esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
## 3 The IACO-Based Spectrum Allocation Method
### Variable Neighborhood Search Process in IACO  
The DE-based ACO is a good way for seeking large search spaces. On the other hand, the combined algorithm has a weakness in which it fails to intensify the search in the promising area. Thus, we use VNS to enhance the local searchability of the improved algorithm. The VNS process in IACO has \(l\)-dimensional solution space, and \(N^{s}\) is the set of neighborhood space for solution \(s\). The process of VNS for local search is shown as follows:  
_Construct the neighborhood \(N^{s}\)_. The main objective of the improved algorithm is to maximize the spectrum utilization. To achieve this we first randomly choose a user \(x_{i}\in s\), and get a benefit list \(L=(b_{1}^{x},b_{2}^{x},...,b_{m}^{x})\) according to matrix \(B\), where \(m\) is the available channel number. Then, we assign the channel with the highest reward \(b_{i}^{x}\) to user \(x_{i}\). Using this procedure, a new set of neighborhoods is constructed. _Local search_. In order to search around the initial solution \(s\), we construct the neighborhood \(N^{s}\) for local search. If the neighborhood search obtains a better fitness value, then the initial solution \(s\) is replaced by \(N^{s}\). Finally, the global optimum with the highest system utility is obtained after finishing the local search.
# Digital-Twin-Based Monitoring System for Slab Production Process
## 3 The Proposed System
### Production Management Module  
#### 3.3.1 Yield Management  
Production output is often the most crucial metric in manufacturing [44]. Therefore, the system incorporates a production management module, utilizing weight sensors to obtain the weight of each slab, aggregated within the DT monitoring system. Production management of steel slabs plays a vital role in modern steel industries. In the management of steel slab production, weight sensors are widely employed to monitor and control the production process. These sensors are typically installed at critical locations, such as the furnace front, entry, and exit of rolling mills, to measure the weight and weight distribution of slabs. The sensors commonly employ pressure-sensing technology, ensuring reliable operation in high-temperature and high-pressure environments.  
Data collection and scale: Weight sensors continuously collect real-time weight data for slabs, often sampling multiple times per second. The scale of the data depends on the speed and demands of the production process, generating a substantial number of data points per hour.  
Data transmission rate: Data generated by weight sensors are typically transmitted at a high speed to the data acquisition system. The transmission rate can be adjusted based on the requirements of the production line, usually at a rate of several thousand data points per second.  
Integration with the DT production monitoring system: Weight sensor data are integrated into the production process monitoring system, allowing real-time monitoring and analysis of slab quality and weight distribution. Data transmission is achieved through Ethernet, ensuring the timeliness and reliability of the data.  
Production output data are not only used for monitoring but also for process optimization. By analyzing the weight distribution of slabs, the production process monitoring system can dynamically adjust certain parameters, such as the furnace temperature and the rolling mill pressure, in real time to ensure the slabs' quality meets specified standards. This feedback control system helps reduce waste, improve production efficiency, and ultimately lower production costs.  
Moreover, the weight data of slabs are utilized for optimizing production planning. The production planning system can adjust production sequences and schedules based on the actual weight distribution of slabs, maximizing production efficiency.  
In conclusion, production output monitoring plays a crucial role in steel slab production management. Through real-time data collection, analysis, and adjustments to the production process, it aids the steel industry in enhancing production efficiency, quality control, and sustainability.
# Effects of Freeze-thaw Cycles on the Mechanical Properties and Microstructure of a Dispersed Soil
###### Abstract  
Dispersed soil is highly sensitive to water and can easily disperse in low-salt water, leading to weakened structures and engineering issues. To investigate the freeze-thaw effects on the mechanical properties and microstructure of dispersed soils in western Jilin Province, we simulated 0, 1, 3, 5, 7, 9, 12, and 15 freeze-thaw cycles. Qualitative and quantitative analyses were performed using numerous methods, including soluble salt determination, density and water content determination, particle size analysis, dispersion identification tests, and scanning electron microscopy (SEM), to investigate the mechanism of deterioration in soil mechanical properties from various perspectives. The research findings indicate that the unconfined compressive strength (UCS) decreased from 156.843 kPa in the unfrozen state to 76.961 kPa and then stabilized. The freeze-thaw action resulted in particle fragmentation, increased soil porosity, and elevated crack content, thereby contributing to soil structure deterioration and strength reduction. Furthermore, the cohesion value (c-value) gradually decreased from 22.196 kPa in the unfrozen state to 7.997 kPa and then stabilized. The angle of internal friction (\(\varphi\)-value) started at 7.514\({}^{\circ}\), peaked at 9.514\({}^{\circ}\), and gradually declined. This comprehensive study provides valuable insights into the variations in soil mechanical properties under freeze-thaw cycles from multiple perspectives.  
dispersed soil; freeze-thaw cycle; dispersibility; microstructure; mechanical properties +
Footnote †: journal: Applied sciences
# Mapping Noise from Motorised Transport in the Context of Infrastructure Management
###### Abstract  
Noise pollution presents significant challenges for urban infrastructure management, highlighting the need for practical assessment tools such as noise maps. These maps enable the visualization and geo-referencing of noise levels, identifying areas requiring immediate intervention and long-term strategic responses. Road sections with traffic exceeding 3 million vehicles per year were selected for measurement. The findings are presented in detail, revealing that the Long-term Day-Night Average Noise Level (I\({}_{\text{den}}\)) exceeds acceptable limits, affecting approximately 1.899 km\({}^{2}\) and impacting around 1200 residents within the exceedance zone. Similarly, the equivalent noise level (I\({}_{\text{Aeq}}\)) surpasses acceptable thresholds over an area of 1.220 km\({}^{2}\), affecting an additional 700 residents. Notably, there were no exceedances of the key noise impact indicators, including high annoyance (HA), high sleep disturbance (HSD), and ischemic heart disease (IHD). Changes in traffic organisation were implemented to address areas that exceed the applicable noise standards, including a ban on trucks and the introduction of local speed limits. The measures have successfully mitigated the noise problem in Grodzisk County (Poland). Further anti-noise initiatives are planned, including planting vegetation along the roadways.  
noise; noise map; road management Article
## 2 Materials and Methods
### U-Net Training  
**U-Net training:** If not otherwise specified, hyperparameters were chosen as provided in ref. [30]. First, model parameters were initialized randomly, forming the initial model M\({}_{0}\). Validation data comprised 5% of training data. A 3-fold cross validation was performed. For model training, an ADAM optimizer with a cyclic learning rate between \(10^{-3}\) and \(10^{-6}\) was used. The mini-batch size was set to 8 images. Training was restricted to max. 100 epochs with early stopping, i.e., if the _Dice Loss_ (i.e., overlap of prediction and ground truth) [35] did not improve after 10 epochs on the validation data, training was terminated. Final segmentation quality was then computed over the mean IoU (mIoU) on the test data.  
**Augmentation:** To enhance the variability of the data, images were augmented using Python Package _Albunmetations_. Variations were stochastically performed with brightness and contrast (\(p\) = 0.75), gamma value (\(p\) = 0.75), Gaussian noise value (\(p\) = 0.5), blurring (\(p\) = 0.5), random rotation between \(0^{\circ}\) and \(30^{\circ}\) (\(p\) = 0.75), and horizontal mirroring (\(p\) = 0.5) [36]. Augmentation was performed for each epoch during training, yielding different training data for each epoch. In addition, for the previously described ROI method, different ROIs were generated for each epoch. Such augmentation approaches help to avoid overfitting of the model on the training data [37] and may also improve model performance [38].
### Benefits of Object Detection for Apple Orchards
The loss in accuracy for the object detection was due to some of the images closely resembling the features of the trained tomato data, such as having similar shape or the tomato being too small and located in a dense region. This was a common issue as the purpose of the experiment was to differentiate between ripe and immature tomatoes [15]. Another one-stage detection algorithm that was used was YOLOv3. Ref. [20] used YOLOv3 to detect tomatoes, while [26] used YOLOv3 to detect apple lesions as the algorithm is known to have strong results with respect to small object detection [26]. Both papers used a similar technique to improve the performance of YOLOv3. By using a different backbone network from the original Darknet-53, both experiments were capable of obtaining higher accuracies [20; 26]. This change was the implementation of DenseNet which allowed YOLOv3 to better utilize the image features compared to Darknet-53. As Darknet-53, having 53 layers resulted in the loss of features and weakening them. With this simple change, YOLOv3-Tomato was able to produce an identification rate of 94.58% [20] and YOLOv3-dense outperformed the original YOLOv3 and Faster R-CNN. Not only can YOLOv3 outperform Faster R-CNN in some applications, YOLOv4 which is the newest version of YOLO at the time was able to produce high accuracy results while being significantly faster than the other models [27]. Some adjustments and techniques that were used to improve the results were seen across the aforementioned papers. A simple step was adding external illumination to the machine to decrease the variance of natural light during the detection process [23]. Ref. [20] introduced training data that had different conditions to allow the model to be more robust to changes of natural conditions, such as illumination. Another approach was seen in [28], which obtained training data with a black cloth behind the white flowers of the trees to have the model set the images to a binary image. This allows for a simple segmentation of the flowers and everything else. The authors in [20; 23] changed to ground truth boxes from rectangle to circle for objects with a circular shape such as apples and tomatoes. This produced better results as the ground truth encapsulated more of the actual object of interest than the rectangular ground truth boxes [20; 23]. The final adjustment that was common between [20; 23; 26] were data augmentation. Whether it was flipping, scaling, flip and then scaling or crop and patching, they all helped the model produce better results, due to inducing the model to more variance. Ref. [26] also indicates that the more images being trained by the model, the higher the F1-score of the model will be. While [23] concluded that having data augmentations allows the model to achieve the same accuracy as a model with no data augmentation and more training images. In general, it can be assumed that implementing data augmentation techniques to the model as well as having more training data will produce the best results for the model.
## 6 Conclusions  
We proposed a method to achieve consistent performance in land-cover semantic segmentation using redundant models for scenarios involving both single and multiple datasets. Redundant models utilize the same network architecture and hyperparameters, but they are trained with different combinations of training and validation data from the same dataset. Using these redundant models, two revised datasets were created. The supplemented dataset retains the same GT class with the confidence levels estimated by the redundant models. Conversely, the modified dataset includes some replacements of the GT class where the confidence was deemed low, based on the dominant class determined by the majority vote of the redundant models. These two datasets were then used to train both the enhanced model and the consensus model.  
Both the models outperformed the raw models, irrespective of the network structures and dataset configurations. It implies that the redundant models can reduce the inconsistency both within and across datasets. Three network structures--simple network, U-Net, and DeepLabV3--were examined, and improvements were consistently observed. This method applies to any pixel-level classifier, as it is independent of the network architecture. Its strength lies in the intuitive assessment achieved by examining the altered sections of the original GT masks. In addition to the performance improvement, it does not require the additional execution time in runtime because it integrates redundancy within the revised training data, allowing for the training of a single, improved model.  
Finally, we estimated the multi-dataset scenario and noted the current limitations and outlined future research directions to address these challenges by developing a more configurable and interactive multi-dataset fusion approach based on redundant models.  
Conceptualization, J.Y.C. and K.-J.L.; methodology, J.Y.C. and K.-Y.O.; software, J.Y.C.; validation, J.Y.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The LoveDA dataset is available online at [[https://github.com/Junjue-Wang/LoveDA](https://github.com/Junjue-Wang/LoveDA)]([https://github.com/Junjue-Wang/LoveDA](https://github.com/Junjue-Wang/LoveDA)) (accessed on 19 May 2025). The CITY-20 dataset is not publicly accessible because of copyright and security concerns.  
The authors thank the anonymous reviewers for their thorough review and valuable suggestions on our manuscript.  
The authors declare no conflicts of interest.
## 4 Performance Evaluation in Edge Scenarios
### Performance Improvement Strategies
#### 4.7.3 Adopting CVM-Based TEE  
Although the methods mentioned above can effectively improve the performance of SGX, its fundamental limitation lies in its design as a user-space TEE. While distrusting the OS, SGX still relies on OS-provided services, such as page table management, leading to inherent performance and security challenges. In contrast, VM-based TEEs, such as HyperEnclave [74], leverage a trusted hypervisor to build a flexible, general-purpose TEE architecture, offering multiple enclave types to suit diverse application needs. CVMs, in particular, are evolving rapidly, enabling fully functional virtual machines where unmodified applications can run seamlessly. Despite introducing a slightly larger TCB, CVMs significantly enhance performance for I/O-intensive tasks [75]. For example, Intel TDX [76] addresses SGX's I/O limitations by supporting trusted I/O, while solutions like Bifrost [77] optimize CVM-I/O performance through techniques such as zero-copy encryption and packet reassembly, achieving up to 21.50% performance gains over traditional VMs. Similarly, FOLIO [78], a DPDK-based software solution, improves network I/O performance for CVMs without relying on trusted I/O hardware, achieving performance within 6% of ideal TIO configurations while maintaining security and compatibility with DPDK applications. Given these advancements, CVM-based approaches are likely to represent the most practical and effective model for implementing TEEs in the future, provided there are no extreme constraints on TCB size.
# Learning Motion Constraint-Based Spatio-Temporal Networks for Infrared Dim Target Detections
## 2 Materials and Methods
### Training Phase  
#### 2.1.1 Time Stream Handling  
Figure 2a contains a single frame with the common wild environment from the 5000 m distance video, and the target is shown in a red rectangle. Figure 2b is the pixel distribution of the frame. The target occupies only a dozen pixels in the entire image, and its pixel distribution is highly similar to the background clutter due to the lack of color, shape, and texture features. Using only a single frame for small target detection makes detection more difficult. Figure 3a displays five successive frames from the same video, and the targets are shown in red rectangles. Figure 3b represents the movement of pixels in an image sequence by optical flow estimation. Compared to the background, the moving small target has obvious trajectory characteristics. The comparison shows that the target has rich motion information between multiple frames, which can be applied to the detection of moving targets.  
Figure 3: An infrared image sequence from the same video. The target is shown with a red rectangle. (**a**) The original infrared image sequence; (**b**) optical flow estimation of the sequence. Pixel movement is rendered with colored blocks.  
Figure 2: A single frame from the from the 5000 m distance video. The target is shown with a red rectangle. (**a**) The original infrared image; (**b**) the pixel distribution of the frame.  
The handling of time streams in this paper is shown in Figure 4. The indices i, i - 1..., i - t in the squares indicate the image sequence. When the new frame updates, the time pipe discards the first frame (Frame\({}_{\text{i-t}}\)) and adds the new image (Frame\({}_{\text{i}}\)) data at the tail. Such an operation can fully ensure the continuity of the target movement and improve the efficiency of memory usage.
## Materials and Methods
### Chemometrics
The constructed calibration models were applied for the prediction of the three analytes during an additional fourth fermentation reaction under identical process conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel** & **Raman (cm\({}^{-1}\))** & **MIR (cm\({}^{-1}\))** & **Function** \\ \hline S1 & 1112–1132 & 1140–1160 & Glucose measurement \\ S2 & 1165–1185 & 1213–1233 & Glucose reference \\ S3 & 868–888 & 1040–1060 & Ethanol measurement \\ S4 & 825–845 & 1060–1080 & Ethanol reference \\ - & 800–1400 & 850–1250 & Spectral range for PLS \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Spectral regions for the evaluation of glucose, ethanol, and biomass (see below) by multiple linear regression (MLR) and partial least squares (PLS) models. The models were characterized by the root-mean-squared error of calibration (RMSEC) and the root-mean-squared error of prediction (RMSEP). Thereby, the RMSEC describes how a model fits the calibration data and is given by:  
\[RMSEC=\sqrt{\sum_{i=1}^{n}\bigl{(}c_{i,true}-c_{i,predicted}\bigr{)}^{2}/(n-p-1)}, \tag{6}\]  
where \(c_{i,true}\) is the concentration determined by reference analysis, \(c_{i,predicted}\) the calculated analyte concentration determined by the PLS or MLR model based on \(n\) calibration samples (\(n=63\)), and \(p\) the number of factors (PLS: \(p=2\)-\(5\)) or regression coefficients (2D MLR: \(p=2\); 4D MLR: \(p=4\)). In addition to the RMSEC values, root-mean-squared errors of cross-validation (RMSECV) were determined in the case of the PLS models. Since RMSEC and RMSECV values were similar, only the RMSEC values are listed in this study. The RMSEP evaluates a calibration model versus an independent sample set and is given by:  
\[RMSEP=\sqrt{\sum_{i=1}^{n}\bigl{(}c_{i,true}-c_{i,predicted}\bigr{)}^{2}/n} \tag{7}\]  
where \(c_{i,predicted}\) is the analyte concentration calculated by the corresponding PLS or MLR model based on \(n\) prediction samples (\(n=21\)).
## 3 Experimental Program  
### Specimen Details  
To evaluate the effects of seismic retrofitting with the WCFST system, one test specimen consisting of a reinforced concrete (RC) moment-resisting frame without seismic details, named the non-seismic reinforced control specimen (NRC), and one frame specimen with the WCFST system, named WCFST, were fabricated. The test specimens were frame structures consisting of a column, upper beam, lower beam, and foundation; the dimensions and reinforcement details of these elements are summarized in Table 1. Details regarding the reinforced concrete frame and CFST frame attached to the WCFST specimen are provided in Figures 3 and 4, respectively. The 28-day compressive strength of the concrete used for the specimens was 21 MPa, while the yield strength of the deformed reinforcement was 400 MPa. Steel with a yield strength of 275 MPa was employed for the CFST frame attached to the WCFST test specimen, and mortar with a compressive strength of 24 MPa was utilized to fill the inside of the steel tubes. The process of constructing the WCFST system on the reinforced concrete frame is shown in Figure 5. After drilling to fix the base plate on the outside of the column, a chemical anchor was constructed to unify the base plate and test specimen. The reinforcement was then arranged on the base plate, followed by welding of the steel tube and base plate to install the CFST frame. Lastly, high-strength mortar was injected inside the CFST frame and the surface was ultimately finished.  
Figure 1: Welded concrete-filled steel tube (WCFST) retrofitting scheme for the reinforced concrete (RC) frame: (**a**) front elevation and (**b**) section.  
Figure 2: Construction process for retrofitting using the WCFST system: (**a**) installation of base plates, (**b**) bonding of base plates using a chemical anchor and placing of reinforcement, and (**c**) installation of a CFST frame.