esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment  
### Experimental Setup and Data Acquisition  
In order to verify the effectiveness of the proposed algorithm in this paper, we conducted in-vehicle experiments in a typical urban environment in Beijing, China. The sensors were provided by the Beijing Key Laboratory of High Dynamic Navigation Technology, and the sensor parameters are shown in Table 1.  
We conducted experiments with frequent and sustained interruptions of GNSS signals to evaluate the performance of the proposed algorithm. The experimental test trajectory is shown in Figure 7, which includes common occlusion environments such as overpasses and tunnels that the vehicle passed through. The total length of the experimental trajectory was 32.96 km. The light blue section, which is 18.99 km long, includes data collected for training the neural network prediction model and determining its parameters. The green trajectory, measuring 7.79 km, was used to assess the algorithm's performance under conditions of frequent GNSS interruptions. The orange trajectory, extending for 6.18 km, was used to evaluate the algorithm's performance during sustained GNSS interruptions. To highlight the superiority of the proposed algorithm, we compared three different integrated navigation methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Parameters** & **Accuracy** \\ \hline IMU & Gyroscope Bias & 20\({}^{\circ}\)/h \\ \hline \multirow{4}{*}{GNSS} & Gyroscope Random Walk & 0.0667\({}^{\circ}\)/\(\sqrt{\text{h}}\) \\  & Accelerometer Bias & 50 mg \\ \cline{1-1}  & Sampling Frequency & 100 Hz \\ \cline{1-1}  & Position Accuracy & 2 m \\ \cline{1-1}  & Sampling Frequency & 1 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor parameters.  
Figure 6: CNN-GRU-assisted model structure diagram.  
1. FGO: factor graph-based integrated navigation method;
2. LSTM-FGO: factor graph-based integrated navigation method assisted by a long short-term memory (LSTM) network;
3. CNN-GRU-FGO: The proposed integrated navigation method, combining convolutional neural network (CNN) and gated recurrent unit (GRU) with factor graph assistance.
# Screening of Scaffolds for the Design of G-Quadruplex Ligands
## 3 Results and Discussion
### FRET Melting Studies
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Legend Concentration**} & \multicolumn{5}{c}{**\(\Delta T_{\rm m}\) (\({}^{\circ}\)C)**} \\ \hline \multirow{4}{*}{5 \(\upmu\)M} & \multirow{2}{*}{**Ligand**} & \multirow{2}{*}{**FmycT**} & \multirow{2}{*}{**F21T**} & \multirow{2}{*}{**FkrasT**} & \multirow{2}{*}{**FvegfT**} & \multirow{2}{*}{**Fpre-MIR150T**} & \multirow{2}{*}{**FdxT**} \\  & & & & & & & \\ \cline{5-8}  & Ter-1 & 0.0 \(\pm\) 0.2 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 1.9 & 0.0 \(\pm\) 1.6 & 0.0 \(\pm\) 0.5 & 0.0 \(\pm\) 0.1 \\  & ISO-1 & 0.0 \(\pm\) 0.3 & 0.1 \(\pm\) 0.0 & 0.0 \(\pm\) 1.0 & 0.5 \(\pm\) 1.2 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.3 \\  & Quin-1 & 0.0 \(\pm\) 1.0 & 0.2 \(\pm\) 0.5 & 0.0 \(\pm\) 0.9 & 0.0 \(\pm\) 2.0 & 0.0 \(\pm\) 0.2 & 0.0 \(\pm\) 0.0 \\  & Napht-3 & 0.0 \(\pm\) 0.1 & 0.8 \(\pm\) 0.3 & 2.0 \(\pm\) 1.0 & 0.6 \(\pm\) 2.1 & 0.0 \(\pm\) 0.3 & 0.0 \(\pm\) 0.4 \\  & Napht-5 & 0.0 \(\pm\) 0.2 & 0.8 \(\pm\) 0.1 & 0.1 \(\pm\) 1.0 & 0.0 \(\pm\) 1.9 & 0.0 \(\pm\) 0.3 & 0.0 \(\pm\) 0.2 \\  & Phen-1 & 0.0 \(\pm\) 0.1 & 2.5 \(\pm\) 0.1 & 0.3 \(\pm\) 0.6 & 0.0 \(\pm\) 2.0 & 0.0 \(\pm\) 0.1 & 0.0 \(\pm\) 0.2 \\  & DPPZ & 0.6 \(\pm\) 0.3 & 0.4 \(\pm\) 0.1 & 0.6 \(\pm\) 0.2 & 0.0 \(\pm\) 1.4 & 0.0 \(\pm\) 0.1 & 0.0 \(\pm\) 0.2 \\  & PhenCOOCH\({}_{3}\) & 0.6 \(\pm\) 0.8 & 3.4 \(\pm\) 0.2 & 3.4 \(\pm\) 1.8 & 0.0 \(\pm\) 2.0 & 0.0 \(\pm\) 0.1 & 0.0 \(\pm\) 0.3 \\  & C\({}_{8}\)NH\({}_{2}\) & 17.4 \(\pm\) 0.4 & 26.8 \(\pm\) 0.3 & 7.2 \(\pm\) 0.4 & 4.5 \(\pm\) 0.5 & 4.6 \(\pm\) 0.0 & 0.1 \(\pm\) 0.1 \\  & C\({}_{5}\)NH\({}_{2}\) & 16.3 \(\pm\) 0.2 & 15.6 \(\pm\) 0.2 & 18.0 \(\pm\) 2.6 & 2.5 \(\pm\) 0.9 & 0.9 \(\pm\) 1.3 & 0.3 \(\pm\) 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The thermal stabilization induced by different scaffolds measured by FRET-melting experiments. lization effect on all G4s. Compounds containing naphthalene scaffolds presented poor G4 stabilization, which was slightly better for FkrasT, in the case of derivatives containing a short polyamine chain linked directly to the ring (Napht-3). All tested scaffolds exhibited negligible stabilization for FdxT (Table 2).
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 2 Preliminaries
### Quality of Experience (QoE)  
QoE refers to the user's assessment of the network service experience under the overall network environment, which is a vital way for content providers and operators to evaluate and improve their service. Generally, we use subjective evaluation-MOS (Mean Opinion Score) to quantify QoE; MOS denotes the levels of experience and can be represented by a single number for evaluation in the range of 1 to 5, as in Table 1.  
However, obtaining QoE by subjective evaluation method is complex, as well as time-consuming and costly; therefore, realization of real-time quality monitoring is difficult. In order to overcome these difficulties, a series of objective evaluation methods are proposed, via some objective key performance indicators (KPIs) of network service. Remember that the QoE itself is a subjective perceptual score, so one can barely replace it with objective system attributes, directly. What we can do is to describe QoE to a certain degree, with a suitable mapping between objective parameters and QoE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experience** & **Excellent** & **Good** & **Fair** & **Poor** & **Bad** \\ \hline
**Score** & 5 & 4 & 3 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean opinion score (MOS) scoring.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 3 Experimental Procedures and Methods
### Freeman's Polarization Calibration Method
However, the complex factors \(Ae^{i\varphi}\) in front of the matrices may be different, and so relative quantities (i.e., normalizing the magnitude and phase of the quad-polarization matrices) are used in the arithmetic process so that they do not impact the solution process for \(R\) and \(T\), and the effect of noise is ignored.
## 5 M2M Communication Models and Paradigms
Subscribers are notified as data/events are produced, reducing the dissemination time and improving the scalability, when compared to request-response. Eliminating the active requests for content, the number of messages, and thus transmissions, is reduced as is the energy consumption of the overall system, which is of extreme importance in scenarios where nodes and networks are resource constrained. This model introduces an additional complexity only in the entity that manages the event notifications, which may be largely compensated by the reduction in the complexity of the nodes. Time and space decoupling allows greater scalability and flexibility than polling-based communications, and allows a more dynamic topology [51], as publishers and subscribers do not need to be actively participating in the interaction at the same time, and since they do not hold references about each others, each can change their location without being necessary to inform the others.
## 5 Investigation of the MIMO-Antenna Diversity Performance
### Diversity Gain (DG)  
The diversity gain (DG) is used in MIMO-system gain enhancement and gives insight into the effects of the diversity on the wireless links. It is calculated using Equation (4) [41]:  
\[\text{DG}=10\sqrt{1-|\text{ECC}|} \tag{4}\]  
It is clear from Equation (4) that the lower value of the correlation coefficient is associated with a greater diversity gain. For an ideal zero correlation coefficient corresponds an ideal constant diversity gain value of 10 [37].  
In this study, the resulting simulated and measured diversity gains calculated using the S parameters stand between 9.990 and 10 in the entire frequency band, as shown in Figure 14. However, the resulting measured diversity gains calculated using the S parameters and radiation efficiency stand between 9.8 and 10 in the entire frequency band, due to the consideration of the efficiency.  
Figure 14: Simulated and measured diversity gains of proposed antenna.  
Figure 13: Measured and simulated ECCs of proposed MIMO antenna.
# Segmentation of River Scenes Based on Water Surface Reflection Mechanism
## 4 Conclusions  
In this study, we focus on the image segmentation of outdoor river scenes. To solve the problem that current methods often missed detection and made false segmentations when applied to complex river scenes, this study proposed a novel segmentation method based on a reflection mechanism of the water surface. An improved LBP feature descriptor was designed for water detection and \(H\) variance was introduced to detect the shadow area of the water's surface. Morphological operation with multiple dilation was employed to eliminate pseudo-water patches wrongly detected by the proposed algorithm and to obtain the largest connected domain in the image as water region. The experiments were performed in simple and complex river scenes respectively where the proposed method was compared with two other river segmentation methods. The results showed the proposed method took the least time and had better and robust performance in both simple and complex river scenes.  
At present, the proposed algorithm has only been proven to be suitable for segmenting water parts in river images. Since the algorithm is designed based on the reflection mechanism of the water surface, it remains to be further studied whether it is effective for other types of images. The design ideas of the proposed algorithm may be helpful to other segmentation algorithms.  
Figure 17: Performance under different sizes of structuring elements in the proposed algorithm. (**a**) An example of simple river scene; (**b**) An example of complex river scene.  
In the future, research can be conducted on anomaly detection of water surfaces based on the proposed method. This study is also important for unmanned surface vehicles (USVs) and river mapping.  
Conceptualization, J.Y.; Methodology, Y.L.; Data curation, Y.Z.; Supervision, P.H., J.Y., G.Z. and D.H.; Validation, W.X.; Writing--original draft, Y.L.; Writing--review & editing, P.H., J.Y. and D.H. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Fundamental Research Funds for the Central Universities (No.2019 QNASA015), the National Natural Science Foundation of China (No. 61803333, 61573313), the Key Technology Research and Development Program of Zhejiang Province (No.2015C03G2010034), and the National Key R&D Program of China (No.2017 YFC1403801).  
The authors declare no conflict of interest.
## 3 Experiments and Analysis
### Model Evaluation Metrics  
For the surface damage identification of wind turbine blades in this subject, the optional evaluation metrics include accuracy, precision, recall, and F1 score.
# FACTMS: Enhancing Multimodal Factual Consistency in Multimodal Summarization
## 4 Fact-Aware Multimodal Summarization
### Cross-Modality Knowledge Interaction  
The interaction of factual knowledge elements between modalities can complement and enhance the crucial information in the multimodal content. Specifically, entities and relations in the article correlate to objects and scenes in the image, which can be exploited to learn the cross-modality representation to capture and enhance facts from both modalities. Thus, a cross-modality knowledge encoder is employed to fuse the two types of knowledge information via transformer attention layers [32]. The input of the attention layer can be regarded as query (\(\mathbf{Q}\)), key (\(\mathbf{K}\)), and value (\(\mathbf{V}\)). The output is a weighted sum of \(\mathbf{V}\), where the weight comes from \(\mathbf{Q}\) and \(\mathbf{K}\). The calculation process is as follows:  
\[\text{Attention}(\mathbf{Q},\mathbf{K},\mathbf{V})=\text{softmax}(\frac{ \mathbf{QK}^{T}}{\sqrt{d_{k}}})\mathbf{V} \tag{7}\]  
where \(d_{k}\) denotes the dimension of the queries and the keys. If \(\mathbf{Q}\), \(\mathbf{K}\), and \(\mathbf{V}\) are all the same, it is called self-attention [32]. If \(\mathbf{Q}\) comes from one modality, \(\mathbf{K}\) and \(\mathbf{V}\) come from another modality, it denotes cross-attention [33; 34], which can fuse the knowledge information of one modality into another modality. The feed-forward network layer constitutes a position-wise fully connected module:  
\[\text{FFN}(\mathbf{H})=\text{ReLU}(\mathbf{HW}_{1}+\mathbf{b}_{1})\mathbf{W} _{2}+\mathbf{b}_{2} \tag{8}\]  
where \(\mathbf{H}\) is the input of the layer, \(\mathbf{W}_{1}\), \(\mathbf{W}_{2}\), \(\mathbf{b}_{1}\), \(\mathbf{b}_{2}\) are trainable parameters, and ReLU is the activation function.  
The cross-modality knowledge encoder is a stack of cross-attention (CrossAttn), self-attention (SelfAttn), and feed-forward network (FFN) layers with layer normalization (LN). The visual knowledge features fused with textual knowledge are calculated as follows:  
\[\mathbf{H}_{of}^{{}^{\prime}}=\text{LN}(\text{CrossAttn}(\mathbf{H}_{v},\mathbf{ H}_{t})+\mathbf{H}_{v}) \tag{9}\]  
\[\mathbf{H}_{vf}^{{}^{\prime\prime}}=\text{LN}(\text{SelfAttn}(\mathbf{H}_{vf}^ {{}^{\prime}})+\mathbf{H}_{vf}^{{}^{\prime}}) \tag{10}\]  
\[\mathbf{H}_{vf}=\text{LN}(\text{FFN}(\mathbf{H}_{vf}^{{}^{\prime\prime}})+ \mathbf{H}_{vf}^{{}^{\prime\prime}}) \tag{11}\]  
Similarly, fused with visual knowledge, the textual knowledge features \(\mathbf{H}_{tf}\) can be obtained. Finally, the fused multimodal knowledge features \(\mathbf{H}_{f}\) are obtained as the integration of knowledge information in the two modalities:  
\[\mathbf{H}_{f}=[\mathbf{H}_{vf};\mathbf{H}_{tf}] \tag{12}\]
# Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation
## 6 Conclusions and Future Research  
In this paper, we proposed a learning model designed to track the isolines of a 2D fluid based on its physical properties as inputs. Compared models such as linear regression and LSTM resulted in unnatural surfaces due to the divergence of inferred level-set values. While the surface represented by the learning representation (1-layer) model was visually significant, it was not as smooth as the surface produced by the proposed model. The LSTM model, designed to ensure temporal continuity between consecutive frames, suffered from the structural characteristic of LSTM, where previous errors continuously accumulate, leading to divergence in later frames. Experiments with linear regression and learning representation (1-layer) models were conducted to see if they could reflect the data trained with a simpler structure compared to our method, but they were less accurate than the proposed model. Training with networks having a more complex structure (3-layer, Hidden layer 256, 128, 64, etc.) than our method was also attempted, but it resulted in increased training and validation loss without convergence. To verify the generality of our method, tests were conducted in a total of five scenes, and in all test scenes, visually natural fluid isolines were successfully represented.  
The model proposed in this paper is independent of time, thus preventing the accumulation of errors in the level set, enabling surface reconstruction with uniform quality. Additionally, by eliminating the computational process of traditional algorithms, it saves time and resources, making it suitable for real-time isoline tracking and use in middleware.  
In the future, we plan to reduce training time and improve the accuracy of the model through correlation analysis with physical properties in anisotropic forms, in addition to the input features used in this paper. Furthermore, we plan to extend our research to efficiently learn level-set inference and surface tension using models such as GNN and CNN, which were not utilized in this paper.  
Conceptualization, J.Y.K. and J.-H.K.; methodology, C.G.S.; software, J.L.; validation, J.W.L., S.-J.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by Basic Science Research Program through the NRF funded by the Ministry of Education under Grant 2022R1F1A1063180 (Contribution Rate: 40%). This work was supported by INHA UNIVERSITY Research Grant (Contribution Rate: 20%). This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00254695) (Contribution Rate: 40%).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
## 1 Introduction
The common behavior that has been figured out is the decrease in flowability and the setting time with the addition of CaO. This could be explained by the high dissolution rate of the calcium and silicon ions within the matrix, which leads to an increase in the hydration heat. This could accelerate the hydration process, decreasing the setting time and flowability [24; 26]. The high dissolution rate is accompanied by high medium alkalinity and a significant water consumption rate, which may cause a deterioration in the fresh and hardened properties [21]. The influence of the CaO addition on the mechanical property is mainly associated with the precursor type and chemical composition, the percentage of CaO, the alkaline activator type and concentration, and the curing conditions. Some scholars have attributed the enhancement in the mechanical properties to the better breaking and dissolution of the matrix atoms, providing a denser C-S-H gel. On the other hand, [PERSON] et al. explained the deterioration in the mechanical properties by the quick setting behavior. This behavior is associated with poor formwork that produces a destabilized C-A-S-H gel. [PERSON] et al. [28] added that the higher hydration heat could lead to water evaporation, causing high porosity and reducing the compressive strength. This was supported by the findings of [PERSON] et al. [25] and [PERSON] et al. [28], which emphasized that applying heat curing led to a reduction in mechanical properties due to water loss. Based on the above discussion, the influence of CaO has not been fully clear on the performance of AAS, in particular composites incorporating healing agents. Further studies are required to assess the influence of calcium oxide on the microbial behavior of alkali-activated composites. The selection of the CaO percentage was 7% of the total binder weight, as highlighted by some studies based on its positive contribution to the development of mechanical properties [4; 11; 26]. The performance of the selected bacteria, \"_Bacillus subtilis_\", could be explored by detecting the ability of the microbe to precipitate calcite crystals in AAS material incorporating 7% CaO of the binder weight. This could be conducted through an experimental investigation of the mechanical, durability, and microstructure properties. The mechanical properties were tested in terms of compressive and flexural strengths. Absorption, porosity, chloride resistance, and surface electrical resistivity were investigated to gain insights into the durability performance of the microbial AAS mortar. Microstructure analysis was traced using a scanning electron microscope (SEM) alongside image energy-dispersive X-ray (EDX) analyses and X-ray diffraction (XRD). \(\downarrow\) decrease. \(\uparrow\) increase.
# Ribosomal Hiberation Factor Links Quorum-Sensing to Acid Resistance in EHEC
## 3 Results  
### Exogenous and Endogenous AHL Signaling Synergistically Promote Acid Resistance in EHEC  
We first established the optimal conditions for acyl-homoserine lactone (AHL)-mediated acid resistance in EHEC O157:H7 EDL933. Exogenous 3-oxo-C6-AHL conferred concentration- and time-dependent survival enhancement under lethal acidity (pH 2.5) (Figure 1A): a 10 \(\upmu\)M AHL elevated viability of 2.3-fold, 4.5-fold, and 3.2-fold after 1, 2, and 3 h. One hundred \(\upmu\)M AHL exerted superior efficacy: 4.9-fold, 9.9-fold (peak at 2 h), and 8.7-fold.  
The 2 h timepoint generated maximal protection for both concentrations (4.5- and 9.9-fold). Survival increases with 100 uM AHL significantly exceeded those with 10 uM at 2 h (2.2-fold difference), defining 100 uM AHL + 2 h acid stress as the optimal regimen.  
To evaluate endogenous signaling, we engineered the AHL-synthetic strain EDL933 \(\Delta m\)_nf/pyenl_. Cross-streaking assays (Figure 1B) confirmed functional AHL production, as the recombinant strain induced robust violacein pigmentation in biosensor CV026--matching the C6-HSL-positive control. Wild-type EDL933 elicited no response, validating the assay's specificity.  
Endogenous AHL synthesis in WT/_pyenl_ significantly enhanced acid resistance (Figure 1C)--1 h: 5.0-fold survival increase; 2 h: 5.5-fold; 3 h: 7.0-fold. Protection escalated temporally (5.0 \(\rightarrow\) 7.0-fold) and surpassed all WT timepoints, demonstrating that _yenl_-driven AHL production confers acid resistance equivalent to 100 uM exogenous supplementation.