esa-sceva/satcom-chunk-collection huggingface dataset:
# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 4 Results and Evaluation
### Real-Time Capabilities  
We conducted a series of experiments designed to assess the performance of our newly developed pipeline, specifically its ability to process video sequences in real-time. These evaluations were carried out on a desktop computer; its specifications are provided in Table 2. We analyzed three video sequences for this purpose, each with a duration of approximately 15 s, a resolution of \(1920\times 1080\) pixels, and a frame rate of 25 fps. The focus of our investigation was on the processing times associated with various stages of our pipeline (EO: expansion operation, AS: artifact suppression, DQ: dequantization, and HB: highlight boosting), examining the impact of both activating and deactivating certain steps. Throughout these experiments, a total of 1175 frames were processed, and the timing outcomes are presented in Table 3. It is worth noting that these results do not account for the time taken to retrieve individual frames from the video file because this aspect is heavily influenced by I/O operations, storage medium speed, and other system-specific variables. These factors can vary significantly depending on the system setup and are not directly related to the performance of our processing pipeline. Therefore, to provide a clearer and  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Pipeline Step** & **Parameters Values** \\ \hline \multirow{3}{*}{Artifact suppression} & \(r=32\) (Local window radius) \\  & \(\epsilon=0.12^{4}\) (Regularization parameter) \\  & \(s=4\) (Subsampling ratio) \\ \hline \multirow{3}{*}{Dynamic range expansion} & \(L_{\text{w,max}}=4000/6000\) (\(4000\) cd/m\({}^{2}\) peak luminance) \\  & \(m_{\text{i}}=0.214\) (Mid-level in) \\  & \(a=1.15\) (Contrast settings) \\  & \(d=2\) (Highlights settings) \\  & \(p=0.2\) (Dampering factor) \\ \hline \multirow{3}{*}{Dequantization} & \(q=5\) (Difference between two consecutive pixels) \\  & \(b=4\) (Radius of the average filter) \\  & \(i=5\) (Number of iterations) \\ \hline \multirow{3}{*}{Highlights boosting} & Parameters of the fast guided-filter to transfer details on the \\  & binary mask: \\  & \(r=20\) (Local window radius) \\  & \(\epsilon=10^{-2}\) (Regularization parameter) \\  & \(s=4\) (Subsampling ratio) \\  & Parameters to apply the expansion mask: \\  & \(M=2000/6000\) (\(2000\) cd/m\({}^{2}\) for highlights boosting) \\  & \(\alpha=4.16\) (Highlight boosting control) \\ \hline Saturation enhancement & \(s=1.3\) (Saturation level) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters values used in our testing.  
more focused assessment of our pipeline's efficiency, we isolated the computation time required by our algorithm from the overall I/O retrieval time. With an average processing time of 15.93 ms per frame, our approach theoretically has the capability to process video sequences with a resolution of 1920 \(\times\) 1080 pixels at a rate of up to 60 frames per second.
## 3 Results
### Swarm Electron Density Results
We note that, for the shorter period analysis in the symmetrical period from \(-500\) days to 500 days with respect to the earthquake, the maximum concentration of the anomalies is post-seismic, and for the analysis in the signal period range between 10 s and 25 s from \(-90\) days to 30 days, the maximum concentration is a \"co-seismic\" effect. Even if statistically significant, these results do not contribute to the main purpose of this paper, i.e., a better understanding of the preparatory phase of medium-large earthquakes. On the other hand, the investigation in the lower frequency band in which the longest period is equal to 25-50 s shows not only highly significant results but also pre-seismic maximum concentrations of anomalies both for the investigation in the time window from \(-90\) days to 30 days and for the one from \(-500\) days to 500 days at about \(-59\) days and \(-290\) days, respectively (see Table S2). Such a result seems to indicate that taking the frequency content of the anomalies into account could help to discriminate the ones possibly caused by the preparation phase of earthquakes from the other ones caused by other phenomena. Finally, Table S2 (in the Supplementary Materials) reports the main statistical parameters for all the performed analyses for 8 years of _Swarm Ne_ data. The statistically significant results (i.e., the ones with \(d\geq 1.5\) and \(n\geq 4.0\)) that precede the earthquake are underlined in green, as in Table S1. It is possible to note that just three out of sixteen results for _Ne_ passed these criteria. Despite this, introducing the analysis of the signal frequency provides more significant results in the lowest frequency band (i.e., the period between 25 s and 50 s).
# Extreme Wave Loading on a Vertical Circular Cylinder
## 2 Experimental Modeling and Data Analysis
### Four-Phase-Based Decomposition Method  
In order to apply the four-phase-based decomposition method presented by [24] to extract the harmonic structure of a nonlinear wave (i.e., higher-order wave components), a crest-focused wave group and three wave groups with phase shifts of \(\pi/2\), \(\pi\), and \(3\pi/2\) were generated in groups. These four waves (Figure 3; labeled as \(\eta_{0}\), \(\eta_{\pi/2}\), \(\eta_{\pi}\), \(\eta_{3\pi/2}\)) were generated by the same paddle signal (i.e., have the same frequency content), but with the phase of each Fourier component being shifted \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\). That is, \(\varphi_{g}\) in Equation (1) equals 0, \(\pi/2\), \(\pi\), and \(3\pi/2\), respectively.  
The wave component at each order is then decomposed as [24]  
\[\begin{array}{ll}\text{linear}:&\eta^{(1)}+O(A^{5})=\frac{1}{4}(\eta_{0}+ \eta_{\pi/2}^{H}-\eta_{\pi}-\eta_{3\pi/2}^{H})\\ \text{2 nd sum}:&\eta^{(2+)}+O(A^{6})=\frac{1}{4}(\eta_{0}-\eta_{\pi/2}+\eta_{ \pi}-\eta_{3\pi/2})\\ \text{third}:&\eta^{(3+)}+O(A^{5})=\frac{1}{4}(\eta_{0}-\eta_{\pi/2}^{H}-\eta _{\pi}+\eta_{3\pi/2}^{H})\\ \text{2 nd diff}+\text{4 th sum}:&\eta^{(2-)}+\eta^{(4+)}+O(A^{6})=\frac{1}{4}( \eta_{0}+\eta_{\pi/2}+\eta_{\pi}+\eta_{3\pi/2})\end{array} \tag{4}\]  
The superscript '\(H\)' indicates this is the Hilbert transform of the corresponding free surface elevation time history. \(\eta^{(1)}\), \(\eta^{(2+)}\), \(\eta^{(3+)}\), \(\eta^{(4+)}\), and \(\eta^{(2-)}\) are the linear component, second-order sum, third-order sum, fourth-order sum, and second-order difference terms, respectively. The second-order difference (long wave) and fourth-order sum terms can be further separated by performing digital frequency filtering.
## 1 Introduction
The contributions of this work can be concluded as follows:  
* To optimize the transport network latency and improve load-balance, a pair-decision resource allocation model for backhaul transport NS is introduced on account of mapping virtual nodes and links in a coordinated way. Here, the mapping objects are substrate network resources and SCs of E2E slices (i.e., including VNUs and their interconnections), and the problem model encloses the formulation of ILP, whose resolution yields the optimal path for VNFs and virtual links mapping and traffic routing. * For further improving extreme QoS (such as 5G ultra-reliable low-latency communications (URLLC)), the above resource allocation problem is formulated to minimize the transport network latency with considering the transmission time and propagation time, subject to the network capacity and link bandwidth constraints. In addition, in order to improve the network resource utilization and load balance, a node importance metric is employed to analyze the DCs' availability and priority in the substrate network. This paper is structured as follows. In Section 2, we present relevant related work, outlining an overview of existing contributions and shortcomings in regard to enabling technologies, VNF placement and virtual network embedding algorithms, and NS and resource allocation. In Section 3, we formally introduce the transport network latency and pair-decision resource allocation model over slices with substrate networks and logical networks. Section 4 formulates the optimal-latency model and describes the solution framework in Section 4. Section 5 evaluates the proposed model and analyzes results. Finally, we conclude the paper and summarize our findings in Section 6.
## 3 Examples
### Field Data Example  
A stacked seismic dataset from East China (Figure 6a) is used to thoroughly examine the effectiveness and applicability of the proposed method. The 2D field data comprise 200 seismic traces in the lateral direction, with each trace containing 700 sampling points spaced at intervals of 2 ms. Notably, there are two wells in these data, situated at CDPs of 32 and 68. The former well is employed for extracting the reflection coefficient distribution and generating a substantial number of reflection coefficients, while the latter serves as a blind well to assess the accuracy of the results.  
Furthermore, the statistical methodology within Hampson-Russell software is employed by us to extract the wavelet and construct the low-frequency wavelet library. The  
Figure 4: Synthetic example of the high-resolution reconstruction. (**a**) The reflection coefficient model. (**b**) The synthetic seismic data (S/N = 15.0).  
Figure 5: (**a-c**) Reconstructed errors for SSI, the DL-based method, and the proposed method, respectively. (**d**) Amplitude spectrum comparison of the synthetic data, outcomes from SSI, the DL-based method, and the proposed method, as well as the true high-resolution data.  
high-resolution results of SSI, the DL-based method, and the proposed method are presented in Figure 6b-d. It can be seen that the weak reflection and thin-layer structure are efficiently recovered by the proposed method compared with other methods, especially as indicated by the arrow. In Figure 7, the amplitude spectrum (AMP) of the output illustrates that the proposed method reconstructs a broader frequency range, where the high-frequency component distinctly exceeds that of other methods.  
A comprehensive comparison between the validation data derived from well-log reflectivity and the inverted high-resolution results is depicted in Figure 8. The 2-6-60-90 Hz bandpass filter is used to generate the validation data to evaluate the accuracy of the inverted results. Apparent in Figure 8 is the effectiveness of the proposed approach (the black curve), displaying not only a strong agreement with the verification data (the red curve) concerning robust amplitudes but also a precise retrieval of weaker amplitudes, including those within the amplification segment. The Pearson correlation coefficient (PCC) of the reconstructed high-resolution data on the blind well is also compared by us. The calculated PCC values of SSI, the DL-based method, and the proposed method are 0.7495, 0.7114, and 0.8012, respectively. It can be concluded that the high-resolution result of our method coincides with the validation data the whole time.  
Figure 6: Comparison of the high-resolution results. (**a**) Field data. (**b–d**) High-resolution results of SSI, the DL-based method, and the proposed method, respectively. The proposed method can better restore thin layers and yield more consistent horizontal high-resolution results (yellow and green arrows).  
Figure 7: The AMP of the field data, the result from SSI, the DL-based method, and the proposed method, respectively.
# Object-Level Semantic Map Construction for Dynamic Scenes
## 2 Related Work  
### Dense SLAM  
The arrival of the Microsoft Kinect device and the sudden availability of inexpensive depth sensors to consumers triggered extensive research aimed at real-time 3D scanning. Systems such as KinectFusion [1] first enabled mapping of the 3D geometry of arbitrary indoor scenes accurately and in real-time, by fusing the images acquired by a depth camera simply by moving a sensor around the environment. Successors to KinectFusion have quickly addressed some of its shortcomings. Ongoing research has produced systems with impressive performance, ranging from scalable extensions [7] and loop closure capabilities [2; 8] to methods that consider runtime limitations [9; 10] in order to enable 3D scanning and mapping of large indoor scenes for maximum robustness and ease of use. Although some researchers have focused on extending the capabilities of such systems to handle very large scenes or to include loop closure [3], others have made the tracking more robust [2] or improved the memory and scale efficiency by using point-based instead of volumetric representations [7], leading to increased 3D reconstruction quality [11]. KinectFusion [12] proved that a truncated signed distance function (TSDF)-based map representation can achieve fast and robust mapping and tracking in small environments. Subsequent work [13; 14] showed that the same principles are applicable to large-scale environments by choosing appropriate data structures. In addition, many research results have been obtained through the expression and organization of dense maps. Surface elements (surfels) have a long history in computer graphics and have found many applications in computer vision [5]. Recently, surfel-based map representations were introduced into the domain of RGBD-SLAM. A map of surfels is similar to a point cloud with the difference that each element encodes local surface properties--Typically a radius and normal in addition to the location. In contrast to TSDF-based maps, surfel clouds are naturally memory efficient and avoid the overhead due to switching representations between mapping and tracking that is typical of TSDF-based fusion methods. [PERSON] et al. [3] presented a surfel-based RGBD-SLAM system for large environments with local and global loop closure. With the advent of artificial intelligence, robots are required to interact well and intelligently with the environment. Although the above methods have achieved good results in camera tracking and map construction, they are assumed to run in static environments and do not add semantic information that can understand the environment, so they cannot meet the requirements of embedded unmanned platform intelligence. Because no semantic information is added, many robots cannot complete complex operations, such as road kinematics planning, obstacle avoidance, and human-computer interaction.
## 5 Conclusions
(a) The comparison of plot-averaged soil matric potential (\(\psi\)) in three groups BC1, BC2, and C from UAV over the period 14 to 21 November 2018 and during the four UAV campaigns. Whiskers denote the 95% confidence interval of each group on each day. Violin plots of \(\psi\) from UAV in each plot and each treatment on (b) Nov16, (c) Nov 19, and (d) Nov-21. The shaded areas of the violin plot denote the distribution of the day's measurement, the white dots denote the median values, and the bottom and top edges of the black bars denote the 25 th and 75 th percentiles respectively. The red circle '\(\bigcirc\)' denotes sensor measurements during flight. Figure S5. Water retention curves estimated from field-measured soil matric potential (\(\psi\)) and soil moisture content using Van Genuchten model. Dot-dash line denotes field capacity (FC) at \(-0.05\) MPa. The willing point (\(-1.5\) MPa) is out of the x-axis range. Figure S6. Overall relationships of (a) canopy chlorophyll content (CCC), (b) NDVI, and (c) LAI to soil moisture content in three groups BC1, BC2 and C. Table S1. Statistics for variables of the biochar experiment. Conceptualization, C.J.K., M.G., S.M., S.W.L. and M.S.J. ; methodology, H.J., M.G., C.J.K. and B.M.C.F. ; software, M.G., C.J.K. and H.J. ; validation, M.G., S.M. and A.M.D.-Q., data processing, H.J., C.J.K. ; investigation, C.J.K., B.M.C.F., J.R.-C., L.M., A.M.D.-Q. and A.S.-S.; data curation, C.J.K., B.M.C.F. and J.R.-C.; writing--original draft preparation, H.J. ; writing--review and editing, M.G., B.M.C.F., S.M., A.M.D.-Q., S.W.L., M.S.J. and H.J. ; visualization, H.J. ; supervision, M.G., S.M., A.S.-S. and M.S.J. ; project administration, M.G., S.M., S.W.L., A.S.-S. and M.S.J. ; funding acquisition, M.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the Joint Call of the Water Joint Programming Initiative (Water JPI) and the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI) of the European Union and partner countries via the Agricultural Water Innovations in the Tropics (AgWIT) project. H.J. is jointly funded by Sino-Danish Center for Education and Research (SDC), Denmark. S.M. acknowledges support from the Swedish Research Council (Vetenskapsradet), Formas, and Sida (VR 2016-06313), and by the Bolin Centre for Climate Research (Research Area 7). Not applicable. Data Availability Statement:Data is contained within the supplementary material. The data presented in this study are available in Supplementary material. We thank all the people who helped in the field and the laboratory, especially [PERSON], [PERSON], [PERSON], [PERSON], and all technical staff from the EEEJN-INTA who helped develop the rice experimental design and advised about regional crop management practices. Also, we want to thank the students [PERSON], [PERSON], and [PERSON] of Universidad Nacional, Liberia, Costa Rica. The authors declare no conflict of interest.
# Does Pressurized Carbon Dioxide Lavage Improve Bone Cleaning in Cemented Arthroplasty?
## 2 Materials and Methods
### Devices and Investigated Carbon Specimens
#### 2.1.3 Determination of the Cleaning Effect  
The two lavage systems were divided into group InterPulse (A), Carbolet (B) and InterPulse + Carbolet (C) as described. A sample size of 10 specimens per group (30 in total) were used. In case of the InterPulse a saline irrigation volume of one liter per specimen was used. The cleaning distance was determined by the splash shield. Following the manufacturer's specifications, a cleaning duration of 30 s was used for the Carbolet.  
The determination of the cleaning effect of the investigated lavage systems were performed using validated and standardized carbon foam specimens (RVC foam; ERG Materials and Aerospace, Oakland, CA, USA) as substitutes for human cancellous bone [35]. The carbon specimens showed a porosity of 30 PPI (pores per inch), which corresponds to 1.2 PPM (pores per millimeter). During the manufacturing process, the specimens were compressed twice, resulting in similar trabecular bone structure (Figure 4, right). The carbon foam specimens were filled with standardized technical fat (Bechem Rhus FA 37; Carl Bechem GmbH, Hagen, Germany), to simulate human bone marrow [36]. One ingredient of the fat was an aluminum-complex soap which we used as contrast medium for the radiological analysis. The fat-filled carbon specimens were coated with a shrink-on tube simulating the cortex (Figure 4, left).  
The evaluation of cleaning effects was determined by using computer tomography scans (SOMATOM Emotion, Siemens Healthcare GmbH, Erlangen, Germany). For this  
Figure 4: (**Left**) Fat filled carbon specimen with shrink tube, (**right**) close up of Carbon foam.  
Figure 3: Top view of: (**left**) Stryker- InterPuls bone cleaning tip, (**right**) KINAMED—Carbojet Wide-Angle Knee—Nozzle.  
purpose, the fat-filled specimens were scanned before and after cleaning with a slice thickness of 0.75 mm. For volume determination, a reference volume was recorded for each scan. The segmentation and calculation of the cleaning volume was performed with the software itk-Snap [37]. The mean flushing depth was calculated using the volume and the geometric parameters. Exclusion criteria were air inclusions or if specimens would not have been fully filled with fat. One specimen in Group A had to be excluded due to air inclusions that would have biased the cleaning effect. Therefore, only 9 out of 10 samples could be analyzed for Group A. Beyond that, no other specimens had to be excluded.
## 2 Possibility Analysis and Testing Method of the Structure Sound Absorption Performance  
### Analysis of the Possibility of Sound Absorption  
Most studies have shown that viscous dissipation and resonant sound absorption [16; 17] are the main sound absorption modes of porous metal foams. Viscous dissipation is due to the velocity gradient of air vibration in pores caused by the existence of pore walls, and acoustic energy is transformed into heat energy dissipation through internal friction [18; 19]. Resonant sound absorption relies on the resonance of the resonator to acoustic energy dissipation. When the frequency of the incident acoustic wave is close to the natural frequency of the resonator, resonance occurs. At this time, the velocity of air vibration reaches the maximum, and the acoustic energy loss reaches the maximum, so the sound absorption effect is the best.  
The existing research have shown that the closed-cell aluminum foam does not have good sound absorption performance [20], but according to mechanism of the above two sound absorption methods, it is feasible to improve its sound absorption performance by improving the structure of the closed-cell aluminum. Some scholars have found that the resonant sound absorption structure composed of holes can improve the sound absorption effect [21; 22]. Other scholars have also found that by compressing to make small cracks appear through the pore wall, a better viscous dissipation effect can be achieved [23]. It can be seen that the installation structure of closed-cell aluminum foam board plays a sound absorption and noise reduction effect in the tunnel is reasonable.
## 3 Results
### Farm Management System Integration: Communication to End Users
Data access could be either unrestricted or limited to a specific demo site, depending on the user's profile. Farmers who agreed to host part of MAGDA's infrastructure were granted an account with access to all available data for their respective national demonstrator site (Figure 9). Available data included Meteor drone flight data, in situ sensor data, forecast model outputs, and agro-hydrological results. Feedback from farmers was collected by Cap 2020 through interviews with users of its in situ sensors in France during the 2023 and 2024 campaigns, conducted independently of MAGDA, including users of the API that allows seamless data integration into third-party systems such as disease modeling applications or farm management software (Figure 10). Figure 10: Numerical weather forecast model using MAGDA’s infrastructure, with comparison to open-loop model forecasts, as displayed to farmers. Figure 9: View of an Italian farmer’s dashboard with access to Meteor drone data and three in-crop in situ sensors. In the scope of MAGDA, stakeholders from all demonstrator sites were informed of new functionalities, and meetings were organized accordingly: France (Burgundy) in March 2023 and May 2024, Italy (Piedmont) in January 2025, and Romania (Brala region) in March 2025. During these meetings, the functionalities of the dashboard and API were presented, and questions were answered. Feedback led to adjustments in the dashboard, including a more efficient display of long time series and the possibility to define user-specific indicators, relatively simple calculations based on available data, adaptable to various crops and user profiles (Figure 11).