esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### FeCo-LDH System Application  
The cycling performance of catalyst materials is a crucial factor in assessing their potential for practical applications. The results (Figure 11a) demonstrated that the catalytic efficiency of FeCo-LDH remained exceptionally high after five reaction cycles under consistent conditions. Additionally, the presence of inorganic anions (Cl\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), and NO\({}^{3-}\)) had minimal impact on the degradation of SMX in the FeCo-LDH/PMS system (Figure S11), further confirming the catalyst's suitability for real-world water purification. Finally, we selected three phenolic pollutants as well as three antibiotics for degradation to examine the applicability of the materials, and the results, as shown in Figure 11b, achieved more than 85% removal for all six pollutants.  
In order to examine the degradation of the target pollutant in a real water body, polluted wastewater from a real sewage plant was selected and the target pollutant SMX was added to it, which contains inorganic anions and humic substances that may affect the degradation effect, so as to determine the performance of the system in real wastewater treatment as well as the selectivity of the non-radicals to the target pollutant [75]. Three-dimensional excitation-emission matrix fluorescence spectroscopy (3D-EEM) can distinguish humic-like, protein-like, microbial metabolites and other fluorescent components in the aquatic environment [76], and localize them by characteristic peaks  
Figure 11: (**a**) Recycling performance of catalyst; (**b**) Removal of different pollutants by catalyst.  
(e.g., Ex/Em = 270/450 nm for humic-like peaks) [77]. The technique analyzes samples without complex pre-treatment and offers high analytical efficiency. Many organic contaminants are present in water at very low concentrations (e.g., pharmaceutical ingredients and their metabolites) and may not be accurately detected by conventional methods, while 3D-EEM provides highly sensitive analysis of these trace organic contaminants [78]. In this study, the transformation of organic matter in FeCoLDH/PMS system was monitored by this technique. The specific details of the technique are given in Text S1. The results of three-dimensional fluorescence and emission matrix fluorescence spectroscopy (3D-EEM) (Figure 12a-c) revealed typical fluorescence peaks (EX: 200-220 nm; EM: 280-320 nm) corresponding to humic acid-like organics in the raw wastewater. It can be seen from Figure 12b that after the addition of the target contaminant SMX, a new peak appears on the 3D-EEM, which can be matched with the characteristic peak of SMX [79]. These results demonstrate that the system not only efficiently degrades target pollutants in real wastewater but also effectively breaks down coexisting organic matter. In natural water treatment, challenges often arise from interfering substances, making the system's anti-interference ability critical for practical applications.
# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 4 Experiments
### Mixed Synthetic Policy  
Table 12 shows the audio tagging and sound event detection results of the dataset synthesized by the mixed policy in Table 1. The maximum event length was set to be 5.0 s, and the mean and standard deviation of the event length were 3.13 and 1.67 s, respectively. The mean length was 0.01 seconds shorter than the longer policy because there were more chances of sound events being cut off. This was the most-difficult dataset, so the F1 scores were overall much lower than the other synthetic policies. First, in the first 3 rows, we compare the CNN, U-Net, and the proposed LUU-Net with the GWRP method. Both U-Net and LUU-Net were much better than the CNN in both the tagging and detection F1 scores. LUU-Net was slightly better than U-Net, with a 3.27-times faster training speed. Comparing GWRP, AlphaMEX, and MEX, they showed similar performances in tagging, but AlphaMEX was the worst at detection. Lastly, we compared different values of the hyperparameter \(\alpha\) with the proposed GTAP method. In tagging, \(\alpha=-0.4\) showed a 2.8 higher F1 than \(\alpha=0\), and \(\alpha=0\) showed a 0.8 higher F1 than \(\alpha=-0.4\). The sum of F1 scores was \((83.3,81.3)\) with \(\alpha=(-0.4,0)\), so \(-0.4\) was the best value for the mixed synthetic policy. The number of steps increased from the 8.64 of U-Net to the 34.92 of LUU-Net with GTAP, so the proposed method was about 4-times faster.  
Figure 11 shows the sound event detection examples for the mixtures generated by the mixed synthetic policy. Various models and various global pooling methods were applied. In the CNN detection example in (c), there were many false detections scattered in the segmentation map. As shown in (d,e), U-Net and LUU-Net provided relatively clean detection results, but there were still many false detections. In (f,g), using AlphaMEX and MEX, it can be seen that the false detections mostly disappeared. In (h), using the proposed GTAP with \(\alpha=-0.4\), it provided very clean detection results.  
Figure 10: Sound event detection examples with various models and global pooling methods. The vertical axis in (**b–h**) represents the class number. (**a**) Spectrogram of the input mixture sample generated by the random onset policy. (**b**) Ground truth labels. (**c–h**) display sound event labels predicted by (**c**) CNN with GWRP, (**d**) U-Net with GWRP, LUU-Net with (**e**) GWRP, (**f**) AlphaMEX, (**g**) MEX, and (**h**) GTAP\({}_{\alpha=0.2}\). The \(x\)-axis is the time in seconds, and the \(y\)-axis represents the frequency (**a**) or event labels (**b–h**).
## 5 Conclusions  
In this paper, we have proposed the FDCNB-MINING algorithm able to determine the appropriate number of computing nodes to mine frequent patterns when the network bandwidth is varying. The FDCNB-MINING algorithm is based on the structure we proposed in [10] for distributed computing environments. Previous studies have assumed a constant network bandwidth, but this assumption fails when there are packet collisions due to many mining tasks being run simultaneously. Therefore, to more closely represent actual situations and to strengthen the environmental structure of CARM, we added a monitor node to observe variable network bandwidths in cloud or distributed computing environments as well as to help the kernel node to select the optimal computing node. Although our results showed that the addition of a monitor node leads to a more stable network, our proposed method exerts only a minimal effect on overall execution efficiency. Through empirical evaluation of six different network bandwidth simulation cases, we found that the FDCNB-MINING algorithm has a good performance in unstable network bandwidth environments and its performance  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Support (\%)** & **45** & **40** & **35** & **30** & **25** \\ \hline Case1 & 0.9922 & 1.0057 & 1.0074 & 1.0117 & 0.9970 \\ Case2 & 0.3996 & 0.3643 & 0.2496 & 0.1854 & 0.1943 \\ Case3 & 0.9090 & 0.8926 & 0.9020 & 0.7831 & 0.8185 \\ Case4 & 0.9054 & 0.8746 & 0.9073 & 0.7900 & 0.6940 \\ Case5 & 0.8455 & 0.7885 & 0.7552 & 0.6538 & 0.5861 \\ Case6 & 0.8875 & 0.8110 & 0.7500 & 0.6597 & 0.6211 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Proportions of execution time for FDCNB-MINING/FLR-mining while varying support from 25% to 45% and Case 1 to Case 6.  
Figure 4: Effect of varying support from 25% to 45% on proportions of execution time in FDCNB-MINING and FLR-mining.  
is also superior to that of the FLR-mining algorithm. The proposed method has been shown to deliver excellent performance in terms of execution efficiency and load balancing.  
Conceptualization, C.-C.L. and K.W.L.; Formal analysis, J.-C.C.; Methodology, C.-C.L., W.-C.L., J.-C.C. and W.-Y.C.; Project administration, K.W.L.; Software, J.-C.C., S.-H.C.; Writing--original draft, W.-C.L.; Writing--review & editing, K.W.L.  
This work was supported by the Ministry of Science and Technology of Taiwan, R.O.C., under grant No. 107-2221-E-992-085.  
The authors declare no conflict of interest.
## 6 Cost Analysis of Smart Contract Execution
### Cost Analysis of System Maintenance per Client
#### 6.3.2 Discussion  
Across all plots, it is evident that the solution scales very well with an increasing number of publishers and subscribers.  
**Subscribers:** Each new subscriber introduces a new cost for the addClient and pullTheAlarm functions. However, these costs are originated by the subscribers themselves (and then can be sustained by them). The other costs (e.g., deploying and storing the root of the Merkle hash tree (MHT) for each round for each publisher) are distributed among all subscribers. Consequently, as the number of subscribers increases, the cost per individual client decreases.  
**Publishers:** For publishers, the introduction of a new cost (due to the storage of the root) is partially offset by the subscribers. The deployment cost is always amortized by the new publishers. As the number of publishers increases, the cost remains nearly constant (with a slight increase) because the cost they introduce is almost fully offset by the publishers themselves. Conversely, as the number of subscribers increases, the per-client cost decreases significantly.  
We can conclude that the solution scales very well with the number of clients.  
Figure 4: Annual Cost per Client with 1 Alarm per Week.  
Figure 5: Annual Cost per Client with 1 Alarm per Month.  
Regarding other factors, as expected, higher round frequencies or higher root publication frequencies result in higher costs. However, even in extreme cases (high-security requirements and a high number of alarms), the cost peaks at around $1200 annually, which can be afforded by a moderately sized organization. Under average conditions, the costs are around $300 annually, akin to a standard subscription fee that a user would pay.
# Text Alignment in the Service of Text Reuse Detection
## 5 Experiments  
We conducted a comparative analysis of our method against the baseline method [5], which we refer to as NW-W2V. Given that we introduced several modifications to the baseline, our goal was to systematically evaluate the impact of each individual change.  
Initially, we focused on assessing the enhancements to the distance function, which resulted in three distinct configurations:  
* NW-Fast: This configuration maintains the same settings as [5] but replaces the Word2 Vec (W2V) model with fastText for word embeddings.
* NW-W2V-Hebrew: This configuration retains the original settings of [5] while incorporating Hebrew- and Aramaic-specific enhancements.
* NW-Fast-Hebrew: Building upon the NW-Fast configuration, this version integrates all enhancements tailored for Hebrew and Aramaic.  
To further assess the impact of the alignment method, we conducted an additional set of tests, replacing the Needleman-Wunsch global alignment algorithm with the Smith-Waterman local alignment algorithm. These tests resulted in four additional configurations:* SW-W2V: Employs the Smith-Waterman local alignment algorithm while retaining the same distance function as in [5].
* SW-Fast: Builds upon SW-W2V, incorporating the fastText embedding model.
* SW-W2V-Hebrew: Extends SW-W2V by integrating Hebrew- and Aramaic-specific enhancements for the distance function.
* SW-Fast-Hebrew: Implements our proposed method, combining all introduced enhancements, referred to as SW-Fast-Hebrew.  
To adjust the distance function parameter \(\alpha\), we evaluated its effect on alignment performance, specifically precision, recall, and \(F_{1}\) score, as shown in Figure 2. The \(F_{1}\) score ranged from 0.86 to 0.88, reaching its peak value of 0.88 at \(\alpha=0.2\). The precision varied between 0.86 and 0.87, peaking at \(\alpha=0.7\). The recall ranged from 0.87 to 0.90, achieving its highest value of 0.90 at \(\alpha=0.1\). Based on these results, we set \(\alpha=0.2\) in all configurations to optimize the balance between precision and recall.
# Two-Dimensional Profile Simulation Model for Five-Axis CNC Machining with Bull-Nose Cutter
## 5 Conclusions  
In the present study, a 2D profile simulation model is proposed for five-axis CNC machining with a bull-nose cutter. Based on the geometry and CL data of the machined workpiece, a simplified surface topography model is established. Based on the performed analyses, the main conclusions can be summarized as follows:  
1. The proposed model can be applied to accurately predict the 2D profile of the workpiece. This scheme can be extended to 3D profiles as well.
2. The proposed model can be applied to predict the 2D profile of planes and free-form surfaces.
3. Although this model is proposed for machining with bull-nose cutters, it can be extended to machining with other tools such as ball-end and drum tools.
4. Tool runout, wear, vibration, and other factors are ignored in the proposed model. However, these errors are inevitable in machining. Further, the phase angle between adjacent CC paths is not considered either. These may lead to error (no more than 20%) between the measurement and simulation results. Overall, the simulation results are consistent with the measurement experiments and can provide a reference for optimizing process parameters.  
J.D.: conceptualization, methodology, validation, results analysis, writing--original draft preparation; Z.C.: supervision and project administration; P.C., J.H. and N.W.: investigation, experiment, data processing and analysis, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work received financial support from the National Natural Science Foundations of China (grant nos. 51775445 and 52175435), the Defense Industrial Technology Development Program (no. XXXX2018213A001), and the Shaanxi Province Major R&D Project (no. 2021 ZDLGY03-07).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 18: Measurement results and simulation results for free-form surface. (**a**) Measured in direction #1 of free-form surface. (**b**) Measured in direction #2 of free-form surface.
# Enhanced Steganography for High Dynamic Range Images with Improved Security and Capacity
## 2 Preliminary
### Hiding Capacity  
In the context of image steganography and image annotation applications, the concealment capacity is consistently regarded as a critical factor. The experimental findings in [PERSON] et al.'s image steganography reveal a small capacity ranging from 0.0010 to 0.0026 bits per pixel (bpp). However, this level of capacity is not considered outstanding.  
In summary, there are two key challenges that require addressing: (1) security, i.e., the need to mitigate abnormal distribution of data, and (2) capacity, i.e., the requirement to enhance the embedding performance.
## 2 Data and Methods
### Datasets  
Two soil moisture products and one evapotranspiration product, namely, in situ soil moisture observations collected from CTP-SMTMN, satellite soil moisture retrievals from SMAP, and modeled evapotranspiration product from the version 2 of the Penman-Monteith-Leuning model (PML-V2), were spatially aggregated into 100 km averages using a simple arithmetic average and then utilized as input for SM2 RAIN to derive precipitation. Rain gauge observations from a China Meteorological Administration (CMA) station were used as a reference for the calibration of SM2 RAIN parameters and the evaluation of the estimated precipitation amount. Additionally, the IMERG Final run precipitation was used to investigate the representativeness of the CMA rain gauge station.
## 4 Discussion
The principle proposed by this article can also be applied in the case of expansion on permanent first molars, while presenting the limitation of not being able to take into account the spontaneous decompensation of these elements as they are constrained by bands that hinder their movement; furthermore, the biomechanical principles of the orthopedic expansion applying forces through the permanent molars may be different due to the more posterior application of strength and changes in the antero-posterior opening pattern of the mid-palatal suture. Several studies have explored the effects of RME using CBCT and other 3D imaging techniques [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. However, these methods often lack a stable reference system, leading to variability in measurements, especially in cases of asymmetric maxillary expansion. The hemimaxillary plane proposed in this study provides a stable reference, allowing for more accurate differentiation between dental and skeletal effects. The ability to distinguish between skeletal and dental contributions in RME allows for a more targeted adjustment of the expansion protocol, potentially reducing the risk of unwanted molar tipping. By providing a clear geometric framework, this method facilitates a more nuanced interpretation of changes, particularly in complex cases such as class II malocclusions, where precise measurement of molar derotation and tipping is critical for successful treatment. Future research should focus on validating the hemimaxillary reference system across a large cohort of patients, including those with varied skeletal growth patterns and stages. Additionally, applying this method in longitudinal studies could provide further insights into the stability of post-expansion results, particularly in assessing the long-term retention of the skeletal and dental changes achieved through RME. Such studies would be invaluable for refining criteria for selecting the most appropriate type of expansion device, ensuring tailored treatments for diverse patient needs. Moreover, a deeper understanding of the interplay between skeletal and dental changes could enhance treatment planning, especially in complex cases requiring precise control over molar positioning.
## 3 Results
### Response of Karsi Groundwater Quality Indicators to Changes in Porosity
#### 3.3.1 Changes in pH of Groundwater at Various Depths under Different Porosity Conditions
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Depth/(m)**} & \multicolumn{3}{c}{**Porosity**} \\ \cline{2-4}  & **0.60** & **0.55** & **0.50** \\ \hline
0.1 & 109,275 (_min_) & 118,925 & 152,060 \\
0.2 & 114,750 & 124,133 & 162,689 \\
0.3 & 113,667 & 127,973 & 184,280 \\
0.4 & 116,592 & 121,809 & 178,578 \\
0.5 & 114,633 & 131,467 & 186,367 (_max_) \\ Average value E & 113,783 & 124,861 & 172,795 \\ Range R & 7317 & 12,542 & 23,677 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The TBC values of different depths of groundwater under different porosity conditions. Figure 13: The TBC curve for different depths of groundwater under different porosity conditions. (i) As shown in the results, in the soil surface karst zone, under varying porosity rates, the total E. coli count (TEC) values in groundwater fluctuated between 357 and 1100 MPN/100 mL, with a variance of 743. This significant fluctuation suggests that the groundwater in this karst surface zone typically experiences severe TEC exceedance, indicative of serious microbial pollution. (ii) As demonstrated in Figure 14, throughout the entire soil surface karst zone, as soil becomes denser or the particle size decreases, the general trend is for groundwater TEC values to increase with increasing soil depth, displaying predominantly non-linear changes. Conversely, in conditions where the soil layer is looser or the soil particle size is larger, the changes in groundwater TEC values with increasing depth are relatively smaller, and the fluctuations are less pronounced. (iii) Within the soil layer of the soil surface karst zone, with smaller porosity, the groundwater TEC values increase, with the average being approximately 2.4 times higher than in looser soil layers, and fluctuations are larger and more unstable.
# One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation
## 4 Training Details
### Loss Functions and Loss Value Balancing  
For the segmentation task, the OFA-Net model resorted to softmax cross-entropy loss function as many others did [14; 15; 16; 17; 18; 19; 20; 21; 22].  
The loss function for object detection is more complex owing to the cause that the detection task requires not only classification outputs but boxes prediction as well. The Softmax cross-entropy loss function is appropriate for classification tasks whereas it is helpless when encountering the boxes prediction problem. Mean square error (or L2 loss) function is appropriate to predict bounding boxes because the bounding boxes prediction problem can be viewed as a regression problem. YOLO loss function [10] gave a good example of combining these two types of loss functions, thus the model of this paper recruited it here.  
The last point about loss functions is \"loss value balance.\" The same thinking is also contained in the YOLO loss function, which assigns different weights to the loss values according to their importance. In this work, detection and segmentation are considered equally important, therefore, an adjustment needs to be carried out to make these two loss values approximately equal. During the training process, our team discovered that the loss value of segmentation is much bigger than that of detection. Therefore, the segmentation loss value should be divided by a big constant number \(K\) to achieve our goal. To find this big constant number, the values of the two loss functions (one for the object detection and the other for the semantic segmentation) were recorded during the first several training iterations, say \(n\) iterations. Then, the big constant number \(K\) can be computed as the Formula (1) and the final loss function is shown as the Formula (2), where \(Loss_{dection}\) and \(Loss_{segmentation}\) indicate the original loss value of the object detection and segmentation respectively, and \(K\) is the big constant number in the Formula (1) to achieve loss value balance.  
\[K=\frac{\sum_{i=0}^{n}Loss_{segmentation}}{\sum_{i=0}^{n}Loss_{detection}} \tag{1}\]  
\[Loss=Loss_{detection}+\frac{1}{K}\times Loss_{segmentation} \tag{2}\]