esa-sceva/satcom-chunk-collection huggingface dataset:
# Calibration of Sliding Friction Coefficient in DEM between Different Particles by Experiment
## 4 Results and Discussion
### Sliding Friction Coefficient between Particles and Boundaries  
The S-S group as defined in Table 5 was used for a packing experiment in a 30 cm \(\times\) 30 cm \(\times\) 30 cm container, and the pads used were made of glass, wood board, flint paper, or crocus paper. Numerical simulation was carried out simultaneously, and the friction between particles and the vertical boundary was ignored. The parameters used in the simulation are shown in Table 6. According to Figure 7b, the sliding friction coefficient of the sphere particles in contact with different boundaries was applied.  
The results of the packing experiment and numerical simulation are shown in Figures 10 and 11. It can be seen from the figure that the packing morphologies obtained by the experiment and the numerical simulation are basically the same, and the packing height of the particles is very close between the methods. The above analysis results indicate the correctness of the experimental method proposed in this study.  
Table 8 displays the average ordinate value and deviation of each group of specific particles after the simulation and experiment, and 10 particles in each group were selected as specific particles. As is shown in Table 8, the deviation between the simulated and experimental results is very small, between 1% and 3%. It is proven that the simulated  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group Name** & \
\begin{tabular}{c} **Average Ordinate** \\ **by Simulation** \\ \end{tabular} &
\begin{tabular}{c} **Average Ordinate** \\ **by Experiment** \\ \end{tabular} & **Deviation** \\ \hline S-S & 3.93 cm & 3.89 cm & 1.03\% \\ E-E & 4.20 cm & 4.25 cm & 1.18\% \\ P-P & 13.89 cm & 14.12 cm & 1.63\% \\ S-E & 4.13 cm & 4.35 cm & 5.06\% \\ S-P & 10.53 cm & 10.66 cm & 1.22\% \\ E-P & 10.50 cm & 10.42 cm & 0.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The repose angle and the deviation of several groups under the condition of glass pads from simulated and experiment results.  
Figure 10: (**a**) Simulated and experimental sandpile configurations of S–S group (glass pad); (**b**) Simulated and experimental sandpile configurations of S–S group (crocus paper pad).  
height is basically consistent with the experimental height, indicating that the experimental method proposed in this study can effectively determine the sliding friction coefficient of particles and boundaries in DEM, and the sliding friction coefficient determined by fitting according to the experimental results can be applied to the numerical simulation of DEM to improve the accuracy of simulation results.
## 5 Discussion
Overall, our proposed work outperformed the related works with 97.91% of accuracy by employing six optimal principal components selected from the CFS method and optimizing the parameters of the Rotation Forest ensemble with the Random Forest as the base classifier. Our work provided about 4.6% higher accuracy than that in [38].
## 1 Introduction  
Endodontic treatment involves a procedure composed of the following sequence of steps: tooth anesthesia, operative field isolation, opening of the pulp chamber, canal scouting, shaping and cleansing of the canals, and three-dimensional (3D) obturation [1]. These phases lead to root canal disinfection, shaping [2, 3], and filling [4]. All the steps are important for achieving endodontic success, but cleansing has an essential role in reducing the bacterial load inside the root canal system [5]. Cleansing can be mechanical and performed with endodontic instruments during the shaping phase [6], or it can be chemical and performed with irrigants [7].  
Irrigants can be divided into (1) substances with strong antibacterial properties, such as 5% sodium hypochlorite, and (2) chelating substances, such as EDTA (ethylenediaminetetraacetic acid) [5]. However, the use of irrigants has some challenges, one of which is their inability to eliminate _Streptococcus faecalis_ bacteria or withstand the action of 5% concentrated sodium hypochlorite [8]. Irrigants, if extruded beyond the apex, can damage the surrounding periodontal tissues [9]. Some physical phenomena are responsible for preventing their adequate action in the apical third or preventing their action within a reasonable time. Among these phenomena is the vapor lock effect [10].
Sensors_**2014**, _14_, 14440-14471; doi:10.3390/s140814440  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Adaptive Jitter Mechanism for Reactive Route Discovery**  
**in Sensor Networks**  
**[PERSON] 1, [PERSON] 2,* and [PERSON] 2**  
\({}^{1}\) Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Universite catholique de Louvain, Louvain-la-Neuve B-1348, Belgium;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Laboratoire d'Informatique (LIX), Ecole Polytechnique, Palaiseau 91120, France;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +33-1-77-57-80-85.  
_Received: 21 April 2014; in revised form: 16 July 2014 / Accepted: 21 July 2014 / Published: 8 August 2014_  
**Abstract:** This paper analyses the impact of jitter when applied to route discovery in reactive (on-demand) routing protocols. In multi-hop non-synchronized wireless networks, jitter--a small, random variation in the timing of message emission--is commonly employed, as a means to avoid collisions of simultaneous transmissions by adjacent routers over the same channel. In a reactive routing protocol for sensor and _ad hoc_ networks, jitter is recommended during the route discovery process, specifically, during the network-wide flooding of route request messages, in order to avoid collisions. Commonly, a simple uniform jitter is recommended. Alas, this is not without drawbacks: when applying uniform jitter to the route discovery process, an effect called delay inversion is observed. This paper, first, studies and quantifies this delay inversion effect. Second, this paper proposes an adaptive jitter mechanism, designed to alleviate the delay inversion effect and thereby to reduce the route discovery overhead and (ultimately) allow the routing protocol to find more optimal paths, as compared to uniform jitter. This paper presents both analytical and simulation studies, showing that the proposed adaptive jitter can effectively decrease the cost of route discovery and increase the path quality.  
**Keywords:** jitter; reactive routing protocol; route discovery; overhead
## 3 Results and Discussion
### SVM Analysis
As can be observed, the ROC plot that produces the best possible prediction yields a point in the upper left corner, i.e., coordinate (0,1) in the ROC space, representing 100% sensitivity (no false negatives) and 100% specificity (no false positives). The area under a ROC plot represents the probability that a randomly selected positive case will receive a higher score than a randomly chosen negative case; in other words, it is the probability of sensing. From the fit using Equation (4), the LD and LOD were calculated (see Figure 6b). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**a**} & \multirow{2}{*}{**b**} & \multirow{2}{*}{**R**} & **\% for** & **\% for** \\  & & & & **p = 93\%** & **p = 50\%** \\ \hline Sensitivity (CV) & 0.99 \(\pm\) 0.01 & 0.014 \(\pm\) 0.002 & 0.923 & 0.2 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SVM parameters for the fits and the 93% and 50% probabilities for petroleum sensing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Test PCA-SVM**} \\ \hline  & **Precision** & **Recall** & **f1-score** & **Support** \\ \hline Without P & 0.89 & 0.93 & 0.91 & 127 \\ With P & 0.95 & 0.93 & 0.94 & 191 \\ Accuracy & & & 0.93 & 318 \\ Macro avg. & 0.92 & 0.93 & 0.93 & 318 \\ Weighted avg. & 0.93 & 0.93 & 0.93 & 318 \\ \hline \hline  & \multicolumn{4}{c}{**Test SVM**} \\ \hline  & **Precision** & **Recall** & **f1-score** & **Support** \\ \hline Without P & 0.99 & 1.00 & 0.99 & 127 \\ With P & 1.00 & 0.99 & 1.00 & 191 \\ Accuracy & & & 1.00 & 318 \\ Macro avg. & 0.99 & 1.00 & 1.00 & 318 \\ Weighted avg. & 1.00 & 1.00 & 1.00 & 318 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters for the external evaluation for each support vector machine (SVM) model. Figure 6: (**a**) ROC curve from the quantum cascade laser/ support vector machine (QCL/SVM) model based on a sigmoid kernel function and (**b**) SVM detection probabilities for each concentration in all soil matrices using the SVM model.
## 3 Results
### Effect of the Focused Spot's Energy\(-\)Flux Density on the Face of Material Removal
The material removal rates for various energy\(-\)flux densities were calculated using the data in Figure 11 and the material removal rate mathematical model; the resulting curves are presented in Figure 12. As shown in Figure 12, the rate of material removal rises as the energy\(-\)flux density rises, and the rise in the rate progressively accelerates. The diamond coating material is removed at a rate of 3.64 \(\times\) 10\({}^{5}\)\(\upmu\)m\({}^{3}\)/s when the energy\(-\)flux density reaches 42.5 J/cm\({}^{2}\). In order to investigate the effect of energy\(-\)flux density on material removal rate, the data in Figure 12 were nonlinearly fitted using the Stirling function of Exponential type, where the independent variable is energy\(-\)flux density and the dependent variable is material removal rate, and the resulting functional equation of the fit is shown in Equation (9). \[\mathrm{y}=65,826.701+\frac{10,623.867\times\left(\mathrm{e}^{0.428\times \mathrm{x}}-1\right)}{0.428} \tag{9}\]  
From the fitting curve in Figure 12, it can be seen that when the femtosecond laser repetition frequency and scanning speed are stable, with the increase of the focused spot energy\(-\)flux density, the intensity of the femtosecond laser etching increases, the width of the etching and the ablation depth increase significantly, and the material removal rate also improves, and the growth rate becomes faster. Figure 11: Ablation widths and depths of the concentrating spot at various energy\(-\)flux densities: (**a**) widths and (**b**) depths. Figure 12: Material removal rates under different energy\(-\)flux densities.
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 4 Results  
### Sound Localization First Round: Horizontal Plane  
The distribution of angular localization perception for the horizontal plane positions in the first round is shown in the violin plot below (Figure 6). Because the data are angular positions at a single fixed azimuth, we considered using circular statistics to address the results.  
To determine whether the participants' perception of location was correctly aligned with the position where the audio was played, we employed the nonparametric V-test [29; 34]. The V-test was used to assess whether there was a significant difference between the mean response angle and the expected angle for each position, with the null hypothesis assuming a uniform distribution of the responses.  
Results indicate that the angular differences were statistically significant at most positions, allowing us to reject the null hypothesis at an alpha level of 0.05. However, at the position of 180\({}^{\circ}\), the difference in the mean angle was notably distinct, highlighting a significant deviation in localization accuracy at this specific point. The results of the V-test for each position, including the mean angle and the V and u statistics, are presented in Table 1, with significance determined at an alpha level of 0.05.  
Figure 6: Violin plot of sound localization perception in the first round. The distribution of localization responses is shown as a function of the azimuth. The violin plot illustrates the spread of responses per angle, with the shape representing a sideways histogram.
# Towards Robust Representations of Spatial Networks Using Graph Neural Networks
## 4 Methods
### Learning on Heterogeneous Graph Representations of Spatial Networks
#### 4.2.2 Modelling Semantics  
Similar to the difference imposed by the heterogeneity of the node and edge types on the graph structure representation, the heterogeneous representations of the spatial networks deals with different semantic types. In heterogeneous graphs, we can explain semantic relations using semantic paths. A semantic path is a relation connecting objects. To illustrate, given two relations: _intersects_ and _within_, a semantic path between two streets could be street--_intersects_\(\rightarrow\) street, a path between a building and a street could be building--_within_\(\rightarrow\) streets. When the semantic path traverses more than two objects, we may refer to it as a meta-path. Now, given the different paths or meta-paths present in a graph, we can see that certain paths will be better suited for certain tasks. For example, if we seek to infer the street type, it may be the case that the street--_intersects_\(\rightarrow\) street path will be better suited than the building--_within_\(\rightarrow\) streets path. It then raises the question, how do we determine the best suited semantic path for a particular task. The attention mechanism has been used in many machine learning approaches [23; 28]. We use the attention mechanism in our neural architecture to compute importance values for the semantic paths with respect to a learning task. We now describe the architecture of the neural model.
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
###### Abstract  
Plant diseases such as drought stress and pest diseases significantly impact crops' growth and yield levels. By detecting the surface characteristics of plant leaves, we can judge the growth state of plants and whether diseases occur. Traditional manual detection methods are limited by the professional knowledge and practical experience of operators. In recent years, a detection method based on deep learning has been applied to improve detection accuracy and reduce detection time. In this paper, we propose a disease detection method using a convolutional neural network (CNN) with multi-scale feature fusion for maize leaf disease detection. Based on the one-stage plant disease network YolCov5s, the coordinate attention (CA) attention module is added, along with a key feature weight to enhance the effective information of the feature map, and the spatial pyramid pooling (SSP) module is modified by data augmentation to reduce the loss of feature information. Three experiments are conducted under complex conditions such as overlapping occlusion, sparse distribution of detection targets, and similar textures and backgrounds of disease areas. The experimental results show that the average accuracy of the MFF-CNN is higher than that of currently used methods such as YoCov5s, Faster RCNN, CenterNet, and DETR, and the detection time is also reduced. The proposed method provides a feasible solution not only for the diagnosis of maize leaf diseases, but also for the detection of other plant diseases.  
deep learning; plant diseases; leaf detection; multi-scale feature fusion +
Footnote †: journal: Medical Image Analysis
## 2 Material and Methods
### Overall Installation Indexes  
The net electricity production of the whole installation (\(\mathrm{P_{el,net}}\)) can be defined by the following expression:  
\[\mathrm{P_{el,net}}=\mathrm{P_{el,ORC}}-\mathrm{P_{el,WE}}-\mathrm{P_{el,HC}} \tag{36}\]  
The overall system's energetic performance (\(\mathrm{\eta_{en}}\)) is calculated as:  
\[\mathrm{\eta_{en}}=\frac{\mathrm{P_{el,net}+LHV_{hydr}\cdot\dot{\mathrm{m}_{ hydr}}}}{\mathrm{Q_{solar}}} \tag{37}\]  
The overall system's exergetic performance (\(\mathrm{\eta_{ex}}\)) is calculated as:  
\[\mathrm{\eta_{ex}}=\frac{\mathrm{Ex_{el,net}+ex_{hydr}\cdot\dot{N}_{hydr}}}{ \mathrm{Ex_{solar}}} \tag{38}\]  
The exergy rate of net electricity production (\(\mathrm{Ex_{el,net}}\)) is equal to the corresponding energy rate (\(\mathrm{P_{el,net}}\)). The solar exergy rate is described based on the Petela model by the following equation [26]:  
\[\mathrm{Ex_{solar}}=\mathrm{Q_{solar}}\cdot\left(1-\frac{4}{3}\cdot\frac{ \mathrm{T_{0}}}{\mathrm{T_{sun}}}+\frac{1}{3}\left(\frac{\mathrm{T_{0}}}{ \mathrm{T_{sun}}}\right)^{4}\right) \tag{39}\]  
The reference temperature (\(\mathrm{T_{0}}\)) is defined at 298.15 K (25 \({}^{\circ}\)C) and the sun temperature (\(\mathrm{T_{sun}}\)) at 5770 K [26]. Similarly, the overall energy and exergy efficiency can be also described on an annual basis taking into account the performance during the year.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Oxygen pressure (\(\mathrm{P_{ox}}\)) & 1.0 bar \\ Hydrogen pressure (\(\mathrm{P_{hydr}}\)) & 1.0 bar \\ PEM water electrolyzer temperature (TWE) & 80 \({}^{\circ}\)C (353.15 K) \\ Reference temperature (\(\mathrm{T_{0}}\)) & 25 \({}^{\circ}\)C (298.15 K) \\ Activation energy of the anode reaction (\(\mathrm{E_{act,an}}\)) & 76 kJ/mol \\ Activation energy of the cathode reaction (\(\mathrm{E_{act,cath}}\)) & 18 kJ/mol \\ Water content at the anode-membrane interface (\(\mathrm{\lambda_{an}}\)) & 14 \(\mathrm{\Omega^{-1}}\) \\ Water content at the cathode-membrane interface (\(\mathrm{\lambda_{cath}}\)) & 10 \(\mathrm{\Omega^{-1}}\) \\ Thickness of the membrane (L) & 100 \(\mathrm{\mu m}\) \\ Gas constant (\(\mathrm{R_{g}}\)) & 8.314 J/mol\(\cdot\)K \\ Faraday constant (FC) & 96,485.3 s\(\cdot\)A/mol \\ Hydrogen low heating value (\(\mathrm{LHV_{hydr}}\)) & 120 MJ/kg \\ Hydrogen chemical exergy (\(\mathrm{Ex_{hydr,chem}}\)) & 236.1 kJ/mol \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the PEM water electrolyzer modeling.
# Theoretical Zero-Thickness Broadband Holograms Based on Acoustic Sieve Metasurfaces
## 4 Discussion
### Actual Situation  
To verify the effectiveness of the proposed hologram design method in practical applications, based on the combination of the FEM and the BEM, holographic imaging for the three frequencies of \(f_{0}/2\), \(f_{0}\), and \(2f_{0}\) is simulated in a case where the hologram is fabricated with a \(0.1\lambda_{0}\)-thick steel plate with holes, as shown in Figure 8a-c. It can be seen from the figures that the images are almost identical to Figures 4c and 5a,f of the ideal rigid screen. The RMSEs between the images and the desired image (Figure 2a) are 0.2008, 0.1780, and 0.1853, respectively. The RMSEs increase only slightly (rates of increase are less than 3.4%) compared to the case of the ideal rigid screen.  
It is worth noting that, the proposed hologram design method may not be well-suited for the background medium of water in practical applications, because most materials are not rigid enough relative to water.  
Figure 8: Holographic images of 0.1\(\lambda_{0}\)-thick steel plate with holes. (**a–c**) The normalized sound energy distributions in the image planes of the hologram fabricated with 0.1\(\lambda_{0}\) -thick steel plate with holes are calculated using numerical simulation when the incident frequencies are \(f_{0}/2\), \(f_{0}\), and 2\(f_{0}\), respectively.  
Figure 7: Holographic images of the cases that may cause errors. (**a–c**) The normalized sound energy distributions in the image planes of the ASM-based hologram with a small displacement of holes from their ideal places are calculated using numerical simulation when the incident frequencies are \(f_{0}/2\), \(f_{0}\), and 2\(f_{0}\), respectively. (**d–f**) The normalized sound energy distributions in the image planes of the ASM-based hologram with a dispersion of hole size are calculated using numerical simulation when the incident frequencies are \(f_{0}/2\), \(f_{0}\), and 2\(f_{0}\), respectively. (**g–i**) The normalized sound energy distributions in the image planes of the ASM-based hologram for incidence wave with a 5\({}^{\circ}\) declination are calculated using numerical simulation when the incident frequencies are \(f_{0}/2\), \(f_{0}\), and 2\(f_{0}\), respectively. Since the incident direction of the plane sound wave is inclined, the position of the image is shifted by \(z\tan 5^{\circ}\).
###### Abstract  
Background: Prolonged manual wheelchair usage often leads to musculoskeletal disorders in the upper body of individuals with spinal cord injury (SCI) due to repetitive, unidirectional movements. To mitigate these issues, targeted exercise of the back muscles--particularly those involving pulling movements of the arms and shoulders--is recommended. Therefore, this study aimed to develop a detachable, two-way propulsion system for manual wheelchairs, enabling propulsion through both pushing forward and pulling backward on the wheelchair pushrims. Methods: The propulsion system was engineered using a planetary gear train to facilitate dual-direction propulsion. Specifically, the planetary gear reverses the rotational direction, allowing the wheelchair to advance forward even when users pull the pushrims backward. Thus, the wheelchair can move forward through either pushing forward or pulling backward actions. Results: A prototype of the proposed system was fabricated using 3D printing technology and its functionality was verified. The prototype successfully demonstrated the two-way propulsion capability and the operation of the attachment mechanism. Additionally, the pilot test confirmed that an individual with SCI was able to propel a manual wheelchair equipped with the two-way propulsion system using both propulsion methods and switch between the methods independently while maintaining stability and safety throughout the test. Conclusion: The developed detachable two-way propulsion system shows significant promise as both a mobility aid and an exercise device, potentially reducing musculoskeletal complications among individuals with SCI who regularly utilize manual wheelchairs.  
welvechairs; shoulder pain; musculoskeletal diseases; rehabilitation; spinal cord injury +
Footnote †: journal: Applied sciences
# Hardware Design of a High Dynamic Range Radio Frequency (RF) Harmonic Measurement System
## 3 Issues in Creating a High Dynamic Range Harmonic Measurement System  
To measure harmonics generated by devices that are not traditionally nonlinear, a high dynamic range (DR) measurement system must be developed. The measurement system must create a highly linear probe signal and must have the ability to measure very weak nonlinear signals in the presence of the large fundamental probe signal.  
There are two important aspects of designing a high DR harmonic measurement system: (1) the use of a high DR receiver to measure the weak nonlinear signals in the presence of the high power probe signal; and (2) generation of a highly linear probe single used to probe a DUT. Both the receiver and probe single generator have their unique problems that must be addressed to generate high fidelity, linearized signals. This section addresses these problems.