esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward the Generation of an Isolated TW-Attosecond X-ray Pulse in XFEL
### Single-Molecule Imaging  
Single-molecule imaging provides a method to obtain direct structural information without crystallization, and has been regarded as the ultimate goal of biological molecule imaging. To obtain reasonably strong scattering signals from a single molecule or for a better signal-to-noise ratio, a sufficient number of photons (\(\sim\)10\({}^{12}\)) per pulse is desired. This strong X-ray pulse can destroy a single fragile molecule. Hence, a very short pulse is needed to extract the structural information from a molecule before its structure is changed by the pulse. It was thought that a femtosecond X-ray free electron laser (XFEL) such as the Linac Coherent Light Source (LCLS) would satisfy this criterion. However, after the operation of LCLS, scientists realized that a femtosecond X-ray pulse is still too long to obtain the information, highlighting the necessity of attosecond X-ray pulses [8]. Thus, terawatt-level XFEL pulses with a pulse duration of a few hundred attoseconds or less are indispensable for single-molecule imaging, which creates new possibilities for research and development in bio-medical science and engineering.  
Several XFELs have been constructed or are under construction [9; 10; 11; 12; 13]. All have produced a few to a few tens of femtosecond pulses. The experimental effort toward the realization of attosecond XFEL has begun. The X-ray Laser-Enhanced Attosecond Pulses (XLEAP) project at the LCLS started in 2017 [14; 15; 16]. There have been many published research works related to attosecond XFEL [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]. Most showed only gigawatt (GW)-level power or a pulse duration longer than 100 as. Because the lengths of attosecond X-ray pulses are about a thousand times shorter than femtosecond X-ray pulses (\(\sim\)GW level), the power of attosecond X-ray pulses should be around the terawatt (TW) level for the same level of scattered photon numbers per pulse. All the experiments that have been performed with current XFEL facilities could be performed with an even higher temporal resolution.  
This article reviews several schemes among the previously published studies [37; 38; 39; 40; 41; 42; 43; 44], assessing the feasibility of each scheme for the realization of the TW-as XFEL. The methods involved in beam emittance modulation or beam tilting are reviewed in Section 2. Schemes with a current modulation are reviewed in Section 3. Section 4 provides a summary and thoughts for future research directions.
## 2 Materials and Methods
### Available Data  
#### 2.2.1 Data Sources and Distribution  
Training and validation data for development of the PTF came from information provided in the New Zealand National Soil Data Repository (NSDR) [14] and the National Soils Database (NSD) [15]. The NSDR contains soil classification and soil morphological descriptions and soil analytical data on sand, silt, and clay content and water retention at 0, \(-5\), \(-10\), \(-20\), \(-40\), \(-100\), and \(-1500\) kPa tensions, which also includes the older NSD. The NSDR is a point (soil profile site) database, and a fundamental assumption in the present modeling is that the water-release relationships found between the soil description data and the associated analytical data (NSDR) can be applied to identical soil description data recorded in the S-map database (and thus be applied to the soil map units containing these soils). S-map itself provides the best available soil survey data for extensive areas of New Zealand, providing the basic soil property data given in Table 1.  
Table 2 shows the numeric distribution of samples and sites among NZSC soil orders, while Figure 1 shows the spatial distribution of the site data. Sites of the NSDR are individual soil profiles, and samples are the laboratory-analyzed soil horizons in a profile. The equivalence between NZSC soils and US Soil Taxonomy may be found in [9]. The point data used for development of the PTF are clearly not spatially representative, nor spatially random, and areas of intensive agricultural development are over-represented at the expense of low-productivity natural vegetation and agricultural land. However, the distribution of point data among NZSC soil orders in Figure 1 provides good coverage of the dominant soil types used for agricultural production. Sparsely distributed NZSC soil orders (e.g., Oxidic and Semiarid) have relatively few samples available and are more likely to be subject to overfitting using regression-based modeling approaches.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**NZSC Soil Order** & **Number of Samples** & **Number of Sites** \\ \hline Allophanic & 554 & 64 \\ Allophanic Brown & 164 & 25 \\ Granular & 51 & 7 \\ Immuture Gley & 148 & 19 \\ Immature Pallic & 958 & 110 \\ Mature Gley & 1005 & 125 \\ Mature Pallic & 743 & 81 \\ Melanic & 104 & 14 \\ Non-Allophanic Brown & 841 & 159 \\ Organic & 57 & 9 \\ Oxidic & 51 & 8 \\ Podzol & 210 & 30 \\ Pumice & 157 & 28 \\ Recent & 691 & 93 \\ Semiarid & 98 & 10 \\ Ultic & 124 & 14 \\ \hline All soil orders & 5956 & 796 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of samples and sites in the assembled NSDR dataset in each NZSC soil order, as well as in total.
## 2 Materials and Methods
### Data Selection
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Parameter/Feature**} & \multicolumn{1}{c}{**Value**} \\ \hline Leading types of publication & Article (44.00\%), conference paper (36.00\%), review article (16.00\%) \\ \hline Leading areas of science & Computer science information systems, computer science artificial intelligence \\ \hline Leading topics & Industrial: Design and Manufacturing (28.40\%), Telecommunication (8.23\%) \\ \hline Leading countries & Germany, Spain, USA, China \\ \hline Leading scientists & [PERSON], [PERSON] \\ \hline Leading affiliations & Chinese Academy of Sciences \\ \hline Leading funders (where information available) & European Union \\ \hline Sustainable development goals (SDGs) & Industry Innovation and Infrastructure, Responsible Consumption and Production, Sustainable Cities and Communities, Good Health and Wellbeing \\ \hline \hline \end{tabular}
\end{table} 
Table 3: General summary of results of bibliographic analysis (WoS, Scopus, dblp). Figure 2: Publications by category according to WoS. The final set of 85 publications constitutes a representative sample of the literature, reflecting both the breadth and depth of current research on AI-based DTs in engineering. This number was achieved through a rigorous selection process based on relevance, topical saturation, and scientific impact, ensuring the inclusion of key studies and influential works. The dataset encompasses a wide range of perspectives, methodologies, and applications, indicating the coverage of major research trends and directions. Saturation was observed because recurring concepts and findings appeared consistently across the included studies, suggesting a comprehensive representation of the field (Figures 5-9). Furthermore, the inclusion of highly cited and peer-reviewed publications strengthens the credibility and scientific relevance of the review. Figure 8: Publications by country. Figure 6: Publications by type. Figure 7: Publications by subject area. This review highlights several potential limitations that may impact the scope and comprehensiveness of the findings. First, the inclusion of only English-language sources introduces linguistic bias, potentially excluding relevant studies published in other languages. Second, the literature search was limited to three major academic databases, which, although comprehensive, may have missed relevant work indexed elsewhere. Third, the exclusion of gray literature--such as technical reports, theses, and industry papers--may limit insight into emerging practices and real-world applications not yet covered by peer-reviewed sources. These limitations underscore the need for cautious interpretation and suggest directions for broader future research.
# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
###### Abstract  
In this paper, we propose a novel machine learning (ML) based link-to-system (L2S) mapping technique for inter-connecting a link-level simulator (LLS) and a system-level simulator (SLS). For validating the proposed technique, we utilized 5G K-Simulator, which was developed through a collaborative research project in Republic of Korea and includes LLS, SLS, and network-level simulator (NS). We first describe a general procedure of the L2S mapping methodology for 5G new radio (NR) systems, and then, we explain the proposed ML-based exponential effective signal-to-noise ratio (SNR) mapping (EESM) method with a deep neural network (DNN) regression algorithm. We compared the proposed ML-based EESM method with the conventional L2S mapping method. Through extensive simulation results, we show that the proposed ML-based L2S mapping technique yielded better prediction accuracy in regards to block error rate (BLER) while reducing the processing time.  
Keywords:link-to-system mapping; exponential effective SNR mapping (EESM); physical-layer abstraction; system-level simulation; machine learning; deep neural network (DNN) +
Footnote †: journal: Sensors
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 2 Related Works
All three algorithms can reduce network congestion to some extent; however, they only consider the load states of nodes or the entire network unilaterally while ignoring the impact of local congestion on the overall network performance and global load. We propose a congestion-aware routing algorithm (CARA) for active wireless sensor networks based on the above analysis. Considering the node's load and the network local load, we define the \"cache remaining rate\" and \"forward average cache residual rate\" to minimize the network packet loss rate. At the same time, to deliver the data collected by the node to the sink node as soon as possible, the algorithm also puts forward the \"forward rate\". Finally, the optimal next-hop node is determined utilizing multiparameter fusion. Simulation results show that the algorithm can significantly alleviate network congestion and reduce network packet loss and network latency under the condition that the sensor node has a continuous energy supply.
## 5 Results and Analysis
### Evaluation of Multi-Objective Segmentation Accuracy of Tomato Plants  
In the comparative experiments, the UNet model served as the baseline, while the encoder part was replaced by two different feature extraction networks, VGG16 and ResNet, resulting in the VGG16-UNet and Res-UNet models, respectively. These models were then trained with the following settings: an initial learning rate of 0.0001, the Adam optimizer to adjust the learning rate, 200 epochs, and a batch size of 2. This setup allowed for a comprehensive comparison of the segmentation performance between the different models.  
To accelerate the convergence speed of the model training and to improve its generalization ability, pretrained weights from the large ImageNet dataset were used in the training of the tomato plant segmentation model via transfer learning. As shown in Figures 9 and 10, VGG16-UNet (pretrain) represents the VGG16-UNet model using pretrained weights, while Res-UNet (pretrain) represents the Res-UNet model using pretrained weights. It should be noted that both models with pretrained weights show a significantly accelerated convergence speed during training.  
The analysis of Table 2 shows that the VGG16-UNet model outperforms the Res-UNet model when no pretrained weights are used. Similarly, when pretrained weights are used, the performance of the VGG16-UNet model exceeds that of the Res-UNet model. Specifically, with pretrained weights, the VGG16-UNet model shows the highest performance, achieving an average intersection over union (_IoU_) and pixel accuracy (_PA_) of 85.33% and 92.47%, respectively. This represents an improvement of 5.02% and 4.08%, respectively, over the VGG16-UNet model without pretrained weights. Furthermore, its IoU values for all segmentation categories (axillary buds, lateral branch, and main branch) are 75.91%, 78.86%,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MIoU** & _mPA_ \\ \hline VGG16-UNet & 80.31\% & 88.39\% \\ Res-UNet & 75.65\% & 83.62\% \\ DeepLabv3+ & 57.94\% & 72.05\% \\ PSPNet & 51.61\% & 54.09\% \\ HRNet & 63.48\% & 69.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance analysis of different semantic segmentation models.  
Figure 9: Comparison of convergence rate and stability during training of each model.  
and 87.01%, respectively, representing an increase of 10.22%, 6.73%, and 2.92% compared to the VGG16-UNet model without pretrained weights. Correspondingly, the PA values for these categories are 87.47%, 89.53%, and 93.15%, representing improvements of 8.88%, 5.55%, and 1.8%, respectively, over the VGG16-UNet model without pretrained weights.
# Thermal Effusivity Tester (TET)--A New Device to Determine Thermal Effusivity of Textiles
## 1 Introduction
The motivation to design and build an additional device for the measurement of thermal perception (with several instruments already available) was to have a system that allows easy measurement and offers the possibility to adjust the measurement parameters to special needs. Therefore, the system was designed to offer more flexibility in the measurement setup and variation in the selected parameters. Despite the targeted flexibility, the new device also should be comparable to the existing systems and thus allow a comparison of various measurements. Similar to Alambeta, TET offers a semi-automated measurement of thermal conductivity and effusivity on single-layered fabric. It differs from the devices described above by two main aspects: it addresses the fabric compression issue mentioned above by targeting a harmonized fabric compression rate instead of a harmonized weight application. Furthermore, temperature control at both fabric sides offers a more precise assessment of the transient heat transfer into the fabric, which allows the measuring of thin single layers. The adjustability of fabric compression, temperatures on both fabric sides and measurement time provide the possibility of simulating various fabric use conditions. The reliability of the device and the measurement approach were tested by comparing the obtained results with available measurement results from Alambeta and TCi systems. Results were also correlated with available results from Kawabata Thermo Labo, as Q\({}_{\text{max}}\) is also usually used in the textile market to describe cool perception of the fabric.
## 1 Introduction
Therefore, two fractions of MC accumulation can be studied: (i) 'free MC' representing the cell fraction of unbound MC, and (ii) 'total MC' including free and protein-bound MC in tissues. Studies performed in situ demonstrated the accumulation of free MC by the bivalves _D. polymorpha_ and _A. anatina_[39; 40]. Moreover, in the laboratory, _D. polymorpha_ accumulated both free and bound MC from the first of a three-week exposure to the MC-producing cyanobacteria _M. aeruginosa_[41]. Free MC can be quantified in a biological matrix using high-pressure liquid chromatography (HPLC) alone [42; 43], with single (LC-MS) or tandem (LC-MS/MS) mass spectrometry [41; 44]. However, those methods require great expertise as opposed to the enzyme-linked immunosorbent assay (ELISA), which is increasingly used [1]. The ELISA method is rapid, sensitive [45], easy to use and interpret [46]. It has been pointed out to generate false-positive results [1], which was not confirmed for some mollusc matrix [47]. Since free MC may represent a small percentage of the total MC concentration in organisms [41; 47], its measurement in sentinel species could be less accurate than that of total MC to reveal levels of water contamination by MC-producing cyanobacteria. However, the quantification of total MC is expensive and long as samples first need to be oxidized, using Lemieux oxidation [48] or ozonolysis [49], to release a compound called 2-methyl-3-methoxy-4-phenylbutyric acid (MMPB). Then MMPB is extracted and analysed by GC-MS or LC-MS/MS [1]. This allows all fractions (free and bound) in a sample to be quantified. However, as the protein-bound MC are known to remain longer in tissues than free MC after the end of exposure [47], the quantification of total MC in bivalves might not reflect short-term variations of environmental MC contamination. Therefore, following the kinetics of both accumulation fractions in bivalves during and after an exposure to MC-producing cyanobacteria is mandatory to evaluate their pertinence to highlight environmental contamination. This study assesses if free or protein-bound MC accumulation by the freshwater bivalves _A. anatina_ and _D. polymorpha_ could be used as a tool to evaluate water contamination by MC-producing cyanobacteria. To study this, we compared the kinetics of accumulation and elimination of free and total MC in both bivalves exposed to three densities of the MC-producing cyanobacteria _P. agardhii_ for three weeks, followed by two weeks of depruration. Concomitantly, the ingestion rate of cyanobacteria and of MC were measured daily to calculate a cumulative and a temporal accumulation factor. Owing to the size differential between the two mussel species, we study the kinetics of free and total MC accumulation in the digestive gland of _A. anatina_, to avoid MC biodissolution, and in the entire body of the smaller mussel _D. polymorpha_. Furthermore, at some specific dates, we also studied total MC contents in whole body of _A. anatina_ and in the digestive glands of _D. polymorpha_, in order to compare the two species in terms of accumulation capacities.
# Spatial Data Reconstruction via ADMM and Spatial Spline Regression +
## 5 Performance Evaluation
### Performance Evaluation
#### 5.3.1 Comparison of Different Algorithms
Incorporating additional features into our model can help to explain the residuals between estimated smooth distribution and the true activity distribution, therefore further increasing estimation accuracy. By comparing Figure 3 and Figure 4, we also can see that incorporating additional features into Constrained Spatial Smoothing becomes more important when the base stations are more sparse. The performance of different methods on the December dataset is worse than on the November dataset. This is because there are multiple holidays in December. The cell phone activities will become more irregular than usual during holidays, as discussed in [PERSON] et al. (2018) and [PERSON] et al. (2019). Figure 6a-c show the distribution surfaces of true cell phone activity volumes, estimated volumes by Patched Estimation, and estimated volumes by Constrained Spatial Smoothing with features when \(n_{\text{BS}}=200\) using the November dataset. We can see that the Patched Estimation approach fits a stepped surface, while our approach gives a much smoother surface. Figure 6: \(Cont\). For time efficiency, experiments based on the Milan Call Description Records (CDR) dataset show that the average time for our approach to converge is less than five minutes on a MacBook Pro with a 2 GHz Intel Core i7 processor, and 8 GB memory. This proves that our system is highly efficient and practical.
## 5 Result Analysis and Discussion  
In this section, this paper explores a proposed method for evaluating the strategic placement of BSSs and dynamic reconfiguration in intelligent distribution networks like the IEEE 118-, 69-, and 33-bus systems and their subnetworks. The assessment considers various load conditions, ranging from 20% to 150% of the rated load, as depicted in Figure 8 for different case studies. To validate this methodology, the authors utilize a 118-bus distribution network with interconnected microgrids, as shown in Figure 9, comparing it with recent approaches in the field. For detailed information on load data and network lines, readers are advised to refer to the provided source [26]. In this study, three microgrids are positioned at nodes 1, 26, and 111. Additionally, specific lines between buses 11-12, 68-69, and 104-105 are coupled with the lines between buses 17-27, 80-99, and 91-115 in the network, respectively. In this regard, Table 1 shows the dynamic coupled switches in each case study. The distributed generation resources in these microgrids include wind turbines, photovoltaic systems, microturbines, and diesel generators to provide operational power. The optimal placement of BSSs is conducted during system operation. The proposed method is then assessed and compared with recent methods, considering network security and reliability indicators. A multiobjective optimization algorithm is employed to optimize the mentioned function, and its results are evaluated against other optimization algorithms discussed.
# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 2 Related Work
### Dehazing for a Static Scene  
Fixed camera environments, such as traffic monitoring and surveillance systems, present unique opportunities for optimizing dehazing algorithms [8; 21]. The static nature of the scene allows for the development of specialized dehazing techniques that leverage prior information about the scene's consistent elements [9; 19]. Background modeling can be employed to estimate the scene in a haze-free state, improving the effectiveness of haze removal in current images. This specialized approach can significantly enhance system reliability and efficiency, particularly in areas prone to frequent haze or fog.  
Figure 1: Progressive Lightweight Dehazing System for a Static Scene captured by Fixed Camera Environments.
# Design and Analysis of Novel Low-Cost Linear Vibration Motor for an Electronic Cigarette
## 4 Design and Optimization
### Magnetic Circuit Optimization  
The Lorentz force applied on the vibration assembly is linearly related to the current in the voice coil. It can be divided into two components. One is current on voice current, and the other is a force factor that consists of the magnetic flux density and total coil length. The input voltage and specifications of the voice coil are fixed. Magnetic circuit optimization (MCO) is used to maximize the Lorentz force by changing the magnetic structure dimensions.  
The total thickness of the magnetic structure (yoke, magnet, and top plate) is fixed at 1.03 mm. The thickness from the yoke to the weight is fixed at 0.88 mm. Each magnetic circuit component and constraint has been marked with a number in Figure 25. MCO #1 and MCO #2 have different magnet thickness (MG) with the same yoke thickness (YK). When MG decreases, the top plate thickness (T/P) increases.  
MCO #3 and MCO #4 are models with different T/P and the same MG. When T/P decreases, YK increases, resulting in decreasing weight thickness. MCO #5 and MCO #6 have different YK with the same T/P. When YK decreases, MG increases, which also causes the weight thickness to increase. The thicknesses of each magnetic circuit component are shown in Table 9.  
The analysis result of the average flux density, force factor, and Lorentz force are shown in Table 10. The optimized result is MCO #2, which has a bigger magnet volume. By comparing all analysis results, it can be found that the magnet thickness is the most essential factor. The Lorentz force of MCO #2 has a 5.36% increase over that of novel spring #3. According to the different novel spring thicknesses, the average flux density differs a little when the relative coil position changes. Based on optimized magnetic circuit results, a new MG (0.53 mm) and T/P (0.20 mm) were applied to novel linear vibration motors, and samples were manufactured to conduct experiments.