esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The escalating heat flux densities in high-performance electronics necessitate superior thermal management. This study enhanced pool-boiling heat transfer, a method offering high heat removal capacity, by leveraging Binder Jetting 3D Printing (BJ3 DP) to create complex porous copper structures without the need for chemical treatments. This approach enables a reliable utilization of phenomena like capillarity for improved performance. Three types of porous copper structures, namely Large Lattice, Small Lattice, and Staggered, were fabricated on pure copper substrates and tested via pool boiling of de-ionized and de-gassed water at atmospheric pressure. Compared to a plain polished copper surface, which exhibited a critical heat flux (CHF) of 782 kW/m\({}^{2}\) at a wall superheat of 18 K, the 3D-printed porous copper surfaces showed significantly improved heat transfer performance. The Staggered surface achieved a conventional CHF of 2342.4 kW/m\({}^{2}\) (a 199.7% enhancement) at a wall superheat of 24.6 K. Notably, the Large Lattice and Small Lattice structures demonstrated exceptionally stable boiling without reaching the typical catastrophic CHF within the experimental parameters. These geometries continued to increase in heat flux, reaching maximums of 2397.7 kW/m\({}^{2}\) (206.8% higher at a wall superheat of 55.6 K) and 2577.2 kW/m\({}^{2}\) (229.7% higher at a wall superheat of 39.5 K), respectively. Subsequently, a gradual decline in heat flux was observed with an increasing wall superheat, demonstrating an outstanding resistance to the boiling crisis. These improvements are attributed to the formation of distinct vapor-liquid pathways within the porous structures, which promotes the efficient rewetting of the heated surface through capillary action. This mechanism supports a highly efficient, self-sustaining boiling configuration, emphasizing the superior rewetting and vapor management capabilities of these 3D-printed porous structures, which extend the boundaries of sustained high heat flux performance. The porous surfaces also demonstrated a higher heat transfer coefficient (HTC), particularly at lower heat fluxes (\(\leq\)750 kW/m\({}^{2}\)). High-speed digital camera visualization provided further insight into the boiling phenomenon. Overall, the findings demonstrate that these BJ3 DP structured surfaces produce optimized vapor-liquid pathways and capillary-enhanced rewetting, offering significantly superior heat transfer performance compared to smooth surfaces and highlighting their potential for advanced thermal management.  
pool boiling; CHF; HTC; porous structure; 3D-printed porous; heat exchanger +
Footnote †: journal: Applied Sciences  
Article Enhanced Pool Boiling via Binder-Jetting 3D-Printed Porous Copper Structures: CHF and HTC Investigation  
[PERSON]  
1 Joint Doctoral Program for Sustainability Research, The University of Electro-Communications, Tokyo 182-8858, Japan; [PERSON]_ADDRESS]  
[PERSON]  
2 Department of Mechanical and Intelligent Systems Engineering, The University of Electro-Communications, Tokyo 182-8858, Japan; [EMAIL_ADDRESS] (T.M.); [EMAIL_ADDRESS] (K.K.); [EMAIL_ADDRESS] (N.F.)
# A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors
## 2 Methodology
### A Filter Bank Model/Interpretation  
Since \(\tilde{X}=\text{diag}\big{(}V^{\text{T}}x\big{)}\), the diagonal entries of \(\tilde{X}\) are formed by the inner product of the DFT eigenvectors and the input \(x\). Let \(V=\left[\begin{array}{cccc}v_{0}&v_{1}&\cdots&v_{N-1}\end{array}\right]\) where the column vector \(v_{k}\) is a DFT eigenvector. Thus  
\[\tilde{X}=\left[\begin{array}{cccc}\langle v_{0},x\rangle&0&\cdots&0\\ 0&\langle v_{1},x\rangle&0&\vdots\\ \vdots&0&\ddots&0\\ 0&\cdots&0&\langle v_{N-1},x\rangle\end{array}\right] \tag{16}\]  
where the notation \(\langle a,b\rangle\) represents the inner (dot) product of two vectors \(a\) and \(b\). We use this to develop a filter bank description for the linear algebra in the above discussion. Specifically, we define the time reversed vector \(v_{0}(-n)\) of \(v_{0}\). Now, filter the sequence \(x\) through the FIR filter \(v_{0}(-n)\) to produce the \(N+N-1=2N-1\) length output sequence \(x_{0}^{filt}\). Down-sample this output \(x_{0}^{filt}\) by \(N\) (i.e., selecting the middle term) yields the dot product \(\langle v_{0},x\rangle\), i.e., \(\langle v_{0},x\rangle=x_{0}^{filt}(N-1)\), where \(x_{0}^{filt}(N-1)\) is the \(N^{\text{th}}\) element of \(x_{0}^{filt}\). A similar procedure is applied to the other eigenvectors.  
Now consider the matrix product \(V\tilde{X}\). Each diagonal entry of \(\tilde{X}\) scales the corresponding column of \(V\), i.e., each eigenvector is scaled by the corresponding filter output. These scaled eigenvectors are then added or subtracted in the specific manner defined by \(u\) to give the real and imaginary components of the DFT result. This is why we call \(u\) the \"combiner\". The combiner adds and subtracts the real parts (from the \(\pm 1\) elements of \(u\)) and the imaginary components (from the \(\pm j\) of \(u\)). The vector \(z\) is only provided for theoretical completeness so that we can combine the real and imaginary components for the DFT \(X\). These steps are summarized in Figure 1.
# Empirical Models for the Viscoelastic Complex Modulus with an Application to Rubber Friction
## 4 Results
### PHR Content  
We used the data published in [20] to study the effect of different concentrations of CB (carbon black) on the DMA data, and by extension, the effects on the identified parameters from both formulations. Five SBR compounds with different parts phr (per hundred rubber) of carbon black, ranging from 0 to 60 phr, are shown in Figure 10. Both models were able to fit the curves with a maximum normalised root mean squared error of 7.75% for the LBEM formulation and 2.12% for the polynomial formulation. From the raw data it can be seen that as the phr of CB increases, so does the storage modulus in the rubbery (low frequency) region. The storage modulus also increases with the phr of CB in the glassy (high frequency) region, albeit at a reduced rate. The peak of the loss tangent is broadly inversely proportional to the concentration of CB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Value** & **Units** \\ \hline \(\zeta_{max}\) & Maximum magnification & 10 & [-] \\ H & Hurst exponent & 0.85 & [-] \\ \(\
u\) & Poisson ratio & 0.5 & [-] \\ \(q_{0}\) & Lower distance cut-off & 2000 & [m\({}^{-1}\)] \\ \(\sigma_{0}\) & Normal pressure & \(0.2\times 10^{6}\) & [Pa] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Commonly used parameters for [PERSON]’s isothermal friction model [11], employed in the present study.  
Figure 9: Estimated friction using 4 rheological models for 3 different DMA data sources.  
From Figure 12 it can be observed that the parameters \(ry_{0}\), \(k\), \(sy_{2}\), \(r_{1}\), \(r_{2}\) and \(p\) of the LBEM model have R-squared values equal to or higher than 90%, indicating a high level of correlation with the carbon black concentration. As expected from the raw data analysis, the parameter \(ry_{0}\) that controls the vertical shift of the storage modulus shows a high correlation. However, the parameter \(k\) that controls the logistic rate of increase or steepness of the curve also shows a high correlation, and this was not easy to spot from a visual analysis of the data. In addition, as indicated from the visual analysis of raw data, the parameter \(p\) that controls the bell function peak value shows a clear correlation with CB concentration. The loss tangent parameter \(sy_{2}\) that controls the bell function's vertical shift at high frequencies after the peak, together with \(r_{1}\) and \(r_{2}\) that determine the bell's rate of increase at low frequencies and high frequencies, respectively, also have R-squared values above 90%.  
On the basis of the observed dependency of critical model parameters on CB content, the LBEM model and to a lesser extent the polynomial model, lend themselves to the creation of virtual hypothetical rubber materials to be simulated within friction models, so that the performance of a new tyre with new materials can be predicted.
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
###### Abstract  
Disaster monitoring is a primary task for wireless sensor networks. Systems for the rapid reporting of earthquake information are a crucial aspect of disaster monitoring. Furthermore, during emergency rescue after a large earthquake, wireless sensor networks can provide pictures and sound information to save lives. Therefore, when accompanied by multimedia data flow, the alert and seismic data sent by the seismic monitoring nodes must be sufficiently fast. We present herein the architecture of a collaborative disaster-monitoring system that can obtain seismic data in a highly energy-efficient manner. In this paper, a hybrid superior node token ring MAC scheme is proposed for disaster monitoring in wireless sensor networks. This scheme consists of set-up and steady-state stages. A clustering approach was proposed for heterogeneous networks during the set-up stage. The proposed MAC operates in the duty cycle mode at the steady-state stage and is based on the virtual token ring of ordinary nodes, the polling all the superior nodes in one period, and alert transmissions with a low-power listening and shortened preamble approach during the sleep state. The proposed scheme can simultaneously satisfy the requirements of three types of data in disaster-monitoring applications. Based on embedded Markov chains, a model of the proposed MAC was developed and the mean queue length, mean cycle time, and mean upper bound of the frame delay were obtained. Using simulations under various conditions, the clustering approach performed better than the pLEACH approach, and the theoretical results of the proposed MAC were verified. We found that alerts and superior data have outstanding delay and throughput performances even under heavy traffic intensity, and the proposed MAC can provide a data rate of several hundred kb/s for superior and ordinary data. Considering all three types of data, the frame delay performances of the proposed MAC are better than those of the WirelessHART and DRX schemes, and the alert data of the proposed MAC have a maximum frame delay of 15 ms. These satisfy the application requirements of disaster monitoring.  
hybrid scheme; frame delay; throughput; lifetime; wireless sensor networks; disaster-monitoring applications 20232320232  
sensing is challenging, considering the small sensor nodes that have limited processing and power supply. Meanwhile, it is difficult for small sensor nodes to perform data-sensing and network transmission functions and satisfy the requirements of the quality of service (QoS) for multimedia communication in WSNs. Owing to technological progress and the diverse sensing requirements of WSN applications, application-specific QoS provisioning techniques have recently been confirmed as crucial research areas for WSNs [4; 5].
## 2 Results and Discussion
### Odor Analysis
#### 2.4.1 Floral and Woody Odor  
From an odor perspective, alcohols showed a higher number of compounds with a descriptor of a floral odor. For example, linalool, which is reported to possess a floral and citrus-like aroma [32], was relatively high in the \(R\). _anne_ species (4.752% \(\pm\) 0.114%). Aside from linalool, benzyl alcohol, phenylethyl alcohol, and citronellol are all described as having floral and rose odors. Among them, phenylethyl alcohol is widely used as ingredient for perfumes and produces a rose smell [33]. Citronellol was previously reported as the floral odor compound in lychee juice [34]. Woody odor attributes in _Rbloodendron_ flowers were mainly associated with alkenes and alcohols. Alkenes showed a higher number of compounds with descriptors of woody and sweet, such as \(\alpha\)-pinene (intense woody), \(\beta\)-pinene (dry woody), and \(\alpha\)-terpinene (woody, piney), which were previously identified in terebinth fruits [17], but accounted for relatively low contents (0.117% \(\pm\) 0.056% to 1.456% \(\pm\) 0.039%) in flowers. Alcohols such as isopulegol and isoborneol also have woody odor characterization and accounted for 1.099% \(\pm\) 0.091% to 3.328% \(\pm\) 0.133% in _Rbloodendron_ flowers, mostly higher than the alkene contents. In addition, \(\beta\)-ionone, well known for its violet odor and described as a complex woody and fruity scent [35], was also found in four flower species.
## 4 Conclusions  
In summary, the detection probability, SNR, and average BER of the signal OAM state carried by non-uniformly correlated high-order Bessel-Gaussian beams in a turbulent atmosphere were derived. The theoretical calculations show that the detection probability of the signal OAM state decreases when the propagation distance, receiver aperture diameter, strength of the turbulent atmosphere, and topological charge increase and the wavelength decreases. The SNR and average BER of the signal OAM state are affected by the beam width. The BER of the signal OAM state decreases with an increase in the SNR when a non-uniformly correlated high-order Bessel-Gaussian beam propagating in a weak turbulent atmosphere has a small beam width. Non-uniformly correlated high-order Bessel-Gaussian beams carrying the OAM state might be very useful for FSO communications.  
Conceptualization, H.Z. and Y.Y.; writing--original draft preparation, Z.C. and Y.G.; writing--review and editing, Z.C. and H.Z.; supervision, Y.C. and Y.Y.; funding acquisition, Y.G., Y.C. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Nos. 2022 YFA1404800 and 2019 YFA0705000) and the National Natural Science Foundation of China (NSFC) (Nos. 12192254, 11974219, 11974218, 12174227, 11904211, and 92250304).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Matrixes for Enzyme Immobilization
### Natural Inorganic Biopolymers  
Inorganic carriers are ideal for enzyme immobilization due to their high thermal, mechanical, and microbial resistance [76]. Suitable materials must be water insoluble, mechanically strong, chemically stable, and have a large a large surface area [77], with examples including silica, silicates, borosilicate, aluminosilicates, alumina, zeolites, ceramics, titania, and other oxides [78].  
Silica stands out due to its mechanical strength, thermal stability, and chemical resistance [79]. Its porous structure and high surface area minimize diffusional limitations, while facilitating enzyme attachment. Surface hydroxyl groups allow for easy functionalization with agents such as glutaraldehyde or 3-aminopropyltriethoxysilane (APTES), making silica suitable for accommodating diverse enzymes [80]. For example, silica-immobilized lipases retain 91-96% of their activity [81].  
Other metal oxides such as titanium, aluminum, and zirconium oxides are also effective carriers for enzymes like lipase, urease, and \(\alpha\)-amylase [29]. Their hydrophilic nature, due to their abundant hydroxyl groups, enhances enzyme attachment and stability [82].  
Ceramics, including metal oxides (SnO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), ZrO\({}_{2}\)) and silico-aluminates (kaolinite and montmorillonite), are particularly valuable due to their high thermal, chemical, and mechanical stability, along with their large surface areas [83]. These supportsenhance enzyme activity and substrate conversion, making them valuable in food processing, biodiesel production, and biosensing [84; 85; 86].  
The combination of inorganic and organic materials enhances their mechanical strength, controlled release, and functionalization, expanding their use in drug delivery and bioengineering [87]. Overall, inorganic carriers, especially silica, ceramics, and oxides, offer versatile and robust support for enzyme immobilization, with potential for further advancement in industrial and biomedical applications.
## 4 Design of the DRFTM
### Trust Update Based on Deep Reinforcement Learning
#### 4.3.2 Deep Reinforcement Learning  
Q-learning is a classical reinforcement learning algorithm employed to acquire the optimal action-value function. However, in the MDP process, When the state and action spaces are extensive, and it demands a substantial number of samples to explore the entire state space, Q-learning-based schemes may encounter difficulties. This limitation is mainly related to Q-table storage and updating. When the state space is large, you need to use a huge Q-table to store the value corresponding to each state-action, which takes up a lot of memory. Also, the speed of updating the Q-table is affected because each iteration step needs to traverse the entire Q-table. In the scenario of underwater acoustic sensor networks, deep reinforcement learning, particularly through the use of Deep Q-Networks (DQNs), offers a strong solution to this problem.
## 4 Real-Time Elbow and T-Junction Detection and Estimation (RT-ETDE) Framework
### T-Junction Estimation
#### 4.4.1 Branch Direction Estimation Based on Band Intensity  
To detect the branch direction, we rely on the fact that an area of points on the branch having high intensities because of small incident angles indicates the branch direction. The highest intensity point indicates the direction of the branch. However, relying solely on the single highest-intensity point for branch direction estimation can be unreliable due to potential intensity errors or the presence of the defect with high intensity. To improve reliability, we propose dividing the outer outlier point cloud into \(C\)-band along the circumferential axis in the \(Y_{S}Z_{S}\)-plane with an angular step \(\delta\alpha_{T}\), as illustrated in Figure 11(b). For each band, we compute the average intensity as a feature to reduce the impact of intensity errors and random defects. We select the \(C\)-band with the highest average intensity, and inside it we select the highest intensity point. The direction of this point is also the branch direction \(\theta_{T}\). Given \(\theta_{T}\), the direction of \(Y_{T}\) and \(Z_{T}\) are obtainable. Therefore, the \(X_{T}Z_{T}\)-plane is well defined even though the origin \(O_{T}\) is unknown. This plane is used to estimate the branch radius in the following section.
# Studies and Rejection of Intercrystal Crosstalk on FPGA in a High-Energy Photon-Counting System
## 4 Intercrystal Scattering Rejection Algorithm
### Realization on FPGA Chip  
To realize the stated function of time and position judgment, the proposed algorithm has been implemented on a cyclone IV FPGA chip with a 200 MHz clock frequency, enabling the identification of a 5 ns time difference. The steps of the algorithm are outlined in Figure 13. Additionally, the algorithm features two distinct criteria with varying detector window widths, which can be interchanged according to the count rate or material of the detected object. This flexibility allows for a more tailored approach that is adaptable to various scenarios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source Intensity**} & \multirow{2}{*}{
\begin{tabular}{c} **No-Load Count** \\ **Rate/Unit** \\ **N\({}_{\text{0}}\)** \\ \end{tabular} } & \multicolumn{4}{c}{**P (\(n\geq 2\))**} \\ \cline{3-6}  & & **t = 2 ns** & **t = 5 ns** & **t = 7 ns** & **t = 10 ns** \\ \hline
30 Ci & 3.44 \(\times\)\(10^{5}\)/s & 0\% & 0\% & 0\% & 0.01\% \\
50 Ci & 5.73 \(\times\)\(10^{5}\)/s & 0\% & 0\% & 0.01\% & 0.01\% \\
100 Ci & 1.15 \(\times\)\(10^{6}\)/s & 0\% & 0.01\% & 0.03\% & 0.06\% \\
300 Ci & 3.44 \(\times\)\(10^{6}\)/s & 0.02\% & 0.13\% & 0.25\% & 0.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The probability of more than two decays occurring within three adjacent detector units for different time gaps.
# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 2 Dynamic Similarity Metrics
### Dynamic Features
#### 2.2.2 Correlation-Based Similarity  
Correlation analysis is a statistical evaluation method that is used to quantify the strength and direction of the linear association between two variables. It is widely used in financial network analysis, asset allocation, portfolio optimisation and risk management [47]. This study applied correlation analysis to measure the affinities or similarities between actor pairs in regard to the temporal sequences of dynamicity values in all SIN. The assumption here is that two actors are similar if they fluctuate in a similar fashion considering any dynamicity measurement (i.e., dynamicity values of one actor increase or decrease with the other at the same time). If \(\delta^{a}(t)\) and \(\delta^{b}(t)\) denote the structural dynamicity, and \(\lambda^{a}(t)\) and \(\lambda^{b}(t)\) denote the temporal neighbourhood dynamics of actor \(a\) and \(b\) at time \(t\), then the evolution similarity between them is computed in regard to the Pearson correlation coefficient. Therefore, the third and fourth dynamic similarity metrics to measure the similarity of proximity between actor pairs \(a\) and \(b\) in this study are constructed as follows:  
\[sim_{3}(a,b)=\frac{\sum_{t}[(\delta^{a}(t)-\overline{\delta^{a}})(\delta^{b}(t )-\overline{\delta^{b}})]}{\sqrt{\sum_{t}(\delta^{a}(t)-\overline{\delta^{a}}) ^{2}}\sqrt{\sum_{t}(\delta^{b}(t)-\overline{\delta^{b}})^{2}}} \tag{7}\]  
\[sim_{4}(a,b)=\frac{\sum_{t}[(\lambda^{a}(t)-\overline{\lambda^{a}})(\delta^{b} (t)-\overline{\lambda^{b}})]}{\sqrt{\sum_{t}(\lambda^{a}(t)-\overline{\lambda^ {a}})^{2}}\sqrt{\sum_{t}(\lambda^{b}(t)-\overline{\lambda^{b}})^{2}}} \tag{8}\]  
Here, \(\delta^{a}(t)\) and \(\lambda^{a}(t)\) represent the structural and neighbourhood dynamicity, respectively, of actor \(a\) at timestamp \(t\). \(\overline{\delta^{a}}\) represents the average structural dynamicity of actor \(a\) over all SINs. Similarly, \(\overline{\lambda^{a}}\) represents the average neighbourhood dynamicity of actor \(a\) over all SINs.
# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 3 Experiment and Comparison  
The experimental environment used Windows 10 operating system, 16G memory, I7-10400F CPU, NVIDIA GTX 3060 TI GPU, Pytorch1.8, and Matlab2018 as the development environment for the test and verification of this method.  
The algorithm was tested using 100 sets of remote sensing images (10 frames per group) with 10 m to 15 m resolution, each with a pixel of 10,240 \(\times\) 10,240, of which 80 sets were used as the training dataset, and 20 sets were used as the test dataset. The targets in the test data were all large vessels ranging from 30 m to 150 m, with individual targets ranging from 3 to 10 pixels in length and 1 to 3 pixels in width.