrics and log structures would differ between networking technologies. In the case of networks that contain loops, additional adaptations should be made in FDLBD localization technique. For this reason, we placed medium and thigh grade for FDLBD in the 'Adaptive' column. DMPF is also adaptive because it is similar to FDLBD, but somewhat less than FDLBD because it relies on RADIUS. On the other hand, SRE-based solutions and PNM are highly focused on specific networking technologies, which makes them unadaptable to other networking technologies. FDLBD, DMPF and PNM are able to cover all devices in the network; thus, their coverage is high. The SRE-based solution has great coverage of the PON part of the network but does not consider aggregation part of the network. Coverage of devices that do not produce logs is not explained in [26]. Depending on the information collected from the logs of other devices, it might be possible to reconstruct the health of devices that do not produce logs but that highly depend on types of information contained in logs.
## 9 Samples of the Results  
### Evaluation of PPO in Robotic Path Planning  
In their work on Proximal Policy Optimization (PPO), [PERSON] et al. [4] evaluated the algorithm across several simulated robotic tasks using the MuJoCo physics engine. The tasks included challenging continuous control problems, such as HalfCheetah, Hopper, and Walker2d. Each task was run for one million timesteps, and the experiments used a fully connected neural network with two hidden layers of 64 units for the policy representation. Key hyperparameters, including the clipping threshold \(\epsilon=0.2\), were tuned to maximize performance. The authors compared PPO against other algorithms like Trust Region Policy Optimization (TRPO), vanilla policy gradient methods, and Advantage Actor Critic (A2C), demonstrating that PPO consistently achieved superior performance in terms of sample efficiency and stability. Figure 16 presents a comparative performance analysis of PPO against various algorithms across seven MuJoCo environments. This figure illustrates the training curves for each method, highlighting the robustness of PPO in achieving higher cumulative rewards over the training period. It effectively showcases PPO's advantage in continuous control tasks, making it a crucial inclusion for understanding its contributions to robotic path planning.  
Figure 16: Training performance of PPO and other reinforcement learning algorithms across MuJoCo environments, showcasing PPO’s superior learning efficiency (Adapted from [4]).
## 3 Results
### Compression Analyses
#### 3.8.2 Kawakita Analysis  
Kawakita plots and Kawakita parameters for non-compacted Avicel PH-101, compacted-1 st cycle Avicel PH-101, compacted-2 nd cycle Avicel PH-101, and compacted-3 rd cycle Avicel PH-101 are presented in Figure 11 and Table 4, respectively.  
The 1/\(b\) parameter obtained from the Kawakita analysis is indicative of the force required for a reduction in the powder bed volume [47]. The value increases for Avicel PH-101 with a higher number of compaction cycles, particularly during the 1 st and 2 nd compaction cycles. Thus, the granules from the 1 st, 2 nd, and 3 rd compaction cycles of Avicel PH-101 are hard enough to impose resistance to a compression/compaction force. The compaction of powders for repeated compaction cycles renders the granules more highly resistive to compression as their 1/\(b\) value increases. Similarly, the foregoing resistance can be interpreted from the reduced \(a\) parameter when the powder undergoes compaction. This parameter, which reflects the maximum powder volume reduction, is reduced to lower values as compression is carried out on powders using a higher number of compaction cycles. Moreover, the extent of particle re-arrangement (\(ab\)) undergoes a decrease for powders of higher compaction cycles in comparison to the raw excipient itself (Table 4).  
Figure 10: Heckel plot of non-compacted and compacted Avicel PH-101.
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Evaluation Metrics  
The evaluation system employs a comprehensive method that combines subjective visual assessment with objective quantitative indicators. Subjectively, various methods are visually compared, focusing on the visual effects of deep-space background satellite images and the effectiveness of noise removal, such as halo suppression.  
Objectively, several indicators are utilized. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) [32] measure the differences between the enhanced and original images, helping to determine whether excessive distortion or information loss has occurred. PSNR quantifies the noise level introduced during enhancement, whereas SSIM evaluates the preservation of structural information, ensuring that the enhancement process maintains target morphology and edge details. Additionally, the Learned Perceptual Image Patch Similarity (LPIPS) [33] metric serves as a deep learning-based perceptual measure. By comparing high-level features extracted from convolutional neural networks, LPIPS can capture perceptual differences more closely aligned with human visual perception than traditional pixel-level methods. Collectively, these metrics provide a comprehensive evaluation of the enhancement process, effectively quantifying the introduction of noise and artifacts while assessing the preservation of fine details.  
This multi-level evaluation framework can fully verify the comprehensive performance of the method in improving the quality of low-light satellite images.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 5 Fibers with Anti-Resonant Guidance
### NANF and DNANF Fibers
* Effective refractive index (n\({}_{\text{eff}}\)) of fiber is low: \(\approx\)1.005 for HC-PBGF, \(\approx\)1.001 for NANF. * Optical nonlinearities resulting from interaction with glass are reduced in proportion to optical overlap coefficient \(\eta\) multiplied by the ratio of effective core area (A\({}_{\text{eff}}\)) of SMF and HCF, approx. 1:4. In a typical HC-PBGF, Kerr nonlinearity is ca. 500\(\times\) lower than in SMF; for NANF, the difference is in the order of 1:10,000. In a fiber with very low \(\eta\) like NANF, the contribution of gas to nonlinear effects can dominate [5]. * Backscattering is mostly produced by surface scattering, which is quite strong in HC-PBGFs, almost absent in ARFs, and filler gas, and dominant in NANF/DNANF, but weak; the contribution of Rayleigh scattering in fused silica is marginal [57]. Backscattering in ARFs is very weak and the fiber can hardly be tested with OTDR--see Section 7.4. * Chromatic dispersion (CD) and polarization mode dispersion (PMD) in HCFs are substantially different from such parameters of solid-core fibers, and very different for each kind of HCF--see Section 7.2 and Tables 2 and 3. * Splicing and termination techniques must ensure hermetic sealing to prevent fiber contamination, e.g., with water vapor--see Sections 6.3 and 6.4. * Contaminated fiber can be purged with pure gas, but this can take several days for a 1 km long fiber with 100 \(\upmu\)m core at 100 kPa overpressure [58]--See Section 6.4. * HCFs exhibit much better stability of latency than solid-core fibers when subjected to variable temperatures, but not strain--see Section 7.3. * HCFs are resistant to ionizing radiation, as color centers and associated bulk absorption in fused silica have little effect on fiber attenuation [59]. However, radiation can deteriorate fiber coatings and the polymer parts of the cable.
# A High Dynamic Weak Spread Spectrum Signal Acquisition Strategy Based on Iterative Local Search
###### Abstract  
To solve the acquisition problem of the satellite-ground microwave link ranging of the China Space Station, iterative local search (ILS) is proposed. In this paper, an iterative-local-search-based acquisition strategy is presented, and a Doppler frequency and code phase transfer model is designed, which can accomplish acquisition by multiple rounds of two-dimensional search. The adaptability of the ILS improved traditional acquisition algorithm to a high dynamic and low signal-to-noise ratio (SNR) environment can be significantly improved without relying  
Partially Matched Filter combined with the FFT (PMF-FFT) algorithm was proposed to realize the rapid search of the Doppler frequency in the received signal. However, in the face of weak signal acquisition, the acquisition time is inordinately long, and real-time acquisition is difficult. In [11], an acquisition algorithm based on the fractional Fourier transform was proposed to address the Doppler change rate problem of a signal. This approach facilitated the search for the Doppler change rate and the second-order change rate. However, certain problems remain unresolved such as the complexity of the acquisition algorithm and the difficulty of implementation. In [12], an acquisition strategy was proposed using the ephemeris to address the acquisition of the prior information. The search range in the frequency domain was greatly reduced based on the prior information signal acquisition. However, this strategy relied on the accuracy of the prior information, and the reliability of the acquisition is difficult to ensure.  
We propose an acquisition strategy based on ILS, which can improve the sensitivity of a contemporary acquisition algorithm in dynamic environments and enhance its adaptability in high dynamic and low SNR environments by changing the strategy without prior information. In the simulation process, the classical parallel code phase search is used as an example to compare the acquisition performance of the algorithm for high dynamic weak signals before and after changing the strategy.  
The rest of this paper is organized as follows. Section 2 describes the model of the space station ranging signal. Section 3 introduces the ILS strategy and transfer model, and the computational load of the parallel code phase search algorithm under the ILS strategy is analyzed. Section 4 verifies the effect of the parallel code phase search algorithm under the ILS strategy on the satellite-ground microwave link signal acquisition of the space station, and the acquisition effect of the parallel code phase algorithm before and after changing the strategy for a high dynamic weak signal is compared. Section 5 summarizes the experimental results and the advantage of the ILS strategy.
# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 1 Introduction
In some tasks, SAM even fails to correctly identify medical target images [14; 15]. To harness SAM's potential in the field of medical image segmentation, some researchers have made improvements to the SAM model. They froze the original parameters of the SAM model and introduced several optimizers in the image encoder. Fine-tuning these optimizer parameters allows the model to better adapt to specific medical image segmentation tasks [16]. However, these approaches are limited to processing individual slices in medical images, neglecting the interconnections between different slices. In comparison to three-dimensional holistic segmentation, the accuracy achieved by segmenting and subsequently stitching together individual slices is lower. To address these issues, this paper proposes an effective plug-and-play three-dimensional tooth CBCT image segmentation architecture named VAG-SAM. The overall architecture of the network is a generative adversarial network composed of a discriminator and a generator. The generator consists of VNet and a SAM encoder with optimizers, while the discriminator is a three-layer convolutional network. VAG-SAM combines the SAM model with traditional convolutional models, fully integrating high-precision segmentation capability and outstanding generalization ability, demonstrating excellent performance in small-sample three-dimensional tooth CBCT image segmentation tasks. The main contributions of this paper are as follows:  
1. We propose PPA-SAM, an innovative fusion method of the Visual Grand Model and traditional convolutional networks. By cleverly integrating the improved SAM encoder into VNet, forming a dual-encoder structure, it significantly enhances the multi-angle feature extraction capability. 2. While retaining the reusable pre-trained weights of the SAM encoder, we introduce optimizers for parameter fine-tuning, making the segmentation network more suitable for three-dimensional tooth segmentation tasks while preserving the advantages of the large model. The combination of GAN (Generative Adversarial Networks) and SAM improves the network's generalization performance, and the utilization of VNet enhances the network's feature extraction capability, demonstrating excellent performance in small-sample tasks. 3. We conducted a comprehensive performance evaluation on a small-sample 3D CBCT tooth dataset, comparing it with other advanced networks and considering different scales of training sample sizes. In this series of experiments, PPA-SAM exhibited outstanding segmentation performance, providing a solid foundation for addressing three-dimensional tooth segmentation tasks.