esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Summary, Conclusions, and Discussion
### Discussion  
For the given assumptions and the system considered, we found through the system simulations that it is feasible to exploit spatial side information to minimize the positioning error. The gains observed in our simplified system, minimizing the error distance, pave the way for either studying fingerprinting-based positioning and spatial side information with a more elaborated system or validating whether the proposed 2D sensor array offers gains in a real system. In this latter case, the fingerprints would be collected from field measurements in a real scenario.  
We postulated one conjecture based on the idea that by increasing the number of spatial points in which the fingerprints are measured, the amount of information should increase, and therefore, a better estimate of the position should be achieved. From the results obtained, such a conjecture is true for the system and assumptions considered.  
We postulated another conjecture stating that a pattern-matching method based on the classification of discretized areas (the method 4-MultFingIn-ClassOut) could be applied to estimating the position. From the results obtained, such a conjecture is true for the system and assumptions considered, and in addition, it allows us to enhance the position estimate compared with that obtained using the other methods considered.  
Looking at the computing and hardware costs alone, as an isolated unit, the proposed approach could appear to be expensive. In order to implement the proposed approach, it is necessary to incorporate the 2D sensor array on the positioning device side. In addition, it is necessary to incorporate on the positioning device side the necessary hardware to support the implementation of a pattern-matching method for processing the spatial side information. However, as stated in the introduction, we aim to transfer the complexity and cost from the infrastructure to the positioning device. Then, it is just a matter of taking a holistic view of the costs, complexity, energy efficiency, etc., in a given use-case to determine whether the implementation of the proposed approach fulfills the expected goals. These aspects are proper to each use-case and therefore cannot be generalized here. The ultimate feasibility in deciding whether to incorporate the proposed approach into a real use-case has to be analyzed on a case-by-case basis.
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
###### Abstract  
Wheat production safety is facing serious challenges because wheat yellow rust is a worldwide disease. Wheat yellow rust may have no obvious external manifestations in the early stage, and it is difficult to detect whether it is infected, but in the middle and late stages of onset, the symptoms of the disease are obvious, though the severity is difficult to distinguish. A traditional deep learning network model has a large number of parameters, a large amount of calculation, a long time for model training, and high resource consumption, making it difficult to transplant to mobile and edge terminals. To address the above issues, this study proposes an optimized GhostNetV2 approach. First, to increase communication between groups, a channel rearrangement operation is performed on the output of the Ghost module. Then, the first five G-bneck layers of the source model GhostNetV2 are replaced with Fused-MBConv to accelerate model training. Finally, to further improve the model's identification of diseases, the source attention mechanism SE is replaced by ECA. After experimental comparison, the improved algorithm shortens the training time by 37.49%, and the accuracy rate reaches 95.44%, which is 2.24% higher than the GhostNetV2 algorithm. The detection accuracy and speed have major improvements compared with other lightweight model algorithms.  
GhostNetV2; wheat disease; attention mechanism; disease severity detection Article  
Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2. April. 2022  
10.3390/app13179987
## 3 Design Optimization
### Design of Experiment  
The OLHD technique, which is a DOE sampling technique suitable for computational experiments, was used in this study. OLHD can improve the space filling characteristics by using optimal conditions and distributing the test points evenly [17]. Considering the fact that there are five design variables, the number of experiments was determined to be 50 [9]. The best experimental point was obtained through analysis for 50 experimental points, and the comparison results with the initial model are shown in Table 3. The demagnetization rate of the best experimental point was 2.466%, which is very low compared to that of the initial model. However, the efficiency and average torque of the best point is slightly lower than those of the initial model. Figure 7 shows the screening analysis result. It can be seen that the average torque, torque ripple, and efficiency are most affected by the change in barrier length, and the demagnetization rate is most affected by the change in rip thickness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Variables** & **Unit** & **Lower Limit** & **Upper Limit** & **Considerations** \\ \hline Barrier length & mm & \(-4\) & 10 & Magnetic flux flow \\ Rib thickness & mm & \(-1\) & 0.5 & Mechanical stiffness and interference \\ Teeth width & mm & \(-1\) & 0 & Coil fill factor and magnetic saturation \\ Teeth thickness & mm & 0 & 1 & \\ Barrier gap & mm & 0 & 2 & Mechanical interference and magnet flux flow \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical constraints of the design variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design Variables** & **Unit** & **Initial Model** & **Best Point** \\ \hline Barrier length & mm & 0 & \(-3.72\) \\ Rib thickness & mm & 0 & 0.07 \\ Teeth width & mm & 0 & 0.44 \\ Teeth thickness & mm & 0 & 0.62 \\ Barrier gap & mm & 0 & 1.36 \\ Demagnetization rate & \% & 6.397 & 2.466 \\ Average torque & N-m & 60.936 & 59.846 \\ Torque ripple & \% & 5.202 & 4.939 \\ Efficiency & \% & 91.550 & 91.398 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results between the initial model and the best experimental point.
# MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries
## 5 Performance Evaluation
### Synthetic Datasets
#### 5.1.2 Effect of Cardinality  
We similarly examined the efficiency of the proposed method with different cardinalities. The results for varying cardinality are presented in Figure 9. For this particular test, we used a dimensionality \(m\) of 6, skyband size of \(K=5\), and datasets cardinality ranging from 75,000 to 300,000. Figure 8(a)-c present the results for the correlated, independent, and anti-correlated datasets, respectively.  
Figure 8: Performances with different data dimensions.  
The proposed technique had the fastest runtime for all three data distributions when the cardinality is low. However, as the data cardinality increases, the performance of the proposed method consistently decreases. As expected for anti-correlated data, the proposed method fails to dominate a reasonable number of objects, and the performance is worse than that for correlated and independent data.
## 4 Transmission Through the Switch
### ABCS-Based Transmission  
If the switch selects ABCS mode, it operates as a backscatter tag, and the received signal \(y_{SR}(t)\) is reflected to \(D\), and the system adapts a simple OOK modulation technique. The amount of reflection depends on the reflection coefficient \(\alpha\):  
\[\alpha=\frac{Z_{l}-Z_{s}*}{Z_{l}+Z_{s}}=Ae^{j\psi},\]  
where \(A\in[0,1]\) is the modulus of the reflection coefficient, and \(\psi\) is the phase.  
When \(y_{SR}(t)\) is the signal received at \(R\), the signal backscattered to \(D\) can be given as follows:  
\[y_{RD}(t) =\alpha y_{SR}(t)\] \[=\alpha\sqrt{\frac{P_{SR}P_{RD}}{d_{SR}^{m}d_{RD}^{m}}}h_{SR}h_{RD} x(t)+n(t)\]  
The switch is considered to have very little modulation; therefore, we can neglect the thermal noise from the backscatter tag [29].  
The SNR of the received signal at \(D\) in ABCS mode is given as follows:  
\[\gamma_{D_{ABCS}}=\frac{A^{2}P_{SR}P_{RD}|h_{SR}|^{2}|h_{RD}|^{2}}{d_{SR}^{m}d_{ RD}^{m}\sigma_{SR}^{2}\sigma_{RD}^{2}} \tag{10}\]  
The power consumption of ABCS mode is primarily determined by the circuit power (\(P_{ckt}\)) required for reflection operations, which is significantly lower than SWIPT's transmission power requirements. The reflection coefficient \(\alpha\) affects both the power consumption and the backscattered signal strength--higher values of \(\alpha\) increase the reflected signal strength but also slightly increase power consumption. This makes ABCS particularly suitable for scenarios where energy availability is limited but maintaining communication links is crucial.
# Neuroprotective Investigation of Chitosan Nanoparticles for Dopamine Delivery
## 3 Results
### ROS Neuroprotection by CS(DA) NPs and Enzymatic Activities
Thus, we focused our attention on these enzymes to obtain a better understanding of how H\({}_{2}\)O\({}_{2}\) was produced after DA addition and how this process was controlled by these scavenging enzymes. Increased H\({}_{2}\)O\({}_{2}\) production was indeed accompanied by a significant increase in the enzymatic activity of both GPx (\(p<0.001\)) and SOD (\(p<0.05\)) in cells treated with free DA when compared to control cells (Figure 5). Figure 3: Dose-dependent effects of free DA and CS(DA) NPs on SH-SYSY cell-viability. (**a**) Optical microscopy imaging of control cells (CTR), cells incubated with free DA (100 \(\upmu\)M) and cells incubated with CS NPs loaded with the same amount of DA. (**b**) Histogram of cell viability after treatment with free DA, CS and CS(DA) NPs with respect to untreated control cells at 25, 50 and 100 \(\upmu\)M DA concentration. A similar behavior was observed when the DA was delivered through the CS NPs (\(p<0.001\) and \(p<0.005\) for the GPx and SOD activity with respect to control cells, respectively). On the other hand, a statistical difference (\(p<0.001\)) in the activity induced by the free and the encapsulated DA was noted only in the GPx enzyme, where the CS(DA) NPs stimulated a greater effect with respect to the free drug (Figure 5a). Although similar behavior was observed when checking the SOD enzymatic activity, no statistical significance was noted between the two delivery systems, which were namely the free and encapsulated drugs (Figure 5b). As expected, the bare CS NPs did not induce any activity in the studied enzymes with respect to the control cells. The observed increased enzymatic activity with the CS(DA) NPs compared to the free DA might be due to the sustained release exerted by the NPs. Furthermore, the NPs could protect the DA from the external environment before its release, while the free drug might be partially oxidized and become inactive in the cell media over the 24-h time experiment. This data might indicate a direct involvement of GPx and SOD in the protective effect of CS NPs against DA-induced oxidative stress. Thus, it is conceivable that the controlled release of DA from CS NPs led to an increase in the enzymatic activity, thus preventing an excessive increase in the H2O2 level. Figure 4: Amount of hydrogen peroxide produced by different incubated SH-SY5Y cell lines. Asterisks indicate statistical differences between the selected data series (\({}^{*}p<0.05\); \({}^{**}p<0.005\)). Figure 5: (a) GPx and (b) SOD antioxidant enzyme activity in control and SH-SY5Y cells treated with 100 \(\upmu\)M DA, CS NPs and CS(DA) NPs. Asterisks indicate statistical differences between the selected data series (\({}^{*}p<0.05\); \({}^{**}p<0.005\); \({}^{***}p<0.001\)).
# A Review of Milk Frauds and Adulterations from a Technological Perspective
## 3 Results and Discussion
### Methods for Detecting Adulterants in Milk
#### 3.2.3. Instrumental Methods (Quantitative)
The most popular techniques include gas chromatography, high-performance liquid chromatography (GC and HPLC), NIR, MID, Raman infrared spectroscopy, enzyme-linked immune sorbent assay (ELISA), capillary electrophoresis (CE), and nuclear magnetic resonance (NMR) [48; 49]. There are several analytical techniques for detecting melamine in liquid and powdered milk, but regulatory agencies prefer and frequently use HPLC to identify this adulterant because of its great sensitivity and molecular specificity. There are many effective detectors that can be used, including mass spectrometry (MS), fluorescence,  
Figure 3: A description of the main milk adulterants, along with information on sample pretreatment complexity and the analytical techniques used for determining the most commonly used (adapted from [PERSON] et al., 2017). FL: molecular fluorescence; GC: gas chromatography; HPLC: high performance liquid chromatography; IR: infrared spectrometry; RI: measurement of refractive index; Scan: scanometry; SP: UV-vis spectrophotometry [48]. and UV-vis spectrophotometry. For the detection of melamine in liquid milk and powdered infant formula, HPLC with UV or fluorescence detection has also been suggested. This was taken into account precisely because, in comparison to HPLC-MS/MS, the equipment needed for these approaches is much simpler and less expensive. Due to the fact that other organic substances absorb in the same spectral region as melamine (about 240 nm), they can, however, be less selective, particularly in the HPLC-UV approach. In addition, infrared spectroscopy and chemometric techniques make for a perfect methodology for evaluating the authenticity and quality of milk sold on the market [48; 50]. Due to their high nitrogen content and low price, melamine, urea, and whey are the most common adulterants used for this purpose. The analytical procedures for their identification are described in Table 4[46]. These methods have a number of drawbacks, including expensive instrumentation, the need for qualified analysts, the length of time, immobility, human interaction with
## 4 Conclusions  
Biomass is a viable renewable and sustainable source of raw material for diverse application and mostly used in the energy industry. In this regard, this study investigates the potentials of cowpea husk, a biomass as reinforcement for polyester as well as the influence of 3% clay addition as a secondary material on the mechanical properties. The effect of clay addition on the mechanical and thermo-mechanical behaviour of formulated composites was investigated for possible application in roofing tiles. The morphological analysis of the mono and hybrid system shows a rough and coarse inhomogeneous surface with voids created due to the addition of CPH filler for the mono reinforced and clay uniformly filling the voids that were created by the CPH in the hybrid composite. Observations made from the mechanical and thermo-mechanical properties of the composite indicates that variations in composition have an impact on the attributes of all the produced composite as they all yielded different results. The addition of cowpea husk improved hardness, tensile modulus and flexural modulus but decreased tensile strength and flexural strength. The visco-elastic test also showed improvement in the damping properties of the hybrid composite below 100 \({}^{\circ}\)C. From the Grey relational analysis, the optimal composition for both mono-reinforced and hybrid composite is 18 percent CPH. This proves the potential of the composite to be used in building application and also serves as a benchmark for future work. Finally, this study has provided data and knowledge to fill the missing gap of using CPH, UPT and clay as a potential and sustainable source of raw material for building application in particular roof tiles. However, further studies on the full optimization of cowpea husk and clay should be carried out to elucidate the exact amount of cowpea husk and clay for maximum tensile and flexural strength.  
Figure 6: Main effect plot of optimal level conditions of factors for (**a**) mono-reinforced composite (**b**) hybrid composite.  
**Author Contributions:** E.N.A.-F.: Conceptualization, Methodology, Writing-original draft preparation, Software, Data curation, Resources; P.A.U.: Visualization, Resources, Investigation; I.I.O.: Investigation, formal analysis; G.M.K.-U.: Visualization, Resources, Investigation; A.B.: Formal analysis, Writing-review and editing; M.I.O.: Writing-review and editing; A.O.O.: Writing-review and editing; A.P.O.: Supervision and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Pan African Materials Institute (PAMI), a regional Center of Excellence in Materials Science and Engineering in West Africa established under the World Bank (WB) African Centers of Excellence (ACE) program, and hosted by the African University of Science and Technology, Abuja, Nigeria. The grant number is AUST/PAMI/2015/5415-NG.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 5 Discussion
### Benefits of MOUNTS
MOUNTS is open-source, with a Github repository ([[https://github.com/sebastienValade/mounts](https://github.com/sebastienValade/mounts)]([https://github.com/sebastienValade/mounts](https://github.com/sebastienValade/mounts))) storing both the source code and a changelog informing on all the notable changes made to the system and website. * Modular architecture, allowing the implementation of new processing algorithms to extract relevant volcanological parameters, or solve specific tasks. As a matter of example, a pre-trained CNN was plugged to detect strong deformation in the interferograms generated by the system. * Automated email alert messaging to dedicated users when specific thresholds are overcome. Interaction with other monitoring systems such as MIROVA is achieved by facilitating access to volcano-dedicated webpages (Figure 3). Strengthening the interactivity between the systems is planned, in particular by sharing database access in order to confront datasets more easily. The operational community such as volcano observatories can use MOUNTS and contribute to its development in a number of ways. The IGEPN (Instituto Geofisico de la Escuela Politecnica Nacional) for example, responsible for volcano monitoring in Ecuador, suggested to add Sangay to the list of monitored volcanoes in order to contribute to the surveillance of this remote edifice. The data available on the platform was used freely, and a collaborative exchange was initiated upon request to provide more specific data processing. The resulting material was further analyzed by IGEPN staff according to their needs, and was used in the activity reports describing the ongoing crisis for public information [87]. (Disclaimers on the data usage and appropriate acknowledgements can be found on the website). Scientific collaborations to investigate specific volcanic processes, or to develop specific methods (based on either the dataset available on the website, or on datasets resulting from more complex analysis) are also welcomed.
# Dynamic Modeling and Characteristic Analysis of Articulated Steering Vehicles
## 2 Dynamic Model of ASVs
### Model of Steering System  
#### 2.2.1 Hydraulic System  
The steering maneuver of an ASV is performed with hydraulic cylinders, and the hydraulic cylinders are arranged on both sides of the articulated point [25]. The rod and rodless cavities of the two cylinders are connected crossly to obtain a cooperative steering moment, as shown in Figure 3a. A steering valve, with the pressure compensation in front of the spool, is used to control the direction of the steering, and its flow is decided by the control current. The articulated steering hydraulic system and the geometric relationship are shown in Figure 3b. Then, the flow and force balance equations of the two steering cylinders can be expressed as follows:  
\[\left\{\begin{array}{l}Q_{L1}=A_{1}\dot{X}_{L}+\dot{P}_{L1}V_{L1}/K+C_{ip}(P _{L1}-P_{L2})\\ Q_{R2}=A_{2}\dot{X}_{R}+\dot{P}_{R2}V_{R2}/K+C_{ip}(P_{R2}-P_{R1})\\ Q_{R1}=A_{1}\dot{X}_{R}+\dot{P}_{R1}V_{R1}/K+C_{ip}(P_{R2}-P_{R1})\\ Q_{L2}=A_{2}\dot{X}_{L}+\dot{P}_{L2}V_{L2}/K+C_{ip}(P_{L2}-P_{L1})\end{array}\right. \tag{12}\]\[\left\{\begin{array}{l}F_{L}=A_{1}P_{L1}-A_{2}P_{L2}-f_{\rm s}\dot{X}_{L}\\ F_{R}=A_{2}P_{R2}-A_{1}P_{R1}-f_{\rm s}\dot{X}_{R}\end{array}\right. \tag{13}\]  
where the flow, volume, and pressure of the two chambers of the two cylinders are represented by \(Q_{ij}\) (\(i=L\), \(R;j=1\), \(2\)), \(P_{ij}\) (\(i=L\), \(R;j=1\), \(2\)), and \(V_{ij}\) (\(i=L\), \(R;j=1\), \(2\)), respectively. \(A_{1}\) and \(A_{2}\), as well as \(X_{R}\) and \(X_{L}\), are the corresponding action areas and displacement values, respectively, whereas \(f_{\rm s}\) is the coefficient of dynamic friction.
## 4 Conclusions  
In this paper, five over-reinforced RC beam specimens were prepared and corroded by an electrical accelerated steel corrosion setup in different ratios. Bending and AE tests were utilized for evaluating the flexural characteristics of the RC beam under the effect of steel corrosion. The following conclusions can be achieved:  
1. In general, the ultimate flexural load of the RC beam decreases along with steel corrosion. Interestingly, the ultimate flexural load of the 10% corroded RC beam was the largest instead of the uncorroded one; the reason is that failure form of the uncorroded RC beam is over-reinforced. It is proof that the balanced-reinforced RC structures should be designed to meet the requirements of compression and flexural strength.
2. The cracking failure modes of the corroded RC beams were analyzed according to the R/A and AF values and their association diagram. Results revealed that for each specimen, shear damage  
Figure 11: Localization of AE events and cracks: (**a**) Uncorroded RC beam; (**b**) 10% corroded RC beam; (**c**) 20% corroded RC beam; (**d**) 30% corroded RC beam.  
dominates during the loading process. Furthermore, the cracks' type tends to transform from shear cracks to tensile cracks along with the increase of steel corrosion ratio.
3. Results concluded from Ib-value curves revealed that the proportion of larger-scale and stronger damage increases along with the steel corrosion. Furthermore, the trend of the Ib-value curve can reflect the formation and extension of cracks, which corresponds highly with the actual failure process and modes. Furthermore, a larger duration of the violent fluctuations in the Ib-value curve results in a higher the ultimate flexural load of the RC beam. Therefore, the violent degree of fluctuations for the Ib-value can be considered as the threshold to alert attention to the severity of the RC structures.
4. The location of cracks and their extension states can be determined by the two-dimensional localization technology of the AE signal. The denser the distribution of the AE events is, the higher the probability that cracks form in this region, which corresponds highly with the macroscopic morphology of cracks and provides a basis for further research on crack growth and spatial form.  
Conceptualization, Y.Z. and Y.C.; Funding acquisition, G.T.; Data curation, Y.Z., G.T., S.W. and S.Y.; Writing--original draft, S.W., S.Y. and X.S.; Writing--review & editing, Y.Z., G.T. and S.W.  
The authors express their appreciation for the financial supports of National Natural Science Foundation of China (Nos. 51478203, 51408258).  
The authors declare no conflict of interest.