esa-sceva/satcom-chunk-collection huggingface dataset:
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 3 Overview of the Discrete Analog Functional Blocks of ArduSiPM
### PC/104 PCB Layout  
PC/104 stands out as one of the most popular embedded computer standards, known for its smaller, stackable form factors and communication interfaces. With dimensions of 90.17 mm \(\times\) 95.89 mm, PC/104 boards differ from typical PC motherboards as they are stacked vertically, allowing for space-saving configurations. Thanks to its versatility, this form factor finds extensive use in various applications beyond satellites. In fact, the On-Board Computer (OBC) of a CubeSat often relies on PC/104 technology. Therefore, the layout of the Cosmo ArduSiPM is designed to adhere to the PC/104 standard, ensuring compatibility with a wide range of applications. This design choice enables the integration of two isolated SiPM detectors on a single board, achieved by selecting electronic components with the smallest possible package size. Additionally, for flexibility, the PC/104 board can be divided by cutting it into two separate and fully independent smaller boards, making it suitable for a variety of purposes.  
Assessing Cosmo ArduSiPM: Overview of Electronics Performance, SiPM Noise Characterization, and Response to Various Photon Fluxes  
The performance of the Cosmo ArduSiPM was extensively evaluated using two separate test benches: one dedicated to electronic performance and another designed for photonic optical tasks. We also conducted a preliminary gamma spectrometry test with a crystal,  
Figure 6: The output of the peak-hold circuit (fuchsia signal) generated by a pulse signal of few ns (yellow signal). The waveforms are acquired with a oscilloscope Lecroy WaveRunner HRO 64 Zi. The peak hold keeps the voltage level at the maximum of the input signal without appreciable discharge. The time scale is 100 ns/division.  
intended as an exploratory probe rather than a detailed study. Initially, our focus was exclusively on the device's electronic characteristics, employing a programmable pulse generator instead of a real SiPM. This setup allowed us to assess the maximum performance achievable, regardless of the sensor model. Subsequently, we used short optical pulses containing just a few photons to operate a Hamamatsu S13660-1325 CS SiPM (Hamamatsu, Hamamatsu City, Japan), a small sensor renowned for its very good timing capabilities. We continued our investigations by analyzing the system's response to various simulated scenarios on an optical test bench. To enhance photon collection, the crystal used in the spectrometry was coupled with a 6 \(\times\) 6 mm\({}^{2}\) SiPM.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 2 Background
### Deep Learning-Based Super-Resolution Method  
The intuition behind the deep learning-based SR is utilizing an advanced neural network algorithm and enhancing the LR digital frame's resolution. In this type of approach, the deep neural network is trained on a large dataset of HR image and its corresponding LR images. This approach allows for mapping between LR image to HR image.  
The initial deep learning-based algorithm was introduced in 2014 [49] as image SR using deep convolutional networks (SRCNNs). In this approach, the authors designed the network in three consistent layers: the first layer extracts the features from the LR image, the second layer does non-linear mapping, and the last layer constructs the HR counterparts. The relationship is represented in Figure 2.  
Apart from SRCNNs, the first Generative Adversarial Network (GAN)-based approach was introduced by [PERSON] et al. [PERSON] et al. (2016) in 2016. In this method, authors designed a generator that upsamples the LR images after the 16 residual convolutions. Here, each residual block consists of a convolutional layer, batch normalization, PReLU activation function, convolutional layer, batch normalization, and element-wise summation layer, respectively. Here, a generator produces an HR image, and then the discriminator classifies whether the generator's produced image is real or fake. In the discriminator, authors followed eight residual blocks, where each residual block consists of convolution, batch normalization, and Leaky ReLU followed by each other.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 5 Experimental Analysis  
In this section, we present experiments on real-world datasets. The experiments are divided into two parts: the first part was designed to test the effectiveness of the proposed anonymization algorithm against composition attack, in comparison with the \(\epsilon-DP\)[41], Hybrid [2], Probabilistic [23], Composition [24] and Mondrian [44] methods. Our experimental results show that the Merging method also provides smaller privacy risks for the composition attacks. The results of this experiment are presented in _Composition Attack_ subsection.  
In the second part, we evaluated the effectiveness of our Merging method in preserving data utility, as compared to the same set of competing methods. The experiment demonstrates that the Merging method preserves more data utility than the other methods. In addition, it has smaller relative query error and better classification accuracy than the competing methods. The results of this experiment are presented in _Data Utility_ section.
## 2 Design of the Proposed Antenna Array  
In this section, the design flow from all layers of the proposed antenna array will be illuminated in detail. Firstly, according to the principle of multi-mode excitation, a square patch and a pair of coupling slots are stacked together as a driven patch. Then, multilayer radiation analysis is applied to guide the design of the parasitic patch array. Finally, we present the 1 to4 CSC feeding network to realize a wide bandwidth and high-gain antenna array.
# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 2 Methodology
### Precise Geocoding  
All the shorelines generated by processing the input dataset are available in the Master SAR geometry and are all co-registered at sub-pixel level. Additionally, the results can be projected in geographic coordinates. Interestingly, the conversion from SAR coordinates to geographic coordinates is performed only for the Master geometry since all the shorelines are precisely co-registered on it. In this way, apart from reducing the computational cost of the geocoding (which is performed only for one image), we also avoid the random perturbation of geocoding errors that would result if each single image of the dataset was geocoded independently. The geolocation of the Master image is performed by taking into account the atmospheric propagation delay correction which is estimated through Numerical Weather Modeling (NWM) and is integrated into the coastal monitoring system in order to ensure a geolocation accuracy close to or better than the spatial resolution of the SAR images [67; 68]. Finally, the extracted land-sea interface is further precisely geolocated using Ground Control Points (GCP).
## 2 Related Work
The path planning and control part of the presented ADAS is mostly based on previously published algorithms for planning kinematically feasible paths of buses [33; 34], identification of vehicle models [35] and control technique [36; 37]. The contribution of this article to the planning part lies in replacing the planner based on state lattices [34] with a more general one, applying an optimisation technique. Moreover, we demonstrate a novel use of the freely available OpenStreetMap data to define the constraints for this planner, thus avoiding the need to have a mapping sensor on the vehicle. When GNSS-based localisation of a vehicle is considered, the vehicle odometry (steering angle and wheel speed) is used to compensate for the relatively low sampling frequency of the GNSS and the occasional outliers in the GNSS-estimated vehicle pose. The recent literature describes algorithms that improve the localisation accuracy during GNSS outages, for example by estimating the lateral velocity of the vehicle [38], and methods for vehicle slip angle estimation [39; 40]. However, these approaches are intended for highly dynamic driving conditions, which are not present at all during the docking manoeuvres considered in this paper. Our system uses the kinematics model of a city bus obtained for small velocities when the effects of the vehicle's dynamics are minimal. This model turned out to be sufficient [35] for the operational speed of the bus while docking, which does not exceed 20 km/h. A human-machine interface (HMI) is essential for successful ADAS operation because it provides a communication interface between the computer system of ADAS and the driver. This communication has to be both distraction-free, to ensure safety, and efficient to ensure that the driving cues or alarms generated by ADAS are displayed timely and clearly to the human driver. Different concepts of the HMI for ADAS have been proposed in the literature, mostly from the viewpoint of ergonomics [41; 42; 43]; however, there is a lack of literature dealing explicitly with HMIs for city buses. Whereas the information can be provided to the driver through visual, acoustic and haptic modalities, including force feedback on the steering wheel, our ADAS uses a relatively simple single-display graphical HMI with additional audible warnings. This is again motivated by the requirements of the bus operator, which wants to have a system that can be easily installed in the existing fleet of electric buses at a reasonable cost. In such a case, the design of the graphical user interface of the HMI [44] becomes particularly important, as the visual cues are the only means to communicate the ADAS decisions to the driver. Therefore, we describe the HMI in detail and draw conclusions about its influence on the correctness of the docking manoeuvres in our tests.
# An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs
## 4 ANFIS: An Introduction  
Recently, a fundamental change has occurred in the methodology of empirical analysis. Because of the nonlinearity and high degree of uncertainty associated with structural behavior, traditional mathematical models are difficult to develop. As an alternative, FIS- and ANN-based models (belonging to \"soft computing\") are being used for many civil engineering problems. Nowadays, ANNs have been accepted as very useful tools for modeling nonlinear systems and are being widely used. FIS has emerged as a useful tool to represent and analyze complex systems [31; 32; 33]. Each method has its own advantages and disadvantages. Whereas in FIS there is no systematic procedure for designing a fuzzy controller, ANNs have the ability to map the input and output datasets through supervised learning and a self-organized structure. For this reason, it was proposed to combine an FIS and ANN together to get ANFIS, which enhances the efficiency of the systems and the modeling of problems using available data. ANFIS is thus an integration of an ANN and an FIS and uses basic FIS rules and the ANN network architecture to update system parameters using existing input and output pairs. ANFIS was first introduced by [PERSON] [34]. In both an ANN and FIS, input parameters pass through the input layer using an input membership function, and the output parameters are seen in the output layer using output membership functions. In this method, the parameters are changed until an optimal solution is reached using a learning algorithm. A basic flow diagram of computations in ANFIS is illustrated in Figure 1. Several fuzzy inference systems have been developed by different researchers [35; 36; 37; 12; 38], who commonly use Mamdani-type and Takagi-Sugeno-type systems. In this study, a Takagi-Sugeno-type system was used.
## 3 Critical Metrics for Industrial Monitoring and Control
### Reliability  
Reliability is an integral part of any industrial monitoring and control system as any slight degradation in communication can potentially result in complete system malfunction. In order to ensure reliable wireless communication, various techniques can be used to mitigate communication problems such as interference and weak signals. Figure 1 gives an overview of the different classes of problems in wireless communication commonly present in industrial environments and their relevant solutions. We present some of the more important solutions developed both in academia and industry in greater detail in the following sections. The PHY aspects of reliability are addressed in Section 6.  
Figure 1: Common wireless communication problems and relevant solutions in typical industrial environments.
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 5 Results
### Evaluation
#### 5.2.1 Computational Costs  
In our scheme, many computing initiatives are combined to enable smooth coordination between drones and blockchain technology. While leader-follower communication involves data transmission and reception, the path planning of the leader drone necessitates processing resources for environmental data analysis and the best route selection. For each drone to participate in the network and use blockchain technology, it is necessary to run and maintain a blockchain node, implement smart contracts to automate swarm operations, and carry out data encryption and verification processes. Drones may participate in resource-intensive calculations or allocation processes, depending on the blockchain's consensus algorithm. Computing power is needed for the swarm's networking and communication, as well as for security measures, data processing, storage, and retrieval. For efficient operation, real-time data processing and resource monitoring are crucial.  
Another level of computing complexity is added by addressing latency issues and enabling real-time decision making. The particular computational requirements vary depending on the complexity of the swarm, the blockchain platform, the number of drones, the frequency of interactions, and the tasks at hand, necessitating a delicate balance between the fundamental operations of the drones and their energy limitations for optimal performance.
# A Model for Working Environment Monitoring in Smart Manufacturing
## 3 Model of a System for Monitoring Working Environments in Smart Manufacturing
### Indoor Dust Control Simulation
Since a CNN is not suitable for implementation in low end wireless nodes, it was planned to be deployed in the middleware layer. Several variants of CNN architectures were tested, some of which contain more than 12,000,000 trainable parameters, so training can be extremely time consuming. However, the goal was that the training does not take long on the average hardware/software platform to allow for possible retraining, which would improve the system. Certainly, it is desirable to measure the duration of the training process in hours instead of days. It was decided to use four convolutional layers with a \"relu\" activation function; there were 64 \(3\times 3\) kernels. Each convolution layer was followed by a pooling layer, the window size was 2 by 2. Finally, there were two fully connected layers with 164 and 4 neurons, and the activation functions were \"relu\" and \"softmax\", respectively. The loss function was \"categorical_crossentropy\", with \"adam\" optimizer, \"accuracy\" metrics, and validation split set to 0.1. There were 154,232 trainable parameters in total. After 100 epochs, the achieved training set accuracy was 0.977. The third step was testing the previously trained network on 1200 images. The achieved test set accuracy was 0.822. The accuracy of 0.822 was not particularly high, so there was an additional adjustment of the CNN and a more significant number of images in the training set.
## 5 Simulation Results  
### Test Questions and Parameter Settings  
In this section, the proposed IHBA is compared with the competitive memetic algorithm (CMA) [34], biogeography-based optimization algorithm (BBO) [35], estimation of distribution algorithm ( EDA) [36], genetic algorithm [19; 21], and particle swarm optimization algorithm [20], and only one of these algorithms was selected for comparison from the literature [37]. So far, the three DIWO algorithms have proved to be the state-of-the-art algorithms for the considered problem with the parameters shown in Table 3. All simulation experiments were implemented on the MATLAB r2020a platform. All programs were executed on an AMD A8-7100 Radeon [EMAIL_ADDRESS] GHz and 12.0 GB RAM in Windows 10 operating system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison** & **Parameters** & **Range** \\ \hline \multirow{6}{*}{TDIWO} & Initial population size & 10 \\  & Maximum population size & 50 \\  & permutation-based shift operator & 5 \\  & Max value & 20 \\  & Min value & 1 \\  & Control parameters & 0.9 \\ \hline \multirow{6}{*}{CMA} & Selection probability of a subclass & 0.5 \\  & Selection probability of populations & 0.5 \\  & Number of populations & 50 \\  & Local search operator & 100 \\ \hline \multirow{6}{*}{BBO} & Number of populations & 40 \\  & Elite retention number & 10 \\  & Max emigration rate & [0,1] \\  & Max migration rate & [0,1] \\  & Max mutation rate & [0,1] \\ \hline \multirow{2}{*}{EDA} & Population size & 50 \\  & Probability vector & 0.3 \\  & Iterative Probability Sampling Parameters & 0.4 \\ \hline \multirow{6}{*}{GA} & Number of populations & [40,50,60] \\  & Max number of iterations & 300 \\ \cline{1-1}  & Crossover rate & [0.7, 0.9] \\ \cline{1-1}  & Mutation rate & [0, 0.2] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Algorithms and Parameters.  
To calibrate the proposed IHBA, a total of 810 instances were randomly generated according to the literature [5,34] written by [PERSON] et al. These instances can be found in [[http://soa.iti.es/problem-instances](http://soa.iti.es/problem-instances)]([http://soa.iti.es/problem-instances](http://soa.iti.es/problem-instances)), accessed on 1 September 2021. These instances combine four factors, including the number of jobs (\(n\)), the number of machines (\(m\)), the number of products (\(s\)), and the number of plants (\(f\)). Each factor has three levels, \(n\in\{100,200,500\},m\in\{5,10,20\},f\in\{4,6,8\}\) and \(s\in\{30,40,50\}\). Thus, a total of \(3^{4}=81\) factor combinations are studied. The processing time in the production stage is randomly generated in the range \([1,99]\) with uniform distribution. The operation time of the transportation stage is obtained in the range \([1,99]\) with uniform distribution. The assembly operation time for the assembly stage is obtained in the range between \(nl\) and \(99 nl\) with uniform distribution.
## 3 Materials and Methods
### RNA Isolation and Quantitative Real-Time PCR Analysis  
A total of 50-100 mg of frozen berry samples representing the different development stages was ground in a mortar and the resultant powder was used for the RNA extraction with the TRIzol extraction reagent (Invitrogen, Carlsbad, CA, USA). Agarose gel electrophoresis was used to assess the quality of every RNA sample and high-quality RNA samples with an A260/A280 ratio of 1.8:2.1 were used as the template material for the qRT-PCR analyses. Specifically, complementary DNA (cDNA) was transcribed using the Takara Prime Script TM-RT PCR reagent Kit (Takara, Shiga, Japan) according to the manufacturer's instructions. Since the monoterpene volatiles showed the most significant variations after the elicitor treatment, we quantified the expression of genes in the terpenoid metabolic pathways by qRT-PCR analysis (ABI7300, Applied Biosystems, Milan, Italy). Relative transcript levels were calculated using the 2\({}^{-\Delta\Delta\text{Ct}}\) method [53] and _VvActin_ was used as the reference for normalization purposes. All reactions, including non-template controls, were performed in triplicate. Sequences of the primers used to amplify the _VvDXS1_, _VvDXS3_, _VvDXR_ and _VvHDR_ genes were referenced from [PERSON] [43]; _VvPNGer_ and _VvPNlinNer1_ were referenced from [PERSON] [47].
# Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach
###### Abstract  
Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management.  
knowledge graph; accident causation correlation analysis accident report; safety management Article  
1 Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi'an710051, China; [EMAIL_ADDRESS] (J.X.); [EMAIL_ADDRESS] (W.T.)  
2