esa-sceva/satcom-chunk-collection huggingface dataset:
# Large-Scale Fine-Grained Bird Recognition Based on a Triplet Network and Bilinear Model
## 2 Background  
### Dataset and Evaluation Metric  
Birds-1096: Our own database (Birds-1096) includes 1096 bird categories, each containing 200-350 images (giving a total of 459,828 images). The original dataset is composed of images of different resolutions, we resize the image to (299, 299, 3) for later use. We randomly selected 10 pictures from each category for testing, thereby assigning 109,600 images to the test set. Targets in the same category have diverse postures and large illumination changes, whereas the between-category differences are very subtle, with similar shapes and colors of the target. Some images from Birds-1096 are displayed in Figure 1.  
CUB200-2011: This dataset is the most widely used dataset for fine-grained image classification. It contains 11,788 images of 200 bird subcategories and is divided into 5994 images for training and 5794 images for testing [15]. Each subcategory consists of 30 images for training and 1130 images for testing, and each image has detailed annotations: a subcategory label, an object bounding box, 15 part locations, and 312 binary attributes. All attributes are visual in nature, pertaining to the color, pattern, or shape of a particular part. Some images from the CUB200-2011 dataset are displayed in Figure 2.  
The classification performances of our approach and another method (for comparison) were evaluated by the accuracy metric, a widely used performance index in fine-grained image classification studies [16, 17].
## 5 Experiment II--Change Detection  
### Reference Map Generation  
In this experiment, we test the capability of height difference maps as derived from the DSMs to detect windthrow areas, caused by a storm event in 2007 in the described test site. Due to the lack of field data, we use the height difference map derived from both available LiDAR data sets as reference (Figure 7a).  
As discussed in the previous section, although the two LiDAR data sets exhibit similar characteristics, the elevation values in the forest regions do not perfectly match, even in the regions with no changes. Therefore, the same median filter as mentioned in Equation (1) is applied separately for  
Figure 7: Reference change mask generation procedure: (**a**) Original height difference map; (**b**) refined height difference map (in (**a,b**), the red areas represent a decrease in height, while green ones represent the increase in height and white represents regions with no height difference and no change); and (**c**) generated change reference mask (green: a decrease in height of 12 m or more; grey: forest mask extracted from WorldView-2 data).  
the two LiDAR DSMs. The filtered height difference map is shown in Figure 7b. Here, the windthrow areas are well highlighted in red, and the noise in the central area is largely removed. The light green color in Figure 7 indicates a height increase from natural tree growth, in which Figure 7a assumes a value of about 5-10 m for the nine year time difference (around 0.6 m per year), which is slightly higher than the 0.3-0.4 m annual growth speed obtained in [37]. However, in the filtered height change map (Figure 7b), the height increase is only 3-6 m. Some regions exhibit a height change of about 9 m, which is assumed to be related to young, fast-growing trees.  
A major task after storms in forest areas is to determine and delineate the damaged regions. Thus, we generated a reference mask for the windthrow areas based on the refined height difference map (Figure 7b). First, a height threshold of 12 m (value selected according to the recommendation given by [PERSON] [45]) was applied to obtain an initial change mask, which shows the areas where the filtered difference map indicates a reduction in height of 12 m or more. Next, morphological erosion and dilation operators were applied to clean the generated change reference mask. In the morphological filter procedure, a squared structuring element of size of 3 \(\times\) 3 was used, and regions smaller than 100 pixels were removed.
# Diode Laser Welding/Brazing of Aluminum Alloy to Steel Using a Nickel Coating
###### Abstract  
Joining Al alloy to steel is of great interest for application in the automotive industry. Although a vast number of studies have been conducted to join Al to steel, the joining of Al to steel is still challenging due to the formation of brittle Fe-Al intermetallic compounds. In this work, the microstructure and mechanical properties of the dissimilar Al/steel joints with and without a nickel coating are comparatively investigated. A homogenous reaction layer composed of FeZn\({}_{10}\) and Fe\({}_{2}\)Al\({}_{5}\) is formed at the interface in the joints without Ni coating, and the joint feature load is only 743 N. To prevent the formation of brittle Fe\({}_{2}\)Al\({}_{5}\), Ni electroplated coating is applied onto a steel surface. It has been shown that a nonhomogeneous reaction layer is observed at the interfacial region: Ni\({}_{5}\)Zn\({}_{21}\) is formed at the direct irradiation zone, while Al\({}_{3}\)Ni is formed at the fusion zone root. The microhardness of the interfacial layer is reduced, which leads to the improvement of the joint mechanical properties. The average fracture load of the Al/Ni-coated steel joints reaches 930 N. In all of the cases, failure occurs at the Ni coating/fusion zone interface.  
laser welding/brazing; aluminum alloy; Ni coating; interfacial microstructure; intermetallic compounds; mechanical properties +
Footnote †: journal: Applied Soft. 2018, 8, 922; doi:10.3390/app8060922
## 4 Error Simulation and Analysis
### Datum Alignment Error  
According to the description in Section 3, we obtain the datum plane by fitting the feature points by the least-squares method. The reference platform coordinate system is established with the normal direction of the plane as the coordinate axis, and the angular error of the coordinate system establishment is simulated. The measurement of feature points can be equated to the measurement of spatial point coordinates. According to the laser tracker and photogrammetric error distribution model, the spatial point coordinate measurement can be described by a normally distributed error.  
Assuming the measuring device is at 2 m, the laser tracker error is approximately 27 \(\upmu\)m (3\(\sigma\)), and the photogrammetric error is approximately 15 \(\upmu\)m (3\(\sigma\)), substitute into (10) and (11). We have designed a simple equivalent model, imported the feature points  
Figure 6: The error in the establishment of the cubic prism vector as a function of the length of the reflected ray. (**a**) error curve for incident ray at 1 m; (**b**) error curve for incident ray at 2 m; (**c**) error curve for incident ray at 4 m.  
into the simulation software, and added the above errors to simulate the datum alignment errors during real measurements. The probability distribution curves of the angular error of the coordinate system after datum alignment are shown in Figure 7a-c. The horizontal coordinates are the angular errors in the \(X\), \(Y\), and \(Z\) axes (expressed as Eulerian rotation angles) and the vertical coordinates are the probabilities.  
The simulation results show that when the measuring equipment is 2 m away from the reference platform, the error distribution of the \(X\), \(Y\), and \(Z\) axes of the coordinate system presents three different forms during the alignment process between the laser tracker and the photogrammetric reference. The limiting errors (3\(\sigma\)) are approximately 8.8\", 5.2\", and 4.2\".
# The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Standley Model
## 1 Introduction
[18] proposed a PSO-optimized fuzzy supervisory system to handle the various trajectories and speed for an armored vehicle, where the parameters of the modified SM were changed adaptively based on fuzzy inference and an optimal knowledge database. Inspired by the work of [18], this paper proposes an improved path tracking control algorithm based on the fuzzy SM (FSM) and PSO to cater for variations in the agriculture robot in terms of speed and road conditions. In this contribution, the primary controller parameters of SM are adjusted by fuzzy inference at first, and then the control gain under different velocities and actuator saturation conditions are further modified by PSO. The fuzzy algorithm is used to adjust the control gain with respect to tracking errors, which not only improves the tracking precision for the automatic turning of agricultural machinery, but also reduces the distance of guided trajectory when the initial lateral error is large. The PSO is employed to further optimize the decision-making of the front wheel steer angle and improve the adaptivity of agricultural machinery towards different vehicle speeds and actuator saturation situations. The main contributions of this paper are as follows: (1) PSO-enhanced control gain adjustment is developed to cater for varying vehicle velocities and actuator saturation conditions; (2) a cascaded path tracking algorithm based on FSM and PSO is proposed for the full-field autonomous navigation of agriculture robot; (3) the autonomous navigation experiments based on numerical simulation, a mobile vehicle and a combine harvester are employed to verify the superiority of PSO-enhanced FSM (PSO-FSM). The structure of this paper is arranged as follows. The fuzzy Stanley model is presented in Section 2. PSO-enhanced control gain adjustment is developed in Section 3. In Section 4, the PSO-FSM is verified by using numerical simulation, path tracking of a mobile robot and an unmanned combine harvester in a slippery soil condition. Finally, Section 5 concludes the conclusions of this work.
# Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
## 3 Methodology
### Initializing of Parameters  
For the training algorithm's initialization, wavelets and membership functions parameters are set from a uniform distribution. From Equations (3) and (7)-(10), we can see that the parameters to be trained in the FWN will be \(p^{r}_{ij}\), \(t_{ij}\), \(d_{ij}\), \(w_{j}\), and \(w_{nk}\). The initial parameters \(p^{1}_{ij}\), and \(p^{2}_{ij}\) are chosen to be the weighted mean and standard derivation of the input data, respectively, while \(p^{3}_{ij}\) is selected as a random number in a pre-selected range (this is discussed in the next sub-section) to shape the Gaussian-type membership function. In this framework, the initialization of the wavelet parameters is based on the input domains defined by the examples of the training sample [30; 31]. The following expressions were used in the initialization of the translation \(t_{ij}\) and dilation \(d_{ij}\) parameters,  
\[t_{ij} = 0.5*(X_{\text{min }i}+X_{max i}) \tag{15}\] \[d_{ij} = 0.2*(X_{max i}-X_{\text{min }i}) \tag{16}\]  
where \(X_{max i}\) and \(X_{\text{min }i}\) are defined as the maximum and minimum of input \(X_{i}\). Lastly, \(w_{j}\) and \(w_{nk}\) can be selected randomly, as obtained in this study.
# Electrical Coupling of Monolithic 3D Inverters (M3 INVs): MOSFET and Junctionless FET
## 2 Structure and Simulation Method  
Figure 1 shows two types of M3 INV structures used in the simulation. The M3 INV consists of N-type and P-type transistors in the upper and lower tiers, respectively. Figure 1a,b are schematics of M3 INV consisting of MOSFET (M3 INV-MOS) [5] and JLFET (M3 INV-JL) [12], respectively. The doping concentrations of source/drain, LDD, and channel in MOSINV-MOS are \(10^{21}\), \(10^{18}\), and \(10^{15}\) cm\({}^{-3}\), respectively [13,14]. The doping concentrations of source/drain and channel of M3 INV-JL is \(10^{20}\) and \(10^{19}\) cm\({}^{-3}\), respectively [12]. The detailed parameters in both M3 INV-MOS and M3 INV-JL are shown in Table 1. Three types of structures for both M3 INV-MOS and M3 INV-JL are simulated: gate lengths/gate oxide thicknesses are 20/0.9 nm, 30/1 nm, and 50/1.1 nm, respectively [14]. SiO\({}_{2}\) was used for the regions in gate oxide, ILD, and bulk oxide (Box). Figure 1c shows the equivalent circuit of M3 INV-MOS and M3 INV-JL. Silvaco's technology computer-aided-design (TCAD) simulator ATLAS [17] was used in this simulation. The models used in the simulation are CVT, SRH, AUGER, and FERMI. The leakage currents of both MOSFET and JLFET in each M3 INV were set equal to \(10^{-8}\) A, in order to simulate the inverter characteristics with same leakage current condition. The \(V_{th}\) variation, \(g_{m}\) variation, \(C_{ngng}\) variation, \(V_{m}\), and noise margin of both M3 INV-MOS and M3 INV-JL were compared through TCAD simulation.  
Figure 1: Schematics of two types of monolithic 3-dimensional (3D) inverter (M3 INV) cell structure. (**a**) Structure of M3 INV-MOS and (**b**) structure of M3 INV-JL. (**c**) Equivalent circuit of M3 INV-MOS (M3 DINV-JL).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbols** & **Description** & **Value/Unit** \\ \hline \(C_{ngrg}\) & Total gate capacitance of the top transistor & F \\ \(\Delta V_{Cagng}\) & Difference of \(V_{ngs}\)s at maximum \(dC_{ngrg}/dV_{ngs}\) of the top transistor at between \(V_{pgs}\) = 0 and 1 V & F \\ \(\Delta V_{gm}\) & Difference of \(V_{ngs}\)s at maximum \(g_{m}\) of the top transistor at between \(V_{pgs}\) = 0 and 1 V & S \\ \(\Delta V_{th}\) & Difference of \(V_{th8}\)s of the top transistor at between \(V_{pgs}\) = 0 and 1 V & V \\ \(\epsilon_{ox}\) & Oxide dielectric constant & 3.9 \\ \(\epsilon_{si}\) & Silicon dielectric constant & 11.8 \\ \(\epsilon_{ILD}\) & ILD dielectric constant & 3.9 \\ \(g_{m}\) & Transconductance (\(g_{m}=d_{nds}/dV_{ngs}\) or \(d_{pds}/dV_{pgs}\)) & S \\ \(l_{nds}/l_{pds}\) & Drain-source currents of top/bottom transistors & A \\ \(L_{c}\) & Contact length & 50 nm \\ \(L_{c}\) & Gate length & 20/30/50 nm \\ \(L_{LDD}\) & Lightly-doped drain length & 10 nm \\ \(T_{\rm BOX}\) & Buried-oxide thickness & 30 nm \\ \(T_{c}\) & Contact thickness & 6 nm \\ \(T_{g}\) & Gate thickness & 30 nm \\ \(T_{ILD}\) & ILD thickness & variable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Device/electrical parameter descriptions and dimensions.
# Postural Control in Children with Cerebellar Ataxia
## 3 Results
### EMG
Comparison among healthy children H, panel (**A**), children with non-progressive PCA NonP, (**B**) and children with slow progressive PCA SlowP, (**C**). One representative subject for each group. Time 0: APA (anticipatory postural adjustment) onset, defined as the first backward shift of the CoP (center of pressure). Black arrows show SOL (soleus) inhibition and the following TA (tibialis anterior) excitation. Note that the time delay between these two reciprocal actions gradually increased in children with NonP and SlowP with respect to H children. \begin{table}
\begin{tabular}{l l l l r r r r r r r r} \hline \hline  & & & & \multicolumn{2}{c}{**SlowP**} & \multicolumn{2}{c}{**NonP**} & \multicolumn{2}{c}{**H**} \\ \hline \multirow{4}{*}{**SlowP**} & **Erector spinae** & **inhibition** & \(-\)68 & \((-\)85 to \(-\)52) & n = 2 & \(-\)79 & \((-\)109 to \(-\)49) & n = 2 & \(-\)102 & \((-\)115 to \(-\)80) & n = 3 \\  & **Biceps femoris** & **inhibition** & \(-\)70 & \((-\)70 to \(-\)70) & n = 4 & \(-\)68 & \((-\)84 to \(-\)64) & n = 3 & \(-\)100 & \((-\)187 to 119) & n = 7 \\  & **Solues** & **inhibition** & \(-\)72 & \((-\)199 to \(\)63) & n = 6 & \(-\)98 & \((-\)130 to \(-\)63) & n = 3 & \(-\)72 & \((-\)146 to \(-\)25) & n = 5 \\  & **Reactons ferrois** & **excitation** & 15 & \((-\)32 to \(141)\) & n = 4 & \(-\)35 & \((-\)112 to \(22)\) & n = 5 & \(-\)72 & \((-\)110 to 57) & n = 5 \\  & **Tibialis anterior** & **excitation** & 31 & \((-\)52 to \(066)\) & n = 5 & \(-\)41 & \((-\)49 to \(26)\) & n = 5 & \(-\)31 & \((-\)49 to \(108)\) & n = 7 \\ \hline \multirow{4}{*}{**SlowP**} & **Erector spinae** & **inhibition** & \(-\)55 & \((-\)99 to \(2)\) & n = 3 & \(-\)111 & \((-\)127 to \(-\)95) & n = 2 & \(-\)90 & \((-\)263 to \(100)\) & n = 4 \\  & **Biceps femoris** & **inhibition** & \(-\)71 & \((-\)90 to \(-\)39) & n = 4 & \(-\)136 & \((-\)160 to \(-\)113) & n = 2 & \(-\)94 & \((-\)199 to \(70)\) & n = 5 \\  & **Solues** & **inhibition** & \(-\)98 & \((-\)184 to \(-\)35) & n = 6 & \(-\)53 & \((-\)100 to \(-\)24) & n = 4 & \(-\)121 & \((-\)259 to \(-\)113) & n = 5 \\  & **Reactons ferrois** & **excitation** & 3 & \((-\)40 to \(24)\) & n = n 3 & \(-\)11 & \((-\)83 to \(162)\) & n = 4 & 68 & \((-\)80 to \(113)\) & n = 3 \\  & **Tibialis anterior** & **excitation** & \(-\)2 & \((-\)51 to \(145)\) & n = 4 & 6 & \((-\)67 to \(45)\) & n = 4 & \(-\)61 & \((-\)101 to \(6)\) & n = 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Latencies (ms) of postural EMG changes with respect to the APA onset (time 0). Median, minimum, and maximum values, together with the number of subjects (n) in which APAs could be identified, for children with slow progressive PCA (SlowP), children with non-progressive PCA (NonP), and healthy children (H). EMG: electromyographic; APA: anticipatory postural adjustment.
## 5 Selenium-Containing Polysaccharides  
### Divalent, Tetravulent, and Zerovalent Selenium in Polysaccharide Structure  
The current literature shows that selenium in Se-polysaccharides may be di-, tetra-, or zero-valent, depending on the nature of the compound and the type of chemical bonds or interactions. Divalent selenium was found in selenoglycans or selenopyranoses [13], tetravalent in selenites of polysaccharides (selenious acid esters) [244], and zerovalent in selenium nanoparticles encapsulated into polysaccharides [245]. However, the structures of numerous selenium-containing polysaccharides described in the literature have not been conveniently studied yet. Certain structural analyses were limited to examining only the amount of selenium bound to the polysaccharide structure, without taking into consideration its chemical environment. Moreover, conclusions regarding selenium binding (oxidation degree, chemical bonds, chemical environment) in some of the published reports  
Figure 8: Formation of the selenious acid ester of carbohydrate.  
Figure 7: Examples of the alkylselenide structures derived from pyranose (**a**) and furanose (**b**), synthesized by [PERSON] et. al. [242].  
were based on insufficient data (e.g., only IR spectra). Unraveling the chemical environment of selenium in selenoorganic compounds requires advanced analytical methods. The principal techniques that should be used for the characterization of selenium-containing molecules are mass spectrometry, X-ray crystallography, Se NMR spectroscopy, X-ray absorption spectroscopy (XAS), and several others [118,214].  
However, the structurally undefined Se-polysaccharides are a relevant research subject, as the introduction of selenium into their structure often has significant effects on the biological activity.
## 3 Overall Algorithm Design
### Visual Recognition Algorithm
To maintain detection accuracy, gradient change information is fully passed to the feature map, thereby optimizing the network's feature fusion capability. The loss function \(L\) comprises regression loss and classification loss. The loss function \(L\) comprises two components: the localization loss \(L_{loc}\) and classification loss \(L_{cla}\). The composite objective function is defined as  
\[L\ =\ L_{loc}\ +\ L_{cla} \tag{15}\]  
The localization loss \(L_{loc}\) employs the Complete Intersection over Union (CIoU) loss function [37], defined as  
\[\mathcal{L}_{CIoU}=1-CloU=1-\left(loU-\frac{D_{2}^{2}}{D_{1}^{2}}-\frac{\xi^{ 2}}{1-loU-\zeta}\right) \tag{16}\]  
Figure 11: Ghost-BottleNeck. Figure 10: Ghost module. where  
* Intersection over Union (IoU) quantifies the overlap ratio between predicted and ground-truth bounding boxes;
* \(D1\): Diagonal distance of the minimum enclosing rectangle of the two boxes;
* \(D2\): Euclidean distance between their centroids;
* \(\zeta\): Aspect ratio consistency parameter calculated by the following. \[\zeta=\frac{4}{\pi^{2}}\left(\tan^{-1}\frac{w^{st}}{h^{gt}}\ -\ \tan^{-1}\frac{w^{p}}{h^{p}} \right)^{2} \tag{17}\]  
Compared to the traditional IoU loss, the CIoU formula addresses non-overlapping cases and the aspect ratio of bounding boxes, thereby improving regression accuracy and convergence speed. When combined with non-maximum suppression (NMS), the CIoU-based method outperforms traditional NMS methods in both repeated detection suppression and detection accuracy.
## 2 Materials and Methods
### Testing N/S-Carbon QDs in the Preservation of Fresh Strawberries  
For testing the possible application of obtained N/S-Carbon QDs as antioxidant/antibacterial agent in food preservation, a solution (0.1 wt. %) of N/S-Carbon QDs was used as a coating for fresh strawberries. Fresh strawberries are known as highly perishable foods with short shelf-life. Their high water content makes them very susceptible to mold growth. So, six fresh strawberries with similar sizes, purchased from the local fruit market, were washed with water, dried gently, and separated into two subgroups of three strawberries each. The first group of three strawberries was uncoated while the other group with three strawberries was coated with the 0.1 wt. % aqueous solution of N/S-Carbon QDs. Both groups of strawberries were left in open air at 20 \({}^{\circ}\)C and 50% RH for two days and weighed periodically.
## 2 Materials and Methods
### CLQ Evaluation Method
#### 2.2.3 Calculation of the CLQ index  
The CLQ index was used to evaluate CLQ and mainly consists of three steps: (1) indicator selection and weight determination; (2) grading and scoring indicators; (3) calculation of the weighted sum of different indicators [10]. The impact of different evaluation indicators on the CLQ should be different; that is, their weight values are different [32]. Therefore, it is critical to set appropriate weight values for the evaluation indicators. In this study, we consulted ten experts from Guangdong Academy of Agricultural Sciences and South China Agricultural University in the form of questionnaires. They ranked the importance of the selected indicators. We combined the expert survey results with an analytic hierarchy process (AHP) to calculate the weight coefficients. In addition, the weight results passed the consistency test (consistency ratio value less than 0.1) [56]. We divided each evaluation indicator into three grades: high, medium, and low (Table 1). The grading of slope refers to China's national standard: the regulation for gradation of agricultural land quality [25]. The grading of the topsoil texture refers to the classification standard of the Harmonized World Soil Database ([[http://www.fao.org/](http://www.fao.org/)]([http://www.fao.org/](http://www.fao.org/)) soils-portal/, accessed on 10 October 2020). The grading of road accessibility refers to a previous study on CLQ evaluation [57]. The grading of the remaining five indicators is based on Jenks classification, which can reduce the intra-class variance and maximize the inter-class variance [58]. We used the scoring method to score the high, medium, and low grades as 3, 2, and 1, respectively. Finally, we calculated the CLQ index as follows:  
\[CLQI=\sum_{i=1}^{n}W_{i}\times Y_{i} \tag{1}\]  
where \(CLQI\) is the \(CLQ\) index; \(W_{i}\) is the weight of the evaluation indicator; \(Y_{i}\) is the score of the evaluation indicator; \(n\) is the number of evaluation indicators (\(n=8\) in this study).