esa-sceva/satcom-chunk-collection huggingface dataset:
# Departure from Flux-Gradient Relation in the Planetary Boundary Layer
## 2 Methodology  
### Site and Instrumentation  
We present results from two measurement campaigns, one offshore and another onshore, where a long-range pulsed profiling Doppler lidar (WindCube WLS70 from Leosphere) was deployed next to meteorological masks for one-year measurement campaigns. Figure 2 shows the location of both sites: one at the FINO3 research platform (55\({}^{\circ}\)11.7\({}^{\prime}\) N, 7\({}^{\circ}\)9.5\({}^{\prime}\) E) and the other next to the Hamburg meteorological mast (53\({}^{\circ}\)31.2\({}^{\prime}\) N, 10\({}^{\circ}\)6.31\({}^{\prime}\) E).  
The WLS70 measured the radial or line-of-sight velocity \(v_{r}\) at four azimuthal positions separated 90\({}^{\circ}\), namely \(v_{r,N},v_{r,S},v_{r,E},v_{r,W}\). This conical scanning pattern is performed with an angle of \(\phi=14.67^{\circ}\) from the vertical. Pulses were sent to the atmosphere at each azimuth for more than 2 s and the wind vector was derived every \(\approx\)10 s every 50 m from 100 up to 2000 m above the lidar level. The lidar probe volume length, defined by the full width at half of the expected maximum (FWHM) of the lidar's along-beam sensitivity function, is \(\approx\)75 m [12]. Due to a persistent negative bias at 300 m, already noticed in previous work [11], this measurement level was removed from this analysis.  
At FINO3, the lidar was placed at 24.5 m above mean sea level (amsl) and measured for a full year next to a 100-m tall meteorological mast, instrumented with cup anemometers at 30, 40, 50, 60, 70, 80 and 106 m. Hereafter, all heights are referred to amsl. [PERSON] et al. [13] combined the first nine months of cup anemometers and lidar measurements to characterize the mean wind profile from 30 m up to 1000 m at the site.  
At the onshore site, the lidar was measuring for a one-year campaign 170-m away from a 280-m high television tower located southeast of downtown Hamburg. The tower is instrumented with METEK USA-1 sonic anemometers from Metek GmbH, at 50, 110, 175 and 250 m. [PERSON] et al. [14] showed a comprehensive introduction of the site and its wind climatology. [PERSON] et al. [12] used these onshore lidar measurements, among others, to assess the influence of baroclinicity on the measured wind profile.
## 5 Results and Discussion
### Quantitative Analysis of Welding Defect Detection  
In general, the quantitative factors describing welding defects are the length of the defect, the angle, and the depth of burial of the defect, but in actual engineering, the length of the defect receives more attention. Therefore, the length of the defect is selected as the characteristic quantity for the quantitative study of welding defects.  
Defect length was the independent variable. We used the condition of 1400 s of heat dissipation under the effect of residual stress as a simulation of the working conditions, set different length values (size are 1 mm, 2 mm, 3 mm, 4 mm, 5 mm, 6 mm, and 7 mm) for crack- and porosity-type defects in the welded seam, set the three-dimensional magnetic  
Figure 8: Defect at 50 mm after 1400 s of heat dissipation. **(a)** Normal component Z; **(b)** Tangential component X; **(c)** Tangential component Y; **(d)** Three-dimensional magnetic modulus gradient.  
Figure 9: Extreme values of three-dimensional magnetic modulus gradient.  
modulus gradient in accordance with the amplitude of the change in the length of the defect, and fit the function curve, as shown in Figure 10. It can be seen from Figure 10 that the relationship between the length of the defect and the three-dimensional magnetic modulus gradient amplitude is an exponential growth relationship; with an increase in the length of the defect, the three-dimensional magnetic modulus gradient is exponentially increased.  
From the above study, it can be seen that the three-dimensional magnetic modulus gradient polarity under the influence of residual stress not only reflects the location of defects such as cracks but also increases exponentially with an increase in the length of defects. Hence, it is feasible to use the three-dimensional magnetic modulus gradient polarity to determine the location of defects such as cracks.
## 3 Important Factors in Efficacy of Amphiphiles
### Role of Enzyme Loading  
According to several studies (reviewed here), the lower enzyme loadings are of specific benefits when amphiphiles are used as additives during hydrolysis of biomass. According to [PERSON] _et al._[85],at a high solid loading of saccharification (26% for steam exploded spruce), when 0.01 (g DM)\({}^{-1}\) PEG is used as an enzyme stabilizer, the absolute improved cellulose conversion is about 16% over a range of 2-6 FPU (g DM)\({}^{-1}\) of enzyme used. However, at enzyme loadings of above 6 FPU, the improvement effect leveled off [85]. Authors reported that in regard to the severity of pretreatment technique used, application of 0.01 (g DM)\({}^{-1}\) PEG at high dry matter loadings (\(\sim\)26%), 24%-45% of enzyme can be saved. These finding were in agreement with those of [PERSON] _et al._[26]. Perhaps application of a high concentration of enzyme can mask the improvement effect that would be obtained from the use of enzyme stabilizers by releasing a high concentration of sugar and leaving no room for improvement in enzymatic hydrolysis yield. In another study [42], it was found that the glucose and xylose yield was improved from 10% to 38.5% and from 3.1% to 26.7%, respectively, when extrusion pretreated CS was hydrolyzed with the aid of casein using 25 FPU of cellulase. However, as the amount of the enzyme was increased to 37.5 and 50 FPU, no significant increase in sugar yield associated with the casein treatment was observed in most cases. This demonstrates the importance of using lower enzyme dosages when applying additives.
# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 7 Experiments and Performance Evaluation
### Comparison with Similar Works  
To assess the effectiveness of the proposed model against comparable models in the field, we selected CATM and CFFTM, both defined as trust models with recommendation defence in underwater networks. To the best of our knowledge, these two models are the most recent and thus provide a valid basis for comparison. The results of this analysis are shown in Figure 13 and Figure 14, representing the evaluation under ballot-stuffing and bad-mouthing attacks, respectively. The testing metrics are defined as follows:  
1. Accuracy rate: the number of malicious nodes detected as a percentage of the total number of malicious nodes.
2. False detection rate: the number of misidentified normal nodes as a percentage of the total number of normal nodes.  
of attacks, even as the percentage of malicious nodes increases. Figure 14a illustrates the resulting average indirect trust under bad-mouthing attacks in the same setting, where the nodes under attack are promoted as malicious. The proposed model excels in detecting dishonest recommendations, resulting in consistently high trust towards the evaluated nodes. As expected, an increase in the number of malicious nodes challenges all models' ability to detect dishonest recommendations, yet the proposed model remains within a manageable range.  
We evaluate the accuracy and false detection of each model as a function of the varied proportions of malicious recommendations. The proposed model demonstrates the highest resilience, maintaining near-perfect accuracy against up to 30% attack intensity for both ballot-stuffing (Figure 13b) and bad-mouthing attacks (Figure 14b). Beyond this point, there is a gradual decline, but the model still outperforms others significantly. In contrast, the CFFTM model, while initially accurate, shows a significant drop in performance even at lower levels of attack intensity. This is mainly attributed to the high false positive rate, where benign nodes are incorrectly considered malicious, as shown in Figures 13c and 14c.
# Two-View Mammogram Synthesis from Single-View Data Using Generative Adversarial Networks
###### Abstract
have proposed complete representation GAN (CR-GAN) [32; 34] and successfully yielded superior-quality multi-view face images from single-view images. These innovative works motivated us to produce two-view mammograms from single-view image data. This is a challenging task, as mammograms have considerably high resolution compared to human face or fashion images, and high fidelity is required for medical-image syntheses. Nevertheless, if deep learning techniques can provide two-view mammograms from single-view data, they could possibly help to increase breast cancer detection rate and reduce the rate of overdiagnosis. Here, we adopt the CR-GAN framework to synthesize novel-view images (i.e., CC views) from MLO-view mammograms. To the best of our knowledge, this is the first work applying novel-view image synthesis in medical imaging. As a preliminary work, this paper is mainly devoted to examining the technical potential for producing clinically useful CC views with high resolution and fidelity. The ultimate goal of this effort is to improve the ability of single-view mammography to diagnose breast cancer using artificial intelligence (AI) technology. CR-GAN consists of two-pathway networks based on WGAN-GP [35]. WGAN-GP is an alternative model to WGAN, in which a gradient penalty is introduced into a loss function for improved training stability. The publicly available code of the original CR-GAN was written for 128 \(\times\) 128 resolution image synthesis [34], and higher-resolution multi-view images of sufficient quality have rarely been presented [28; 29; 30; 31; 32; 33]. Consequently, we incorporate a technique to progressively increase the image resolution, as proposed in PG-GAN, with which 1024 \(\times\) 1024 resolution images have been successfully synthesized [23]. Moreover, we introduce feature matching loss, as used in previous works [36; 37], to improve the image quality. The main contributions of this work are as follows:  
* We explore the possibility that CR-GAN can provide two-view mammograms from single-view image data;
* With the aim of higher resolution and superior quality mammogram syntheses, we implement two adaptations of CR-GAN: 1. Progressive growing technique; 2. Feature matching loss. In the remainder of this paper, Section 2 first explains CR-GAN and then presents the approaches for the two adaptations. Then, we describe the image data sets and image pre-processing steps used for training. Next, after specifying our experimental setups, we refer to the methods used for evaluating the similarity between the target and synthesized images. Section 3 compares the CR-GAN with our proposed methods, in terms of the synthesized images and evaluation results. Section 4 discusses the advantages, limitations, and future perspectives of our proposed methods. Finally, Section 5 summarizes and concludes this work.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 3 Realistic Scenarios and Modelling Issues
### Group Mobility Model  
In some circumstances, groups of nodes will move in ways that depend on other nodes in a group. These group mobility models are commonly based on the reference point group mobility model first published by [PERSON] et al. (1999). In RPGM, nodes are grouped together with a logical centre (the reference point and not necessarily a node) and a scope within which all the nodes in the group move randomly (note: the nodes move randomly about their pre-defined reference points). The reference point also moves, thus dragging the nodes in that group with it. Different scenarios could be modelled by controlling the movement of the reference point. For example, a troop of soldiers will move as a group during a military operation, with the group's coverage area or footprint progressing as the operation develops. An example of a variation is the structured group mobility model ([PERSON] and [PERSON], 2004, [135]), where the nodes' mobility within the group area could be random or scripted, and the centre of each group maintains orientation and state of motion (i.e., whether moving or not). The nodes' positions are determined by their distance from and angular relationship to the centre. It is postulated that this model could be used for fire fighters and battlefield simulations.
## 2 System Description
### Massive MIMO Model
From Figures 1 and 2, we can observe that asymptotically the channel hardening effect is reliable enough when \(N_{t}>10K\), but as we mentioned, this is just asymptotic approach and there is not strict rule for the criterion of channel hardening effect. Figure 1: Example of channel hardening effect, when \(N_{t}=4\), \(40\), \(400\) and \(K=4\), CDF: cumulative distribution function.
# Stochastic Predictive Energy Management of Multi-Microgrid Systems
## 8 Conclusions  
In this paper, a novel control structure for integrated operation management of IMGs was proposed. The main goal was to represent the MMG system as a predictable entity to the main grid. To that end, a decentralized tracking control problem was designed for each MG and a two-stage stochastic MPC approach was adopted to derive the solution strategy. In the proposed scheme, any power deviation of the MMG system from the scheduled trajectory will be penalized. Considering the significant role of the MMG system in the next-generation power systems, designing systematic and fair cost allocation methodologies to motivate MGs participation in the cooperative energy management strategies is of vital importance. In this sense, a novel methodology for allocating the penalty cost among MGs using the line flow sensitivity factors was proposed. Analyzing other systematic cost allocation strategies is under study by the authors. Experimental results demonstrated the capabilities of the proposed approach to be incorporated as an efficient high-level controller in practical applications. According to the obtained results, adopting the proposed operation management strategy, a reduction of about 47% in the average unplanned daily power exchange of the IMGs with the main grid can be achieved. The reduction of unplanned power exchange with the main grid will result in a more predictable power exchange of the MMG system with the main grid as well as less penalty cost. From the viewpoint of the main grid, since the unscheduled power exchanges can be seen as disturbances, reducing it will result in lowering the complexity of power management of the main grid and more efficient reserve management strategies. Future studies of the authors are related to integrating learning-based methodologies in the EMS of MMG systems. Learning the behavior of MGs under different operating conditions will help develop more efficient EMSs in a less uncertain environment. Besides, recent advances in real-time monitoring and situational awareness of MGs will be deployed for early detection of system behavior changes from the predefined trajectories to activate the required responsive actions.  
Conceptualization, N.B., J.M.G., A.A.-M., and A.T.; Methodology, N.B., A.A.-M., A.M.; Software, N.B., A.M.; Validation, N.B., A.A.-M., J.M.G., and J.C.V.; Formal Analysis, N.B., A.M., A.A.-M., A.T.; Writing--original draft preparation, N.B., A.A.-M., and A.M.; Writing--review and editing, All. All authors have read and agreed to the published version of the manuscript.  
This work was supported by VILLUM FONDEN under the VILLUM Investigator Grant (No. 25920): Center for Research on Microgrids (CROM).  
The authors declare no conflict of interest.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Cse-Cic-Ids2017 Results  
#### 4.3.1 Performance on Minority Class  
Table 4 presents precision, recall, and F1-score comparisons for the minority classes (Bot and infiltration) using traditional oversampling methods (SMOTE, ADASYN, ROS) and several GAN variants (WGAN, CWGAN, WGAN-GP) as well as their SNNL-enhanced versions. The best results are highlighted in **bold**, and the second-best are underlined.  
Figure 1: F1-score comparison by majority class on NSL-KDD dataset.  
For the Bot class, traditional methods (SMOTE, ADASYN, ROS) achieve high recall but suffer from low precision, resulting in F1-scores below 0.56. In contrast, all GAN-based methods substantially improve precision, leading to consistently higher F1-scores. WGAN-GP+SNNL and CWGAN+SNNL attain the top performance with F1-scores of 0.664 and 0.677, respectively, indicating that the inclusion of SNNL enhances the quality of generated samples and promote a better precision-recall trade-off for minority-class detection.  
For the infiltration class, traditional methods yield relatively balanced performance, with F1-scores in the range of 0.630-0.643. GAN-based methods, while achieving higher precision (e.g., 0.833), typically suffer from lower recall, which initially leads to reduced F1-scores. However, the integration of SNNL consistently improves recall across both WGAN and CWGAN variants. For example, WGAN-GP+SNNL improves recall from 0.363 to 0.442, raising the F1-score from 0.493 to 0.559. Similarly, CWGAN+SNNL improves upon CWGAN's F1-score from 0.446 to 0.487. Although these results for infiltration still fall short of the best traditional baselines in absolute terms, they indicate that SNNL helps alleviate recall degradation and improves the overall balance of precision and recall in GAN-based augmentation.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 4 Design
### The Use of Decentralized Identifiers
#### 4.3.3 Resource URL Delegation  
There can be cases where a domain owner would like to delegate the administration of a \(Resource_{URL}\) to a third-party _controller_. Compared to the assignment process, discussed in the previous section, with the delegation process, the public key used by the controller is defined in a DID document owned by the controller. Therefore, a controller can generate new \(Resource_{URL}\) identifiers (prefixed with the delegated \(Resource_{URL}\)), and assign them to IoT devices that it controls. The delegation process is implemented by generating a new item in the assertion list of type \"url\" and by setting the publicKey claim of that element equal to the DID of the controller.
## 2 Experimental Program
### Accelerated Corrosion Test  
Steel atmospheric exposure test is the most common and near-real corrosion test method, but its corrosion time is long and limited by regional climate and environment. The laboratory accelerated corrosion method is more efficient and can greatly shorten the corrosion cycle. However, due to the lack of technology and theory, its accelerated corrosion effect is less reproducible than real corrosion, and the size of components is limited by equipment space. To obtain the uniformly corroded circular steel tube members that meet the test requirements in atmospheric environment within a relatively short period, the accelerated corrosion test by artificial periodic spraying was used in natural environment.  
Neutral salt spray solution was prepared according to the specification (ISO 9227: 2006). The NaCl concentration was 50 g/L and the pH value was 6.5-7.2. Both the test temperature and humidity were based on outdoor natural environment in Fuzhou, China. The solution was sprayed onto the surface of members in a mist form, and the spraying period was arranged at 8:30-11:30 and 14:30-17:30 everyday with an interval of 40 min. The surface of specimen was uniformly wetted as much as possible to alternate wetting and drying. To ensure the same rust status of steel tubes at different positions, the specimens were turned over once a week.  
After the accelerated corrosion test, the weight loss method was used to determine the corrosion ratio of uniform corroded specimen [1; 2; 12; 13; 14; 15; 24]. First, the specimen was washed with 12% dilute hydrochloric acid solution; then, the specimen was washed with water; next, it was neutralized with lime water; next, the specimen was washed with water again; next, the specimen was dried; finally, the specimen was weighed and the corrosion ratio \(D_{\mathrm{w}}\) was calculated according to Equation (1).  
\[D_{\mathrm{w}}=\frac{W_{0}-W_{1}}{W_{0}}\times 100\% \tag{1}\]
## 3 Results
### Model Estimations and Comparisons  
The original reflectance spectra were used as the control group. The corresponding PLSR, BPNN, and MEA-BPNN heavy metal estimation models were established using the reflectance spectra after first derivative, second derivative, and reciprocal logarithmic transformations. Finally, R\({}^{2}\), RMSE, and RPD were used for evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Elements** & **Transformation** & **Wavelength (nm)** & **Correlation Coefficient** \\ \hline \multirow{4}{*}{Cd} & OR & 2380 & \(-\)0.139 \\  & FD & 1710 & \(-\)0.540 ** \\  & SD & 2151 & \(-\)0.602 ** \\  & RL & 462 & 0.150 \\ \hline \multirow{4}{*}{Cr} & OR & 2009 & \(-\)0.559 ** \\  & FD & 433 & \(-\)0.742 ** \\  & SD & 480 & 0.731 ** \\  & RL & 2009 & 0.577 ** \\ \hline \multirow{4}{*}{Pb} & OR & 2051 & 0.384 * \\  & FD & 1333 & 0.691 ** \\ \cline{1-1}  & SD & 534 & \(-\)0.610 ** \\ \cline{1-1}  & RL & 2051 & \(-\)0.384 * \\ \hline \end{tabular}  
* * Represents significance level \(p\) < 0.05, ** represents \(p\) < 0.01.  
\end{table}
Table 4: Most sensitive spectral bands after different spectra pretreated of each metal pollution stress of soil.  
Figure 7: Correlation coefficient between soil spectral reflectance and (a) Cd, (b) Cr and (c) Pb content.
## 6 Conclusions  
In this paper, we present a systematic survey of core technologies in UAV-assisted SAGINs. Following an exposition of the architectural principles and distinctive features of SAGINs, we analyze the functional significance and inherent challenges of UAV communications within this paradigm. The hierarchical network structure introduces complexities in real-time global network governance, particularly impacting the realization of 6G objectives including network stability, controllability, and ultra-low latency. Potential solutions such as SDN and NFV are discussed as enablers for cross-domain integration. In addition, we methodically examine critical technical dimensions: (1) routing protocol design, (2) security assurance mechanisms, (3) trajectory optimization methodologies, (4) resource management frameworks, and (5) UAV deployment strategies. Practical implementations are examined across multiple domains including disaster relief, smart city applications, and precision agriculture. Finally, we present the identification of critical challenges that require immediate attention and propose future research trajectories in three key dimensions: application-specific optimization, security enhancement, and large-scale network coordination.  
Conceptualization, P.Z. and S.C.; methodology, P.Z. and L.T.; software, X.Z., P.L. and R.W. ; investigation, S.C., X.Z., P.L. and G.W.; resources, G.W. and R.W.; data curation, J.W. and L.T.; writing--original draft preparation, P.Z., S.C., X.Z., P.L. and G.W.; writing--review and editing, P.Z., S.C. and L.T.; supervision, J.W. and L.T.; All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257, partially supported by the Natural Science Foundation of Shandong Province under Grants ZR2023 LZH017, ZR2024 MF066, and 2023 QF025, and partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences) under Grant 2023 ZD010.  
All authors declare that no potential conflicts of interest exist.