esa-sceva/satcom-chunk-collection huggingface dataset:
# Approaches and Challenges in Internet of Robotic Things
## 5 Open Research Challenges
**Localization problems:** The navigational duties performed by robots remain restricted to motion modeling and position analysis, with little discussion of trajectory planning [27]. **Noise problems:** Noise is a serious problem in robotic movement, depending on the surface resistance and pushback in the joints. **Accurate localization:** The measurements produced by the small sensors that are frequently used with humanoid robots are noisy and inconsistent. As a result, precise navigation, which is thought to be mostly addressed for wheeled robots, remains a difficult challenge for humanoid robots.
## 2 Saturable Absorber Fabrication and Characterization
### Metallic-SWCNT Film Characterization  
To initially characterize the samples, the metallic carbon nanotube films were transferred onto flat substrates (SiO\({}_{2}\)/Si for SEM and Raman measurements or soda-time glass for optical absorption investigation). A scanning electron microscope (SEM) was used to analyze the topography and homogeneity of the film surface. Images for both film thicknesses are shown in Figure 1a,b, respectively (obtained with a Raith E-Line Plus microscope), demonstrating that the films have similar nanotube random composition independent of the film thickness. The nanotubes inside the films were tightly arranged, pure, without residual contamination. To confirm the type of nanotubes used, the optical absorption spectra of the samples were obtained (Figure 1c), measured in a 300-3000 nmspan using a Cary 5000 UV-Vis-NIR spectrometer. For both samples with different thicknesses, a typical interband transition M\({}_{11}\) for metallic single-walled carbon nanotubes with the maximum around 710 nm was observed. This result clearly confirms that the nanotubes exhibited a metallic type of conductivity. The Raman measurements were performed using an inVia Qontor Renishaw spectrometer with a 532 nm laser excitation line in a backscattering configuration. The results are shown in Figure 1d. The spectra present two main modes G and G' (2D), typical for all carbon nanotubes, and a less intense RBM mode, which is characteristic for single-walled carbon nanotubes only. The relatively low intensity of the D mode indicates that the carbon nanotubes were clean without contamination and surfactant residues and contained slight defects, probably appearing as an effect of the nanotubes being closely packed inside the films.
Commonly used spectroscopic techniques for VOCs detection are Fourier-transform infrared spectroscopy (FTIR) and differential optical absorption spectroscopy (DOAS) [24]; however, their sensitivity, selectivity, and fragile optical setup are not always sufficient for harsh applications. Recently, newly commercialized MIR detectors and lasers, especially quantum cascade lasers (QCLs) [25] and interband cascade lasers (ICLs) [26], have stimulated the development of high-performance, compact, and rugged gas sensors. Traditionally, TLAS use a discrete narrow absorption lines of small molecules for gas sensing. For larger molecules, however, so many lines overlapping with each other results in the spectral features being broad and smooth, except for occasional spikes [23; 27]. These spectral features are distinct from those of the discrete narrow absorption lines with a Lorentzian, Gaussian, or Voigt profile. Detection of trace gas with broadband absorption is much more difficult than with an isolated narrow spectral line. Extra effort should be made to cope with the challenges of the broadband of larger molecules. In this paper, we primarily review tunable laser-based broadband absorption spectroscopy for trace gas detection in 2008-2018. After a brief overview of the principle (Section 2), we discuss the system configuration, including MIR tunable lasers, detectors, and optical configuration in Section 3. We discuss broadband spectroscopic techniques concerning derivative spectroscopy (Section 4), WMS(Section 5), and optical frequency comb spectroscopy (Section 6). Section 7 is a collection of MIR gas sensing applications. Section 8 gives conclusions and prospects.
## 3 Result and Discussion  
### Chemical Analysis: X-Ray Fluorescence Spectroscopy  
Thermo Scientific X-ray (XRF) Epsilon Spectrometer with a reference material of Montana soil SRM 2710 was used to determine the chemical composition of both metaclay (MC) and metakaolin (MK). Clay samples were ground into fine powder with mortar and pestle and 2 g of each of the samples transferred to the sample holder. Samples were held for 10 min in order to get rid of oxygen and moisture. The method was calibrated using geological calibration of oxides. Sample measurements were ran in the XRF spectrometer for 10 min for each result (Table 1). The metaoxides composition predicts the compressive strength of geopolymers [39]. The percentage of SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) determines the compressive strength of geopolymer ceramics. MK has a lower SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) ratio than MC, hence a lower compressive strength was expected in MKGC compared to MCGC (Table 1). The CaO and Fe\({}_{2}\)O\({}_{3}\) percentage in the XRF composition of MK is less than that of MC chemical composition. In a study by [PERSON] et al. (2016), metal oxides having composition, SiO\({}_{2}\) = 50.5%, Al\({}_{2}\)O\({}_{3}\) = 26.57%, CaO + Fe\({}_{2}\)O\({}_{3}\) = 15.9% showed higher compressive strength compared to metal oxides with composition, SiO\({}_{2}\) = 65.77%, Al\({}_{2}\)O\({}_{3}\) = 26.72%, CaO + Fe\({}_{2}\)O\({}_{3}\) = 1.38% of source material [39].
## 3 Results
### Leica Pegasus Backpack  
#### 3.2.1 Data Acquisition  
As reported in Section 2.2.2, the Pegasus system incorporates a GNSS, even though it is supposed to work efficiently in indoor environments too. Compared to the other systems, it hence requires an initializing procedure, useful for both the GNSS and INS, consisting of walking for a few minutes in an outdoor space. At the end of the survey, an analogous process is required.  
The scan data are acquired by the rear laser scanner, and hence it avoids recording data on the operator. A minimum measuring range of 40 cm makes this system suitable for very narrow places, even though it is bigger and heavier than the other two tested systems.  
During our examinations, the surface textures were acquired by the cameras and the optional flash light was installed and turned on. The texture quality varies, with very interesting results in the small indoor spaces (the flash light range is about 6 m) but some overexposure is evident outdoors. In many applications, the image information can be very helpful to support the data interpretation and to provide engaging models for visualization purposes.  
With Leica software supporting the Pegasus system, during post-processing it is possible to consider targets or natural points, if topographically measured, as constraints, and to use them for the linear adjustment of the scanned data, also by only considering a part of the whole trajectory. To make the acquired datasets (Figure 10) as comparable to the others as possible, we preferred not to use to this option.  
Figure 10: Leica Pegasus Backpack datasets: (**a**) path 1; (**b**) path 2.  
Figure 9: Kaarta Stencil, path 1. Cross Section 1 (reference data in white, compared dataset in colour).
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Evaluation and Discussion
#### 3.4.4 Discussion  
In this part, we will discuss the performances between SSENets and other models:  
1. In Section 3.4.1, Table 7 shows SSENets achieves a better performance in terms of accuracy, precision, specificity and \(F_{1}\) score, compared with other models. It proves that the designed embedded SSE module, which selects feature maps of different depths as inputs, and can improve the effectiveness of the model by recalibrating the feature maps by squeeze operator and excitation operator.
2. As shown in Tables 8 and 9, the testing accuracy has been improved more in comparison to the training accuracy, which shows that SSENets has a better generalization ability. Besides, all the models get low detection accuracy at the third fold cross-validation. The reason is that its testing set contains about two-thirds of the background images, which makes the number of cracks images in training set is far more less than background images. Though this situation will  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Epochs** & **FLOPs** & **Running Time** \\ \hline SSENets & 100 & 2.54 G & 95 min 49 s \\ Xu’s Model & 100 & 2.53 G & 94 min 52 s \\ Resnet18 & 100 & 1.82 G & 53 min 8 s \\ Resnet34 & 100 & 3.67 G & 74 min 38 s \\ Resnet50 & 100 & 4.12 G & 138 min 51 s \\ \hline \hline \end{tabular}
\end{table}
Table 10: Computational efficiency and complexity of models.  
Figure 6: Histograms of 5-fold cross-validation. (**a**) The detection accuracy of training; (**b**) The detection accuracy of testing.  
affect the training results of models, SSENets still achieve a higher detection accuracy than other models. Considering the great improvement in the specificity factor, which is shown in Table 7, we conclude that SSENets can reduce the proportion of background images that are classified as crack images.
3. Taking advantage of depthwise separable convolution, SSENets has smaller FLOPs and a shorter running time, compared to Resnets. Therefore, SSENets can greatly reduce the complexity of the model and improve the calculation efficiency, thus improving the detection performance of the model.
4. Though SSENets could achieve a high detection accuracy in most situations, it still has limitations. As the number of negative samples in the training set decreases, the detection accuracy of SSENets will decrease, so we will devote future work to improving this problem.
## 2 Materials and Methods
### CBCT Imaging and Image Processing  
CBCT imaging was performed for each patient under the following conditions: a dental CBCT machine (Finecube, Yoshida Seisakusho Co., Ltd., Tokyo, Japan) was used for imaging in the normal mode (tube voltage, 90 kV; tube current, 4 mA; imaging time, 16.8 s) with the patient's Frankfurt plane parallel to the floor. CBCT images were recorded with a slice thickness of 0.147 mm, a field of view of 81 \(\times\) 74 mm, and a voxel size of 0.146 mm. The images were converted to Digital Imaging and Communication in Medicine (DICOM) files, and the DICOM data were transferred to a computer for analysis. DICOM analysis software (OsiriX MD, version 8.0.2, Pixmeo, Switzerland) was used for linear measurements and bone analysis software (TRI/3D-BON, RATOC, Tokyo, Japan) was used for bone mineral density analysis.
## 3 Results
### Satellite Retrieval and Crop Growth Model Sensitivity Analysis
Furthermore, the footprint of the flux tower measurements themselves depends on meteorological conditions and can be affected by process occurring outside the boundaries of the field [81; 99]. Overall, however, strong performance is seen for the majority of site-years analyzed providing confidence in the retrievals. Figure 8: Actual versus theoretical \(|\mathrm{r}|\) performance of the \(\Delta\mathrm{LUE}_{\mathrm{Canopy}}\) retrievals at all sites at different levels of smoothing and values of \(\Delta\). N = 5071. Figure 9: Actual versus theoretical RMSE performance of the \(\Delta\mathrm{LUE}_{\mathrm{Canopy}}\) retrievals at all sites at different levels of smoothing and values of \(\Delta\). N = 5071.
# Geo-Spatial Context Provision for Digital Twin Generation
## 3 A Digital Twin Architecture Linking CPS Behavior and Produced Operational Data
### Sample Model Variants for the Logistic Use Case  
The general modeling approach is depicted in Figure 4 based on the use case scenario described in the previous section. Each active component is modeled as its own subject. The connections between the subjects contain messages to implement the entire CPS behavior.  
Figure 3: Open Control Cycle of S-BPM Activity Bundles, reprinted from (Ref. [23], p. 31).  
As part of the proposed digital twin architecture, subject-oriented models are used to depict the individual behavior of CPS components and their interactions with each other as part of different processes. Figure 5 shows the subject interaction diagram of the components involved in the transport preparation process on a high level of abstraction. All models were created using the Compunity suite, which uses slightly different terminology, e.g., component interaction diagram instead of subject interaction diagram and step instead of state. For the sake of understandability, we chose to utilize the more general terms common to subject-orientation consistently throughout this article. Figure 5 shows which messages flow between the different subjects that are part of the process, with the notification of the room triggering the robot's behavior, and the robot issuing commands to its attached add-ons (here modeled as separate subjects). The content of the messages is detailed in terms of an abstract data structure that needs to be derived from the respective use case. It can also be aligned with existing data models. Depending on the chosen level of abstraction, the behavior of the robot and its add-ons could be unified into a singular subject, or separated even further. As was mentioned in the previous sub-section, if the digital behavior models are used in the sense of design-integrated engineering, a refinement to a fine-grained level will be necessary to put them into operation. Considering that the digital twin should meet the requirements of its potential users, a high level of abstraction will still be useful in cases where detailed information on behavior is not needed and might overwhelm a CPS stakeholder.  
To demonstrate the modeling of component behavior we detail the part of the transport preparation process concerning the selection of a sensor through the robot (step 4). The behavior of the robot, as described above, was now modeled in a SBD, as seen in Figure 6. It follows the general activities of \"move to way-point\" and \"interact with environment\" as outlined above. According to the SID in Figure 5, the arm unit and scanner were modeled as separate subjects with their encapsulated behavior. The behavior is again depicted on a high level of abstraction.  
Figure 4: General modeling approach.
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods
### 3D Reconstruction
#### 2.5.2 Tie Point Refinement  
To ensure the final sparse point cloud had minimal errors, it was necessary to remove outliers during the tie point refinement process. Metashape reconstructed all visible points that appeared in at least two photos. However, points that were visible on only two photos may have had poor accuracy. \"Image count\" filtering removed the unreliable points (Figure 8). The next criteria pertain to the maximum re-projection error. This was calculated in normalized units for all images where tie points were measured. The next solution was reconstruction uncertainty, which is the ratio of the largest semi-axis to the smallest semi-axis of the error ellipse of the triangulated 3D point coordinates [29]. The error ellipse corresponds to the uncertainty of the point triangulation alone without taking into account the propagation of uncertainties from the interior and exterior orientation parameters. The ideal setting for these parameters was based on the trial-and-error experiences mentioned in Table 6. This workflow was repeated for both spherical and DSLR data processing.  
After the refinement process, the final 3D point cloud of each sensor (Figure 9) was ready to compare and evaluate in the next step.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Calibration Parameters** & **Reconstruction Uncertainty** & **Re-Projection Error** & **Image Count** \\ \hline Threshold value & 30 & \(\sim\)1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The experimental values for the threshold definition of noise filtering.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Alignment Settings** & **Values** \\ \hline Tie point threshold & 8000 \\ \hline Key point threshold & 40,000 \\ \hline Camera calibration methods & Two methods: Brown fisheye model (self-calibration) \(|\) [PERSON]’s method (chesboard calibration) \\ \hline Accuracy mode & High \(|\) Generic preselection \\ \hline \hline \end{tabular}
\end{table}
Table 5: Image alignment settings in Agisoft Metashape software.  
Figure 8: Three methods used for noise filtering from the reconstructed 3D point clouds: re-projection error (**a**), reconstruction uncertainty (**b**), and image count (**c**) threshold. For each step, point clouds were reduced by a certain amount.  
Figure 9: The network design is illustrated on the reconstructed 3D point cloud, accompanied by the relevant software: X3 camera with 20 stations (**a**), X2 camera with 20 stations (**b**), and DSLR camera with 38 stations (**d**), all implemented in Agisoft Metashape software to generate 3D point clouds. The RTC 360 laser scanner with two stations (**c**) performed the last process of this workflow.
###### Abstract  
In addressing eutrophication and enhancing water quality, this study builds upon previous research involving the development of an Efficient Phosphorus Removal Composite (EPRC), a material created using modified industrial wastes (steel slag and fly ash) as adsorbent substrates, supplemented with a binder and porosity-forming agent. In this investigation, the EPRC was further enhanced through the addition of zirconium oxychloride octahydrate, resulting in the production of Zr-EPRC particles as reinforced phosphorus removal materials. Comparative experiments were conducted to assess different methods for preparing Zr-EPRC, the static adsorption performance, and dynamic adsorption behavior. The optimal preparation of Zr-EPRC was achieved by separately modifying the base materials, steel slag and fly ash. Loading with mass ratios of zirconium chloride octahydrate to fly ash and steel slag at 0.4 and 0.6, respectively, for a duration of 12 h at a pH of 10 yielded the best results. In static adsorption experiments conducted at temperatures of 15 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 35 \({}^{\circ}\)C, Zr-EPRC exhibited saturated phosphorus adsorption capacities of 11.833 mg/g (variance = 0.993), 12.550 mg/g (variance = 0.993), and 13.462 mg/g (variance = 0.996), respectively. Zr-EPRC emerges as a cost-effective and readily available solution with promising stability for general wastewater treatment applications, contributing significantly to the mitigation of eutrophication and the improvement of water quality.  
phosphorus; steel slag; fly ash; zirconium modified Article
# WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
## 4 Experimental Section
### Generalization Ability  
Given that our approach was trained using the DPDD training dataset [1], a natural question arises concerning the model's generalization to images from different datasets. To address this, the performance of our method on alternative test sets was assessed. Figure 8 depicts the outcomes of our network trained on the DPDD dataset when applied to our custom-made dataset's test set. The restored image details are prominently discernible, showcasing the efficacy of our model's training on the DPDD dataset. Table 3 illustrates the quantitative comparison of our custom-made dataset's test set. A noticeable enhancement in the image quality achieved by our model can be ascertained, indicating its favourable generalization capacity to images captured by other cameras. The underlying reasons for this effect could lie in the deep learning network utilizing wavelet transforms. Particularly, owing to its inherent low information loss property, the network's transferability is enhanced and the generalization ability of networks with significant depth is also improved.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**WtT** & **IFA** & **GCN** & **PSNR** & **SSIM** & **MAE** \\ \hline \multirow{3}{*}{\(\surd\)} & & & & 23.67 & 0.705 & 0.436 \\  & & & & 24.65 & 0.757 & 0.409 \\  & \(\surd\) & & & 24.56 & 0.751 & 0.414 \\  & & \(\surd\) & & 24.03 & 0.736 & 0.423 \\  & \(\surd\) & & & 25.12 & 0.765 & 0.399 \\  & & \(\surd\) & & 25.04 & 0.763 & 0.401 \\  & \(\surd\) & \(\surd\) & & 24.77 & 0.749 & 0.411 \\  & \(\surd\) & \(\surd\) & & 25.37 & 0.774 & 0.394 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study.
# Navigational Risk Assessment in Offshore Wind Farms Using Spatial Ship Domain Models
## 2 Materials and Methods
### Seabed Characteristics in the Study Area  
The FEW Baltic II offshore wind farm is located in the southern Baltic Sea, approximately 50 km north of Ustka, within the area defined by the \"Lawica Slupska\" sheet of the Geological Map of the Baltic Sea Floor (1:200,000). Geologically, the area lies on the eastern edge of the East European Craton and includes glacially shaped terrain formed during the final stages of the Pleistocene deglaciation [33].  
The site covers approximately 41 km\({}^{2}\) at water depths between 30 and 50 m, with most of the area lying between 40 and 46 m. The seabed consists primarily of fine to medium sands, with interlayers of silty and clayey sediments, and occasional gravel and boulders. Morphological features include sand waves, low ridges, sand domes, and elongated depressions, resulting in under-keel clearance (UKC) variability important for navigational risk modeling.  
Turbines at Baltic II will be mounted on large-diameter steel monopile foundations (~9 m diameter, embedded ~40 m), supporting Siemens SG 14-236 DD (14 MW) direct  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dimensionality**} & **Accounts for Vessel** & **Considers Vertical** & **Reflects Maneuvering** \\  & & **Size/Speed?** & **Risks (UKC/OHC?** & **Capability?** \\ \hline PIANC guidelines & 2D (horizontal) & No & No & Partially (via generic \\  & 2D (horizontal) & Partially (via & No & Yes (limited to turning/ \\  & 2D (horizontal) & stopping distance) & & stopping distance) \\ [PERSON] (2000) [23] & 3D (horizontal + vertical) & Yes & Yes & Yes (detailed maneuvering \\  & & & & behavior under \\  & & & & different conditions) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of spatial ship domain models used in the analysis.  
drive wind turbines. A total of 25 turbines are planned [34]. These foundations are adapted to local geotechnical conditions and may create localized scour zones that can modify bathymetry in their immediate vicinity. While such effects may influence UKC near turbine bases, they are spatially limited and have negligible impact on overall vessel maneuverability across the OWF. Bathymetric and sediment variability are explicitly included in the RN model to reflect realistic conditions affecting deep-draft navigation.