esa-sceva/satcom-chunk-collection huggingface dataset:
# Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks
## 1 Introduction
Moreover, in recent years the agricultural industry has been progressively increasing the practical use of Artificial Intelligence (AI) algorithms, such as Machine Learning (ML) and Deep Learning (DL), in planting, harvesting and processing of fruit and vegetables, in particular using computer vision systems (CVS), processing images of fruit or vegetables to classify different types of fruit and vegetables, detection for harvesting, quality control and accurate detection of diseases or defects in fruit [10]. Considering the above exposure, a prototype CVS equipment is proposed to solve the problem of estimating the quality of raspberries in the harvesting tray at the entrance of the fruit processing plant, contributing to a more objective estimation of the quality of raspberries in the harvesting tray. 1. Develop and construct a controlled environment that allows imaging of the raspberry trays. 2. Capture color images (RGB) in the controlled lighting environment. 3. Develop software to process the images with object-detection algorithms based on convolutional neural networks (CNN) and determine the quality of the raspberry trays. 4. Perform classification according to defects or diseases present in raspberries and determine the quality of raspberry trays. This article is organized as follows: Section 2 corresponds to related work. It first shows some recent scientific papers using computer vision methods and DL algorithms applied to fruit studies This is followed by a review of patents showing the different existing solutions for quality control of fruit at different stages of processing Section 3 presents a description of the quality control prototype, an overview of the Faster R-CNN object detector algorithm, and finally describes the dataset development process. Section 4 presents the results of the training and evaluation process of the object detector models for each class of defect or disease to be detected. Then, with the trained models, the quantitative evaluation of the best detector models for each class is presented, together with the conceptual test. Section 5 presents the conclusions of the work carried out and the future work to be developed.
# Wide Scanning Angle Millimetre Wave 1 \(\times\) 4 Planar Antenna Array on InP at 300 GHz
## 2 Design of the Unit-Cell
#### Array Design
Figure 8: (**a**) Modified defected ground plane, (**b**) modified layout of 1 \(\times\) 4 array. The detailed gain analysis of the modified array structure is shown in Figure 10. The peak realised gain as a function of frequency and steering angle shows that by introducing MSWIGP, the gain performance is more symmetric. Although the overall gain has decreased by 0.85 dB, the array presents a more balanced performance across the operating bandwidth. Further, the SLLs (shown in Figure 10b) show an improvement of 1-2 dB while maintaining symmetry on the performance along with positive and negative angles. The introduction of MSWIGP has mitigated the effects of mutual coupling and substrate waves. It can be argued that in order to improve the results further, the unit-cell antenna structure needs to be refined such that the gain profile along \(\theta\) is more uniform, thereby providing a constant gain in the array configuration. The improved 1 \(\times\) 4 array has a gain variation of 3 dB for a scanning angle of 100\({}^{\circ}\). By comparing the results presented in Figures 6 and 10, it is evident that without using MSWIGP, the array had a gain variation of more than 4 dB for angles \(\geq\) 40\({}^{\circ}\). The SLL of the original array was below 7 dB for extreme angles. By utilizing the MSWIGP structure, the gain variation is decreased to 3 dB for an improved SLL better than 8 dB. This has resulted in improving the scanning range of the array while keeping the fabrication process similar to the original design. Figure 10: (**a**) Steering pattern for 1 \(\times\) 4 array at 300 GHz, (**b**) SLL of array, (**c**) peak realised gain vs. frequency for varying steering angles, (**d**) peak realised gain vs. steering angle for different frequencies. Figure 4:0 Comparison of reflection coefficient between MSWIGP and the original design.
# Performance of Dynamic Time Division Broadcast Protocol with Rateless Coding
## 2 System Models
### System Model of DTDBC-RC
In this case, \(S_{1}\) (\(S_{2}\)) decodes \(S_{2}\)'s (\(S_{1}\)'s) message by combining the message received from both \(S_{2}\) (\(S_{1}\)) in the first time slot and from \(R\) in the third time slot. In _Case II_, at least one source's message (\(S_{1}\) or \(S_{2}\)) cannot be decoded successfully at \(R\). In this case, \(R\) feeds back a NACK and there is no retransmission occurred at \(S_{1}\), \(S_{2}\), or \(R\). Thus, an outage event occurs and \(S_{1}\) and \(S_{2}\) begin a new round of information exchange as described above (In TWRC, outage probability is usually defined for the whole system, not for any single source (see [2; 3] and the references therein). In the paper, we also define the system outage probability. Therefore, if \(R\) cannot decode successfully any source's message, there is no improvement in system outage probability if \(S_{1}\) or \(S_{2}\) retransmits in the third time slot. Thus, there is no need to assume retransmission in the third time slot if at least one source's message cannot be decoded successfully at \(R\).). **State 2: both \(S_{1}\) and \(S_{2}\) decode successfully. ** In this state, \(S_{1}\) and \(S_{2}\) feed back ACKs to each other and begin a new round of information exchange, as described above. The above two states are depicted in Figure 1, where \(T_{DTDBC-RC}\) is the transmission length of the first and second time slots (it will be calculated in Section 3) due to the channel reciprocity assumption. Note that (i) the one-bit ACK/NACK feedback is assumed to be received without delay or error, e.g., through a dedicated control channel; (ii) in one-round information exchange of DTDBC-RC, there are at most three time slots and two time slots for the whole system and for each source, respectively. Moreover, the transmission lengths of the first and second time slots are random depending on the channel quality of the direct link because the sources transmit in rateless coding way. Therefore, the transmission length of one-round information exchange is \(2T_{DTDBC-RC}\) when both \(S_{1}\) and \(S_{2}\) succeed in the first and second time slots (corresponding to State 2) or when \(R\) cannot decode successfully at least one source's message (corresponding to _Case II_ in State 1), or is \(T\) when both \(S_{1}\) and \(S_{2}\) cannot decode successfully in the first and second time slots and \(R\) successfully decodes both sources' messages (corresponding to _Case I_ in State 1); and (iii) we extend the equal-length time slot assumption in [7; 8] to the case with unequal-length time slot in the paper (see Figure 1), i.e., both the first and second time slots are with length \(\alpha T\) in TDBC-IR like DTDBC-RC (The protocol in [7; 8] corresponds to the case when \(\alpha=\frac{1}{3}\) for TDBC-IR in the paper.). Thus, the outage probability, expected rate, and DMT analysis for TDBC-IR in Section 3 is for the unequal-time length case.
## 5 Performance Evaluation and Analysis
### GRU Performance  
The GRU model facilitates efficient resource reservation in the V2X network by accurately predicting future vehicle positions. This process begins with the GRU layers analyzing historical position data, identifying patterns and sequences in the movements of vehicles over time. By capturing these temporal dependencies, the model can generate forecasts regarding the future positions of both the ego vehicle and its neighboring vehicles. These predictions are crucial for anticipating changes in the network, such as whether vehicles will move closer to or further from the ego vehicle, or if they will enter or exit the ego-vehicle sensor's field of view (FOV). Such foresight is vital for planning the allocation of communication resources.  
The model dynamically adjusts the resource reselection counter (\(C_{reset}\)), determining the duration for which channels are reserved. Accurate predictions help avoid both the over-allocation and under-allocation of resources, optimizing bandwidth use. In essence, the GRU model ensures efficient and reliable data transmission by enabling precise and adaptive resource management.
# Lineal Energy of Proton in Silicon by a Microdosimetry Simulation
## 3 Results and Discussions
### Lineal Energy Contribution from Various Secondary Species
In previous SEEs studies, LET was usually treated as a single value for each incident particle with specific kinetic energy. However, [PERSON] et al. [4] and [PERSON] et al. [5] presented that, in some cases, LET verses SEEs cross-section is not a one to one function. In other words, the SEEs cross-section sometimes has large variation even though the LET is the same. In Table 1, the calculated LET of each type of secondary particle via the mean of its kinetic energy are listed. The calculation is done by two commonly used tools, namely LET124 and SRIM. LET124 is from the Tandem Van de Graaff Accelerator Facility at Brookhaven National Laboratory's and SRIM is from [PERSON] et al. [7]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Z \#**} & \multirow{2}{*}{**Mean Energy (MeV)**} & **LET (LET24)** & **LET (SRIM-2013)** \\  & & * **(MeV-cm\({}^{2}\)/mg)** & **(MeV-cm\({}^{2}\)/mg)** \\ \hline
2 & 5.32 & 0.5949 & 0.588 \\
3 & 2.83 & 1.878 & 2.134 \\
4 & 3.00 & 3.003 & 3.138 \\
5 & 1.93 & 4.292 & 4.197 \\
6 & 1.53 & 5.118 & 4.853 \\
7 & 2.84 & 6.41 & 6.006 \\
8 & 3.66 & 7.416 & 7.126 \\
9 & 3.85 & 8.381 & 8.162 \\
10 & 4.60 & 9.358 & 8.172 \\
11 & 3.55 & 9.69 & 8.544 \\
12 & 2.76 & 9.684 & 8.186 \\
13 & 1.98 & 8.616 & 6.748 \\
14 & 1.20 & 6.719 & 5.973 \\ \hline \hline \end{tabular}  
* Calculated by LET124 from Tandem Van de Graaff Accelerator Facility, Brookhaven National Laboratory, USA. ** Calculated by SRIM [9].  
\end{table} 
Table 1: The calculated LET using mean energy of secondary particles generated by 200 MeV proton irradiate on silicon. Figure 5: \(y\) spectra in 100 nm silicon irradiated by a 200 MeV proton beam. In the Table 1, even though both calculations are based on Bethe stopping formula but with different correction factor, the results can have more than 20% difference. When we look back to Figure 5, which plots the \(y\) in the SV for each kind of secondary particles, the \(y\) is not a single value or a symmetric distribution such as Gaussian distribution. Instead, the distribution is focus on low \(y\) region and has a very long tail in high \(y\) region. Take \(Z=12\) which is magnesium for example, the calculated LET is around 7 MeV-cm\({}^{2}\)/mg, but the maximum \(y\) can be larger than 50 MeV-cm\({}^{2}\)/mg, which is seven times higher than the mean. Therefore, using single value LET to estimate the SEEs is risky because the LET can only present the low y region, but the event which contribute SEEs can mostly from the high \(y\) tail. Due to the counts in high y region is quite less as compared to low y region, it has only little effect on the mean but may have significant effect on SEEs cross-section.
###### Abstract  
This paper presents a control strategy to regulate voltage and share reactive power from distributed generators in a microgrid when electric vehicles (EVs) are connected and disconnected at different nodes and times. The control strategy considers fixed and variable virtual impedances created in the microgrid (MG) when loads change (EVs are connected or disconnected). Fixed virtual impedance is related to the distance from each house to the power inverter and is used as an input of the primary control. Variable virtual impedance is associated with the distance from each EV to the power inverter and is also used as an input of the primary control. Thus, the control of the inverter seeks to regulate the voltage where the EVs create variations in the network. The results show that the control strategy regulates well the voltage of different nodes, and the reactive power is distributed to renewable generators based on the distance from the loads to the inverters. By considering the fixed and variable virtual impedances in the primary control, voltage can be regulated, assuming various consumptions of EVs in the network. This result is promising for reactive power control and sharing for each distributed generator (DG) in a microgrid where a great number of EVs affect the operation.  
reactive power sharing; microgrid; virtual impedance; power transfer; voltage control Article
## 1 Introduction
[15] employed the contact angle measurement method for wettability analysis and varied the conditions for various rock samples to derive the results. In addition, flotation tests involve creating rock particles in powder form and conducting experiments in which the wetting state of the rock surfaces affects the buoyancy and settling mechanisms of the experimental fluid. [PERSON] et al. [16] provided insights into the wettability of rock particles and the characteristics of rocks that influenced this test. However, considering temperature variations and reaction times in the context of nanofluid reactivity is important because they are essential factors that need to be adequately addressed in fluid flow experiments. These factors can significantly impact the effectiveness of the injection method and, consequently, oil recovery. This study assessed the potential of GPTMS-SiO\({}_{2}\) nanofluids to enhance wettability in carbonate reservoirs. Flotation tests were conducted to analyze key factors--nanoparticle concentration, reaction temperature, and time--that influence wettability. The quantification of contact-angle variations highlighted the impact of the nanofluid. These findings underscore the potential of GPTMS-SiO\({}_{2}\) nanofluids to enhance wettability in carbonate reservoirs. Furthermore, fluid flow experiments, considering the nanofluid's reaction time, were conducted on both crude oil and core plug samples from an operating oil field. These experiments analyzed pressure differentials, recovery rates, and contact-angle changes induced by nanofluid injection, providing crucial insights into the applications of GPTMS-SiO\({}_{2}\) nanofluids for improving fluid flow in carbonate reservoirs.
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## 1 Introduction  
Travel time is one of the main indexes that reflect the traffic operation level of a freeway, and it is also the basis for Advanced Traveler Information System (ATIS), Traffic Guidance System (TGS), and Traffic Control System (TCS). Nowadays, large amount of heterogeneous data provides the new solution of travel time estimation. Meanwhile, semantic technology can deal with these complex, large-scale, and heterogeneous data. However, the challenges and difficulties of travel time prediction are identified below.  
* Diverse influencing factors such as weather, holidays, traffic accidents, out of sample prediction, and mechanisms contributing to congestion. It is difficult to describe and predict the influence mechanism by using traditional conventional mathematical models.
* The complexity and incompleteness of basic data. Although many flow detectors and video detection equipment are on the freeway, captured data are incompatible, redundant, and include error or loss. To avoid these, techniques that use multi-source data to improve the accuracy of travel time prediction are extremely important.  
In China, practical application of travel time prediction focuses mainly on the following two aspects.  
The first aspect is the prediction of travel time by map navigation providers using their personalized GPS data. Many map service providers employ their personalized data for travel time forecast services and commercial products. For instance, Bai-du, Gao-de, and other Chinese map providers collect real-time GPS data from users while providing map navigation services. Then, a correlation algorithm is proposed to obtain the travel time prediction result at road sections, which depends on the market share of the map navigation service. As people use the navigation service with greater frequency, the GPS data will be more complete and the prediction accuracy will be higher. However, according to the Chinese market report, the market share of Bai-du and Gao-de services are presently 29.3% and 32.6%, respectively. Therefore, the accuracy of results should be further improved by increasing market share.  
The second aspect is the prediction based on the traffic detection data of urban traffic managers and historical data. In recent years, numerous fixed detector devices have been installed in most urban roads and rural freeways to predict travel time; these devices include inductive loops, video recorder, microwave, and laser detectors. However, unavoidable damage to flow detection equipment and transmission error of partial data make traffic detection data incomplete, redundant, or incorrect. In addition, different detectors have different data formats and data accuracy. If the Traveler Information Service system uses inaccurate data, it cannot recommend optimal travel routes nor warn of potential traffic congestion, and users cannot determine optimal departure times nor estimate their arrival times.  
Theoretical research on freeway travel time prediction can be divided into two categories based on single source data and multi-source data.
# Designing an Interactive Visual Analytics System for
## 5 Interactive Precipitation Data Analysis System
### Station-Specific Analysis Interface
#### 5.1.1 Precipitation Frequency Analysis  
Two visualizations are added to support precipitation frequency analysis to show IDF curves and exceedance probability. IDF curve visualization (Figure 3A) shows the frequency of extreme rainfall events based on their intensity and duration. It provides probability estimates of extreme rainfall events. For evaluating return periods expressed in years, different probabilities are applied with 2 years (50%), 5 years (20%), 10 years (10%), 25 years (4%), 50 years (2%), and 100 years (1%). The return periods characterize events ranging from 2-year (frequent but minor storms) and 5-year (common storms) to 100-year (extreme events). In the visualization, periods are represented with different color attributes. The x-axis shows the duration of the rainfall event, and the y-axis indicates precipitation intensity (inch/hr). Each curve demonstrates an inverse relationship between intensity and duration, where rainfall intensity typically decreases as the duration increases for any given return period. The IDF curve evaluates flood risks to help plan climate change adaptation in flood control systems in urban areas [45]. Users can hover over the curves to view specific rainfall intensity values.  
Exceedance probability visualization (Figure 3B) represents the probability of precipitation intensities. It uses intensity-exceedance frequency (IEF) curves that assess the risks of extreme rainfall events by evaluating historical data. The Weibull distribution is used because precipitation values are often positively skewed, with many small and few extreme values. Exceedance probability is computed by sorting and ranking precipitation events using \(P(X\geq x)=\frac{i}{n+1}\). This provides an empirical estimate of the survival function (\(P(X\geq x)=1-F(x)\)), where \(F(x;\lambda,\kappa)=1-\exp(-(x/\lambda)^{\kappa})\) is the cumulative distribution function (CDF). Here, \(\lambda\) indicates the scale parameter and \(\kappa\) represents the shape parameter, using the Weibull estimator (\(F(i,n)=\frac{j}{n+1}\)) [46]. As precipitation values are always positive, this approach to computing exceedance probability is broadly used in evaluating precipitation data [8; 47]. The visualization presents hourly precipitation on the y-axis and the computed exceedance probability on the x-axis. An asymmetric logarithmic scale is applied to show hourly precipitation intensities because of their predominantly small magnitudes.
## 4 Experimental Results and Analysis
### Degradation Analysis of Load Carrying Capacity  
The ultimate load carrying capacities of specimens are shown in Table 1. The degradations of ultimate load carrying capacities of corroded specimens relative to non-corroded specimens are shown in Figure 10. Table 1 and Figure 10 show that the load carrying capacities of specimens decrease with the increase of eccentricity. The reductions of ultimate load carrying capacities of specimens increase with the increase of corrosion ratio. The negative degradation ratio of load carrying capacity of specimen with eccentricity of 35 mm is mainly due to the smaller thickness and load carrying capacity of the non-corroded specimen C35-0 compared to the corroded specimens C35-30 and C35-90. In general, the relationships between the load carrying capacity degradations and the corrosion ratios of uniformly corroded specimens under various working conditions are linear. The slopes are greater than 1, and increase with the increase of eccentricity. That is to say, the degradations of load carrying capacities of specimens increase with the increase of eccentricity at the same corrosion ratio, and the reductions of ultimate load carrying capacities of specimens are greater than that of material strength.  
Figure 9: Load–axial displacement curves of specimens: (**a**) concentrically loaded; (**b**) eccentricity of 15 mm; and (**c**) eccentricity of 35 mm.
# A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions
###### Abstract  
Real-time monitoring and fault diagnosis of bearings are of great significance to improve production safety, prevent major accidents, and reduce production costs. However, there are three primary concerns in the current research, namely real-time performance, effectiveness, and generalization performance. In this paper, a deep learning method based on stacked residual dilated convolutional neural network (SRDCNN) is proposed for real-time bearing fault diagnosis, which is subtly combined by the dilated convolution, the input gate structure of long short-term memory network (LSTM) and the residual network. In the SRDCNN model, the dilated convolution is used to exponentially increase the receptive field of convolution kernel and extract features from the sample with more points, alleviating the influence of randomness. The input gate structure of LSTM could effectively remove noise and control the entry of information contained in the input sample. Meanwhile, the residual network is introduced to overcome the problem of vanishing gradients caused by the deeper structure of the neural network, hence improving the overall classification accuracy. The experimental results indicate that compared with three excellent models, the proposed SRDCNN model has higher denoising ability and better workload adaptability.  
dilated convolution; residual network; LSTM; bearing fault diagnosis +
Footnote †: journal: Applied Sciences