esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Molecular Mechanisms Behind the Resistance of Biofilms to Antimicrobial Agents
### Persister Cells  
Persister cells, a distinct group of dormant and highly antibiotic-tolerant bacterial cells, are essential in understanding the resistance of biofilms to antimicrobial treatments. The specialized cells found within biofilms have been recognized as a key factor in biofilm resistance and play a significant role in the recurrence of bacterial biofilms in medical and industrial settings [155]. In contrast to genetically resistant cells, persister cells exhibit a temporary form of resistance that arises not from genetic mutations but from their metabolic inactivity and dormancy, allowing them to avoid the effects of antibiotics [156]. Persister cells have been observed in a diverse array of bacterial and fungal species, such as _P. aeruginosa, E. coli, S. aureus, C. albicans, A. baumannii_, and _B. cereus_[12; 157; 158; 159; 160]. Studies indicate that persister cells exist in multiple bacterial species, especially in biofilms. In biofilms of _P. aeruginosa_, specific clusters of persister cells are found in the deepest layers, where oxygen and nutrients are scarce [157]. The association of these cells with mechanisms like the suppression of protein translation, induction of dormancy, and development of antibiotic tolerance highlights their role in surviving amid antimicrobial agents [12]. The presence of persister cells in biofilms rises markedly when they are subjected to elevated levels of antibiotics. Research has shown that biofilms contain more persister cells when exposed to antibiotic concentrations that significantly surpass the minimum inhibitory concentration needed to eradicate planktonic cells [161]. This phenomenon highlights the significance of persister cells in maintaining biofilm viability, even in the face of rigorous antimicrobial treatments. The existence of persister cells is well-established. However, complex molecular processes that require more investigation regulate their creation and reactivation. A primary mechanism involves toxin-antitoxin systems, as shown by the hipBA module in _E. coli_. In this approach, the HipA toxin phosphorylates target proteins, blocking critical cellular activities such as translation and inducing a dormant state in cells. This dormancy offers protection against antibiotics that depend on active cellular processes for effectiveness [162; 163]. Moreover, the stringent response, governed by (p)ppGpp signaling, is essential for persister cell production since it modulates bacterial metabolism under stress conditions. This communication reduces growth rates and energy expenditure, facilitating the shift into a metabolically dormant, antibiotic-tolerant condition [164; 165]. Persister cells resume activity when external stressors, such as antibiotics, are removed. Reactivation is often linked to metabolic pathways, with energy generation as a key trigger for exiting dormancy [166; 167]. Mechanisms and illustrations of biofilm resistance to antimicrobial agents [Table 1].
## 3 Results and Discussion
### SG1-SG4 Exhibit Cytotoxicity to Colon Cancer Cells
In addition, SG1 also showed more toxic effects on these three normal cells as compared to SG4. A possible reason for this may be the different polyphenols contents in SG1 and SG4 or other composition and structural variances. Interestingly, HT-29 cells showed a survival rate of less than 60%, and three normal cells had a survival rate of more than 90%, following treatment with SG4 for 48 h at a concentration of 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. Therefore, the anticancer activity and signaling cascade of SG4 against HT-29 cells were further investigated using SG4 at a concentration of 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for 48 h. Previous studies reported the inactivation of PI3K (an upstream molecule of Akt) and Akt may be triggered by fucoidan, leading to cellular apoptosis [24]. Overexpression of protein kinase B (PKB)/Akt is frequently described in many types of human cancers [25]. Moreover, apoptosis induced by the inhibition of PKB/Akt predominantly occurs in cancer cells rather than in normal cells [26]. The reason that SG4 is more cytotoxic to cancer cells than to normal cells may be attributed to the differential expressions of PKB/Akt between cancer cells and normal cells. However, further experiments are still needed to elucidate the precise mechanism. In summary, SG4 was found to be a better candidate as an anti-colon cancer agent due to its relatively high cytotoxicity to HT-29 cells, low cytotoxicity to normal cells, and high production yield.
# Open-Pit Map: An HD Map Data Model for Open-Pit Mines
## 2 Application Scenario of HD Maps for Open-Pit Mines
### Application Scenarios of HD Maps in Open-Pit Mining
no predefined road exists, paths are generated in real-time using information from the HD map about drivable areas. HD maps must provide accurate descriptions of information such as blasting stockpile details, material information, and ore grade levels. HD maps also need precise positioning of the road network and various location areas, establishing a topological map of the entire scene's road network and creating precise paths between operational points. A conceptual representation of the scenario is provided in Figure 3.  
Safety alert and real-time road condition scenarios. In open-pit mining, roadside equipment and sensors on transportation vehicles identify information about road obstacles, road surface water accumulation, snow conditions, weather conditions, and more. This information is transmitted to the control center in real-time and is updated on the dynamic information layer of the HD map. It is promptly relayed to any potentially affected equipment. When the mining operation is planning explosives handling and blasting activities, it is essential to establish alert zones for both the construction phase and the blasting phase. During these phases, the entry of equipment and personnel is strictly prohibited. The HD map must clearly define the boundaries of these restricted areas.
## 4 Comparison and Discussion
### Ablation Study
#### 4.1.3 Gaussian Convolution Kernel  
The Gaussian convolution kernel in the model discussed in this paper is replaced with a traditional convolution kernel. Random initialization is used for training. Figure 13 and Table 8 show the prediction results of the two models.  
From the results, it can be seen that the model in this paper achieves a better prediction performance. The convolution operation in this paper extracts inertial and delay information from the data by employing the Gaussian kernel. There are two trainable parameters in the convolution kernel used in this paper, which determine the shape of the Gaussian curve. Therefore, the convolution kernels used in this paper can learn well for different inertia and delay features. The analysis is carried out in two aspects.  
(1) The problem of data characteristics: Due to the large delay and inertia characteristics of CFB unit data, it is difficult for the traditional CNN algorithm to identify the inertia and delay characteristics of each feature. Traditional CNN is used to mine periodic information in a time series. In contrast, the bed pressure prediction task has a short time interval and is time sensitive. Therefore, the periodic information and periodic patterns of the data are not obvious in this task for CFB units.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Control Group**} & \multicolumn{2}{c}{**Experimental Group**} \\ \cline{2-5}  & **MAE** & **MAPE/\%** & **MAE** & **MAPE/\%** \\ \hline Dataset 1 & 0.0379 \(\pm\) 0.0003 & 6.81 \(\pm\) 0.07 & 0.0440 \(\pm\) 0.0006 & 7.90 \(\pm\) 0.11 \\ Dataset 2 & 0.0165 \(\pm\) 0.0005 & 3.33 \(\pm\) 0.12 & 0.0185 \(\pm\) 0.0001 & 3.74 \(\pm\) 0.02 \\ Dataset 3 & 0.0327 \(\pm\) 0.0021 & 6.68 \(\pm\) 0.39 & 0.0398 \(\pm\) 0.0006 & 8.04 \(\pm\) 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The prediction comparison table of differential processing of input variables.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Control Group**} & \multicolumn{2}{c}{**Experimental Group**} \\ \cline{2-5}  & **MAE** & **MAPE/\%** & **MAE** & **MAPE/\%** \\ \hline Dataset 1 & 0.0379 \(\pm\) 0.0003 & 6.81 \(\pm\) 0.07 & 0.0440 \(\pm\) 0.0006 & 7.90 \(\pm\) 0.11 \\ Dataset 2 & 0.0165 \(\pm\) 0.0005 & 3.33 \(\pm\) 0.12 & 0.0185 \(\pm\) 0.0001 & 3.74 \(\pm\) 0.02 \\ Dataset 3 & 0.0327 \(\pm\) 0.0021 & 6.68 \(\pm\) 0.39 & 0.0398 \(\pm\) 0.0006 & 8.04 \(\pm\) 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The prediction comparison table of differential processing of input variables.  
Figure 13: The prediction comparison diagram of Gaussian convolution kernel.  
(2) The problem of data size: The model requires more inductive bias to improve prediction performance on the small datasets [39]. The Gaussian convolution kernel in this model contains more inductive bias. Therefore, CNN with Gaussian convolutional kernels performs better on smaller datasets than traditional CNN.
## 3 Results and Discussions
### Damage Classification Using the Deep Learning Approach
#### 3.3.1 Classification of AE Signals Based on the Damages Stage
The processing time for alexNet is 2 h 07 min, for SqueezeNet, it is 61 min 16 s, and for the new model, it is 36 min 32 s.  
Based on all the training modes performed to automatically classify the damage stages of AlSi10 Mg specimens, a few observations were made. First, the training time of AlexNet is greater than that of squeezeNet and the new model, especially for the high number of parameters [61]. Secondly, the ReLu activation function converges quickly and therefore takes much less time than the models trained with the sigmoid function. The sigmoid activation function is a fraction between 0 and 1. As the layers multiply, the global gradient becomes exponentially small, so each step along the gradient causes only a tiny change to the weights [62]. With ReLu activation, on the other hand, the gradient is either 0 or 1, so the global gradient is neither too small nor too large [63]. Figure 17: Classification based on the damage stage using TM 2: (a) AlexNet, (b) SqueezeNet, and (c) Proposed model CNN.
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 6 Conclusions  
In this paper, we proposed a cloud-edge collaborative data offloading strategy that optimizes the transmission path within a node group to reduce the data offloading delay and balance delay differences between nodes. Our approach leverages the data offloading capabilities of multiple nodes in the cloud and edge computing environments to provide high-quality data services to a more significant number of users despite limited bandwidth. Our experiments demonstrated that WFO could significantly enhance the number of users  
Figure 11: Variance and standard deviation of WFO and FCFS conducted on different bandwidths. WFO is better than FCFS in most cases.  
Figure 10: The performance gap exists between WFO with priority and WFO without priority. (**a**) Small node topology; (**b**) Large node topology.  
accessing the network simultaneously while also improving the service capacity of a data center. Furthermore, our experiments were conducted on networks with an increasing number of nodes, demonstrating the effectiveness of WFO in networks of varying sizes. The performance of WFO was compared to other data offloading strategies, keeping the number of users, data volumes, data center bandwidths, forwarding delays, and link delays constant. The findings revealed that WFO outperformed the other strategies regarding the number of users and latency. Specifically, WFO served up to 80% more users than FQ and FCFS. When the final node has offloaded all data, the resulting delay is up to 80% better than other solutions. WFO can synchronize data between the cloud and the edge. Our future research will concentrate on improving data offloading between terminals and the edge through wireless channels and achieving rapid convergence of data from the edge to the cloud. The former aims to enable efficient data sharing among terminals, while the latter focuses on enabling rapid aggregation capabilities from the edge to the cloud. Additionally, the paper suggests that dynamic networks should be considered when offloading data in a cloud-edge collaboration network.  
Conceptualization, S.L., Y.X. and J.X.; Methodology, Z.L. and J.X.; Software, J.Q.; Formal analysis, Y.X.; Writing--original draft, S.L.; Writing--review & editing, S.L.; Supervision, Z.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable.  
In order to prevent the data from being abused, we open our data based on reasonable requests. Please contact authors with a formal application form to access the data from [EMAIL_ADDRESS]. The authors declare no conflict of interest.
# Analysis of the Working Performance of a Back-to-Back Geosynthetic-Reinforced Soil Wall
## 3 Results and Discussion
### Parametric Study on the Reinforcement Arrangement
#### 3.3.1 Post-Construction Deformation of the Wall  
The FEM analysis results on the lateral deformation revealed that there was a significant reduction between 7.0 and 8.0 m length, as shown in Figure 13. The deformations with the 7.0 and 8.0 m cross-arranged reinforcements were approximately 554.9% and 114.8% of the baseline case, respectively. The deformations with the same-layered reinforcement were approximately 379.4% and 96.7% of the deformation from the baseline case, respectively. Hence, the cross-arranged reinforcement performed worse than the same-layered reinforcement, with the former being 146.2% and the latter being 118.7% of the deformation.  
It was verified that the key point in controlling post-construction deformation was whether there were any reinforcement layers that were completely covered with geosynthetics. The post-construction settlement was smaller with multiple layers of fully covered reinforcements (e.g., \(L\) = 8.0 m), which was 96.9% of the baseline case. An additional length was needed for the cross-arranged reinforcement to achieve the same performance on the deformation of the wall. For the BBGRSW, the settlement with a cross-arranged 8.0 m long reinforcement was approximately 95.4%, of which 7 m was a same-layered reinforcement. This indicated that the reinforcement length along with the arrangement of the reinforcement was a key parameter for post-construction settlement.  
Figure 12: Maximum tension with different reinforcement lengths. (**a**) Left side; (**b**) right side.
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 6 Computational Complexity  
This section shows the comparison of the computational complexity and elapsed time. As shown before, a spherical microphone array is considered. Thus, the spherical harmonic order needs to be taken into consideration. Thus, only DPD-MUSIC and the proposed method are considered.
## 4 Conclusions  
A statistical model has been proposed that describes the mesoscale size effect on the strength of open-hole woven-reinforcement composites under tension, building on methods proposed in previous works by the authors. The statistical model relies on a discretization of the component into sub-volumes, subjected to the same equivalent stress, computed with a mesoscale finite element model built from the local orientation of the woven fiber reinforcement, measured experimentally. An equivalent stress is defined at the material mesoscale, with four quadratic terms, similar to the Tsai-Hill failure criterion. The maximum equivalent stress is the random variable in the model, whose probability is governed by the scale effect. The statistical model calibrated on experimental results of open-hole tensile tests showed a remarkable accuracy in describing the mesoscopic scale effect governing the failure of woven tows. The mesoscale finite element model  
Figure 4: Model cumulative distribution function of the maximum equivalent stress for the 24 mm and 36 mm specimen types. Experimental observations are shown as individual markers.  
that accounts for the variation of the stress fields due to the material heterogeneity, and the stress intensification induced by the notch, is a cornerstone of the proposed method. From the mesoscale stress field computed with the finite element model, the statistical model accurately predicts the experimental results with a 94% coefficient of determination. For a given probability of failure, the model predicts a lower critical equivalent stress in wider specimens.  
In summary, the proposed method is an efficient tool to predict the component strength accounting for both the material heterogeneity and the scale effect that governs the failure of the carbon fiber tows of the woven composite. It is worth noting that the proposed methodology, while used here to study the circular notch size effect, has the potential to be applied to unnotched specimens, other types of notches, or composites with different types of woven reinforcements, including hybrid ones. Another possible development is the homogenization of the mesoscale model in a homogeneous, macroscale one, to provide a readily applicable design criterion for notched composite parts.  
Conceptualization, D.S.P.; methodology, A.F. and D.S.P.; investigation, A.F.; writing--original draft preparation, A.C., A.F. and C.B.N.; writing--review and editing, D.S.P.; supervision, D.S.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Calibration Experiment
### Dual-Robot Base Coordinate Calibration Experiment  
As shown in Figure 8, the right side is the ABB robot (\(i=1\)), and its base coordinate system is \(\{B_{1}\}\), the left side is the KUKA robot (\(i=2\)), and its base coordinate system is \(\{B_{2}\}\), and the measurement data are shown in Table 10. Figures 9 and 10 show the position of the robot \(i=1\) and robot \(i=2\) end adapters.  
According to the data in Table A2, construct the objective function, calculate the positions of \(p_{1}\), \(p_{2}\), \(p_{3}\) relative to the base coordinate system \(\{B_{1}\}\) and \(\{B_{2}\}\), and calculate \(H_{B_{1}}^{G}\) and \(H_{B_{2}}^{G}\) by Formulas (6), (8), (9). Then, based on Formula (4), the base coordinate transformation relationship \(H_{B_{2}}^{B_{1}}\) of the ABB robot relative to the KUKA robot is calculated.  
\[H_{B_{1}}^{G}=\begin{bmatrix}0.030&-0.858&-0.037&1601.866\\ 0.858&0.031&-0.019&-122.710\\ 0.023&-0.042&0.999&94.0803\\ 0&0&0&1\end{bmatrix}\]  
\[H_{B_{2}}^{G}=\begin{bmatrix}-0.103&0.848&-0.016&1545.780\\ -0.848&-0.103&0.003&78.066\\ 0.001&0.019&0.999&131.368\\ 0&0&0&1\end{bmatrix}\]  
\[H_{B_{2}}^{B_{1}}=\begin{bmatrix}-0.989&-0.084&-0.054&3124.378\\ 0.085&-0.991&-0.013&-178.282\\ -0.072&-0.023&0.997&149.727\\ 0&0&0&1\end{bmatrix}\approx\begin{bmatrix}-1&0&0&3124\\ 0&-1&0&-178\\ 0&0&1&145\\ 0&0&0&1\end{bmatrix}\]  
Through the base coordinate transformation relationship \(H_{B_{2}}^{B_{1}}\), the base coordinate relationship of the two robots in this dual-robot collaborative platform can be obtained. The \(X\)-axis of the base coordinates of the two robots is parallel and reversed, and the distance is \(p_{x}=3124\) mm; the \(Y\)-axis is parallel and reversed, and the distance is \(p_{y}=178\) mm; the \(Z\)-axis is parallel and the same direction, and the distance is \(p_{z}=145\) mm. This result is consistent with the platform design value.  
The error between the experimental results and the design value of the collaboration platform comes from:  
1. Processing accuracy of robot end adapter and calibration tooling  
In order to make the calibration simple and fast, an adjustable magnetic base and an easy-to-remove end adapter are used in the experimental equipment uses. Therefore, the calibration accuracy will be affected by its processing error in calibration.
2. Repeated positioning error of robots and measuring accuracy of the wire sensor  
The fast calibration method for base coordinates of the dual-robot is limited by the accuracy of the wire sensor used in the measurement, and the error also comes from the repeated positioning accuracy of the robot to be calibrated.
3. Actual installation error of the dual-robot collaborative platform  
There will be a certain deviation between the actual value and the design value of the dual-robot collaborative platform due to factors such as the installation site, installation tools, and installlers.  
Figure 10: Robot \(i=2\) tool end adapter location diagram: (**a**) calibration point \(k=1\); (**b**) calibration point \(k=2\); (**c**) calibration point \(k=3\).
## 5 Results on ML/DL-Based Modulation Recognition
### Performance Comparison of Modulation Recognition Techniques  
Table 18 presents how well modulation recognition techniques perform in UWA communication. Key metrics include training loss, complexity, accuracy, and precision. The training loss and complexity were explained earlier. Average accuracy shows the overall rate of correct predictions across all modulation types. It is calculated as the ratio of correctly predicted signals to the total number of signals tested:  
\[\text{Accuracy}=\frac{\text{Correct Predictions}}{\text{Total Predictions}} \tag{3}\]  
Precision reflects how reliable the system is when identifying a specific modulation type. It is given by  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
where TP is true positives and FP is false positives. A confusion matrix is used to compute these values. It shows how many predictions are correct or incorrect for each class and evaluates classifier performance in modulation recognition and other tasks.  
The models in Table 18 demonstrate a clear trade-off between accuracy and complexity. Lightweight classifiers (such as SVM and compact CNNs) run in under 5 ms and use fewer than 200 K parameters, yet still achieve over 98% accuracy and near-perfect precision. Mid-range hybrids and contrastive-learning networks push accuracy above 99% (with some even reaching 100%), but they incur higher computational costs. At the extreme, NAS-driven and transformer-style architectures require millions of parameters and seconds of processing time for only modest accuracy gains. Models based on deep complex networks or pure contrastive loss show wider accuracy variability (64-73%), reflecting sensitivity to signal conditions and feature design. Overall, these results highlight the need to balance model size, inference speed, and classification performance for real-time modulation recognition in underwater acoustic systems. Overall, these modulation recognition methods achieve high accuracy (often \(>\)90%) and precision (up to 100%), with low-complexity models enabling efficient real-time use and deep hybrid architectures delivering marginal improvements at higher computational cost.