esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Examples
### Uniform Circle Formation  
As shown in Figure 2, considering a multiple mobile robots system, consists of nine robots, we set the center of the circular formation at (0, 0) m on the plane. The radius of the desired enclosing uniform circular formation is set as \(R_{i}=100\) m, \(i=1,2,..9\). The initial positions of robots are generated randomly.  
The simulation results are shown in Figures 3 and 4. From Figure 3, we can observe that the group of mobile robots move asymptotically to the pink dots so as to form the prescribed uniform circle under the designed control law (12). As shown in Figure 4, the evolution of the difference between the current position and the required counterpart between each pair of adjacent robots shows that the desired uniform circle formation can be achieved asymptotically. We conclude that using the proposed control law, the multi-robot system has the characteristics of order preservation and collision avoidance.  
Figure 3: Trajectories of nine robots on the plane at \(t\in(0,10]\) when \(k_{r}=1\) and \(k_{\theta}=1\).  
Figure 2: Desired uniform circle on a plane.
## 4 Availability  
A range of nitrate-rich vegetables, such as those outlined in Table 1, are widely available in supermarkets and grocery stores in high-income countries, such as the UK and United States (US). Meanwhile, beetroot juice'shots' can be purchased in many health food shops, in some supermarkets, and via the internet in both the UK and US. For individuals in high-income countries actively trying to increase their nitrate intake, availability is unlikely to be a major barrier to consumption for either nitrate vehicle. However, this may not be the case in other settings, such as low- and middle-income countries, where the intake of vegetables is often much less than 100 g per day [44].  
High rates of hypertension are observed in Africa, where almost 50% of adults aged 25 years and above have elevated blood pressure [55]. Interestingly, [PERSON] et al. [56; 57] demonstrated considerable potential for dietary nitrate to help combat hypertension in Tanzania, a country in Sub-Saharan Africa experiencing a hypertension epidemic. Specifically, 60 days of dietary nitrate supplementation reduced 24 h ambulatory systolic BP by around 10 mmHg, suggesting considerable potential for dietary nitrate as an anti-hypertension strategy in this setting. Although this study administered nitrate in the form of beetroot juice'shots', which were provided to participants via the research team, this strategy may not be viable for many individuals in Tanzania or other lower- or middle-income countries outside of a research setting, given limited availability (alongside cost restrictions). On the other hand, the growth and purchase of native vegetables rich in nitrate (Table 2) could represent a vital tool to help combat the high rates of hypertension in such settings.
## 5 Experimental Results
### Security Analysis
#### 5.6.6 Scalability and Real-World Deployment  
The D-H protocol demonstrates excellent scalability in IoT environments. Each device independently computes its session key using efficient modular exponentiation, minimizing computational overhead. As presented in Section 5.5, experimental results show that even in high-density IoT networks, the average communication delay per device increases by only 3 milliseconds. This minimal overhead underscores the protocol's practicality and suitability for large-scale deployments, ensuring both efficiency and performance in resource-constrained settings.
## 2 Multifocused Analysis Methodology
### Groundwater Agriculture Problem: Multifocused Analysis
#### 2.1.1 Environmental Analysis  
The environmental problem emerges as one of the most crucial impacts. In fact, this issue can involve important problems for the agricultural sector and its irrigation requirements, especially for groundwater irrigation proposals. In an arid climate, climate change can lead to a decrease in precipitation, consequently reducing the natural recharge of aquifers. These negative conditions are a limiting factor for agricultural development in those areas of the world [38]. Reduced rainfall as a result of climate change is not the only environmental impact, but also rising temperatures and other collateral effects. In fact, some analyses and studies focused on climate forecasting suggest a gradual temperature increase, with warmer and drier summer periods. Therefore, water reservoirs and lakes exposed to solar radiation can lose more water by evaporation, and crops will demand greater amounts of water. From a climate perspective, analysis of these data shows a clear tendency from semiarid areas to severely arid conditions, almost becoming desertified areas.  
Water resource management in the agriculture sector emerges as a crucial and relevant factor, affecting irrigation and slightly increasing crop water requirements. Desertification and soil erosion are then collateral problems as a result of poor agricultural practices and inefficient use of irrigation strategies, leading to the loss of soil moisture in semiarid areas. Presently, the climate change problem is leading to unsustainability due to the overexploitation of some aquifers. Other physicochemical problems have also been identified as a consequence of such overexploitation. Leachates, pesticides, and inorganic fertilizers can come into contact with the aquifer and produce a contaminated environment. As an additional drawback to the overexploitation problem, groundwater salinization is becoming extreme. Consequently, water is unusable for either agriculture or human consumption unless highly expensive pretreatment is carried out. Finally, CO\({}_{2}\) emissions produced by pumping irrigation in agriculture must be also estimated and analyzed. Traditional agriculture has mostly been based on diesel equipment. Obviously, these systems do not contribute to mitigating climate change effects, but increase emissions. Therefore, alternative solutions based on clean and renewable energy technologies in the agriculture sector can promote the reduction of emissions.  
Figure 3: Multifocused analysis for agriculture groundwater pumping purposes.
## 5 Simulative Assessment  
Here a simulative performance assessment of a LoRaWAN network working with the LBT AFA MAC protocol described in Section 4 is presented.  
Simulations were run using OMNeT++ [89; 90] and the Framework for LoRa (FLoRa) [91], which in turn adopts components of the Internet Networking (INET) Framework [92], for modeling the LoRaWAN class A EDs, the LoRa physical layer, and the wireless channel. Conversely, the LBT AFA medium access mechanism was implemented from scratch.  
The LoRa SFs imperfect-orthogonality is implemented as in [73]. As shown in Figure 4, before a message transmission attempt each ED runs a check function to create the list of channels available for transmission according to the ETSI limitations on the maximum allowed cumulative ToA per hour (\(ToA_{200\text{kHz}}^{tot/h}\)). This function runs when the LBT AFA procedure starts and after each backoff.  
The metrics here chosen to evaluate the LoRaWAN network performance are the Message Loss Ratio (MLossR), the Message Drop Ratio (MDropR), the Message Delivery Ratio (MDelivR), and the end-to-end delay (e2 eD).  
The MLossR refers to the percentage of lost messages over the total number of transmitted messages, measured at the Application layer, according to Equation (2),  
\[MLossR=\Big{(}\frac{\mathcal{N}_{LOSTmsg}}{\mathcal{N}_{TXmsg}}\Big{)}\times 1 00=\Big{(}1-\frac{\mathcal{N}_{RXmsg}}{\mathcal{N}_{TXmsg}}\Big{)}\times 100 \tag{2}\]  
where \(\mathcal{N}_{TXmsg}\), \(\mathcal{N}_{RXmsg}\), and \(\mathcal{N}_{LOSTmsg}\) are the number of messages transmitted, correctly received and lost, respectively, measured by each ED.  
The MDropR refers to the percentage of messages that are discarded due to exceeding the maximum number of backoffs over the total number of Application layer messages received by the MAC layer, measured by each ED, according to Equation (3),  
\[MDropR=\Big{(}\frac{\mathcal{N}_{DROPmsg}}{\mathcal{N}_{GENmsg}}\Big{)}\times 1 00 \tag{3}\]  
where \(\mathcal{N}_{GENmsg}\) and \(\mathcal{N}_{DROPmsg}\) are, respectively, the number of messages received from the Application layer and discarded by the MAC layer after reaching the maximum number of backoffs, measured by each ED.  
The MDelivR represents the ratio between the number of delivered messages and the number of sent messages, measured at the Application layer and expressed as a percentage, according to Equation (4),  
\[MDelivR=\Big{(}\frac{\mathcal{N}_{RXmsg}}{\mathcal{N}_{GENmsg}}\Big{)}\times 1 00=\Big{(}1-\frac{\mathcal{N}_{LOSTmsg}+\mathcal{N}_{DROPmsg}}{\mathcal{N}_{ GENmsg}}\Big{)}\times 100 \tag{4}\]The e2 eD is the time difference between the message generation at the source node (\(T_{Gen}\)) and the message reception at the destination node (\(T_{RX}\)), measured at the application level, according to Equation (5).  
\[e2 eD=T_{RX}-T_{Gen} \tag{5}\]  
In the simulations the source is an ED, the destination is the network server and the delay introduced by the communication between the gateway and the network server is assumed equal to 0.
## 3 Simulation
### Fan Noise Simulation Result Analysis  
The frequency domain feedback ANC algorithm (FDFBANC for simple) is frequently used, which is based on the theory of internal model control. It has a simple structure and good steady state character. In contrast with the proposed SEDFBANC algorithm, the secondary path impulse response needs to be estimated for the FDFBANC algorithm.  
In this simulation, fan noise was adopted as the primary noise source; its frequency spectrum is shown in Figure 14. It can be filtered using a band pass filter to obtain a narrowband noise with a frequency range between 320 Hz and 350 Hz. The steady-state and noise-reduction properties of the proposed SEDFBANC and FDFBANC algorithms were compared in this simulation. The proposed SFDFBANC algorithm's step size was set at 0.7, and the FDFBANC algorithm's step size was 0.9. The acoustic environment adopted a sound absorption duct sound field, as shown in Figure 4. Figure 5's impulse response  
Figure 12: MSE curves of the three algorithms.  
Figure 13: The three algorithms’ error signal spectrum.  
was used for the FDFBANC algorithm in this simulation. Both of the algorithms' error spectra were shown in Figure 15. It can be seen that the proposed FDFBANC algorithm has a similar noise reduction to that of the FDFBANC algorithm.  
Compared to before the ANC control, the proposed SFDFBANC algorithm's noise reduction is about 14 dB.  
Both of the algorithms' minimum mean square error (MSE) curves are shown in Figure 16. It can be seen that both of the algorithms have comparable convergence rates; however, the steady-state error of the SFDFBANC algorithm is lower than that of the FDFBANC algorithm before 150 s. For the FDFBANC algorithm, the perfect model was adopted, so the internal synthetic signal \(x(n)\) is highly correlated with expected signal \(d(n)\). While the proposed SFDFBANC algorithm directly used the error signal \(e(n)\) as the internal reference signal \(x(n)\), it has a lower correlation with the expected signal than the FDFBANC algorithm. As the time grows, the proposed SFDFBANC algorithm gradually closes the gap with the FDFBANC algorithm due to the feedback of the error signal \(e(n)\). After 200 s, the proposed SFDFBANC algorithm was almost consistent with the MSE of the FDFBANC algorithm.  
Figure 14: Spectrum of fan noise.  
Figure 15: Error signal spectra of SFDFBANC and FDFBANC algorithms.
## 3 Image Feature Extraction and Feature Matching Based on SIFT
### Scale Space Construction  
Spatial scale coordinate transformation is performed on the detected image to obtain the scale space sequence. In the following, the main spatial contour of the scale space sequence is extracted, which is marked as a feature vector to complete the corner feature extraction of key points at different resolutions. The scale space constructed is invariant to scale changes, which is achieved by blurring and down-sampling the image through the Gaussian function [12; 13; 14]. In order to make the calculation relatively efficient, the Gaussian difference scale space is calculated by the Gaussian difference function, through which the Gaussian difference pyramid is generated, as shown in formula (1):  
\[G(x,y,k\sigma)-G(x,y,\sigma)\approx(k-1)\sigma^{2}\
abla^{2}G \tag{1}\]  
Figure 2: Schematic diagram of charging port feature extraction and matching.  
In the formula, \(x\) and \(y\) are the scale coordinates. \(\sigma\) is the image smoothness coefficient. \(G(x,y,\sigma)=\frac{1}{2\pi\sigma^{2}}e^{-(x^{2}y^{2})/2\pi\omega^{2}}\) and \(k-1\) is a constant.
# Validation of a Manual Methodology for Measuring Roundness and Cylindicity Tolerances
## Appendix A  
As it is defined in Section 2.1.1, Equation (3) expresses the relationship between the distance from each point of the profile to the origin, \(r_{i}\), the LSC center, and the radial error between the considered point and LSC, \(e_{i}\). By trigonometry, using the angle difference formula, Equation (A1) is obtained.  
\[r_{i}=R+c\cos\theta_{i}\cos\alpha+c\sin\theta_{i}\sin\alpha+e_{i}\] (A1)  
Considering that \(a=c\cos\alpha\), \(b=c\sin\alpha\), the final expression for \(r_{i}\) is derived, Equation (A2).  
\[r_{i}=R+a\cos\theta_{i}+b\sin\theta_{i}+e_{i}\] (A2)  
By rearranging the terms and taking \(e_{i}\) off, the expression for \(S\), is defined by Equation (4) of Section 2.1.1.  
According to its definition, LSC is the circle which minimizes \(S\), being necessary to equal zero the partial derivatives of \(S\) with respect to \(a\), \(b\), and \(R\), Equation (A3).  
\[\left\{\begin{array}{l}\frac{\partial S_{i}}{\partial R}=\sum\limits_{i=1}^{ n}2(R+a\cos\theta_{i}+b\text{sen}\theta_{i}-r_{i})=0\\ \frac{\partial S_{i}}{\partial a}=\sum\limits_{i=1}^{n}2(R+a\cos\theta_{i}+b \text{sen}\theta_{i}-r_{i})(-\text{sen}\theta_{i})=0\\ \frac{\partial S_{i}}{\partial b}=\sum\limits_{i=1}^{n}2(R+a\cos\theta_{i}+b \text{sen}\theta_{i}-r_{i})\cos\theta_{i}=0\end{array}\right.\] (A3)By rearranging the terms and separating the sums, this leads to the following system, Equation (A4).  
\[\left\{\begin{array}{l}nR+a\sum\limits_{i=1}^{n}\cos\theta_{i}+b\sum\limits_{i=1 }^{n}\text{sen}\theta_{i}=\sum\limits_{i=1}^{n}r_{i}\\ R\sum\limits_{i=1}^{n}\text{sen}\theta_{i}+a\sum\limits_{i=1}^{n}\text{sen} \theta_{i}\cos\theta_{i}+b\sum\limits_{i=1}^{n}\text{sen}^{2}\theta_{i}=\sum \limits_{i=1}^{n}r_{i}\sin\theta_{i}\\ R\sum\limits_{i=1}^{n}\cos\theta_{i}+a\sum\limits_{i=1}^{n}\cos^{2}\theta_{i}+ b\sum\limits_{i=1}^{n}\text{sen}\theta_{i}\cos\theta_{i}=\sum\limits_{i=1}^{n}r_{i} \cos\theta_{i}\end{array}\right.\] (A4)  
In the particular case that the points \(Pi\) are equidistant, Equation (A5) is satisfied.  
\[\left\{\begin{array}{l}\sum\limits_{i=1}^{n}\sin\theta_{i}=\sum\limits_{i=1 }^{n}\cos\theta_{i}=\sum\limits_{i=1}^{n}\sin\theta_{i}\cos\theta_{i}=0\\ \sum\limits_{i=1}^{n}\text{sin}^{2}\theta_{i}=\sum\limits_{i=1}^{n}\cos^{2} \theta_{i}=\frac{\pi}{2}\end{array}\right.\] (A5)  
Applying Equations (A4) and (A5) in combination, the expressions for \(a\), \(b\), and \(R\), are obtained, Equation (A6).  
\[\left\{\begin{array}{l}R=\frac{\sum\limits_{i=1}^{n}r_{i}}{n}=\frac{\sum \limits_{i=1}^{n}\sqrt{x_{i}^{2}+y_{i}^{2}}}{n}\\ a=\frac{2\sum\limits_{i=1}^{n}r_{i}\cos\theta_{i}}{n}=2\overline{x}\\ b=\frac{2\sum\limits_{i=1}^{n}r_{i}\sin\theta_{i}}{n}=2\overline{y}\end{array}\right.\] (A6)
# Fertility Detection of Hitching Eggs Based on a Convolutional Neural Network
## 4 Conclusions  
In this paper, we proposed a novel method used for the fertility detection of hatching eggs by combining a convolution neural network and the heartbeat signal of hatching eggs. We collected  
\begin{table}
\begin{tabular}{c c} \hline \hline
**R** & **Accuracy (\%)** \\ \hline
4 & 94.52\% \\
8 & 96.58\% \\
16 & 99.62\% \\
32 & 97.73\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of accuracy with different values of hyper parameter R.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Method** & **Feature Extraction Method** & **Classification Method** & **Date** & **Accuracy** & **Classification** \\ \hline Proposed method & SR-CNN, & SR-CNN, & 99.62\%, & Fertile and Dead \\ [PERSON] et al. (2017) [8] & TB-CNN & E-CNN & 5-days & 99.5\% & Fertile, Dead and Infertile \\ [PERSON] et al. (2013) [5] & Gabor-filter & K-means clustering & 4-days & 84.1\%. & Fertile and Non-fertile \\ [PERSON] et al. (2010) [7] & Thresholding by & Criterions of & & \\ Histogram-based WPCM & blood vessels & Middle-stage & 99.33\% & Fertile and Non-fertile \\ [PERSON], et al & k-means, LDA, SVM & k-means clustering, & 96\%, 100\% & Fertile and Infertile \\  & & LDA, SVM & 4-days & 100\% & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of experimental results.  
the heartbeat signals of hatching eggs by the method of PhotoPlethysmoGraphy and preprocessed the collected data to reduce noise interference. We analyzed the collected data from different data dimensions. First, we designed a sequence convolution neural network model E-CNN used for tackling the heartbeat signal sequence. Then, in order to solve the problem that E-CNN is not able to deal with noisy signals, we designed SR-CNN which combines the SE block and the residual learning unit in a convolution neural network to enhance the ability to extract features from the heartbeat signal waveform. Experimental results show that the two network architectures we designed are effective in hatching eggs survival identification with a detection accuracy of up to 99.50% and 99.62% respectively on our test set. The proposed methods performed well, while we did not achieve the classification of weak embryos. Thus, it is going to be attempted to classify fertile eggs, dead eggs, and weak embryos in more detail in future work.  
[PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] gave guidance in the experiments and data analysis.  
This work was supported by the National Natural Science Foundation of China under grant No. 61771340, Tianjin Science and Technology Major Projects and Engineering under grant No.17 ZXHLSY00040, No.17 ZXSCSY00060 and No.17 ZXSCSY00090, and the Program for Innovative Research Team in University of Tianjin (No.TD13-5034).  
The authors declare no conflict of interest.
## 3 Analysis of the Pull-Out Test Results
### Elastic Ultimate Load  
Under fatigue load, the elastic ultimate load of the specimen showed a decreasing trend. Figure 8 shows the relationship between fatigue loading times and the ratio of elastic ultimate load between non-fatigue loading specimens and fatigue loading specimens of 8 mm rebar planting specimens under different anchor depths.  
The elastic ultimate load ratio of the specimen decreased with the increase in the fatigue loading times. When the anchor depth of the rebar was 20d, the specimen was minimally affected by the fatigue load. With the increase in fatigue loading times, the  
Figure 8: Relationship curve between elastic ultimate load ratio and fatigue loading times.  
Figure 7: Load–slip curve: (**a**) specimen of 8 mm steel bar (10d); (**b**) specimen of 8 mm steel bar (15d); (**c**) specimen of 8 mm steel bar (20d); (**d**) specimen of 6 mm steel bar (10d); (**e**) specimen of 6 mm steel bar (15d); (**f**) specimen of 6 mm steel bar (20d).  
elastic ultimate load of the specimen under 100,000 times of fatigue loading was still 92.3% of that of the non-fatigue loading specimen. When the anchor depth of the rebar was 10d, the reduction of the elastic ultimate load by the fatigue load was the most obvious. Under 100,000 times of fatigue loading, the elastic ultimate load was only 55.1% of the non-fatigue loading specimen, and the performance of the specimen was greatly reduced. When the loading times reached more than 50,000 times, the reduction of the elastic ultimate load of the specimen increased, and the slope of the curve suddenly changed. On the whole, an increase in anchor depth is beneficial to reduce the influence of the fatigue load on the decrease in the elastic ultimate load.
## 3 Results
### Relative Influence of Rainfall and Temperature on SOS/EOS  
We investigated the relationship between SOS/EOS and pre-SOS/pre-EOS rainfall and temperature by developing the linear mixed models to evaluate the SOS/EOS response. The AIC values recorded for all competing model structures for SOS and EOS ranged from 6313.0 to 6382.2 and from 5436.1 to 5462.4, respectively. Based on AIC comparison of competing model structures, the random intercept mixed models of SOS and EOS with temperature as the sole significant predictor were identified to have the lowest value. This shows that temperature regulates the start and end of the rubber growing season. Table 3 shows the resulting model equations for SOS and EOS. The linear mixed model revealed a decreasing trend of SOS/EOS with an increase in temperature (Figure 9).  
The results suggest that the random effect of pixel plays a role in influencing both SOS and EOS. All pixels in Figure 9 consistently showed a decreasing trend in SOS/EOS. This indicates that although there is rubber multiclonal planting, all clones in the study area respond similarly to temperature. In general, the level of SOS/EOS response to pre-SOS/EOS temperature were calculated to be \(-25.26\) days/\({}^{\circ}\)C and \(-13.78\) days/\({}^{\circ}\)C, respectively. These negative coefficients indicate that an increase in temperature advances the SOS/EOS dates, and vice versa.
# Effect of Performance by Excessive Advanced Fuel Injection Timing on Marine Diesel Engine
## 1 Introduction
Numerous previous studies have presented models for diagnosing engine performance using condition-based maintenance (CBM). [PERSON] developed a performance map using artificial neural networks (ANNs) to predict fuel consumption and output [18]. [PERSON] et al. developed models for predicting faults in the cooling system and the lubricating system of marine diesel engines [19]. A model for predicting fuel consumption, average effective pressure, and exhaust gas temperature for a methanol engine was proposed by [PERSON] et al. [20]. [PERSON] and [PERSON] introduced a diagnostic model based on an ANN for propulsion engines of operating vessels [21]. While various models have been developed and presented for diagnosing and predicting the condition of marine diesel engines, studies that connect diagnostic results to problem improvement are scarce. In this study, the combustion state of the marine engine in operation was diagnosed using an angle-based sampling method utilizing angle sensors to collect combustion pressure data. The \"i-MEP\" equipment, which incorporates output measurement and combustion analysis techniques [22; 23], was employed for this purpose. The target engine under measurement was experiencing issues such as engine start failures, excessive noise, and vibrations. The root cause of these problems was identified as the occurrence of reverse torque due to an excessively advanced fuel injection timing angle. Based on the diagnostic results obtained from the measured combustion pressure, the causes of engine start failures, noise, and vibrations, as well as the identified issues and outcomes for restoring the engine to normal operation, are reported.