esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental System and Definition
### Definition of Feature Points and Radius Ratio  
In order to explore the coupling effect between bubble collapse and splash of droplet, four feature points were selected on the bubble wall and droplet surface, respectively, and their dynamic processes were quantitatively analyzed and discussed.  
Figure 2 shows the definition of feature points and radius ratio. The Cartesian coordinate system takes the point \(O\) as the origin. The intersection point between the positive X axial direction and the bubble wall is defined as \(N_{bx}\); the intersection point between the negative \(Y\) axial direction and the bubble wall is defined as \(N_{by}\); the intersection point  
the cavitation in the droplet by the laser. In the experiment, the droplet is not completely spherical due to the effects of the pipeline and the droplet. After reasonable equivalence, the droplet and the bubble are regarded as ellipsoids. The dimensionless parameter radius ratio \(\lambda\) is defined as follows:  
\[\lambda=\left(\frac{R_{bx}^{2}R_{by}}{R_{dx}^{2}R_{dy}}\right)^{\frac{1}{3}} \tag{1}\]
# The Potential of Aquafaba as a Structure-Shaping Additive in Plant-Derived Food Technology
## 5 Functional Properties of Aquafaba in Food Technology
### Gelling and Thickening Properties  
When cooking seeds, there is a complete or partial denaturation of proteins that interact with water-soluble carbohydrates and retain water molecules, forming a gel, coagulate or sediment. During cooking, there is also the Maillard reaction [16; 21]. Chickpeas have been found to have the best gelling properties and that the ability to gel is inversely correlated with the content of insoluble fiber [24]. The gelling and thickening process takes place in products with a higher water content (e.g., mousse) [50]. In products with a high dry matter content that are heat-treated (e.g., meringue), it only occurs to a small extent [20]. [PERSON] et al. [30] have proven that aquafaba subjected to high pressure treatment improves its structure and gelling ability.  
A promising area of use for aquafaba is 3D printing of food. A major problem faced by researchers is the shaping of the rheological properties of materials (paints, pastes, filaments) used for printing [51]. The specific structural-forming properties of aquafaba may contribute to the development of this new food production technique [52].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Legume Seeds** & **Foaming Capacity, \%** & **Foaming Stability, \%** & **Emulsifying Capacity, \%** & **Emulsifying Stability, \%** & **Emulsifying Activity Index, m\({}^{2}\)/g** & **Reference** \\ \hline \hline \begin{tabular}{c} haricot beans; \\ whole green lentils; split \\ yellow peas \\ \end{tabular} & \(39;97;93\) & - & - & - & - & [24] \\ \hline \hline \begin{tabular}{c} yellow soybeans \\ \end{tabular} & 65 & - & 49 & - & 20 & [25] \\ \hline \begin{tabular}{c} haricot beans; whole \\ green lentils; split \\ yellow peas \\ \end{tabular} & - & - & - & - & 23; 47; 16 & [26] \\ \hline \hline \
\begin{tabular}{c} lim beans \\ \end{tabular} & 348–660 & 23–82 & - & - & - & [32] \\ \hline \hline
\begin{tabular}{c} green peas \\ \end{tabular} & 575–725 & 75–90 & - & - & - & [49] \\ \hline \hline \end{tabular}  
* “-”—not conducted.  
\end{table}
Table 3: Cont.
## 4 Calculation Method for Vertical Bearing Capacity of SDRN File
### Pile Tip Resistance  
It is found that, the response of core pile and cemented soil at the expanded section is almost synchronous by the theoretical calculation [2] and numerical simulations [4]. Therefore, it is reasonable to take the nodular pile and the cemented soil at the expanded section as a whole.  
For MC pile, the cross section of the whole mixing area is taken to calculate the tip resistance [13]. In addition, the adjustment coefficient of the MC pile tip resistance is \(\zeta\) = 0.6\(\sim\)0.8 [13]. Considering the influence of the geological conditions and integrated management, the adjustment coefficient of SDRN pile tip resistance could be taken as \(\eta_{3}\) = 0.6.  
\[Q_{pk}=\eta_{3}q_{bk}A_{b}, \tag{6}\]  
where \(Q_{pk}\) is the standard value of pile tip resistance of SDRN pile, \(\eta_{3}\) is the coefficient of pile tip resistance, \(q_{bk}\) is the standard value of ultimate pile tip resistance, which could refer to that of the precast pile in field; \(A_{b}\) is the sectional area of the expanded section.  
In summary, the calculation formulas for the ultimate vertical bearing capacity of SDRN pile are as following:  
\[Q_{u1}=Q_{sk1}+Q_{pk}, \tag{7}\]  
\[Q_{u2}=Q_{sk2}+Q_{pk}, \tag{8}\]  
\[Q_{u}=\min(Q_{u1},Q_{u2}). \tag{9}\]
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 7 Conclusions  
The existing equations and design steps for microstrip filters found in books may introduce some degree of error in the computed parameters or in the effect of the filter, like in the desired cutoff frequency. To mitigate these sources of error, our proposed software, MicroStripOptim implements several computer algorithms. MicroStripOptim was created to facilitate the design of microstrip low-pass filters and analyze the effects of parameter variations on filter performance. One significant contribution of this paper is the ability to optimize filters with a specific total length requirement. This optimization feature allows designers to achieve desired filter characteristics while adhering to length constraints, enhancing the overall design process. Furthermore, the effectiveness of the designed filters was validated by comparing the scattering parameters plot obtained from simulations with the measured values on a fabricated PCB. The satisfactory results obtained for the cutoff frequency and maximal attenuation provide validation for the accuracy and reliability of MicroStripOptim as a design tool.  
Future work. As future work, the following aspects are proposed: first, to include new types of filters, such as elliptic filters and Bessel filters; second, to add options for high-pass, band-pass, and band-stop filters; and finally, new optimization methods are already being developed.  
Conceptualization, A.G.-J. and L.A.G.-D.; methodology, A.G.-J., R.S.-G. and L.A.G.-D.; software, R.P.-A. and L.A.G.-D.; validation, R.S.-G., J.R.N. and A.G.-J.; formal analysis, M.A.-E. and R.G.-F.; investigation, R.S.-G. and L.A.G.-D.; resources, R.G.-F. and I.E.Z.-H.; data curation, R.P.-A. and L.A.G.-D.; writing--original draft preparation, [PERSON]-G., [PERSON]-J. and L.A.G.-D.; writing--review and editing, J.R.N., [PERSON]-E. and R.G.-F.; visualization, I.E.Z.-H.; supervision, [PERSON]-J.; project administration, L.A.G.-D.; funding acquisition, R.G.-F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data that support the findings of this study are available from the corresponding author upon reasonable request. The authors declare no conflicts of interest.
## 3 Experimental Results
### Discussion and Error Analysis
The distribution of the intersection over union (IoU) values of 3D binary images of all unhealthy patients data is shown in Figure 9 for correct and wrong classification. Most of the thresholded functional networks have little or no overlap with the lesion mask. The normalized versions of these histograms are provided in Figure 10. The two distributions were observed to be highly skewed values of 2.11 and 1.94 for IoU of correctly and wrongly classified images, respectively, indicating non-Gaussian distribution. These histograms show that the category (correctly classified or wrongly classified) are estimated to be equal across the different IoU values as also confirmed by the \(p\)-value of 0.75 in the \(t\)-test carried out from the IoU distribution, which indicates non-significance (\(>\)0.05) in a difference between the two categories. To qualitatively illustrate this statistical fact, Figure 11 provides a scenario where images with or without overlap are correctly or wrongly classified. No direct effect of the tumor on the thresholded functional networks targeted is observed in our dataset. This observation can explain the possibility of transfer learning from healthy to unhealthy data. Nonetheless, we found a useful but not perfect transferability, and therefore, a discrepancy should exist. This could be in the intrinsic shape of the functional network of unhealthy patients, which may be distorted when located in the vicinity of the tumor. Figure 8: Confusion matrix of functional network prediction by proposed CNN model on all individual functional brain networks as classes LANG, SAL, VAN, DMN, IFPCN, rFPCN, and DAN. (**a**) Confusion matrix of brute transfer learning from healthy to unhealthy data. (**b**) Confusion matrix of brute training on healthy and validation with all unhealthy data. (**c**) Confusion matrix of weight transfer learning from healthy to unhealthy data. Figure 10: Normalized Histogram of IoU of network and lesion mask. Figure 9: Histogram of IoU of network and lesion mask.
## 2 Materials and Methods
### Autoaggregation and Coaggregation  
Autoaggregation was assessed using the modified method described by [PERSON] et al. (2017) [25] to confirm the adhesion ability of the cell to the intestinal wall. The selected strain was cultivated in TSB for 24 h at 30 \({}^{\circ}\)C and then centrifuged at 9425\(\times\)\(g\) for 5 min. The cells were washed twice and adjusted to OD\({}_{600}\) of 0.3 with PBS (pH 7.4). The suspension was left to stand at room temperature and monitored at different time intervals (4, 5, and 24 h). Then, the absorbance of the suspension was measured at 600 nm (A\({}_{\text{t}}\)). The autoaggregation percentage was calculated according to the formula:  
\[\text{Autoaggregation}\left(\%\right)=\left(1-\frac{\text{A}_{\text{t}}}{ \text{A}_{0}}\right)\times 100 \tag{3}\]  
A\({}_{\text{t}}\) = absorbance after 4, 5, and 24 h at 600 nm; A\({}_{0}\) = absorbance of 0 h at 600 nm.  
The coaggregation analysis was performed according to [PERSON] et al. (2010) [26] and [PERSON] et al. (2017) [27]. In the coaggregation analysis, the method for preparing the bacterial suspension was the same as that for the autoaggregation assay. Then, 2 mL bacterial suspension of probiotic strain (selected strain) and 2 mL pathogenic strains were mixed. The mixtures were incubated at room temperature without agitation. The absorbance at 600 nm of the mixtures described above was monitored during incubation for 4, 5, and 24 h. The coaggregation was calculated according to the formula:  
\[\text{Coaggregation}\left(\%\right)=\left(\frac{\left(\text{A}_{\text{pat}} +\text{A}_{\text{probio}}\right)}{2}-\text{A}_{\text{mix}}\right)\div\left( \frac{\text{A}_{\text{pat}}+\text{A}_{\text{probio}}}{2}\right)\times 100 \tag{4}\]  
A\({}_{\text{pat}}\), A\({}_{\text{probio}}\) = the absorbance of the pathogen and the probiotic strain at 0 h, respectively; A\({}_{\text{mix}}\) = the absorbance of the mixed culture at different times.
# Development of a Submillimetric GNSS-Based Distance Meter for Length Metrology +
## 2 GNSS Data Processing Approach  
### Basic Carrier-Phase Equation  
Unlike the traditional EDM distance determination, where electromagnetic signal path travels several hundred metres between both ends of the measured baseline, the GNSS-based distance has to be indirectly obtained from electronic signals that travel from satellites to the antennas which involves distances around 22,000 km. The general equation for a carrier phase observable can be written as follows  
\[\begin{split}\[PERSON]_{i}^{k}(t)&=\rho_{i}^{k}(t_{i})+ \Big{[}c-\rho_{i}^{k}(t_{i})\Big{]}dt_{i}-cdt^{k}-l_{i,\Phi}^{k}(t)+T_{i}^{k}(t )+\lambda N_{i}^{k}\\ &\quad+\Big{[}\[PERSON]_{i}(t_{0})-\[PERSON]^{k}(t_{0})\Big{]}+\delta_{i}+ \delta^{k}+\delta m_{i,\[PERSON]}^{k}(t)+\lambda\epsilon_{i}^{k}(t)\end{split} \tag{1}\]  
where:  
\[\begin{split}\[PERSON]_{i}^{k}(t)&\quad\text{carrier phase measurement collected at receiver time $t_{i}$, in m}\\ \rho_{i}^{k}(t)&\quad\text{geometric distance between the receiver $i$ and the satellite $k$, in m}\\ c&\quad\text{speed of light in vacuum, in m/s}\\ \dot{\rho}_{i}^{k}(t)&\quad\text{topocentric range rate of the satellite $k$ with regard to the receiver $i$, in m/s}\\ dt_{i}&\quad\text{receiver clock offset, in s}\\ dt^{k}&\quad\text{satellite clock offset, in s}\\ l_{i,\Phi}^{k}(t)&\quad\text{ionospheric delay evaluated at epoch $t_{i}$, in m}\\ T_{i}^{k}(t)&\quad\text{tropospheric delay evaluated at epoch $t_{i}$, in m}\\ \lambda&\quad\text{carrier phase wavelength, in m}\\ N_{i}^{k}&\quad\text{integer ambiguity for the carrier phase observable, in cycles}\\ \Phi_{i}(t_{0})&\quad\text{initial carrier phase offset for the receiver $i$, in m}\end{split}\]\(\Phi^{k}(t_{0})\) initial carrier phase offset for the satellite \(k\), in m  
\(\delta_{i}\) receiver hardware delay, in m  
\(\delta^{k}\) satellite hardware delay, in m  
\(\delta m^{k}_{i}(t)\) carrier phase multipath error, in m  
\(\epsilon^{k}_{i}(t)\) random noise, in cycles  
For the sake of simplicity, the notation assumes the use of only one constellation (e.g., GPS).
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 2 Methodology
### EDC Deployment Strategies  
This section presents the proposed decentralized strategy based on clustering for solving the EDC placement problem. Clustering offers an unsupervised alternative that has been widely used in various fields. A commonly used group of clustering algorithms is the centroid-based clustering methods, such as K-Means [17], which operate on the assumption that similar points naturally form clusters in feature space. While these methods can yield satisfactory results, their high computational complexity often limits them. This paper presents a distributed framework for large-scale base station deployment using cloud computing. Such extensive network deployments demand significant computing power due to the vast numbers of data involved. As a case study, we focus on unsupervised clustering, specifically the K-Means algorithm, to illustrate how cloud computing technologies can be effectively leveraged for distributed parallel processing in large-scale deployments.  
K-Means  
K-Means [17] effectively identifies well-separated clusters by minimizing the inertia criterion. Specifically, K-Means requires prior knowledge of the number of clusters to detect, and it then groups points to minimize the within-cluster sum of squares, as defined by:  
\[\sum_{i=0}^{n}\left(\|x_{i}-\mu_{j}\|^{2}\right) \tag{2}\]  
where \(\mu_{j}\) is the mean of the samples in the cluster and \(x_{i}\) is the \(i\)-th sample.  
This method is often used to partition a dataset into \(K\) groups automatically. It begins with the selection of \(K\) initial cluster centers (centroids), which are then iteratively refined by minimizing the distance between each point of the cluster and its centroid. In the EDC placement problem, K-Means clustering is used to identify \(K\) clusters of base stations and place \(K\) edge servers at their centers to minimize the distance within each cluster sum of squares [18].  
The selection of the initial centers highly influences the effectiveness of the K-Means algorithm, as it may converge to a local minimum. Therefore, proper initialization is crucial for achieving the best final solution. To obtain a set of high-quality initial cluster centers, several methods have been proposed, including the K-Means++ method [19].
# PDASTSGAT: An STSGAT-Based Multipath Data Scheduling Algorithm
## 4 Evaluation  
### Simulation Environment  
The performance evaluation was carried out using NS3.29 and PyCharm 2020.3.5 x64 as the development environment. The NS3 implementation encompasses the fundamental MPTCP module along with the round-robin algorithm, Average-RTT, Fastest-RTT, and PDAA3C scheduling algorithms. The PyCharm implementation involves a deep learning packet scheduler based on a spatiotemporal graph convolutional neural network algorithm. The simulated topology consists of a client and a server. The client and server are connected through four asymmetric and independent paths. Path 1, Path 2, and Path 4 are configured with a bandwidth of 2 Mbps and a delay of 10 ms, while Path 3 is configured with a bandwidth of 4 Mbps and a delay of 10 ms. The remaining configurations of the links are provided in Table 2.  
To reflect the reliability of addressing the low link resource utilization issue in an asymmetric path environment in this experiment, a pointer was set in the MPTCP module of NS3 to capture the link status and ensure data integrity. Additionally, the congestion control algorithm used in the experiment was the MPTCP-BBR algorithm, which prevents data unreliability caused by severe congestion on the paths.
# Airborne Radar Staggered PRF Coherent Processing Method for Down-Looking Target Detection
## 2 Airborne Radar Clutter Model and Signal Model
### Airborne Radar Clutter Model
Target detection is performed by obtaining range-Doppler results for each PRF set. However, when the clutter power exceeds the target power, the target may be obscured by the clutter. In such cases, the target's presence and the number of target detections can be determined by establishing a set of rules. Figure 3 illustrates this scenario. Assuming three PRF sets are employed for target detection, the target's existence can be confirmed if it is detected twice. If the target lies within the clutter overlap region and remains undetected in two PRF sets, a target miss occurs. Utilizing the RD map of different PRF echoes and applying the Chinese remainder theorem for ambiguity resolution enables accurate determination of the target's distance and velocity. However, the conventional Multi-PRF approach independently detects targets in each PRF signal group, leading to degraded target detection performance in low SNR conditions due to inefficient use of energy within the main beam time. Modern non-coherent detection methods using multiple sets of RD results suffer from inconsistent range cells across PRF signals, resulting from large time gaps between PRF groups, which hinders effective non-coherent accumulation for high-speed targets that exhibit significant start position variations in different PRF echoes. Additionally, the presence of matching errors poses challenges in resolving distance and Doppler ambiguity.
# Topology Optimization of Functionally Graded Structure for Thermal Management of Cooling Plate
## 4 Conclusions and Future Works  
This paper proposed a topology optimization of the cooling plate with functionally graded structure for thermal load management to reduce the average temperature and temperature difference of the overall structure. We first utilized the Brinkman penalty force to distinguish the solid and fluid phases, and then the local volume constraint with a variable filtering radius was introduced to obtain functionally graded structure for thermal load management. The measurement indicators, including the maximum temperature and the standard error of the temperature distribution, were introduced to evaluate the radiation performance of the cooling plate. Numerical experiments, including uniform and nonuniform thermal loads, were carried out to demonstrate the effectiveness of the proposed method for thermal management, which show that the designed cooling plate with a functionally graded lattice structure significantly improved the measurement indicators when compared with a conventional straight mini-channel cooling plate.  
The proposed method is not limited to cooling plate design at a small scale for battery thermal management, but it can be extended to structural design in a large scale such as thermal management of electronic vehicles. Further works will consider manufacturing-constrained topology optimization for cooling plate design, especially the length scale and manufacturing error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average Temperature** & **Maximum Temperature** & **Standard Error** \\ \hline SIMP & 305.9 & 333.85 & 8.82 \\ Lattice structure & 304.49 & 327.04 & 8.45 \\ Proposed method & 303.18 & 324.07 & 7.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of temperature response of various cooling plate designs (unit: K).  
Figure 7: Comparison of different local filter ranges.  
**Author Contributions:** L.T.: writing--original draft preparation, funding acquisition; J.L.: software; B.Y.: writing--review and editing; L.L.: methodology. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Key Research Platform and Project of General Universities in Guangdong Province: 2024 ZDZX3116, Ningbo Key research and development Program: 2023Z134.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Acknowledgments:** The authors are grateful to the editor and the anonymous reviewers for their insightful comments on improving this paper. The authors thank the help given by [PERSON] in the visualization and investigation of this paper.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 4 Datasets for Dense Video Captioning
### Characteristics and Limitations of Each Dataset
Moreover, while comprehensive, its annotations might not be finely tuned to the specific terminology or nuances of highly specialized fields. 14. TVC TVC stands out as a large-scale dataset equipped with multimodal descriptions and embraces diverse context types such as video, subtitle, or both, along with rich human interactions. However, the annotations within this dataset may vary in their level of detail, and some descriptions could be more centered around one modality. 15. ViTT ViTT centers on text-to-video retrieval tasks, making it immensely valuable for video-text understanding and alignment. It provides detailed annotations that link textual descriptions to corresponding video segments. Still, when compared to general-purpose video datasets, its focus is narrower, concentrating on specific types of videospertinent to the text-to-video retrieval task. Additionally, the annotations might lean towards the terminology and context commonly seen in text-video pairing scenarios. 16 VC_NBA_2022 The VC_NBA_2022 dataset presents a specialized collection of basketball game videos, covering a multitude of in-game events. It delivers detailed and specific annotations for shooting incidents along with player-centric details, offering an exhaustive comprehension of basketball game situations. Nonetheless, the dataset's annotations could occasionally be skewed towards certain aspects of the game, perhaps overlooking other vital elements. Furthermore, the quality and consistency of these annotations can fluctuate, which might influence the efficacy of model training. 17 WTS WTS has a distinct focus on traffic safety scenarios, making it highly pertinent to studies involving pedestrian and vehicle interactions. It includes thorough annotations comprising textual descriptions and 3D gaze data for in-depth analysis. Although, in contrast to general-purpose video datasets, it has a more confined scope concerning video content as it concentrates solely on traffic safety circumstances. Furthermore, its annotations may exhibit bias towards traffic-related terminology and specific facets of pedestrian and vehicle behavior.