esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Future Scope and Applicability of the Proposed Model in Seaports
### Augmented Traffic Management Systems  
Our deep learning model can be further extended to enhance overall traffic management systems within seaports. By analyzing the real-time video data from CCTV cameras,the model can provide insights into the traffic patterns, congestion points, and potential bottlenecks. This valuable information can be used to optimize the traffic flow, reroute vehicles efficiently, and implement dynamic traffic management strategies. Consequently, seaport authorities can achieve better resource allocation, minimize delays, and ensure smoother operations.
## 4 Semantic Understanding of Object Layer
### Local Area Semantics
#### 4.2.1 Visual Dictionary  
The visual dictionary, also known as the visual codebook, maps feature data onto individual codewords to generate feature vectors with codebook length [62]. The construction of a visual dictionary is essentially a cluster problem, and the visual codewords correspond to the cluster center. In the task of geographic scene understanding, the visual dictionary connects the image visual features with the scene semantics.  
Whether the design of a visual dictionary is effective mainly includes three aspects: resolution, compactness and universality. (1) The resolution of the visual dictionary is reflected in the similarity between visual words. The lower the similarity, the higher the resolution. (2) The compactness is reflected in the choice of codebook length, which corresponds to different classification accuracies. A high recognition rate can be achieved by selecting a suitable visual dictionary. (3) The universality mainly refers to whether the visual dictionary needs to be relearned if the data of new categories are added. Existing dictionary learning includes generative (unsupervised) and discriminative (supervised) approaches. [PERSON] et al. design a universal visual dictionary and a category visual dictionary to compete for the description of image content. The universal visual dictionary is used to describe all image scene classes, and the category visual dictionary for a certain scene class can be obtained by adaptive learning from the universal visual dictionary [63]. If an image belongs to a given class, a category visual dictionary is more suitable for describing the image than a general visual dictionary. On the contrary, the general visual dictionary is more suitable to describe the image than the category visual dictionary. However, traditional visual dictionaries are prone to a lack of clear meaning or polysemy. To solve the above problems, [PERSON] et al. use semantic attributes to clarify semantic meaning and integrate semantic attributes into the visual dictionary to remove the ambiguity of visual words [64].  
Figure 4: Target detection schematic. (**a**) is the original remote sensing image; (**b**) reflects that the candidate locations of the targets are found in the image; (**c**) reflects the results of target identifications.
## 2 Non-Gaussian Stress  
### Definition of Kurtosis  
For a zero-mean stationary random signal \(z(t)\), the normalized kurtosis is defined as the fourth-order moment of \(z(t)\) divided by the square of the second-order moment. Denoted as  
\[K_{u}=\frac{\mathrm{E}[z^{4}(t)]}{\mathrm{E}^{2}[z^{2}(t)]} \tag{2}\]  
When the kurtosis \(K_{u}\) = 3, the random signal is called Gaussian. When the kurtosis \(0<K_{u}<3\), the signal is called sub-Gaussian. When the kurtosis \(K_{u}>3\), the signal is called super-Gaussian. Sub-Gaussian and super-Gaussian signals are collectively referred to as non-Gaussian signals. Figure 1 shows the probability density distribution of random signals with different kurtosis values. It can be observed that leptokurtic signals have an increased distribution at large amplitudes.  
Generally speaking, if the kurtosis of a non-Gaussian stress process is greater than 3, its fatigue damage will be greater than that of Gaussian stress with the same PSD. This is because the non-Gaussian stress distribution has a longer tail, meaning larger stress amplitudes will lead to a greater accumulation of damage. Since leptokurtic stress can significantly reduce the fatigue life of a structure, this article focuses on discussing structural fatigue under leptokurtic stress conditions.
## 4 Digital Baseband Precoding and Combining
### Complexity Analysis
Hence, \(N_{rel}\times N_{t}\times N_{rel}^{RF}\) operations need to be performed while deriving the relay analog beamformer, and \(N_{r}^{RF}\times N_{r}\times N_{rel}\) operations are required for designing the relay analog combiner. Digital baseband processing components at communicating nodes require complexity in the order of \(O\big{(}N_{s}^{3}\big{)}\). Finally, the overall computational cost of the proposed hybrid beamforming scheme can be approximated by the following expression  
\[C\approx\Big{(}KN_{t}^{2}N_{rel}+N_{r}N_{r}^{RF}+N_{rel}N_{t}N_{rel}^{RF}+N_{r}^ {RF}N_{r}N_{rel}\Big{)}+N_{s}^{3}, \tag{54}\]
# Experimental Study of Material Proportioning for Similar Modeling of Brittle Rocks
## 3 Experimental Results and Analysis
### Sensitivity Analysis of Factors
#### 3.2.3 Sensitivity Analysis of Tensile Strength  
As shown in Figure 7, the tensile strength of the specimens increased with the increasing concentration of the rosin-alcohol solution, reaching its maximum value when the concentration was 25%. The tensile strength of the specimens also increased with the increasing dosage of the rosin-alcohol solution, peaking when the solution content reached 5.5%. However, with the increasing quartz sand content, the tensile strength of the specimens initially decreased, then increased, and then decreased again, reaching a minimum when the quartz sand content was 10%. The gypsum content showed no significant effect on the tensile strength of the specimens.  
The bonding effect of the rosin-alcohol solution enhanced the tensile strength of the specimen. However, a high quartz sand content reduced the cohesion between the materials, leading to opposing trends in their impact on the tensile strength. The range analysis of tensile strength (Table 6) indicated that the rosin-alcohol solution concentration had the most significant impact on the tensile strength of the specimens, with a range of 0.12, while the gypsum content had the least effect, with a range of 0.03. The sensitivity ranking of the factors influencing tensile strength is as follows: D > E > B = A > C.
# Strans-YOLOX: Fusing Swin Transformer and YOLOX for Automatic Payement Crack Detection
## 3 Methodology
### STrans-YOLOX
#### 3.2.3 \(\alpha\)-IoU-NMS  
During the post-processing stage, the NMS algorithm sorts all the detection boxes according to their scores. Then, it selects the box with the maximum score and suppresses the other redundant boxes. However, NMS simply employs the IoU index to suppress the redundant boxes by setting the scores of the boxes with large overlaps to zero. Therefore, when there are occlusion and overlapping objects, NMS is prone to suppress the boxes wrongly, resulting in missing detection. Inspired by [31], we propose the \(\alpha\)-IoU-NMS algorithm in this article, which combines the \(\alpha\)-IoU and NMS algorithms.  
(1) Traditional NMS  
The traditional NMS algorithm sorts the detection boxes of each class according to their scores in descending order. Then, it selects the detection box with the maximum score and suppresses the other detection boxes whose overlaps with the maximum score detection  
Figure 4: The architecture of the GAGM.  
box exceed the setting threshold. Suppose there are two detection boxes, \(A\) and \(B\), and \(iou\) is the ratio of the intersection and union of \(A\) and \(B\), as shown in the following formula:  
\[iou=\frac{in\sec tion}{union}=\frac{A\cap B}{A\cup B} \tag{8}\]  
The traditional NMS method is defined by:  
\[S_{i}=\left\{\begin{array}{l}S_{i},iou(M,bi)<N_{t}\\ 0,iou(M,bi)\geq N_{t}\end{array}\right. \tag{9}\]  
where \(M\) is the box with the maximum current confidence score, \(b_{i}\) is the other detection box, \(N_{t}\) denotes the setting threshold, \(S_{i}=0\) represents the redundant box, and \(S_{i}=S_{i}\) means the different object box.  
(2) \(\alpha\)-IoU-NMS  
In order to obtain more accurate bounding box suppression, we apply the power transformation to the IoU in the NMS to generate the power IoU, which is called \(\alpha\)-IoU. The \(\alpha\)-IoU can be calculated by:  
\[\alpha-iou=iou^{\alpha} \tag{10}\]  
The formula for the \(\alpha\)-IoU-NMS is defined by:  
\[S_{i}=\left\{\begin{array}{l}S_{i},\alpha-iou(M,bi)<N_{t}\\ 0,\alpha-iou(M,bi)\geq N_{t}\end{array}\right. \tag{11}\]  
In the \(\alpha\)-IoU-NMS algorithm, an adjustable power parameter is introduced to meet different levels of box suppression accuracy. Experiments show a positive effect in the case of \(\alpha<1\) for the pavement crack detection task, since \(\alpha\)-IoU is a monotonically increasing function with a limit when \(\alpha<1\). When the IoU of the object is large and the overlap degree is high, the suppression will be strict. When the IoU of the object is small and it is more likely to detect potential objects, the suppression will be not so strict. Thus, it can alleviate the phenomenon of missing detection, especially when there are occlusion and overlapping objects. Therefore, it is shown that the proper selection of \(\alpha\) (\(\alpha<1\)) helps to improve the box suppression accuracy by adaptively increasing the probability of low IoU objects.
## 2 Three-Layer Altitude Spectrum Model of Refractive-Index Fluctuations
Based on the Markov approximation [25], the only larger inhomogeneities than the scale of the beam size can contribute to the beam wander, so the exponential spectrum for the Kolmogorov turbulence in the boundary layer is used,  
\[\Phi_{nB}(\kappa,z)=0.033\ C_{n}^{2}(z)\ \kappa^{-11/3}\Bigg{[}1-\exp\Bigg{(}- \frac{\kappa^{2}}{\kappa_{0}^{2}(h)}\Bigg{)}\Bigg{]}, \tag{4}\]  
where \(\kappa_{0}(z)=C_{0}/L_{0}(h)\), here, the scaling constant \(C_{0}\) is chosen in the range \(1\leq C_{0}\leq 8\pi\), here the value of \(C_{0}\) is \(2\pi\), and \(L_{0}(h)\) is the outer scale varying with the altitude. Here, in the same way, the outer scale factors are introduced in the refractive-index fluctuations power spectra of the free troposphere and the stratosphere,  
\[\Phi_{nF}(\kappa,z)=0.015\widetilde{C}_{nF}^{2}(z)\ \kappa^{-10/3}\Bigg{[}1-\exp \Bigg{(}-\frac{\kappa^{2}}{\kappa_{0}^{2}(h)}\Bigg{)}\Bigg{]}, \tag{5}\]  
\[\Phi_{nS}(\kappa,z)=0.0024\widetilde{C}_{nS}^{2}(z)\ \kappa^{-5}\Bigg{[}1-\exp \Bigg{(}-\frac{\kappa^{2}}{\kappa_{0}^{2}(h)}\Bigg{)}\Bigg{]}. \tag{6}\]  
And the three-layer altitude spectrum model was developed [24]. In order to study the combined influence of the Kolmogorov turbulence and the non-Kolmogorov one on laser propagation for an uplink/downlink communication channel, based on Equations (4)-(6), a modified three-layer altitude spectrum model of refractive-index fluctuations is suggested, which has the following form,  
\[\Phi_{n}(\kappa,z) = \Phi_{nB}(\kappa,z)+\Phi_{nF}(\kappa,z)+\Phi_{nS}(\kappa,z). \tag{7}\]  
Propagation along a vertical or slant path requires a \(C_{n}^{2}(h)\) profile model to describe properly the varying strengths of optical turbulence as functions of altitude \(h\). For the \(C_{n}^{2}(h)\) profile, the most widely used Hufnagel-Valley model is chosen [1]  
\[C_{n}^{2}(h) = 0.00594(v/27)^{2}(10^{-5}h)^{10}\exp(-h/1000) \tag{8}\] \[+ 2.7\times 10^{-16}\exp(-h/1500)+C_{n}^{2}(0)\exp(-h/100),\]with the wind speed \(v=21\) m/s and \(C_{n}^{2}(0)=1.7\times 10^{-14}\) m\({}^{-2/3}\). For the \(\widetilde{C}_{nF}^{2}(h)\) profile in the free troposphere, this form is taken as suggested in [24],  
\[\widetilde{C}_{nF}^{2}(h)=2.2\bigg{(}\frac{k}{L_{1}}\bigg{)}^{-1/6}C_{n}^{2}(h), \tag{9}\]  
where \(L_{1}\) is the propagation distance in the free troposphere, \(k\) denotes the optical wave number, and \(k=2\pi/\lambda\), here \(\lambda\) is the optical wavelength. For the \(\widetilde{C}_{nS}^{2}(h)\) profile in the stratosphere, this form is taken as suggested in [24],  
\[\Phi_{nF}(\kappa,z)=0.015\widetilde{C}_{nF}^{2}(z)\ \kappa^{-10/3}, \tag{2}\]0  
where \(L_{2}\) is the propagation distance in the stratosphere. In addition, the outer scale changes with altitude, according to the empirical formula [26]  
\[\Phi_{nF}(\kappa,z)=0.015\widetilde{C}_{nF}^{2}(z)\ \kappa^{-10/3}, \tag{2}\]1
## 3 Modelling and Parameterisation
### Relative Importance and Partial Dependence of Independent Variables  
The relative importance of the independent variable \(Q^{2}_{xi}\) is obtained by calculating the average of the output results of all decision trees, as shown in Equations (9) and (10) below:  
\[\left\{\begin{array}{c}Q^{2}_{x_{i}}=\frac{1}{M}\sum\limits_{m=1}^{M}Q^{2}_{x_ {i}}(T_{m})\\ Q^{2}_{x_{i}}(T_{m})=\sum\limits_{j=1}^{J-1}d_{j}\end{array}\right. \tag{9}\]  
where \(T\) is the \(m\)-th tree in the model, M is the total number of trees in the model, \(j\) is the branch node, and \(d_{j}\) represents the increase in squared error at the branch node \(j\) of the independent variable during the \(m\)-th iteration.  
The GBDT model can output the nonlinear relationship between rail transit ridership and each built environment independent variable through partial dependence plots (PDP).  
Taking a certain independent variable \(X_{b}\) as an example, by marginalising the output results of the model on the feature distribution of other variables \(X_{i}\), its partial dependence \(\overline{\Gamma_{b}}(X_{b})\) on ridership can be obtained, as shown in Equation (10) below:  
\[\overline{\Gamma_{b}}(X_{b})=\frac{1}{N}\sum\limits_{i=1}^{N}[\Gamma(X_{b},X_ {i})] \tag{10}\]  
where \(\overline{\Gamma_{b}}(X_{b})\) represents the partial dependence value between the target variable \(X_{\text{b}}\) and the dependent variable obtained by marginalising other variables \(X_{\text{i}}\). The Monte Carlo method is generally used to estimate it with the average value of the training data.
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 6 Prospects and Challenges  
It has been proven that LTP can treat a variety of malignant cancers. LTP can kill tumors directly, and can kill cancer cells by affecting the functions of immune cells such as DC, macrophages and T cells. LTP can induce immunogenic cell death of tumor cells, regulate tumor microenvironment, and increase immune response to kill tumors. Therefore, LTP stimulating the immune response is key to improving the cure rate of patients. However, current research on LTP in cancer immunotherapy has the following problems. First, studies of LTP in immunotherapy are still in their infancy, most of which are in vitro tests. Further animal or clinical experiments are needed to explore the anti-tumor effects of LTP by activating immune cells. Second, the underlying mechanism of LTP stimulating the immune response is still unclear. Third, as immune cells are affected by LTP in a time-/dose-dependent manner, it is difficult to determine the optimal LTP dose to activate the body's immune response. Our team [19] treated peripheral blood lymphocyte samples of 20 healthy adult volunteers with LTP, and found that when low doses of LTP were used,  
Figure 3: Immunogenic cell death and the activation of T cell. LTP induced immunogenic death of cells, causing an increase of DAMPs. The DAMPs activate dendritic cells. Then, mature dendritic cells presented tumor antigens to anti-tumor T cells.  
lymphocyte activity was activated. Fourth, immune cells have different sensitivities to LTP. Therefore, when LTP is used to stimulate the immune response, the key is to use the best time and method to treat different immune cells. This is also a huge challenge for LTP in tumor immunotherapy.  
Conceptualization, X.S. and G.Z.; writing--original draft preparation, X.W.; writing--reviewing and editing, X.S. and G.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the General Program of National Natural Science under Grant NO. 51677146 and the Shaanxi Province Key Research and Development Program (Grant NO. 2018 ZDCXL- SF-02-03-01).  
Data Availability Statement: Data sharing not applicable.  
We would like to thank [PERSON] for providing linguistic assistance for this manuscript.  
The authors declare no conflict of interest.
## 4 Conclusions  
The present study demonstrates the GMAW-based WAAM of low alloy steels using metal-cored wire. The Box-Behnken design was employed to perform the experiments with the considerations of process variables of wire feed speed, travel speed, and voltage. BW and BH were selected as the response variables. The TLBO algorithm was used for the optimization of the response variables. The following significant conclusions can be drawn from the present study:  
* ANOVA was employed for statistical analysis. For BW, the regression model term, along with the linear, square, and interaction terms, was found to be significant, while the regression model term and a linear model had a significant impact on deciding the BH response. Multivariable correlations were developed through machining variables for selected responses of BW and BH. A normal probability plot yielded a good statistical analysis for ANOVA and a better future outcome of the proposed model.
* The non-significance of lack-of-fit for both BW and BH indicated that the obtained regression equations are adequate and reliable for the prediction of future values of BW and BH. The negligible deviance between R\({}^{2}\) and Adj. R\({}^{2}\) values for both BH and BW showed the fitness of the model for the present data and the prediction of new observations.
* The single-objective optimization results showed a maximum BH of 7.81 mm, and a minimum BW of 4.73 mm. Pareto fronts provided a trade-off between two competing objectives, and the operator has the option of selecting the appropriate Pareto point, depending on the specified values of BW, and BH.
* The comparison of the predicted and experimental values for the responses showed an acceptable error. This revealed the ability and suitability of the TLBO algorithm for the evaluation of required bead geometries using the GMAW-based WAAM process.
* A multi-layer structure free from any disbonding was successfully manufactured at the optimized variables. Based on the obtained results, the authors suggest that the optimum parametric settings would be beneficial for the deposition of layer-by-layer weld beads for the additive manufacturing of components.  
Conceptualization, J.V., R.C., N.P. and H.P.; methodology, J.V., R.C., N.P. and H.P.; software, V.K.P., D.Y.P. and K.G.; validation, R.C. and J.V.; formal analysis, V.K.P., D.Y.P. and K.G.; investigation, J.V. and R.C.; resources, J.V. and R.C.; data curation, R.C.; writing--original draft preparation, R.C., N.P. and H.P.; writing--review and editing, J.V., R.C., V.K.P., D.Y.P. and K.G.; visualization, R.C.; supervision, R.C. and K.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data presented in this study are available in this article.  
The authors would like to thank ORSP and PDEU for sponsoring the research project in the SRP scheme via project number ORSP/R&D/SRP/2021/010.  
The authors declare no conflict of interest.
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 2 Proposed Methodology  
This section presents the proposed fast reliability scheduling algorithm (FRSA) and its system architecture, including the re-transmission mechanism to recover transient errors, how to implement the static segment of FlexRay, and the communication reliability improvement method.
## 3 Results
### Throwing Accuracy  
In relation to throwing accuracy, the type of ball used was not associated with throwing effectiveness, neither in men from 9 m (\(\chi^{2}\) = 0.52, \(p\) = 0.772) and 7 m (\(\chi^{2}\) = 3.92, \(p\) = 0.141) nor in women from 9 m (\(\chi^{2}\) = 0.13, \(p\) = 0.935) and 7 m (\(\chi^{2}\) = 0.86, \(p\) = 0.650) (Figure 4). Furthermore, the highest effectiveness percentages were reported, in men, for the 9 m throws with the NB (39.50%) and in the 7 m throws with the TBR (40.40%). In women, higher effectiveness was reported for the 9 m throws with the NB and with the TBR (34.80%, both) and for the 7 m throws with the NB (37.90%).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Throws on Goal**} & \multirow{2}{*}{**G**} & \multirow{2}{*}{**Throwing Distance**} & **TBNR** & **TBR** & **NB** & \multirow{2}{*}{**F Int**} & \multirow{2}{*}{\(p\)} & \multirow{2}{*}{\(\eta_{p}{}^{2}\)**} \\ \cline{4-4} \cline{6-9}  & & & \(\chi\pm\) SD & & \(\chi\pm\) SD & & & \\ \hline \multirow{4}{*}{Velocity Target} & \multirow{2}{*}{M} & 9 m & 5.11 \(\pm\) 1.21 & 6.03 \(\pm\) 1.31 & 6.36 \(\pm\) 1.14 & & & \\  & & 7 m & 5.25 \(\pm\) 1.39 & 6.50 \(\pm\) 1.53 & 5.53 \(\pm\) 1.39 & & & \\ \cline{2-9}  & \multirow{2}{*}{F} & 9 m & 6.32 \(\pm\) 1.03 & 6.18 \(\pm\) 1.21 & 6.50 \(\pm\) 1.02 & 1.16 & 0.333 & 0.104 \\  & & 7 m & 6.05 \(\pm\) 0.96 & 6.23 \(\pm\) 1.15 & 5.95 \(\pm\) 1.08 & & & \\ \hline \multirow{4}{*}{Accuracy Target} & \multirow{2}{*}{M} & 9 m & 5.17 \(\pm\) 1.80 & 5.50 \(\pm\) 1.26 & 6.25 \(\pm\) 1.42 & & & \\  & & 7 m & 5.14 \(\pm\) 1.27 & 6.39 \(\pm\) 1.58 & 6.42 \(\pm\) 1.35 & & & \\ \cline{2-9}  & \multirow{2}{*}{F} & 9 m & 6.05 \(\pm\) 1.19 & 6.14 \(\pm\) 0.95 & 6.05 \(\pm\) 1.19 & & & \\ \cline{2-9}  & & 7 m & 5.18 \(\pm\) 1.31 & 5.68 \(\pm\) 0.87 & 5.45 \(\pm\) 0.76 & 0.44 & 0.650 & 0.042 \\ \hline \hline \end{tabular} Notes: G = gender; M = Male; F = Female; F Int = statistical value of the ball–distance interaction; p = significance level; \(\eta_{p}{}^{2}\) = effect size.  
\end{table}
Table 1: Male and female players’ perception level of throwing velocity (Likert scale 1–10) according to throwing distance (7 and 9 m), ball type (traditional ball with no resin—TBNR; traditional ball with resin—TBR; and new ball—NB) and instruction received (velocity and accuracy targets).
## Materials and Methods
### Preparation of LLA66 Construct  
The LLA66-encoding gene was synthesized by Invitrogen with codons optimized for _S. cerevisiae_ expression. The forward primer (5'- GGGAAATATTAAGCTTAACAAAATGATCCCCACGAATTGTC-3') with a HindIII site and the reverse primer (5'-AGATGCATGCATCGAGTTAGAATTGAAATTGAG-3') containing an Xhol site, were used to amplify the gene. PCR was performed in a final volume of 50 \(\mu\)L containing 20 pmol of an amplification primer pair for 30 cycles at 30 s at 98 \({}^{\circ}\)C, 60 s at 55 \({}^{\circ}\)C, and 1.5 min at 68 \({}^{\circ}\)C, with a 3 min preheat at 98 \({}^{\circ}\)C and a 10 min final extension at 68 \({}^{\circ}\)C. The PCR product was subjected to agarose gel electrophoresis in TAE buffer and stained with ethidium bromide. The part of the gel containing the band of the correct size--about 1 k-bp--was excised, and the DNA was recovered using a DNA extraction kit. The purified DNA was ligated into the HindIII/Xhol-digested pYES2/CT expression vector (Invitrogen) by incubation for 4 h at 22 \({}^{\circ}\)C to generate the expression plasmid pYES2/CT-LLA66.