esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Coarse Classification  
Initially, images are coarsely classified into three classes based on the weight information where the weight values are grouped into their natural distribution. The Jenks Natural Breaks (JNB) classification [54] technique is used to estimate the inherent natural distribution in the weight  
Figure 3: Example images obtained from the experiments with ArduCAM (**upper row**) and Huawei P9 Lite (**bottom row**).  
information of the fruit and vegetable. The Accumulated Squared Deviation from the Mean (ASDM) of each class is reduced, and hence, the Accumulated Squared Deviation (ASD) among means of different classes is increased. A set of individual weights of each fruit or vegetable in a class \(i\) is represented as \(w_{i}\), where the cardinality of \(w_{i}\) is considered as \(l_{i}\). An integrated ordered vector of all weight sets of fruit and vegetable classes is denoted as:  
\[W=\{w_{1},w_{2},w_{3},\ldots,w_{n}\}=\{\varphi_{1},\varphi_{2},\varphi_{3}, \ldots,\varphi_{m}\}, \tag{1}\]  
where \(n\) is the maximum number of classes and \(m=\sum_{k=1}^{n}l_{k}\). To estimate the ASDM, a set of mean weights w.r.t. each class in \(W\) is represented as:  
\[M=\{\mu_{w1},\mu_{w2},\mu_{w3},\ldots,\mu_{wn}\}. \tag{2}\]  
The accumulated deviation of individual weight value in \(w_{i}\) from mean \(\mu_{wi}\) of a class \(i\) is estimated as:  
\[\sigma_{i}^{ASDM}=\sum_{k=1}^{l_{i}}w_{i}(k)-\mu_{wi}\,, \tag{3}\]  
where \(\mu_{wi}=\frac{\sum_{k=1}^{l_{i}}w_{i}(k)}{l_{i}}\). The ASD among means of different classes is estimated based on \(W\) for all possible combination range distribution that can be described as:  
\[\sigma_{i}^{ASD}=\sum_{j=1}^{n}\sum_{k=1}^{m}\varphi_{k}-\mu_{i}, \tag{4}\]  
where the minimum value of \(\sigma_{i}^{ASD}\) represents the increased inter class deviation and hence the optimal distribution. A Goodness of Variance Fit (GVF) metric is maximised to estimate the effectiveness of the distribution. The GVF considered as a normalised difference of accumulated squared variance between class means and the weights of individual fruit and vegetables is described as:  
\[GVF_{i}=\frac{(\sigma_{i}^{ASDM}-\sigma_{i}^{ASD})}{\sigma_{i}^{ASDM}}. \tag{5}\]  
This is an iterative process where greater values of GVF indicates more effective distribution. This weight based coarse distribution groups the different varieties of a fruit or vegetable. This grouping helps in learning more effective features for the classification of the same species of a fruit or vegetable in the fine classification phase.  
Figure 4: A stack based sequential description of the proposed approach: (**a**) A progressive coarse to fine classification based approach, and (**b**) AdaBoost CNN optimisation for fine classification of fruit and vegetables.
## 2 Use Case (UC) Description, IOP Methodology and Concept Applied  
### Methodology  
The methodology [1] indicates that one of the first steps is to define the use case that will be examined, so as to move on with determining the link of interaction between the Equipment Under Test (EUT) and the system under test. Each link of interaction among the devices is characterized by a Basic Application Profile (BAP), which specifies the standard/option of a standard that is used in that particular link of interaction. Afterwards, the Basic Application Interoperability Profile (BAIOP) is specified, which shows the test steps for interoperability testing. Figure 1 summarizes the methodology, as is indicated in [1]. After the BAIOP creation, the design of experiments, presented in the following section, determines how to alter the parameters to evaluate interoperability. Altering important parameters can compromise the system interoperability. The statistical analysis that follows can give important information on how the parameters affect the overall system. The methodology can be applied in all use cases that entail this specific interaction.  
The Use Case described in this work can be considered as a sub-category of the use case described in [18], where the integration of a new smart meter is examined under the concept of a Demand Side Management DSM exercise. Whereas the use case described here is simpler and it only involves a small number of actors, the main difference is that here focus is given on the design of experiments and the effect of basic parameters in the overall system performance. In addition, an example of experimental results is presented, which follows the use case description and the design of experiments, thus, giving a concrete example of the theoretical part and proving the effectiveness of the whole methodology.
# The Possibility of Using Bee Drone Brood to Design Novel Dietary Supplements for Apitherapy
## 3 Results and Discussion
### Eggshell Quality Analysis  
The quality of eggshells as a potential natural source of bioavailable calcium was first evaluated regarding its mineral content and microbial contamination. Table 3 summarizes the results of the mineral profile of powder from hen eggshells obtained from various hen rearing systems. The main bioelement present in eggshell powder was found to be calcium (31.9 g/100 g on average) in the form of calcium carbonate, which constitutes 96% of the mineral composition. This element was accompanied by significant amounts of magnesium (average 483.1 mg/100 g), sulfur (average 446.99 mg/100 g), sodium (96.12 mg/100 g), and potassium (average 52.45 mg/100 g). Additionally, eggshells have been shown to contain trace amounts of the essential elements, chromium and copper (values in the range of 0.04-0.3 mg/100 g), and only traces of cadmium and arsenic were found, as well as lead and nickel. Furthermore, the effect of the hen rearing system was found negligible (\(p\) < 0.05). The results presented are comparable to those reported by [PERSON] et al. [20] who showed that eggshells are essentially composed of calcium (382 mg/g), sodium (5.1 mg/g), phosphorus (4.4 mg/g), and potassium (1.4 mg/g). Furthermore, [PERSON] et al. [24] showed a similar level of calcium in eggshells, in the range of 360 to 400 mg/g of eggshell.  
Although microbiological contamination was detected for 80% of the fresh shells tested, numerous (uncountable) colonies were found for 15% of the samples only (Figure S1). Surprisingly, the caged eggs were more contaminated than the organic ones. Rod-type bacteria and streptococci were identified mainly in the microscopic image (Figure S2). The microbiological analysis of the heat-treated powders did not show any grown colonies in both caged and farm egg powders.
## 4 Simulation Results
For \(\alpha=1.57\) and \(\Phi=0.018\), the WMR touched down the zero level of potential energy within almost 3.7 s. This implies that those tuning parameters are very effective at manipulating the convergence rate resulting in possibly shortening the entire navigation time of the WMR. As shown in Figures 7 and 8, to explore the effectiveness of proposed control law in depth, various initial conditions were provided to the WMR. Specifically, Figure 7 includes the performance of WMR navigation for 16 different initial positions along with \(45^{\circ}\) orientation angle while Figure 8 includes the performance of the global convergences based on two different initial positions and eight different initial orientations. It is clearly seen from all simulation results that the WMR successfully passed through the forbidden sets (i.e., local minima) and pushed itself to the desired set of posture. Figure 4: Performance of robust switching control for initial condition (\(-1\),\(0\)) with 45\({}^{\circ}\). (**a**) Trajectory of WMR on (_x,y_)-plane; (**b**) orientation of WMR \(\theta(t)\); (**c**) (_x,y_) navigation potential energy; and (**d**) forward velocity of WMR \(v(t)\) (first SW in (**a**) indicates the position where the first switching occurs). Figure 3: Performance of robust switching control for initial condition (2,1) with 45\({}^{\circ}\). (**a**) Trajectory of WMR on (_x,y_)-plane; (**b**) orientation of WMR \(\theta(t)\); (**c**) (_x,y_) navigation potential energy; and (**d**) forward velocity of WMR \(v(t)\) (1 st SW (Switching) in (**a**) indicates the position where the first switching occurs). Figure 5: Model uncertainty estimation. (**a**) Case for Figure 3 and (**b**) case for Figure 4.  
Figure 6: Performance of switching control based on the tuning parameters (initial condition (2,1) with 45\({}^{\circ}\)). Figure 8: Trajectories of WMR for different initial orientations. (**a**) and (**c**) Initial orientations –135, –90, –45, and 0\({}^{\circ}\); (**b**) and (**d**) initial orientations 45, 90, 135, and 180\({}^{\circ}\). Figure 7: Trajectories of WMR for different initial positions. (**a**) Initial positions (2,0), (0,2), (–2,0), and (0,–2); (**b**) initial positions (2,2),(–2,2),(–2,–2), and (2,–2); (**c**) initial positions (1,2),(–1,2),(–1,–2); and (1,–2), and (**d**) initial positions (2,1),(–2,1),(–2,–1), and (**e**,–1).
# Zinc Oxide-Based Rotational-Linear Triboelectric Nanogenerator
## 2 Materials and Methods  
### Design and Fabrication of the Rotational-Linear Tribogenerators (RL-TENG)  
For the design of the RL-TENG, Autodesk Fusion360 was used, while the fabrication of the device was performed using a 3D printer (Creality Ender 7). The main printing parameters are as follows:  
1. Material: PETG (polyethylene terephthalate glycol);
2. Layer height: 0.2 mm;
3. Perimeters: 4;
4. Infill: 40% (triangles).  
The design of the RL-TENG is shown in Figure 1a. It consists of (a) 3 pairs of contact-separation type triboelectric generators, i.e., T1, T2, T3 (Figure 1b), (b) the circular base upon which the TENGs are mounted and (c) a circular cam that is attached to the axis of rotation and is perpendicular to the base plane.  
In Figure 1c, we can see a part of a triboelectric pair. The triboelectric material was placed on the surface of the component using double-sided aluminum tape. The area of the triboelectric surfaces that contacted each other is 1 \(\times\) 1.67 cm\({}^{2}\). Figure 2 is a photograph of the RL-TENG. We can identify the three triboelectric pairs, i.e., T1, T2 and T3, as well as their wiring. In the central area of the tribogenerator, the cam is placed (not shown in the figure). Two different cams were used to control whether the triboelectric pairs worked in a synchronous or asynchronous way. The cam is mounted to the rotating axis (vertical to the plane, not shown in figure) to provide the rotational motion.  
For the triboelectric layers, Kapton(r) (DuPontTM, Kingston, ON, Canada) and Kapton(r) with a ZnO thin film deposited by sol-gel on the surface were used. The thickness of both Kapton(r) layers was 75 \(\upmu\)m. Briefly, a sol-gel solution was prepared by dissolving the appropriate amount of Zinc Acetate Dihydrate (C\({}_{4}\)H\({}_{10}\)O\({}_{6}\)Zn, Merck, Germany, and urea (CO(NH\({}_{2}\))\({}_{2}\), The Science Company, Lakewood, CO, USA) in ethanol (C\({}_{2}\)H\({}_{5}\)OH, Carlo Erba Reagents, Emmendingen, Germany) using magnetic stirring, at 60 \({}^{\circ}\)C for 30 min, in order to end up with a 40 mM solution containing 5w/w% of urea. The sol-gel was left to cool to room temperature prior to use for 24 h. ZnO films were prepared by ten successive spin coatings on Kapton films. After each deposition, the film was annealed at 300 \({}^{\circ}\)C in air. A detailed description of the ZnO thin-film fabrication and deposition process as well as its performance as a triboelectric layer can be found in [18].  
Figure 1: **(a)** Schematic of the energy-harvesting device, **(b)** a magnified view of a triboelectric pair showing the movable and stationary triboelectric surfaces and **(c)** a part of a triboelectric pair.  
Figure 2: Photograph of the RL-TENG. We can identify the three triboelectric pairs (T1, T2 and T3).
## 2 Materials and Methods
### Research Data and Pre-Processing
#### 2.2.6 Sample Point Data  
Historical grassland fire data were primarily sourced from records maintained by forestry and emergency management departments at multiple government levels, resulting in a dataset of 150 fire incidents. Due to regional variations in grassland fire reporting practices, the data collected from Huangnan Prefecture are the most complete, while records from other states and counties are less comprehensive. Nevertheless, these data limitations did not significantly affect the analyses conducted in this study.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 4 Detection of Various Damage Types Using ABT-ABT on a 3-mm Quasi-Isotropic Composite Plate
### Multimode Guided Wave Detection
#### 4.3.1 Damage Detection Using Pure S0 Mode  
A signal comparison between the pristine area, the delamination by the Teflon insert, and the impact damage in the 3-mm quasi-isotropic composite plate is shown in Figure 23.  
Figure 23a shows the strong single wave packets for S0 mode in all cases. A high signal-to-noise ratio was obtained. It was found that no obvious amplitude change was observed for the case of the Teflon insert, whereas a large amplitude drop could be clearly observed due to the presence of impact damage. The frequency spectrums were calculated using time windowing on the acquired signals to obtain the main wave packet and remove the trailing waves. In addition to the amplitude drop, a frequency shift was clearly observed for the case of impact damage in the frequency spectrum, as shown in Figure 23b. Impact damage, in the form of matrix cracking, fiber breakage, and interlaminar delamination, significantly reduced local stiffness. Therefore, there was a local defect resonance due to the impact damage. The interaction between the incident wave and the standing wave due to the local defect resonance may have caused a frequency shift, as observed in Figure 23b. Therefore, impact damage was successfully detected using pure S0 mode through ABT-ABT. However, S0 mode was not sensitive to the delamination simulated by the Teflon insert. This was due to the motion characteristics of the S0 mode's shape.  
Figure 22: Experimental setup for damage detection in the 3-mm quasi-isotropic CFRP composite plate: (**a**) delamination simulated by the Teflon insert; (**b**) impact damage.  
Figure 23: Signal comparison between the pristine area, the Teflon insert, and the impact damage using pure S0 mode at 300 kHz on a 3-mm quasi-isotropic composite plate: (**a**) time-domain signals; (**b**) frequency spectrums.
# A Polishing Processes Optimization Method for Ring-Pendulum Double-Sided Polisher
## 1 Introduction
However, the uncertainty disturbances during the machining process can affect the polishing efficiency and the precision of the processed optical elements. Therefore, the disturbance rejection control of the RDP's radial-feed system requires further investigation. Various control methods, such as proportional-integral-derivative (PID) control [19; 20; 21], sliding mode control [22; 23; 24], and model predictive control (MPC) [25; 26; 27; 28], are commonly used in practical systems. PID control is known for its simplicity and ease of implementation, effectively suppressing small disturbances. However, its performance may be compromised when facing complex uncertainty perturbations. Sliding mode control excels at attenuating uncertainties but may introduce significant control oscillations, posing challenges in applications. MPC demonstrates excellent disturbance rejection capabilities by optimizing control actions based on a predictive model to minimize future errors. Despite its strengths in handling disturbances, MPC has limitations when dealing with substantial interferences that exceed the predictive model's capabilities. In comparison, robust model predictive control (RMPC) offers superior disturbance rejection capabilities [29; 30; 31; 32; 33; 34]. By considering system uncertainties and employing robust optimization techniques, RMPC enhances its ability to withstand disturbances. However, it is important to note that even RMPC may have limitations when confronted with significant interferences, necessitating additional considerations during implementation. This paper proposes an optimization method for the polishing process. The proposed method focuses on improving the radial-feed speed accuracy by establishing a single-tube RMPC system for the radial-feed system. By effectively mitigating disturbances during processing, this control system significantly enhances the performance of the radial-feedspeed control in the RDP, resulting in improved polishing efficiency and accuracy for the RDP. The subsequent sections of this paper are structured as follows. This paper is organized as follows. In Section 2, we establish the model for the radial-feed system of the RDP. In Section 3, we conduct the control structure of the RDP radial-feed system, highlighting the design of the single-tube robust model predictive control system. In Section 4, we present simulations and experiments that validate the effectiveness of our proposed method. Section 5 concludes the whole paper.
# Study on Vibration Reduction Performance of Gear Pairs Made by a High-Strength Fe-Mn Damping Alloy
## 5 The User Subprogram of LS-Dyna Software for the Constitutive Model of the Damping Alloy Material
### Calculation Process  
Based on the constitutive equation of a new type of Fe-Mn damping alloy material, we wrote a UMAT subprogram of the non-linear constitutive model of the damping alloy material with FORTRAN language and applied the nonlinear constitutive model of damping alloy materials to the simulation of the finite element software LS-Dyna. The flow chart is shown in Figure 11. The real lines represent the parameters imported into the subroutine by the main program and the data passed in the subroutine. In contrast, the dashed lines represent the data that needed to be returned by the subroutine to LS-Dyna. The numerical calculation steps of the constitutive equation of damping alloy materials were as follows:  
1. We first imported the material attributes \(\rho\), \(\
u\), \(E_{0}\), \(\alpha\), \(\beta\), \(C_{1}\), and \(\theta_{1}\) from the LS-Dyna main program, with the variables at the beginning of the time step being the deformation gradient tensor \(F_{ij}^{t}\)\(F_{ij}^{t+\Delta t}\), time increment \(dt\), and an array of historical variables;
2. We then loaded the strain tensor \(E_{ij}^{t}\), the stress tensor \(S_{ij}{}^{t}\) and the viscous response stress \(K_{ij}^{t}\) of the previous step from the user-defined array of historical variables;
3. We calculated the strain tensor \(E_{ij}^{t+\Delta t}\) and the increment of the strain tensor \(\Delta E_{ij}^{t}\) of the current step length;
4. We calculated the nonlinear elastic stress increment and viscoelastic stress increment \(\Delta K_{ij}\);
5. We calculated the increment of the stress tensor \(\Delta S_{ij}{}^{t}\);
6. We calculated the stress tensor \(S_{ij}{}^{t+\Delta t}\);
7. We calculated and returned the Cauchy stress tensor \(\sigma_{ij}^{t+\Delta t}\) at the end of the incremental step;
8. We then updated the state variables \(E_{ij}^{t+\Delta t}\), \(K_{ij}^{t+\Delta t}\), \(S_{ij}{}^{t+\Delta t}\), etc.
# State-of-the-Art Model for Music Object Recognition with Deep Learning
## 2 Related Work
### Object Detection-Based Approaches  
In recent years, with the development of computer vision technology, the convolutional neural network (CNN) based on deep learning [6] technology has been increasingly applied to OCR and has achieved good results. During this period, a large number of object detection algorithms appeared, which were roughly divided into two categories: a one-stage detection algorithm and a two-stage detection algorithm.Typical one-stage detection models, such as YOLO [7; 8; 9], SSD [10] and retina-net [11], do not require a regional proposal stage and directly generate the category probability and coordinate position values of objects. The final detection result can be obtained directly after a single detection, so it has a faster detection speed. On the contrary, two-stage detection algorithms, such as Fast R-CNN [12], Faster R-CNN [13], and R-FCN [14], have higher detection accuracy levels but slower speeds. Naturally, the exploration of CNN application in the OMR system is also in full swing.  
[PERSON] et al. [15] proposed a baseline for general music object detection, which uses three different object detection models (Faster R-CNN, u-net [16], RetinaNet) for three different datasets (deepscores [17] as well as using MUSCIMA++ [18], capital) to detect notes. The test results of each model were given in terms of average precison.  
[PERSON] jr. et al. [19] identified the notes in two stages. In the first stage, the input score image was segmented into a binary image using the semantic segmentation model, and the overall detection problem was decomposed into a set of binary pixel classification problems, and then the connected component detector was used to derive the final detection proposal. The experiment was aimed at the MUSCIMA++ dataset, showing the detection results of symbols in terms of f-scores.  
[PERSON] et al. [20] proposed the Deep Watershed Detector. They use ResNets [21] to predict dense energy maps that were used to predict the location, class, and bounding box of each symbol and could directly input the entire image without cropping each staff. They gave the final results for DeepScores and the MUSCIMA++ dataset. Their method was found to work well on small symbols, but there were problems such as inaccurate bounding boxes and undetectable rare classes.