esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 LCI System Design Framework
### Axial Resolution  
The axial resolution in an LCI system is determined by the smallest change in OPD that the system can detect, determined by the spectral resolution of the spectrometer. Previous work utilising the LCI system described in Section 3.2.1 has demonstrated submicrometric accuracy across step heights ranging from 5 \(\upmu\)m to 8 \(\upmu\)m with a precision of \(\pm\)56 nm [12]. However, if transparent samples are measured with multiple reflective layers, the smallest discernible distance between layers will be limited by the coherence length of the light source and the axial resolution will be as shown in Equation (17). The larger the bandwidth of the light that is sent into the interferometer, the narrower the coherence function/PSF upon performing an FFT. Increasing the bandwidth of the light source for a detector of set pixel number will result in a reduction in the maximum achievable measurement range as shown in Equation (13); thus, a balance must be found:  
\[\delta_{z}=\frac{2 ln2}{\pi}\times\frac{\lambda_{0}^{2}}{\Delta\lambda}. \tag{17}\]  
When performing tomographic measurements, the resolution of the instrument is limited by the coherence function due to potential reflectors interfering with one another. It has been shown for measurements of singular reflecting surfaces the accuracy of measurement is not limited by the peak PSF with step-height measurements with submicrometric accuracy [12].  
Figure 4: LCI sensor design framework, optimising hardware choices for key operating criteria.
## 3 Methodology  
### Overall Model Architecture  
Formally, the proposed model aims to understand a sign language video containing many glosses and learn a mapping \(R:V\to G\) that recognizes the input video frame sequence \(V\) as a gloss sequence \(G\). The overall model framework is shown in Figure 2. Given a video, a multi-layer convolutional network first extracts spatial features from each frame and generates various feature maps with diverse receptive fields. Then, a fusion module transforms each feature map to feature vector and concatenates all the multi-scaled features. Next, the sequence composed of fused features is learned by temporal module. Finally, the CTC decoder is used to align the sequence for model training and inference.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 2 Related Work
### Voxel-Based Deformable Registration  
Voxel-based deformable registration has traditionally relied on intensity-driven optimization frameworks, often augmented with anatomical priors. For example, [PERSON] et al. [31] jointly optimize fissure segmentation and registration to improve the alignment near the inter-lobar boundaries, while [PERSON] et al. [24] propose lobe-wise registration strategies to accommodate the sliding motion between lobes better. These methods incorporate domain knowledge to improve their physiological plausibility but often depend on manual annotations and are sensitive to segmentation errors.  
To enhance the robustness in regions affected by noise, low contrast, or modality variations, several studies have introduced hand-crafted descriptors into the voxel-based pipeline. The Modality Independent Neighborhood Descriptor (MIND) [32], for instance, encodes local self-similarity patterns and has been widely adopted as a replacement for the raw intensity in similarity metrics, particularly for multi-modal or artifact-prone registrations. More recently, deep-learning-based methods such as VoxelMorph [33] and DLIR [34] have been proposed as data-driven alternatives to iterative registration. These models typically adopt U-Net-style encoder-decoder architectures [35] to directly regress dense voxel-wise displacement fields from input image pairs. Trained in an unsupervised manner using a combination of similarity and smoothness losses, these networks replace conventional optimization with a single forward pass, enabling efficient inference.  
Despite their advantages, voxel-based models remain computationally demanding and are inherently sensitive to intensity inconsistencies, noise, and modality shifts. These limitations have motivated the exploration of alternative data representations--such as point-based methods--that aim to improve efficiency and robustness, especially in the presence of large deformations and variations in appearance.
# Advances in Riblets Design
## 10 Highlighted Challenges  
The previous sections provided a review of research studies that focused on examining the impact of riblet surfaces on flow properties within the boundary layer. These studies have consistently demonstrated the positive effects of riblet geometries in reducing drag. However, there are specific activities that should be pursued to further advance this field, and corresponding recommendations for future studies are outlined.  
It is crucial to recognize that while drag reduction is highly advantageous for the aviation industry, it is important to consider other factors, such as the aeroacoustic properties of riblet surfaces and their potential degradation over time. Moreover, as the turbulent spots typically exhibit relatively small levels of fluctuating wall pressure sources, it is recommended to conduct noise studies in a controlled experimental environment, such as an anechoic wind tunnel, to minimize external noise interference.  
Although great effort has been made to optimize riblet design for drag reduction, it is equally important to focus on the lifetime and durability of the riblets with consideration of the aircraft maintenance schedule. In addition, to achieve optimum riblet performance, fabrication techniques should be further advanced to address the need for lower cost and versatility, as previous studies reveal that a combination of different configurations and sizes should be applied in dissimilar aircraft surfaces to achieve optimum performance.  
Moreover, production uncertainties encompass the challenges that arise due to variations in the manufacturing process. Riblets, being intricate structures, might exhibit variability in terms of geometry, dimensions, or material properties during production. Incorporating these uncertainties into the optimization process ensures that the designed riblet configuration remains effective even when faced with these variations. This enhances the robustness of the design against real-world production conditions.  
All in all, in the realm of aircraft design, where multiple goals like drag reduction, noise control, and overall robustness are important factors, an optimization approach stands out as crucial. This strategy involves tackling conflicting objectives simultaneously. Specifically, in the context of managing noise and drag, a multi-objective optimization strategy becomes essential. This involves using techniques such as surrogate modeling and genetic algorithms to strike a balance between noise and drag reduction. However, the complexity increases when we consider two key factors: production uncertainties and triplet degradation over time.  
Figure 16: Drag reduction measurements for scaled-up riblet sample of a cargo door in the oil channel [66].
## 5 Summary and Discussion
### Challenges and Limitations  
Although the integration of vibration and AE sensors with IoT shows great potential in the remote monitoring of port cranes; to achieve its widespread application and efficient operation, a series of complex challenges and limitations need to be addressed. These issues cover multiple key aspects, such as sensor performance, IoT integration, and data processing, posing obstacles to practical implementation and long-term stable operation of the technology.  
Deeply analyzing these challenges and limitations not only helps to accurately locate bottlenecks in technological development but also provides a basis for formulating targeted solutions. Only by properly addressing these issues can the advantages of the remote monitoring system be fully utilized, the safety and reliability of port crane operation be enhanced, and the process of intelligent port operation and maintenance be promoted.
## 2 Related Works
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Strengths** & **Weaknesses** \\ \hline PbQR [21] & Stability of communication links, improving packet transmission rate, reducing delay in the routing process, high scalability & Applying a greedy approach to obtain Q-value from Q-table, high dependence on RSU, not considering traffic lights as warning signs \\ \hline Q-LBR [22] & Low routing overhead, high packet transmission rate, stability of communication links, suitable for urban areas during natural disasters, high scalability & Not considering a mechanism for adding drones to the network, not providing techniques to calculate the optimal height of drones, not specifying the number of drones \\ \hline RLRC [23] & Considering the optimal energy consumption in electric vehicles, using SARSA for optimizing the routing process, scalability & High bandwidth consumption, high delay, low throughput, not paying attention to motion direction \\ \hline HQVR [24] & Determining the learning rate based on the link quality, increasing packet delivery rate, reducing the effect of the node mobility on the convergence speed of Q-learning algorithm, low dependence on infrastructure (RSUs) & High dependence of Q-learning algorithm to beacon messages, slow convergence speed of the learning algorithm, applying an exploration technique based on a specific probability \\ \hline QVDRP [25] & High delay-tolerant, increasing packet delivery rate, reducing the number of duplicated control messages, considering relative velocity of vehicles & Slow convergence speed of the learning algorithm \\ \hline GPSR [26] & Reducing routing overhead, reducing delay in the network & Not considering factors, like velocity, movement direction, and link lifetime in the routing process \\ \hline QGrid [27] & Reducing the number of states in Q-learning algorithm, appropriate convergence speed, reducing communication overhead, determining the discount factor based on vehicle density & Designing an off-line routing, not designing a congestion control mechanism in the network, fixing Q-table during the simulation process, not considering the effect of intersections and buildings on the transmission quality in each grid, not considering parameters such as speed, movement direction, and link lifetime in the routing process, introducing a centralized reinforcement learning-based routing algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The strengths and weaknesses of the related works.
# Complementary Segmentation of Primary Video Objects with Reversible Flows
## 5 Experiments
### Experimental Settings  
We test the proposed approach on three widely used video datasets; their ways of defining primary video objects are different. Details of these datasets are described as follows:  
1. **SegTrack V2**[59] is a classic dataset in video object segmentation that is frequently used in many previous works. It consists of 14 densely annotated video clips with 1066 frames in total. Most primary objects in this dataset are defined as ones with _irregular motion patterns_.
2. **Youtube-Objects**[1] contains a large amount of Internet videos, and we adopt its subset [69] that contains 127 videos with 20,977 frames. In these videos, 2153 keyframes are sparsely sampled and manually annotated with pixel-wise masks according to the video tags. In other words, primary objects in **Youtube-Objects** are defined from the perspective of _semantic attributes_.
3. **VOS**[15] contains 200 videos with 116,093 frames. On 7467 uniformly sampled keyframes, all objects are pre-segmented by 4 subjects, and the fixations of another 23 subjects are collected in eye-tracking tests. With these annotations, primary objects are automatically selected as the ones whose average fixation densities over the whole video fall above a predefined threshold. If no primary objects can be selected with the predefined threshold, objects that receive the highest average fixation density will be treated as the primary ones. Different from **SegTrack V2** and **Youtube-Objects**, primary objects in **VOS** are defined from the perspective of _human visual attention_.  
On these three datasets, the proposed approach, denoted as **CSP**, is compared with 22 state-of-the-art models for primary and salient object segmentation, including: **RBD**[34], **SMD**[70], **MB+**[48], **DRFI**[2], **BL**[71], **BSCA**[47], **MST**[72], **ELD**[4], **MDF**[5], **DCL**[53], **LEGS**[13], **MCDL**[7] and **RFCN**[73], **ACO**[22], **NLC**[74], **FST**[23], **SAG**[57], **GF**[60], **PN+**[75], **DFI**[76], **FSal**[77] and **PFS**[78].  
In the comparisons, we adopt two sets of evaluation metrics, including the intersection-over-union (IoU) and the precision-recall-\(F_{\beta}\). Similar to [15], the precision, recall and IoU scores are first computed on each video and finally averaged over the whole dataset so as to generate the mean average precision (mAP), mean average recall (mAR) and mean average IoU (mIoU). In this manner, the influence of short and long videos can be balanced. Furthermore, a unique \(F_{\beta}\) score can be obtained based on mAR, mAP and a parameter \(\beta\), the square of which is set as 0.3 to emphasize precision more than recall in the evaluation.
# A New Calibration Method for Broadband Complex Resistivity Measurement System of Rocks and Ores
###### Abstract  
The complex resistivity (CR) measurement constitutes a practical methodology for investigating the internal structures of rocks and ores alongside their mineralogical compositions and the chemical properties of fluids. However, during complex resistivity testing, particularly at high frequencies, the leakage current caused by the distributed capacitance of the instrument's acquisition channels reduces the measurement accuracy. Additionally, the contact impedance of the measuring devices and the electromagnetic coupling effects of the measurement cables further affect the complex resistivity test results of samples. To accurately characterize samples' intrinsic induced polarization (IP) properties, we developed a broadband complex resistivity measurement system (1 mHz-100 kHz) for rocks and ores, comprising a complex resistivity analyzer and a sample holder, employing the four-electrode method. In this study, we establish a circuit model for the measurement system to analyze the influence of the distributed capacitance of the acquisition channels on the test results at elevated frequencies. We derive the error terms inherent in the instrument's measurements across various circuit design configurations and propose a novel method for calculating the distributed capacitance of the instrument's acquisition channels, the parasitic capacitance of the sampling resistor, and for calibrating data by reversing the polarity of the excitation signal. Furthermore, we investigate the effect of contact impedance within the measurement setup on test results and design two sample-testing devices. Through extensive testing on multiple circuit models and samples, the system achieves an accuracy of up to 1% within the 10 M\(\Omega\) range. Its overall performance surpasses that of the Solartron 1260A impedance analyzer and traditional signal source forward connection calibration methods. This advancement holds significant implications for complex resistivity measurements and the study of rock physical properties.  
rock physics; spectral induced polarization; data acquisition and calibration; distributed capacitance; contact impedance +
Footnote †: journal: Applied sciences  
Article
# Compliant Human-Robot Collaboration with Accurate Path-Tracking Ability for a Robot Manipulator
## 3 Experiments and Results
### 3D Path-Tracking Tests
#### 3.3.1 Free Motion (FM)  
For this test as shown in Figure 7a-c, the robot follows the defined helix path, without external forces applied to the end effector as presented in Figure 7a. This demonstrates the case of fully-assisted interaction because the robot moves from the starting point \(s=0\) to the final point \(s=1\), see Figure 7b. This case serves as the reference to evaluate the error in the results of Section 3.4. The desired velocities \(f_{d}\) in Figure 7c, which are used in (16) to obtain the desired joint velocities, are calculated from passing the constant virtual force \(f_{v}\) projected to the tangent direction of motion of the path through the admittance law as described in Section 2.
# Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments
## 4 Simulation Verification Analysis
Numerous experiments and tests [30; 31] have demonstrated that these measures significantly enhance the accuracy and reliability of fault detection, establishing a robust data foundation for the early diagnosis of bearing faults. In this study, the primary noise sources identified were the vibrations of the bench and interference from surrounding equipment. To maintain the consistency of fault characteristic signals, the working conditions of the bearing (such as rotational speed and load) were kept constant during the experiments, ensuring that the sound sensor's performance remained stable and capable of accurately capturing sound signals. Figure 26: Location of the sound sensor. Figure 25: Structural diagram of the test bench.
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 3 Damping Extraction Methods
### Wavelet-Based Damping Extraction  
In this section, we present a comprehensive analysis of wavelet analysis, a fundamental tool in signal processing. We discuss its mathematical foundations, including the integral transform formulation and the importance of mother wavelets. Key properties and their implications will be highlighted, with a special focus on the transformative capabilities of wavelets in analyzing signals over time. From the essential conditions for wavelet functions to their energy conservation attributes, we navigate through this mathematical field of study, concluding in practical applications, including signal alignment and damping extraction.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 2 Related Work
Based on this, both channel state information (CSI) and residual energy information (REI) of sensor nodes are considered to choose one cooperative node for cooperative transmission, which can significantly prolong network lifetime compared with non-cooperative MAC protocols. In summary, the existing research work on improving energy efficiency and extending network lifetime can be improved in the following aspects. Firstly, the existing cooperative MAC protocols mainly use single cooperative node and do not consider the cooperation of multiple cooperative nodes. By exploiting the potential multiple cooperative nodes, the cooperative transmissions can improve the energy efficiency and extend network lifetime more effectively. Furthermore, with the application of randomized DSTC (RDSTC) technique [40] to cooperative communication, the cooperative MAC protocols using multiple cooperative nodes are feasible [41; 42]. Secondly, most of existing cooperative MAC protocols consider either the energy efficiency of each node or the energy utilization of the entire network, and lack a comprehensive consideration of both factors. In general, in order to optimize energy consumption for each data packet transmission, transmission power optimization should be adopted in the cooperative MAC protocols and channel quality (or channel state information) should be considered in the process of relay selection, and in order to optimize the entire network lifetime, the residual energy information should be considered. Therefore, this paper proposes a power-optimized cooperative MAC (PO-CMAC) protocol based on multiple cooperative nodes and considering both the energy consumption of each data packet transmission and network energy utilization. Each node located in one-hop transmission range of both the sender and its recipient contends to become a cooperative node and participate in cooperative transmission according to the instantaneous channel quality between itself and the sender/recipient, thus reducing the transmission energy consumption and improving energy efficiency. Meanwhile, the sender can optimize its transmission power and each cooperative node's transmission power according to the proposed LETPO algorithm, thus balancing the energy consumption of the local nodes. Through the above two points, the PO-CMAC protocol can efficiently extend entire network lifetime.
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
## 4 Experimental Results
### Rate-Distortion Efficiency
#### 4.3.3 Subjective Quality Comparison  
To show the benefits of the proposed framework in terms of subjective quality, we visualize the reconstructed point clouds of color attributes generated by different PCAC methods in Figure 8. For a fair comparison, we obtained the visual results of all methods at approximately equal bit rates. We can see that there are apparent blurry artifacts and blocky artifacts around the longdress's eyes and londress's fingers for TMC13v6 and TMC13v14. In general, our method achieves the best subjective quality by having smooth texture on flat regions and retaining more clear edges in sharp regions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **TMC13v6** & **TMC13v14** & **SparsePCAC** & **Deep-PCAC** \\ \cline{2-5}  & **BD-Rate (\%)** & **BD-Rate (\%)** & **BD-Rate (\%)** & **BD-Rate (\%)** \\ \hline longdress & \(-33.2\) & \(20.1\) & \(1.6\) & - \\ solider & \(-39.1\) & \(23.8\) & \(-10.8\) & \(-50.7\) \\ redandblack & \(-38.5\) & \(5.8\) & \(-5.7\) & - \\ loid & \(-15.1\) & \(67.5\) & \(-10.2\) & - \\ average & \(-31.5\) & \(29.3\) & \(-6.3\) & \(-50.7\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: BD-rate(%) comparisons of the proposed method with TMC13 and PCAC.  
Figure 7: Performance comparison. (**a**) longdress. (**b**) redandblack. (**c**) soldier. (**d**) loot.