esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Computational Overhead Analysis  
In task offloading and resource allocation problems, computational overhead is an important indicator for evaluating the practical feasibility of an algorithm. The dynamic gradient descent-based task offloading method (D-GTOM) proposed in this paper has a lower computational complexity compared to traditional algorithms. The algorithm mainly updates the offloading decisions in real time based on network states and resource variations, thus avoiding the high computational overhead required by large-scale parallel computation. Specifically, in each iteration, D-GTOM only involves updating local network information and computing gradients, making its computational burden relatively light.  
To quantify the computational overhead of D-GTOM, we have conducted a theoretical analysis. Let the complexity of each gradient computation in one iteration be \(O(n)\), where \(n\) is the number of variables in the task offloading decision, which is typically related to the number of satellites and the task scale. Let the total number of iterations be \(T\), then the total computational overhead can be expressed as \(O(T\cdot n)\). Furthermore, since communication between satellites is required, the communication cost cannot be ignored. Assuming the communication complexity is \(O(N\cdot K)\), where \(N\) is the number of satellites and \(K\) is the complexity of the network topology, the overall computational overhead becomes  
\[O(T\cdot n)+O(N\cdot K)\]  
Although computational overhead increases with the network scale and task complexity, compared to traditional global optimization methods, D-GTOM achieves optimal offloading decisions with relatively low computational overhead by utilizing local information updates and gradient descent optimization strategies. To further reduce computational overhead, future research may explore adaptive computation strategies and hierarchical optimization methods to improve the algorithm's efficiency in practical applications.
## 4 Conclusions  
This paper presents a novel body-nonlinear time-domain Green's function method (TGF_N_V) for evaluating the equivalent roll damping of floating bodies on the free surface with viscous dissipation effects. The TGF_N_V is derived through the definite problems based on \"fairly perfect fluid.\" The TGF_N_V is different from the existing body-nonlinear time-domain Green's function (TGF_N) [25] or linear time-domain Green's function method with viscous dissipation effects (TGF_V) [21] as follows. As compared to TGF_N, the viscous effects of the fluid can be approximately taken into account. As compared to TGF_V, the nonlinear effects of the large amplitude roll can be precisely considered.  
The TGF_N_V is employed to evaluate the equivalent roll damping of hull sections with bilge keels, in which there exist both of viscous and nonlinear effects. The numerical results suggest that the viscous effects are more important than the nonlinear effects in the roll motion, and TGF_N_V can give desirable numerical results as compared with CFD or EFD results. In contrast, those inviscid Green's function methods (TGF or TGF_N) are not appropriate for estimating the equivalent roll damping of hull sections with bilge keels.  
Figure 6: Equivalent roll damping using different methods (TGF [25], TGF_N [25], TGF_V_N) as compared with results from CFD [15] and EFD [15]. (**a**) Equivalent roll damping under roll amplitude \(\theta=0.15\) rad; (**b**) Equivalent roll damping under roll amplitude \(\theta=0.20\) rad; (**c**) Equivalent roll damping under roll amplitude \(\theta=0.25\) rad.  
**Acknowledgments:** This project is supported by the National Natural Science Foundation of China (Grant No. 51509053, No. 51579056 and No. 51579051). The second author wishes to thank the [PERSON] Visiting Chair professorship of Chinese Ministry of Education, supported and hosted by Harbin Engineering University.  
**Author Contributions:** [PERSON] and [PERSON] developed the body-nonlinear time-domain Green's function method with viscous dissipation effects; [PERSON] developed the numerical algorithm for calculating the roll damping; [PERSON] performed the numerical calculations; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Progress in THz Rectifier Technology: Research and Perspectives
## 5 Conclusions  
In this review, several aspects of THz diode technology have been discussed. Rectennas are considered a complementary solution to solar cells due to their various discussed advantages. However, although significant improvements in the design and fabrication process have been seen in recent years, the diode rectifier remains the most critical part of the system, with a maximum operating frequency of 343 THz and efficiency lower than 1%. Furthermore, the compatibility with the desired figures of merit still remains an open question. Currently, the use of a rectenna has only been successfully demonstrated for harvesting microwave and radio frequency. In light of the results discussed above, it is not possible to obtain a rectifying diode able to rectify frequencies at hundreds of THz with the current technology. However, we find that the results obtained for the infrared-frequency rectifying diode around 30 THz are more encouraging. Therefore, a new generation of diodes with small resistance values must be developed to transfer more power to the load. This will require significant creativity and efforts in the design, materials, and manufacturing processes.  
Investigation, methodology, data curation, R.C.; conceptualization and writing, R.C. and P.L.; referenced papers selection, organization, and supervision of the manuscript, F.D.P. and P.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in the references section.  
**Acknowledgments:** R.C. and P.L. acknowledge INRS, Montreal, Canada for useful discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Efficient GPU Implementation of the McMurchie-Davidson Method for Shell-Based ERI Computations
###### Abstract  
Quantum chemistry offers the formal machinery to derive molecular and physical properties arising from (sub)atomic interactions. However, as molecules of practical interest are largely polyatomic, contemporary approximation schemes such as the Hartree-Fock scheme are computationally expensive due to the large number of electron repulsion integrals (ERIs). Central to the Hartree-Fock method is the efficient computation of ERIs over Gaussian functions (GTO-ERIs). Here, the well-known McMurchie-Davidson method (MD) offers an elegant formalism by incrementally extending Hermite Gaussian functions and auxiliary tabulated functions. Although the MD method offers a high degree of versatility to acceleration schemes through Graphics Processing Units (GPUs), the current GPU implementations limit the practical use of supported values of the azimuthal quantum number. In this paper, we propose a generalized framework capable of computing GTO-ERIs for arbitrary azimuthal quantum numbers, provided that the intermediate terms of the MD method can be stored. Our approach benefits from extending the MD recurrence relations through shells, batches, and triple-buffering of the shared memory, and ordering similar ERIs, thus enabling the effective parallelization and use of GPU resources. Furthermore, our approach proposes four GPU implementation schemes considering the suitable mappings between Gaussian basis and CUDA blocks and threads. Our computational experiments involving the GTO-ERI computations of molecules of interest on an NVIDIA A100 Tensor Core GPU (NVIDIA, Santa Clara, CA, USA) have revealed the merits of the proposed acceleration schemes in terms of computation time, including up to a 72\(\times\) improvement over our previous GPU implementation and up to a 4500\(\times\) speedup compared to a naive CPU implementation, highlighting the effectiveness of our method in accelerating ERI computations for both monatomic and polyatomic molecules. Our work has the potential to explore new parallelization schemes of distinct and complex computation paths involved in ERI computation.  
two-electron repulsion integrals; McMurchie-Davidson method; quantum chemistry; parallel algorithm; GPU +
Footnote †: journal: Applied sciences
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Service Management Layer  
The uppermost layer in the architecture is the service management layer. This layer manages the end-to-end services provided to end-users. It receives filtered events from the resource management layer. Within this layer lies the architecture repository, the Configuration Management Database (CMDB), that saves all inventory, configurations, and filtered events related to all devices. Service delivery and management represent the organizational strategic objectives that require effective and efficient support. Therefore, the services should be planned in a systematic and standard way to guarantee service continuity and interoperability. In this architecture, we recommend using the ITILv3 framework. ITILv3 best practices define five core processes, namely, service strategy, service design, service transition, service operation, and continual service improvement. Service operation management further defines five service operation processes, namely, incident management, problem management, event management, service request fulfillment, and access management. The ITILv3 framework can be adapted for smart healthcare systems' service management by incorporating smart healthcare's technical and medical requirements. Defining a service management layer requires comprehensive strategy development and implementation. ITILv3 defines a priority framework to prioritize incidents based on impact and urgency. Italso defines the IT support roles and levels that handle different incident complexities and time constraints.
## 4 Dataset Development
### Shape Deformations
#### 4.4.2 Data Capture  
Following the creation of these surfaces, we rendered 2D images and captured corresponding depth maps. This process involved two steps:  
1. **Rendering 2D Images:** Each deformed surface was rendered under controlled lighting conditions to capture the specular reflections characteristic of real-world environments.
2. **Depth Map Acquisition:** Depth maps \(\mathcal{D}\) were generated for each configuration, providing the precise geometric ground-truth information essential for training.  
This methodical approach to generating a diverse and realistic dataset enables our deep learning model to learn robust features applicable across a wide range of real-world surfaces.
# Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20
## 4 Experimental Results  
In this section, the noise characteristics of GPS, the GLONASS code, and carrier phase observations from the two Samsung S20 devices are first investigated. The positioning performance of the SF-PPP model while employing the reliable stochastic model is then evaluated.
## 2 Results  
### Identification and Molecular Cloning of McJAZ Genes from M. canadensis  
To identify the putative _McJAZ_ family genes in _M. canadensis_, in total, twelve well-known JAZ protein sequences from _A. thaliana_[19] and nine reported JAZ protein sequences from _A. annua_[24] were collected and used as queries to perform a BLASTP search based on the previous transcriptome data (SRP132644) at the National Center for Biotechnology Information (NCBI) [67]. Finally, we identified and annotated eight non-redundant full-length _McJAZ_ genes in the _M. canadensis_ transcriptome database. Next, we obtained seven amplicons corresponding to seven distinct full-length _McJAZ_ genes through PCR amplification with gene specific primers. Subsequently, the purified amplicons were ligated with pClone007 Blunt Simple vector, and the resulting products were transformed into _Escherichia coli_ DH5\(\alpha\) and sequenced, respectively. After this, we successfully cloned seven full-length _McJAZ_ genes, and denoted them as _McJAZ1-McJAZ7_ (Figure 1 and Table 1). As expected, all of these McJAZ proteins contained a N-terminus conserved TIFY domain and a C-terminus Jas domain (also named CCT_2 motif). These findings were further corroborated by using the online SMART tools ([[http://smart.embl-heidelberg.de/](http://smart.embl-heidelberg.de/)]([http://smart.embl-heidelberg.de/](http://smart.embl-heidelberg.de/)), accessed on 25 April 2021) with the default parameters (Figure 1 and Table 1).  
Next, sequence analysis showed that the length of the _McJAZ_ genes coding sequences (CDs) ranged from 372 (_McJAZ7_) to 1041 (_McJAZ3_) base pairs (bp), which was similar in length to the known _AtJAZ_ CDs ranging from 396 (_AtJAZ8_) to 1059 (_AtJAZ3_) bp in _A. thaliana_[19]. Furthermore, these candidate _McJAZ_ genes encoded predicted products which varied in length from 123 (McJAZ7) to 346 (McJAZ3) amino acids (aa) residues, with molecular weights (\(M_{\rm W}\)) ranging from 13.87 kilodalton (kDa) (McJAZ7) to 36.89 kDa (McJAZ3), and the isoelectric point (pl) ranged from 6.12 (McJAZ2) to 9.96 (McJAZ6) (Table 1). Notably, the pI features of most of McJAZ proteins except for McJAZ2 were greater than 7, indicating that most of the McJAZ proteins were basic proteins. Taken together, all of these results provide an earlier basis for further characterization of JAZ family proteins or related networks in _M. canadensis_.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Gene Name** &
\begin{tabular}{c} **Genbank** \\ **Acression No.**1 ** & **Length CDs 2 ** & **Amino** \\ **Acids 3 ** & **\(M_{\rm W}\) (kDa) 4 ** & **pl 5 ** & **TIFY Motif 6 ** & **Jas Motif** & **Protein** \\ **(aa) 7 [FOOTNOTE:7]Footnote 7: The ‘G’-’-’-’
###### Abstract  
A wearable antenna functioning in the 2.4 GHz band for health monitoring and sensing is proposed. It is a circularly polarized (CP) patch antenna made from textiles. Despite its low profile (3.34 mm thickness, 0.027 \(\lambda_{0}\)), an enhanced 3-dB axial ratio (AR) bandwidth is achieved by introducing slit-loaded parasitic elements on top of analysis and observations within the framework of Characteristic Mode Analysis (CMA). In detail, the parasitic elements introduce higher-order modes at high frequencies that may contribute to the 3-dB AR bandwidth enhancement. More importantly, additional slit loading is investigated to preserve the higher-order modes while relaxing strong capacitive coupling invoked by the low-profile structure and the parasitic elements. As a result, unlike conventional multilayer designs, a simple single-substrate, low-profile, and low-cost structure is achieved. While compared to traditional low-profile antennas, a significantly widened CP bandwidth is realized. These merits are important for the future massive application. The realized CP bandwidth is 2.2-2.54 GHz (14.3%), which is 3-5 times that of traditional low-profile designs (thickness \(<\) 4 mm, 0.04 \(\lambda_{0}\)). A prototype was fabricated and measured with good results.  
characteristic mode; parasitic loading; slit loading; low-profile; wearable antenna
# Improvement of Electro-Optical Properties of PSLC Devices by Silver Nanowire Doping
## 2 Materials and Methods
### Fabrication Process of PSLC Device  
The PSLC mixture with negative LC, RM82 and IR651 were mixed together according to the ratio in Table 1 with magnetic stirrer for 30 min at 60 \({}^{\circ}\)C. The silver nanowire solution was diluted to a concentration of 0.5 mg/mL using ethanol, and dispersed for 1 h by ultrasonication. Then 20, 40, 100, or 200 mL of silver nanowire solution were added to 0.1 g PSLC mixed solution, respectively, resulting in 0.01, 0.02, 0.05, 0.1 wt% of silver nanowires concentration in PSLC. After another 30 min of ultrasonication, the mixtures were placed at room temperature for 72 h to remove the ethanol.  
The PSLC mixtures with different silver nanowire concentration were filled into a cell with 5 \(\upmu\)m gap and ITO electrode on both internal surface by capillary force at 60 \({}^{\circ}\)C. The samples were cooled on hot stage to room temperature slowly after the cells were fully filled. Then, the samples were photo polymerized by 320-400 nm UV light with light intensity of 500 mW/cm\({}^{2}\) for 300 s at room temperature.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### Algorithm
#### 3.4.1 Node Clustering  
The sensor nodes are organized into clusters to form virtual sensor nodes. Neighboring sensor nodes are grouped to satisfy (5). \(\mathbf{V}\) represents a set of sensor nodes \(\mathbf{I}\) that have been grouped using an appropriate clustering algorithm. It is verified that all sensor nodes in the \(v\)th group, \(\mathbf{V_{v}}\), are situated within the trapezoid \(\mathbf{A}\), thereby satisfying (5). If any sensor node within \(\mathbf{V_{v}}\) does not reside within \(\mathbf{A}\), clustering is repeated. This guarantees that the groups satisfy (5). The clustering algorithm is selected based on the distribution of sensor nodes. The altitude of the receiver drone, \(h\), is set to the highest value that satisfies (7). Minimizing the number of virtual sensor nodes reduces the receiver drone's flight time.  
Figure 5: Concept of trajectory control algorithm.  
```
1:I
2:\(Trajectory\), Times
3:# Node clustering
4:functionIsIntTrapezoid(\(\mathbf{V_{v}}\))
5:for\(i\in\mathbf{V_{v}}\)do
6:if\((x_{i},y_{i},z_{i})\
otin\mathcal{A}\)then
7:return False
8:endif
9:endfor
10:return True
11:endfunction
12:functionClusterNodes(I)
13:\(\mathbf{V}\leftarrow\) AnyClusteringAlgorism(I)
14:\(clusters\leftarrow\emptyset\)
15:for\(v\) in range(\(|\mathbf{V}|\))do
16:if\(IsInTrapezoid(\mathbf{V_{v}})=TRUE\)then
17:\(clusters.append(\mathbf{V_{v}})\)
18:else
19:\(clusters.append(\text{ClusterNodes}(\mathbf{V_{v}}))\)
20:endif
21:endfor
22:return\(clusters\)
23:endfunction
24:functionCalculateFilmingTime(\(clusters\))
25:\(times\leftarrow\emptyset\)
26:for\(c\in clusters\)do
27:\(\tau_{max}\gets 0\)
28:for\(i\in c\)do
29:if\(\tau_{i}>\tau_{max}\)then
30:\(\tau_{max}\leftarrow\tau_{i}\)
31:endif
32:endfor
33:\(times.append(\tau_{max})\)
34:endfor
35:return\(times\)
36:endfunction
37:\(clusters\leftarrow\text{ClusterNodes}(\mathbf{I})\)
38:# Graph generation
39:\(\mathbf{G}\leftarrow\text{GenerateGraph(clusters)}\)
40:# Trajectory determination
41:\(Trajectory\leftarrow\text{TSP(G)}\)
42:\(Times\leftarrow\text{CalculateFilmingTime(clusters)}\)
```  
**Algorithm 1** Trajectory control algorithm.
# Distribution and Prediction of Incremental Cutter Flank Wear in High-Efficiency Milling
## 6 Wear Prediction of Flank Face of Cutter Tooth
### Validation of the Predictive Model for Flank Wear
Through the response analysis of the normalization, the normalized response of the output layer of each subset of the BPNN flank-wear prediction can be obtained, as shown here:  
Among them, \(\text{Tar}_{\text{max}}=1\); \(\text{Tar}_{\text{min}}=-1\). As can be seen from Figure 15, the distribution and similarity of the subsets do not differ significantly when compared to the normalized form. The similarity in Kr between the actual value Tar of the training set and the output value Val is 0.92559; the similarity in Kr between the actual value Tar of the verification set and the predicted value Val is 0.86433; the similarity in Kr between the actual value Tar of the test set and the predicted value Val is 0.8848; all similarities are greater than 0.8, which proves that the normalized response of each subset output layer has a good convergence effect, and the overall similarity in Kr between the actual value Tar and the predicted value Val is 0.9145 \(>\) 0.8. \[\text{Tar}=\frac{(\text{Tar}_{\text{max}}-\text{Tar}_{\text{min}})(\Delta_{i}- \Delta_{\text{min}})}{\Delta_{\text{max}}-\Delta_{\text{min}}}+\text{Tar}_{ \text{min}} \tag{33}\]  
The four statistical indexes, MSE, MAE, RMSE, and R\({}^{2}\), were used as the evaluation criteria for the accuracy of the BPNN to predict the instantaneous wear model of the cutter tooth flank, as shown in Table 4. The results are shown in Table 5.  
Figure 15: BPNN wear prediction normalized response of each subset output layer. (**a**) Training set; (**b**) validation set; (**c**) test set; (**d**) summary set. According to Table 5, the predicted values of the BPNN prediction model can better reflect the measured values, and the predicted results of the model are accurate. The generalization is excellent, and the instantaneous incremental wear of the flank face of the cutter tooth can be predicted. When compared to the existing methods for predicting tool wear based on given cutting conditions over a period of time, the proposed method based on BPNN learning has significant advantages in the efficiency and accuracy of predicting the instantaneous incremental wear of the cutter tooth under different instantaneous contact stiffness conditions. When the BP neural network learning model is successfully trained, the model can easily adapt to new cutting conditions. Under new cutting conditions, the number of samples required is very small, or even only a single sample is required. Extract and select special signal features that are relatively more sensitive to the instantaneous wear increment of the cutting surface after the cutter teeth and less sensitive to cutting conditions as inputs to the
# Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks
## 4 Methodology
### Feature Selection  
To improve the accuracy of the model and reduce computational costs, this study uses the Boruta feature selection method to select the variables to be incorporated into the breakdown prediction model. Boruta is a feature selection algorithm that utilizes the random forest framework to identify significant features in a data set. Compared to traditional random forests, Boruta has the advantage of not requiring manually defining the threshold of feature importance [27]. Instead, it utilizes random forests to obtain an importance hit and selects variables with importance values higher than the hit. This approach is more effective, as it eliminates the subjective nature of manually setting a threshold and allows for a more objective and data-driven selection process. Boruta first creates shadow features that are randomized copies of the dataset and appends them to the original dataset. It then creates a random forest to obtain the importance of all features and compares the importance of the original features to the importance of the shadow ones. Original features of greater importance than the maximum importance of shadow features are considered significant. This approach provides robust feature selection results that can handle noisy and correlated data. In this paper, we employ not only the traffic data directly collected from the sensors, but also the differences between the features of the traffic data from consecutive time intervals (such as \(\mathrm{q}_{\mathrm{t}}^{\mathrm{curr}}-\mathrm{q}_{\mathrm{t-1}}^{\mathrm{ curr}}\)) and adjacent segments (such as \(\mathrm{q}_{\mathrm{t}}^{\mathrm{curr}}-\mathrm{q}_{\mathrm{t}}^{\mathrm{down}}\)) to analyze the impact of the changes in traffic conditions on the occurrence of breakdown events.