esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application Example, Results and Discussion
### Automatic Measurement Validation  
The first step before the measurements can be ranked is to remove those without useful information. This is done automatically as mentioned before by using established curve similarity metrics in conjunction with the presented slope-based signature calculation algorithm. The software implements several similarity methods, all of which compare two time series to each other. The complete functionality is encapsulated in a separate software tab and depicted in Figure 4.  
All metrics calculate and assign a score to each measurement and afterwards rank them with descending similarity in the middle list (yellow rectangle in Figure 4). The user can inspect candidate curves and reference together in an interactive graph view (purple) and afterwards apply the final validation with the buttons and the following list selection: The measurements moved to the upper list (red) are set to invalid, the status of those in the middle list are not changed and finally the lower list (green) marks its contents as valid. The option _Remove low SNR_ (blue) automatically proposes measurements as invalid that do not show enough signal amplitude by calculating the measurements signal to noise ratio (SNR) and comparing it to the threshold given in the spin box.  
For validation of the proposed signature algorithm, we created a test subset including the runs 52, 261, 267, 343 and 374 and calculated the distance with respect to the reference run 270 for all available curve distance methods. The proposed slope-based signature algorithm yields the same signature + for the reference and all runs of the subset except for run 52, which was assigned the signature **-+**. In Figure 5 run 52 shows a significant drop and therefore a different slope characteristic compared to the other runs, which is represented by the signature value. Referring to Table 3, the calculated curve similarities based on the four curve distance methods listed in Section 3.1 reveal that run 52 has a very similar distance to the reference compared to at least one of the other runs for the respective method. If the signature would not be used to sort out run 52, it would be on the same similarity level as the other curves, ignoring the significant difference in slope characteristics.  
Figure 4: Exemplary use of the auto validation function. Validation of all CO measurements performed with In\({}_{2}\)O\({}_{3}\)-based sensors. The blue curve is the user-supplied reference, the green curves are the candidates as selected in the middle (yellow rectangle) and lower list (green rectangle). The upper list (red rectangle) holds all measurements that are greater than the selected reference threshold (light blue rectangle) and are therefore sorted out.
## 6 Conclusions  
In this work, a three-dimensional (3D) finite-element (FE) framework has been employed to accurately quantify how skin and proximity losses are influenced by varying lay lengths in multi-segment underground cables. Unlike simpler two-dimensional (2D) methods--which treat sub-conductors as though they are parallel--this 3D approach fully accounts for helical transposition. The key findings can be summarized as follows:  
1. _Influence of Lay Length on AC Losses:_ Over the tested range of lay lengths (0.1 m-5.0 m at 50 Hz), the AC-to-DC resistance ratio \(\rm R_{AC}/R_{DC}\) varied from about 1.32 (for very tight twists) to nearly 1.72 (for gentle twists). This demonstrates that twisting can markedly change the current distribution and resultant AC losses;
2. _3D FE Accuracy vs. 2D Simplifications:_ Two-dimensional models assume parallel sub-conductors and thus overestimate eddy-current losses, effectively treating lay length as infinite. By contrast, the proposed 3D model reveals how shorter lay lengths help redistribute current more uniformly;
3. _Exponential-Saturation Relationship:_ An exponential-saturation model was found to predict \(\rm R_{AC}/R_{DC}\) with high accuracy (\(R^{2}\approx 0.996\)) across all studied twists, offering a convenient analytical tool for cable designers;
4. _Total Cable Resistance vs. Per-Meter Perspective:_ Although the per-unit-length AC resistance is reduced at shorter lay lengths, the overall helical path is longer, which can increase the total cable resistance. Understanding both effects is key to balancing mechanical robustness with minimal AC losses.  
Looking ahead, further studies may consider higher frequencies or transient phenomena, more complex conductor arrangements (e.g., partially insulated strands). Such advancements would continue to refine the design guidelines for high-voltage cables, helping manufacturers and regulators ensure that both mechanical and electromagnetic performance requirements are efficiently satisfied.  
Conceptualization, S.A.; methodology, S.A. and S.H.K.; simulation, S.A.; validation, S.A. and S.H.K.; formal analysis, K.T.V.G.; writing--original draft preparation, S.A.; writing--review and editing, S.H.K. and K.T.V.G.; supervision, S.H.K. and K.T.V.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in this article.  
The authors wish to thank City St George's, University of London for providing research facilities.  
The authors declare no conflicts of interest.
## 4 Potential Decolorizing Enzymes and Their Characteristics
### Manganese Peroxidases (MnPs)  
Manganese peroxidase (EC 1.11.1.13) is another lignin-modifying enzyme firstly detected in _P. chrysosporium_ by [PERSON] et al. [143]. It is secreted by many Basidiomycota species such as _Panus tigirus_, _Agaricus bisporus_, _Bjerkandera_ sp., and _Nematoloma frowardi_[142]. Additionally, this enzyme has been found in bacteria _Bacillus pumilus_, _Panei-bacillus_ sp. [144], _Azospirillum brasilense_ and _Actinobacterium S. psammitoicus_[142]. MnPs are glycoproteins with a molecular weight ranging between 38 and 62.5 kDa, comprised of approximately 350 amino acids. The overall structure is shown to be similar to that of LiPs, with two domains, a heme group in the middle, 10 major helices, and a minor helix. MnPs have five disulfide bonds unlike LiPs. Another major characteristic of MgP is the manganese-binding site. The additional disulfide bond participates in the Mn-binding site alongside Aspartic and Glutamic acid residues [145].
# Situational Awareness and Problems of Its Formation in the Tasks of UAV Behavior Control
## 6 Areas of Further Research on Situational Awareness for Highly Autonomous UAVs
At the UAV-group level, the sensor fusion problem is to combine the data coming from the UAVs in the grouping into a single information picture to form the situational awareness of the grouping. An important task is to create representation formats for various situational awareness elements, which would be the most effective in terms of information support for behavior control algorithms of both individual UAVs and teams of UAVs solving a common task. In our opinion, standardization of such formats is reasonable, as in this case, there isa possibility to accumulate libraries of tools for the formation of situational awareness elements. These libraries will significantly reduce the time and cost of creating appropriate subsystems for the UAV behavior control system. When a HA-UAV is used for observational tasks, there is a problem of situational awareness formation for \"external\" use, when a HA-UAV or a group of HA-UAVs perceives and evaluates the current situation not only for its use by the HA-UAV behavior control algorithms but also for some consumer, which is outside the HA-UAV. Usually, in such tasks, \"raw\" data on the current situation, such as a video signal, is transmitted to the consumer. It would be more expedient to enable situational awareness onboard HA-UAV, which meets the goals of the information consumer. This approach would considerably reduce requirements on the channel's bandwidth for linking the HA-UAV with the consumer. Another area of research in situational awareness for UAVs is related to the tools for implementing algorithms for the formation of situational awareness elements. As the already available experience in solving problems of similar nature and level of complexity shows, machine learning provides the most effective tools at our disposal. We can confidently say that the methods and tools of machine learning represent the primary tool for forming the elements of situational awareness. In this field, technologies of creating situational awareness elements using various neural architectures, including both feed-forward neural networks and recurrent networks, require consideration. Deep networks and deep learning have considerable potential. In our opinion, the use of reinforcement learning is also very promising. In this case, the formation of the corresponding neural architectures must be carried out to meet best the solution of the problem of the formation of situational awareness, taking into account the limitations on the characteristics of the computing facilities available onboard the VA-UAV. This requirement means that there is a need to develop information technologies that effectively implement situational awareness formation algorithms within the available computing resources.
## 3 Results and Analysis
### Multiscale Association Analysis  
For the unified research object, observation and analysis at multiple scales can provide basis and method at micro and molecular scales to solve the macroscopic performance problem. The relationship between multiple scales is shown in Figure 18. The influence indices of water at multiple scales on the interface of crumb rubber-asphal mixture are shown in Table 12.  
Figure 16: Influence of water on the asphalt–aggregate interaction energy.  
Figure 17: Influence of water on interaction energy density.  
Through molecular dynamics simulation calculation, it can be known that the interaction energy and interaction energy density of the asphalt and aggregate at the molecular scale decrease with water participation; the interaction energy density of basalt and CR decreases by 38.2%, and the interaction energy density of limestone and CR decreases by 64.5%. Due to the decrease in the interaction energy between CR and the aggregate, the adhesion ability between the asphalt and the aggregate at the microscale decreases obviously under the wet condition, the \(W^{\prime}\) of basalt and CR decreases by 6.51%, and the \(W^{\prime}\) of limestone and CR decreases by 12.98%. Since the adhesion ability between the crumb rubber asphalt and aggregate is an influencing factor on the crack resistance of the crumb rubber-asphalt mixture, the decrease in adhesion ability between the asphalt and aggregate at the microscale leads to the decrease in the crack resistance of the macro crumb rubber-asphalt mixture. The \(D_{E}\) of medium-temperature fatigue failure decreases by 4.11%, and the \(D_{E}\) of low-temperature fracture failure decreases by 15.52%. It can be seen from the above that the influence of water on the interface between the crumb rubber-asphalt and the aggregate is basically the same at the molecular scale, microscale and macroscale. The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scales** & **Indexes** & **Unit** & **Dry** & **Wet** \\ \hline Molecular scale & Basalt-Asphalt \(E_{\rho}\) & (kJ/mol/A2) & 6.131 & 3.789 \\  & Limestone-Asphalt \(E_{\rho}\) & (kJ/mol/A2) & 11.944 & 4.243 \\ Micro scale & Basalt-Asphalt \(W^{\prime}\) & (\%) & 100 & 93.49 \\  & Limestone-Asphalt \(W^{\prime}\) & (\%) & 100 & 87.02 \\ Macro scale & Medium temperature & - & 36.83254 & 35.3172 \\  & fatigue failure \(D_{E}\) & - & 0.077 & 0.06505 \\  & Low temperature & & & \\  & fracture \(D_{E}\) & - & 0.077 & 0.06505 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Multiscale indices of the influence of water.  
Figure 18: Multiscale association analysis.  
macroracking of the asphalt mixture can further be analyzed for its internal mechanism and changes caused at the micro and molecular scale, so as to provide a certain theoretical basis for further improving the performance of asphalt mixtures. However, due to the influence of the size effect, the correlations of the three scales need to be further studied.
# Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks
## 3 Materials and Methods
### Design of the Model
#### 3.1.2 Drone and Object Characteristics  
The drone's flight along the route is the main element of monitoring. During the mission, the drone observes the object using the payload (i.e., camera and possibly other sensors). The data obtained are stored or processed for further use. The implementation of this part depends on a wide range of parameters. These parameters can be divided into two groups: drone and object characteristics.  
According to the IEEE standard [1], the UAV flight performance is characterised by the following drone characteristics:  
1. Height: Generally drone flights take place at low altitudes and mostly in automatic mode, as it is limited by the capabilities of the target load and free airspace. This parameter should be considered at the route planning stage.
2. Speed: The monitoring of extended objects can be considered as flying at a constant average speed, depending on the payload or the capabilities of the drone. However, the drone may fly at different speeds at various stages of the mission, for example, while collecting data compared to the flight back to the base station. Thus, it is necessary to take into account average and maximum speed independently.
3. Radius of flight: If several drones of the same type are used in a system, the drones can use each other's infrastructure, i.e., a return to the take-off point may not be necessary. Therefore, instead of the flight radius, we will use a range that is equal to the doubled flight radius.
4. Endurance: For endurance requirements, we will consider preflight preparations and maintenance.
5. Manoeuvrability of aircraft: The manoeuvrability of the aircraft is taken into account when preparing the flight route.
6. Take-off: Take-off is performed in accordance with UAV specifications.  
Hence, the model will consider range, average and maximum speed as general drone parameters.  
An individual object to be monitored is of linear structure, such as pipelines, electric power transmission lines or similar, where it is possible to organize checkpoints for drones. The following parameters are important for this model:  
Figure 1: A statechart diagram of the monitoring process.  
Checkpoints: places where drones can undergo preflight procedures. A preflight procedure includes checklist execution, battery charging and maintenance if required.
2. Distance: the distance between two nearest checkpoints. Determined not only by the distance on the plane but also by the topography of the surface.
3. Intersection points: points of intersection of several sections of the monitored object.
## 6 Conclusions  
The main novelty of this research is development of the shape memory alloy-pounding tuned mass damper (SMA-PTMD) that uses the SMA material to accelerate energy dissipation in a bounding tuned mass damper. In this study, two different types of PTMDs (spring steel type and pendulum type) are equipped with a shape memory alloy sponge to suppress the vibration of a suspended piping system. The SMA-PTMDs are tested in both free vibration and forced vibration cases. To investigate the influence of energy dissipating material (SMA) on the PTMD device, the SMA-PTMD results are compared with those of a previous study by the authors [45], which consists of a TMD control and a VE-PTMDs control.  
In the free vibration experiment, the beating phenomenon is observed in the responses of the pipe with the SMA-PTMDs control. The control times are reduced from 26.5 s (without control) to 2.5 s (spring steel type) and 2.9 s (pendulum type). The damping ratios are increased from 0.58% (without control) to 4.9% (spring steel type) and 4.0% (pendulum type). SMA-PTMDs suppress the vibration of the piping system effectively. Both the shortened time and the increased damping show that SMA-PTMDs suppress the vibration of the pipe effectively and efficiently. In the forced vibration experiment, the frequency responses of the piping system under the control of SMA-PTMDs have one peak on each side of the piping system's resonant frequency which are different compared to the single peak frequency responses under the control of VE-PTMDs. By comparing the damping effects of the same types of SMA-PTMDs and VE-PTMDs, it can be observed that SMA material considerably improves the performance of PTMDs both across the entire frequency domain and especially at the resonant frequency.  
The results presented in this paper demonstrate the ability of SMA-PTMDs to suppress the vibration of a suspended piping system effectively and efficiently for both free vibration and forced vibration. The numerical model of SMA as a bounding energy dissipating material has not been studied in this paper and will be the subject of future work. Additionally, the nonlinearity of SMA in collision also requires more detailed research.  
All authors discussed and agreed upon the idea and made scientific contributions. [PERSON], [PERSON], and [PERSON] designed the experiments. [PERSON] and [PERSON] performed the experiments and analyzed the data. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] revised the paper.  
This work is partially supported by the Scientific Research Fund of the Institute of Seismology and the Institute of Crustal Dynamics, China Earthquake Administration (Grant number IS201616250) and the National Natural Science Foundation of China (Grant number 51808092). The authors would also like to thank the support from the China Scholarship Council.  
The authors declare no conflict of interest.
## 4 Discussion  
### Quality of Remote Sensing Observations of the Start of Season  
The accuracy of DBF_SOSsat was about one week for Finnish sites, which is in line with results obtained in Siberia [70]. Moreover, our observations are in agreement with a map of satellite-derived greening-up vegetation in Fennoscandia for the period 1982-2002 by [PERSON] et al. [77] Figure 2a, but DBF_SOSsat occurred slightly later in the northern boreal zone than in the map for a relatively warmer period (2000-2006) [28] Figure 5. The sites Aulanko, Kevo, Saariselka and Varrio were also used for evaluation of the NDVI-based method in studies by [PERSON] et al. [28,78] that showed higher  
Figure 12: **(a–f)** Spatial distribution of yearly anomalies, the deviations (in days) from grid cell mean 2003–2010, of the start of season of deciduous broad-leaf forest. Upper panel: remote sensing estimate (DBF_SOSsat). Lower panel: model-derived estimate (DBF_SOSmod).  
correlation between field observations and the NDVI-based start of season in Aulanko and Kevo, but larger RMSE and bias for all four sites than in our results (Table 2).  
For the evaluation of DBF_SOSat, we compared visual observation of bud break of birch on a few trees with the satellite signal on green-up for many pixels covering an area of several kilometres. As described by [PERSON] et al. (2016), several factors could cause deviations between the in situ observation and DBF_SOSat: (a) the inter-individual variability in bud break, (b) systematic time lags between bud break and the date derived from the satellite signal and (c) the heterogeneity in terrain and land cover. Concerning (a), no information on the observer error was given with the ground observations, but the visual observations at Finnish sites were carried out twice a week and we therefore assumed an observers error of about three days. The within-population variation in the date of bud break can range between a few days to up to four weeks and varies from year to year depending on weather patterns (K  
only available at a grid resolution of 25 km \(\times\) 25 km, but a higher resolution SWE product (augmented using the optical FSC data) is under development at the FMI.
# Glass Defect Detection with Improved Data Augmentation under Total Reflection Lighting
## 3 Principle and Improvement of Defect Detection Algorithm
### Add a Small Target Detection Layer  
In the process of extracting image features with the help of a convolutional neural network, the local receptive field can perceive the ever-expanding original image as the convolution layers deepen and the resolution of the feature map decreases. Moreover, the closer the feature map is to the top level, the more attention it tends to pay to global information. As a result, the deep features extracted by the deep neural network are extremely unsuitable for small target detection. It should be admitted that shallow feature maps, with relatively small local receptive fields and more position and detail information, can partially compensate for the deficiency of deep features. However, if single shallow features are employed to detect targets, they will cause serious false positives and missed detections due to a lack of guidance from advanced semantic information. In YOLOv5, the feature maps of 60 \(\times\) 60, 30 \(\times\) 30, and 15 \(\times\) 15, which are obtained from an input image with a size of 480 \(\times\) 480 after 8, 16, and 32 downsampling operations, are the exact places where the network conducts target detection. Among these feature maps at three scales, the feature map downsampled 8 times has the smallest local receptive field. Therefore, it is difficult to learn the feature information of small targets with a resolution lower than 8 \(\times\) 8. To solve this problem, a detection header with 4 downsampling operations is added to the original network model. A feature output is added after the first CSP1 module of the backbone network, which is connected to the neck to conduct feature fusion at the same scale. After the CSP1 module and convolution operations, it is input into the newly added small target detection layer for prediction. The improved network model is represented in Figure 10.  
To validate the improving effect of adding a small target detection layer on the model's ability to detect small targets, the model is trained and validated. The results are shown in Figure 11. The improved model can correctly identify and detect small targets on the mobile phone's flat glass, whereas the original model fails to detect some small targets. This proves the effect of such an improvement in enhancing the detection of small targets.  
Figure 9: Optimized 1 detection image.