esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Evaluation  
The performances and accuracies of the ANN, ExtraTrees, GBDT, LightGBM, RF, and XGBoost models in E estimation are evaluated using four metrics, which are correlation coefficient (R), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE) and mean absolute error (MAE):  
\[R=\frac{\sum_{i=1}^{n}\big{(}X_{i}-\overline{X}\big{)}\big{(}Y_{i}-\overline{ Y}\big{)}}{\sqrt{\sum_{i=1}^{n}\big{(}X_{i}-\overline{X}\big{)}^{2}}\sqrt{ \sum_{i=1}^{n}\big{(}Y_{i}-\overline{Y}\big{)}^{2}}} \tag{15}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(X_{i}-Y_{i})^{2}} \tag{16}\]  
\[NSE=1-\frac{\sum_{i=1}^{n}(X_{i}-Y_{i})^{2}}{\sum_{i=1}^{n}\big{(}X_{i}- \overline{Y}\big{)}^{2}} \tag{17}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\big{|}X_{i}-Y_{i}\big{|} \tag{18}\]where \(n\) is the number of samples, \(X_{i}\) and \(Y_{i}\) denote the observed and predicted values, respectively; and \(\overline{X}\) and \(\overline{Y}\) are the mean of the observed and predicted values, respectively. Higher values of R and NSE (close to 1) and lower values of RMSE and MAE (close to 0) indicate good prediction accuracy of soil evaporation models.
# A Graph Neural Network Node Classification Application Model with Enhanced Node Association
## 6 Conclusions  
On the basis of graph neural network node classification, this paper proposes a graph convolutional neural network classification model based on the introduction of external nodes, with full consideration of the potential relationships between nodes and the corresponding graph structure information. Combining graph convolutional neural network, graph attention mechanism, early stop algorithm, and Dropout algorithm, the construction of the model is completed. Using the outcomes of classification on both datasets, this study concludes that this model has a significant classification advantage. In the public Cora dataset, the classification accuracy of the model proposed in this paper is 85.1%. In a limited sample of the Stock dataset, the proposed model produced accurate classification results with an accuracy of 85.3%. This result infers the possibility and potential benefits of applying this model to problems of a comparable nature in the future. Due to the two-sided character of the results, however, the application of the model may present certain difficulties and obstacles. The confidentiality of government and corporate information, for instance, can pose significant obstacles to data access. To circumvent these issues, classification must be accomplished in consultation with the relevant authorities and with their sanction. In accordance with legal and ethical restrictions, the model's application area must be expanded to the greatest extent feasible. For me, this study is not an endpoint, but a good starting point. To assess the adaptability and practical value of the model across various disciplines, the Stock dataset will be expanded to include over 4000 stocks from the Chinese stock market, and additional enormous datasets will be selected for experiments. The model will be applied in diverse disciplines to demonstrate its validity and utility, as well as to emphasise its wide range of applications. Through this study, valuable insisgths and directions will be provided to researchers. In future work, this paper aims to pursue innovative approaches in emerging fields and fundamental algorithms related to graph neural network algorithms.  
Conceptualization, Y.Z. ([PERSON]) and Y.X.; methodology, Y.Z. ([PERSON]); software, Y.Z. ([PERSON]); validation, Y.Z. ([PERSON]), Y.X. and Y.Z. ([PERSON]); writing--review and editing, Y.Z. ([PERSON]); supervision, Y.X. and Y.Z. ([PERSON]); project administration, Y.X.; funding acquisition, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Nature Science Foundation of Heilongjiang Province grant number LH2022F036. This research was funded by General Research Project on Higher Education Teaching Reform of Heilongjiang Provincial Education Department grant number SJGY20200347.  
Figure 5: Line graph of the classification results of the model in the Stock dataset.
## 4 Results and Discussion
### Bearing Performance Analysis  
Figure 6 shows the pressure distribution of the designed radial bearing. The shaft speed was 21,000 rpm, and the eccentricity was 0. The bearing supply pressure is 70 bar, and discharge pressure is 60 bar. Under these operating conditions, the recess pressure was predicted to be 65.9 bar, and the pressure distribution in the circumferential direction was periodic around the recess. Furthermore, as shown in the figure, the highest pressure was found at the recess and the pressure decreased toward both ends in the axial direction. This indicates that, owing to the pressure difference, the CO\({}_{2}\) lubricant supplied to the recess flowed out through both ends in the axial direction. Additionally, a high-pressure region was created in the surrounding central area with the recess where the pressure was relatively uniform; this is usually called a virtual recess.  
Figure 7 shows the minimum film thickness according to the applied load to the radial bearing. As shown in the figure, the minimum film thickness decreases as the applied load increases. When a load of 2000 N was applied, the minimum film thickness was predicted to drop to 20 \(\upmu\)m, corresponding to  
Figure 5: Stiffness and flow rate for recess pressure ratio (\(p_{s}\) = 70 bar, \(p_{e}\) = 60 bar, \(\omega\) = 21,000 rpm). (**a**) Stiffness; (**b**) Flow rate.  
Figure 6: Pressure distribution of radial bearing (\(p_{s}\) = 70 bar, \(p_{e}\) = 60 bar, \(\omega\) = 21,000 rpm).  
the eccentricity ratio of 0.5. Accordingly, the designed bearing was predicted to be able to support radial loads up to 2000 N at the rated speed.  
Figure 8 shows the bearing stiffness and damping versus the rotor speed. The predicted results demonstrate that the stiffness and damping in the vertical direction (x direction in Figure 2) were identical to those in the horizontal direction (y direction in Figure 2) at all rotating speeds. Such a symmetric dynamic characteristic occurs because the rotor was operated at a concentric position. Since both the stiffness and damping in the vertical direction are identical to those in the horizontal direction, it is expected that the pump-drive turbine will have the characteristics of an isotropic rotor. In addition, when the rotating speed is low, the hydrostatic effects are dominant compared to the hydrodynamic effects, resulting in small cross-coupled stiffness. However, as the rotor speed increases, cross-coupled stiffness increases almost linearly. The damping coefficients were almost identical for all rotor speeds and the cross-coupled terms of damping were predicted to be trivial compared to the direct terms.
# Two-Machine Job-Shop Scheduling Problem to Minimize the Makespan with Uncertain Job Durations
## Appendix A Proofs of the Statements
### Proof of Theorem 8  
We consider an arbitrary vector \(p\in T\) of the job durations from set \(T\) and relevant vectors \(p_{1,2}\) and \(p_{2,1}\) of the durations of jobs from set \(\mathcal{J}_{1,2}\) and set \(\mathcal{J}_{2,1}\), respectively. Set \(S^{\prime}_{1,2}\) contains an optimal permutation \(\pi^{\prime}_{1,2}\) for the problem \(F2|p_{1,2}|C_{max}\) with job set \(\mathcal{J}_{1,2}\) and with vector \(p_{1,2}\) of the job durations. Set \(S^{\prime}_{2,1}\) contains an optimal permutation \(\pi^{\prime}_{2,1}\) for the problem \(F2|p_{2,1}|C_{max}\) with job set \(\mathcal{J}_{2,1}\) and with vector \(p_{2,1}\) of the job durations. We next show that the pair of job permutations \((\pi^{\prime},\pi^{\prime\prime})=((\pi^{\prime}_{1,2},\pi_{1},\pi^{\prime}_{ 2,1}),(\pi^{\prime}_{2,1},\pi_{2},\pi^{\prime}_{1,2}))\) is an optimal pair of job permutations for the problem \(J2|p,n_{i}\leq 2|C_{max}\) with scenario \(p\in T\) (the jobs in permutations \(\pi_{1}\) and \(\pi_{2}\) are ordered in increasing order of their indexes). From the proofs of Lemmas 2 and 3, we obtain the value of \(C_{max}(\pi^{\prime},\pi^{\prime\prime})=\max\{c_{1}(\pi^{\prime}),c_{2}(\pi^{ \prime\prime})\}\)  
\[=\max\{\max\{\sum_{l_{1}\in\mathcal{J}_{1,2}\cup\mathcal{J}_{2,1}\cup \mathcal{J}_{1}}p_{i1},C_{max}(\pi^{\prime}_{2,1})\},\max\{\sum_{l_{1}\in \mathcal{J}_{1,2}\cup\mathcal{J}_{2,1}\cup\mathcal{J}_{2}}p_{i2},C_{max}(\pi^ {\prime}_{1,2})\}\},\]  
which cannot be reduced. Therefore, \(C_{max}(\pi^{\prime},\pi^{\prime\prime})=C_{max}\). An optimal pair of job permutations for the problem \(J2|p,n_{i}\leq 2|C_{max}\) with vector \(p\in T\) of the job durations belongs to the set \(<S^{\prime}_{1,2^{\prime}},S^{\prime}_{2,1}>\). As vector \(p\) is arbitrary in set \(T\), the set \(<S^{\prime}_{1,2},S^{\prime}_{2,1}>\) contains an optimal pair of job permutations for all vectors from set \(T\). Due to Definition 4, the set \(<S^{\prime}_{1,2},S^{\prime}_{2,1}>\subseteq S\) is a dominant set of schedules for the problem \(J2|l_{ij}\leq p_{ij}\leq u_{ij},n_{i}\leq 2|C_{max}\) with job set \(\mathcal{J}\).
# Research and Application of Slag-Nanosilica Stabilizer for Silt Subgrade
## 4 Results and Discussion
### Laboratory Test Results
#### 4.2.2 Split Test  
It can be seen from Figure 3 that the split strength of stabilized soil increased rapidly in the early period and gently in the later period with the increase in curing age. In the early period of SNS-S, the growth rate of the split strength was greater than that of CS-S, and when the split strength reached a flat level, the split strength of SNS-S was greater than that of CS-S. Among them, the 28 d split strength of 12% SNS-S was the highest at 0.671 MPa, which was 26.1% higher than that of CS-S. This was followed by the 28 d split strength of 10% SNS-S at 0.665 MPa, which was 25% higher than that of CS-S. Therefore, the addition of SNS instead of CS can effectively improve the shortcomings of low split strength of traditional CS-S [32] and significantly improve the shortcomings of poor cohesion of silt itself so that the structure of stabilized soil is more compact, thus improving the tensile property of SNS-S.  
Figure 2: Change of unconfined compressive strength with time.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 2 Important Applications of UAVs in Smart Cities
### Delivery Services  
Companies like Amazon and Google are testing drones for delivering goods to customers. Drones can potentially make deliveries faster and more efficient, especially in congested urban areas [24].
## 3 Results  
To compare DMDCt against csDMD as well as show its effectiveness in dynamic denoising and reconstruction, three tests were performed. For each case, both csDMD and DMDCt were tried with several levels of sparsity and then, the best results were picked for comparison. The root mean square error (RMSE) defined below was used as the comparison metric for all noise-free cases  
\[\text{RMSE}\triangleq\frac{1}{\sqrt{n}}\left\|\mathbf{Z}_{rec}-\mathbf{Z}_{ ref}\right\|_{F} \tag{16}\]  
where \(\mathbf{Z}_{rec}\) is the reconstructed dataset, \(\mathbf{Z}_{ref}\) is the reference dataset, and \(n\) is the total number of elements of the dataset. A lower RMSE value represents a better reconstruction. For the noisy cases,the peak value to noise ratio (PVNR), inspired by PVNR defined in [19] and as defined below was used as the comparison metric  
\[\text{PVNR}\triangleq 20\log_{10}\frac{\max\left|\mathbf{Z}_{ref}\right|}{\text{RMSE }}\left(\text{dB}\right) \tag{17}\]  
A higher PVNR value represents a less-noisy reconstruction.  
The original implementation of csDMD was partly based on the method of compressive sampling matching pursuit (CoSaMP) [20]. We used the Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) algorithm [21] to solve the \(l_{1}\)-regularization problem; thus, to ensure all the differences between the results of the two methods are due to the methods themselves and not the \(l_{1}\)-regularization algorithms, the csDMD was re-implemented by using OWL-QN rather than CoSaMP.
## 3 Results, Analysis, and Simulations
### Simulation Planning Results Based on Link Budget, Coverage, and Capacity Calculations
#### 3.3.2 Scenario 1-2: O2O Downlink--SS-SNR  
In Figure 5, the average SS-SINR obtained from the prediction using X sites of groodeB is 17 dB. In other words, the SINR ratio is allocated into the mean values; meanwhile, this paper revealed an SS-SINR with a minimum value of \(-5\) dB and a maximum value of 30 dB.
## 4 Results and Analysis
### The Effect of Random Frequency  
The random frequency analysis was subject to a triaxle agitation with a power spectrum of 0.04 G\({}^{2}\)/Hz from 20-1000 Hz and is subsequently increased by 6 dB for frequencies of 1000-2000 Hz to give an overall vibration of 7.7 G RMS (Root Mean Square). This was applied for a duration of 1 h while the unit was non-operational. This test is designed to emulate the broadband random vibration that equipment and products may undergo. Random vibrations can occur in operational environments, such as aircraft, space vehicles, and land vehicles, in addition to shipping transportation.  
The results are again expressed as displacement and strain on the components and board due to the effects of the random vibration (see Figure 12a,b). The random vibration affected the board and components in terms of displacement and strain significantly more than harmonic vibration. This may due to the tri-axis nature of the agitation or magnitude of the stresses. Subsequently, this causes the failure of some of the components. Perhaps unsurprisingly, the components that failed were mostly the larger components situated at the top of the board, which, due to their size and mass, were subjected to more energy from the vibration. The PCB vibration fatigue life prediction is shown in Figure 13.  
Figure 11: The lifetime (years) against probability of failure (%).
## 2 Materials and Methods
### Models of the Auditory Nerve Firing Rates
#### 2.1.2 Electrical Stimulation  
A CI generates electrical impulses and injects them via its electrode array into the cochlea in order to trigger action potentials of the AN fibers. We used the continuous interleaved sampling (CIS) strategy to generate electrical impulses [25] and predicted the AN firing rates using the model of [PERSON] [13].  
First, the CIS strategy is explained. The signal is pre-processed with a head-related transfer function for frontal incidence of sound and are high-pass-filtered for pre-emphasis; then, automatic gain control is applied. The signal passes a 22-channel filter bank. The band envelopes are extracted by rectification and low-pass filtering and are input into a multi-band compressor that reduces the dynamic ranges. In each stimulation cycle, the eight channels with the largest activity are selected, which is referred to as 8-of-22 channel stimulation. The band envelopes of the selected channels modulate biphasic pulse trains, the pulses of which are interlaced in time for each channel to avoid simultaneous stimulation of multiple channels. The modulated biphasic pulse trains drive the electrode array.  
Second, the firing rates of the ANs were predicted from the electrical pulse output by the electrode array using the model of [PERSON]. The model is overviewed as follows. A spatial spread function predicts from the electrical pulses the effective input currents of the AN cells. The spatial spread function models the distribution of the electric field across the cochlea as a two-sided symmetrically decreasing exponential. In particular, the strength of the electric field decreases exponentially with distance from an electrode. The electrical fields of the individual electrodes are superimposed for all positions across the cochlea. The model of an AN cell comprises the models of the cell membrane, the membrane noise, the refractoriness of the cell, and latency and jitter of the action potentials. The cell membrane is modelled as an RC low-pass, taking a current as input and outputting the membrane depolarization potential. An action potential is triggered whenever thepotential exceeds a threshold. To model the membrane noise, low-pass-filtered zero-mean Gaussian noise is added to the depolarization potential before thresholding. To model refractoriness after the release of an action potential, a cell has a dead time, after which the threshold potential is restored following an exponential function of time. Finally, the action potential is slightly delayed and jittered.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 6 Conclusions  
With advancements in the field of wireless communication and sensor technology, new techniques and protocols are proposed for UASNs. These kinds of networks have become popular among researchers because of applications such as disaster prevention, ocean exploration and resource discovery. Different centralized and distributed network routing approaches have been proposed by researchers to make the communication efficient in underwater.  
In this paper, using the concept of decentralized network a Self-Organizing and Scalable Routing Protocol (SOSRP) is proposed where each node forms a local connectivity based on the information acquired from the neighboring nodes and performing the data transmission. The protocol utilizes a multi-hop communication technique to transmit the sensed data to the sink node. Each node formulates the routing table using control packets broadcasted in initialization and neighbor discovery phase, and path selection is based on the information of hop count and distance to base station (sink node) from the transmitting node. The Matlab platform was used to simulate the protocol along with both proper energy and propagation models for acoustic communication to contemplate the undersea conditions. The performance of the protocol was measured against end-to-end delay, energy consumption and number of hops in the path by varying network size and transmission range for optimal and pragmatic behavior of SOSRP. Through different simulations, it was found that an optimal transmission range and network size can improve the performance of the protocol by decreasing the number of hops in the generated path. The results show that SOSRP provides stable operation, scalability, fault tolerance, and isolation detection for UASNs.  
Conceptualization, S.K.H., M.-A.L-N.; Methodology, S.K.H., J.P.; Software, S.K.H., W.H.; Validation, S.K.H., M.-A.L-N. and J.P.; Formal Analysis, M.-A.L-N.; Investigation, S.K.H., W.H.; Resources, S.K.H., M.-A.L-N. Data Curation, S.K.H., M.-A.L-N.; Writing-Original Draft Preparation, S.K.H.; Writing-Review & Editing, S.K.H., M.-A.L-N., PO.; Visualization, S.K.H., M.-A.L-N.; Supervision, M.-A.L-N., J.P.; Project Administration, J.P., P.O.; Funding Acquisition, P.O., J.P.  
This research was funded by University of Malaga (Campus de Excelencia Internacional Andalucia Tech).  
**Acknowledgments:** University of Malaga (Campus de Excelencia Internacional Andalucia Tech).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 20: End-to-end delay for longest path fortwo particular simulations: Test 1 (**a**) and Test 2 (**b**).
# Decoding _Scn2a_ Variants: Bridging Genetics and Phenotypes in Autism Spectrum Disorder
## 3 Results
### Genotypic Data
\begin{table}
\begin{tabular}{l l l} \hline
**Reference** & **Variant(s)** & **Notes** \\ \hline [PERSON] et al. [59] & c.2919+4 deIT & NM\_001040142 \\ \hline [PERSON] et al. [63] & c.34G\_A, c.2464G\_G\_A, c.2645G-A, c.2877C-A, c.2932T-C, c.4264A-G, c.4896, 4897 insT, c.4996C-T, c.4256 dA, c.605+IG-T & Multiple variants \\ \hline [PERSON] et al. [58] & p.-Arg102 Gin, splice acceptor variant & Missense and splicing \\ \hline [PERSON] et al. [60] & 1.1 Mb deletion including SCN2A and SCN3A & Large deletion \\ \hline [PERSON] et al. [61] & p.-AP04N & Alanine to lysine \\ \hline  & c.34G\_A, c.1289A-C, c.5339G-T, c.2932T-C, c.5318C-T, c.5272A-C, c.2877C-A, & \\ [PERSON] et al. [57] & c.605+IG-T, c.3849*t27C-C, c.4801G-T, c.2635G-A, c.4938, 4939 nsGAT, c.4591C-T, & Multiple variants \\  & c.823C-T, c.252C-A, c.1712G-A, c.632G-A, c.2464G-A & \\ \hline [PERSON] et al. [64] & c.3370A/c, c.2272A/c, c.1094C-T, c.3922C-T, c.1819C-T, c.5536C-T, c.3435\_346 delCG, & Multiple types \\  & c.469 dEdA, c.1843*t16A-T & & & \\ \hline [PERSON] et al. [65] & c.2932T-C, c.2674G-A, c.5192C-A, c.4644G-C, c.6407G-G, c.2774T-C, c.5318C-T, & Multiple variants \\  & c.1184G-A, c.4543C-T, c.5157G-A, c.47807-A, c.4886G-A, c.5638G-A & \\ \hline [PERSON] et al. [62] & p.Leu78s & & De novo \\ \hline  & Q18118E, M1548V, E.340A, E999K, R1319 QQ (2), R18827, H1652P, H9300, P1622S, & \\ [PERSON] et al. [49] & c.605+IG4T, V1528K-T, C1107 Wt15, R1235V, A1737W, K1933M, N803 Ks19, K1387 Sfs4, & All de novo \\  & R1435T, T1711 Ks*, G1744E, c.386+2T-C & \\ \hline [PERSON] et al. [56] & c.4550, c.451 del, c.605+IG-A, c.1570C-T & Subjects 3F, 3M, 4M \\ \hline \end{tabular}
\end{table} 
Table 4: Specific genetic mutations in SCN2A studies. This table summarizes reported SCN2A variants across multiple studies, including the specific genetic changes, associated protein alterations, and relevant notes such as inheritance patterns or variant types. Variants include missense, nonsense, splice-site, frameshift, and large deletions.
# How Does _Bacillus thuringiensis_ Crystallize Such a Large Diversity of Toxins?
###### Abstract  
_Bacillus thuringiensis_ (\(Bt\)) is a natural crystal-making bacterium. \(Bt\) diversified into many subspecies that have evolved to produce crystals of hundreds of pesticidal proteins with radically different structures. Their crystalline form ensures stability and controlled release of these major virulence factors. They are responsible for the toxicity and host specificity of \(Bt\), explaining its worldwide use as a biological insecticide. Most research has been devoted to understanding the mechanisms of toxicity of these toxins while the features driving their crystallization have long remained elusive, essentially due to technical limitations. The evolution of methods in structural biology, pushing back the limits in size of amenable protein crystals now allows access to be gained to structural information hidden within natural crystals of such toxins. In this review, we present the main parameters that have been identified as key drivers of toxin crystallization in \(Bt\), notably in the light of recent discoveries driven by structural biology studies. Then, we develop how the future evolution of structural biology will hopefully unveil new mechanisms of \(Bt\) toxin crystallization, opening the door to their hijacking with the aim of developing a versatile in vivo crystallization platform of high academic and industrial interest.  
pore-forming toxin (PFT); pesticidal protein; bacteria; crystals; crystalline formulation; bioinsecticide; biotechnology; structural biology  
**Key Contribution:** Using _Bacillus thuringiensis_ to produce microcrystals of exogenous proteins could be envisioned by capitalizing on the knowledge acquired on its crystallization machinery.
## 4 Field Tests and Analyses
### Field Tests  
In order to ascertain the efficacy of the novel method, a combined long and short hole cutting approach was employed to conduct cutting and full-section blasting tests on a Class III surrounding rock section. Furthermore, two types of oblique cutting tests were designed as a control group. The layout of the boreholes for the three tests is illustrated in Figure 7, and the design parameters are illustrated in Tables 5-7, respectively.  
Figure 6: Cloud map of deep hole blasting damage at different times: (**a**) t = 370 ms; (**c**) t = 460 ms; (**d**) t = 530 ms; (**b**) t = 1000 ms.