esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 1 Introduction
Recent studies show use of coordinated multi-point techniques in the mmWave wireless access [22; 23; 24; 25], which not only improve coverage and capacity, but also provide more robust communication capabilities. In addition, mmWave technology is also capable of performing accurate user positioning by means of directional beamforming and higher system bandwidth, as shown in [9; 10; 11; 12] with the single BS. In [22], the author analyzed the coverage performance improvement by serving a user with multiple geographically separated transceivers. Network capacity and coverage gain are also shown in [23], for the heterogeneous systems using stochastic geometry tools. The authors in [24] presented a low complexity cooperation method for the joint transmission, wherein the set of coordinating BSs is obtained by selecting one strongest BS in each tier and extended it in [25] by explicitly considering the impact of blockage density in multi-tier heterogeneous network. Despite the coverage and/or capacity improvements obtained because of multi-point connectivity, as shown in [22; 23; 24; 25], it is yet unclear how concurrent connections with multiple spatially distributed transceivers improve the quality of user experience by providing better positioning quality and achievable user-rate. In this paper, we explore the viability of using coordinated multi-point reception for mmWave based localization and improving the connectivity in the presence of random link blockage. In particular, we focus on time-synchronous conditions, where each user is concurrently communicating with multiple spatially separated receivers. We show that mmWave and massive MIMO with the multi-point connectivity not only contributes in achieving high data rate but also provides more reliable connectivity and accurate positioning capability. The main contribution of this paper are:  
* We derive the CRLB for localization in a mmWave based multi-point connectivity scenario, which is subsequently solved to obtain the lower bound on positioning accuracy across spatially distributed receivers. * We reformulate closed-from expressions for the Fisher Information Matrix (FIM) and provide insight on the dependence of different parameters on the achievable positioning quality. * We provide a robust receive beamforming strategy that can retain a stable connectivity to combat the uncertainties of mmWave channel. * We validate the performance of the formulated theoretical bounds via extensive numerical results, i.e., quality of positioning and achievable user-rate with the joint reception. The reminder of this paper is organized as follows. In Section 2, we provide system architecture and explain the performance metrics. In Section 3, we describe calculation of positioning accuracy using CRLB. The numerical results are presented in Section 4. We give our conclusions in Section 5.
# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 2 Related Work
### Research Pertaining to Surface Defect Detection  
Over the past two decades, numerous researchers have explored the application of object detection technologies for surface defect detection [15; 16]. In recent years, there has been a rapid increase in research on few-shot learning in object detection. Many of these studies have focused on improving existing methodologies in the field of few-shot learning (see Table 1). Among these techniques, generative adversarial networks (GANs), an unsupervised learning algorithm renowned for its potent generative capabilities, has been extensively deployed in defect detection tasks across various domains. [PERSON] et al. [17] provided an encompassing overview of the theoretical foundations, technological evolution, and practical implementations of GAN-based defect detection, thereby furnishing a base for research in this area. In addition, [PERSON] et al. [18] proposed a novel two-stage object detection framework based on convolutional neural networks (CNNs), where the localization and classification tasks were decoupled via two dedicated modules, ultimately enhancing the precision of FPCB surface defect detection. Furthermore, [PERSON] et al. [19] integrated atrous convolution into deep networks and designed a surface defect detection model based on convolutional block attention module and atrous convolution, aiming to optimize the model's real-time detection capabilities. Additionally, [PERSON] et al. [20] introduced an innovative task configuration that enhances cross-domain generalization through image-level meta-enhancement and feature-level perturbation. In comparison to mainstream approaches, their proposed method exhibits superior performance. In a parallel development, [PERSON] et al. [21] advanced a rapid steel strip surface defect detection network (DCAM-Net) based on deformable convolutions and attention mechanisms. Tailored to address a wide spectrum of complex and irregularly distributed steel strip defects, DCAM-Net employed a novel enhanced deformation-feature extraction block (EDE-block), which substantially elevated the detection efficiency of steel strip surface defects. These contributions encompassed additional surface defect detection methods that we drew upon in our research on steel surface defect detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Methods** & **Performance** & **Disadvantages** \\ \hline \hline \multirow{6}{*}{[PERSON] et al. [17]} & \multirow{6}{*}{GAN} & GAN uses the concept of game to train the generator and discriminator to detect the defect region. & The old problems in the application of GAN network, such as mode collapse, gradient disappearance and training instability, have not all been solved. \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Existing methods in surface defect detection research.
# Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification
## 4 Performance Analyses
### Batch Verification
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CPU** & **OS** & **RAM** & **Compiler and Library** \\ \hline Intel i7-12700z & Ubuntu 14.04 & 32 GB DDR5 4800 MHz & GNU C/C++ \& PBC 0.5.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental environment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Signature Length** & \
\begin{tabular}{c} **Sign** \\ **Computation** \\ \end{tabular} &
\begin{tabular}{c} **Verify** \\ **Computation** \\ \end{tabular} & **Sign Time (ms)** & **Verify Time (ms)** \\ \hline He & \(4|Z_{p}|\) & \(3T_{add}+3T_{mul}\) & \(6T_{mul}+7T_{add}\) & \(1.01\) & \(4.81\) \\ Huang & \(1|G|+2|Z_{p}|\) & \(3T_{mul}+T_{p}+T_{e}\) & \(2T_{mul}+2T_{p}+T_{e}\) & \(4.08\) & \(3.63\) \\ Our & \(4|Z_{p}|\) & \(3T_{add}+3T_{mul}\) & \(6T_{mul}+7T_{add}\) & \(0.99\) & \(5.26\) \\ \hline \hline \end{tabular} In the above table, \(|Z_{p}|\) and \(|G|\) denote the binary length of an element in group \(Z_{p}\) and \(G\), respectively.  
\end{table} 
Table 2: Efficiency comparison of the CLS schemes. can be sent to the user. Then, the user calculates \(\pi_{2}=e(\sum_{i=1}^{m}[x_{i}]P,\sum_{i=1}^{m}[y_{i}]P)\) and \(\pi_{3}=e(\sum_{i=1}^{m}[xy_{i}]P,P)\). Finally, check if the equation \(\frac{\pi_{2}}{\pi_{1}}=\pi_{3}\) holds. If all equations in the three steps hold, it outputs \"1\". Otherwise, it outputs \"0\". In this way, when verifying the signatures from m users, the pairing operation can be completed twice locally rather than increasing with the number of users.
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 4 Experiment  
### Datasets and Evaluation Metrics  
The model is experimented on the datasets Microsoft Video Description (MSVD) [25] and Microsoft Research-Video to Text (MSR-VTT) [26]. The MSVD dataset contains 1970 short videos, 1200 videos for the training set, 100 videos for validation set, and 670 videos for the testing set. The dataset MSR-VTT contains 6513 videos for the training set, 497 videos for validation set, and 2990 videos for the testing set. BLEU@4 [16], CIDEr [17], METEOR [18], and ROUGE_L [19] are adopted as evaluation indexes.
# Bi-Photon Entangled Airy Beams through Unstable Ocean Turbulence
###### Abstract  
Orbital angular momentum (OAM) carried by bi-photon entangled Airy beams in unstable oceanic turbulence is theoretically investigated. The analytical expression of the spatial coherence radius of a spherical wave in the unstable stratification oceanic turbulence is derived to obtain the relative detection probability of bi-photon entangled Airy beams. The relative detection probability of OAM carried by bi-photon entangled Airy beams acquires significant enhancements--up to 62% compared to the same measurement for the single photon system over a distance of 100 m. Lower-order bi-photon entangled Airy beams with larger wavelengths, and main ring radius are less affected by oceanic turbulence. A larger main ring radius also contributes to reducing the fluctuations in detection probability. Oceanic turbulence dominated by salinity fluctuations induces more adverse effects on the OAM, as well as a stronger strength of the turbulence.  
Airy beams; bi-photon entanglement; orbital angular momentum; oceanic turbulence +
Footnote †: journal: Marine Science and Engineering  
1
Footnote 1: School of Science, Jiangnan University, Wuxi 214122, China  
2
Footnote 2: Corresponding.org/licenses/py/4.0/.
## 3 Results and Discussion  
### Equilibrium MD Simulations  
#### 3.1.1 Defining Terminologies in a Confined System  
Figure 3 shows a typical density and pressure distribution near the metallic surface [23]. The fluid molecules form a dynamic structure near the surface called density layering. This non-homogeneous distribution of fluid atoms results in a high local pressure variation near the surface [40]. Several terminologies are defined in order to closely investigate the solid/fluid interface. The center-to-center height (\(H_{\rm cc}\)) is defined as the distance between the innermost solid layers. The innermost solid layer is defined as the most probable location of innermost solid atoms. The parameter \(\delta_{\rm b}\) is the distance between the innermost solid layer and the defined solid/fluid boundary as shown in Figure 3. As the definition of solid/fluid boundary is assumed as a plane in this work, a new definition of solid/fluid boundary can be easily applied to the continuum analysis [12]. Then, the effective channel height is a function of \(\delta_{\rm b}\); \(H_{\rm fluid}\equiv H_{\rm cc}-2\delta_{\rm b}\).
## 4 Measurement and Results/Discussion
### I-180 Slope
However, with experienced surveyors, this technique is a time-proven technique and may economically overcome difficult environmental factors (except thick snow covering), which may pose challenges to other techniques discussed. The UAS-based technique used to be an expensive technique, and it had been used only in limited applications. With the availability of UAS systems at an affordable price, and with enhanced camera quality, this method has become a convenient option. The technique provides an unmatched high-resolution data set compared to both the TS and the DSS methods. UAS seemed particularly advantageous for applications on wide and rugged terrains. Although, this technique required two skilled crew members, including an operator with a UAS pilot's license and an assistant to monitor highway safety. UAS also resorted to dedicated software to compile and analyze an extremely large data set, which might be expensive and time-consuming depending on the system being used. It was, however, noted that the data reduction time for UAS based technique was not noticeably longer than other techniques. Although cross-comparison of errors for the three techniques was contemplated, it was ultimately not executed due to the significant differences in measurement principles, as previously mentioned. As previously noted, the DSS was intended to measure relative movement rather than changes in coordinates, while UAS and TS provide total deformation. Nonetheless, all three methods effectively indicated the location of the failure zone.
# Cross-Lingual Named Entity Recognition Based on Attention and Adversarial Training
## 5 Conclusions  
In order to improve the accuracy of cross-lingual named entity recognition, this paper proposes a cross-lingual named entity recognition method based on attention mechanism and adversarial training, which firstly introduces the M-BERT pre-trained language model. The obtained word vectors can enhance the word-word dependencies, adds the attention mechanism based on the adversarial migration model and obtains the semantic representation of the aligned word vectors by enhancing the more accurate text representation vectors. Finally, the best entity annotation is obtained. In addition, the accuracy of the cross-lingual NER task is improved by introducing adversarial training to obtain the shared features and word boundary information of both languages. The experimental results show that the method in this paper shows good results for named entity recognition in low-resource  
Figure 4: Case Study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **WeiboNER (F1)** & **People-Daily2004 (F1)** \\ \hline Model & **0.5192** & **0.5322** \\ Without attention & 0.5012 & 0.4834 \\ Without position & 0.5107 & 0.5214 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiments.  
languages; however, name entity recognition not only relies on word-level features, but also on sequential features for entity-type classification. In the future, we will experiment with language-agnostic information at the sentence level.  
Methodology, L.H.; Resources, J.D.; Writing--original draft, H.W.; Writing--review & editing, L.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by R&D Program of Beijing Municipal Education Commission (KM202210009002), the National Natural Science Foundation of China (61972003), and the Beijing Urban Governance Research Center.  
Not applicable.  
We would like to thank the anonymous reviewers for their helpful comments. We would like to thank the referees for their comments, which helped improve this paper considerably.  
The authors declare no conflict of interest.
## 2 System Model
Some aspects of interest that should be noted: (i) each satellite only has \(19\) visible positions from the point of view of the cluster and experiences the erasure probability values in decreasing order \(\varepsilon_{1}\in\{0.9,0.8,\cdots,0.01\}\) until it is just above the cluster, where it experiences the smallest value \(\varepsilon_{1}=0.01\), and from this point on it begins to experience the erasure probability values in increasing order \(\varepsilon_{1}\in\{0.01,0.1,\,\cdots,0.9\}\); (ii) however, this topology has \(M=35\)--that is, when the first satellite is still visible to the cluster in another \(16\) iterations, some of the remaining satellites are visible, the first being only visible in the \(19\) first positions and the last satellite only being visible in the last \(19\) positions; (iii) this means that when \(s\) increases, the number of iterations in which a satellite is visible to the cluster increases, but at the same time the number of positions in which the \(K\) satellites of the constellation are visible decreases; (iv) even for some topologies it would not be possible to have the \(K\) satellites in the same iteration (e.g., \(K=4\) and \(s=9\))--in such circumstances the non-visible satellites can be considered in the analysis with \(\varepsilon_{k}=1\)._  
Moreover, in the considered model neither retransmission policies nor communication between satellites are considered. Having in mind that low cost satellites may have limited computational capabilities, it is also assumed that collisions are destructive; i.e., we do not consider multiuser detection or successive interference cancellation at the LEO satellites. Following [36], the average number of packets transmitted per slot is defined as \(G\), and the number of users accessing the channel at the same time-slot is modeled as a Poisson random variable \(U\), so that the probability that \(u\) users transmit in the same time-slot is  
\[\mathbb{P}[U=u]=\frac{G^{u}e^{-G}}{u!}. \tag{2}\]
## 4 SnZn-Based Solders  
### Microstructure  
The matrix microstructure of the SnZn solder mainly consists of \(\beta\)-Sn phase and needle-shaped \(\alpha\)-Zn phase, in which the coarsening \(\alpha\)-Zn phase greatly decreases the mechanical properties of SnZn solder [74]. Besides, the existence of Zn-rich phase greatly reduces the oxidation resistance of the SnZn solder, which limits the practical use of the SnZn solder. Different from other Sn-based solders, the interfacial IMCs layer of the SnZn/Cu solder joint is mainly Cu\({}_{5}\)Zn\({}_{8}\), which increases the risk of fracture of solder joints during service due to its poor reliability [75].  
It is generally known that soldering process parameters have a great influence on the microstructure and properties of solder joints. [PERSON] et al. [76] found the thickness of Cu\({}_{5}\)Zn\({}_{8}\) layer increased with the reflow time and temperature, and the addition of Ni and Ag nanoparticles could effectively suppress the IMC growth behavior. Besides, with the addition of Ni nanoparticles, the grain growth of needle-shaped \(\alpha\)-Zn phase could also be inhibited because Zn-Ni IMCs and Ni nanoparticles increased the heterogeneous nucleation sites, as shown in Figure 12[77]. Due to high surface energy and large special surface area, Al\({}_{2}\)O\({}_{3}\) nanoparticles can also provide nucleation sites during the solidification process of the solder. So, it can not only inhibit the coarsening of dendrite Sn-Zn eutectic structure, but also refine the microstructure of the composite solder [78].  
The gathering of ZrO\({}_{2}\) nanoparticles at the phase boundaries decreased surface energy of the boundaries and inhibited the growth of Zn-rich phase [79]. Therefore, the average grain size and spacing of Zn-rich phase were significantly reduced by the addition of ZrO\({}_{2}\) nanoparticles, as shown in Table 1. It was also reported that the addition of ZrO\({}_{2}\) changed the thickness and composition of interfacial IMCs in SnZn-ZrO\({}_{2}\)/Cu solder joints. Due to the refining effect of ZrO\({}_{2}\) nanoparticles, less Zn atoms could migrate from the solder bulk to the interface to form Cu\({}_{5}\)Zn\({}_{8}\) layer while the migration of Cu atoms became easier, resulting in the increased thickness of Cu\({}_{6}\)Sn\({}_{5}\) layer. A small addition of  
Figure 11: Corrosion rate of SnBi–xnano Cu\({}_{6}\)Sn\({}_{5}\) composite solder: x = 0%, 0.03%, 0.05%, 0.1% [59].  
ZnO nanoparticles can significantly decrease the sizes of the \(\beta\)-Sn dendrites [80]. The refinement can be attributed to the absorption of ZnO on the grain surface, which reduces the growth rate of \(\beta\)-Sn grains.
## 1 Introduction
The main work presented in this paper can be summarized as follows: (1) To minimize the loss of UGS information during the model down-sampling process, and in line with the characteristics of UGS (a low-rank feature, analysis in later section), an enhanced UGS feature extraction backbone network called RPS-ResNet was proposed. RPS-ResNet replaces the large kernel convolutional layer and the max-pooling layer in the origin ResNet-50 with a reverse Pixel Shuffle approach, which is proposed in this paper. Specifically, the last residual convolutional layer in the origin ResNet-50 was also removed to reduce the model's parameters without compromising accuracy. (2) Instead of using cross entropy or binary cross entropy for segmentation tasks, Focal Loss and the Dice coefficient are combined for GSRPnet training, and the effects of these two losses on the segmentation accuracy of a UGS under different weights are discussed. (3) To validate the correctness and effectiveness of the ideas and modules proposed in this paper, ablation studies were conducted. In addition, five segmentation models were introduced to illustrate the superiority of GSRPnet.
## 3 Timescale-Scalable Posture Detection Algorithm
### TSPDA--Initialiser  
The Initialize module defines the global variables (reference graph, target graph, slice count, target minimum accuracy, reference start frame, and reference end frame) required in the algorithm.  
The Initialize module also compares the reference start frame and reference end frame with the target graph to calculate the relative accuracy (here, lower values are better). Additionally, it defines the sum start frames and sum end frames, which are accumulated values for each frame. The Initialize module is represented by the Algorithm 1 and Equations (1) and (2).  
Figure 1: The operation sequence of the modules composing the TSPDA.  
```
let (referenceGraph) be ([pre-recorded reference pose] composed with [X as time and Y as sensor data]) let (targetGraph) be ([recorded graph by patient] composed with [X as time and Y as sensor data]) let (sliceCount) be ([Desired Slice Count] when comparing the similarity between [[targetGraph] and the [candidates]]) let (targetMinimumAccuracy) be ([minimum target accuracy] between [targetGraph and referenceGraph]) let (referenceStartFrames) be ([Y values of referenceGraph's first X value]) let (referenceEndFrames) be ([Y values of referenceGraph's last X value]) let (totalFrameCount) be (Total count of X value for targetGraph) let (sumStartFrames) be (empty list) for x = 1 to totalFrameCount do let sumStart be 0 let sumEnd be 0 let graph = targetGraph[x] let targetMax = max(graph) let targetMin = min(graph) let targetDiff = targetMax-targetMin let frameCount = (frame count of the graph) (equation #1) (equation #2) sumStartFrames[x] = sumStart sumEndFrames[x] = sumEnd end
```  
**Algorithm 1**Pseudo code for the Initializer module of the TSPDA.  
\[sumStart = \sum_{y=1}^{frameCount}\Big{[}\Big{|}\frac{referenceStartFrames[y]- graph[y]}{targetDiff}\Big{|}\Big{]} \tag{1}\] \[sumEnd = \sum_{y=1}^{frameCount}\Big{[}\Big{|}\frac{referenceEndFrames[y]- graph[y]}{targetDiff}\Big{|}\Big{]} \tag{2}\]  
Equation (1) is a formula for calculating _sum start_ (in which a lower number corresponds to higher accuracy), based on the reference start frames and the target graph.  
Equation (2) is a formula for calculating the sum end by applying Equation (1) above to the reference end frames.