esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### River Sediments  
Results relative to river sediments are shown in Figure 4. The concentrations of \({}^{238}\)U are all smaller than 1.2 ug/g; this value corresponds to the typical natural abundance of uranium in the environment. It is almost constant along the stream. The concentration of \({}^{236}\)U is at the detection limit upstream of the NPP and slightly increasing downstream. This can also be seen in the \({}^{236}\)U/\({}^{238}\)U ratio.  
The increase in the \({}^{236}\)U/\({}^{238}\)U ratio observed downstream of the NPP indicates detectable contamination, likely due to NPP releases accumulated during reactor operation, whereas no significant increase in U concentration C is observed. In fact, due to the uncertainties affecting the measurements, a possible DC variation would only have been detectable if DC/C \(>\) 15%. Using a mass balance approach, we calculated the expected percentage increase as a function of the assumed isotope ratio of the contaminant, using the measured values of the upstream and downstream isotope ratios (2.0 \(\times\) 10\({}^{-10}\) and 1.5 \(\times\) 10\({}^{-9}\), respectively). The curve thus obtained (Figure 5) shows that the minimum  
Figure 3: \({}^{238}\)U concentration measured in river water samples. Values below the detection limit are not shown.  
value of the isotope ratio that would produce an increase in a concentration lower than its uncertainty is \(10^{-8}\), which is hardly attributable to any other source than NPP releases.  
The \({}^{240}\)Pu/\({}^{239}\)Pu isotopic ratios are nearly constant, considering the uncertainties, and fluctuate around the value of 0.18 (Figure 6). This agrees with global fallout values in the northern hemisphere. In fact, the distribution of \({}^{240/239}\)Pu values in marine sediments (Figure 7) shows a peak at about 0.18 [26; 27].  
We obtain an identical distribution for \({}^{236}\)U/\({}^{238}\)U ratio, peaked at 2.8 \(\times\) 10\({}^{-9}\), compatible with global fallout values.  
Concentration values of \({}^{239}\)Pu are \(\leq\)20 fg/g and increase slightly downstream, by a factor \(\sim\)2, except at the river mouth (sample F8). In this case, mass balance indicates that the isotopic ratio of the contaminant is expected to be the same as the upstream value within \(\pm\)0.04, in agreement with observation.  
Figure 4: \({}^{236/238}\)U isotopic ratio (in orange, on the left) and \({}^{238}\)U concentration (in green, on the right) measured in river sediments. Values below the detection limit are not shown.  
Figure 5: Expected percent upstream-downstream increase in U concentration as a function of the assumed isotopic ratio \(\rm R_{f}={}^{236}\)U/\({}^{238}\)U. The horizontal blue line represents the upper limit of the observed concentration.
## 3 Proposed Model
### Applications  
HCAE is a general framework that can be applied to different scenarios such as transportation, healthcare, social networks, economy, and finance. Take transportation scenarios as an example: (1) Construct TKG, using vehicles, routes, and events, etc. as entities:A specific vehicle travelling on a particular route during a certain period, and a special event occurring on a specific route, etc., are represented as relationships. Vehicle type, color, the route starting and ending points, and event type, etc. are used as entity attributes. (2) Encoding attribute information: This can reflect behavioral patterns under different conditions, such as route selection varying based on weekdays or weekends and other start and endpoints. It helps in learning the correlations between entities, such as certain events that significantly impact specific types of vehicles. It also provides decision-making foundations, such as planning public transportation routes based on road segment attributes. (3) Mining high-order structural information: Learning high-order relationships between vehicles and roads can reflect the way in which a vehicle drives certain road sections for an extended period, while high-order connections between roads can demonstrate the way in which congestion on a road section often affects neighboring road sections, and high-order relationships between events and related entities, such as a road construction events, affect related vehicles and road sections. (4) Multi-scale historical modeling: Long-term history can capture vehicle driving habits and patterns. A medium-term account can learn changes in road congestion over the past month or two. A short-term history can predict traffic peaks during daily traffic hours. Therefore, the HCAE model can predict future traffic conditions, traffic flow, and optimal route planning. Compared with the use of KG or historical data alone, HCAE effectively combines attribute, structural, and multi-scale historical information, improving traffic-related prediction and planning tasks.
# Saturation-Based Airlight Color Restoration of Hazy Images
## 4 Experimental Results and Discussions
### Performance Evaluation Using Ground Truth Image
To detect the saturation of the airglow, the dominant color output method is used to cluster the LAB images, and the classified clusters are assigned an area score to select the optimal airglow area. In addition, the a and b channels of the airglow are white-balanced by running a modified gray world hypothesis in LAB. To dehaze the color-restored fog image, a weighted depth map is computed by analyzing the correlation between brightness, saturation, and the depth of the fog. To solve the problem of traditional dehazing where objects in the close-range view are darkened when visibility is increased, we divide the image based on the mean of L, so that different distances are weighted differently. To address shading and noise in the sky area, we also take advantage of the fact that the restored airglow is achromatic to separate distant objects from the airglow. The parameters of the depth map are estimated using multiple linear regression based on their linear nature, and the final output depth map is applied to the atmospheric scattering model to run dehazing. The quantitative and visual analysis results of the existing method and the proposed method demonstrate that the proposed method addresses the problems of existing color restoration dehazing methods, such as scenes with fog and objects with similar saturation and scenes with widespread highly saturated objects, and shows superior color restoration performance. In terms of dehazing, the proposed method preserves objects in the close-range view that are less affected by fog while improving the visibility of the distant view, thus preserving the information in the original image as much as possible. It also handles distant objects separately from airglow, while also addressing shading, noise, etc., in the airglow area due to severe visibility enhancement. Conceptualization, Y.-S.C. and N.-H.K. ; software, Y.-S.C.; validation, N.-H.K. ; formal analysis, Y.-S.C.; investigation, Y.-S.C. and N.-H.K. ; data curation, Y.-S.C. and N.-H.K. ; writing--original draft preparation, Y.-S.C. and N.-H.K. ; writing--review and editing, Y.-S.C. and N.-H.K. ; visualization, Y.-S.C.; project administration, N.-H.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data availability StatementData are contained within the article. The authors declare no conflict of interest.
# VIS-HAPT: A Methodology Proposal to Develop Visuo-Haptic Environments in Education 4.0
## 4 The VIS-HAPT Methodology
### Recommendations for Application in a Student Environment and Field Studies
#### 4.4.3 Learning Gains  
Learning gains are used to test whether the use of the visuo-haptic environment has a positive effect on student outcomes. Initially, the pre-test and post-test are administered to both the experimental and control groups. The pre-test is an indicator of the student prior knowledge about a given subject. The experimental group interacts with the visuo-haptic environment, while the control group is given traditional content. Finally, the post-test is also administered to the whole student sample. To measure the effectiveness of the applied methodology in the student learning process, it is useful to define either an absolute learning gain: \(G=<\mathit{Post}>-<\mathit{Pre}>\), where \(<\mathit{Post}>\) and \(<\mathit{Pre}>\) are the average post-test and average pre-test for either the experimental or control groups. Additionally, a relative learning gain may be calculated [37], such that: \(g=(<\mathit{Post}>-<\mathit{Pre}>)/(100-<\mathit{Pre}>)\). For a given student, the relative learning gain is the actual student learning gain relative to the maximum student learning gain they may attain.  
For the results to be meaningful, we recommend that during the process (a) the sample is as large as possible, for both the experimental and control groups; (b) a professor teaches both the experimental and control groups, to mitigate as much as possible the \"teacher effect\"; and (c) the pre-test and post-test are administered under similar and controlled conditions to both groups.  
We also suggest validating the significance of the results by means of \(t\)-test and \(p\)-value analyses, having defined null (\(H_{0}\)) and alternative (\(H_{1}\)) hypotheses, or by the effect-size considering the average gains and their standard deviations [38; 39].
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 3 Results and Analysis
### Exposure Assessment of Disaster-Prone Environment  
The exposure of the disaster-prone environment reflects the hydrological and geomorphological environment of Liaocheng City. As shown in Figure 3, the distribution  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Return Periods** & **Very Low** & **Low** & **Medium** & **High** & **Very High** \\ \hline
10a & 18.83\% & 34.53\% & 20.66\% & 19.28\% & 6.70\% \\
50a & 16.94\% & 34.31\% & 20.86\% & 20.14\% & 7.75\% \\
100a & 17.37\% & 34.68\% & 20.47\% & 19.40\% & 8.08\% \\
200a & 19.47\% & 34.40\% & 18.06\% & 18.66\% & 9.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Area share of disaster-causing factors.  
Figure 2: Zoning maps indicate the severity of disaster-causing factors.  
of flood exposure in Liaocheng City is scattered; the exposure is generally low, but the flood exposure in the central part and part of the southwest is strong. The areas of very high exposure are mainly concentrated in Dongchangfu District and Shenxian County, accounting for 10.42% of the total area, which is closely related to the high density of the river network, lower elevation, gentle terrain, and low vegetation cover in these areas. In Liaocheng City, the very-low-exposure and low-exposure areas account for 21.64% and 31.41% of the total area, respectively, and are mainly distributed in the northern part of the city, with Linqing City being the least exposed because of its relatively steep slope and high vegetation cover.
## 8 Conclusions
Based on the accuracy of the measurement method and the spatial variability, the number of taken samples should be adapted;
* The analysis was based on the decision tree presented in Figure 1. However, the presented decision tree is not definitive, and could be extended to include other events, which were not included here. This includes extending the decision tree to include events of the propagation phase, events of defective repair that can cause damage to the structure, events, such as unsuccessful reactive repair, etc. ; and
* The decision tree was observed as a stationary process, meaning that the probabilities and costs of events were not associated with their duration. However, the time component of all the processes in the decision tree could be taken into account, in order to further investigate in which way the duration of certain events influences the cost efficiency of the measurement. The main aim of the paper was to present a holistic procedure for pre-posterior analysis in the quantification of the Vol for chloride content measurement, with an associated elaboration of the chloride parameter identification, measuring techniques, and repair methods. In other words, the intention was to show a procedure according to which infrastructure managers and operators can quantify the value and cost effectiveness of a certain step in the life-cycle process, such as measurement of the chloride content. Thus, as such, the paper forms a valuable basis for further research and enhancements in the area of pre-posterior analysis and Vol analysis for reinforced concrete structures affected by the ingress of chlorides. **Author Contributions**: Conceptualization, I.Z. and M.P.S.A. ; methodology, I.Z., J.C.e.M. and A.S.; software, I.Z. ; validation, I.Z. and M.P.S.A., J.C.e.M. and A.S.; formal analysis, I.Z. and M.P.S.A. ; investigation, I.Z. ; resources, I.Z., J.C.e.M. and A.S.; data curation, I.Z. and M.P.S.A. ; writing--original draft preparation, I.Z. and M.P.S.A. ; writing--review and editing, I.Z. ; visualization, I.Z. and M.P.S.A. ; supervision, J.C.e.M. and A.S.; project administration, I.Z. ; funding acquisition, I.Z. All authors have contributed substantially to the work reported. All authors have read and agreed to the published version of the manuscript. **Funding**: This research received no external funding. **Acknowledgments:** The author would like to acknowledge the financial and educational support from the COST TU 1402 Action (www.cost-tu1402.eu), received through the Training School grant. **Conflicts of Interest**: The authors declare no conflict of interest.
# Carbon Emission Analysis of RC Core Wall-Steel Frame Structures
## 3 Results  
### Background Information of the Office Building  
The case study in this work is a super high-rise office building with an RC core wall-steel frame structure in Nanjing, China. This structure is composed of three main components: the external steel frame, the concrete core tube, and the roof steel structure. The building has an above-ground floor area of approximately 31,400 m\({}^{2}\) and a footprint of 1227.5 m\({}^{2}\). It consists of 21 above-ground floors, one roof level, and one mezzanine for mechanical equipment. The height of the ground floor, the 21 st floor, and the standard floor are 8.95 m, 4.55 m, and 4.2 m, respectively, resulting in a total height of 101.3 m. The steel used in the structure is of grade Q355B, and the concrete grades range from C30 to C60. The external steel frame structure is composed of frame beams and columns.  
The structural design of the building is based on relevant codes including [54; 55]. The design lifespan of the building is 50 years, with a structural safety level of Category II and a structural importance factor of 1.0. The seismic design category is C, with a basic snow load of 0.65 kN/m\({}^{2}\) (return period of 50 years) and a basic wind pressure of 0.40 kN/m\({}^{2}\) (return period of 50 years). The ground roughness is classified as Category B. The seismic fortification intensity is 7 degrees, with a design basic seismic acceleration value of 0.10 g. The seismic design grouping is Group I, and the site category is III. As mentioned, the structural system consists of an RC core-steel frame structure, with the cross-shaped steel columns and concrete core tube designed to seismic performance level II, and the steel beams designed to seismic performance level III.  
The structural calculation of the building is conducted utilizing YJK4.1 software to ensure its safety and reliability. The model of the standard floor is shown in Figure 2, and the main computational results are presented in Table 1. The wind load and seismic calculations are also included, with the detailed results presented in Tables A5-A7 in Appendix A. The results indicate that the safety and reliability of the structure meet the required standards [54; 55].
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 4 Discussion
### Related Work
#### 4.1.2 Gait Recognition for Coal Mine  
The existing works on gait recognition in coal mines mainly use neural networks to process image data. [PERSON] et al. [19] proposed a gait recognition method via similarity learning named two-stream neural network (TS-Net) based on a densely connected convolution network (DenseNet) and stacked convolutional autoencoder (SCAE). The experimental results show that the proposed TS-Net model has a gait recognition accuracy of 92.22% in the UCOMP-GAIT dataset. [PERSON] et al. [20] proposed a gait recognition method by combining wavelet packet transforms (WPT) and principle component analysis (PCA). Gait recognition results on the CASIA-A database showed the highest accuracy of 97.16% using WPT and PCA. [PERSON] et al. [21] proposed an improved YOLOv4 network for personnel detection based on thermal infrared images. Experimental results show that on the infrared personnel image dataset underground coal mine, the improved YOLOv4 model achieves 96.25% and 48.2 fps in global AP and detection speed, respectively. These image-based works are heavily affected by illumination.  
Figure 19: Comparison of recognition accuracy of different models.
# Analysis and Design of Infrared Search and Track System with Afocal Zoom Telescope
## 3 Image Wandering Analysis of an IRST
### System Design Considerations  
* IRST is located on a circular sweep platform, and its imaging system consists of a forward afocal telescope, FSM, and an imaging objective. Paraxially, its effective focal length \(f_{IRST}^{\prime}\) can be described as \[f_{IRST}^{\prime}=f_{OBJ}^{\prime}\times m_{\text{tele}}\] (20)  
By reasonably allocating the magnification \(m_{\text{tele}}\) of the telescope and the effective focal length \(f_{OBJ}^{\prime}\) of the objective, we can reduce the inertia of FSM, improve its working bandwidth, and achieve a small volume envelope of the IRST system. Meanwhile, the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Spectral range & 3.7 \(\upmu\)m\(\sim\)4.8 \(\upmu\)m \\ Effective focal length & 60 mm\(\sim\)360 mm \\ F number & 4 \\ FPA array & 640 \(\times\) 512 \\ Pitch size & 15 \(\upmu\)m \\ Frame rates & 50 fps \\ Relative distortion & \(\leq\)5\(\%\)\(\%\) \\ MTF & \(\geq\)0.4 (30 \(\upmu\)m \(\pm\) 4 whole FOV) \\  & 60\({}^{\circ}\)/s, \(f^{\prime}=\) 60 mm \\ Scan speed & 180\({}^{\circ}\)/s, \(f^{\prime}=\) 180 mm \\  & 360\({}^{\circ}\)/s, \(f^{\prime}=\) 360 mm \\ Back focal length & \(\geq\)28 mm \\ Size & \(\leq\)270 mm \(\times\) 160 mm \(\times\) 120 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Requirements for a 6\(\times\) zoom IRST optic.  
residual image wandering caused by projection transformation can be controlled to ensure the imaging performance.
* Magnification of the telescope  
Different from ordinary telescopes, in which the magnification \(m_{\text{tele}}=tan\theta_{out}/tan\theta_{in}\), where \(\theta_{in}\) is the chief ray angle of incidence from verified FOV, and \(\theta_{out}\) is the angle of emergence. The linearity of the angular magnification needs to be guaranteed in the IRST system, which means \(m_{\text{tele}}=\theta_{out}/\theta_{in}\).
* Angular velocity of FSM  
For the convenience of servo control, the angular velocities \(\omega_{scan}\) and \(\omega_{FSM}\) of the platform and FSM follow \(\omega_{FSM}=m_{\text{tele}}\omega_{scan}/2\) in the angle range of \(m_{\text{tele}}\omega_{scan}/2\times t_{int}\), where \(t_{int}\) is the integral time.
* Image wandering  
Image wandering affects the image performance directly. Refering to Figure 2, if we choose the dynamic MTF reduction of up to 0.5 at 30 lp/mm as a design criterion, maximum image wandering can be determined as a 0.6-pixel pitch.
## 3 Methods
### Ecosystem Structure Evaluation Method  
The evaluation of ecosystem landscape structure is based on indicators derived from the ESA WorldCover land cover dataset, produced by Impact Observatory. These maps are based on Sentinel-2 imagery from the European Space Agency, with a resolution of 10 m. Each map is a composite of LULC (land use and land cover) predictions for the entire year, classified into nine categories, to generate a representative snapshot for each year. The dataset achieves an average accuracy of over 75% (Figure 3).  
Figure 3: Remote sensing interpretation map of overall land cover types in the study area in 2020.  
The habitat quality index measures the rate of improvement in biological habitat quality within the study area, reflecting the suitability of the regional ecosystem for sustaining wildlife. Landscape fragmentation is primarily determined by the Patch Number (NP), which represents the total number of patches of a specific land use or land cover type in a landscape image. A higher NP value indicates greater fragmentation or dispersion of an ecosystem type, necessitating a combined analysis with the average patch area index for a more comprehensive assessment. Natural Habitat Connectivity evaluates the overall connectivity between key ecological spatial patches within the study area, serving as an indicator of ecosystem stability and ecological security. Landscape stability is assessed using three key metrics: landscape spread, patch density, and total edge contrast. A higher landscape spread, coupled with lower patch density and reduced total edge contrast, indicates greater landscape stability. Landscape richness is quantified using the Shannon diversity index, which captures the diversity of landscape elements or ecosystems in terms of structure, function, and temporal dynamics, reflecting the complexity and heterogeneity of landscape types.
## 3 Results  
### Sensor Characterisation  
The PVDF-ITO thin film had a transmittance of 55-72% in the wavelength range from 400 nm to 800 nm, as shown in Figure 3a. With the excitation laser light at 532 nm, the sensor had a transmission of 63%. The frequency response was evaluated by receiving a photoacoustic signal generated from a black tape, which was placed \(\sim\)1 mm away from the sensor tip to separate the signal from the trigger-induced noise (Figure 3b). The power spectrum of the photoacoustic signal indicated that the transparent ultrasound sensor had a centre frequency of 17.5 MHz, and a \(-\)10 dB bandwidth of 5-30 MHz (Figure 3c).
## 2 Materials and Methods
### Electrochemical Measurements in MFC\({}_{\text{DSM}}\)  
To assess how _C. pasteurianum_ DSM 525 performed in MFC\({}_{\text{DSM}}\), polarization/power curves and electrochemical impedance spectroscopy (EIS) were carried out in an AUTOLAB PGSTAT 30 potentiostat/galvanostat (NOVA 2.1 software).  
Initially, the polarization curves were obtained at a low scan rate (1 mV\(\cdot\)s\({}^{-1}\)) starting from the open circuit potential (OCP) down to zero. OCP denotes the maximum operating voltage of the system, where the current is zero. MFC\({}_{\text{DSM}}\) operated without an external resistance until OCP stabilized. Subsequently, the power curve was derived from the polarization curves according to Equation (1).  
\[P=\frac{i\,U}{A} \tag{1}\]  
where \"P\" is the power density (W m\({}^{-2}\)), \"\(i\)\" is the current (A), \"\(U\)\" is the voltage (V), and \"\(A\)\" is the anode geometric area (m\({}^{2}\)).  
EIS was performed at OCP, from 100 kHz to 0.01 Hz. Ten frequency points per decade with a root mean square (r.m.s.) sinusoidal perturbation were employed; the amplitude was 10 mV.