esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact Testing of 3D Re-Entrant Honeycomb Polyamide Structure Using Split Hopkinson Pressure Bar
## 3 Materials and Methods  
### Specim and Material  
The 3D RH polyamide specimens still used the same structural form as the quasi-static test. The unit cell is shown in Figure 1, and the sample photographs are shown in Figure 2. The specimens were made of polyamide (PA2200) material which was supplied by the supplier EOS, Germany, and were fabricated by using additive manufacturing. The material properties of polyamide are listed in Table 1.  
A total of 30 polyamide specimens with five configurations (i.e., C1-C5) were designed, with 6 of the same specimens per configuration. The parameters are given in Table 2, and the design variables are indicated in bold. It is worth noting that, due to the size limitation of the SHPB test equipment, the unit size of the specimen in the SHPB test was set to one third of that of the quasi-static test specimen, that is, to reduce the unit size in the same proportion in three directions. Each specimen had the array of \(N_{\mathrm{X}}\times N_{\mathrm{y}}\times N_{\mathrm{z}}\) unit cells. Four main parameters, displayed in grey, were taken into consideration, i.e., the length of the vertical strut (\(H_{0}\)), the horizontal length of the oblique strut (\(L_{0}\)), the re-entrant angle between the vertical strut and the oblique strut (\(\theta\)) and the thickness of the oblique strut cross section (\(t_{2}\)), as shown in Figure 1a. C1 and C2 were designed to investigate the effect of the re-entrant angle \(\theta\). C1, C4 and C5 were designed to investigate the effect of \(L_{0}\). C3 was designed to investigate the effects of both \(t_{2}\) and \(\theta\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Material** & **Young’s Modulus E (MPa)** & **Tensile Strength \(\sigma_{\mathrm{B}}\) (MPa)** & **Elongation \(\varepsilon_{\mathrm{B}}\) (\%)** & **Density (g/cm\({}^{3}\))** \\ \hline PA 2200 & 1500–1650 & 48 & 18 & 0.93 \\ \hline \end{tabular}
\end{table}
Table 1: Material properties of polyamide [27].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Specimen** & \(t_{1}\) (mm) & \(t_{2}\) (mm) & \(\theta\) (\({}^{\circ}\)) & \(L_{0}\) (mm) & \(H_{0}\) (mm) & \(L_{x}\) (mm) & \(L_{z}\) (mm) & \(N_{x}\), \(N_{y}\) & \(N_{z}\) \\ \hline C1 & 1.2 & 1.0 & **70** & 2.6 & 5.4 & 6.4 & 7.22 & 7 & 3 \\ \hline C2 & 1.2 & 1.0 & **60** & 2.6 & 5.4 & 6.4 & 6.18 & 7 & 3 \\ \hline C3 & 1.2 & **0.5** & **50** & 2.6 & 6.0 & 6.4 & 7.34 & 7 & 3 \\ \hline C4 & 1.2 & 1.0 & 70 & **3.1** & 5.4 & 7.4 & 6.85 & 6 & 3 \\ \hline C5 & 1.2 & 1.0 & 70 & **3.6** & 5.4 & 8.4 & 6.49 & 5 & 3 \\ \hline \end{tabular} Note: Please refer to Figure 1 for the notations.  
\end{table}
Table 2: Design parameters for each specimen (C1–C5).  
Figure 2: Photograph of sample specimen (C4).
# Design and Experiment of Grain Litter for Sorghum Harvester
## 3 Research on Structure and Parameters of Grain Lifter
### Determine the Length of the Grain Lifter
#### 3.2.1 Determine the Front Length of the Grain Lifter  
The front part of the grain lifter is used to straighten the fallen sorghum so as to reduce the leakage loss during the sorghum harvest. Obviously, the smaller the angle between the grain lifter and the horizontal direction, the more conducive to the righting of the lodging sorghum. However, this may lead to the increase in the length of this section of grain lifter, on the contrary, if the angle of the grain lifter is larger, although the length of the grain lifter can be reduced, it will increase the difficulty of righting the lodging sorghum plants, and even lead to the accumulation and break of the lodging sorghum plants in severe cases. Considering the righting process of lodging sorghum, the stalk has been sliding backward on the surface of the grass lifter with the push of the spiral blade. Therefore, in order to ensure the stable post-sliding of the stalk, the angle between the upper surface of the straw holder and the horizontal plane (equal to the sum of the angle between the axis of the straw holder and the horizontal plane and the half of the cone angle of the straw holder), that is, \(\theta+\beta\) is less than the friction angle \(\varphi\) between sorghum stem and centralizer, that is:  
\[\theta+\beta\leq\varphi \tag{3}\]  
Considering that although sorghum plants are relatively tall, their stem structure is a columnar structure with hard skin outside the stem pith, so in the righting process, only half of the height of the stem should be held. It can ensure the stem upright state requirements, therefore, when designing the grass aid, the position height of the row head should only exceed \(1/2\) height of the stalk. If the front length of the centralizer is \(l_{1}\) and the height of the sorghum plant is \(h\), then:  
\[\frac{l_{1}}{\cos\beta}\sin(\theta+\beta)\geq\frac{1}{2}h \tag{4}\]  
Namely:  
\[l_{1}\geq\frac{h\cos\beta}{2\sin(\theta+\beta)} \tag{5}\]  
After substituting the relevant measured data, there is \(l_{1}\geq 146.63\) cm.
## 3 Reliability Evaluation Method
### Reliability Evaluation Algorithm  
1. Work Time Calculation  
Assuming the average life cycle of the CNC grinding machine tool is \(T_{L}\) years, the average annual maintenance time of the life cycle is \(T_{M}\), the annual average normal downtime is \(T_{S}\), and then the normal working time in the life cycle of the CNC grinding machine \(T_{W}\) is as shown in Equation (34).  
\[T_{W}=(365\times 24-T_{M}-T_{S})\times T_{L} \tag{34}\]  
2. Quantity of Random Data Calculation  
The quantity of generated random data of time between failures of each subsystem of each CNC grinder can be calculated by Equation (35). In Equation (35), \(i\) is the number of the subsystem (\(i=1\), \(2\),..., \(n\)), \(n\) is the amount of the subsystem, and \(MTBF_{i}\) is the MTBF of the \(i\)th subsystem.  
\[k_{i}=\frac{T_{W}}{MTBF_{i}} \tag{35}\]  
3. Dimension of Random Data Calculation  
To enhance the accuracy, \(m\) CNC grinders are selected for simulation. The dimension of random data \(d_{i}\) generated by the \(i\)th subsystem can be calculated by Equation (36).  
\[d_{i}=m\times k_{i} \tag{36}\]  
4. Random Data Generation  
After calculating the distribution type and parameters of distribution function of each subsystem, the random data of time between failures of each subsystem can be generated according to Equations (29) to (33). The random data matrix \(T_{i}\) of the \(i\)th subsystem is shown in Equation (37).  
\[T_{i}=\left[\begin{array}{cccc}T_{00}^{i}&T_{01}^{i}&\cdots&T_{0k_{i}}^{i}\\ T_{10}^{i}&T_{11}^{i}&\cdots&T_{1k_{i}}^{i}\\ \vdots&\vdots&\ddots&\vdots\\ T_{m0}^{i}&T_{m1}^{i}&\cdots&T_{mk_{i}}^{i}\end{array}\right]_{(m\times k_{i})} \tag{37}\]  
5. Sum of Every Time Between Failures  
After all subsystems' random data are generated, the sum of every time between failures can be calculated by Equation (38).  
\[T_{sum}=\sum_{i=1}^{n}\sum_{u=1}^{m}\sum_{v=1}^{k_{i}}T_{uv}^{i} \tag{38}\]  
where \(k_{i}\) is the amount of random data of the \(i\)th subsystem.  
6. Sum of Fault Amount Calculation  
It is necessary to calculate the sum of fault amount of each subsystem and each CNC grinding machine in the life cycle, as shown in Equation (39).  
\[n_{sum}=m\sum\limits_{i=1}^{n}k_{i} \tag{39}\]  
7 System MTBF Calculation  
The system reliability of \(MTBF_{s}^{m}\) can be calculated by the Monte Carlo simulation method, as shown in Equation (40).  
\[MTBF_{s}^{m}=\frac{T_{sum}}{n_{sum}}=\frac{\sum\limits_{i=1}^{n}\sum\limits_{u= 1}^{m}\sum\limits_{v=1}^{k_{i}}T_{uv}^{i}}{m\sum\limits_{j=1}^{n}k_{j}} \tag{40}\]  
Taking into account the monotonicity of different distribution functions, the interval estimation of system reliability of the CNC grinding machine can be calculated by replacing the point estimation value with the upper and lower bound of interval estimate value to generate random data in step 4.
## 3 Results and Discussion
### Active and Driven Drum Dynamic Balance Effects  
Based on the active and driven drum dynamic balance test based on transfer characteristics, the layout and transmission path of the threshing drum on the combine harvester were simulated. The unbalanced amount of each drum at different speeds was measured under two different conditions of single drum and double drum. The experimental data was shown in Table 4.  
Figure 9: Threshing drum end face radial weight unbalance phase change zone.  
According to test results, it was found that the unbalanced phase of the drum produced different degrees of change after the new drum was connected in parallel. However, it was always within the wrap angle of the sprocket as shown in Figure 10.  
When a drum had two sprocket wheels with a transmission relationship at the same time, that was one drum had two transmission paths, the drum unbalanced phase appeared as a sprocket wrap angle range that was biased toward the dominant segment. As shown in Figure 10, the unbalanced amount of the driven drum had a complicated influence on the unbalanced amplitude of the active drum through the transmission characteristic of the chain drive. The active drum unbalance amplitude sometimes increased and sometimes decreased. The variation of the fluctuation was large, but in general, the unbalance of the driven drum had less influence on the unbalanced amplitude of the active drum.  
The driven drum was connected to the active drum through the chain drive, which can be equivalent to the load of the active drum. So that the transmission chain between the two drums generated tensile force and lateral vibration, which had obvious influence on the unbalanced phase of the active and driven drum. And the unbalanced phase of the drum was always within the wrap angle of the sprocket in the driven drum end surface radial weight balance test. Taking into account the above two points, when constructing the double threshing drums dynamic balance model, the influence of the chain drive on the unbalanced phase would be considered. The influence of the transmission characteristics on the unbalanced amplitude would not be considered.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Rotating Speed**} & \multicolumn{2}{c}{**Active Drum**} & \multicolumn{2}{c}{**Driven Drum**} \\ \cline{3-6}  & & **Amplitude** & **Phase** & **Amplitude** & **Phase** \\  & & **(mm/s)** & **(°)** & **(mm/s)** & **(°)** \\ \hline \multirow{4}{*}{Single drum} & 600 & 0.754 & 0 & - & - \\  & 700 & 0.712 & 10 & - & - \\  & 800 & 1.285 & 33 & - & - \\  & 900 & 1.635 & 42 & - & - \\  & 1000 & 1.865 & 48 & - & - \\  & 600 & 0.608 & 8 & 0.544 & 130 \\  & 700 & 0.783 & 20 & 0.644 & 131 \\  & 800 & 1.213 & 17 & 0.754 & 136 \\  & 900 & 2.476 & 14 & 1.926 & 138 \\  & 1000 & 2.865 & 28 & 2.19 & 144 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Unbalance of each active and driven drum at different speeds.  
Figure 10: Threshing drum unbalanced phase change zone.
## 1 Introduction
This is why we limit our considerations to transform-limited pulses, and describe the effects of OAM-temporal coupling in the ring of the ultrafast vortex where the pulse peak intensity is maximum, which generally does not coincide with the ring of maximum pulse energy, as demonstrated below. It turns out that the effects of OAM on the transform-limited pulse with maximum peak intensity are directly related to the properties of its symmetric temporal lobe around the pulse peak, which is, on the other hand, the only portion of the pulse of interest in nonlinear optics applications. Specifically, the involved magnitudes are the instantaneous frequency (derivative of the phase of the pulse with respect to time) at the instant of pulse peak, or \"central\" frequency, \(\omega_{c}\), and the duration of the central lobe around the peak of the pulse, defined by the concavity of the lobe, and which we call \"central\" duration, \(\Delta t_{c}\). As seen below, these two magnitudes are trivially determined by means of a measurement of the pulse frequency spectrum. We then find qualitatively similar OAM-coupling effects in the most intense ring to those occurring in the most energetic ring. The central frequency of the pulse in the most intense ring is the same as the central frequency of the ultrafast laser source, and therefore independent of the imprinted topological charge. The duration of the central lobe is larger than that obtainable from the laser source without OAM, and increases with the magnitude of the topological charge. This effect is a result of the existence of a lower bound, \(\sqrt{|l|}/\bar{\omega}_{c}\), to the duration of the central temporal lobe that is satisfied by all existing ultrafast vortices in their most intense ring. If, as in many situations, the ring of maximum pulse energy coincides with the ring of maximum pulse peak intensity, then the pulse temporal shape in this ring is always such that the two inequalities, \(\Delta t>\sqrt{|l|}/\bar{\omega}\) for the pulse duration as a whole, and \(\Delta t_{c}>\sqrt{|l|}/\bar{\omega}_{c}\) for the duration of the central temporal lobe, are satisfied.
## 3 Applications of Cold Atmospheric Pressure Plasma
### Medicine
ROSs contribute to oxidative stress and can cause cell death [89]. However, it is important to tailor the plasma process parameters to produce a controllable amount of ROS to obtain the desired biological responses. The mechanism of CAPP action in the case of cancer includes the induction of apoptosis, DNA damage, cell cycle arrest at the S-phase and increase of the intracellular ROS concentrations [79]. CAPP can open new horizons in oncology because it seems to be a promising complementary therapy to conventional cancer treatments (chemotherapy, surgery, radiation therapy, immunotherapy and others). The anti-melanoma activity of a softjet CAPP device was studied by [PERSON] et al. Three different human melanoma cells (skin cancer) were successfully treated using air and N\({}_{2}\) gases. The results showed preferential killing of melanoma cells, apoptotic pathways by triggering apoptotic genes in all three melanoma cell lines. However, the exact mechanism for the inhibitory effect of CAPP has remained unknown [85]. [PERSON] et al. provided evidence of plasma-activated Ringer's saline (PAR) as a promising therapy option for treating Osteosarcoma (a type of malignant bone cancer). They used two types of plasma jet devices (single-electrode needle device operating in helium and pin-type electrode with a grounded outer electrode operating with argon) to obtain PAR. The cytotoxic effects of PAR in human and mouse osteosarcoma was investigated [88]. Based on the above-mentioned properties, it can be said that plasma medicine holds enormous application and development potential (Figure 3) [47]. CAPP seems to be ready to enter the healthcare area for various medical therapies. However, a lot remains to be done (conduction and evaluation of preclinical and clinical trials with standardized protocols), in order to fully understand the interactions between plasma and biological cells and tissues (both in vitro and in vivo) [26; 49].
# Policy-Compliant Maximum Network Flows
## 4 Policy-Compliant Paths and Algorithms
### Labeled Networks and Policy-Compliant Paths  
Given a certain flow network \(G=(V,E)\) and an FSA \(M\), we define a labeling function \(l:E\to\Sigma\) that attaches one symbol to every edge of the graph, which becomes a labeled flow network. Before, we were interested in all paths from source to sink, in order to maximize the total flow value of the network. Now, we constrain the allowed paths to only those that are accepted by the FSA \(M\). Formally, we define a policy-compliant path \(p\) from \(s\) to \(t\) in \(G\) to be a sequence of edges from \(E\), such that the symbols that are encountered, in order, constitute a word that is accepted by \(M\). Thus, in addition to (5)-(7) and (10), we add the following constraint:  
\[\delta^{*}(q_{0},l(\epsilon_{1})l(\epsilon_{2})\dots l(\epsilon_{n}))\in F \tag{14}\]  
Thus, we are looking for paths from source to sink that are accepted by the automaton. Now we can define the policy-compliant maximum flow problem (PCMF) as calculating the maximum flow \(|f|\)from \(s\) to \(t\) in a labelled flow network \(G_{f}\), subject to the constraint that all paths that realize the flow \(f\) are accepted by an FSA \(M\), i.e., they are policy-compliant paths.
# Efficient Construction of the Equation Automaton
## 2 Preliminaries
### Regular Expressions and Finite Automata
#### 2.1.2 Finite Automata and Recognizable Languages  
A nondeterministic finite automaton (NFA) is a quintuple \(\mathcal{A}=\langle Q,A,q_{0},\delta,F\rangle\) where \(Q\) is a finite set of states, \(A\) is the alphabet, \(q_{0}\in Q\) is the initial state, \(F\subseteq Q\) is the set of final states, and \(\delta:Q\times(A\cup\{\varepsilon\})\to 2^{\mathcal{Q}}\) is the transition function. The size of an automaton \(\mathcal{A}\), denoted by \(|\mathcal{A}|\), is the number of its states. The automaton \(\mathcal{A}\) is called _deterministic_ (DFA) if there is only one initial state, \(|\delta(q,\varepsilon)|=0\) and \(|\delta(q,a)|=1\), for any \(q\in Q\), for any \(a\in A\). A path in \(\mathcal{A}\) is a sequence \((q_{i},a_{i},q_{i+1})\), \(i=1,\cdots,n\), of consecutive transitions. Its label is the word \(w=a_{1}a_{2}\cdots a_{n}\). A word \(w=a_{1}a_{2}\cdots a_{n}\) is recognized by the automaton \(\mathcal{A}\) if there exists a path labeled \(w\) such that \(q_{1}=q_{0}\) and \(q_{n+1}\in F\).  
The language recognized by the automaton \(\mathcal{A}\), denoted by \(L(\mathcal{A})\), is the set of words it recognizes. The right language of a state \(q\) in the automaton \(\mathcal{A}\), denoted by \(\overset{\rightarrow}{L}_{q}(\mathcal{A})\), is obtained by setting \(q\) to be the initial state, i.e., \(\overset{\rightarrow}{L}_{q}(\mathcal{A})=\{w\in A^{*}\mid\delta(q,w)\cap F\
eq \emptyset\}\).  
We say that \(\mathcal{A}\) is acyclic if the underlying graph is acyclic. The language associated with an acyclic automaton is finite.  
Let \(\sim\) be an equivalence relation over \(Q\). For \(q\in Q\), \([q]\) denotes the equivalence class of \(q\) w.r.t. \(\sim\) and, for \(C\subseteq Q\), \(C/_{\sim}\) denotes the quotient set \(C/_{\sim}=\{[q]|q\in C\}\). We say that \(\sim\) is right invariant w.r.t. \(\mathcal{A}\) if and only if the following conditions hold:  
* \(\sim\subseteq(Q-F)^{2}\cup F^{2}\) (final and non-final states are not \(\sim\)-equivalent),
* for any \(p,q\in Q\), \(a\in A\), if \(p\sim q\), then \(\delta(p,a)/_{\sim}=\delta(q,a)/_{\sim}\).
# Incremental Dilations Using CNN for Brain Tumor Classification
## 2 Materials and Methods
### Dataset
After preparing the data and applying the mentioned augmentation techniques, the data is split into training and validation sets. Each model is trained using backpropagation on the training set and after each _Epoch_ (one iteration through the complete training set), the validation accuracy is calculated. Figure 3: Augmented images: Each of the 21 displayed images are generated from the same input image by introducing slight variations such as rotation, sheer, zoom, brightness, shift and horizontal flip with the aim of building a generalized and robust dataset. Figure 2: **(a) Original Image: Section of brain from a MRI scan (b) Finding largest contour: Detecting the overall shape of the skull structure. (c) Calculating extreme points of contour: Selecting the best points to fit the entire brain into the frame with minimum loss of data (d) Cropping based on extreme points: The brain structure from the MRI image is the main focus area of the new image. **  
Using the validation accuracy, checkpoints are created. These checkpoints store the best weights that can later be used for model inferencing or for further training. The step by step flow, is mentioned in Algorithm 2. ```
Input Image Dataset Output Trained Brain Tumor Classifier
1:Model Training
2:Randomly initialize neural network with random weights
3:\(Accuracy\gets 0\)
4:for\(epoch=1,2,\ldots,N_{epochs}\)do
5:for\(image=1,2,\ldots,N_{batch\_size}\)do
6:\(Image_{data}\gets Resize(Image)\)
7:\(Image_{data}\gets Augmentation(Image_{data})\)
8: Input layer \(u(t)\) takes \(Image_{data}\) and sends it to the hidden layers
9:Hidden layers
10: Large \(dilation\_rate\) Coarse features
11: Small \(dilation\_rate\) Finer features
12: Output layer \(w(t)\) returns diagnosed results
13: Calculate error rate \(e(t)\)
14: Update weights using \(back\_propagation\)
15: Using \(Adam\_Optimizer\) to minimize \(e(t)\)
16:endfor
17:Model Check-pointing
18: Using Validation set, Calculate \(Validation\_Accuracy\)
19:if\(Validation\_Accuracy\geq Accuracy\)then
20: Checkpoint model, Save Weights
21:\(Best\_Weights\gets Weights\)
22:endif
23:endfor
```  
**Algorithm 2** Dilated CNN Classifier Algorithm.
###### Abstract  
Performance and economic efficiency of the fuel cell (FC)/battery/super capacitor (SC) hybrid 100% low-floor tramcar is mainly determined by its energy management strategy. In this paper, a train traction model was built to calculate the power output and energy consumption properties of the hybrid tramcar. With the purpose of reducing hydrogen consumption, the genetic algorithm was adopted to optimize the original energy management strategy. The results before and after the optimization show that the power requirement of the tramcar can be satisfied in both situations with the fuel cell (FC) module non-stopped. The maximum output power of the FC is reduced from 170 kW to 101.21 kW. As for the SC, a two-parallel connection module is used instead of the three-parallel one, and the power range changes from \(-\)125-250 kW to \(-\)67-153 kW. Under the original energy management strategy, the battery cannot be used efficiently with less exporting and absorbent power. Its utilization ratio is improved greatly after optimization. In sum, the equivalent total hydrogen consumption is reduced from 3.3469 kg to 2.8354 kg, dropping by more than 15%.  
fuel cell; hybrid; tramcar; energy management strategy; genetic algorithm +
Footnote †: journal: Applied Sciences