esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Polymer Reusability  
The reusability of the poly(n-VIM/C\({}_{6}\)-bis-VIM) polymer with a monomer to crosslink ratio of 11.50 to 1 was investigated by performing multiple DSPE cycles. Therefore, after each loading step the sorbent was regenerated, dried, and reused. In Figure 6, the quantity of bound phenolic acids after one to five DSPE cycles is displayed. Further data is given in Table S12. The applied polymer showed excellent reusability results for DHB with a maximum adsorption decrease of 0.6%. Good reusability results were also obtained for the ChlorA and CaffA with a maximum decrease in adsorption of 5.3 and 13.4%, respectively. A considerable decrease in the adsorption performance was observed for FerA and CinA with a maximum decrease of 30.7 and 50.3%, respectively. It is noteworthy that the quantity of bound ChlorA, CaffA, FerA and CinA mainly decreased after the first DSPE cycle and remained constant after the subsequent cycles. It is possible that the washing solution containing LiCl salt was responsible for this trend by influencing the binding properties of the sorbent. Since the FerA and CinA generally exhibited the weakest binding strength, they were probably the most affected by this effect. By optimizing the washing solution, it is likely to also result in a better reusability for FerA and CinA.  
In summary, it was shown that the [C\({}_{6}\)-bis-VIM] [Br] crosslinked polymer could be regenerated and reused for the majority of analytes making it a good candidate regarding sustainable analytical sample preparation.
## 6 Discussion  
### Industrial Applicability  
The proposed framework combining autoencoders and Gaussian Mixture Models (GMMs) for unsupervised classification and Remaining Useful Life (RUL) prediction demonstrates significant industrial applicability. By eliminating the need for labeled data, the method enables scalable implementation across various industrial sectors, including aviation, manufacturing, and power generation. Predictive maintenance strategies based on this approach can lead to a substantial reduction in unplanned downtime and maintenance costs. The ability to classify engine health states without supervision provides a reliable early warning system for potential failures, allowing for proactive maintenance scheduling.  
One of the major benefits of the methodology is its adaptability to real-world engine datasets. Unlike the NASA CMAPPS dataset, real-life engine operational data does not contain explicit failure labels, making supervised learning techniques difficult to apply. By leveraging unsupervised learning, the proposed framework successfully addresses this challenge, making it a practical solution for industries in which failure data is limited or unavailable.
# A Data Processing and Distribution System Based on Apache NiFi
###### Abstract  
The monitoring of physical and logical networks is essential for the high availability of 5G/6G networks. This could become a challenge in 5G/6G deployments due to the heterogeneity of the optical layer. It uses equipment from multiple vendors, and, as a result, the protocols and methods for gathering monitoring data usually differ. Simultaneously, to effectively support 5G/6G networks, the optical infrastructure should also be dense and ensure high throughput. Thus, vast numbers of photonic transceivers operating at up to 400 Gbps are needed to interconnect network components. In demanding optical solutions for 5G and beyond, enterprise-class equipment will be used--for example, high-capacity and high-density optical switches based on the SONiC distribution. These emerging devices produce vast amounts of data on the operational parameters of each optical transceiver, which should be effectively collected, processed, and analyzed. The aforementioned circumstances may lead to the necessity of using multiple independent monitoring systems dedicated to specific optical hardware. Apache NiFi can be used to address these potential issues. Its high configurability enables the aggregation of unstandardized log files collected from heterogenous devices. Furthermore, it is possible to configure Apache NiFi to absorb huge data streams about each of the thousands of transceivers comprising high-density optical switches. In this way, data can be preprocessed by using Apache NiFi and later uploaded to a dedicated system. In this paper, we focus on presenting the tool, its capabilities, and how it scales horizontally. The proven scalability is essential for making it usable in optical networks that support 5G/6G networks. Finally, we propose a unique optimization process that can greatly improve the performance and make Apache NiFi suitable for high-throughput and high-density photonic devices and optical networks. We also present some insider information on real-life implementations of Apache NiFi in commercial 5G networks that fully rely on optical networks.  
Apache NiFi; big data; data processing; ETL
## 4 Discussion
### Plant Water Condition and Phytolith  
Although the soil water content did not affect the water content of plant shoots, the water potential of plant leaves and the water content of plant roots indicated that soil water content affected plant water conditions, with major differences observed between 30% and 60% of the soil water holding capacity (Table 2). Soil water content is a major factor in increasing plant water uptake, but increases in the latter cease beyond the limit point and the same water potential value is maintained even when soil moisture increases [41]. In this study, only the phytolith accumulation in _P. australis_ increased with high soil water content between 30% and 60% of soil water holding capacity, while that in _P. multifida_ was not affected. Previous research reported that higher soil water content increases phytolith accumulation in plants, describing the change as \"more water--more phytoliths\" [42]. However, in most previous studies, the relationship between soil water and phytoliths was analyzed in Gramineae [17; 43; 44; 45], with a major focus on the water management of food crops and phytolith accumulation [17; 44; 45]. In contrast, no previous research on the relationship between soil water content and phytolith accumulation was available for Pteridaceae species. In the present study, we determined that soil water content did not affect phytolith accumulation in _P. multifida_ as much as in _P. australis_.
# Detection of Parking Slots Based on Mask R-CNN
## 4 Method for Detecting Parking Slots Based on Mask R-CNN  
Mask R-CNN is a multi-task deep neural network that combines the ideas of FPN and fully convolutional networks (FCN) [31] base on Faster R-CNN. It does not only effectively detect the marking-points, but also generates high-quality segmentation mask for the marking-points.  
Marking-points are defined as the cross-points of parking lines, showing a T or L shape, as shown in the red rectangles in Figure 5. The main steps of the proposed scheme are as follows: the first part is image preprocessing, including selecting the region of interest (ROI) and contrast enhancement. Next, the mask of marking-points is generated by Mask R-CNN and then, using the LSD algorithm, the guidelines and parallel lines are found, based on the mask. Finally, candidate parking slots can be determined. Figure 6 illustrates an overall flow chart of the proposed scheme.  
Figure 4: Calibration transformation from the world coordinate system to the undistorted image. (**a**) Original fisheye image, the green circles are the feature points selected in the world coordinate system. (**b**) Undistorted image, the red circles are the corresponding feature points in the undistorted image.  
Figure 3: Transformation relationship between the bird’s-eye view coordinate system and world coordinate system.  
When the driver is driving towards the parking slots, marking-points will appear on both sides of the vehicle, as shown in the blue rectangles in Figure 7. We define the ROI instead of detecting marking-points on the entire image, which reduces computing resources. Besides, the further away from the AVM image center, the more severe the distortion. For the vertical parking slots in particular, some marking-points are blurred, as shown in the red rectangles in Figure 7. Blurred marking-points increase the difficulty of sample annotation. Therefore, it is difficult to detect all the markings based on the entire image.  
Figure 5: Typical types of marking-points. (**a,b**) Vertical parking slots; (**c,d**) parallel parking slots; (**e,f**) slanted parking slots.  
Figure 6: Overall flow chart of the proposed scheme.  
Figure 7: Original AVW image.  
The specific ROI parameters are shown in Figure 8, where \(L\) and \(W\) represent the length and width of the ROI region, \(L_{1}\) and \(W_{1}\) represent the length and width of the vehicle. The geometric constraints are \(L=L_{1}+2*L_{2}\), \(W=W_{1}+2*W_{2}\).
# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 2 Artificial Intelligence Technologies for Smart Greenhouses
### Robotics and Algorithms for Precision Agriculture and Smart Greenhouses
#### 2.4.2. Commercial Development of Robotic Systems for Smart Agriculture (Inhouse and Outdoors)
In most cases, closed-loop control algorithms, such as MPC, may impose new constraints on manipulating variables and often on computational resources [10]. Despite the strain on computational resources, the continued use of MPC was validated by unique benefits and capabilities, including rolling optimization, which provided the operators with sufficient time to initiate inverse responses, address sensor failure, change the computing objectives, and address time delays between sensors. The MPC system could help streamline the performance and accuracy of the UAV systems, which are affected by light intensity, soil moisture, relative humidity, pH, and the concentration of nitrogen, phosphorous, and potassium [103; 135; 103]. The observations made by [103; 135; 113] concerning the regulation of the greenhouse microclimate were in agreement with the research of [PERSON] et al. [137] on the factors predicting the economic return and fruit quality of greenhouse-grown-table grapes in Northern China. Proper regulation of greenhouse parameters was a key prerequisite for higher table grape yields and profit margins. The observations reinforce current research on the benefits of greenhouse microclimate control. For example, the DynalLight system and dynamic climate control in InfoGrow improved the quality of greenhouse crops [136]. Similarly to [136], [PERSON] et al. [11] confirmed that predictive modeling for greenhouse light management could yield significant cost savings. In contrast to [136] and [PERSON] et al. [11], [PERSON] et al. noted that cost savings in predictive modeling were largely dependent on the choice of the algorithm and MPC [138]. The latter study preferred particle swarm algorithms. On the downside, the selection of bio-inspired algorithms may not suffice to offset the existing challenges. Even in cases where the dynamic climate control in InfoGrow was present, and it was challenging to mitigate the impact of local environmental conditions, the benefits associated with timely detection of powdery mildew and management of pests outweighed the drawbacks [135; 13; 13; 111; 113]. The variable test set accuracy (85-90%) of the Adam, RMSprop, Adamax, and Nadam optimizers depicted in Table 3 should not be perceived as a major impediment to the adoption of algorithms for smart farms [69]. The test accuracy can be improved in subsequent training of the algorithms. Moreover, new computational approaches have been employed to enhance the training and test set accuracy.
# An Improved Method for Named Entity Recognition and Its Application to CEMR+
## 4 Experiments and Analysis
### Parameter Setting  
The parameters of our model are listed in Table 2. We obtained word representation with word embedding dimension 256, char embedding dim 64, POS embedding dim 64, and position embedding dim 128. The filter width, numfilter, dilation, and block number of the iterated dilated CNN module were set to 3, 256, [1,1,2] and 4, respectively. Other parameters can also be seen in Table 2.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**CCKS2017**} & \multicolumn{5}{c}{**CCKS2018**} \\ \hline
**Entity** & **Count** & **Train** & **Test** & **Entity** & **Count** & **Train** & **Test** \\ \hline Symptom & 7831 & 6846 & 1345 & Symptom & 3055 & 2199 & 856 \\ Check & 9546 & 7887 & 1659 & Description & 2066 & 1529 & 537 \\ Treatment & 1048 & 853 & 195 & Operation & 1116 & 924 & 192 \\ Disease & 722 & 515 & 207 & Drug & 1005 & 884 & 121 \\ Body & 10,719 & 8942 & 1777 & Body & 7838 & 6448 & 1390 \\ Total & 29,866 & 24,683 & 5183 & Total & 15,080 & 11,984 & 3096 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics on different categories of entities in the dataset. CCKS, China Conference on Knowledge Graph and Semantic Computing.  
To compare with the traditional conditional random field model, we constructed feature templates for the CRF as shown in Table 3. In the feature templates, \(U01\) indicates the feature number, and \(W[-1,0]\) indicates the word at the previous position of the current word. Similarly, \(W[0,0]\) and \(W[1,0]\) indicate the words of the current word and the next word.
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
###### Abstract  
Algorithms that automatically search for routes have various applications, such as games, navigation, and robots, and are used to create optimal paths in the space of real and virtual worlds. This paper introduces an indoor navigation algorithm allowing rescuers to create a topology map of floor plans, given that the amount of data provided by conventional mapping constructs is limited. Traditional pathfinding algorithms include the A* algorithm, which has demonstrated excellent performance; however, its applicability is limited in the case of high-resolution images, as the number of nodes increases significantly. Here, we use the corner node algorithm (CNA) to improve pathfinding performance by preprocessing the data to create a topology map for the pathfinding algorithm. The algorithm can reduce the number of nodes required for the pathfinding process by generating topology maps in advance through two preprocessing processes: node creation and node connection. This effectively reduces the number of nodes to be processed, as well as the error rate when creating more accurate routes. Furthermore, a generated topology map can be reused by the algorithm unless its structure changes, which enhances efficiency. For the user, this means that the sought-after mapping information of the floor plan is quickly made available for navigation. With this algorithm, we created an augmented reality navigation solution to display virtual objects for rescuers, to demonstrate the enhanced performance of our approach.  
algorithm; AR; HoloLens; indoor disaster; navigation; pathfinding; topology map; XR +
Footnote †: journal: Applied
# Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation
## 4 Experiments and Results
### Results and Analysis  
#### 4.3.1 Comparison with Other Semi-Supervised Methods  
In this paper, we design an attention module and apply it to the V-Net network. It can use the interdependence between channels to learn the spatial weight information combined with the feature map, and to obtain some structural regions with strong correlation. The final feature of each channel is the weighted sum of the features filtered by the correlations between channels and the original features. The feature correlation of the channels simulates the remote semantic dependence between features. It helps to maintain the relationship between different channel feature maps, enlarge the inconsistency between categories, and make the feature maps transmitted by the skip-connection have stronger semantic information.  
As shown in Table 2, on the 2018 left atrium segmentation dataset, we replace the backbone network V-Net of the DTC algorithm with Attention V-Net, comparing it with the recurring results of the basic framework DTC. The effect of Attention V-Net is 0.56% higher on Dice, 0.74% higher on Jaccard, 0.16 voxel greater on ASD, and 0.32 voxel greater on 95 HD. Our method outperforms all the other semi-supervised networks in both Dice (89.08%) and Jaccard (80.48%), and achieves competitive results on other metrics. We compare our framework with four semi-supervised segmentation methods, including entropy minimization approch (Entropy Mini) [30], uncertainty-aware mean teacher model (UA-MT) [11], shape-aware adversarial network (SASSNet) [12], and dual-task consistency model (DTC). Please note that we use the official code and results of Entropy Mini andUA-MT, and reimplement the SASNet and DTC for left atrium segmentation. Table 2 shows the quantitative comparison of these methods. It can be found that our method achieved the better accuracy than other semi-supervised segmentation methods on all the evaluation metrics. It shows that our structure can improve the regional similarity of cardiac segmentation results, and also has a significant improvement in the accuracy of the boundary. Thus, our experiments can prove that our attention mechanism can enhance the accuracy of model segmentation, and help to improve the performance of left atrium segmentation.
# Fast-RRT: A RRT-Based Optimal Path Finding Method
## 4 Simulation and Result
### Find Near-Optimal Path  
The algorithm's ability to find a near-optimal path is also measured. The algorithm needs to find an obstacle-free path from \(x_{start}\) to \(x_{goal}\), whose length differs from the optimal path's length is less than threshold \(r\). The environment used in this experiment is the same as Section 3.1, the environment is shown in Figure 8, the length of the environment is 1200, the width of the environment is 900, and \(d_{goal}\) is 1000. For this environment, the length of the optimal path is 1035.  
Similarly, the average and variance of the search time are applied to evaluate the efficiency of the algorithm. The differences between the length of the near-optimal path and the length of the optimal path are set to 5%, 10%, 15%, 20%, and 25%, respectively. At a threshold of 5%, we need to find a path with a length of less than 1087, as the length of the optimal path is 1035. The RRT* is used as a control algorithm to verify the efficiency of the  
Figure 14: Comparison of the search time between Fast-RRT algorithm and RRT algorithm in the narrow passages as a function of different step sizes.  
proposed algorithm. The step size is also set to 30 as the same as the Fast-RRT algorithm. In each experiment, we ran the Fast-RRT algorithm and the RRT* algorithm 100 times and recorded the running time. Finally, the average and variance of the search time of these two algorithms were calculated. The final results are presented in Figure 15.  
As expected, the average and variance of the search time within the Fast-RRT algorithm is smaller than those of the RRT* algorithm. At a threshold of 5%, the RRT* algorithm shows an average and variance of the search time of 13.34 s and 5.75 s, respectively. Impresively, the average and variance of the search time of the Fast-RRT algorithm are 0.322 s and 0.207 s, respectively, which is only 2.4% and 3.6% of the RRT* algorithm. When the threshold is set up to 10%, 15%, 20%, and 25%, the average search time of the Fast-RRT algorithm is 2.9%, 3.4%, 4.0%, and 6.1% of the RRT* algorithm, respectively. Therefore, the search speed of the Fast-RRT algorithm is 20 times faster than the RRT* algorithm. Moreover, the variance of search time within the Fast-RRT algorithm is 3.4%, 2.4%, 2.0%, and 2.1% of the RRT* algorithm, demonstrating enhanced stability.  
Thanks to the superior performance mentioned above, our Fast-RRT possesses great potential in the design and navigation of robots. After using sensors such as lasers and depth cameras to build an environment map, our Fast-RRT algorithm can be used to find a feasible path from the starting point to the target point. Moreover, due to the advantages of fast search speed and small search time variance, our Fast-RRT can be further applied to UAV navigation and other path search tasks that require high real-time performance.
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
## 3 Analysis and Verification
### Influencing Factors of Balancing Performance
#### 3.3.3 Effect of Output Resistances  
The three-cell-to-three-cell balancing mode with \(\rm R_{01}=\rm R_{02}=1\)\(\Omega\) and \(\rm R_{01}=\rm R_{02}=5\)\(\Omega\) is selected for simulation to study the relationships between output resistors and balance performances. The results are shown in Figure 9.  
According to Figure 9a,b, when the switching cycles are the same, \(\rm P_{in}\) and \(\rm P_{out}\) corresponding to the balancing circuit are decreased by the increase in the output resistances. Figure 9c shows that changing the output resistors leads to a smaller effect on the balancing efficiency for the same switching period. Figure 9d, for the same switching cycle, the longer balancing time results from larger output resistances. In summary, smaller output resistances help to increase \(\rm P_{in}\) and \(\rm P_{out}\), and a shorter balancing time and faster balancing speed will be obtained.  
Figure 7: Effect of converter turns ratios on balancing performances: (**a**) input powers and output powers, (**b**) balancing efficiencies and balancing times.  
Figure 8: Effect of switching cycles on performances in different balancing mode: (**a**) input powers, (**b**) output powers, (**c**) balancing efficiencies, (**d**) balancing times.  
Figure 9: Effect of output resistances on balancing performances: (**a**) input powers, (**b**) output powers, (**c**) balancing efficiencies, (**d**) balancing times.