esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis for Forced Vibration Excited by Piezoelectric Sheets
### Analysis for Frequency Domain Response of Touch Beams  
Using Equation (22) of the theoretical derivation results, the frequency-domain response of the cantilever touching beam is solved by using MATLAB and the related curves are drawn. The 3 rd-6 th order frequency domain response of the touch beam is shown in Figure 8. From left to right are the 3 rd, 4 th, 5 th, and 6 th order frequency domain responses.  
Available from Figure 8, the following can be noted.  
* The touch beam has a significantly increased amplitude of the forced response when the excitation frequency is close to its natural frequencies, and the magnitude of the forced response of the touch beam becomes small when it is far from the natural frequency of the touch beam itself. Therefore, the touch beam needs to operate at its resonance frequency to be effective. As the order of the natural frequency increases, the displacement response of forced vibration will gradually decrease, and the displacement response of the sixth order decreases to about one-sixth of that of the third order. However, the frequency of the sixth order is 24,221 Hz, which is beyond the sound range that humans can recognize and so will not make the subject uncomfortable due to noise, showing that this excitation frequency is more suitable as a working frequency.  
Meanwhile, the effect of changing electromechanical parameters is also needed to be investigated in the frequency domain, for observing the influence of parameters on the forced response when the frequency changes. For this, the 6 th-order forced response is the working frequency used as the research subject. Keeping the other parameters unchanged, and changing the four parameters of piezoelectric constant, excitation voltage, modal stiffness, and vibration mode damping ratio, provides the relevant curves from three different values as shown in Figure 9.  
From Figure 9, the following points are worth noting.  
* With the increase of excitation voltage and piezoelectric constant, the displacement of the forced response of the touch beam gradually increases. Within the interval of resonance, especially, the increase values of the forced response are more significant. However, with the increase of the elasticity modulus, the modal stiffness increases, and therefore the displacement of forced response gradually decreases. Within the intervals of resonance, the decrease values of the forced response caused by the elasticity modulus are more significant. In addition, increasing the modal damping ratio can reduce the displacement of forced response in the resonance intervals significantly. However, out of the resonance intervals, this kind of influence on forced response is not obvious.  
Figure 8: 3 rd–6 th order frequency domain response of the touch beam
## 2 Materials and Methods
### Theoretical Analysis  
#### 2.4.1 Fluid Mechanics  
The flue gas duct is a rigid container; thus, according to the law of energy conservation, the thermal equilibrium equation of the thermal fluid inside the tube can be expressed as Equation (1).  
\[q_{fg}=\overset{\bullet}{m}_{fg}\Big{(}h_{fg,in}-h_{fg,out}\Big{)} \tag{1}\]  
Considering that the flue gas is a mixture of many chemical compositions, the enthalpy depends on the chemical composition of fuels, the excess air ratio and gas temperature. The exergy calculation model developed by [PERSON] et al. was adopted to calculate the exergy of the flue gas [20]:  
\[h_{fg}-h_{0}=c_{p,fg}\Big{(}T_{fg}-T_{0}\Big{)} \tag{2}\]  
\[\overset{\bullet}{E}x_{fg}=c_{p,fg}\overset{\bullet}{m}_{fg}\Big{[}\Big{(}T_{ fg}-T_{0}\Big{)}-T_{0}\Big{(}\ln\frac{T_{fg}}{T_{0}}\Big{)}\Big{]} \tag{3}\]  
\[c_{p,fg}=\frac{c_{p,CO_{2}}}{a_{C}+b_{N}+c_{H}+d_{S}}\times\frac{m_{tot,steo}} {m_{fg}}+f_{A} \tag{4}\]  
\[c_{p,CO_{2}}=0.1874\times 1.000061^{7_{6}}\times T_{fg}^{0.2665} \tag{5}\]  
The mass flow rate of the flue gas (\(\overset{\bullet}{m}_{fg}\)) can be determined by Equation (6) [21]:  
\[\overset{\bullet}{m}_{fg}=\rho_{fg}\cdot v\cdot s_{\text{in}} \tag{6}\]  
Based on [PERSON]'s law of cooling [22], the average surface heat transfer coefficient \(h_{s}\) between the flue gas and the internal surface of the tube can be calculated by Equation (7):  
\[h_{s}=\frac{q_{fg}}{s_{in}\cdot\Delta t} \tag{7}\]  
where \(\Delta t\) is the logarithmic mean temperature difference between the flue gas flux and the internal surface of the tube, which can be calculated by Equation (7) [23]:  
\[\Delta t=\frac{\Big{(}t_{fg,in}-t_{fg,out}\Big{)}}{\ln\Big{[}\Big{(}t_{tube }-t_{fg,out}\Big{)}/\Big{(}t_{tube}-t_{fg,in}\Big{)}\Big{]}} \tag{8}\]  
In order to investigate the convective heat transfer intensity between the fluid and the tube, the Nusselt number between the thermal fluid (flue gas) and the internal surface of the tube (\(Nu_{in}\)) and the Nusselt number between the cold fluid (ambient air) and the outer surface of the tube (\(Nu_{out}\)) were adopted to investigate the heat transfer behavior. In thepresent work, the Zukauskas model was adopted to compute the \(Nu_{in}\) and \(Nu_{out}\), which are expressed as Equations (9) and (10), respectively [15]:  
\[Nu_{in}=0.023(\text{Re}_{in})^{0.8}\cdot(\text{Pr}_{in})^{0.3} \tag{9}\]  
\[Nu_{out}=0.35(\text{Re}_{out})^{0.6}\cdot(\text{Pr}_{out})^{0.36}\bigg{(}\frac {\text{Pr}_{out}}{\text{Pr}_{air}}\bigg{)}^{0.25} \tag{10}\]  
where the Reynolds number (Re\({}_{i}\)) can be calculated by Equation (11), while the Prandtl number (Pr) can be ascertained by using the Industrial Furrace Design Handbook [24].  
\[\text{Re}_{i}=v_{i}d_{i}/u_{i} \tag{11}\]
## 3 Results
### Composition of the Grain Protein Fractions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Genotype** & \multicolumn{3}{c}{**Alvand**} & \multicolumn{3}{c}{**Mihan**} & \multicolumn{3}{c}{**Impression**} \\ \hline Treatments & WW & MD & SD & WW & MD & SD & WW & MD & SD \\ \hline HMW & 27.3 & 27.2 & 28.7 & 33.3 & 35.2 & 35.2 & 41.3 b & 41.4 b & 48.0 a \\ LMW-D & 4.3 & 4.6 & 5.0 & - & - & - & 1.6 & 1.5 & 1.0 \\ LMW-B & 31.2 & 30.2 & 31.0 & 39.2 & 35.6 & 36.0 & 26.7 & 28.0 & 28.1 \\ LMW-C & 37.2 _ab_ & 37.9 \(a\) & 35.3 \(b\) & 27.5 & 29.2 & 28.8 & 30.6 \(a\) & 31.9 \(a\) & 22.8 \(b\) \\ \hline \(\Sigma\) & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline HMW/LMW & 0.37 & 0.37 & 0.40 & 0.50 & 0.54 & 0.54 & 0.70 b & 0.67 b & 0.92 a \\ \hline
**Genotype** & \multicolumn{3}{c}{**Discus**} & \multicolumn{3}{c}{**Rumor**} & \multicolumn{3}{c}{**Hypery**} \\ \hline Treatments & WW & MD & SD & WW & MD & SD & WW & MD & SD \\ \hline HMW & 27.3 b & 29.2 b & 32.6 a & 32.0 b & 32.4 b & 36.0 a & 31.2 b & 30.9 b & 41.2 a \\ LMW-D & 5.1 B & 5.3 B & 6.8 A & 6.4 B & 6.5 B & 10.6 A & 5.2 & 4.8 & 3.9 \\ LMW-B & 36.3 & 34.5 & 35.2 & 32.8 **ab** & 33.7 **a** & 30.8 **b** & 32.9 & 33.4 & 31.7 \\ LMW-C & 31.4 \(a\) & 31.0 \(a\) & 25.4 \(b\) & 28.5 \(a\) & 27.8 \(a\) & 22.7 \(b\) & 30.7 \(a\) & 30.8 \(a\) & 23.2 \(b\) \\ \hline \(\Sigma\) & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline HMW/LMW & 0.37 b & 0.41 b & 0.48 a & 0.47 b & 0.48 b & 0.56 a & 0.45 b & 0.45 b & 0.70 a \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relative share of the glutenin protein sub-fractions (% of total SDS-PAGE lane intensity for each treatment within a variety), depending on the treatments (well watered: WW, moderate drought stress: MD, severe drought stress: SD) of the Iranian (Alvand, Mihan) and German wheat genotypes (Impression, Discus, Rumor and Hybery). For each variety, different letters within the same row represent significant differences between treatments (small letters: within HMW; capital letters: within LMW-D; bold letters: within LMW-B; italic letters: within LMW-C); No letter means no significant differences in this group; \(p<0.05\). (\(n=4\)).
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
###### Abstract  
As the population continues to grow, the need for increased food production has become increasingly evident. However, the agricultural workforce is decreasing due to industrialization. To address this gap, numerous research efforts are underway to adopt robots for harvesting. While significant progress has been made, especially in fruit harvesting using robotic systems, there are still inefficiencies and areas where human intervention is necessary. This paper proposes an algorithm for selecting the position of a mobile manipulator for fruit harvesting using an inverse reachability map to achieve automation and enhance efficiency. Additionally, a method for collision-free path planning in joint space using the model predictive artificial potential field (MPAPF) algorithm is proposed. The effectiveness of the proposed position selection and path planning algorithms was validated through harvesting simulations and a collision-free path was validated in experiments.  
harvesting robot; mobile manipulator; inverse reachability map; collision-free path planning; artificial potential field +
Footnote †: journal: Applied sciences  
1
## 1 Introduction
This architecture comprises extended fully convolutional networks (FCN) and deeply supervised nets (DSN). The model learns and aggregates multi-scale and multi-level features from the low-level convolutional layers to the high-level convolutional layers during the training of the model. Direct supervision for features of each convolutional stage is provided by DSN. On one hand, the results of DeepCrack are very promising--the network is able to identify cracks in different scenes. However, some testing samples such as Figure 10 in [31] indicate that in some cases, this architecture has problems with misidentifying shadows as cracks. It is clear that precise image acquisition techniques are crucial for successful further processing of these images. Employment of different lighting and illumination conditions such as shown in [33] might increase the success of further identification of cracks in images. However, in many cases this kind of laboratory-based experimental setup is not feasible and fine-tuning of illumination conditions is not possible. Moreover, in some cases acquired images might contain shadows. The detection and identification of concrete cracks in the presence of shadows on the analyzed images remains a challenging task. Appearance of shadows results in various challenges in the detection of objects. Thus, multiple methods for shadow detection and removal were developed over the years. Early papers on shadow detection and removal have been presented in [34; 35]. A comprehensive survey of various shadow detection and removal methods published in literature could be found in [36]. More recently, artificial-neural-network-based deep-learning methods have also been deployed for the detection and removal of shadows [37; 38; 39]. Although several researchers have tried to solve the problem of shadow removal, it remains a complex topic with a moderate to good success rate [36]. The shadow removal process might have a severe impact on the quality of the analyzed image. That can make further object detection even more challenging than before the removal of the shadow. This paper discusses challenges related to the identification of cracks on concrete surfaces in presence of shadows. This paper is organized as follows: the introduction and a detailed literature review of the different concrete-crack detection methods and shadow detection and removal approaches is presented in Section 1. concrete-crack detection challenges associated with real-time images are discussed in Section 2. Difficulties related to the detection and removal of shadows from real-time images are outlined in Section 3. Finally, discussion and conclusion are presented in Section 4.
## 3 Results
### Anaerobic Digestion of Microcalga Chlorella Protothecoides
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Batch** & **CODin** & **COD** & **TSin** & **VSin** & **VS** & **pH** & **pH** \\
**Condition** & **(g/L)** & **Removal (\%)** & **(g/L)** & **(g/L)** & **Removal (\%)** & **Initial** & **Final** \\ \hline H & 32.7 \(\pm\) 0.0 & 23 & 11.6 \(\pm\) 0.1 & 10.2 \(\pm\) 0.1 & 39 & 6.81 & 6.72 \\ HExt & 21.3 \(\pm\) 0.0 & 25 & 11.6 \(\pm\) 0.3 & 10.1 \(\pm\) 0.4 & 52 & 7.09 & 6.85 \\ HPEnz & 29.1 \(\pm\) 0.0 & 14 & 10.0 \(\pm\) 0.0 & 8.60 \(\pm\) 0.1 & 53 & 6.85 & 6.95 \\ HPA & 27.0 \(\pm\) 0.0 & 53 & 12.1 \(\pm\) 0.1 & 10.5 \(\pm\) 0.1 & 35 & 6.98 & 6.80 \\ HPU & 26.3 \(\pm\) 0.0 & 25 & 11.7 \(\pm\) 0.1 & 10.2 \(\pm\) 0.1 & 40 & 6.88 & 6.72 \\ AA & 32.1 \(\pm\) 0.0 & 53 & 11.6 \(\pm\) 0.0 & 9.30 \(\pm\) 0.0 & 67 & 7.21 & 6.87 \\ I & 11.4 \(\pm\) 0.0 & 43 & 5.10 \(\pm\) 0.1 & 1.10 \(\pm\) 0.0 & 7 & 7.38 & 6.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mixtures’ chemical composition: COD, TS, VS, and pH. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mixture** & \multicolumn{3}{c}{**Methane Yield**} \\ \cline{2-4}  & **(L CH4/kg CODn)** & **(L CH4/kg CODr)** & **(L CH4/kg VSin)** \\ \hline H & 56 & 242 & 180 \\ HExt & 138 & 550 & 290 \\ HPEnz & 61 & 440 & 206 \\ HPA & 107 & 418 & 276 \\ HPU & 102 & 411 & 263 \\ AA & 81 & 153 & 279 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Methane yield per VS and COD at the beginning, and per COD removed, at the end of the anaerobic digestion of microalgae. gas production was carried out in the final phase of the experiment, and this period may have been insufficient for the complete consumption of all the substrate still available and resulted in a low COD removal. The highest methane yields, in terms of COD and VS fed at the beginning of AD, were in the HExt, HPA, and HPU units (Table 3), corresponding to the highest methane volume achieved, as mentioned before. The highest methane yield of 290 L CH\({}_{4}\)/kg VSin obtained for the lipid extraction process can be considered as an optimal pretreatment method if the tested algal materials do not hold any solvent residues. The extracted microalga in our study showed a higher methane yield than the value reported by [PERSON] et al. [30] of 250 L CH\({}_{4}\)/kg VS obtained in a semi-continuous stirred tank. The application of ultrasonic pretreatment has been reported to easily disrupt algae cell walls, improve substrate solubility, and enhance the crude protein digestibility [31; 32]. [PERSON] et al. [28] reported an improved methane yield of 327 L CH\({}_{4}\)/kg VS using sonication pretreatment of microalga _Chlorella protothecoides_. This value is higher than the 263 L CH\({}_{4}\)/kg VS attained in this study, yet those authors performed the AD using a basal medium for microbial growth supply, making a comparison between the methane yield values difficult. The comparison of the behavior of the whole heterotrophic algae units (H) with those that digested the pretreated algae reveals that the application of any pretreatment is advantageous in terms of methane production.
# Disposal and Recycle Economic Assessment for Aircraft and Engine End of Life Solution Evaluation
## 5 Aircraft Disposal and Recycle Economic Indicator
### Definition of Disposal and Recycle NPV (\(I_{\text{D\&R},\text{NPV}}\))  
It is known that NPV is the aggregation of the yearly differences between the present value of cash inflow and present value of cash outflow over a period of time, as seen in Equation (15). A positive NPV indicates that the project earnings generated in present money exceeds the costs spent, also in present money. Therefore, an investment with a positive NPV will be profitable, and an investment with a negative NPV will result in a net loss. In the domain of aircraft disposal and recycle, the valorization value is comparable with the cash inflow, while the disposal and recycle cost is the cash outflow. The disposal and recycle NPV \(I_{\text{D\&R},\text{NPV}}\) is then formulated as shown in Equation (16).  
\[NPV=\sum_{t}\frac{CI_{t}-CO_{t}}{(1+i)^{t}}, \tag{15}\]  
\[I_{\text{D\&R},\text{NPV}}=\sum_{n_{\text{D\&R}}}\frac{(V_{\text{valorization }})_{n_{\text{D\&R}}-(C_{\text{D\&R}})_{n_{\text{D\&R}}}}}{(1+i)^{t_{\text{D \&R}}}}=\sum_{m_{\text{D\&R}}}\frac{(V_{\text{s}alvage})_{n_{\text{D\&R}}-(V _{\text{residual}})_{n_{\text{D\&R}}}}}{(1+i)^{t_{\text{D\&R}}}}, \tag{16}\]  
where \(CI\) is the cash inflow, and \(CO\) is the cash outflow, \(i\) represents the discount rate, \(t\) is the specific year of the project counted from the time of start, and \(n_{\text{D\&R}}\) is the current year of conducting the D&R process. Assuming that the valorization profile is equally distributed over the duration of the disposal and recycle project, then \((V_{\text{valorization}})_{n_{\text{D\&R}}}\) and \((C_{\text{D\&R}})_{n_{\text{D\&R}}}\) are averaged values for each year. Therefore, we obtain the simplified Equation (17) with a discount multiplier \(D\) (Equation (18)):\[I_{D\&R\_NPV}=D\Big{(}(V_{\text{nulorization}})_{n_{D\&R}}-(C_{D\&R})_{n_{D\&R}} \Big{)}=D\Big{(}(V_{\text{sululorg}})_{n_{D\&R}}-(V_{\text{residual}})_{n_{D\&R }}-(C_{D\&R})_{n_{D\&R}}\Big{)}, \tag{17}\]  
\[D=\sum_{n_{D\&R}}\frac{1}{(1+i)^{n_{D\&R}}}=\frac{1}{i}-\frac{1}{i(1+i)^{n_{D \&R}}}. \tag{18}\]  
When \(I_{D\&R\_NPV}>0\), a positive aggregation of the differences between the present valorization value and the present D&R cost is obtained. The value valorized is able to cover the cost spent for D&R process within a certain period, which indicates that the D&R process introduces a positive effect, and that the D&R solution should be chosen. In general, the larger the \(I_{D\&R}\), the better the solution. Alternatively, when \(I_{D\&R\_NPV}<0\), the D&R process makes a net loss. When \(I_{D\&R\_NPV}=0\), the D&R process does not introduce any effect.
## 3 Results and Discussion
### Physico-Chemical Characterization of the Hydrolysates
#### 3.1.3 Degree of Hydrolysis (DH)  
The data in Figure 2A show the hydrolysis degree of black, red, and white kidney bean protein isolates after subject to Alcalase for 60 min at 37 C. The DH was significantly (\(p\leq 0.05\)) increased with hydrolysis time but not significant differences between DH values after 30 and 60 min. The highest DH of BKH, RKH, and WKH were obtained after 30 min, reaching 29, 27, and 25%. So, the BKH exhibited the highest DH with about a 14% relative increase over WKH and RKH. Similar results were obtained by [PERSON], et al. [21] who found that DH of kidney bean protein isolate was 33% after hydrolysis with popsin 1% for 6 h. In addition, [PERSON], et al. [72] found that camel whey protein isolates was hydrolyzed by 27% by Alcalase after 4 h. Furthermore, [PERSON], et al. [70] found the highest DH = 27% of popsin black bean protein hydrolysate achieved after 120 min. Moreover, the highest DH (75%) when [PERSON] and [PERSON] [36] used Flavourzyme(r) (6%, 360 min) to hydrolyze black gram bean protein isolate. The obtained DH in the range of previous studies.  
Figure 1: SDS-PAGE profile: (Lanes, 1–3) are for protein isolates and lanes 4–6 for protein hydrolysates (30 min Alcalase hydrolysis at 37 \({}^{\circ}\)C), isolated from black (B), red (R), and white (W) kidney bean seeds.M, molecular marker.
# Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm
## 3 Improved Genetic Algorithm Combining the Historical Optimal Population
### Fitness Function  
When using genetic algorithms to solve TSPs, it is usually possible to use the inverse of the path length or directly use the path length as the fitness function. When using the inverse of the path length as the fitness function, it is necessary to select individuals with larger fitness values; when using the path length, it is necessary to select individuals with smaller fitness values. In this work, the path length was directly chosen to use as the fitness function, which was simple and intuitive to calculate, as shown in Formula (6).  
\[f=\sum_{i=1}^{N}\sum_{j=1,i\
eq j}^{N}k_{ij}d_{i,j} \tag{6}\]  
For the path planning problem for conformal antenna surface detection, the fitness function is more complex. This function is described in Section 4.2.
# Experimental Identification of Characteristic Curves of
## 1 Introduction  
Supersonic vacuum generators, also known as vacuum ejectors or simply ejectors, are essential components in modern industrial automation systems [1, 2, 3]. A key performance metric for ejectors is the Total Evacuation Time (TET), which measures the time required to reach the minimum pressure, with shorter times preferred for evacuating a reference volume [4]. This research focuses on the empirical prediction of the TET, which is referred to as the Predicted Total Evacuation Time (PTET). Other key design metrics for ejectors include the characteristic curve \(\dot{m}_{s}(p)\), which relates the change in the absorbed flow rate (or secondary flow) to the working pressure [5], and the polytropic coefficient evolution curve \(n(p)\), which relates the change in the polytropic coefficient to the working pressures. These metrics provide the basis for comparing ejectors and improving theirperformance. Since TET directly influences the efficiency and suitability of the ejectors for various applications [6], accurately predicting PTET relies on precisely capturing both the characteristic curve \(\hat{m}_{s}(p)\) and the polytropic curve \(n(p)\). Precise experimentation setups allow for evaluating the variation of flow rate and the polytropic coefficient across different working pressures, leading to the development of the \(\hat{m}_{s}(p)\) and \(n(p)\) curves.  
The operational performance of the ejector is primarily determined by its internal geometry [7]. Based on the Venturi principle, the ejectors use compressed air as the primary flow to create the secondary flow rate of air from the secondary inlet. When the secondary inlet connects to a closed tank, this process empties the tank, enabling the use of the generated vacuum for automation applications, such as the use of vacuum cups for moving objects [8]. This process is captured with the \(\hat{m}_{s}(p)\) curve. The minimum pressure is achieved when the ejector can no longer absorb air, which causes the secondary inlet flow rate to drop to zero. Figure 1 shows the internal configuration of an ejector and the two working flow rates. Thermodynamically, the emptying of an air-filled tank has a polytropic impact, which is shown by the polytropic \(n(p)\) curve [9].  
This paper is organized as follows. Section 2 reviews the existing literature on the experiments on ejector performance. Section 3 explains the mathematical model for forecasting the evacuation time, including the empirical polynomial formulas, outlines the experimental setups for data collection, and introduces a new ejector design, claimed to have optimized performance, which is to be tested with the validated models. Section 4 presents the experimental findings and compares them with the results of the mathematical model. Finally, Section 5 discusses the results, while Section 6 draws the conclusions of the study.
# RoseCliff Algorithm: Making Passwords Dynamic
## 4 Implementation
### One-Time Number  
An automated one-time number serves as a dynamic authentication mechanism for a user, which is valid for a single transaction or login session. The server enhances security by generating a unique code that changes rapidly. When an unauthenticated user attempts system access or a transaction, the network server's authentication manager generates a number using a one-time password algorithm. Unlike conventional one-time passwords transmitted over a network, this number remains confidential on the server.  
In our implementation, we employ the 'Sieve of Atkin' [32] for prime number generation due to its improved time and memory efficiency. We compile a list of prime values slightly exceeding 10 million, from which we extract a million primes, each with at least eight digits. This extensive prime number set plays a crucial role in the password encryption process. The dynamically chosen prime number adds an extra layer of security, as it is not a static constant, thus making it challenging for attackers to predict or reuse. Utilizing a one-time prime number significantly reduces the window of opportunity for the potential interception and exploitation of cryptographic data.