esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characterization of the Cross-Linked Chitosan Beads  
In order to obtain better efficiency in the FD&C Red 40 dye removal process, the effect of each component (GO and TiO\({}_{2}\)) was evaluated by incorporating them into CS-GLA beads previously synthesized and characterized in a recently published work where were discussed the FT-IR, XRD, SEM, and TGA characterization of CS-GLA, CS-TiO\({}_{2}\)-GLA, and CS-GO-TiO\({}_{2}\)-GLA beads [21]. FT-IR spectrum highlights the cross-linking reaction of CS with GLA due to the stretching band presented at 1641 cm\({}^{-1}\) due to the time-like bond from the amino groups of CS and the carbonyl groups of GLA. Similarly, the existence of amorphous regions and the signals obtained from XRD for the CS-GO-TiO\({}_{2}\)-GLA sample at angles 20 = 25\({}^{\circ}\), 38\({}^{\circ}\), 48\({}^{\circ}\), 54\({}^{\circ}\), 62\({}^{\circ}\), 68\({}^{\circ}\), 74\({}^{\circ}\), and 82\({}^{\circ}\), corresponds to the tetragonal crystalline planes of the anatase phase of TiO\({}_{2}\)[14]. The CS-GO-TiO\({}_{2}\)-GLA beads presented a small peak at 20 = 13\({}^{\circ}\), corresponding to the presence of the dispersed GO sheets. SEM analysis (Figure 1) demonstrated the changes in roughness when GO and TiO\({}_{2}\) were added to the CS-GLA beads due to the cross-linking and intercalation of the nanomaterials with the polymeric chains.
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 4 Results and Discussion  
For a full understanding of the sea motion effect on a floating pulsed LIDAR, we proceed in a stepwise fashion. First, we estimate values of rotational angle and translational velocity representative of the experimental conditions of the WINDSEA_02 buoy at sea. Then, the effect of motions is studied on the theoretical horizontal velocity measurement using the model described in Section 3.1. Finally, the effect of wave induced motion on FLS measurement is studied on the Fecamp experimental campaign and the correction is applied to the floating WindCube v2(r) measurements.
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 3 Results
### Survey A  
To evaluate the penetration of electromagnetic waves through the wood wall in the frequency range up to 20 GHz, we firstly carried out a 1D survey on the Type I wall, as shown in Figure 1a. The survey line is also indicated by a line with an arrow in the photo in Figure 3. Note that, due to height limitation of the experiment room, the wall specimen has been rotated anticlockwise by 90 degrees. Figure 6 shows the acquired raw radar profile in the VV polarization. At the depth of around 40 cm and y position of 0.5 m, we can see a distinct hyperbolic reflection stemming from a supporting steel pipe behind the wall. This verifies that the high-frequency electromagnetic waves have sufficient penetration through the wooden walls, which are dry and impose low attenuation to electromagnetic waves. Above the steel pipe reflection, we can also discern two clear reflections at the depths of 5 cm and 15 cm, which come from the top and bottom of the embedded wooden girts. Besides these, two horizontal reflections from the interior and exterior covering boards are also observed. Nevertheless, due to the wave scattering, the raw radar profile cannot provide a good lateral resolution to identify the damage.  
Figure 6: Raw radar profile acquired along the 1D survey line on the Type I wall specimen (Figures 1a and 3).
## 7 Numerical Simulations and Data Analysis
### Numerical Simulations Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \((n,m)\) & \(\theta_{1}=0.5\) & \(\theta_{1}=1\) & \(\theta_{1}=1.5\) & \(\theta_{1}=2\) & \(\theta_{1}=3\) \\ \hline  & 0.0919(0.0165) & \(-0.0094(0.0079)\) & \(-0.0082(0.0108)\) & \(-0.0223(0.0137)\) & \(-0.0098(0.0090)\) \\ \((10,10)\) & 0.0881(0.0158) & \(-0.0088(0.0078)\) & \(-0.0207(0.0137)\) & \(-0.0145(0.0143)\) & \(-0.0054(0.0091)\) \\  & 0.0721(0.0120) & \(-0.0084(0.0065)\) & 0.0018(0.0083) & \(-0.0036(0.0111)\) & 0.0138(0.0078) \\ \hline  & 0.0281(0.0078) & 0.0095(0.0074) & \(-0.0098(0.0079)\) & \(-0.0188(0.0091)\) & \(-0.0127(0.0065)\) \\ \((20,15)\) & 0.0317(0.0078) & 0.0061(0.0075) & \(-0.0068(0.0078)\) & \(-0.0161(0.0095)\) & \(-0.0079(0.0065)\) \\  & 0.0207(0.0086) & 0.0090(0.0064) & 0.0034(0.0079) & \(-0.0056(0.0078)\) & 0.0043(0.0060) \\ \hline  & 0.0132(0.0037) & 0.0060(0.0025) & 0.0025(0.0067) & 0.0057(0.0045) & \(-0.0270(0.0049)\) \\ \((25,25)\) & 0.0132(0.0038) & 0.0062(0.0024) & 0.0040(0.0068) & 0.0047(0.0045) & \(-0.0242(0.0049)\) \\  & 0.0055(0.0036) & 0.0061(0.0022) & 0.0076(0.0063) & 0.0128(0.0041) & \(-0.0165(0.0045)\) \\ \hline  & \(-0.0094(0.0014)\) & \(-0.0039(0.0010)\) & \(-0.0714(0.0051)\) & \(-0.0048(0.0024)\) & \(-0.0510(0.0038)\) \\ \((30,40)\) & \(-0.0092(0.0014)\) & \(-0.0064(0.0011)\) & \(-0.0760(0.0051)\) & \(-0.0060(0.0023)\) & \(-0.0504(0.0038)\) \\  & \(-0.0135
# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
## 2 Materials and Methods
### Precipitation Data  
The IMERG GPM data was downloaded from the Precipitation Measurement Missions website that uses the algorithm version V06B ([[http://pmm.nasa.gov/data-access/downloads/gpm/](http://pmm.nasa.gov/data-access/downloads/gpm/)]([http://pmm.nasa.gov/data-access/downloads/gpm/](http://pmm.nasa.gov/data-access/downloads/gpm/)) accessed on 22 March 2020). The IMERG is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team. The IMERG GPM is available in three products, namely Early, Late, and Final. All of the IMERG products have a temporal resolution of one hour and a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\). The Early-run product is produced with the first run of the IMERG algorithm, which has a latency of about 4 h from the observation time. Another product is produced when the algorithm is run for the second time, with more information from the satellites at a latency  
Figure 1: Path track of the Tropical Storm Imelda before and after its landfall in Freeport, TX at 17:45–18:00 UTC 17 September 2019 (green line shows the path of the Tropical Strom Imelda). Precipitation estimates are based on radar data.  
of about 14 h. This product is called Late-run. The Final-run product is developed when the monthly gauge analysis data is obtained, and it has a latency period of about 3.5 months from the observation time. The baseline for the post-real-time Final-run half-hour estimates is calibrated so that they sum to the monthly satellite-gauge Final-run combination. This product is developed mainly for research purposes. The Final-run product is adjusted with a gauge-network which converts it to a more accurate product relative to the Early- and Late-run products. Since the radar products have an hourly temporal resolution, the IMERG products used in this study were also converted to an hourly format at the end of each hour.  
The National Weather Service/National Centers for Environmental Prediction (NWS/NCEP) Stage-IV Quantitative Precipitation Estimates (QPEs) gridded radar-rainfall ([[http://data.eol.ucar.edu/](http://data.eol.ucar.edu/)]([http://data.eol.ucar.edu/](http://data.eol.ucar.edu/)) accessed on 22 March 2020) serves as the source of radar precipitation measurements. The operational Stage-IV precipitation radar data production started its journey in 2001 at NWS River Forecast Centers (RFCs) and has been continued since then [64]. It contains bias-adjusted, quality-controlled hourly rain-gauge precipitation records with a 4 \(\times\) 4-km spatial resolution. The measurements are in a cumulative hourly format at the end of each hour.
###### Abstract  
The terrestrial gross primary productivity (GPP) plays a crucial role in regional or global ecological environment monitoring and carbon cycle research. Many previous studies have produced multiple products using different models, but there are still significant differences between these products. This study generated a global GPP dataset (NI-LUE GPP) with 0.05 deg spatial resolution and at 8 day-intervals from 2001 to 2018 based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO\({}_{2}\) concentrations, radiation components, and nitrogen (N) index. To simulate the global GPP, we mapped the global optimal ecosystem temperatures (T\({}_{\rm opt}^{\rm exo}\)) using satellite-retrieved solar-induced chlorophyll fluorescence (SIF) and applied it to calculate temperature stress. In addition, green chlorophyll index (Cl\({}_{\rm green}\)), which had a strong correlation with the measured canopy N concentrations (\(r=0.82\)), was selected as the vegetation index to characterize the canopy N concentrations to calculate the spatiotemporal dynamic maximum light use efficiency (\(e_{\rm mcr}\)). Multiple existing global GPP datasets were used for comparison. Verified by FLUXNET GPP, our product performed well on daily and yearly scales. NI-LUE GPP indicated that the mean global annual GPP is \(129.69\pm 3.11\) Pg C with an increasing trend of 0.53 Pg C/yr from 2001 to 2018. By calculating the SPAtial Efficiency (SPAEF) with other products, we found that NI-LUE GPP has good spatial consistency, which indicated that our product has a reasonable spatial pattern. This product provides a reliable and alternative dataset for large-scale carbon cycle research and monitoring long-term GPP variations.  
1  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
1
# Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
###### Abstract  
Previously published studies have proposed fatigue life prediction models for dense graded asphalt pavement based on flexural fatigue test. This study focused on the fatigue life prediction of High Modulus Asphalt Concrete (HMAC) pavement using the local strain-stress method and direct tension fatigue test. First, the direct tension fatigue test at various strain levels was conducted on HMAC prism samples cut from plate specimens. Afterwards, their true stress-strain loop curves were obtained and modified to develop the strain-fatigue life equation. Then the nominal strain of HMAC course determined using finite element method was converted into local strain using the Neuber method. Finally, based on the established fatigue equation and converted local strain, a method to predict the pavement fatigue crack initiation life was proposed and the fatigue life of a typical HMAC overlay pavement which runs a risk of bottom-up cracking was predicted and validated. Results show that the proposed method was able to produce satisfactory crack initiation life.  
p _Applied_  
_sciences_  
Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
# Drag Reduction in Compressible Channel Turbulence with Periodic Interval Blowing and Suction
###### Abstract  
This paper employs direct numerical simulation (DNS) to investigate the influence of blowing and suction control on the compressible fully developed turbulent flow within an infinitely long channel. The spanwise blowing strips are positioned at uniform intervals along the bottom wall of the channel, while the suction strips are symmetrically placed on the top wall. The basic flow (uncontrolled case) and the controlled cases involving global control and interval control are compared at \(Ma=0.8\) and \(1.5\). Although the wall mass flow rate remains constant across all controlled cases, the applied blowing/suction intensity and spanwise strip areas exhibit significant variations. The numerical results indicate that augmenting the blowing/suction intensity will alter the velocity gradient of the viscous sublayer in the controlled region. Nonetheless, a reduction in the area of the controlled region diminishes the impact of blowing/suction on drag reduction on the entire wall. The spatially averaged velocity profiles on the wall for cases with identical wall mass flow rates are nearly indistinguishable, suggesting that the wall mass flow rate is the primary factor influencing the spatially averaged drag reduction rate on the entire wall, rather than the blowing/suction intensity or the injected energy. This is because the wall mass flow rate influences the average peak position of the Reynolds stress, which, in turn, affects the skin friction drag. An increase in the wall mass flow rate correlates with a heightened drag reduction rate on the blowing side, while simultaneously leading to a rising drag increase rate on the suction side.  
compressible channel flow; interval control; blowing; suction; drag reduction 2025  
5
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 4 Performance Evaluation
### Results and Analysis
#### 4.2.6 Simulation Results for Different Desired Queue Length  
In this subsection, we change the desired queue length while keeping the other parameters constant, i.e., \(q_{r}=100\), \(200\), \(300\) packets, respectively. From Figure 7, it is obvious that CCAIB has good convergence performance whether the desired queue length is \(100\), \(200\) or \(300\) packets. In addition, larger desired queue length allows for larger window size. Therefore, increasing the buffer size of the router is an effective way to increase the window size.
## 4 Experiment and Results
### Effect of External Diameter of Pipes on MPPT Signal  
Experiments involving Group A were conducted for steel pipes with different external diameters. The external diameters of the pipes were 50 mm, 54 mm, and 60 mm, and all the wall thicknesses were 10 mm. The magnetizing current was 15 A in the experiments. The fitted curves of the peak-to-peak values of the internal axial crack signals for different pipes are shown in Figure 14.  
Figure 12: The specimens.  
Figure 13: (**a**) Probe structure. (**b**) Experimental signal.  
For internal axial cracks of the same size, as the external diameter of the pipe increases, the peak value of the experimental signal first increases and then tends to level off, as shown in Figure 15b. For cracks with different depths, the signal of the internal axial crack decreases significantly as the defect depth decreases. The trends of the experimental signals with the external diameter of the pipe for 1.5 mm depth and 1 mm depth defects are consistent with the simulation results.
## 4 Experiments
### Comparative Experiments on Rice Grain Dataset  
#### 4.7.1 Quantitative Analysis  
To further evaluate the deployability of our model in resource-constrained scenarios, we conducted a quantitative assessment of inference efficiency across all compared lightweight SR models. The comparison models include CARN [7], IMDN [8], SAFMN [32], ShuffleMixer [52], and SwinIR-S [12]. All tests were carried out under a \(\times\)4 scale factor using weight files pretrained on public datasets to ensure fairness. Table 3 summarizes not only the PSNR/SSIM and FLOPs but also the average inference time (in milliseconds) per 1280 \(\times\) 720 rice image on a single NVIDIA RTX 4080 GPU.  
As shown in Table 3, DRFAN achieves a PSNR of 38.09 dB and an SSIM of 0.9165, surpassing all baseline models in reconstruction accuracy. In terms of computational cost, the DRFAN maintains a reasonable balance with 1057K parameters and 65.1G FLOPs. Its inference time of 41.3 ms is higher than those of ultra-fast models such as SAFMN (8.9 ms) and ShuffleMixer (17.8 ms), but this trade-off yields significantly better reconstruction quality. Compared to SwinIR-S, the DRFAN offers improved accuracy (+0.19 dB PSNR, +0.0015 SSIM), with only a modest increase in runtime (41.3 ms vs. 31.5 ms).  
These results demonstrate that the DRFAN achieves a favorable balance between performance and efficiency, making it a promising solution for industrial inspection systems where image fidelity is critical but computational resources may be limited.
# Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
## 3 X-ray Photoelectron Spectroscopy
### Ambient Pressure X-ray Photoelectron Spectroscopy  
Following our discussion on XPS, it is crucial to delve into a more specialized variant of this technique which is the focus of our study: ambient pressure X-ray photoelectron spectroscopy, commonly abbreviated as APXPS [29]. The evolution from conventional XPS to APXPS embodies our relentless pursuit of enhancing our investigative tools, facilitating experiments under conditions closer to reality.  
XPS operates in ultra-high vacuum (UHV) conditions. While this ensures high precision and clarity in our measurements by reducing contamination and spectral interference, it somewhat limits the scope of conditions under which materials can be studied. Often, real-world conditions, especially in heterogeneous catalysis, energy storage, and environmental science, involve interactions occurring at non-vacuum conditions. This discrepancy between UHV conditions of XPS and ambient conditions of practical scenarios necessitated an adaptation.  
APXPS bridges the gap by allowing researchers to perform XPS measurements at elevated pressures, extending from UHV up to atmospheric conditions, and sometimes even higher. This transition is not merely a trivial escalation in pressure; it signifies a monumental leap. By probing materials under conditions where actual reactions take place, APXPS provides insights that are directly translatable to real-world applications. For instance, in situ investigations of catalytic processes can be observed as they occur, paving the way for groundbreaking discoveries in reaction mechanisms.  
Key to the success of APXPS is the differential pumping system, which maintains the X-ray source and detectors in UHV while the sample is subjected to higher pressures. This ensures the X-rays' integrity and prevents contamination of the sensitive equipment. The capability to vary pressure allows researchers to mimic a plethora of environments, from the gas-solid interfaces in fuel cells to the liquid-solid boundaries in biological systems.  
In essence, while XPS laid the foundation of surface science investigations with its prowess to determine elemental composition and chemical states, APXPS has expanded this frontier. It ensures that our experimental conditions are not just a mere approximation, but rather a true reflection of practical environments.
# Crop Prediction Model Using Machine Learning Algorithms
## 4 Crop Analysis and Prediction Benefits and Challenges
### Benefits  
The following are the key advantages farmers can receive from utilizing machine learning on their farms:  
* More effectiveness: This approach is more effective and accurate in identifying patterns and saving farmers time and resources because a larger volume of data can be evaluated by machine learning in a shorter amount of time than with previous methods.
* Increased crop yield: Using many data sources for analysis, including weather patterns, soil quality, and historical machine learning algorithms, can help farmers make more informed decisions that increase crop yields.  
Figure 2: AI-based crop analysis and prediction.  
* Lower costs: Machine learning may assist farmers in maximizing the use of resources, such as water, fertilizer, and pesticides, by offering insights into crop development and health. This can save expenses while lowering how much of an impact agriculture has on the environment.
* Early disease detection: Farmers can take preventative measures to stop the spread of illness and reduce crop loss by identifying early indicators of crop diseases with machine learning. Once the model is sufficiently trained, it can detect anomalies such as discoloration on growth size in the early stages of disease much faster than humans would notice.
* Improved crop management: By offering insights into variables such as soil moisture, temperature, and nutrient levels, ML algorithms can assist farmers in improving their crop management tactics. This can assist farmers in making data-driven decisions regarding the best time to water, fertilize, and sow their crops.  
Overall, using ML in crop analysis and prediction can help farmers optimize their crop yields, reduce waste, and increase profitability while promoting sustainable farming practices.
# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
## 3 Analysis of Knowledge Representation Systems
### Ontology Scope
#### 3.5.1 Object  
Most of the compared KR systems define the _Object_ from various perspectives as an essential component of their ontologies. KnowRob [99] defines the \"Object\"in a large ontology from very generic classes such as _SpatialThing_ to specific ones, e.g., _Refrigerator-Freezer_. It contains about 7000 object classes [101]. In addition to this, KnowRob 2.0 [68] considers physical, social, and mental entities as objects participating in an event. OROSU [69] takes the basic Object definition from the IEEE suggested upper merged ontology (SUMO) ([[https://en.wikipedia.org/wiki/Suggested_Upper_Merged_Ontology](https://en.wikipedia.org/wiki/Suggested_Upper_Merged_Ontology)]([https://en.wikipedia.org/wiki/Suggested_Upper_Merged_Ontology](https://en.wikipedia.org/wiki/Suggested_Upper_Merged_Ontology))). We could not find the object definition in CARESSES [70]; however, it is defined as a sub-class of _Topic_. PMK [71] defines the objects in _WSObjectClass_ and their features in the _Features_ class. Its main _WSObjectClass_ uses the standardized concepts of SUMO: _Artifact_ for object and _Collections_ for group-level, while the new concept _ArtifactComponent_ for component-level. SARbot [72] defines the concept of environment objects (e.g., victims, bookshelves, etc.) represented as individual classes. It also contains the objects that may be initially unknown for the robot, but can be recognized to update the environment ontology. IEQ [73] does not describe the concept of objects in natural language. However, it shows the ontological concepts and relations of logical sentences about objects. SmartRules [74] concentrates on the notion of manageable objects (MOs). It is comprised of three subcategories of object classes: active objects that include _physical_ objects; _virtual_ objects, i.e., databases, files, web services; _static objects and movable_ objects. ARBI [75] defines different objects ranging from object connection, components (e.g., hinges, joints, etc.), to human-scale objects (e.g., documents, table) in the perception ontology and their attributes such as size, color, state (static or dynamic, open or closed), shape, and other features. Worker-cobot [76] does not provide an object definition. However, it uses Drools [102] for pattern matching with the ReeOO algorithm [103] by combining it with object-oriented (OO) concepts such as abstraction, inheritance, and encapsulation. Its agent descriptive ontology indicates different objects and entities of the shared domain (i.e., electric motor, worker, robot). APRS [77] describes solid objects (e.g., parts) with general information related to their basic elements. Its _PointTypeclass_ stores dynamic information, while the _PartType_ class holds both static and dynamic information about parts of the object. A finished kit becomes the instance of the _PartsTrayType_ class.