esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Testing of a New Bionic Corn-Ear-Picking Test Device
## 5 Conclusions  
A comparative test was conducted using two different mechanical methods for corn ear picking. When the average moisture content of corn straw was 75%, the average tensile fracture force applied for ear picking was 456.43 N, and the average bending fracture force was 51.86 N. When the average moisture content of the straw was 15%, the average tensile fracture force was 278.80 N for ear picking, and the average bending fracture force was 38.50 N. The results show that the bending fracture forces are much smaller than the tensile fracture forces required for the picking of corn in two different harvest periods.  
A new bionic device for corn ear picking was designed based on the bending fracture principle. The utility of this device was proven by the test in this study, and it achieved corn ear picking with little damage occurring to the corn ears and straw. More importantly, ear picking could be accomplished successfully despite the corn having various growth directions and scion heights.  
It was proven in the bench test that the ear-picking device was designed based on a sound principle and had a reasonable structure. An orthogonal test was performed on the factors that affected the ear picking. Based on the results, the corn-feeding and snapping roller speeds were found to have significant effects on the ear-picking rate. The optimal combination for ear picking is a rotational speed of the snapping rollers of 780 r/min, a corn-feeding speed of 1.5 m/s, and a 40\({}^{\circ}\) angle between the snapping rollers and clamping conveyor. Finally, the functional relationship between the influencing factors and the ear-picking rate was obtained.  
Figure 11: Visual diagram of picking rate and its influencing factors.  
**Author Contributions:** Conceptualization, L.Z. and J.Y.; Methodology, Q.Z.; Validation, L.Z. and X.F.; Resources, Q.Z.; Data Curation, X.F.; Writing--Original Draft Preparation, L.Z.; Writing--Review and Editing, J.Y. Visualization, Q.Z.; Supervision, J.Y. and Q.Z.; Project Administration, Q.Z.; Funding Acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by National Natural Science Foundation of China (Grant No. 51375206), and Jilin Provincial Science and Technology Development Plan (20170204015 NY).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The relevant data are all included in the paper.  
**Conflicts of Interest:** All authors declare no conflict of interest.
## 4 Experiments
### Comparison with Other KD Methods  
To demonstrate the effectiveness and robustness of the proposed method, we compared it with other mainstream KD methods for the CIFAR100 and Tiny ImageNet datasets, and the experimental results are shown in Tables 4 and 5. All KD methods can improve network performance to some extent, as seen by the statistics presented in the tables. However, not all improved KD methods consistently outperform conventional KD methods under different teacher-student structures. The proposed method consistently outperforms the traditional KD methods under a variety of teacher-student architectures for both datasets. In addition, the student network outperforms the teacher network in certain teacher-student architectures. Specifically, for a ResNet18 student trained by a ResNet50 teacher on CIFAR100, the proposed method results in a 2.7% improvement in the student network performance, and the accuracy of the student network is 1.83% higher than that of the teacher network. The Oto method, which is an extension of MSKD, further improves the accuracy, its performance is slightly worse than the SOTA distillation method HSAKD  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Teacher** & **Baseline** & **Student** & **Baseline** & **Exit1** & **Exit2** & **Exit3** & **Exit4** & **Ensemble** \\ \hline ResNet152 & 80.88 & ResNet50 & 79.34 & 79.32 & **80.80** & 81.78 & 81.74 & 83.20 \\ ResNet152 & 80.88 & ResNet18 & 78.47 & 75.06 & 77.40 & **80.15** & 80.50 & 81.73 \\ ResNet152 & 80.88 & ResNet10 & 76.19 & 73.19 & 75.12 & **77.52** & 78.02 & 79.74 \\ ResNet50 & 79.34 & ResNet18 & 78.47 & 74.37 & 77.62 & **80.09** & 80.65 & 81.42 \\ ResNet50 & 79.34 & VGG8 & 70.36 & **71.20** & 69.79 & 70.95 & 72.98 & 76.05 \\ VGG13 & 74.64 & VGG8 & 70.36 & **71.17** & 70.59 & 71.10 & 72.65 & 76.27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracies (%) obtained for the CIFAR100 dataset. The first early exit that exceeds the performance of the benchmark method is marked in **bold**.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Teacher** & **Baseline** & **Student** & **Baseline** & **Exit1** & **Exit2** & **Exit3** & **Exit4** & **Ensemble** \\ \hline ResNet34 & 66.40 & ResNet34 & 66.40 & 59.52 & 63.80 & **68.65** & 70.38 & 71.38 \\ ResNet34 & 66.40 & ResNet18 & 64.40 & 59.86 & 62.30 & **65.30** & 68.04 & 69.86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracies (%) obtained for the Tiny ImageNet dataset. The first early exit that exceeds the performance of the benchmark method is marked in **bold**.  
when the teacher network is ResNet152, but is excellent with the rest of the teacher-student architectures. The SOTA HSAKD [28] method augments data for self-supervised learning, and data augmentation aids its effectiveness. Furthermore, we used the AutoAugment [40] strategy in OtO, and Table 4 shows that the proposed method superior to the previous KD method at this time.
## 3 Results
### Time-Mean Eddy Propagation
The most remarkable feature of eddy dynamics in the Argentine Basin is the anticyclonic (anticlockwise) propagation of eddies around the Zapiola Rise (ZR). The ZR is a seamount that rises about 1500 m above the Argentine Abyssal Plain (Figure 4). A very strong anticyclonic recirculation gyre extending from surface to bottom and transporting about 100 Sv exists around this topographic anomaly [38]. The eddy propagation around the ZR mimics the recirculation gyre with velocities comparable to the mean flow. The eddy propagation velocities on the northern flank of the seamount are the strongest in the South Atlantic Ocean and often exceed 10 km/day. On the southern flank of the seamount, eddy propagation is slower with velocities of 1-3 km/day. It is interesting to note that the mean SSH slope is steeper to the south of the seamount crest than to the north (see contours in Figure 1), meaning that the eastward surface geostrophic velocity is greater south of the ZR than the westward surface geostrophic velocity north of the ZR. This suggests that eddy propagation velocities around the ZR are mostly affected by currents at depth rather than by the near-surface flow. The ACC in the Southern Ocean has a sufficiently strong eastward component that reverses the inherent westward direction of eddy propagation and forces them to move in the direction of the mean flow. As illustrated in Figure 3, the zonal eddy propagation velocities within the ACC are much more variable in space than those within the South Atlantic subtropical gyre. Some regions of the ACC show very slow either eastward or westward eddy propagation, but the Drake Passage, the Scotia Sea, and the region east of 20\({}^{\circ}\)W are characterized by relatively swift eastward eddy propagation with speeds reaching  
Figure 4: Bottom topography based on ETOPO2 Global Relief Model (color shading) and eddy propagation velocities (vectors) in the Argentine Basin. The black and blue contours denote the 200-m and 4000-m isobaths, respectively. Abbreviations: BMC—Brazil-Malvinas Confluence, AAP—Argentine Abyssal Plain. 4-5 km/day. The latter segment of the ACC is zoomed in Figure 5, showing the contours of the time-mean SSH overlaid on the mean eastward eddy propagation velocity. It appears that the intensification of the eastward eddy propagation happens in the locations where the ACC is also stronger, associated with steeper SSH gradients and, consequently, stronger surface geostrophic flows. There are two bands of steep SSH gradients or fronts within this segment of the ACC: (i) the Sub-Antarctic Front (SAF) centered approximately along \(-\)0.2 m mean SSH contour (red contour in Figure 5) and (ii) the Polar Front (PF) centered at about \(-\)0.6 m mean SSH contour (blue contour in Figure 5) [45; 46].
The factors influencing project cost include code reusability, the complexity of requirements, documentation, and testing. Cost evaluation is typically measured in hours required to complete specific functionalities. The desirability of requirements is directly proportional to the value they provide [9]. Additionally, every project faces various uncertainties or risks that necessitate effective risk management strategies in order to address both external and internal challenges. It is essential to prioritize requirements while also considering the associated risks. Desirability is also directly related to cost. The time it takes to bring a product to market is significantly influenced by the availability of the resources necessary for job completion. Factors such as concurrent development, the need for training, and adherence to industry standards also impact time-to-market. The instability of requirements is another critical factor; requirements often change, particularly during the development phase, due to shifts in the market, government regulations, or industry standards [10]. Therefore, it is advisable to first execute the more stable requirements. The debate highlights that while various dimensions are significant, not every dimension needs to be considered in the prioritization process. The selection of what to prioritize is contingent on the specific situation and circumstances [11]. This article introduces a comprehensive framework integrating the Analytical Hierarchical Process (AHP) and an advanced grouping methodology to address the deficiencies of current prioritization practices in IoT-enabled edge solutions. This framework not only refines existing approaches to mitigate the limitations and subjectivity of traditional grouping models but also expands the capacity of AHP to handle a broader range of requirements efficiently. The framework is organized into two main modules: the Pre-processing Module, where requirements are collected and stored in a repository, including real-time requirements, and the Prioritization Module, which is tasked with grouping, reviewing, and prioritizing these requirements. Within the Prioritization Module, the Grouping Processing Unit (GPU) classifies requirements using a newly proposed grouping approach; the Review Processing Unit (RPU) assesses the requirements post-grouping; and the AHP Processing Unit (APU) is responsible for the final prioritization of the requirements. The remainder of the article is structured as follows: Section 2 provides a literature review that critiques existing methods and identifies the gaps that our framework addresses. Section 3 describes the proposed framework in detail, explaining the workings and components of each module. Section 4 discusses the validation and conducts a case study to demonstrate the framework's practical applications and effectiveness. Section 5 sets out the challenges and limitations associated with implementing the framework. Finally, Section 6 concludes the article with a summary of our findings.
## 3 Results and Discussion
### Evaluation of the Mechanical Performance of the Selected Asphalt Mixtures
#### 3.5.2 Water Sensitivity of the Selected Asphalt Mixtures  
The water sensitivity test was carried out to characterize the two asphalt mixtures with 75% SSA and the equivalent conventional mixtures. The main results obtained with this test (IST\({}_{d}\), ITS\({}_{w}\) and ITSR), including the average densities and air void contents of the different asphalt mixtures, are shown in Table 11.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fraction** & **AC 14 Surf 35/50 (BB)** & **AC 20 Bin/Base 35/50 (MB)** \\ \hline NA 14/20 & – & 15\% \\ NA 10/14 & 40\% & 17\% \\ NA 4/10 & 22\% & 23\% \\ NA 0/4 & 35\% & 44\% \\ Commercial filler & 3\% & 1\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Composition of the conventional asphalt mixtures with natural aggregates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Equivalent** & \multicolumn{2}{c}{**Marshall Stability (kN)**} & \multicolumn{2}{c}{**Deformation at Failure (mm)**} \\ \cline{2-5}
**Binder Content** & **AC 14** & **AC 20** & **AC 14** & **AC 20** \\ \hline
4.0\% & 20.2 & 13.7 & 3.7 & 3.2 \\
4.5\% & 17.9 & 16.6 & 3.8 & 3.7 \\
5.0\% & 21.5 & 18.2 & 3.8 & 3.8 \\
5.5\% & 19.7 & 17.0 & 4.1 & 3.8 \\
6.0\% & 20.9 & 16.8 & 4.6 & 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Marshall stability and deformation results for selected mixtures with 75% SSA.  
The excellent performance of the mixtures with SSA verified in the preliminary study was confirmed in this phase, which means that the developed asphalt mixtures with 75% SSA guarantee good durability even in the most adverse atmospheric conditions.  
The mixtures with SSA performed better than the equivalent mixtures produced only with natural aggregates. The performance of the AC 20 mixture with SSA was inferior to that of the surface course mixture (AC 14), but still higher than the 70% limit already mentioned. This result is mitigated because AC 20 is less exposed to atmospheric agents.  
Moreover, higher air void content values were obtained in the mixtures with SSA, mainly due to the highly rough surface of the SSA, which reduces the workability of the mix. In this regard, the AC 14 mixture with SSA stands out, with a porosity of 7.8% (above the specifications limit). This high porosity resulted in a slight reduction in dry tensile strength (ITSd) values. However, the increased air void contents of the asphalt mixtures with SSA did not reduce their water sensitivity test results (ITSR), confirming the excellent affinity between SSA and bitumen.
# Green Scalar Function Method for Analyzing Dielectric Media
## 2 Theoretical Background
### Scalar Analytical Solution  
The scalar approximation simplifies the analysis of electromagnetic wave scattering by reducing the problem to a scalar field rather than dealing with the full vector nature of electromagnetic fields. This approach is especially useful when the variations in the wave field occur mainly in a single component, allowing for simpler mathematical handling and greater computational efficiency. By using scalar fields, we can take advantage of well-established mathematical techniques, such as Fourier series, to express the fields in terms of their angular harmonics. Since analytical solutions for cylindrical symmetries are studied in the literature, we opted to use cylindrical dielectric media. In order to compare the numerical results, the analytical solution presented in Section 2.1 in [27] is used. Thus, to achieve this, a homogeneous cylinder is considered to obtain the scattered field in cylindrical coordinates in terms of a Fourier series in the angular coordinate. Lets us consider an incident wave front in plane \(y=0\) parallel polarized with respect to the \(z\)-axis; thus, the electric field in cylindrical coordinates expressed as a Fourier series in the angular coordinate is given by  
\[E^{(1)}(\rho,\phi)=E_{0}e^{ikx}=E_{0}\sum_{m=-\infty}^{\infty}i^{m}J_{m}(k\rho )e^{im\phi} \tag{16}\]  
where \(E_{0}\) is the amplitude of the incident wave, \(\phi\) is the angle that the scattered wave forms with respect to the incident wave and \(J\) is the Bessel function of first kind. Once we have expressed the incident field in Fourier terms as a function of the angular harmonics, we can give the expression for the scattered field \(E^{(3)}\) as  
\[E^{(3)}(\rho,\phi)=E_{0}e^{ik\rho\cos\phi}=E_{0}\sum_{m=-\infty}^{\infty}i^{m} \alpha_{m}H_{m}(k\rho)e^{im\phi} \tag{17}\]  
This Equation (17) satisfies the Sommerfeld radiation condition at infinity. Here, \(\alpha_{m}\) is a constant describing the continuity of the dielectric field derived by the boundary conditions as obtained in [28] for a perpendicular polarization across the \(xz\)-plane, which is given by  
\[\alpha_{m}=\frac{n!J_{m}^{\prime}(ka)J_{m}(nka)-J_{m}(ka)f_{m}^{\prime}(nka)}{ n!J_{m}(nka)H_{m}^{\prime}(ka)-J_{m}^{\prime}(kna)H_{m}(ka)} \tag{18}\]  
where \(k=2\pi/\lambda\) is the modulus of grating vector \(\mathbf{k}\), \(n\) is the refractive index of the dielectric media and \(a\) is the cylinder radius. The scalar approximation provides significant insight into the scattering problem by simplifying the analysis. However, it should be noted that the solutions obtained using this scalar approximation are analogous to those derived from the full vector treatment in the case of TE (Transverse Electric) polarization. In this context, TE polarization implies that the electric field is perpendicular to the plane of incidence, making the scalar approximation of the field a valid representation of the vector field under these specific conditions, as it is described in [25].
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 3 Proposed Method
### Data Preprocessing  
Since the vibration signal measured by sensors carries vital information, it should be transformed by the appropriate action and sensitive features, which can efficiently reflect the bearing working condition will be obtained. To avoid missing sensitive features, almost all features in the time and frequency domains are acquired. In order to improve the convergence speed of the model and the accuracy of the model, all features will be performed by feature scaling after the features are obtained. The details are given in Algorithm 1.  
Figure 1: Flowchart of the proposed method. (**a**) Data preprocessing: Features are obtained through the time domain and frequency domain, forming a feature set (\(FS\)) matrix with a size of \(n\times m\); (**b**) MIC (Maximum Information Coefficient) calculation: relevance between features and features are calculated to obtain an FF-MIC matrix with a size of \(m\times m\) by MIC, and relevance between features and fault categories are calculated to obtain the FC-MIC matrix with a size of \(m\times p\) by MIC; (**c**) relevance calculation: a threshold \(FF\_threshold\) is set to distinguish strong irrelevance features from \(FS\), and a threshold \(FC\_threshold\) is set to distinguish strong relevance features from \(FS\); (**d**) obtaining of feature subsets: according to the thresholds, Subset1 and Subset2 are obtained; (**e**) merging of feature subsets: An intersection operation is applied to merge Subset1 and Subset2, obtaining a final subset \(F-Subset\); (**f**) PCA: PCA is employed to further reduce the dimension of \(F-Subset\).
## 4 Results and Discussion
### Edge Defects  
For studying the morphologies and edge defect sizes at the workpiece exit end, only a small part containing the edge defects was cut from all the machined components using wire electrical discharge machining. When the feed rate and spindle speed were 0.237 mm/r and 500 r/min, respectively, the exit edge defect morphologies under five different depths of cut are displayed in Figure 5. Figure 5a-e shows the morphologies of the edge defects in the length direction, while Figure 5f-j show the morphology of the edge defects in the height direction. In Figure 5, we can clearly see that the brittle characteristics of 17 vol.% SiC\({}_{\text{p}}\)/2009 Al composites were very obvious, and brittle SiC particles in the Al matrix led to very rough fracture morphologies. As the depth of cut gradually increased, the values of length and height of the edge defects increased significantly.  
Figure 5: Edge morphology on the exit surface, at the depth of cut of: (**a,f**) 1 mm, (**b,g**) 1.5 mm, (**c,h**) 2 mm, (**d,i**) 2.5 mm, (**e,j**) 3 mm.
# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 4 Proposed Framework
### Multi-Fidelity Model Composition and Target Model Substitution
#### 4.3.1 Multi-Fidelity Model Composition for Dynamic System  
Figure 10 shows the structure of the multi-fidelity model of the dynamic system. The internal models (i.e., \(M_{1}\) to \(M_{n}\)) are expressed with the DTSS formalism mentioned in Section 2; the \(SM_{DS}\) has a similar form to the DTSS so that overall models can be simulated with the existing simulation engine.  
Figure 10: An example of the multi-fidelity model’s structure for a dynamic system: Fidelity-unchanged and fidelity-changed cases.  
\[SM_{DS}=\langle X,Y,S,FCC,sf,of\rangle\]  
\(X=\{X_{in}\}\cup\{\mathrm{U}_{i=1}^{n}M_{i}.State\}\) is the set of inputs;  
\(M_{i}.State\) is the current state input of internal model \(M_{i}\);  
\(Y=\{\mathrm{U}_{i=1}^{n}M_{i}.X_{in}\}\cup\{\mathrm{U}_{i=1}^{n}M_{i}.Activate\}\) is the set of outputs;  
\(M_{i}.X_{in}\) is the set of bypassing outputs to \(M_{i}\);  
\(M_{i}.Activate\) is the set of activating outputs to \(M_{i}\);  
\(S=\{(M_{i},State,bChange)|M_{i}\in\{M_{i}\},\ State\in M_{i}.S,\ bChange\in\{ True,\ False\}\}\)  
is the set of internal model states;  
\(FCC=\{(v,r)|v\in IRV,\ r\in R\}\) is the set of fidelity change conditions;  
\(sf:X\times S\times C\to S\) is the selection function;  
\(of:X\times S\to Y\) is the output function.  
The specification of the \(SM_{DS}\) is given after Figure 10. The \(SM_{DS}\) has two types of input sets: an external input set from outside of the \(MFM_{DS}\) and a state input set. The two types of output sets for input bypassing and model activation are allowed by the \(SM_{DS}\). Fundamentally, the \(SM_{DS}\) receives the external input \(X_{in}\), and sends it to a current internal model \(M_{i}\) in the form of the output \(M_{i}.X_{in}\). The \(SM_{DS}\) receives the state input \(X_{i}.State\) from the \(M_{i}\) and checks whether a model change is needed. If a change is required, the \(SM_{DS}\) sends the output for model activation \(M_{j}.Activate\) to the altered model \(M_{j}\). The \(SM_{DS}\) has two sets of states: (1) a set of internal model states including the current internal model, the state variable of the model, and the Boolean variable to check whether there has been a model change; and (2) a set of fidelity change conditions. Finally, the \(SM_{DS}\) employs two functions: a selection function \(sf\) and an output function \(of\). The \(sf\), which is mapped to the state transition function of the DTSS model, decides whether the current internal model is changed with the \(FCC\) when the \(SM_{DS}\) receives an external input. If the current internal model does not need to be changed, the \(of\) sends the external input to the model. Otherwise, the \(of\) sends the state of the current internal model and the external input to the altered internal model (blue fonts are relevant to the fidelity-unchanged case and red ones correspond to the fidelity-changed case in Figure 10).