esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Limitations  
This study was conducted using an \"open-loop\" control mode, since the power was imposed by the experimenters. Consequently, the movements were pre-programmed or predetermined without continuous feedback from the sensory inputs. In real performance conditions, such as running, the subject is in a closed-loop condition and the EEG response could also be used as a pace controller [48]. This closed-loop configuration is probably the most appropriate exercise model for understanding individual critical threshold [105] and subjective experiences of fatigue, such as in a marathon race [106], and for understanding the limits of exercise tolerance with an integrative model [107; 108]. However, our experimental study is limited by the small sample size, which restricts the statistical power and may affect the reliability and generalizability of the results. A smaller sample size increases the risk of type II errors, where true effects may not be detected, and reduces confidence in effect sizes estimates. This limitation may also lead to greater variability and margin of error in the results, which could impact the robustness and reproducibility of our findings. Therefore, although our results provide valuable preliminary information, further research with larger and more diverse samples is essential to validate and extend these findings.  
Conceptualization, C.B., E.M., M.B. and J.M.; methodology, V.B., C.B., M.C., E.M., M.B., S.E., P.B., V.P., J.M., P.R., B.M. and C.C.; software, C.B., E.M., S.E., C.D., F.R. and A.B.; formal analysis, V.B., C.B., L.P., E.M. and J.M.; data curation, E.M. and T.C.; writing, V.B., C.B. and M.C.; review and editing, L.P.; project administration, C.B., M.B., S.E., L.F., V.P., J.M. and C.C.; funding acquisition, C.B., M.B. and J.M. All authors have read and agreed to the published version of the manuscript.  
This study was conducted with the financial support of the French Defense Procurement Agency (DGA/AID) through a RAPID Project named MEEGAPER, grant number 112906033.  
This study was conducted in accordance with the Declaration of Helsinki, and approved by the Research and Ethics Committee of the Leon Berard center under the ref A 13-160 number 2013-A00503-42 for studies involving humans.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Multi-Lidar System Localization and Mapping with Online Calibration
## 3 Back-end Optimization and Map Construction
### Loop Detection Based on Scan Context  
In this study, the Scan Context environment descriptor is selected. The initial step involves reorganizing the point cloud using a ring and sector methodology. This procedure maps the 3D point cloud into a 2D feature map, as illustrated in Figure 5.  
By segmenting the point cloud into rings and using a small sector for finer division, distinct indexed regions can be located within the two-dimensional feature map. The feature map's values correspond to the highest reflection point's elevation in the respective area. This approach extracts a two-dimensional feature map while retaining the spatial structural characteristics of the point cloud, as exemplified in Figure 6.  
Upon extracting the Scan Context descriptor, defining a distance calculation formula is necessary to assess the similarity between two descriptors. Given the Scan Context of two point clouds, denoted as \(I^{q}\) and \(F\), the cosine distance of the column vectors in the two feature matrices is used as the distance measure, and the result is then normalized by division as expressed in the following:  
\[d(I^{q},I^{c})=\frac{1}{N}\sum_{i=1}^{N}\left(1-\frac{c_{j}^{q}c_{j}^{c}}{c_{ j}^{q}c_{j}^{c}}\right) \tag{27}\]  
Directly computing the distance of column vectors is inadequate when dealing with scenarios involving perspective switches within the same scene. Consequently, it is crucial  
Figure 5: Schematic diagram of Scan Context segmentation.  
Figure 6: Scan Context two-dimensional feature map.  
to calculate the distance between all column vectors of the two Scan Contexts, selecting the minimum value as the final distance.  
To ensure efficient searching and real-time performance of the SLAM system, sparse optimization is also necessary. Introducing the concept of the ring key facilitates obtaining a descriptor insensitive to rotation from the column vector in the Scan Context descriptor:  
\[\psi(r_{i})=\frac{r_{i0}}{N} \tag{28}\]  
Given that ring characteristics are unaffected by the vehicle's orientation, the ring key becomes rotation insensitive. While it contains less information compared to the complete Scan Context descriptor, it enhances candidate frame searching efficiency. By constructing a Kd-tree and selecting the Scan Context corresponding to the multiple historical ring keys most similar to the ring key of the current scene, the nearest scene can be identified using the distance calculation formula described in the preceding section. This entire scene recognition process is depicted in Figure 7.
## 3 Evaluation
### Simulation  
Previously, the method of predicting the bending shape of the manipulator by applying the minimum potential energy theory was described. In addition, stiffness, which is a parameter necessary for prediction, and the movement distance of a cable whose movement was restricted due to physical limitations, were obtained through an experiment. As we mentioned earlier, it is complicated to derive the minimum energy value when the manipulator is bent. An optimizer was used to solve this complex equation. The MATLAB optimization function \"_fmincon_\" was used to consider all cases. The algorithm used was \"_sequential quadratic programming_ (SQP)\". The total pulled distance of the cable was increased in units of 0.01 mm. Following this, the minimum of the potential energy was identified by changing the distributed length of each joint. Figure 8a shows the results of the simulation conducted with different thicknesses of flat springs placed at each joint. The bending shape of the manipulator depended on the combination of flat springs used for each joint. Additionally, the displacement that the cable can move was determined according to the flat spring assembled to the joint. The order of the thickness of the flat springs assembled from the proximal-part joint to the distal-part joint of the manipulator is shown in Figure 8b. The configuration T1, the red text, has a 0.08 mm thick flat spring on the proximal joint  
Figure 7: Design of transmittance and prototype of the middle segment: (**a**) The side view; (**b**) The top view; (**c**) A prototype fabricated by assembling all segments and flat springs.  
and a 0.14 mm thick flat spring on the distal part. Maximizing the difference between the thicknesses of the flat springs of the proximal joint and the distal joint could help maximize the difference in the curvature of the manipulator. Among the various configurations, the four selected flat spring configurations that could increase the difference in curvature were simulated. For each manipulator, the cable was pulled in steps of 0.5 mm each to achieve a displacement of 2.0 mm diagonally (\(y\)-axis and \(z\)-axis). The configuration of flat springs marked in blue and green, and those marked in red and black appeared to bend with a similar tendency when the cable is pulled slightly.  
Figure 8: Manipulator bending shape according to four flat springs combinations (**a**) Simulation result of pulling the driving cables of four different manipulators in 0.5 mm increments up to 2 mm; (**b**) Indication by color what thickness of flat springs each manipulator is composed of.
# Characterizations of Interfacial Solar Water Evaporation
## 2 Intrinsic Properties
### X-ray Photoelectron Spectroscopy  
When a sample is illuminated by an X-ray beam with energy greater than its work function, some incident X-ray photons are absorbed by the electrons of the component atoms, which is known as the photoelectric effect, and it results in the generation of core holes and emission of photoelectrons. For X-ray photoelectron spectroscopy (XPS), the electron binding energy can be determined by measuring the kinetic energy of expelled photoelectrons and knowing the energy of the incoming X-rays, which is easily determined from their wavelength. The X-ray photoelectron spectrum plots the intensity of the emitted photoelectrons versus the binding energy, and it can be used to determine the oxidation states of the elements in addition to the elemental composition of a specimen by a straightforward peak matching procedure because the electrons of the atom in a particular chemical state have their unique binding energies.  
[PERSON] et al. [17] developed a bi-layered hybrid biofoam made of bacterial nanocellulose and RGO to generate steam by interfacial solar evaporation. They used XPS to establish that GO had undergone chemical changes during the base wash. They separated three peaks in the C 1s spectrum that corresponded to the sp2 domains (C=C with a binding energy of 284 eV) and oxidized sp3 domains (C-O with a binding energy of 286 eV and C=O with a binding energy of 288 eV). They calculated a C/O ratio of 1.74 from the area below the peaks, which indicated that around 57% of the as-synthesized GO surface was oxidized. The C/O ratio rose to 4.95 after a thorough base wash, which indicates that only 20% of the GO surface was oxidized (Figure 4a,b). [PERSON] et al. [8] used one-step easy surface oxidation to transform SnSe nanomaterials into a new photothermal-photocatalytic dual-functional core-shell composite. Upon solar irradiation from a single sun, the resulting SnSe@SnO2 exhibited good capabilities for synchronous pollutant destruction and water evaporation. They used XPS to clarify the elemental composition of SnSe@SnO2 and identified Se, Sn, and O-related peaks. The presence of C was attributed to the substrate used to prepare the XPS samples. High-resolution XPS spectra of Sn 3d provided further access to the chemical states of Sn and showed that the Sn 3d5/2 and 3d3/2 peaks broke into two components. The peaks centered at 495.0 and 486.6 eV were assigned to Sn-O bonding, and the peaks at 493.5 and 485.1 eV were attributed to SnSe. The XPS results supported the presence of SnO2 with SnSe (Figure 4c,d).  
Figure 3: Raman spectra. (**a**) Black titania. Reproduced with permission from ref. [13], Copyright 2016, American Chemical Society. (**b**) The bi-layered photothermal membrane. Reproduced with permission from ref. [14], Copyright 2017, The Royal Society of Chemistry. (**c**) MoO3-x nanoflowers. Reproduced with permission from ref. [15], Copyright 2019, Elsevier. (**d**) The asymmetric plasmonic structure. Reproduced with permission from ref. [16], Copyright 2018, Elsevier.
# Research on Grouting Pressure of Earth-Rock Dams Reinforced by Polymer Based on Discrete Elements
## 2 PFA-Reinforced Rockfill Materials Technology  
### Technical Principles  
When using PFA-reinforced rockfill materials technology for grouting reinforcement, a high-pressure grouting system and grouting catheter are used to inject a polymer into the dam crest and specific areas below the dam slope. These polymers can rapidly expand and solidify after the chemical reaction. The key to this technology is that the polymer material can fill the pores in the rockfill so that the rockfill particles can be effectively cemented together without increasing the overall size of the dam. This cementation increases the resistance of particle redistribution so that the rock-fill particles can form a tightly bonded composite material, which significantly improves the integrity and stability of the dam. It is worth mentioning that this polymer material exhibits flexible characteristics after curing. The special structure formed by this polymer and rockfill particles gives the dam a unique ability to consume seismic energy through its deformation. This ability is particularly important for earth-rock dams located in seismically active areas. In addition, the flexibility of the polymer can also make the dam adapt to a certain degree of deformation, to maintain good stability during long-term operation.
## 2 Data and Methods
### Machine Learning Algorithms
#### 2.2.2 XGBoost  
XGBoost [43] is an optimized version of the Gradient Boosting algorithm [46]. The boosting algorithm is a type of ensemble approach of learning algorithm that accomplishes the learning task by building and combining multiple base estimators. The base estimator of XGBoost is the regression tree (CART) [47]. XGBoost uses the boosting algorithm to continuously correct the fitting effect, and each tree is grown on the residuals of the previous tree, and the prediction is obtained by weighting the ensemble output of all regression trees. Like the RF algorithm, XGBoost also supports random row sampling and column sampling of the training set to avoid overfitting. However, XGBoost features a few improvements, such as it (1) supports linear classifiers to reduce generalization errors and improve better prediction accuracy by using [PERSON]'s method and second-order Taylor series; (2) reduces the possibility of overfitting by application of regularization; and (3) combines multi-threading and data compression to make the algorithm as efficient as possible. Presently, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Long Name** & **Units** \\ \hline TA & Air temperature & deg C \\ SW\_IN & Shortwave radiation, incoming & W m\({}^{-2}\) \\ LW\_IN & Longwave radiation, incoming & W m\({}^{-2}\) \\ VPD & Vapor pressure deficit & hPa \\ PA & Atmospheric pressure & kPa \\ P & Precipitation & mm d\({}^{-1}\) \\ WS & Wind speed & m s\({}^{-1}\) \\ NETRAD & Net radiation & W m\({}^{-2}\) \\ TS & Soil temperature at the shallowest & deg C \\ SWC & Soil water content at the shallowest & \% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of input features for the model training. XGBoost is widely used in data science competitions and is considered to be an advanced evaluator with ultra-high performance in both classification and regression. In this study, the XGBoost model was implemented using the XGBoost package ([[https://github.com/dmlc/xgboost](https://github.com/dmlc/xgboost)]([https://github.com/dmlc/xgboost](https://github.com/dmlc/xgboost)) accessed on 12 October 2020) with Scikit-learn interface.
# Numerical Simulation of Flow Field of Submerged Angular Cavitation Nozzle
## 3 Results and Discussion
### Influence of Cylinder Section on Cavitation  
There is an optimal ratio between the length and the diameter of the nozzle cylindrical section [31; 32]. Therefore, different dimensionless lengths of the cylindrical segment (\(Ld/d=2\), 2.5, 3, 3.5, 4) are set for numerical simulation, while other structural parameters are kept unchanged to explore the influence of the length of the cylinder section on the cavitation effect. In order to facilitate comparison, the nozzle inlet is set as 0 mm for drawing. The axial velocity distribution of the nozzle jet and the axial steam volume fraction distribution at \(d/2\) are shown in Figures 18 and 19.  
As can be seen from Figure 18, when the axial distance is less than 40 mm (inside the nozzle), the axial velocity of the jet corresponding to different \(Ld\) is essentially the same, indicating that the change in \(Ld\) has little effect on the interior of the nozzle jet. But the longer the \(Ld\), the longer the isokinetic nucleus. While the axial distance is greater than 40 mm (external flow field), the axial velocity of the jet corresponding to different \(Ld\) fluctuates up and down. The length of the dimensionless cylindrical section with a value of 3 has the largest axial velocity of the jet.  
From Figure 19, it can be seen that when the axial distance is located in the contracted section (0 mm-26 mm), the steam volume fraction at \(d/2\) for different \(Ld\) is 0. While the axial distance is within the cylindrical section (31 mm, 32.25 mm, 33.5 mm, 34.75 mm, and 36 mm at the end of the cylindrical section for different \(Ld\)), the steam volume fraction at \(d/2\) decreases with the extension of \(Ld\). As \(Ld\) increases, the fluid boundary layer gradually becomes thicker, making the cavitation in the cylindrical section smaller. When the axial  
Figure 19: Effect of \(Ld\) on the cavitation.  
Figure 18: Effect of \(Ld\) on the fluid velocity.  
distance is located in the external flow field (41 mm, 42.25 mm, 43.5 mm, 44.75 mm, and 46 mm at the end of the nozzle for different \(Ld\)), the vapor volume fraction at the length of the dimensionless cylindrical section with a value of 3 is substantially higher than the one corresponding to the rest of parameters. It also has the best cavitation effect. This is different from the findings of [PERSON] et al. [32], who argue that when the length to diameter ratio is 2, the vapor fraction distribution region reaches the maximum. This shows that with the change in nozzle diameter, the length to diameter ratio corresponding to the best cavitation effect also changes, and the nozzle diameter should be confirmed first.
## 3 Results and Discussion
### HR-SEM Analysis  
The study of the surface morphology of spinel MnFe\({}_{2}\)O\({}_{4}\) and the morphology of the Ce doped MnFe\({}_{2}\)O\({}_{4}\) NCs was achieved with the aid of HR-SEM analysis. The obtained SEM pictures of MnFe\({}_{2}\)O\({}_{4}\) (Figure 5a) and Ce doped MnFe\({}_{2}\)O\({}_{4}\) NCs (Figure 5b,c), respectively, demonstrated a sphere-shaped morphology and also exposed a combination and agglomeration of grains. An agglomerated and sphere-shaped morphology was noticed, which is mostly due to the magnetic characteristics of the samples [47; 48]. The surface of Ce doped MnFe\({}_{2}\)O\({}_{4}\) ferrites shows the agglomerated coalescence behavior. Furthermore, it can be seen that the Ce substitution in MnFe\({}_{2}\)O\({}_{4}\) NCs influence the surface morphology of nanocrystalline MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\) NCs.
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 3 Methods for Industrial Data Distribution
### Building Blocks
#### 3.1.3 Latency-Aware Proxy Selection  
In the context of wireless industrial networks, one could leverage the set of nodes present at the edge of the network to distribute functions that are currently being implemented by the CNC. Many flavors of distributed data management exist in the networking  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **C1** & **D1** & **D2** & **C2** \\ \hline
**Data cache** & Netw. Contr. & LCAs & Proxies & Netw. Contr. \\ \hline
**APP** & \multicolumn{4}{c}{publish–subscribe} \\ \hline
**NET** & RPL non-st. & RPL st. & source routing \\ \hline
**MAC** & \multicolumn{4}{c}{CSMA/CA} & WirelessHART \\ \hline
**PHY** & \multicolumn{4}{c}{802.15.4 (2.4 GHz)} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layout of the compared methods.  
literature, depending on which edge devices are used. In [24], the authors consider a distributed approach and use the multitude of sensor nodes present in an industrial physical environment (e.g., a specific factory) to implement a decentralized data distribution, whereby sensor nodes cache data they produce and provide these data to each other upon request. In this case, the choice of the sensor nodes where data are cached must be made to guarantee a maximum delivery latency to nodes requesting those data. The objective function of the proxy selection minimizes the number of proxies. The main constraint is put on the guarantee that the value of the average end-to-end data access latency by the consumers in the network has to remain below a given time threshold imposed by the industrial operator. Each node has to be assigned to one and only one proxy, and all nodes are considered for potentially being selected as proxies.
## 3 Results and Discussion
### Determination of Total Phlorotannins Content by DMBA Assay  
The results in Table 2 show the quantitative phlorotannins and total phenol content extracted from _S. linifolium_ determined with DMBA and Folin-Ciocalteu reagent, respectively. The amount of phlorotannins was 10.087 g/g extract, about 10% of dry weight; meanwhile, the total phenol content was 22.19 \(\pm\) 3.42 mg/g GA. The phlorotannins were quantitatively analyzed with DMBA and compared to phloroglucinol. The findings concurred with the following published studies: The total phenolic content of _Sargassum filicinum_ and _Sargassum yendoi_ was 20.57 to 88.97 mg GA/g DW [45]. For a cytoplasmic and membrane extract of brown alga _Sargassum_ sp., the total phenolic compounds ranged from 0.006-0.65 and 6.72-21.99 g phloroglucinol/g100 DW, respectively [46]. The phenolic content in _Sargassum duplicatum_, _Sargassum crassidium_, and _Sargassum. polyicstummg_ was 24, 21, and 19 mg GA/g dw [47]. The total phlorotannin content extracted from _Sargassum fusiforme_ was 88.48 \(\pm\) 0.30 mg PGE/100 mg extract, which represents the maximum phlorotannin content and highest antioxidant activity [48]. The lowest amount of phlorotannin content was found in _Sargassum duplicatum_ 4.45 \(\pm\) 0.11 mg phloroglucinol/g DW [49].  
\begin{table}
\begin{tabular}{c c} \hline
**Total Phenolic** & **Total Phlorotannin** \\
**(mg/g GA)** & **Content (mg/g Dry Weight)** \\ \hline
22.19 \(\pm\) 3.42 & 10.87 \\ \hline GA: gallic acid. & \\ \hline \end{tabular}
\end{table}
Table 2: Total phenolic and phlorotannin content of _S. linifolium_ brown alga.  
Figure 2: The FT-IR spectrum of phlorotannins extract from _S. linifolium_ marine alga.