esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrawideband Low-Profile and Miniaturized Spoof Plasmonic Vivaldi Antenna for Base Station
## 6 Conclusions  
Here, a broadband and miniaturized plasmonic antipodal Vivaldi antenna is designed and measured, where plasmonic metamaterials are loaded on the antipodal Vivaldi antenna to improve the antenna performance. Compared of the typical AVA, the electrical size of the plasmonic AVA is reduced. The operating frequency is from 1.8 GHz to 6 GHz, which covers the 2G/3G/4G/5G communication band. The simulated and measured results of the plasmonic agree well. The measured results show that the radiation pattern of the plasmonic AVA is stable and the average gain of the antenna is 7.24 dBi.  
Conceptualization, Y.J.Z.; data curation, L.H.D.; formal analysis, L.H.D.; methodology, L.H.D.; resources, C.T.; writing--original draft preparation, L.H.D.; writing--review and editing, Y.J.Z.; supervision, Y.J.Z.; project administration, Y.J.Z.; funding acquisition, C.T. and Y.J.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 61971469, and by Science and Technology Commission Shanghai Municipality (STCSM), grant numbers 18 ZR1413500 and SKLSF02017-05. The authors declare no conflict of interest.
## 2 Related Work
### Robustness Evaluation
#### 2.2.2 Image Data  
Vehicle detection algorithms require large amounts of realistic weather image data to be evaluated, but obtaining such images is difficult under adverse weather. Cityscapes [29], BDD100K [30], KITTI [31], Waymo [32], and nuScenes [33] are great image datasets that provide real traffic images in the field of autonomous driving, but they lack images from adverse conditions and do not classify the severity of adverse conditions. DAWN [34], which is a collection of 1000 images from real traffic environments. These images are categorized into fog, snow, rain, and sandstorms, but the image quality is low, and the quantity is also limited.  
In order to evaluate the robustness of models, it is feasible to simulate real-life interferences into clean datasets in a synthetic way. As an example, ImageNet-C [35], introduced for image classification, covers 15 types of interferences, including noise, blur, weather, and digital distortions. It must be noted, however, that many of the interferences analyzed in these research works are hypothetical, introducing bias into practical situations. Rain100H and rain800H are datasets specially synthesized for rainy weather, but the number of images is small, and the fidelity of the images is not high. In summary, datasets for evaluating vehicle detection robustness in various adverse conditions are very scarce.
# Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach
## 3 Bayesian Filtering MBPU Method
### Framework of TSPA-Based Bayesian Filtering MBPU  
In summary, this paper proposes the TSPAEKF, TSPACKF, and TSPAUIF MBPU methods, which are all Bayesian filtering MBPU methods. The MBPU methods with filtering functions proposed in this paper are essentially different from methods proposed in previous research. This paper proposes MBPU methods with good noise robustness, and it is more meaningful that this paper proposes a TSPA-based Bayesian filtering MBPU method framework, whose schematic representation is shown as Figure 2. First, we propose a method based on the first step of TSPA to estimate the distance and azimuth gradients (Equation (9)). The gradients estimated by this method can eliminate the continuity assumption. Then, the equations are obtained that correspond to different Bayesian filtering models. Finally, the unwrapped phase is obtained by unwrapping one pixel at a time according to the heapsort strategy. Through this framework, some other Bayesian filtering methods can also be transplanted to MBPU methods, such as particle filtering, unscented particle filtering, and adaptive filtering. The proposed framework has great significance for broadening the research of MBPU methods.
## 4 Applications of WSN in Urban Areas
### Disaster, Emergency, and Security Applications
Each methodology targets specific urban resilience challenges but faces unique implementation constraints. The  in Table 6 shows that the method increases the performance of the particular metric, whereas the blank space indicates that the method does not consider the performance of the particular metric. The summary conclusions of the disaster, emergency, and security applications are listed below. * The studies emphasize the critical role of WSNs in enhancing urban resilience and managing disasters, addressing challenges like energy consumption, data management, scalability, security, and reliability. * Various network topologies and architectures, including tree, mesh, star, and cluster-based configurations, are implemented to optimize sensor deployment, data collection, and disaster response. * Mesh Network Topology offers reliability and comprehensive coverage for centralized networks. Star Network Topology is effective for addressing overcrowding and safety, while Cluster-Based Topology excels in urban facility administration and emergency response through hierarchical structures. * Centralized Architectures are preferred for structured data aggregation and resource management, while Decentralized Architectures provide flexibility and adaptability in disaster scenarios. * Each topology and architecture has specific benefits, making a combined approach ideal for enhancing urban resilience and safety in disaster management and security applications.
Passive Self-Ligating Bracket Systems: A Scoping Review of Their Claims Regarding Efficiency and Effectiveness in Orthodontic Treatment  
[PERSON]  
[PERSON]  
[PERSON] 1  
[PERSON] 1  
[PERSON] and [PERSON]  
1  
Footnote 1: email: c.c.  
The passive self-ligation technique is claimed to have the least frictional resistance compared to other types of ligation systems [2]. This technique allows for the direct transmission of force from the archives to the teeth and their supporting structures without the dissipation of force by the ligature system, resulting in reduced friction [3]. It has been stated that passive self-ligating brackets, especially the Damon system, offer several advantages over other types of bracket systems, such as faster alignment of teeth [4; 5; 6], greater efficiency in chairside time [6], less need for extraction [7], improved control of tooth position [3], reduced patient discomfort [7; 8], decreased demand for anchorage devices [9], and limited use of intraoral expansion appliances [3; 7; 8]. PSLB systems are also claimed to significantly increase the posterior region of the arches and improve the stability of the transverse dental arch dimension over time [10; 11]. Additionally, some studies conclude that it promotes periodontal health and infection control due to the reduced need for elastic ties, plaque accumulation, and plaque clusters [12; 13].  
However, PSLBs have also their drawbacks. The primary disadvantage is their cost, as they are typically more expensive than conventional metal braces and may not be covered by insurance. Moreover, repair and treatment delays may result from a broken or stuck locking mechanism. The effectiveness of smaller brackets may diminish when rotating larger teeth. Some studies question the effectiveness and efficiency of PSLB systems compared to other types of bracket systems in terms of alignment stage [14; 15; 16], space closure [15; 16; 17; 18], patient comfort [19; 20; 21], arch development [14; 15; 16; 20; 21; 22; 23], stability of the results [11; 24], periodontal health [25; 26; 27], and apical root resorption (APP) [3].  
Because of this debate, this article aims to conduct a scoping review from the past decade's (2013-2023) literature concerning the assertions made about PSLB systems, in comparison with other types of bracket systems (ASLB, CB).
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 2 Credible Kalman Filter Model  
### Filter Model Description  
In this paper, GNSS attacks are divided into two categories depending on their impact on the positioning domain: GNSS jump attacks and GNSS slow-change attacks. GNSS jump attacks happen when the GNSS is jammed or spoofed, causing invalid or large deviation in the positioning result. This type of GNSS attack is relatively easy to recognize, including GNSS jamming attacks, GNSS-generated attacks and GNSS repeater attacks. GNSS slow-change attacks happen when the positioning result drifts off slowly after the GNSS spoofing attack. This type of GNSS attack, including induced spoofing attacks, a not easy to identify. If the mobile terminal has suffered from a GNSS jump attack or a GNSS slow-change attack during movement, the new observation data will be abnormal, as mentioned above. With the feedback correction of the Kalman filter, the error will propagate and make the positioning results not credible [25]. This paper proposes a credible navigation algorithm based on an auxiliary sensor system, and a credible Kalman filtering framework (CKF) which includes an optimized Kalman filter framework with the addition of a credible decision-making module with two state-parameter update models (credible update model and auxiliary update model). Through sliding the credible-verification window, GNSS attacks that may occur in mobile terminals are continuously monitored, and the adaptive selection of the state-parameter update model is performed. If there is no attack alarm, the current GNSS signal is determined to be credible, and the credible update module is selected to perform position prediction, and state parameters are updated and corrected. If a GNSS attack warning does occur, it is determined to be untrusted, and the auxiliary update model is selected for position prediction. In the end, the credible navigation and positioning results are produced (see Section 3 in detail).  
The credible Kalman filter framework is shown in Figure 1. In theory, the credible Kalman filter model can be applied to any kind of auxiliary sensor system that can provide the navigation position information (the auxiliary sensor system here includes IMUs, odometers, visual odometers, cameras, and other sensors that can provide absolute or relative position information. For example, the auxiliary navigation sensor could be an inertial navigation system (INS), a differential drive encoder (DDE) system or a DDE/INS integrated navigation system). In order to better explain the principle based on the self-developed credible navigation test terminal, this paper selects the DDE/INS-integrated navigation system as an auxiliary sensor system to derive the credible Kalman filter model. If other auxiliary navigation sensors are used instead, the credible Kalman filter model needs to be derived based on the corresponding sensor. The following is a theoretical modeling of the GNSS, DDE/INS auxiliary sensor system and credible Kalman filter.
## 5 Conclusions  
Using SEM, XRD, electrochemical impedance spectroscopy and mechanical tests, it was shown that the increase of rGO content up to 1 wt.% in the sintered composite led to the microstructure improvement, density close to the theoretical one, high crystallinity and microhardness up to 13 GPa. Via the impedance spectroscopy, it was shown that Z0 possesses the highest conductivity values at temperatures >450 \({}^{\circ}\)C, while Z1 composite has the lowest conductivity among all the compositions and the highest activation energy of 1.35 eV. Using the impedance spectroscopy and EDX data, it was revealed that carbon phase agglomeration takes place in Z2 composite resulting in the damage of the 3D carbon subphase network. The performed thermodynamic analysis showed that SiC decomposition with the formation of free carbon is favorable and results in the formation of a porous near-edge layer for all investigated composites.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12010190/s1](https://www.mdpi.com/article/10.3390/app12010190/s1)]([https://www.mdpi.com/article/10.3390/app12010190/s1](https://www.mdpi.com/article/10.3390/app12010190/s1)), Figure S1: SEM image of the cross-section of Z0 sample with pointed areas where EDX spectra were collected., Table S1. Distribution of elements in Z0 sample according to taken EDX spectra (in atomic %)., Figure S2. EDX spectrum of cross section of Z0 sample., Figure S3. SEM image of the cross-section of sample Z2 with pointed areas where EDX spectra were collected., Table S2. Distribution of elements in sample Z2 according to taken EDX spectra (in atomic %)., Figure S4. SEM image of Z0 ceramics bulk and carbon, and zirconium elemental distribution maps., Figure S5. Particle size distribution of used in powder bed SiC measured by dynamic light scattering method.  
**Author Contributions**: Conceptualization, A.G. and V.K.; methodology, A.G. and V.K.; validation, O.G. and I.S.; formal anal E.B. and A.G.; investigation, I.S., O.G., E.B. and A.G.; resources, V.K. and O.K.; data curation, A.G. and O.K.; writing--original draft preparation, A.G.; writing--review and editing, A.G. and O.K; visualization, A.G.; supervision, V.K.; project administration, V.K.; funding acquisition, V.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Russian Science Foundation (RSF) grant number 18-19-00255.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
SEM and EDX measurements were performed at the Research Park of St. Petersburg State University Interdisciplinary Resource Centre for Nanotechnology. Raman spectroscopy data of sintered ceramics were obtained at the Centre for Optical and Laser Materials Research of St. Petersburg State University Research Park.  
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.
# Machine Learning-Based Malicious X.509 Certificates' Detection
## 4 Feature Engineering
### Features from Standard Checking  
RFC 5280 documents common agreements of CAs, certificate users, governments, and several organizations to X.509 certificates. Any implementation of RFC 5280 should be conforming to its criterion. [PERSON] et al. (2011) implemented a frame for checking whether certificates issued by CAs complied with standards defined by RFC 5280, CAs, and browser corporations. The results showed the number of unmatched certificates was decreasing and the percentage reduced to 0.02% in 2017. Although the rate of inconsistent certificates is lower, it is necessary to check certificates' match with RFC 5280. Therefore, we extract 10 features to represent checking results. Table 5 illustrates the details of features from standard checking.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Name** & **Feature Description** & **Value Type** \\ \hline issuer\_country & The country of issuer & integer \\ \hline subject\_country & The country of subject & integer \\ \hline digital\_signature & The keyusage information & integer \\ \hline content\_commitment & The keyusage information & integer \\ \hline serial\_number\_length & The length of serial number & integer \\ \hline has\_expire & Whether certificate has expired & integer \\ \hline issuer\_is\_empty & Whether issuer information is empty & integer \\ \hline public\_key\_parse\_error & Whether meet error while parsing public key & integer \\ \hline validation\_parse\_error & Whether meet error while parsing validation information & integer \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sample features from basic analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Name** & **Feature Description** & **Value Type** \\ \hline serial\_number\_not\_conforming & Whether serial number is positive and no longer than 20 octets & integer \\ \hline after\_smaller\_than\_before & Whether after-time is small than before-time in validation & integer \\ \hline extension\_exist\_in\_wrong\_version & Whether extensions are math with certificate version & integer \\ \hline decipher\_and\_encipher\_error & Whether the presence of decipher and encipher is conforming & integer \\ \hline explicit\_text\_exceed & Whether the length of explicit text exceeds 200 & integer \\ \hline only\_consist\_reasons\_error & Whether cRLDistributionPoint only consist reasons & integer \\ \hline keycertsign\_not\_conform\_ca & Whether the set of keycertsign is conforming with ca field & integer \\ \hline cA\_with\_empty\_subject & Whether subject information is empty and the subject is ca & integer \\ \hline CRLissuer\_with\_empty\_subject & Whether subject information is empty and the subject is & integer \\  & CRLissuer & \\ \hline subject\_with\_subaltname\_no\_critical & Whether subject information is empty and subject alternative & integer \\  & name is not critical & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Details of features from standard checking.
(**a**) Cell area: According to the cell spreading, these surfaces were categorized into three groups of \(\zeta\)-potential, and the respective cell area values were averaged: negative (blue), moderately positive (green), and highly positive (orange). Note that cells favor only a certain range of moderately positive surface charges and cannot tolerate high charges of around +50 mV. Osteoblasts on the four moderately positive surfaces RGD, APTES, PPAAm, and PEI (+1 to +10 mV, green box) exhibit the highest cell area after 1 h (1791 \(\pm\) 275 \(\upmu\)m\({}^{2}\)). This cell area changes only slightly within 24 h (1669 \(\pm\) 66 \(\upmu\)m\({}^{2}\)). Cells on the five negatively charged surfaces Au, PSS, Ti, Matrigel, and Col I (\(-\)120 to \(-\)3 mV, blue box) initially have a smaller cell area after 1 h (838 \(\pm\) 114 \(\upmu\)m\({}^{2}\)) but spread within 24 h to reach the same level of cell area as on moderately positive surfaces (1513 \(\pm\) 108 \(\upmu\)m\({}^{2}\) after 24 h, arrow). Cells on the three highly positively charged surfaces PPI-G2, PPI-G4, and PDADMA (+50 to +55 mV, orange box) show initially significantly increased spreading (1321 \(\pm\) 89 \(\upmu\)m\({}^{2}\)), but the cell area decreases dramatically within 24 h (510 \(\pm\) 65 \(\upmu\)m\({}^{2}\) after 24 h, arrow). Plot: Microscopic images of PKH26 stained cells after 1 h on Ti, PPAAm, and PDADMA as representatives for each category. Cell area after 1 and 24 h in mean \(\pm\) sem, \(\zeta\)-potentials in mean \(\pm\) SD, Mann–Whitney-U test, \(p\) < 0.05; significances after 1 h: \(-\) vs. + and \(-\) vs. ++; significances after 24 h: \(-\) vs. ++ and \(+\) vs. ++. (**b**) Circularity: A perfect circle is defined by a value of 1.0, and an increasingly elongated polygon is represented by a value converging toward 0. While the cell shape on negatively and moderately positively charged surfaces changes from circular to a more elongated shape within 24 h, cells on highly positively charged surfaces exhibit an impaired cell spreading and retain their circular morphology. Plot: \(\zeta\)-potentials in mean; circularity after 1 and 24 h in medians (see detailed values in Table S1). Abbreviations: Au (gold), PSS (polystyrene sulfonate), Ti (titanium), Matrigel (basement membrane matrix), Col I (collagen type I), RGD (arginine-glycine-aspartate sequence), APTES (3-aminopropyltriethoxysilane), PPAAm (plasma polymerized allylamine), PEI (polyethyleneimine), PPI-G4 (polpropyleneimine dendrimer generation 4), PDADMA (polydiallyldimethylammonium chloride), and PPI-G2 (polyppropyleneimine dendrimer generation 2).
###### Abstract  
Metastasis in colorectal cancer is responsible for most of the cancer-related deaths. For metastasis to occur, tumor cells must first undergo the epithelial-to-mesenchymal transition (EMT), which is driven by the transcription factors (EMT-TFs) Snail, Slug twistl, or Zeb1, to promote their migration. In the distant organs, tumor cells may become dormant for years, until signals from their microenvironment trigger and promote their outgrowth. Here we asked whether CD147/EMMPRIN controls entry and exit from dormancy in the aggressive and proliferative (i.e., non-dormant) CT26 mouse colon carcinoma cells, in its wild-type form (CT26-WT cells). To this end, we knocked down EMMPRIN expression in CT26 cells (CT26-KD), and compared their EMT and cellular dormancy status (e.g., proliferation, pERK/pP38 ratio, vimentin expression, expression of EMT-TFs and dormancy markers), and angiogenic dormancy (e.g., VEGF and MMP-9 secretion, healing of the wounded bEND3 mouse endothelial cells), to the parental cells (CT26-WT). We show that knocking-down EMMPRIN expression reduced the pERK/pP38 ratio, enhanced the expression of vimentin, the EMT-TFs and the dormancy markers, and reduced the proliferation and angiogenic potential, cumulatively indicating that cells were pushed towards dormancy. When macrophages were co-cultured with both types of CT26 cells, the CT26-WT cells increased their angiogenic potential, but did not change their proliferation, state of EMT, or dormancy, whereas the CT26-KD cells exhibited values mostly similar to those of the co-cultured CT26-WT cells. Addition of recombinant TGF\(\beta\) or EMMPRIN that simulated the presence of macrophages yielded similar results. Combinations of low concentrations of TGF\(\beta\) and EMMPRIN had a minimal additive effect only in the CT26-KD cells, suggesting that they work along the same signaling pathway. We conclude that EMMPRIN is important as a gatekeeper that prevents cells from entering a dormant state, and that macrophages can promote an exit from dormancy.  
CD147/EMMPRIN; CT26 cells; knockdown; dormancy; EMT; angiogenesis; proliferation Article
# Multi-Scale Receptive Fields Convolutional Network for Action Recognition
## 4 Experiments  
### Experimental Settings and Datasets  
#### 4.1.1 Datasets  
We evaluate MSRFNet on three action recognition datasets, HMDB51 [11], UCF101 [12], and Kinetics-400 [13]. HMDB51 was introduced in 2011, and is collected mainly from movies. It consists of 6776 video clips divided into 51 action classes, each containing a minimum of 101 video clips. HMDB51 has three official splits for public use. One year later, UCF101 is introduced, which contains 13,320 video clips from YouTube, with 101 action categories. UCF101 also has three official splits, similar to HMDB51. Most previous papers either report the top-1 recognition accuracy on split 1, or the average accuracy over three splits. Kinetics-400 is now the most widely adopted large-scale dataset. It contains a total of 300,000 video clips of 400 action classes, with at least 400 videos for each class. For this large dataset, we use its training set (240,000 clips) for training, and the validation set (20,000 clips) for testing.