esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Experiment Setup
#### 3.1.4 Incremental Learning Policy  
This study uses the Progressive Incremental Learning (PIL) strategy to optimize the network's training process. This strategy divides the training into three stages, following a \"from easy to difficult\" curriculum learning paradigm, gradually enhancing the network's ability to handle different types of noise [47]. In the first stage, fixed-parameter Gaussian noise (\(\sigma\) = 30, 50, and 70) is used to construct the training sets. A step-by-step training approach is adopted to optimize the network parameters, and the chain parameter initialization strategy is employed to save and transfer the training weights from each sub-stage. In the second stage, more challenging blind Gaussian noise (\(\sigma\sim U(30,70)\)) is introduced. The optimal weights from the previous stage are used as initialization, enabling the network to adapt to dynamically changing noise levels. The final stage employs a progressive mixed noise training strategy, where the network is trained sequentially on single noise cases from Case 1 to Case 4, followed by optimizing on complex mixed noise (Case 5), which includes all types of noise, thus systematically improving the network's robustness. As shown in Figure 4, the model trained with incremental learning exhibits lower and more stable training errors compared to conventional training without incremental learning.  
The proposed network is implemented using PyTorch version 1.9.0+cu111 and trained on a system equipped with an NVIDIA GeForce RTX 4090 GPU (manufactured by NVIDIA Corporation, Santa Clara, CA, USA). For optimization, the Adam optimizer is used to update the network parameters [48], with a batch size of 20. The training epochs are differentiated for each stage: for the Gaussian noise removal task, the training lasts for 100 epochs, while for more complex noise removal tasks, the training is extended to 200 epochs to ensure convergence.  
The learning rate follows a stage-wise decay strategy, starting at 5 \(\times\) 10\({}^{-4}\), and is reduced by 2 \(\times\) 10\({}^{-4}\) at the 50 th and 70 th epochs. To further improve the network's performance, the gradient loss weight \(\lambda\) is dynamically adjusted during training. Specifically, an adaptive weight adjustment function based on the training epoch is designed. This function ensures that during the early stages of training, the network focuses on basic feature learning with a smaller \(\lambda\) value, while in later stages, the network gradually increases the constraint on fine detail features with a growing \(\lambda\) value.  
Figure 4: The training errors with/without incremental learning. Blue curve denotes the gauss noise data training without incremental learning. Red curve denotes the gauss noise data training with incremental learning.
# Multi-Module Combination for Underwater Image Enhancement
## 3 Proposed Method
### Color Deviation Detection Module
Consequently, we utilized the reference images in the UIEB dataset as the basis for calculating the \(K\)-value and set the average \(K\)-value of 0.9 for the 890 reference images as the reference value. The \(K\)-value can be adjusted as necessary. Specifically, in this paper, images with a \(K\)-value greater than 0.9 are classified as exhibiting color bias. Conversely, images without color deviation do not undergo compensation by the adaptive color channel.
## 3 Results and Discussion  
### Effect of FY-07 on the Growth and Metabolism of WJ-1  
To study the feasibility of compound flooding by FY-07 and WJ-1 strains, the strains WJ-1, FY-07, and the mixed bacteria (WJ-1: FY-07 = 1:1) culture experiment was carried out. The growth of these strains is shown in Figure 2. The growth of strain WJ-1 is divided into four stages--the lag phase, exponential phase (log phase), stationary phase, and death phase. WJ-1 exhibited the same lag phase, log phase, and stable phase under the condition of single culture and mixed culture. This indicated that the growth of the two strains had no effect on each other under the condition of adequate nutrition. However, under the mixed culture condition of FY-07, the decline period of WJ-1 was advanced because the nutrient consumption was exhausted in the late stage of strain growth, and there was a competition for nutrients between the strains.  
The growth of the strain reduced the surface tension of the fermentation broth due to formation of the metabolizing surfactant. Therefore, the metabolic status of strains can be reflected by analyzing the change of surface tension. The reduction of surface tension by growth and metabolism of experimental strains is shown in Figure 3. Under the condition of degradation medium, WJ-1, FY-07, mixed WJ-1 and FY-07 bacteria reduced the surface tension to 30 mN/m from 70 mN/m. However, the time for WJ-1 to reach the minimum surface tension was basically the same under the condition of single culture and mixed culture, which was earlier than that of FY-07. This shows that both WJ-1 and FY-07 produced some surfactant to reduce the surface tension, but WJ-1 produced the biosurfactant faster than FY-07. It mainly emulsified the crude oil due to the biosurfactant produced by the WJ-1 metabolism, while FY-07 mainly relied on the biopolymer produced to form plugging control. The presence of strain FY-07 did not reduce the emulsification of strain WJ-1.  
Figure 1: Schematic diagram of the experimental setup for core flooding experiments.  
Figure 2: Growth curve of _Pseudomonas aeruginosa_ WJ-1 with and without FY-07.
However, several challenges remain for increasing and stabilizing crop productivity, as tropical soils typically have low nutrient concentrations, with phosphorus (P) and nitrogen (N) being the most limiting elements for production in Brazil [9]. Phosphorus, in addition to having a high fixation and immobilization potential in the soil, also comes from finite and non-renewable sources. Consequently, the debate over the inability to meet future food demands sustainably has been revisited in the scientific community [10]. Given this scenario, research is needed to address the peculiarities of phosphorus dynamics in the soil and its use in more efficient management systems, involving production, the decomposition of residues, and nutrient cycling. These studies aim to increase crop productivity and phosphorus inputs into the soil, making it possible to reduce the use of mineral fertilizers. If conducted responsibly, the agricultural sector has the potential to mitigate the environmental damage caused by food production while contributing to environmental conservation. Therefore, research investigating management practices and technologies related to this issue is of great social, economic, and ecological importance. The significance of these studies, combined with the fact that in more complex production systems, soil attributes can be used reliably to assess soil quality and conservation, underscores the need for further research in this area. Studies evaluating the physical, chemical, and biological characteristics of soils under NTS and/or ICLFSs have been somewhat neglected. We hypothesized that long-term no-till systems combined with crop inoculation by _Azospirillum brasilense_ modify soil chemical and biological properties in ways that enhance enzymatic activity, making these bioindicators reliable tools for evaluating soil health in sustainable cropping systems. We also hypothesized that such inoculation promotes root development and enhances phosphorus uptake, particularly in low-mobility forms, thereby increasing crop productivity and enabling the more efficient use of residual phosphorus in the soil. This study aimed to investigate whether long-term conservationist management practices, such as no-tillage and inoculation with _A. brasilense_, influenced soil bioinolicators, including chemical, biological, and enzymatic attributes, and how these attributes correlated with crop productivity. Additionally, it evaluated the residual effects of phosphate fertilization and its interactions with inoculation on soil phosphorus fractions and the productivity of crops grown in rotation systems.
## 2 Related Work
### Optimal Placement of Static UAVs  
Earlier studies focused on the deployment of fixed-position UAVs in a number of scenarios, including both single-UAV and multi-UAV configurations. In [19], the authors introduced an evolutionary algorithm for disaster areas, optimizing the terrestrial and UAV base station positions to reduce their count, enhance response times, and ensure sufficient bandwidth. In [20], an optimal UAV deployment strategy was suggested for high-speed wireless networks, to increase the number of served high-priority GNs. The main objective of these studies was to address the problem of developing a single UAV, in order to increase its coverage. However, as the need for wireless connectivity grows, it becomes more and more important to investigate the deployment of multiple UAVs while trying to achieve even wider coverage, improving the efficiency of wireless networks. In [21], the challenge of establishing wireless connectivity in infrastructure-limited terrestrial networks was addressed by using MBSs mounted on UAVs, aiming to ensure communication coverage for distributed GTs by minimizing the number of MBSs through a polynomial-time spiral arrangement algorithm. For high-rate wireless communications systems, an iterative 3D multi-UAV deployment strategy was suggested in [22]. The goal was to maximize system throughput while considering co-channel interference and quality of service (QoS)requirements. However, the abovementioned studies failed to consider multiple UAVs' high levels of mobility and flexible movement design.
# Extending Cooling Rate Performance of Fast Scanning Chip Calorimetry by Liquid Droplet Cooling
## 3 Results
### Liquid Cooling of Sensors with Samples  
For a further test of liquid droplet cooling on the sensor, HDPE and indium samples were loaded on the sensor and cooled by room temperature ethanol droplets. Samples and cooling liquids were chosen such that any interaction between the solvent and the samples was excluded. Note that using a solvent for the polymer under study or a water-containing liquid for hygroscopic materials such as polyamides as cooling liquid is not possible. The efficiency improvement by liquid droplet cooling for sample-loaded sensors is less pronounced due to the larger heat capacity of the sample-sensor system and the low thermal conductivity of the polymer sample. Figure 9 shows HDPE (a) and indium (b) crystallization on the same sensor (XI395) using just heater voltage off switching (air cooling, black) and synchronized liquid droplet with voltage off cooling (liquid cooling, red).  
Figure 8: Influence of droplet size and sensor type: (**a**) sensor XI395, ethanol at room temperature, droplet size from ca. 1 to 200 \(\mu\)L, (**b**) different sensor size, cooled with 200 \(\mu\)L chilled ethanol (XI395 60 \(\times\) 80 \(\mu\)m\({}^{2}\) and UFS1 e500 \(\mu\)m).  
Sample crystallization can be followed as an exothermic effect, resulting in a reduced cooling rate. From Figure 9, it can be seen that the HDPE crystallization temperature decreases with increasing measured cooling rate, while the indium crystallization onset is shifting towards higher temperatures due to faster cooling of the indium sample compared to the sensor membrane. An increase in cooling rate by more than one order of magnitude is achieved by using droplet cooling in both cases.  
Delamination due to temperature gradient was also not observed, probably because of fast cooling of the membrane under the sample, comparable to the sample cooling itself.
## 4 Calibration Method
### Transit Tilt Error Calibration Method  
As shown in Figure 8, if the transit tilt error exists in the laser tracker and we do not consider the installation eccentricity error of test rods, after the standing axis rotates 180\({}^{\circ}\), the transit tilt error (\(i\)) can be expressed as  
\[i=arctan(O_{3}O_{4}/O_{4}P) \tag{8}\]  
where \(O_{3}O_{4}\) is the offset between transit axis before and after standing axis rotation, and \(O_{4}P\) is the distance between the two ends of test rod A and B.  
However, the test rods are noncoaxial with the transit axis due to the installation eccentricity error of \(O_{3}O_{3}^{\prime}\) and \(O_{4}O_{4}^{\prime}\). Therefore, the transit tilt error(\(i\)) can be calculated as  
\[i=arctan\big{[}\big{(}MN+O_{3}O_{3}^{\prime}\cdot\sin\theta_{1}-O_{4}O_{4}^{ \prime}\cdot\sin\theta_{2}\big{)}/O_{4}P\big{]} \tag{9}\]  
where \(MN\) is the offset between the central axis of test rod A and B. The distance (\(O_{4}P\)) can be obtained using the mechanical design value of the rotary stage. The offset (\(O_{3}O_{4}\)) between the central axis of test rod A and B can be measured by the telecentric measurement system. The telecentric measurement system is established at position 2, as shown in Figure 4. The transmitter and the receiver are horizontally placed, and the green LED light emitted from the transmitter is projected to test rod A; the image of test rod A will be recorded and displayed on the monitor. The initial position of the central axis of test rod A is recorded as \(M\). The standing axis rotates 180\({}^{\circ}\), and the green LED light emitted from the  
Figure 8: Transit tilt error. (**a**) Without installation eccentricity error of test rods. (**b**) With installation eccentricity error of test rods.  
Figure 7: Transit offset error. (**a**) Transit offset error model. (**b**) The image of test rod A and B displayed on the monitor.  
transmitter is projected to test rod B; the position of the central axis of test rod B is recorded as \(N\), as shown in Figure 9. The offset (\(MN\)) between the central axis of test rod A and B can be obtained.
## 4 Results
### Experimental Setup  
To validate the efficacy and versatility of the proposed approach, we conducted a comparative analysis with various classification models such as SSRN [20], DBDA [21], CS2 DT [54], Group_Aware(G_A) [29], MSTViT [55], and an SVM network structure with RBF kernel [10]. To ensure a fair comparison, all experiments were performed in a PyTorch 1.13.0 environment on an Intel Core i9-12900H CPU, 16 GB RAM (Intel, Santa Clara, CA, USA), and NVIDIA GeForce RTX 3070 8 GB GPU platform (NVIDIA, Santa Clara, CA, USA). Common training parameters were uniformly set across all frameworks to facilitate fair comparisons under consistent conditions. The hyperspectral image patch size was fixed at \(9\times 9\), batch size at 16, learning rate at 0.0001, and the number of epochs at 200, with training conducted using the Adam optimizer. In addition to the shared parameters, other experiment-specific parameters were configured based on literature guidelines. The SVM model's penalty parameter \(C\) and RBF kernel parameter \(\gamma\) were optimized using GridSearchCV in sklearn 1.1.3. It is noteworthy that differences may exist between the classification performances reported in this study and those from the original literature due to the standardized parameter settings adopted here.  
Evaluation metrics included overall accuracy (OA), average accuracy (AA), and Kappa statistic (\(\kappa\)) to quantitatively assess classification performance. The results presented in this study are an average of 10 trials with randomly selected training samples from the datasets.
## 6 Conclusions  
In the context of an increasing concern toward the development of enhanced solutions for traffic safety improvement, this article has addressed the issues that are related to the use of VLC systems in communication-based vehicle safety applications. A new VLC system has been introduced and its experimental evaluation has been presented. The system has been developed using a more problematic approach, based on a standard power VLC emitter and a wide-angle VLC receiver. Although this method is more suitable for automotive applications, the SNR is affected. The VLC system has been tested for variable communication ranges, under variable SNRs, and for different modulation and coding techniques.  
This work has also investigated the opportunity of using adaptable modulation techniques, coding techniques, and data rates, depending on the existing context. Thus, this article has made a significant step toward a context adaptive VLC system that is capable of maximizing the performance in each environment.  
Another important contribution of this article comes from the experimental evaluation of the system in variable conditions, especially in snowfall and blizzard conditions. These tests have showed the highly disruptive effect of these weather phenomena, and confirmed that the system is still able to maintain communication.  
Future work on this project involves further enhancing the system's robustness to noise and further increasing the communication distance. Additionally, this project involves the development of reconfigurable VLC architectures and context-aware VLC systems. These future works are meant to fully demonstrate and confirm the suitability of VLC systems for automotive applications.  
Conceptualization, S.-A. and A.D.; methodology, A.-M.C., M.D. and V.P.; software, S.-A.A.; validation, A.-M.C., M.D., V.P. and M.P.; investigation, A.-M.C. and S.-A.A.; formal analysis A.-M.C., M.D. and M.P.; resources A.-M.C., M.D. and V.P.; original draft preparation, S.-A.A. and A.-M.C.; writing--review and editing, A.-M.C., M.D., V.P. and M.P.; visualization, A.-M.C. and S.-A.A.; supervision, M.D., V.P. and A.-M.C.; project administration, M.D. and A.-M.C.; funding acquisition, M.D. and A.-M.C. All authors have read and agreed to the published version of the manuscript.  
This work was founded by a grant of the Romanian Ministry of Research and Innovation, CCCDI-UEFISCDI, project number PN-III-P1-1-2-PCCDI-2017-0917, contract 21 PCCDI/2018, within PNCDI III. This work also was funded by Ministry of Research and Innovation within Program 1-Development of national research and development system, Subprogram 1.2-Institutional Performance-RDI excellence funding projects, contract no. 18 PFE/16.10.2018.  
The authors declare no conflict of interest.
## 2 A Novel 3D Discrete Element Numerical Analysis Method for Rockburst  
### Shortcomings of Previous Rockburst Analysis Methods  
The occurrence of rockbursts is extremely complex and affected by many factors. It is difficult to use mathematical analysis methods to simply analyze its inoculation mechanism and catastrophic process. In contrast, numerical methods have become a powerful tool for studying rock mechanical behaviors in recent years, and the numerical simulation of the rockburst catastrophic process can effectively reveal its disaster-causing mechanism. Considering that there are a large number of micro cracks and other defects in the rock itself, the characteristics of the rock mass are mainly characterized by structural discontinuity due to the cutting effect of cracks and joints. Therefore, numerical methods based on discontinuous medium mechanics have been widely applied [18]. Currently, scholars usually use the discrete element method (DEM) to simulate the rockburst ejection characteristics. The discrete element block can produce displacement, rotation, and even complete separation under the action of external force and can accurately recognize the new contacts generated in numerical simulations. Block discrete element code (3 DEC) is part of the DEM, and the shape of the polygonal block in 3 DEC is closer to the engineering rock mass. The computational region is discretized into blocks by intersecting discontinuous surfaces, and each block is divided into a mesh by the finite element method. The mechanical motion characteristics of each element can better simulate the rock failure characteristic and more truly reflect the mechanical response of discrete medium under dynamic and static loads during rockburst.  
Based on the cognition of the rockburst mechanism and the gradual maturity of numerical simulation, some scholars have conducted numerical simulation research on rockburst risk evaluation in combination with the rockburst discriminant index. The quantitative relationship between rockburst risk parameters (such as rockburst location and intensity grade) and numerical indexes is established, which can consider the influence of parameters such as strength-stress ratio, energy ratio, and critical buried depth [19; 20]. By analyzing the distribution range of the rockburst index in numerical space, the location and damage depth of the rockburst can be effectively determined. Summarizing the previous rockburst indexes, most of the existing indexes only consider the three-way compression state of the rock mass, ignoring the possible two-way compression and one-way tension state, which cannot truly reflect the stress state of the rock unit before and after excavation. With the further revelation of the rockburst mechanism, the increase in engineering practice, and the in-depth development of numerical research, the research about the rockburst index gradually shows a significant trend from the single index to the composite multi-parameter index.
## 5 Conclusions  
This paper carried out a series of model tests under a plane strain condition to investigate the stability of high backfill slopes moisturized by rainfalls, considering the influences of rainfall duration and intensity. Based on the extended model cases, a BP neural network model was used to develop a prediction model considering the rainfall duration and intensity. Based on the test result and analyses, the following conclusions can be drawn:  
1. The deformation of the slope increased sharply after reaching a certain surcharge. Increasing the rainfall intensity and duration made the lateral displacement of the slope become more significant, and reduced the stability of the backfill slope.
2. The moisture content was distributed almost parallel to the slope surface after rainfall. Increasing the rainfall intensity caused the soil near the slope surface to be saturated faster. All the slip planes were close to an arc-like sliding, but the rainfall influenced the sizes of the sliding mass.
3. Based on the calibrated model, a model base including 112 cases was established considering the factors of surcharge load, and rainfall intensity and duration. The BP neural network was adopted to build a model for predicting the stability of high backfill slopes under rainfall. The trained BP model shows the average relative error of 1.02% and the goodness of fitness of 0.999, indicating a good prediction effect.  
Conceptualization, Z.Z. and L.Q.; methodology, W.W.; formal analysis, W.W.; investigation, W.W.; resources, J.Z.; data curation, W.W.; writing--original draft preparation, L.Q.; writing--review and editing, Z.Z. and L.Q.; visualization, L.Q.; supervision, G.Y.; project administration, G.Y.; funding acquisition, G.Y. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shanghai Science and Technology Development Funds, Grant, No. 22 ZR1466600, and the National Natural Science Foundation of China (NSFC), Grant Nos. 41972272 & 41772281.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.  
The authors appreciate the financial support provided by the Shanghai Science and Technology Development Funds (Grant Nos. 22 ZR1466600) and the National Natural Science Foundation of China (NSFC) (Grant Nos. 41972272 & 41772281).  
The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test** & **T-1** & **T-2** & **T-3** & **T-4** \\ \hline Prediction result & 1.108 & 1.090 & 1.060 & 1.045 \\ \hline \end{tabular}
\end{table}
Table 7: Safety factor prediction results of the model tests.