esa-sceva/satcom-chunk-collection huggingface dataset:
# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 5 Conclusions  
For PBR detection, especially for those illuminators with frequency, PRI and PW agile, it brings many challenges in following target tracing due to heavy clutters. Thus, combining with the features of PBR, a preprocessing operation is introduced before target tracing. In this paper, we propose a PBR cluttering suppression algorithm based on dilation morphology of non-uniform grid. According to the acquisition geometry of PBR, the nonuniform grid construction method is proposed at first. Then, iteratively separate false-alarm clutters from the point data based on dilation morphology. We perform experiments utilizing both simulated data and field data. Experiment results show that the proposed algorithm can effectively filter most false alarm clutters. Besides, combining with the tracing algorithm, it can enhance the PBR tracing performance, reduce the occurrence probability of false tracks and meanwhile save time. Furthermore, the theory of the proposed algorithm is also applicable for 3-D passive tracking, if the non-uniform grid for dilation is modified into cube. In current algorithm, the non-uniform grid is calculated through the first order Taylor expansion. Its reminder term is larger compared to the one of the higher order Taylor expansion. To balance the computation cost and the model accuracy, it is meaningful to exploit the maximum acceptable magnitude of measurement error in different positions. Future researches will focus on building up more specific non-uniform grid for clutter suppression combined with the target tracing, especially for the grids close to the baseline direction.  
[PERSON] and [PERSON] implemented the methods and designed the experiment. [PERSON] and [PERSON] performed the experiments and analyzed the data. [PERSON] supervised the research. [PERSON] wrote the paper. All authors of the article provided substantive comments.  
**Funding:** This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Total Number of Traces** & **Mean Trace Length** & **Max Trace Length** & **Time Consuming (s)** \\ \hline SNN-Kalman & 23 & 254.5 & 734 & 0.8536 \\ MCSNG-SNN-K & 13 & 196.6 & 424 & 0.6467 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The performance indexes of SNN-Kalman and MCSNG-SNN-K.  
Figure 15: Tracing results of SNN-Kalman and MCSNG-SNN-K: (**a**) SNN-Kalman tracing result; (**b**) MCSNG-SNN-K tracing result.
## 4 Discussion
### Discussion on the Load Bridging Effects and Trends by Each Significant Factor
#### 4.1.2 Effect of the Payload Height on the Bending Ratio of a Board  
The height of the payload was identified as a significant factor influencing the bending of the pallet deck. When analyzing the height on a range between 500 mm and 2 m, and for a range of friction coefficients and number of columns, it can be observed that the effect of height is highly dependent on the other variables, as shown in Figure 11. [PERSON] [15] conducted preliminary experimentations on the effect of payload height. Using large, corrugated boxes (arranged in two columns), [PERSON] identified the fact that by increasing the height of the unit load, there was a significant decrease in the overall board bending response. A similar trend can be observed in the model's response for unit loads consisting of three columns. However, as the boxes became smaller, the effect of height was no longer relevant. The movement of the boxes, such as in Figure 10, for six columns, was mostly downwards. There was no rotational movement on unit loads with low payload heights having the same total weight per column, so the load perceived by the board was not changed, therefore it generated the same board deflection ratios. For the majority of boxes used in the field, changing the height of the boxes or adding additional layers of the same package will not significantly change the resulting deflection.  
Figure 10: Finite element model simulations for 3, 4, 5, and 6 columns and three combinations of payload height and pallet and box friction coefficients. (**a**) Low pallet friction with short payload height; (**b**) high pallet friction with tall payload height; and (**c**) low pallet friction with tall payload height. Colors shown represent displacement (U). Blue equals no movement, red equals large displacements.  
Figure 9: Effect of number of columns on board bending ratio by height (mm), box friction and pallet friction coefficients.
# KGNER: Improving Chinese Named Entity Recognition by BERT Infused with the Knowledge Graph
## 5 Overall Results
### Performance against Using Different Knowledge Graph  
* **CN-DBpedia** [34] is a large open-field encyclopedic KG developed by the Knowledge Work Lab of Fudan University, which involves a large number of entities and relationships. CN-DBpedia has been refined by eliminating those triples with entity names of less than 2 in length or containing special characters. There are 5.17 million triples in the improved CN-DBpedia.
* **HowNet** [35] is a large-scale language knowledge base for Chinese vocabulary and concepts, in which each Chinese word is annotated with semantic units called sememes. If we take (word, contain, sememes) as a triple, HowNet is a language KG. Similarly, the official HowNet is refined by eliminating those triples with entity names less than 2 in length or contang special characters. There are 52,576 triples in the improved HowNet.  
Figure 5: Ablation study on the four datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Knowledge Type** & **Weibo** & **Ontonotes** & **MSRA** & **Resume** \\ \hline tail entity & 71.9 & 82.1 & 95.7 & 96.4 \\ relation & 66.6 & 82.2 & 95.8 & 96.1 \\ both & 72.0 & 82.1 & 95.8 & 96.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adding different knowledge to the sentence (\(F1\)).  
* **MedicalKG** is a Chinese medical concept KG developed by [12]. There are four types of hypernym(sysptoms, diseases, parts, and treatments) and 13,864 triples in it.
* **Medicine_NER** is the Clinical Named Entity Recognition (CNER) task released in CCKS 2017.  
We use the three different knowledge graphs to perform substantial experiments on five datasets. Table 4 shows the experimental results. Compared with HowNet and MedicalKG, Weibo and Resume achieve the excellent \(F1\) score on CN-DBpedia, but Ontonotes obtains the highest \(F1\) score on HowNet. We speculate the following reasons: the Weibo and Resume dataset are obtained from the Internet and better match the knowledge in CN-DBpedia; Ontonotes is in the news domain and can acquire more knowledge from the HowNet. Finally, Medicine_NER only achieves the best results for MedicalKG. From the above results, we conclude the correct selection of KG is of great benefit to domain-specific tasks.
# CFD Estimation of a Resistance Coefficient for an Egg-Shaped Geometric Dome
## 4 Discussion  
The comparison of CFD results and experimental tests in terms of total resistance value (Figure 8), total resistance coefficient (Figure 9), and shape of water free surface  
Figure 14: CFD results \(Re=7.8\times 10^{5}\): 3D streamlines for water fraction.  
Figure 12: CFD results on the symmetry plane for \(Re=7.8\times 10^{5}\): water vorticity.  
(Figure 10) show good agreement for \(Re>4\times 10^{5}\). It is achieved despite the simulations conducted for steady state conditions and using the Reynolds averaging approach. It should be underlined that a good agreement between simulations and experimental tests is obtained for the water free surface for \(Re=7.8\times 10^{5}\). In the marked regions in Figure 10, nearly identical shapes of induced front wave can be observed (region marked white). The high wake region can also be observed in the same location in the CFD model and experimental tests (region marked red). More differences appear with smaller \(Re\) numbers, i.e., between \(Re=2.5\times 10^{5}\) and \(Re=3.5\times 10^{5}\), possibly due to the Reynolds averaging approach and steady-state simulation. The values for \(Re<2\times 10^{5}\) were not compared due to the uncertainty level for experimental tests.  
It can be observed that the total resistance coefficient depends on the draught value. It is consistent with other studies; although a majority of them relate to the high ratio T/D (draught to cylinder diameter) of vertical cylinders, similar trends can be observed [15,19]. One of the few studies on similar cases (low aspect ratio) [20] also show that the total resistance coefficient is directly dependent on the draught. Figure 3 in [20] indicates that the total resistance coefficient varies depending on the cylinder diameter (tests were conducted for the constant draught), i.e., depending on the T/D ratio. It is also confirmed for the test at cylinders with a low aspect ratio, as presented in Figure 6 in study [15]. The work [21] presents the results of drag coefficient research for a fully immersed cylinder. However, it can be seen that the drag coefficient is almost constant to some range of \(Re\). This graph cannot be used as a reference to the presented case study, but in principle, a similar trend should be expected for the investigated egg-shaped dome. The total resistance coefficient value of the egg-shaped dome is expected to be smaller than those presented in Figure 15.
## 3 Building Blocks of the Overall DFE Architecture
### Digital Mixer  
The digital mixer removes the IF of each carrier and translates each carrier frequency by using an angle applied externally, as plotted in Figure 11. The local oscillator outputs consecutively accumulated values for a certain angle. The angle information that the local oscillator needs to operate on can be initialized by referencing the look-up table (LUT) in which the I (in-phase) and Q (quadrature) initial values were stored, according to which case of the ten cases was assumed. The exponential value from the local oscillator was obtained by operating on the accumulated angle each clock cycle. This value and the FIR3 (Farrow interpolator) output were the inputs to the complex multiplier from which the final output of the digital mixer was obtained.  
Figure 10: Continuous-time impulse response of FIR3 (Farrow interpolator).  
Figure 12 illustrates how the digital mixer behaves when it receives the output from FIR3, assuming case 10 (+10 MHz, \(-\)10 MHz) is selected for LTE CA. Carrier 1 output is shifted to the minus direction (leftward) relative to the input while carrier 2 output is shifted to the plus direction (rightward) relative to the input. The amounts of frequency translation in the digital mixer are (carrier 1, carrier 2) = (\(-\)10 MHz, +10 MHz), (\(-\)10 MHz, +7.5 MHz), (\(-\)10 MHz, +5 MHz), and (\(-\)5 MHz, +5 MHz) for cases 10, 11, 12, and 13, respectively, while bypassed for cases 1-6.
###### Abstract  
This paper presents a theoretical study on the effects of selected defects (oxygen vacancies and substitutional Fe\({}_{\text{Zn}}\) atoms) on the structural and electronic properties of a 2D ZnO/graphene heterostructure. Spin-polarized Hubbard- and dispersion-corrected density functional theory (DFT) was used to optimize the geometrical configurations of the heterostructure and to analyze the equilibrium distance, interlayer distance, adhesion energy, and bond lengths. Charge density difference (CDD) analysis and band structure calculations were also performed to study the electronic properties of the heterostructure. The results show that the presence of defects affects the interlayer distance and adhesion energy, with structures including oxygen vacancies and Fe\({}_{\text{Zn}}\) substitutional atoms having the strongest interaction with graphene. It is demonstrated that the oxygen vacancies generate localized defect states in the ZnO bandgap and lead to a shift of both valence and conduction band positions, affecting the Schottky barrier. In contrast, Fe dopants induce strong spin polarization and high spin density localized on Fe atoms and their adjacent oxygen neighbors as well as the spin asymmetry of Schottky barriers in 2D ZnO/graphene. This study presents a comprehensive investigation into the effects of graphene on the electronic and adsorption properties of 2D ZnO/graphene heterostructures. The changes in electronic properties induced by oxygen vacancies and Fe dopants can enhance the sensitivity and catalytic activity of the 2D ZnO/graphene system, making it a promising material for sensing and catalytic applications.  
2D ZnO; van der Waals heterostructure; graphene; spin-polarized density functional theory; band structure; defect engineering; hydrogen adsorption +
Footnote †: journal: Applied Physics Letters  
**Citation:** [PERSON] Defect-Induced Modulation of a 2D ZnO/Graphene Heterostructure: Exploring Structural and Electronic Transformations. _Appl. Sci._**2023**, _13_, 7243. [[https://doi.org/10.3390/app13127243](https://doi.org/10.3390/app13127243)]([https://doi.org/10.3390/app13127243](https://doi.org/10.3390/app13127243))
## 4 Discussion
### Low-Temperature Stress Impacts on the Photosynthesis of Sandrice  
The morphological and phenotypic results indicated that the AGB, BBL, Plant Height, SD, and Leaf RWC of sandrice seedlings were barely affected by a four-day exposure to 4 \({}^{\circ}\)C cold stress. A study on cold stress in quinoa seedlings, a food crop native to the alpine regions of South America known for inheriting elite genes for cold-stress adaptation, found that a cold-tolerant quinoa line exhibited limited changes in Plant Height after 11 h of 5 \({}^{\circ}\)C low-temperature stress [44]. Similarly, sandrice also displayed a comparable cold tolerance to _F. tataricum_, a plant known for its robust resistance to cold environments [27]. These results suggested that sandrice exhibits strong tolerance to cold stress.  
Additionally, photosynthesis, a fundamental process in plant physiology, serves as a crucial indicator for assessing the impact of abiotic stresses on plants [45]. This study observed significant changes in several representative photosynthesis parameters (Fv'/Fm', Phi2, PhiNO, and PhiNPQ) in sandrice following low-temperature stress (Figure 1). Specifically,Fv'/Fm' showed a significant reduction under low-temperature stress, indicating impaired maximum photosynthetic efficiency in the low-temperature treatment groups. Phi2 and PhiNO also markedly decreased in the cold-stressed group compared to the control, suggesting a reduced efficiency in light energy captured by PSII under cold stress. In contrast, PhiNPQ significantly increased in the cold-stressed group, indicating enhanced non-photochemical quenching and heightened photoproduction mechanisms in sandrice that mitigate damage. This result was consistent with previous studies that showed a weakened efficiency in converting light energy into chemical energy, and higher PhiNPQ levels as a protective mechanism for the photosynthetic system of leaves under stress [46; 47]. Overall, while cold stress negatively impacts photosynthetic efficiency and light energy  
Figure 8: Q-PCR validation of the transcriptome data. The line graph and the histogram with error bar (\(\pm\)SE, \(n\) = 3) depict the relative expression calculated using the 2\({}^{-\Delta\Delta\text{C}}\) method and the FPKM values generated by the RNA-Seq. The blue, green, and orange histograms represent genes examined in DL, AEX, and DK, respectively. _CYP5B1_, Flavonoid 3′-monooxygenase; _C4H_, Cinamate-4-hydroxylase; _CHI_, Chalcone-flavanone isomerase; _CHS_, Chalcone synthase; _4 CL_, 4-comarate-CoA ligase; _UFGT_, Anthocyanidin 3-O-glucosyltransferase; and _FNS_, flavone synthase.  
utilization in sandrice, the plant activates robust photoprotective mechanisms to manage the low-temperature stress effectively.
## 3 UAV AG Communication Channel Modeling and Characterization
### A UAV AG Multi-Mobility Multi-Trajectories Channel Model
#### 3.1.1 Generation of Tx Trajectories  
To enhance the randomness of the model, Markov chains were introduced, by setting random azimuths as well as elevation angles to simulate UAV arbitrary trajectories. Markov chains are stochastic processes in the state space, which undergo transitions from one state to another [34]. The process requires \"memorylessness\", i.e., the probability distribution of the next state can only be determined by the current state, and the events preceding it in the time series are irrelevant. The process contains three elements: state space; transfer matrix; and initial state. A sequence of random variables \(X=\{X_{0},X_{1},\cdots,X_{i},\cdots\}\), \(X_{n}\) depends only on \(X_{i-1}\), i.e.,  
\[P(X_{i}|X_{0},X_{1},\cdots,X_{i-1})=P(X_{i}|X_{i-1}),i=1,2,\cdots \tag{8}\]  
where \(P(X_{i}|X_{i-1})\) is the transition probability distribution. The flight angle of the UAV was determined by the azimuth angle \(\alpha\) and the elevation angle \(\gamma\), and we used a Markov chain to randomly generate the azimuth and elevation angles. \(\mathbf{A}_{p}^{T}(t)\) was expressed as  
\[\mathbf{A}_{p}^{T}(t)=\mathbf{A}_{ini,p}^{T}+\mathbf{V}_{p}t, \tag{9}\]  
where \(\mathbf{A}_{ini,p}^{T}(t)\) was the initial coordinate of the \(p\)th Tx antenna, \(\mathbf{A}_{p}^{T}(t)\) was the coordinate of the \(p\)th Tx antenna, after introducing a Markov chain, \(\alpha\) was in \([\alpha_{\text{ini}},\alpha_{\text{end}}]\), and \(\gamma\) was in \([\gamma_{\text{ini}},\gamma_{\text{end}}]\). The moving range of the UAV was controlled by the azimuth angles \(\alpha_{\text{ini}}\), \(\alpha_{\text{end}}\) and by the elevation angles \(\gamma_{\text{ini}}\), \(\gamma_{\text{end}}\). By setting the values of \(\alpha_{\text{ini}}\), \(\alpha_{\text{end}}\), \(\gamma_{\text{ini}}\), \(\gamma_{\text{end}}\), the moving range of the UAV could be adjusted. The speed vector of the Tx \(\mathbf{V}_{p}\) was expressed as  
\[\mathbf{V}_{p}=\begin{bmatrix}v_{P,x}\\ v_{P,y}\\ v_{P,z}\end{bmatrix}=\begin{bmatrix}V_{P,x}\text{cos}\text{sin}\gamma\\ V_{P,y}\text{sin}\text{sin}\text{sin}\gamma\\ V_{P,z}\text{cos}\gamma\end{bmatrix}. \tag{10}\]  
The initial state probabilities were all \(1/(\alpha_{end}-\alpha_{ini})\), and the initial transition matrix was subject to Poisson distribution. In the case of equal time, a Markov chain was used to obtain the state chain in turn. After multiple screening of the simulation, the following two random trajectories were obtained, as shown in Figure 3.
# Multi-Directional Displacement Threshold Energy and Crystal Irradiation Damage Model
## 5 Conclusions  
Based on the existing crystal irradiation defect model, a novel defect number calculation model is proposed that incorporates variations in crystal type and deviation threshold energy across different crystal directions. To account for the uncertainty caused by differences in departure threshold energy for different impact directions, an example using GaAs establishes a single-collision atomic displacement probability model for the face-centered cubic type and provides the corresponding calculation formula for displacement probability. Additionally, the Markov process is introduced to describe the state transition process of cohesive energy and threshold displacement energy for each crystal orientation under continuous collision. Three probabilistic transition models are presented that comprehensively consider different crystal types, irradiation intensity, and vacancy recovery after annealing. The results demonstrate that as energy increases, this method yields defect numbers similar to those obtained by using the arc-NRT model but with a higher degree of accuracy than linear models. Moreover, the resulting probability distribution aligns closely with SRIM simulation results. These findings indicate that the proposed new model effectively captures nonlinear growth trends arising from displacement excitation energy loss and phonon excitation.  
Conceptualization, T.Z.; methodology, T.Z. and H.-Z.H.; validation, Y.Z., Y.-F.L. and S.G.N.; investigation, Y.Z.; resources, Y.-F.L.; data curation, Y.Z.; writing--original draft preparation, T.Z.; writing--review and editing, T.Z. and S.G.N.; supervision, H.-Z.H.; project administration, H.-Z.H.; funding acquisition, H.-Z.H. 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 under the contract No. 2020 YFB2008900.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 3 Methods
### Primary Segmentation Branch  
We adopt the network architecture from [19] as the primary segmentation branch. Specifically, this branch consists of three modules: an encoder \(E\), a segmentor \(S\), and an auxiliary generator \(G\). The encoder takes point clouds as input, where each point contains a 3D coordinate and its accompanying attributes provided by specific datasets, and outputs per-point features. The segmentor uses these features as input to predict segmentation \(P_{main}\), i.e., the probability that the points belong to each class.  
The auxiliary generator \(G\) is used only during the training process to assist in the inductive generalization to unseen classes. It takes a class embedding \(t_{u}\) as input, which is the embedding of a given class label encoded by pre-trained Natural Language Processing (NLP) models, i.e., GloVe [37] + Word2 Vec [38]. Note that \(t_{u}\) is disturbed by random noise \(z_{u}\) before being fed into the generator in order to introduce randomness and diversify the generated data. The generator outputs the features \(\tilde{F_{u}}\) of the given classes, which share the same dimensions as those of encoder \(E\). In this way, the primary segmentation branch can produce the visual features \(F_{s+u}\) for both the seen and unseen classes.
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 3 Regulation of RHO Family GTPases
### Guanine Nucleotide Exchange Factors (GEFs)  
GEFs are able to selectively bind to their respective RHO proteins and accelerate the exchange of tightly bound GDP for GTP [8]. Typically, GEFs profoundly reduce the affinity of RHO proteins for GDP, leading to its displacement from GDP and subsequent association with GTP [76; 77]. This reaction involves several stages including an intermediate state in which the GEF is in the complex with the nucleotide-free RHO protein. This intermediate does not accumulate in the cell and rapidly dissociates because of the high intracellular GTP concentration, leading to the formation of the active RHO-GTP complex. The main principle driving this mechanism is based on the binding affinity of nucleotide-free RHO protein being significantly greater for GTP than for GEF proteins [76; 78]. Cellular activation of RHO proteins and their cellular signaling can be selectively uncoupled from GEFs through the overexpression of dominant-negative mutants of RHO proteins (e.g., threonine 19 in RHOA is replaced with asparagine) [79]. Dominant-negative mutants form a tight complex with their cognate GEFs, preventing them from activating endogenous RHO proteins. RHOGEFs are classified into two distinct families: DBL homology (DH) domain-containing proteins, and dedicator of cytokinesis (DOCK) proteins [80; 81].