esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Impacts of Data Corruption  
Prior research has empirically demonstrated the substantial impact of data corruption on model performance across learning paradigms. In the realm of language models, [PERSON] et al. (2020) [10] found that missing rare tokens during pre-training led to incomplete token embeddings that limits the model's ability to capture fine-grained semantics.  
Missing data reduce the available context, weakening learned representations and hindering downstream tasks such as summarization or question answering [11; 12]. Noisy data, particularly in large-scale corpora, introduce biases and degrades model robustness. [PERSON] et al. (2020) [3] highlighted that noisy training data increase the likelihood of generating biased or low-quality outputs, while filtering and robust training objectives mitigate such effects. Semantic noise, such as contradictory or irrelevant text, reduces the model's ability to retain factual knowledge and generalize across tasks [13].  
For reinforcement learning, data corruption affects state observations, which are critical for decision making. Missing features reduce state informativeness, leading to suboptimal policies, particularly in partially observable environments (POMDPs) [14]. Studies by [PERSON] et al. (2019) [15] demonstrated that missing features disrupt state-transition dynamics, causing instability in model-free RL algorithms and inaccurate environment models in model-based RL. [PERSON] et al. (2017) showed that noisy features distort latent state representations, leading to poor decision making in high-dimensional environments [4]. [PERSON] et al. (2015) [16] found that Q-values fluctuate with noisy observations, resulting in unstable policies. Moreover, noise hampers transfer learning, as policies trained in corrupted environments fail to generalize to clean environments [17]. These studies collectively highlight how data corruption undermines performance in both paradigms, though through different mechanisms: contextual understanding and factual accuracy in language models versus compounding errors in sequential decision making for reinforcement learning.
## 4 Proposed System Model
### Data Permission Control  
The proposed system is intended to provide authorized access control and permission-based sharing of services. Several permission levels are set for subject to access services of the object. The permission levels determine privileges for the user to access services. These levels are defined according to the sensitivity level of data. Moreover, the permission is granted to subject based on its privilege level; what type of data is requested by the subject. The data permission levels are as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject** & **Object** & **Misbehavior** & **Misbehavior-Time** & **Penalty** \\ \hline Subject-A & Object-X & Multiple requests in 3 min & 2019/3/27 11:12 & Request halted for 2 h \\ Subject-B & Object-Y & Canceled request & 2019/4/1 1:05 & Request halted for 10 min \\ Subject-C & Object-Z & Frequent requests & 2019/4/3 3:09 & Request halted for 3 h \\ \hline \hline \end{tabular}
\end{table}
Table 6: Subject Misbehavior Record.  
* _L0: Data is not accessible,_
* _L1: Data can be used in aggregated computation without revealing the raw data_ _[_10_]__,_
* _L2: Data is partly allowed, and_
* _L3: Data or service is accessible._  
Hence, all of the above-mentioned levels determine the acceptance or denial of users' access request. L0 defines that the subject is not legitimate to avail the requested service. Such services can be personal information of object. Legitimacy of the subject is determined by the object. In L1 and L2, service is shared with some restrictions of usage. In L3, service is fully accessible to subject.
# A Dynamic Numerical Simulation on the Grouting Timing in Retained Rib of Pillarless Mining
## 2 The Principle of the Dynamic Numerical Simulation
### Surrounding Rock Constitutive Model  
#### 2.2.1 The Constitutive Model of Caved Zone  
Gangue, as a stack of rock blocks with compressible properties, contains a large number of pores. The double-yield constitutive model in FLAC3D takes both the shear-tension yield and the permanent volume reduction caused by the compression of the material into account. The double-yield constitutive model can well simulate the re-compaction process of gangue [29]. The yield criterion is shown in Equation (4) [24]:  
\[\left\{\begin{array}{l}f^{s}=\sigma_{1}-\sigma_{3}N_{\varphi}+2c\sqrt{N_{ \varphi}}\\ f^{t}=\sigma^{t}-\sigma_{3}\\ f^{v}=\frac{1}{3}(\sigma_{1}+\sigma_{2}+\sigma_{3})+\mathrm{p}_{c}\end{array}\right. \tag{4}\]  
where \(N_{\varphi}=\frac{1+\sin\varphi}{1-\sin\varphi}\), \(\varphi\) is internal friction angle, \(c\) refers to the cohesion, \(\sigma^{t}\) indicates the tensile strength and \(\mathrm{p}_{c}\) stands for the cap pressure.  
Figure 1: The principle of dynamic numerical simulation on pillarless mining.  
Generally, the gangue cohesion is 0 MPa with an internal friction angle of 30\({}^{\circ}\)[30; 31]. The Salamon stress-strain equation can be used to invert the cap pressure parameters [32], as shown in Equation (5):  
\[\sigma=\frac{E_{0}\epsilon_{v}}{1-\epsilon_{v}/\epsilon_{v}^{m}} \tag{5}\]  
where \(E_{0}\) is the initial secant modulus, \(\epsilon_{v}\) refers to the volumetric strain and \(\epsilon_{v}^{m}\) indicates the maximum volumetric strain. With the gangue expansion ratio, \(\epsilon_{v}^{m}\) can be obtained, as shown in Equation (6) [33]:  
\[\epsilon_{v}^{m}=\frac{K_{p}-1}{K_{p}} \tag{6}\]  
\(E_{0}\) can be obtained through laboratory tests. The existing research indicates that the initial secant elastic modulus of gangue is about 80 MPa [33; 34].
## 2 Materials and Methods
### Quantitative Reverse-Transcriptase PCR for RNA Sequencing Results Validation (RT-qPCR)  
To validate the RNA sequencing results of the DEGs, quantitative RT-qPCR was performed. The primers for RT-qPCR were designed with the PrimerQuest Tool by Integrated DNA Technologies ([[https://www.idtdna.com/PrimerQuest/Home/Index](https://www.idtdna.com/PrimerQuest/Home/Index)]([https://www.idtdna.com/PrimerQuest/Home/Index](https://www.idtdna.com/PrimerQuest/Home/Index)) (accessed on 2 September 2023)) [38], and the eligibility of primers was tested with the NetPrimer tool by PREMIER Biosoft ([[http://www.premierbiosoft.com/NetPrimer/AnalyzePrimer.jsp](http://www.premierbiosoft.com/NetPrimer/AnalyzePrimer.jsp)]([http://www.premierbiosoft.com/NetPrimer/AnalyzePrimer.jsp](http://www.premierbiosoft.com/NetPrimer/AnalyzePrimer.jsp)) (accessed on 2 September 2023)) to avoid the formation of unwanted structures like hairpins and self and cross dimers in the designed product, while ensuring melting temperatures with a difference of up to 5 degrees Celsius for both forward and reverse primers. The designed primer pairs were the following: for F3H forward 5'GAGCATCTCCAATTCCCTCC3' and reverse 5'TTTGGCGGTGTGTGTGGGG3', for DFR forward 5'TCAGCATACCCAATTCC3' and reverse 5'CTCATTACGACCAGATCC3', for bHLH forward 5'AACCA TGTCCTCGCTGA3' and reverse 5'CTCTATCGTTGCGCCTAAGA3' and for actin forward 5'GAACTGGGTTCTTCGG3' and reverse 5'GCTGATTGTGGAGATGA3'. Samples were diluted with nuclease-free water and 2 uL of the diluted RNA was used per reaction in RT-qPCR which was performed using the Luna(r) Universal One-Step RT-qPCR Kit (NEB #E3005). Three biological replicates and three technical replicates were used, and the conditions for the RT-PCR were 1 cycle of 55 \({}^{\circ}\)C for 10 min, 1 cycle for the initial denaturation of 95 \({}^{\circ}\)C for 1 min, 40 cycles of 95 \({}^{\circ}\)C for 10 s for denaturation, 58 \({}^{\circ}\)C for 30 s for extension, with an additional stage of High-Resolution Melting to generate the melting curves according to LighCycler96(r) (Roche) Instrument 95 \({}^{\circ}\)C for 60 \({}^{\circ}\)C s, 40 \({}^{\circ}\)C for 60 s, 65 \({}^{\circ}\)C for 1 s and 95 \({}^{\circ}\)C for 1 s. Relative quantification analysis was conducted and the results were analyzed using the built-in tools of the LighCycler96(r) instrument (software version v 1.1). Actin was used as a reference gene and the Ct values obtained from RT-qPCR revealed the relative expression of each gene compared to actin's levels of expression by the Livak Method (2\({}^{-\Delta\Delta\text{CT}}\)) [39].
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 5 Experimental Results
### Comparison with SOTA Methods  
In our study, we conducted a comparison of our method against three leading font generation (FFG) methods for Korean character generation, each embodying a unique strategy for font creation, to assess the novelty and efficiency of our approach, as follows: (1) FUNIT [33], which employs dual encoders and the Adaptive Instance Normalization (AdaIN) module to seamlessly merge content and style, marking its place in the global disentanglement of style and content. (2) MX-Font [2], a method that avoids direct component label conditioning in favor of a more expert-driven analysis of local concepts like the left-side sub-glyph, efficiently extracting multiple style features for generalization across languages previously unseen. This illustrates the significance of detailed local style features in enhancing the adaptability and versatility of font generation. (3) LF-Font [1], which pioneers a localized style representation through factorization of the low rank matrix, enabling the precise extraction of features of the components. (4) DM-Font [4], which leverages a dual-memory architecture to generate high-quality fonts from a few samples effectively. It captures global structures and local component-wise styles, aligning well with our approach as both methods apply component-based synthesis to maintain the structural integrity of glyph-rich scripts like Hangul. (5) FM-GAN [6] utilizes mixing regularization for unsupervised font synthesis, enabling the generation of font styles with limited supervision. This baseline is relevant to our objective of creating fonts with minimal reference data and demonstrates how different unsupervised techniques can achieve competitive results. For Chinese fonts, we excluded LF-Font and compared our results with VQ-Font [3] as it is the most recently proposed FFG method. We trained all the models on our training data using the same hyper-parameter setting provided by the authors and used their official GitHub repositories as baseline methods. For VQ-Font [3] we attempted to train the model on our Korean dataset. However, the model did not generalize to Korean characters. This may be attributed to the pre-training of VQ-Font in primarily Chinese and Latin scripts, which differ structurally from Hangul. The complex positional nature of Hangul components, where characters are composed of stacking subjyphs vertically and horizontally, may require specialized handling not inherently present in VQ-Font'sarchitecture. For this reason, we only used VQ-FONT to compare generation results for Chinese characters.
# Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank
## 4 Experimental Results
### Pr(\(i\), \(N_{area}\)) and Deg(\(i\))  
From Table 9, the values of \(\rm R_{0.85}\)-\(\rm R_{0.15}\) have a higher chance of being positive excepted for \(i=5\) and 7 and larger \(\rm N_{area}\). Hence, the related probabilities \(d=0.85\) (i.e., \(\rm R_{0.85}\)) are often better than those of \(d=0.15\) (i.e., \(\rm R_{0.15}\)).  
Furthermore, from Table 9, if the adjacent node subsets of two different nodes are the same, e.g., \(V(i)=V(j)\), we have \(\rm Pr(\it i\), \(\rm N_{area})=Pr(\it j\), \(\rm N_{area})\) for all \(\rm N_{area}\). For example, for nodes 1, 3, and 4 in Figure 1, \(V(1)=V(3)=V(4)\), and their probabilities of having at least \(\rm N_{area}=1\), 2,..., 8 burned areas are identical: 1.0000, 0.9642, 0.9620, 0.9586, 0.9524, 0.9349, 0.8530, and 0.5144 for \(d=0.85\), respectively, i.e., \(\rm Pr(\it i\), \(\rm N_{area})\) = \(\rm Pr(\it j\), \(\rm N_{area})\) for \(i=1\), 3, 4, and all values of \(\rm N_{area}\).  
However, the total number of possible state vectors is different for a particular number of \(\rm N_{area}\). For example, for nodes 1, 3, and 4, \(V(1)=V(3)=V(4)\), the number of vectors for nodes 1, 3, and 4 are different for \(5\leq\rm N_{area}\), e.g., the 36276, 36212, and 36180 correspond to nodes 1, 3, and 4 when \(\rm N_{area}=5\).  
It can be seen from Figure 2 for \(d=0.85\), the higher degree node seems to have a higher probability in lower \(\rm N_{area}\) but lower probability in higher \(\rm N_{area}\). For example, nodes 5 and 6 exhibit a degree of 6, which is the highest among all the nodes in Figure 1. \(\rm Pr(\it i\) = 6, \(\rm N_{area})\) is always the highest among all other nodes for \(\rm N_{area}=1\), 2,..., 7 areas, but \(\rm Pr(\it i\) = 6, \(\rm N_{area}=8)\) ranks third from last. Similarly, \(\rm Pr(\it i\) = 5, \(\rm N_{area})\) is less than \(\rm Pr(\it i\) = 6, \(\rm N_{area})\) and its \(\rm Pr(\it i\) = 5, \(\rm N_{area}=8)\) is the least. On the contrary, \(\rm Pr(\it i\) = 2, \(\rm N_{area})\) is always the least for \(\rm N_{area}=1\), 2,..., 7 but that of \(\rm Pr(\it i\) = 2, \(\rm N_{area}=8)\) is not the least.  
Hence, it is necessary to extinguish wildfires in the areas with a higher degree in the beginning. However, the nodes with a lower degree for larger \(\rm N_{area}\) in the final stage of wildfire propagation require further attention.
## 2 Materials and Methods
### Decision Tree Models and Neural Network Models
The second layer involves the application of fuzzy logic rules, which are typically of the form \"If-Then\" statements. The outputs of this layer are the product of the input membership values applied to each rule, representing the fired strength of the rules. The third layer normalizes the rule strengths from the previous layer, dividing each rule's output by the sum of all rule outputs. This ensures that the sum of all outputs from this layer equals 1, setting the stage for a combination in subsequent layers. In the fourth layer, each rule's output is multiplied by the function it maps to (usually a linear function of the inputs). This layer produces a weighted output for each rule based on the normalized firing strengths. The final layer sums all incoming signals from the defuzzification layer to produce a single output from the system. The ANFIS model was trained using a hybrid learning rule that combines the least squares method and the backpropagation gradient descent method. This approach allows for the efficient and precise tuning of membership function parameters to minimize a specified error function, typically the mean-squared error between the predicted and actual values. To validate the ANFIS model, we employed k-fold cross-validation. This method involves dividing the dataset into k subsets and iteratively training the model on k - 1 subsets while using the remaining subset for validation. This technique not only provides insights into the model's generalizability but also helps in identifying overfitting issues. The performance of the ANFIS model is highly dependent on the structure and parameters of the membership functions. We optimized these parameters, including the number of rules and the type of membership functions, using grid search and genetic algorithms to find the best combination that minimizes the prediction error.
## 2 Silymarin Extraction Methods: From Conventional to Novel
### Enzyme-Assisted Extraction  
Enzyme-Assisted Extraction (EAE) employs enzymes to facilitate the breakdown of plant cell walls, thereby enabling the release of bioactive compounds. Enzymes, including cellulases, hemi-cellulaases, pectinases, and a combination of these, have been used for the pretreatment of plant materials. The primary function of cellulase and hemi-cellulase is to act upon cell walls. They act on cell wall components, hydrolysing them and increasing the permeability of the cell wall, thus resulting in a higher yield of the metabolite. However, enzymatic treatment is influenced by a number of variables, including enzyme concentration, temperature, duration, and pH, which collectively affect the enzyme activity.  
A study by [PERSON] et al. investigated the optimisation of EAE for the extraction of silybin from the seeds of milk thistle [28]. The researchers optimised conditions for enzymatic hydrolysis, comparing EAE with conventional ethanol extraction. The defatted seeds were treated with a cellulase enzyme under the optimised conditions, and then subjected to ethanol extraction. For comparison, seeds were also extracted using ethanol under reflux conditions. The study revealed that EAE significantly increased silybin yield, achieving 24.81 \(\pm\) 1.93 mg/g defatted seeds in comparison to 10.42 \(\pm\) 1.65 mg/g with ethanol extraction, representing a 138% increase. Infrared (IR) spectroscopy confirmed that EAE did not alter the extract's structure. Scanning Electron Microscopy and Transmission Electron Microscopy (TEM) images indicated that EAE effectively disrupted seed cell walls, enhancing silybin release.
## 4 PFAC Calibration Results
### Sentinel-3A -3B Cal/Val Results in Tandem Phase  
Sentinel-3B was flying in tandem 30 s ahead of Sentinel-3A from 6-June-2018 to 17-October-2018 (Figure 25). Figure 26 presents the Cal/Val results for Sentinel-3A and Sentinel-3B during this tandem phase along sea surface Passes No. 14 (S3A), No. 278 and No. 335.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cal/Val Site/ Satellite** & **Sentinel-3A** & **Sentinel-3B** & **Jason-3** \\ \hline Gavdos & \(-\)11.98 mm (No. 14) & - & \(-\)4.34 mm (No. 18) \\  & \(-\)10.08 mm (No. 335) & - & \(-\)2.78 mm (No. 109) \\ CRS1 & \(-\)3.68 mm (No. 278) & \(+\)4.55 mm (No. 14) & \\ RDK1 & \(-\)6.00 mm (No. 335) & \(+\)4.85 mm (No. 71) & \\ CDN1 Transponder & \(+\)4.90 mm (No. 14) & \(-\)3.46 mm (No. 335) & \(+\)5.30 mm (No. 18) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Latest calibration results for satellite altimetry missions as determined by four operational Cal/Val sites that comprise the Permanent Facility for Altimetry Calibration in west Crete, Greece. The Gavdos, CRS1 and RDK1 Cal/Val sites are used for sea-surface calibration, whereas the CDN1 is used for range transponder calibration.  
Figure 24: The Jason-3 calibration results for the sea-surface height, based on ascending pass No. 109 and using the Gavdos Cal/Val. The results cover cycles 1-150 with Geophysical Data Records-version D (GDR-D) data.  
Figure 26: Sea-surface calibration results for Sentinel-3A and Sentinel-3B satellites during their tandem phase.  
Figure 25: Sentinel-3A and -3B flying (not to scale) in tandem from June to October 2018 over west Crete, Greece and the transponder (CDN1) and sea-surface (Gavdos) permanent Cal/Val sites. Similar situations occurred for S3A and S3B for Pass No. 278 and No. 335 over the CRS1 and Gavdos sea–surface Cal/Val sites.  
Over the sea surface and during tandem, the bias difference of Sentinel-3B with respect to Sentinel-3A is calculated to be +20.66 mm (average). This implies that Sentinel-3B seems to measure sea-surface height by +2 cm higher than Sentinel-3A, at least during their tandem phase. Nonetheless, this result is based only on very few cycles and thus cannot be statistically significant but only indicative.  
During the tandem Sentinel-3A and Sentinel-3B phase, transponder calibrations were carried out at the CDN1 Cal/Val site (Figure 27). One of them was not successful, as Sentinel-3B's altimeter was operating in Low Resolution Mode. Here also, it can be seen that Sentinel-3A seems to measure a slightly shorter range than Sentinel-3B in that short transponder Cal/Val analysis.  
Both results in sea surface and transponder analysis corroborate each other during the tandem phase of Sentinel-3A and Sentinel-3B. The analysis continues with crossover Cal/Val results.
## 3 Test Details
### Solutions  
From the previous experiments and the theoretical analyses, it is believed that both the enabling load and contacting area play important roles in improving the electrical performance under TH for area array packages/printed circuit board interfaces. To get better TH performance, increasing the enabling load helps, and increasing the contacting area (with a minimum contact force requirement being met) also helps.  
There are relationships between metallurgical surface finish and key tip design parameters. Larger contacting areas are able to provide more space for more gas tight contacting points. When the contact force meets the minimum requirement, increasing contacting area actually helps improve the contacting efficiency by giving full play to the potential of metallurgical surface finish. Additionally, with more gas tight contacting points, the possibility of all a-spots being covered by foreign materials under TH is less. This paper proposed a design optimization methodology to correlate tip radius with asperity spots and contact resistance requirements. The proposal proved to be efficient.  
Increasing the enabling load is the typical industrial solution, but it leads to extra cost in loading mechanism design, and in printed circuit boards design, etc. For instance, in order to get to higher load, a thicker backing plate and/or a thicker loading plate is needed. The extra material to be used can result in huge costs when it comes to high volume manufacturing. Besides, with a higher enabling load, it is very likely that extra effort is needed to accommodate for the risk due to creep on the maximum load end. More seating plane areas may be needed. This can result in less pin count, and needless to say, it is not desirable.  
Thus, in industrial application, the design strategy proposed in this paper shall be a reasonable first step to take in order to optimize the electrical performance under TH and to save cost. If this route is insufficient, other solutions such as increasing the enabling load may come in as a second step.
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 2 Autonomous Underwater Vehicles  
Two distinct AUVs are considered in this work. The first one is COMET, which is shown in Figure 1. COMET can work in a swarm mode with up to 10 AUVs. The acoustic communication system enables them to have an accurate positioning information [15]. A precise underwater positioning is useful not only for swarm navigation, but also for field data collection. These surveyed data are associated with a more reliable ocean coordinates. This AUV can operate in depths up to 300 m and its battery can last up to 20 h. COMET is around 2 m long and it weights less than 40 kg.  
The second AUV is NemoSens, which is depicted in Figure 2. NemoSens is a micro AUV, and it is 90 cm long, has a diameter of 120 mm, and weights less than 9 kg. This micro AUV can operate in a swarm mode with up to seven vehicles, which extends the mission coverage [16]. NemoSens is able to perform missions up to a depth of 300 m, with a battery autonomy up to 10 h. Another differential feature of this AUV is its flexible system. With the open Linux architecture, it is possible to test and employ any other navigation algorithm.  
Figure 1: AUV COMET.