esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 3 Methods
### Carafe Up-Sampling  
Up-sampling plays a pivotal role in dense prediction tasks such as semantic segmentation. Commonly used up-sampling operators include interpolation methods and transposed convolutions. Interpolation methods rely solely on spatial positions to define the up-sampling kernel, ignoring the semantic information embedded in the feature map. Consequently, they have a limited receptive field, which often causes adjacent target regions to merge or disappear during up-sampling, a problem particularly evident in dense and fragmented crack targets. In contrast, transposed convolutions learn up-sampling kernels through the network, enabling better integration of semantic information. However, these kernels are applied uniformly across all image regions, lacking adaptability to local variations. Additionally, transposed convolutions significantly increase the parameter count and computational complexity, presenting challenges in scenarios where computational efficiency is critical.  
To address the unique challenges of pavement crack segmentation tasks, this study adopts the Carafe up-sampling operator as a replacement for traditional up-sampling methods. Carafe is a lightweight and efficient approach that generates content-aware up-sampling kernels, enabling better integration of contextual information. These dynamically generated kernels facilitate adaptive feature reorganization, achieving more effective up-sampling. The computational workflow of Carafe is depicted in Figure 4.  
The Carafe operator can be divided into two modules: the up-sampling kernel prediction module and the feature reassembly module.  
In the up-sampling kernel prediction module, for a feature map \(x\in R^{H\times W\times C}\) to be up-sampled by a factor of \(\sigma\), the channel dimension is first reduced to ccc using a \(1\times 1\) convolution. Assuming the size of the up-sampling kernel is \(K\times K\), the shape of the up-sampling kernel to be predicted for each output location is \(\sigma H\times\sigma W\times K^{2}\). The compressed feature map is then subjected to another convolution operation, transforming its shape to \(H\times W\times\sigma^{2}K^{2}\). The data in the channel dimension of the feature map is then flattened in the spatial dimension and normalized using softmax, resulting in an up-sampling kernel of shape \(\sigma H\times\sigma W\times K^{2}\).  
In the feature reorganization module, for each point in the up-sampled output feature map, the corresponding position in the input feature map is located, and a feature sub-map from the \(K\times K\) neighborhood at that position is extracted. This sub-map is then multiplied with the up-sampling kernel of the corresponding point in the output feature map via a dot product operation. A \(1\times 1\) convolution is applied to compress the number of channels to  
Figure 3: Haar Wavelet Down-sampling.  
\(C^{\prime}\) dimensions, producing the output \(x^{\prime}\). It is important to note that the same up-sampling kernel is shared across different channels at the same position.
# A New Observation in Spiral and Curl Antenna Configurations above the Ground Plane
## 3 Curl Antenna
### Electromagnetic Coupling Feed
The fabricated antenna is rotated on a turntable, while the horn antenna is fixed. Finally, we summarize the differences between the curl and spiral antennas. The differences are summarized in Table 1. It is emphasized that the curl has an antenna part vertical to the ground plane (the part \(o\)-\(o^{\prime}\) in Figure 5). In contrast, the spiral antenna does not have a vertical part. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Antenna**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Arms** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Antenna Part Vertical** \\ **to the Ground Plane** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Antenna Height** \\ **Range (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{   }}}}}}}}}}}}}\)) \\ **Range (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}\)) \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **3 dB Axial Ratio** \\ **Bandwidth in Its** \\ **Height Range (\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Input Impedance** \\ **(\(Z_{\text{in}}=R_{\text{in}}+j\;X_{\text{in}}\))** \\ **in Its Height Range (\%)** \\ \end{tabular} } \\ \hline curl & 1 & present & 0.10–0.15 & 3–8 & \(\mid\!X_{\text{in}}\!\mid\!>R_{\text{in}}\) \\ spiral & 2 & absent & 0.15–0.25 & 8–24 & \(\mid\!X_{\text{in}}\!\approx 0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Differences between curl and spiral antennas. Figure 9: Radiation patterns of a curl antenna at \(f=0.993f_{0}\).
# A Review on Ultrafast Laser Enabled Excellent Superhydrophobic Anti-Icing Performances
## 4 Advances and Challenges of ULSASs
### Passive Superhydrophobic Anti-Icing Surfaces
#### 4.1.2 Ultralow Ice Adhesion Strengths  
Besides delaying ice embryo nucleation, lowering ice adhesion strengths is also one of the most important properties of ULSASs [147]. In some severe icing environments (e.g., ice wind tunnel conditions), freezing processes inevitably occur on SHSs [81]. How to lower ice adhesion strengths is the key for avoiding ice accretion.  
Centrifuge adhesion tests and push-off adhesion tests are the two most common methods [148; 149]. The former determines the adhesion strengths by measuring the maximum shear forces or pulling forces at the moment of ice shedding during the centrifugal rotating while the latter records the maximum shear forces of the ice column detachment by the horizontal pushing. Although the reported ice adhesion strengths in literature cannot be compared accurately owing to the differences in test methods, it is widely regarded that the surfaces with ice adhesion strengths below 100 kPa are icephobic surfaces, and the ice adhesion strength of 20 kPa is the benchmark for passive anti-icing surfaces, where ice could be removed by wind blow or slight vibration [30; 150]. To date, many ULSASs have been designed and fabricated with ultralow ice adhesion strengths [151; 37; 152; 153; 154; 155; 37]. [PERSON] et al. tested the ice adhesion strengths of different ULSASs under various icing conditions and found that the cross-like DLIP pattern with the size of 2.6 \(\upmu\)m showed the lowest ice adhesion strengths of 6-10 kPa [154]. As shown in Figure 14a, the accumulated ice layer on laser-structured surfaces could be easily removed by vibration. Considering that the solidification of environmental-related mixed liquids always took place in practical applications, we adopted a femtosecond laser to fabricate the superomniphobic surface showing excellent repellent ability for multiple liquids from deionized water to mixed solutions and then to organic liquids [78]. Figure 14b shows the ice adhesion strengths of different liquids on the designed surfaces. It was found that our fabricated surfaces had ultralow ice adhesion strengths for various liquids. Even after multiple deicing cycles, the ice adhesion strengths could be maintained below 20 kPa. However, some challenges still exist [11; 30], such as poor icephobic durability (discussed in the next section), the poor environmental adaptivity, and the difficulty of large-scale fabrication. These issues greatly limit ULSASs for practical applications.
## 2 Study Area and Data  
### Study Area  
The research area is situated in Henan Province, China, spanning from 31\({}^{\circ}\)23\({}^{\prime}\)N to 36\({}^{\circ}\)22\({}^{\prime}\)N and from 110\({}^{\circ}\)21\({}^{\prime}\)E to 116\({}^{\circ}\)39\({}^{\prime}\)E [2]. Figure 1 shows the land-use types within this study area. Henan Province experiences a transitional climate, lying between the subtropical humid and warm temperate semi-humid monsoon climates, with an average annual precipitation ranging from 500 to 900 mm. Henan is a significant agricultural province, predominantly cultivating wheat and maize as its main crops, especially during the winter season.
## 5 Security Analysis
### Security Proof  
This section discusses the security proof of the proposed scheme. Our proposed scheme is constructed by re-simulating the CP-ABE scheme published in [23], which has been proved to be secure by attaining full security in the standard model utilizing the dual system encryption approach under static assumptions. Therefore, we provide a security analysis of the enhanced scheme on hidden access policy presented in Theorem 1. While in theorem 2 we discussed our scheme against traffic analysis.  
**Theorem 1**.: _PHLC preserves the privacy of access policy against the polynomial-time adversary in the security parameter \(\lambda\)._  
Proof.: In the PHLC scheme, the attribute's location in the access policy is converted into a hidden value using X-OR operation. So, this hidden access policy stored in Cloud together with \(CT=(C_{0},C_{1}\{C_{x}\}_{1\leq x\leq 1},\ HV))\). DU with attributes set \(s\) that satisfied access structure \(\mathbb{A}=((\mathbf{A},\mathbf{\rho})\mathcal{T}))\) can decrypt \(CT\). The adversary \(\mathcal{A}\) who has no knowledge about the scheme used to convert the hidden access policy could not launch the brute force attack to guess the attribute string within polynomial time. Furthermore, they are unable to sniff any sensitive data from the modified access policy established as \(HVx\). DU is only permitted to validate their attributes in the hidden access policy, and it is forbidden to inspect any attributes in the attribute universe unless they collude with others. \(\square\)  
**Theorem 2**.: _PHLC secures against Traffic Analysis._  
Proof.: In our scheme, the \(\mathbf{CT}\) is stored in Cloud storage together with \(HV\). We assume that an adversary \(\mathcal{A}\) successfully analyses the packet during the transmission to the Cloud and gain access to ciphertext and hidden value (\(CT=(C_{0},C_{1}\{C_{x}\}_{1\leq x\leq 1},\ HV)\)), thus the adversary \(\mathcal{A}\) could not read the message because it is in the unreadable format (ciphertext). In this case, we assume that the symmetric encryption key based on AES is secured. In addition, the adversary \(\mathcal{A}\) also could not gather any information from the access policy because the access policy is in the ciphertext. Hence it is intractable to compute the access policy. \(\square\)
# A Bio-Inspired Control Strategy for Locomotion of a Quadruped Robot
## 2 The Foot WT Planning
### Modeling and Kinematics Analysis of a Quadruped Robot  
A simplified quadruped robot plane model is shown in Figure 1, which consists of nine links: a body, four femurs and four shins. To simplify the analysis and control, the joints configuration of the robot is all-knee.  
Figure 1: Schematic and parameters of the quadruped robot model.  
The coordinate system \(Oxy\) and \(O_{b}x_{b}y_{b}\) are the world coordinate system and the local coordinate system fixed in the geometric center of the robot's body respectively. The orientations are shown in Figure 1, where \(x_{b}\) points to the forward direction of the robot, \(y_{b}\) is perpendicular to the robot's body and pointing to its back from its abdomen, and \(z_{b}\) is determined by the right hand rule of the coordinate system. To make the modeling more general, a leg is chosen arbitrarily to establish the single leg D-H coordinate system, where the right front leg is selected and the coordinate system \(O_{0}x_{0}y_{0}\) is located at its hip joint, as shown in Figure 1. The point \(P(x,y)\) is the position description of the robot foot in the coordinate system \(O_{0}x_{0}y_{0}\). According to the D-H notation, the D-H parameters can be described as shown in Table 1.  
Where \(\theta_{h}\) and \(\theta_{k}\) are the balance position angles of hip and knee joints, respectively; \(\theta_{1}\) and \(\theta_{2}\) are the rotation angles of the hip and knee joints with respect to their balance position, respectively; \(l_{1}\) and \(l_{2}\) are the lengths of the femur and shin, respectively. By the D-H notation, the angles \(\theta_{1}\) and \(\theta_{2}\) corresponding to point \(P(x,y)\) can be solved via the inverse kinematics, which is given by  
\[\theta_{1} =\arcsin\frac{x^{2}+y^{2}+l_{1}{}^{2}-l_{2}{}^{2}}{2l_{1}\sqrt{x^ {2}+y^{2}}}-\arctan2(x,y)-\theta_{h} \tag{1}\] \[\theta_{2} =\arccos\frac{x^{2}+y^{2}-l_{1}{}^{2}-l_{2}{}^{2}}{2l_{1}l_{2}{}^ {2}}-\theta_{k}\]
# Understanding Forest Health with Remote Sensing-Part II--A Review of Approaches and Data Models
## 2 Trends in Close-Range RS Approaches for Assessing FH
### Close-Range RS Approaches--Wireless Sensor Networks (WSN)
[107] divided the canopy into height layers according to phytosociological standards and found a strong relationship with different LiDAR metrics using regression models. Similar approaches were used by [PERSON] et al. [126] and [PERSON] et al, [127] to represent the understory relevant for birds and deer and to detect forest regeneration [128]. A more recent study applied a 3D segmentation algorithm to estimate regeneration cover and achieved an accuracy of 70% [129]. LiDAR-derived information about the vertical structure is also used for the assessment of forest fuels and their vertical distribution, which are important input variables in forest fire models supporting fire management [130; 131]. As a result, LiDAR-RS is a powerful tool for monitoring FH indicators. It delivers detailed and accurate information about forest properties down to the scale of the individual tree. Nowadays, LiDAR is widely applied in RS research as a reference to test the accuracy of other methods as well as in practical forest inventories in the boreal zone of Scandinavia [102]. The development of new sensors will lead to multi- or hyperspectral LiDAR technology, which will combine the advantages of today's LiDAR and optical sensors. These systems will be able to collect accurate 3D information and calibrated spectral information without facing the problems of varying illumination in the tree crowns. Furthermore, the resolution of the data will increase, enabling parameter extraction at the branch level [132]. Until now the application of LiDAR has been relatively costly because it is limited to airborne missions covering local to regional areas; currently there are no active space-borne missions providing data for mapping forest vegetation. The Global Ecosystem Dynamics Investigation (GEDI)-3D-LiDAR of NASA, which is scheduled for 2019, will provide measurements of 3D vegetation structures which can be used to model vertical stand structure, biomass, disturbance and recovery on a regional to a global scale. Although a footprint size of 25 m will not enable the high resolution mapping that we are familiar with from small footprint LiDAR, the scientific objectives of the mission include modelling finer scale structure [133]. A combination of Landsat, EnMAP, FLEX, HySPIRI and GEDI LiDAR will combine structural and spectral information and improve the modelling, prediction and understanding of FH [134; 135; 136; 137; 138; 139]. Examples of the main findings from LiDAR studies in forest analysis are given in Table 6.
## 3 Application
### Computing Time Comparison  
It was mentioned in Section 3.2 that the PEN method is orders of magnitude faster than top88. However, the predictor profits from a computationally intensive training. So it is interesting to attempt a comparison which takes into account the training time.  
The PEN computing time for a single geometry \(t_{\text{PEN}}\), including its share of training time, obviously depends from the number of geometries predicted \(e_{\text{P}}\) on the basis of one single training process:  
\[t_{\text{PEN}}=\frac{T_{\text{P}}}{e_{\text{P}}}+t_{\text{P}} \tag{37}\]  
where \(t_{\text{P}}\) is the computing time per single geometry and \(T_{\text{P}}\) is the training time. The break-even point (BEP) is given by the number of predictions \(e_{\text{BEP}}\) for which both methods require the same time (including training time contribution). To calculate the BEP \(t_{\text{PEN}}\) is set equal to \(t_{\text{TO}}\), which is the computing time to optimize a single geometry using the conventional method. It results as follows:  
\[e_{\text{BEP}}=\frac{T_{\text{P}}}{t_{\text{TO}}-t_{\text{P}}}. \tag{38}\]  
Table 5 shows the computing times of the different methods as well as the BEP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **mse** & **mae** & \(\kappa_{\mathbf{0.01}}\) & \(\kappa\) \\ \hline Mean & 0.093 & 0.113 & 79.9\% & 86.4\% \\ SD & 0.07 & 0.072 & 9.8\% & 7.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of indicators.  
Figure 15: Additional sample geometries: (**a**) generated with predictor–evaluator network (PEN); (**b**) conventionally generated validation data.  
When evaluating the results of this comparison, the following points should be considered:  
* Due to the fact that \(t_{\text{P}}\ll t_{\text{TO}}\), the BEP, for a given reference method, essentially depends on the training time.
* The training time in turn depends on the convergency condition. Within the framework of this project, an extensive study about the proper choice of the convergency criterion could not be made. The present choice allowed for good results. It can be expected that the training time could be reduced after a targeted study in this sense.
* Of course, the training time also depends on the hardware used for training. By using a high performance hardware, the training time, and thus, the BEP, can be strongly reduced without affecting the versatility of the method in everyday use.
* This comparison does not include a study of the effect of the problem size (number of design variables).
# Auditory Perception of Impulsiveness and Tonality in Vocal Fry
## 2 Materials and Methods
### Listening Experiments
#### 2.2.2 Experiment 2  
In Experiment 2, the voice quality in synthesized vowels was evolving via glottal parameters Fo\({}_{\text{M}}\) and Q\({}_{\text{OM}}\). Each stimulus had a length of 1.7 s, so that the length of the middle segment was long enough for auditory judgement. Starting with modal phonation mode using Fo\({}_{\text{n}}\) and Q\({}_{\text{n}}\), the two glottal parameters were continuously varied after 450 ms to Fo \(=\) Fo\({}_{\text{n}}-\Delta\)Fo and Q\({}_{\text{O}}=\) Q\({}_{\text{O}n}-\Delta\)Qo with a short transition of 150 ms. At 1.2 s, the  
Figure 4: Examples of waveforms of stimuli (gray) and illustrations of synthesis parameter values (black). (**a**) The synthesis parameters Fo and Qo tested in Experiment 1 were constant for the duration of the stimulus resulting in vowels with temporally homogeneous voice qualities. (**b**) In Experiment 2, the parameters Fo and Qo were varied throughout the stimulus.  
parameters were increased back again to their initial values \(\mathrm{Fo_{n}}\) and \(\mathrm{Q_{On}}\) using another 150 ms transition, and were then kept constant until the end of the stimulus.  
The listeners participating in Experiment 2 were asked to ignore the short-term context (i.e., the initial and final segments of the stimulus) and to rate only the mid-vowel segment. As in Experiment 1, listeners rated the mid-vowel segment with regard to perceived tonality, impulsiveness, and naturalness and also judged the presence or absence of binary fry.  
The 80 stimuli of the main corpus used in Experiment 2 combined different values of parameters \(\mathrm{Fo_{n}}\)&\(\mathrm{Fo}\) and \(\mathrm{Q_{On}}\)&\(\mathrm{Q_{On}}\). The amplitude quotient \(\mathrm{Q_{n}}=\mathrm{Q}\mathrm{a}=1\) was kept constant. The 80 random fundamental frequencies \(\mathrm{Fo_{n}}\) of the context segments (i.e., the initial and final segments of stimuli) were uniformly distributed in the interval \(\mathrm{Fo_{n,M}}=100\ldots 150\) Hz. The corresponding 80 random Fo values of the mid-vowel segment were normally distributed in the interval \(\mathrm{Fo_{M}}=10\)-120 Hz (mean 65 Hz, standard deviation 20 Hz). Similarly, 80 random open quotients were uniformly distributed in the interval \(\mathrm{Q_{On}}=0.2\)-0.7 for the context segments and normally distributed in the interval \(\mathrm{Q_{On}}=0.1\)-0.5 (mean 0.3, standard deviation 0.08) for the mid-vowel segment.  
To test the intra-rater reliability, 15 stimuli randomly chosen from the main corpus were rated a second time after presentation of the main corpus.
## 1 Introduction  
Aviation Self-lubricating Spherical Plain Bearings (SSPBs) are widely used in aerospace and other precision transmission fields because of their advantages of self-lubrication, large carrying capacity, and being maintenance free. Due to the requirements of lightweight and high reliability of aircraft, the staking quality of SSPBs is required to be very strict [1, 2, 3, 4, 5]. Therefore, the research on the roller staking process for the installation of the SSPBs (bearing for short) become an increasingly serious public concern. The roller staking process is a developing installation method for SSPBs, which realizes the connection of bearings with housing chambers through the local and gradual plastic deformation of the flanging lip of the V groove (also known as incremental forming technology). The incremental forming technology uses small deformation to replace the large deformation to minimize forming loads and increase workability [6; 7; 8].  
Research on the roller staking process is important to improve the reliability of the connection between the bearing and the housing. There is some representative research on the roller staking process: [PERSON] et al. established a 3D elastic-plastic finite element model of the roller staking process, and the influence of the staking time, the staking load and the rotation speed of the roller tool on the staking quality was studied [9]. [PERSON] et al. proposed staking load analytical equations, and described the metal flow in the roller staking by a 3D finite element model. At the same time, they also designed and fabricated the experimental setup of the roller staking process, and analyzed the influence of the housing chamfering parameters on the staking quality [10; 11].  
However, to the authors' knowledge, there are fewer studies on the deformation degree of the flanging lip, the feeding displacement control, and its impact on the staking quality after the roller staking process. Defects such as a significant increase in the starting torque of the bearing, material damage and crushing of the flanging lip of the V groove, and the bearing \"locking\" often appear after the roller staking process, which seriously affect the staking quality and production efficiency. Therefore, the reliable process-loading control methods and precise process parameter indexes will come into the creation of efficacious staking quality. The purpose of this paper is to analyze the mechanical state of the roller staking process and give a loading control method for the high-quality roller staking process and the parameter indexes. The MS14101-9 self-lubricating spherical plain bearing was used as an example in this paper. Based on the numerical simulation and experimental analysis of the roller staking process, five deformation states of the flanging lip during the roller staking process were defined and the reasonable feeding displacement was determined. Meanwhile, a Multi-Stage Composite Loading (MSCL) process control method was proposed, and the staking quality of this method was verified by tests.