esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### TEC Responses to the Geomagnetic Storm  
The TEC during the storm revealed noticeable enhancements. The features demonstrated in Figures 4 and 6 contributed to both the latitudinal and longitudinal dependences. EIA was noticed toward the southern hemisphere in the Asian-Australian sector (130\({}^{\circ}\) E) at 07:15 UT on 23 June. This was mainly due to the sudden direction change of the prompt penetration electric fields at the moment and the eastward increasing electric field lifting ionosphere towards higher latitudes, which were quite unique compared to the previous geomagnetic storms [3; 4; 5; 6]. While ~12.5 TECU depletion was discovered in the American sector (70\({}^{\circ}\) W) at 20:00 UT on 23 rd; The TEC depletion in the Europe-Africa sector even reached the equatorial region when the SYM-H dropped to its minimum value of ~201 nT at 04:00 UT, 10 h after the SSC. Both two hemispheres were greatly impacted by the rapid variation of IMF Bz, treated as strong disturbances [26]. The VTEC values from the multi-GNSS observations demonstrated differences of different observations in contributing to the VTEC calibration. Eighteen stations in three longitudinal sectors were analyzed by using MGEX observations; a ~200% increase of TEC was noticed in the southern hemisphere in the Asian and European-African sectors, while clear TEC depletions were noticed in AREG, KOUR, and ABMF in the American sector. Similar TEC variations using BDS observations were described by [PERSON] [4], discovering both positive and negative responses in the St. Patrick's Day Storm. In summary, during the main phase of this storm, the Asian and southern hemisphere of the European sectors demonstrated TEC enhancement and the American sectors represented TEC inhibition.
# Meta-YOLO: Meta-Learning for Few-Shot Traffic Sign Detection via Decoupling Dependencies
## 5 Conclusions  
In this work, we are devoted to addressing the problem of few-shot traffic sign detection. Firstly, we propose a feature decorrelation module that can remove the statistical correlation between relevant and irrelevant features, exploiting the characteristics of Random Fourier Features and sample weighting. Secondly, we design a few-shot traffic sign detection framework based on meta-learning, Meta-YOLO, based on a network feature decorrelation module (FDM), which can prompt the network to take full advantage of features from different scales and is able to learn the general knowledge and proper fast adaptation strategies with the learnable learning rate set for each parameter. Thirdly, meta-YOLO outperforms the three competitive baselines and improves the mAP of few-shot detection by 39.8%. Comparing with state-of-the-art methods, our performance is also better than most other detectors. The results of meta-YOLO's performance variation under different iterations show that two-stage meta-learner model \(\mathcal{F}\) owns the ability to quickly learn parameters. A large number of ablation studies confirm the positive impact of the FDM, meta-learner and three-head mechanism during detection.  
We designed a three-head mechanism to obtain the information regarding different categories and levels, but we did not completely integrate the information obtained by different heads. This is the limitation of our work. Perhaps the idea of residual connection is helpful to alleviate this problem and we will continue to improve this problem in the future.  
Conceptualization, X.R. and W.Z.; methodology, X.R.; software, X.R.; validation, X.R., M.W. and C.L.; formal analysis, X.R.; investigation, X.R.; resources, X.R.; data curation, X.W.; writing--original draft preparation, X.R.; writing--review and editing, X.R.; project administration, W.Z.; funding acquisition, X.R. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by National Natural Science Foundation of China (No. 51805312, 52172388).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors would like to express their appreciation to the developers of Pytorch and OpenCV, the authors of Grad-CAM and dataset provider Shanghai Smart Vehicle Integration Innovation Center Co., Ltd. and Shanghai University of Engineering Science.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initialization Term** & **Adaptation Term** & **Base** & **Novel** \\ \hline  & \(\surd\) & 58.2 & 21.8 \\ \(\surd\) & \(\surd\) & 67.9 & 45.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The ablation of meta-learner module \(\mathcal{F}\).
## 1 Introduction
Second, new services can be flexibly and efficiently created through the well defined interfaces of IMS such as _Session Initiation Protocol_ (SIP) [1] and Parlay-X. In particular, new IP services can be nicely integrated with existing telecom services through this architecture. Non-IMS Internet solutions typically interact with telecom network through a gateway (such as _Gateway GPRS Support Node_ (GGSN) in GPRS). Such approaches cannot easily manipulate telephone numbers, and cannot take full advantages of telecom-grade call control, QoS, and security because GGSN does not provide access to such telecom features as _Call Session Control Function_ (CSCF) does. With NGN/IMS, Chunghwa telecom has experimented with several potential services [3] to identify the restrictions and shortcomings of the overall service platform by service verification technique, a prototyping approach to verify if the related network components qualify for fulfilling the service requirements.
## 2 State of the Art
The proposed methodology is based on the semantic languages created by the World Wide Web (W3C), RDF, and OWL (see Section 5.2. for further information). These XML-based syntaxes allow the representation of knowledge as triples, i.e., a three-entity statement in the form of subject-predicate-object expressions. This atomic structure forms a directed graph; hence, ontologies can be defined as graphs, in which each class is a node (vertex), and is connected to other classes via properties or relations (edges). Some studies have considered the use of graph databases to implement RDF stores [18] or to build an ontology for an automated vehicle's context model [19]. However, there is not yet a de jure standard for the construction or design of ontologies in the automotive domain. Furthermore, the proposed methodology bases the construction and representation of the ontologies in graph databases. Among the analyzed references, to the best of our knowledge, sound developments based on graphs do not exist. Thus, is no dominant, standardized methodology based on these databases. In this work, we used Neo4j as the graph database to host the ontology and the Cypher query language to interoperate with it. This database deploys the ontology as a database resource, fostering its utilization to address the new challenges of the labeling industry, such as global networking(e.g., using the Bolt network protocol for client-server communication), Big Data, advanced algorithms (e.g., pathfinding), and visualization applications.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 3 Physiological Roles
### Auxiliary Transcriptional Regulation  
The mode of action of TA systems is not limited to the nuclease activities of the toxins. As a global transcriptional regulator, some antitoxin and/or TA complexes can alter the gene expression patterns of many genes. MqsRA was shown to bind to the palindromic sequence within the promoter regions of non-TA genes, including _rpoS_ and _cspD_, therefore having an influence on processes such as stress response, motility and biofilm formation [39; 41]. Deletion of _mqsR_ resulted in the repression of 239 genes and the induction of 76 genes in the log-phase, suggesting that MqsR has a genome-wide influence in gene expression either through its nuclease activity and/or via MqsA [41]. The antitoxin DinJ of YafQ (ribosome-dependent nuclease) also regulates non-TA genes as a transcriptional regulator [42]. DinJ binds to the LexA palindrome within the promoter of _cspE_, a cold shock protein [42]. As CspE plays a role in the translation of _rpoS_ mRNA, DinJ influences _rpoS_ expression through _cspE_[42].  
The toxin's role in transcriptional regulation is to influence the stability of the antitoxin and its binding capacity to DNA [40]. When RelBE proteins are produced at the proper stoichiometric ratio, two TA complexes cooperatively bind DNA to repress the expression. The presence of excess toxins (RelE) abolishes cooperative binding, causing de-repression of transcription to occur [40]. Preferential proteolysis of antitoxins by stress-inducible proteases must alter the toxin-antitoxin ratio, leading to a change in efficacy of transcriptional regulation by these complexes.
## 16 [PERSON]'s Relation and Planck's Constant  
In our case, where we want to compare some calculated characteristics of the model with known values, we proceed as follows. Define a \"beat wavelength\" \(\lambda_{beat}\) as the distance travelled by the droplet in the time \(T=\frac{2\pi}{\Delta\omega}\) between two consecutive cycles of the beat:  
\[\lambda_{beat}=v_{av}\ T=\frac{2\pi}{k\omega_{0}}\ \frac{1}{v_{av}} \tag{32}\]  
Next define an \"average momentum\" \(p=m\ v_{av}\) (\(m\) and \(v_{av}\) as defined in Section 14). Equation (32) may then be written in the form:  
\[p=\frac{K}{\lambda_{beat}} \tag{33}\]  
\[K=\frac{2\pi}{k\omega_{0}}m=\ \frac{1}{c}\ \frac{q^{2}}{4\pi\epsilon_{0}}\ \sqrt{\mu^{\tau}\tau}.\beta,\ \text{and}\ \beta=\sqrt{\pi^{2}\alpha\ \alpha^{*}} \tag{34}\]Equation (33) is a relation remarkably similar in form to [PERSON]'s equation \(p=h/\lambda\). We now compare the numerical value of the constant \(K\) with the value of [PERSON]'s constant \(h=6.6256\ \times 10^{-34}\ J.s\). The constant \(K\) in the droplet theory depends on the factor \(\beta\), so that it depends on how the charge is distributed within the droplet. The precise value of the factor \(\beta\) is thus unknown: In the theory as it stands now, no assumptions were introduced (nor were they needed) about how the charge density varies throughout the droplet. The only thing known is that the factor is of order unity: \(\beta=O(1)\) (see the remarks about the orders of \(\alpha\) and \(\alpha^{*}\) in Sections 6 and 10). The best estimate that presently can be made is therefore \(\beta=1\). The other constants in Equation (34) have the value \(c=2.9979\times 10^{8}m/s,\ q=1.6021\times 10^{-19}C\), \(\epsilon_{0}=8.8544\times 10^{-12}N^{-1}m^{-2}C^{2}\), \(\mu=206.85\) and \(\tau=3477.1\). The resulting value of \(K\) becomes:  
\[K=6.52\ \times 10^{-34}\ J.s\ for\ \beta=1 \tag{35}\]  
The difference with [PERSON]'s constant is 1.5 %. It is an error that can be considered to be well within the expected accuracy margins, in view of the exploratory nature of the derivations (one-dimensional approach, just three terms included in the matched asymptotic expansion, linearisations, etc.).
## 3 Experimental Validation
### Ti6 Al4V Validation
#### 3.2.2. Levd Ml Algorithm Validation  
In this Section, the Levd ML is validated using Ti6 Al4V data. Figure 16 plots the loss function with respect to the k-fold considered for the validation. According to Figure 16, the computed validation loss functions are close to 0.04, with only the one computed for k = 1 showing a larger value close to 0.14.  
Figure 15: Validation of the investigated Probability ML algorithm: (**a**) Gumbel plot for the validation dataset for k = 3; (**b**) P–P plot for k = 3 by considering the training and the validation data.  
The predictive capability has been thereafter verified on the validation fold for which the loss function shows the largest value, i.e., for k = 1. Figure 17 compares the LEVD computed experimentally and with the ML algorithm: Figure 17a for the validation dataset and Figure 17b for a training fold randomly chosen.  
According to Figure 17a, the LEVD ML algorithm works well even for the first fold, i.e., for the dataset characterized by defects significantly larger than those found in the other datasets, for which the Probability ML has shown worse performance (Figure 14a). \(R_{exp}^{2}\) and \(R_{ML}^{2}\) are very close, being equal to 0.95 and 0.93. The slope and the intercept show acceptable differences, with the two curves showing similar trends. Accordingly, the LEVD ML has proven to work better than the Probability ML for the T16A14V dataset showing an \"anomalous\" trend. As for the other analyses, the LEVD ML overlaps with the experimental LEVD for the training dataset (Figure 17b).
###### Abstract  
In this paper, we propose a grouping-based dynamic routing, core, and spectrum allocation (RCSA) method for preventing spectrum fragmentation and inter-core crosstalk in elastic optical path networks based on multi-core fiber environments. Multi-core fibers enable us to considerably enhance the transmission capacity of optical links; however, this induces inter-core crosstalk, which degrades the quality of optical signals. We should thus avoid using the same frequency bands in adjacent cores in order to ensure high-quality communications. However, this simple strategy leads to inefficient use of frequency-spectrum resources, resulting in spectrum fragmentation and a high blocking probability for lightpath establishment. The proposed method allows one to overcome this difficulty by grouping lightpath-setup requests according to their required number of frequency slots. By assigning lightpath-setup requests belonging to the same group to cores according to their priority, the proposed method aims to suppress inter-core crosstalk. Furthermore, the proposed method is designed to mitigate spectrum fragmentation by determining the prioritized frequency bandwidth for lightpath-setup requests according to their required number of frequency slots. We show that the proposed method reduces the blocking of lightpath establishment while suppressing inter-core crosstalk through simulation experiments.  
Article  
Academic Editor: [PERSON]  
25 April 2025  
Revised: 19 May 2025  
Accepted: 21 May 2025  
Published: 23 May 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Grouping-Based Dynamic Routing, Core, and Spectrum Allocation Method for Avoiding Spectrum Fragmentation and Inter-Core Crosstalk in Multi-Core Fiber Networks. _Future Internet_**2025**, _17_, 222. [[https://doi.org/10.3309/617060232](https://doi.org/10.3309/617060232)]([https://doi.org/10.3309/617060232](https://doi.org/10.3309/617060232))  
**Copyright:** 62 2025 by the authors.  
Luoense MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Determination of Key Parameters
### Control Mechanism of Aperture on Roof Breaking
From Equations (17) and (18), when \(R=0\) (i.e., no dense drilling), the moment of inertia \(I_{y}\) and the static moment \(S_{y}^{*}\) on the cross section can be simplified as:  
\[I_{y}=\frac{bh^{3}}{12} \tag{19}\]  
\[S_{y}^{*}=\frac{1}{8}bh^{2}-\frac{1}{2}bz^{2} \tag{20}\]  
From Equations (19) and (20), the bending moment \(M_{O2}\) and shear force \(F_{SO2}\) on the O section can be expressed as:  
\[M_{O2}=\frac{qb(l_{1}+l_{2})^{2}}{2}-F_{A}l_{0} \tag{21}\]  
\[F_{SO2}=qb(l_{1}+l_{2})-F_{A} \tag{22}\]  
By substituting Equations (19) to (22) into Equations (13) and (14), the normal stress \(\sigma_{O2}\)(z) and shear stress \(\tau_{O2}\)(z) on the O section can be expressed as follows:  
\[\sigma_{O2}(z)=\frac{M_{O2}}{I_{y}}z=\frac{12z}{bh^{3}}\Bigg{[}\frac{qb(l_{1} +l_{2})^{2}}{2}-F_{A}l_{0}\Bigg{]} \tag{23}\]  
\[\tau_{O2}(z)=\frac{F_{SO2}S_{y}^{*}}{I_{y}b}=\frac{6}{bh^{3}}\Bigg{(}\frac{h^ {2}}{4}-z^{2}\Bigg{)}[qb(l_{1}+l_{2})-F_{A}] \tag{24}\]According to Equations (23) and (24), the maximum normal stress and maximum shear stress on the O section can be expressed as follows:  
\[\sigma_{\rm O2 max}=\frac{M_{O2}}{I_{y}}\frac{h}{2}=\frac{6}{bh^{2}}\Bigg{[}\frac {qb(l_{1}+l_{2})^{2}}{2}-F_{A}l_{0}\Bigg{]} \tag{25}\]  
\[\tau_{\rm O2 max}=\frac{F_{SO2}S_{\rm symex}^{*}}{I_{y}b}=\frac{3}{2}\frac{1}{bh} [qb(l_{1}+l_{2})-F_{A}] \tag{26}\]  
Similarly, the bending moment \(M_{B2}\) and shear force \(F_{SB2}\) on the B-section can be expressed as:  
\[M_{B2}=\frac{qbl_{2}^{2}}{2} \tag{27}\]  
\[F_{SB2}=qbl_{2} \tag{28}\]  
The conditions for the success of the roof cutting in Equations (25) to (28) are as follows:  
\[S_{y}^{*}=\frac{1}{8}bh^{2}-\frac{1}{2}bz^{2} \tag{20}\]0  
\[S_{y}^{*}=\frac{1}{8}bh^{2}-\frac{1}{2}bz^{2} \tag{20}\]1  
Simplified:  
\[S_{y}^{*}=\frac{1}{8}bh^{2}-\frac{1}{2}bz^{2} \tag{20}\]2  
\[S_{y}^{*}=\frac{1}{8}bh^{2}-\frac{1}{2}bz^{2} \tag{20}\]3  
According to Formulas (31) and (32), the normal stress and shear stress on section B are positively correlated with the aperture \(R\). The roof cutting height H \(>\) 7.8 m, \(b=\) 14 m, \(q=\) 0.5 MPa, \(l_{2}=\) 22.2 m obtained is substituted into Formulas (31) and (32), and the pore size \(R\) should be greater than 20 mm.
## 2 Problem Formulation  
### The General MOSL Problem  
We extend the OSL problem, where the odor source is static, to the case of moving source resulting in the MOSL problem. The objective is to localize a mobile target, characterized by a \(D\)-dimensional position \(\mathbf{p}_{s}(t)\) varying with time \(t\). This target is assumed to release scalar information in the environment, such as an odor, heat, radioactivity, a sound or radio wave; without losing generality we assume the intensity of this information to be positive. We thus note \(u:\mathbb{R}^{D}\times\mathbb{R}\mapsto\mathbb{R}^{+};(\mathbf{p},t)\to u( \mathbf{p},t)\). This information \(u\) is assumed to be _diffused_ and _transported_ by the environment. This can be modeled by a _partial differential equation_ of variable \(u\), which exact formulation depends on the problem at hand. We assume furthermore that at any given time \(t\), function \(u\) has an _unique point_\(\mathbf{p}_{\text{max}}(t)\) where the information intensity \(u\) is maximum. We also assume that the diffusion and transport mechanism is such that \(\mathbf{p}_{\text{max}}(t)\) is very close to the position of the target \(\mathbf{p}_{s}(t)\) so that for all practical purposes we may assume that \(\mathbf{p}_{s}(t)\approx\mathbf{p}_{\text{max}}(t)\), that is, both terms can be used interchangeably. Our problem is then to find \(\mathbf{p}_{\text{max}}(t)\), which is then equivalent to finding the position of the target at the same date. Another thematic close to MOSL is geofencing [42] where Unmanned Aerial Vehicles (UAV) can be used in a constrained area, typically cities, for tracking and tracing missions, as for the track of stolen cars.  
The measure, made by the system's sensors, will be denoted by function \(f:\mathbb{R}^{D}\times\mathbb{R}\mapsto\mathbb{R}^{+};(\mathbf{p},t)\to u( \mathbf{p},t)+\beta(\mathbf{p},t)\), where \(\beta(\mathbf{p},t)\) is a noise. We assume that the Signal-to-noise Ratio (SNR) is sufficient so that the maximum of \(f\) still coincides with the maximum of \(u\) for all practical purposes. In our simulations, we considered that function \(u\) had values in \([0,1]\) and the noise was additive white Gaussian, uncorrelated in space and time, with a standard deviation \(\sigma\) taken between 0 and 0.25 except where indicated. We leave issues raised by a poor SNR for future research.
## 2 Materials and Methods
### Satellite-Based Soil Properties from GLEAM  
GLEAM [55,56] is a set of algorithms to derive the different components of terrestrial evaporation and soil moisture (surface and root-zone) from satellite-based datasets of different hydro-climatic variables at a daily timescale. It employs the Priestley and Taylor equation [72] to estimate potential evaporation for different land cover types (short and tall vegetation, bare soil, ice and snow covers, and open water). A semi-empirical, land cover-dependent multiplicative stress factor is used to translate the potential evaporation estimates into actual evaporation. For tall and short vegetation, the stress factor is based on vegetation optical depth [73] and root-zone soil moisture. For bare soil, the stress factor is dependent on surface soil moisture only. Interception loss is estimated using the Gash analytical model [74]. To model surface and root-zone soil moisture, GLEAM applies a multi-layer soil water balance. The number of soil layers and the depth of the root-zonedepend on land-cover type with a maximum depth of 10 cm for bare soil, 100 cm for low vegetation, and 250 cm for tall vegetation, respectively. The soil water available for evaporation, either via transpiration or bare soil evaporation, is estimated using static soil properties (e.g., \(\theta_{\text{W}}\)) taken from the database of Global Gridded Surfaces of Selected Soil Characteristics generated by the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS, [75]). Finally, GLEAM incorporates a data assimilation module which ingests soil moisture observations via a Newtonian nudging scheme. In the version of GLEAM used in this study (GLEAMv3.5b), the latest satellite-based estimates of surface soil moisture from ESA-CCI [76] are assimilated, which include observations from the Soil Moisture Active Passive (SMAP) satellite. GLEAMv3.5b data at daily and 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) resolution are available for download at www.gleam.eu (accessed on 1 April 2022).