esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Transcriptomic and TF Analyses  
In order to understand the effects of climate on grape volatiles at transcriptional levels, the RNA sequencing method was conducted on grape samples. After raw quality filtering, the results comprised about 185.73 Gb of clean sequence data with 40 to 45 million clean reads of each sample. Altogether, 77.33% to 91.23% reads were successfully mapped on _Vitis vinifera_ reference genome (GCF_000003745.3_12X) via HISAT. Basic information of RNA sequencing was listed in Table S4.  
The analyses of DEGs were completed in TS and US at each stage. Results are shown in Figure 2A (FC > 2, \(p\) < 0.01) and are listed in Table S5. The most abundant DEGs were  
Figure 1: Volatile profiles of grape berries. Compounds are labeled with numbers consistent with Tables S2 and S3. (**A**): The top 25 compounds correlated to developmental stages. (**B**): 3D plot of principal component analysis; (**C**): Significantly varied compounds between US and TS at each stage (FC > 2, \(p\) < 0.01). TS: Turpan sample; US: Urumqi sample.  
found at S4, whereas the fewest DEGs were witnessed at S2. The Venn graph depicts DEGs at all five stages (Figure 2B). Chaperone protein DnaJ (GenBankBL ID: 100261077) and desiccation-related protein PCC13-62 (GenBank ID: 100252547) were diversely expressed in TS and US at all stages. RT-qPCR verification were included to ascertain their expressional profiles, and results are listed in Table S1. As shown, the expressional value of _DnaJ_ in TS was higher than that in US at S4, but it was upregulated in US at other stages as compared to TS. Validated by RT-qPCR, the expressions of _PCC13-62_ in US were higher than in TS from S2 to S4.  
Additionally, DEGs were enriched by the KEGG pathway and divided into seven primary categories, in which the top 25 pathways are depicted in Figure 2C. A total of 906 DEGs were highly expressed in US, while 846 DEGs were upregulated in TS (FC > 2, \(p<0.01\)). The two groups of DEGs were annotated into 192 and 206 pathways, respectively (Table S6). As classified, the majority were involved in metabolic pathways and biosynthesis of secondary metabolite categories. In contrast to those in US, the DEGs highly expressed in TS were also related to circadian rhythm and endocytosis. Conversely, DEGs that were upregulated in US were partly enriched into cytochrome P450.
# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
###### Abstract  
This study examines the chlorophyll a content and turbidity in the shallow dam reservoir of Lake Dobczyce. The analysis of satellite images for thirteen wavelength ranges enabled the selection of wavelengths applicable for a remote determination of chlorophyll a and turbidity. The selection was completed as the test of the significance of the coefficients in the equation, which calculates the values of the parameters on the basis of reflectance. The reflectance of the reservoir surface differs from the reflectance of individual water components, and the overlapping of spectral curves makes it difficult to isolate the significant reflectance. In the case of Lake Dobczyce, the significant reflectance was for wavelengths 665, 705, 740, and 842 nm (chlorophyll a) and for wavelengths 705, 740, and 783 nm (turbidity). In the model, the natural logarithm of chlorophyll a or turbidity was a linear combination of the natural log reflectance and the squares of those logarithms. A lake surface reflectance also includes the bottom reflectance. The reflectance obtained from the Sentinel-2 satellite was corrected with a bottom reflectance determined using the Lambert-Beer equation. The reflectance of a given surface may vary with the position of both the satellite and the sun, atmospheric pollution, and other factors. Correction of reflectance from satellite measurements was performed, as reflectance changes for the reference surface; the reference reflectance was assumed as the first reflectance of the reference surface observed during the study. The models helped to develop the maps of turbidity and chlorophyll a content in the lake.  
Sentinel-2; chlorophyll; turbidity; lake; concentration modeling of contaminants +
Footnote †: journal: Applied Sciences
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 3 Framework for Security Monitoring and Multi-Domain Trust Management
### Event Brokering  
The glue in the monitoring framework is the information brokering mechanism. It enables different parties to connect each other and share information and knowledge. The proposed paradigm for information distribution is to 'publish and subscribe'. In this paradigm, the information is published to a central element broker, which then forwards information to components that have subscribed to that particular information flow. The approach increases the scalability and flexibility of 5G security monitoring as:  
* It is easy to add new heterogeneous event sources to the system without changes to the broker or existing inferencing applications.
* Inferencing components can be integrated to the system for processing and inferencing of event streams as needed and reused in different applications.
* Events are efficiently provided only for those components that are interested on them.  
Security event information sources include, for instance, SDN controllers that feed the broker with SDN topology, and authentication events, as well as traffic statistics. Different 5G network  
Figure 3: The monitoring framework. VNF: Virtual Network Function; SDN: Software-Defined Network; TLA: Trust Level Agreement.  
functions (e.g., for mobility management, routing, authentication, traffic acceleration) may also collect and publish security event information while deep traffic inspectors intercept and analyse traffic flows--headers and payloads--in detail.  
The framework deploys 'big data' technologies for information brokering and inferencing. Existing general purpose complex event processing as well as publish-and-subscribe platforms, supporting cluster computing, are utilized to handle large numbers of event streams and infer knowledge in near-real time.
## 5 Experimental and Simulation Setup Analysis
### Performance Metric Evaluation Measurements Used  
We use performance metric evaluation measurements based on the differences derived in the configuration of the ML and DLMIDPSMs, such as accuracy, precision, f1-score, and recall. Here, we denote TP as the True Positive, TN as the True Negative, FP as the False Positive, and FN as the False Negative for the performance metric measurements applicable below.  
1. **Accuracy**: Evaluation of the classification models by identifying the proportion of correct predictions in the given dataset.
2. **Precision**: Ratio of correctly identified labels to the absolute number of positive classifications, based on the formula below.
3. **Recall**: Ratio of correctly identified labels to the total absolute number of occurrences of those labels.
4. **F1-score:** Average geometric based on precision and recall.
## 6 Conclusions  
Both outer and inner rings of intershaft bearings rotate and the rotating speeds are different. The FCFs of intershaft bearings are relevant to the two speeds, so the calculation formulas of the FCFs of the intershaft bearings are different from those of traditional REBs. Calculation formulas for the FCFs of the intershaft bearings were obtained in this paper.  
The order tracking method used in fault diagnosis for REBs only considers one rotating speed, so it is not applicable to intershaft bearings. Then, a correspondent order tracking method, order tracking in the rotating speed difference domain, was proposed in this research.  
The feature extraction of the intershaft bearing's fault is more difficult than that of traditional REBs because the vibration signal of the intershaft bearings cannot be measured directly. The weak signal of the intershaft bearings is buried in strong vibration and interference as a result of indirect measurement. To solve this issue, a scheme based on linear prediction, SK, and order tracking in rotation speed difference domain is proposed to extract the weak fault features of intershaft bearings. A prewhitening method, based on linear prediction, is adopted to preprocess the fault data to remove stationary signals and preserve the nonstationary and impulsive signals. Optimal band-pass filter parameters were selected using the SK. The fault signal was also analyzed through the order tracking in the rotation speed difference domain when the bearings operate at variable speeds. The above three methods were combined in the proposed scheme. The simulated signals and experimental data were utilized to validate the effectiveness of the proposed scheme and the analysis results show that the proposed scheme can successfully extract the weak fault features of intershaft bearing.  
Figure 27: Analysis results for inner ring fault data using algorithm proposed in this paper: (**a**) kurtogram and (**b**) envelope order spectrum.
# Towards Robust Neural Rankers with Large Language Model: A Contrastive Training Approach
## 5 Evaluation Result
### Analysis of the Retrieval Robustness
#### 5.2.1 Comparison Analysis on Retrieval Robustness of Different IR Models  
The results shown in Figure 3 indicated that the method proposed in this study performed the best overall in terms of various metrics, both in avg d. and worst d. The decline in performance for the proposed method was the lowest among all models in almost all experiments, demonstrating a better resilience to adversarial attack compared to the original BERT-based Reranker model. In terms of the defensive ability against query attacks, the performance of traditional IR models and neural network-based IR models had their own advantages and disadvantages, which was consistent with the conclusions of the previous study [7]. Moreover, we noticed that the robustness of the models varies across different datasets: on WikiQA, the robustness of traditional IR models was superior to that of PLM-based IR models. Although the proposed method in this study could improve the robustness of the original PLM-based IR models, it still did not show a significant advantage compared to traditional models; on ANTIQUE, PLM-based IR models outperformed traditional IR models completely. We believed that this result was related to the number and quality of candidate document lists in the dataset, as well as the levels and quantities of relevance annotations. In comparison to ANTIQUE, WikiQA had a smaller number of candidate documents in the test set and relatively simpler query-document matching tasks, resulting in a less pronounced gap. On the other hand, ANTIQUE had more candidates, higher quality relevance annotations, and some more difficult query tasks, leading to a larger gap in the results of different models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MAP** & **MRR@10** & **MRR** & **nDCG@10** & **P@10** \\ \hline BM25 & 0.192 & 0.506 & 0.509 & 0.510 & 0.238 \\ RM3 & 0.176 & 0.433 & 0.437 & 0.488 & 0.228 \\ DRMM & 0.187 & 0.515 & 0.530 & 0.404 & 0.234 \\ KNRM & 0.203 & 0.574 & 0.579 & 0.443 & 0.255 \\ DR & 0.438 & 0.677 & 0.679 & 0.578 & 0.317 \\ Reranker & 0.511 & 0.747 & 0.752 & 0.654 & 0.363 \\ Our (DR) & 0.438 & 0.677 & 0.681 & 0.558 & 0.310 \\ Our & **0.529** & **0.770** & **0.771** & **0.659** & **0.367** \\ (Reranker) & \multicolumn{1}{c}{} & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Retrieval performance of each model on ANTIQUE. The best result is **bold-faced**.
# Content Management Based on Content Popularity Ranking in Information-Centric Networks
## 4 Performance Analysis
### Average Hop Count
The results of the experiments in terms of AHC are demonstrated in Figure 2. In this graph, the maximum number of content a client can request was 40, and one server could handle a maximum of 12 clients at a time. The performance trend in terms of AHC was similar while we varied these two parameters. The \(Y\)-axis indicates AHC values, and the \(X\)-axis shows the cache sizes for each of the server repositories in MB. All three content caching schemes s  
Figure 2: Average hop count against increasing cache sizes. by achieving a decreasing value of AHC as the cache size increased. However, LCD still needed a higher number of hops than LCE, and even LCE took more hops than the proposed CPR-based caching scheme. For example, when the servers' cache size was 50 MB, servers utilizing the three different content caching schemes, CPR-based, LCE, and LCD, required 4.6, 4.95, and 5.1 hops on average, respectively, to deliver each requested form of content to the clients. As the cache size increased to 150 MB, the servers needed approximately 2, 3, and 3.5 hops on average, respectively, while utilizing the three different content caching schemes mentioned above. Therefore, it can be concluded that the CPR-based caching scheme outperformed the LCE caching scheme and the LCD caching scheme in terms of Average Hop Count, and the LCE caching scheme performed better than the LCD caching scheme.
# A Novel Secondary Side Series LCD Forward Converter with High Efficiency and Magnetic Reset
## 1 Introduction
### Analysis of the Turn-Off Characteristics of the Switch  
During the switch-on period, \({\rm{D_{2}}}\) was in on state, the inductor \(L_{2}\) and the capacitor \(C_{2}\) resonated in series, and the equivalent circuit is shown in Figure 4.  
According to Figure 4, we can obtain the following equation.  
\[{u_{\rm{C2}}}(t)+L_{2}C_{2}\cdot\frac{{d^{2}u_{\rm{C2}}}(t)}{{dt^{2}}}=0 \tag{13}\]  
When the switch is turned on, the current through \(C_{2}\) increases from \(I_{L1,10}\) and the voltage across \(C_{2}\) decreases from \(V_{\rm{C2,10}}\). Therefore, the initial conditions \({u_{\rm{C2}}}(0)=V_{\rm{C2,10}}\) and \({u^{\prime}}_{\rm{C2}}(0)=-\frac{{I_{L1,10}}}{{\rm{C_{2}}}}\) can be obtained. According to these initial conditions, we can obtain the following by solving Equation (13).  
\[{u_{\rm{C2}}}(t)={V_{\rm{C2,10}}}\cdot\cos(\frac{t}{{\sqrt{L_{2}{\rm{C_{2}}}}} })-{I_{L1,10}}\sqrt{\frac{{L_{2}}}{{\rm{C_{2}}}}}\cdot\sin(\frac{t}{{\sqrt{L_ {2}{\rm{C_{2}}}}}}) \tag{14}\]  
Figure 4: Equivalent circuit of Series Resonance of the capacitor \(C_{2}\) and the inductor \(L_{2}\).  
Since \(I_{L1,t0}\) is approximately zero at the moment when the switch is turned on, by solving Equation (14), the time for the voltage across \(C_{2}\) drop to zero can be obtained using the following equation.  
\[t_{m}=\frac{\pi\sqrt{L_{2}C_{2}}}{2} \tag{15}\]  
To ensure that the switch is turned off at a lower voltage, the voltage across \(C_{2}\) should be reduced to zero before the switch is turned off. Therefore, \(t_{m}\) should be less than the switch-on time \(dT\), which can be obtained using the following equation.  
\[\frac{\pi\sqrt{L_{2}C_{2}}}{2}\leq dT \tag{16}\]  
According to Equation (16), \(L_{2}\) should satisfy the following.  
\[L_{2}\leq\frac{4D^{2}}{\pi^{2}f^{2}C_{2}} \tag{17}\]
## 5 Discussion
### Analysis and of Coherence of Differential Interferogram  
As defined and described in Section 3.4, the average coherence coefficient of the high-quality interferogram that yields the annual average deformation rate and time series is calculated, and the results are shown in Figure 14. Due to Sentinel\(-1\)A's advantage in time sampling of, the proportion of values with coherence greater than 0.9 is 3.22% higher than that of ALOS/PALSAR\(-2\). However, the overall coherence is lower than that of ALOS/PALSAR\(-2\), and the proportion of regions with coherence greater than 0.6 is 6.98% lower than that of ALOS/PALSAR\(-2\). In some areas with more artificial construction and less vegetation, Sentinel\(-1\)A shows extremely strong coherence, and ALOS/PALSAR\(-2\) still has some sporadic low-coherence areas inside the high-coherence areas. The coherence of Sentinel\(-1\)A is strongly correlated with the type of surface scatterers, while the variation in the coherence of ALOS/PALSAR\(-2\) across different surface scatterers is relatively small. The decoherence of ALOS/PALSAR\(-2\) is mainly caused by its long temporal baseline. In conclusion, ALOS/PALSAR\(-2\) has a coherence advantage over Sentinel\(-1\)A in areas with dense vegetation. This difference in coherence leads to a lower effective detection area in the Sentinel\(-1\)A deformation-rate results, so the final number of detected landslides will be lower than the number detected by PALSAR\(-2\) (especially in areas with high vegetation coverage).  
Figure 13: Schematic diagram of visible and geometric distortion areas; (**a**,**b**) are schematic diagrams of the visual and geometric distortion areas of Sentinel\(-1\)A and ALOS/PALSAR\(-2\), respectively; (**c**) shows the incident angle of Sentinel\(-1\)A and ALOS/PALSAR\(-2\); (**d**) is a statistical diagram of Sentinel\(-1\)A and ALOS/PALSAR\(-2\) visibility, layover, shadow, and layover-shadow areas. Here, the ribbon settings of (**a**,**b**) are consistent with (**d**).
# Material Characterization for Reliable Resin Transfer Molding Process Simulation
## 1 Introduction
Thus, the solver can provide the filling time and properties (thicknesses, fiber volume contents, geometry) of the final product [11]. However, accurate information on the material properties is needed for reliable simulations of the RTM process: in particular, reinforcement permeabilities and resin viscosity. Several works in the literature are available for the experimental determination of the permeability of RTM reinforcements [12]. Most of them evaluate the in-plane permeability [13; 14], concluding that radial flow (liquid injected from transverse direction at well-defined locations to evaluate 2D permeability) and linear flow (liquid injected from one end of the composite preform to evaluate 1D permeability) experiments give consistent results. The through-the-thickness permeability is more complex to determine [15; 16; 17; 18; 19; 20; 21] and is usually neglected because of the small thicknesses of the preforms. Therefore, a well-defined procedure for the experimental determination of the permeability tensor of fiber textiles and resin viscosity has been formulated in this paper in order to provide a reliable method to simulate RTM production of industrial components using PAM-RTM software. The key steps, from material characterization to numerical simulations, are illustrated by referring to the production of a real component in order to compare to a real-life test case. The permeabilities of four different types of textile reinforcements (three carbon fibers and one glass fiber) were experimentally measured. Moreover, the viscosity of a synthetic oil (15W40, used in glass textile permeability tests) and orthophthal resin (Lavesan LERPOL 666/S RAL 9010) was tested. Permeability tests were modelled in the case of carbon fiber reinforcement tests, while in the case of glass fibers (GFRP), a naval component was reproduced. The analyzed GFRP part represents coverage for an aft-peak, placed aft-ward the cockpit, and it belongs to a series of pieces that are mounted on a 15.85 m (52 ft) cruising sailing yacht. This choice was sustained by the wider experience and higher amount of available data that RTM technology shows in marine industry compared to automotive, where production information represents more sensible data.
# Boosting Safety Protocol Compliance with Eye-Tracking in Chemical Plants
## 2 Materials and Methods
### Experiment Environment
#### 2.3.3 VR Simulation System  
The VR simulation system was developed to accurately represent the chemical production environment. It included interactive workstations where participants could engage in tasks such as monitoring raw material quality, adjusting processing parameters, and responding to safety alarms. The Unity 3D engine was used to develop the XR simulation environment, providing an interactive and immersive experience. The simulation was integrated with the HTC SRanipal SDK, which allowed for real-time eye-tracking data processing. The gaze data were analyzed through event handling scripts within Unity, where deviations from predefined safety-critical zones were used to trigger corrective feedback. The eye-tracking data processing involved fixation detection algorithms to determine whether the participant's gaze remained within critical areas. Statistical analysis algorithms in MATLAB R2024b were used to process performance metrics such as reaction time and accuracy.
## 2 Image Quality Evaluation Results
### MTF Measurement Setup and Method  
Figure 2a shows the experimental setup to evaluate image quality degradation. The slanted-edge target (50 cm in width and 100 cm in height) utilized for MTF measurements was placed 100 cm in front of the camera (AMA-02011M, ANTKR, Bucheon, Republic of Korea). The camera had a resolution of 1980 \(\times\) 1080 pixels and operates at 60 frames per second (fps). The slanted-edge target was illuminated by two halogen lamps (FOMEX H1000, FOMEX, Seoul, Republic of Korea) at a 45-degree angle, with all other light blocked. The illuminance on the target was maintained at 1000 lux, and the light uniformity was over 85% across the target. This uniform lighting condition enabled precise MTF measurements with minimal noise. The camera sensor was covered by waterproof housing, and the lens was protected by a 0.7 mm thick, 90% transparent flat optical cover glass (Borofloat, FINE CHEMICAL INDUSTRY, Gimhae, Republic of Korea). To compare how surface properties of the lens affect the image quality, glass with a hydrophobic coating (Cytop CTL-809M, ACGchemicals, Exton, PA, USA) and uncoated hydrophilic bare glass were used as the lens cover glass.  
For the experiments, a slanted-edge target was captured when the camera lens surface was clean and when it was contaminated by single and multiple droplets. Then, the Region of Interest (ROI) (550 pixels in width and 1000 pixels in height) at the center of the target in the image was analyzed using an image analysis program (Imatest Master, Imatest-24.1), which is widely used for MTF measurements.  
To contaminate the lens cover glass with a single droplet, we placed a droplet in the center of the cover glass using a micropipette (HHT-D20, HTL Co., Warszawa, Poland), as shown in Figure 2b. Also, to contaminate the lens cover glass with multiple droplets, a spray module with a single spray volume of 0.05 mL was used 15 cm from the camera, as shown in Figure 2c.
# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 3 Methodology
### Dynamic Feature Enhancement Module
#### 3.2.3 Feature Fusion  
The final stage of our feature enhancement module is the fusion of weighted features to generate a comprehensive representation. The fusion process consists of the following steps:  
\[F_{l}^{weighted}=w_{l}\cdot F_{l}^{transform} \tag{8}\]  
\[F_{concat}=[F_{1}^{weighted},F_{2}^{weighted},F_{3}^{weighted},F_{4}^{weighted}] \tag{9}\]  
\[F_{enhanced}=\text{BN}(\text{ReLU}(\text{Conv}_{1\times 1}(F_{concat})))+F_{concat} \tag{10}\]  
The fusion block includes the following:  
* Weighting of each transformed feature by its corresponding attention weight;
* Concatenation of weighted features (4 \(\times\) 64 = 256 channels);
* 1 \(\times\) 1 convolution (256 \(\to\) 256) for feature integration;
* Batch normalization and ReLU activation;
* A residual connection to preserve original information.  
The residual connection is particularly important as it allows the model to maintain access to the original feature information while benefiting from the refined representation, ensuring robust feature propagation throughout the network.  
The enhanced feature representation \(F_{enhanced}\) effectively combines multi-scale information with learned importance weights, providing a rich foundation for subsequent direction-aware detection. This adaptive fusion approach significantly improves the model's ability to handle the unique vertical text patterns and complex layout structures found in traditional Mongolian documents.