esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Ultrasonic Nondestructive Testing for Breeding of Meat Pigeons
## 4 Discussion
The correlation coefficients between the BMT and BMW at points A and B were 0.907 and 0.897, respectively. Therefore, points A and B should be selected as the most appropriate sites for UNDT of the BMT of meat pigeons. UNDT is widely applied in the livestock and poultry industries in many countries [13; 14; 15; 16] and commonly used in China for the breeding of Peking ducks [7; 13; 24] and, to a lesser extent, for chickens [9], geese [8], and meat pigeons [6]. A study conducted by [PERSON] et al. to assess the correlation between carcass yield and BW, BMT, and the sternal crest of Beijing ducks found that the correlation coefficient between BW and BMT measured by ultrasound was 0.82 [23]. A study by [PERSON] et al. found that the correlation coefficients of the slaughter weight, live BW, and BMT of three species of Peking ducks was 0.50-0.62 [13]. Moreover, [PERSON] et al. reported that the correlation coefficient between the BMT and BW of Peking ducks was 0.46 [24]. [PERSON] et al. found a high correlation coefficient between the live BW and weight of the measured tissue components of Muscovy ducks of 0.70-0.86 [25]. Meanwhile, the results of the present study showed a significant positive correlation between the live BW, carcass weight, and BMT of Carneau and Silver King pigeons (\(p<0.01\)); the correlation coefficients between breast muscle thickness and weight in meat pigeons (0.907 and 0.897) are significantly higher. Hence, UNDT is a useful method for the breeding of high-quality meat pigeons and to further develop the meat pigeon industry and market. Despite its demonstrated value, the widespread adoption of ultrasonic measurement technology faces challenges. Equipment costs may restrict its use in small-scale farms, and measurement stability can be influenced by operator skill and environmental conditions [22]. Therefore, it is possible to develop automatic ultrasound image analysis system using artificial intelligence technology, reducing human-induced variability [9]; develop cost-effective and portable ultrasound devices to improve accessibility [15]; and combine UNDT with molecular genetics to further enhance breeding efficiency [21].
## 1 Introduction
Our main contributions are (1) applying the VG algorithm to the TC integration scheme to give a new fusion scheme, (2) designing a new satellite selection algorithm by utilizing the correlation term that determines the User Equivalent Range Error while guaranteeing the real-time capability of the system and the accuracy of the observation data, (3) making a complete performance comparison of the navigation algorithm results with the LC and VG-free systems, as well as providing a comprehensive evaluation of the satellite selection algorithm. This paper is organized as follows: Section 2 outlines our combined navigation system model and algorithm. Section 3 describes our given satellite selection algorithm. Section 4 presents simulation experiments on our navigation model and satellite selection algorithm and evaluates the results. In the last section, Section 5, the paper is summarized and discussed.
# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## 4 Analysis and Experiments
### Ablation Experiment and Analysis
#### 4.4.5 Comparison of Different Attention Modules  
This subsection compares the performance with the already proposed attention modules [49, 50, 51]. Table 6 compares our proposed DMANet with different attentions on the VeRi-UAV dataset. Using a contrastive attention (CA) [1] module, we determine the significance of a feature based on the sum of all the parts. By reweighting the part feature, practical cooperation among the part features is derived. In comparison, for DMANet, Rank-1 and mAP are 2.6% and 9.12% higher than CA. DMANet allows for more equitable attention to the characteristics of different channels. It is essential for vehicle ReID tasks because the feature channels in different vehicle images may have different importance and expressiveness. Contrastive attention, by contrast, focuses only on the differences between positive and negative samples on each channel and may not be able to utilize the information from all channels fully. Polarized filtering and enhancement are two essential concepts for high-quality pixel-wise regression, and they are combined in the polarized self-attention (PSA) [51] block. In comparison, for DMANet, mAP is 6.29% higher than PSA. DMANet introduces more feature diversity by dividing multiple sub-feature maps into channel dimensions and spaces. Diversity can help improve the network's ability to capture the features in different vehicle images and improve the vehicle re-identification performance. However, PSA folds in both channel and spatial dimensions, which results in a certain level of information loss. It has a particular impact on the ability of fine-grained  
Figure 5: **Comparison of dual mixing attention module (DMAM) in different backbones.** The blue bar represents the performance of the original backbone, and the orange represents the performance after adding DMAM. **(a)** Rank1 results of different backbones; **(b)** mAP results of different backbones.  
feature discrimination in vehicle re-identification tasks. Table 6 shows that, compared with the baseline, DMANet contributes 6.29% of the mAP improvement of VeRi-UAV. In the end, by fusing attention mechanisms, DMANet can provide more flexibility so that the model can adaptively select and adjust different attention mechanisms according to the needs of specific tasks.
## 5 Conclusions  
Experiments show that hotspot information and domain knowledge significantly impact document recommendation. The heterogeneous literature recommendation method proposed in this paper employs a heterogeneous network to depict the literature's external and internal association relationships. It aggregates the hotspot information network and domain knowledge information network related to query terms into the heterogeneous network, making the literature recommendation method domain-specific and emphasizing the characteristics of the literature. Compared with existing models, our model has improved HR and NDCG by approximately 4.5% and 4.2%, respectively.  
Previous models have yet to consider how to reflect the domain characteristics and how to depict the various types of associational relations among documents. The experiments in this paper show that heterogeneous networks of literature are essential for describing multiple types of relationships among documents. It is worthwhile to study how the heterogeneous network of literature can be combined with traditional models and how to make the various valuable information for literature recommendation effectively.  
In addition, the model provides some relief for data scarcity and cold start problems. This method expanded the query terms so that uncommon query terms that may not initially be queried for the right paper can be queried after the information is extended.  
Based on the achievements as well as limitations, we believe several directions still need to be further investigated. (1) The best results of the experiment appear when TOP \(N=50\), but when \(N\) is relatively large, there are too many candidates for the recommended literature, making the recommendation unfeasible. Consider how to improve the effectiveness of recommendations when the list is short. (2) In addition to incorporating domain knowledge and hotspot information, constructing a heterogeneous network is also a worthy research problem to improve the recommendation effect by reasonably incorporating more related information.  
Conceptualization, W.C.; methodology, W.C.; software, W.C.; validation, Y.X., Y.W.; formal analysis, W.C.; investigation, W.C.; resources,W.C.; data curation, Y.Z.; writing--original draft preparation, W.C.; writing--review and editing, Y.Z., Y.X. and Y.W.; visualization, Y.Z.; supervision, W.C.; project administration, W.C.; funding acquisition, W.C. and Y.X. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Performance metrics of NDCG for the three baseline models and our proposed model in metallurgical datasets.  
**Funding:** This work is supported by Yunnan Basic Research Project (Grant Nos. 202001 AT0 70046) and the Open Fund of Yunnan Key Laboratory of computer technology application in China (Grant No.140520200151).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are included within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
Atom-based measurements are receiving increasing attention, such as time [1], gravity [2] and electromagnetic fields [3; 4; 5], due to their invariable level structures that can be used as self calibrating criterions for measurements of these quantities. Atoms have also been successfully used for magnetometry due to their sensitivity to magnetic field [6; 7]. Rydberg atom, highly excited atom with principal quantum number \(n>10\), has a large electric polarizability (\(\propto n^{7}\)) and a big microwave-transition dipole moments (\(\propto n^{2}\)) [8], which make it a good candidate for measuring external electric fields [9; 10; 11; 12; 13; 14; 15]. Rydberg electromagnetically induced transparency (EIT) [16; 17], providing a nondestructive detection of Rydberg states, has been investigated in recent years. Rydberg EIT can be used to measure a large dynamic range of the electric fields of electromagnetic radiation, including microwave fields [10; 11; 12], millimeter waves [13], static electric fields [14; 15] and subwavelength imaging of microwave electric-field distributions [18; 19] and field inhomogeneities [20].  
Measurement of the microwave field for frequency larger than 1 GHz is mainly based on an Autler-Townes (AT) effect [21] that is induced by the microwave field coupling nearby Rydberg levels [10]. Rydberg-EIT is employed to measure the AT splitting (\(f_{AT}\)) that is proportional to the microwave electric field, i.e., \(f_{AT}=\Omega_{MW}=\frac{\mu_{MW}E}{h}\), with \(\Omega_{MW}\) the Rabi frequency of microwave field, \(\mu_{MW}\) the dipole matrix element microwave field coupled and \(h\) reduced Planck constant. The Rydberg EIT-AT spectrum leads to a direct International System of Units (SI) traceable, self-calibration and broadband measurement of microwave electric fields, that has the capability to realize measurementson a fine spatial resolution. The approach of EIT/AT-based electric-field measurement is recently investigated by several groups around the world [9; 10; 11; 12; 13]. However, the AT splitting shows the nonlinear dependence on the coupling \(\Omega_{MW}\) at a weak electric-field range, which yield a large deviation of the microwave electric-field measurement. Here, we present the measurements of \(f_{AT}\), induced by a 15.21-GHz microwave field coupling the \(66S_{1/2}\)-\(65P_{1/2}\) Rydberg transition in a cesium room-temperature vapor cell. We investigate the dependences of the \(f_{AT}\) on the \(\Omega_{MW}\) and the coupling (probe) Rabi frequency \(\Omega_{c(p)}\). The results are in accord with the theoretical simulation of the density matrix theory.
# Evaluating the Reliability of Powder Bed Fusion for Biomedical Materials: An Experimental Approach
## 4 Discussions
Recent experimental research conducted with Ti Grade I powder demonstrated that using a laser power of 120 W, a scanning speed of 500 mm/s, and a hatching distance of 0.12 mm resulted in parts with an ultimate tensile strength of 441 MPa, which is higher than the values reported in other similar studies on the same material [51]. There is a direct correlation between Vickers hardness and the total energy delivered by the laser beam. The reduction in hardness may be primarily attributed to microstructure roughening. The Vickers hardness values measured on the SLM samples were higher compared to the hardness of cast parts made from CoCr alloys. For example, at the same cobalt content level (59%), SLM parts manufactured from CoCrWMo powder exhibited a hardness of 544 HV1, whereas conventional Colado CC parts had a hardness of 407 HV1 [52]. In the case of pure titanium, the SLM samples showed hardness values ranging from 234 to 260 HV1, which were closer to the hardness of similar parts obtained through traditional manufacturing methods (255-290 HV1) [53]. While our current implementation provides consistent results across repeated runs, future extensions of this work could explore probabilistic or uncertainty-based formulations of direct methods to further support safety-critical applications. One major challenge associated with Selective Laser Melting is the surface quality of the manufactured parts. Without any post-processing, the Ra roughness measured on the parts ranged between 5 and 50, while the Rz roughness varied from 27 to 255, depending on the material used and the configured process parameters. In almost all cases, a reduced laser power significantly increased the surface roughness due to the presence of semi-melted grains on the outer boundaries of the parts. This phenomenon, along with other undesirable issues such as the balling effect, micro-pores, asymmetric streaks, and scratches, are prone to occur on surfaces during SLM manufacturing. To achieve a homogeneous surface and reduced roughness, the process parameters responsible for scanning the outer boundary of the parts should be optimized for each powder material type. The ANOVA method offers a valuable approach to optimize process parameters by developing an empirical mathematical model through statistical techniques, mathematical combinations, and response surface methodology [54]. Additionally, post-processing techniques such as sandblasting with alumina, polishing with carborundum, and ultrasonication are commonly used to enhance the surface quality of PBF parts. It is the authors' aim to support reproducibility of our research. Despite the data limitation, we provided detailed descriptions of all relevant input parameters, including images of the processed samples and threshold values. This ensures replicability of the methodology using similar types of input data.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 3 Proposed Approach
### Image Acquisition and Preprocessing  
The MR headset camera continuously captures video frames of the surroundings, which undergo pre-processing before being fed into the detection model. Pre-processing includes resolution calibration to adjust image dimensions for optimal processing, contrast  
Figure 1: Workflow of the proposed MR-based vial detection system. This figure illustrates the key steps in the proposed system: hand-tracking for user interaction, spatial mapping to create a 3D environment, and image acquisition through the HoloLens 2 camera, followed by preprocessing to optimize detection. The AirTap gesture (tapping the air with a finger) is used for selecting and interacting with objects in the MR environment, allowing the user to engage with the system effectively. The Lightweight YOLOv4 model is then used for vial detection, generating bounding boxes, and projecting them into the MR space for real-time visualization and interaction.  
enhancement to improve image clarity for better feature extraction, and noise reduction to remove distortions, improving detection accuracy [22]. Once an image is captured, 2D coordinates of detected vials are annotated and projected into 3D space, ensuring accurate positioning within the MR interface.
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 3 Watermark Embedding
### Method 3 (M3): Adaptive LSB Modification of Grouped Spectral Coefficients  
In order to improve the quality of the watermarked image and at the same time be able to recover the hidden information with high accuracy, the previous method was slightly  
Figure 1: Block diagram for the base process of watermark embedding.  
modified. In contrast, the information bits are inserted into least significant bits of the spectral coefficients for the given channels. Although it requires casting real spectrum coefficients to integer type and then setting the last bit to zero or one, this approach provides satisfactory results as far as visual perception and recovery accuracy are concerned. Except for the rounding operation, statistically, only half of the least significant bit replacement operations will result in a change of the coefficient value.
# Efficient Multi-Objective Simulation Metamodeling for Researchers
## 2 Test Environment and Methods
### Metamodel
#### 2.1.1 Gaussian Regression (GR) Metamodel  
This metamodel employs the \"GPFDA\" package ([[https://github.com/gpfda/GPFDA-dev](https://github.com/gpfda/GPFDA-dev)]([https://github.com/gpfda/GPFDA-dev](https://github.com/gpfda/GPFDA-dev)), accessed on 23 May 2023) for R [34] and treats outputs as independent, though this is not a restriction on all Gaussian regressions.  
For an objective \(x\), the FEs in memory relate values of the objective to values of the inputs, so if there are \(n\) FEs in memory, we can express \(Memory=\{(x_{1},t_{1}),(x_{2},t_{2}),\ldots(x_{n},t_{n})\}\) where \(t_{j}\) is a vector of input values corresponding to \(x_{j}\).  
The Gaussian regression model for \(x\) is then:  
\[\begin{split} x_{i}&=f(t_{i})+e_{i},\,i=1,\ldots,n \\ e_{i}&\sim i.i.d.N(0,\sigma_{e}^{2})\\ f(.)&\sim GP(\mu(.),k(.,.))\,\,where\,k(t_{i},t_{j}) =COV(f(t_{i}),f(t_{j}))\end{split} \tag{1}\]  
The mean function used is \(\mu(.)=0\), which means the covariance function of a Gaussian process is the only item that has to be estimated in order to characterize that Gaussian process.  
The Matern covariance function is used here, so:  
\[\begin{split} k(t_{i},t_{j})&=v_{0}\frac{(d\sqrt{ 2v})^{v}}{\Gamma(v)2^{v-1}}K_{v}(d\sqrt{2v})\\ d&=|t_{i}-t_{j}|^{2}\end{split} \tag{2}\]  
where \(K_{v}\) is the modified Bessel function of order \(v\), and \(v=1.5\) is used.
## 3 Results
### Validation of Analytical Solutions  
To validate the accuracy of analytical solutions obtained using the HBM in combination with the PLM, the numerical calculation with the fourth-order Runge-Kutta algorithm is conducted, as shown in Figure 3. Comparisons between the analytical and numerical simulations are presented for two sets of parameters. The numerical solutions of the forward and backward frequency sweep are represented by cross and circle marks, respectively. In order to compare the analytical solutions with the numerical simulations, we utilize the amplitude-frequency response of the ERS-TQZS VI system \(\widehat{z}_{1}=\widehat{Z}_{11}\sin(\Omega\tau)+\widehat{Z}_{12}\cos(\Omega \tau)=\widehat{Z}_{1}\cos(\Omega\tau+\varphi_{1})\). By observing Figure 3, it becomes evident that there is a good agreement between both methods, which validates the satisfactory accuracy of the HBM in combination with the PLM. Therefore, in the subsequent analysis of this paper, unless otherwise specified, we will use the approximate analytical method.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 3 Method
### Update Parent Population Parameter  
Note that before the parent populations are generated, the constraints of the objective problem have to be considered. To deal with this issue, we define the constraint violation value \(CV(I_{\mathbf{U}},\mathbf{p})\) for each user \(k\) as  
\[CV(I_{\mathbf{U}},\mathbf{p})=\left|\frac{p_{k}h_{Uk}}{\sum_{i=k+1}^{K}p_{i}h_{Uii}}-P_{ \text{SIC}}\right|^{\dagger}+\left|R_{k}-R_{\text{th}}\right|^{\dagger},\ \forall k\in\mathcal{K}, \tag{16}\]  
where two parts of the right-hand side are referred from (10c) and (10d), respectively. The operator \(|x|^{\dagger}\) indicates the absolute value of \(x<0\), and returns \(0\) otherwise. It is noted that the feasible solution of (16) has to be equal to \(0\), and the quality of the parameter depends on how much smaller the \(CV(I_{\mathbf{U}},\mathbf{p})\) is. If every value of \(I_{\mathbf{U}}^{hp}\) and \(\mathbf{p}^{hp}\) are conform to the constraint requirement, the member will be served for the unconstraint update procedure. Otherwise, the infeasible solution will be decided by the \(CV(I_{\mathbf{U}},\mathbf{p})\) and niching scenarios. Obviously, the population diversity depends on the solution associated with an isolated sub-region significantly.  
For those values of \(I_{\mathbf{U}}^{hp}\) and \(\mathbf{p}^{hp}\) which are not conformed to the constraint requirement, there are two scenarios to identify the solutions \(I_{\mathbf{U}}^{\prime}\) and \(\mathbf{p}^{\prime}\).
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
###### Abstract  
Hundreds of processor cores or modules are integrated into a single chip. The traditional bus or crossbar is challenged by bandwidth, scalability, and silicon area, and cannot meet the requirements of high end applications. Network-on-chip (NoC) has become a very promising interconnection structure because of its good scalability, predictable interconnect length and delay, high bandwidth, and reusability. However, the most available packet routing NoC may not be the perfect solution for high-end heterogeneous multi-core real-time systems-on-chip (SoC) because of the excessive latency and cache cost overhead. Moreover, circuit switching is limited by the scale, connectivity flexibility, and excessive overhead of fully connected systems. To solve the above problems and to meet the need for low latency, high throughput, and flexibility, this paper proposes PCCNoC (Packet Connected Circuit NoC), a low-latency and low-overhead NoC based on both packet switching (setting-up circuit) and circuit switching (data transmission on circuit), which offers flexible routing and zero overhead of data transmission latency, making it suitable for high-end heterogeneous multi-core real-time SoC at various system scales. Compared with typically available packet switched NoC, our PCCNoC sees 242% improved performance and 97% latency reduction while keeping the silicon cost relatively low.  
Network on Chip (NoC); packet connected circuit; low latency +
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# A Numerical Analysis on Lateral Resistance of Pile-Bucket Foundation for Offshore Wind Turbines
## 4 Results of Numerical Analysis
### Comparison of Bearing Capacity and Displacement of PF, BF and P-BF  
According to the above, the PF, BF and P-BF (\(d\) = 6 m, \(l\) = 80 m, \(D\) = 18 m, \(h\) = 12 m) are established, respectively, where \(d\) is pile diameter, \(l\) is pile length, \(D\) is bucket diameter, and \(h\) is the height of bucket. As shown in Figure 6, the lateral bearing of the P-BF is much higher than the SF and BF. The lateral bearing capacity of the BF base is lower than the PF. Under the same load, the displacement of the BF is much larger than that of the PF, as is shown in Figure 7. Thus, we can conclude that the P-BF has much better anti-overturning ability than the BF under the same lateral load and lateral bearing capacity than the PF.  
Figure 6: Comparison of the bearing capacity.  
Figure 7: Comparison of lateral displacement between P–BF and BF.