esa-sceva/satcom-chunk-collection huggingface dataset:
# Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera
## 2 Materials and Methods  
The software used in this work was MATLAB 2019a, along with its Deep Learning toolbox, running on a Windows 10 Pro operating system on hardware equipped with an Intel(R) Core(TM) i9-10900X CPU @ 3.70 GHz 3.70 GHz processor, manufactured in Dalian, Liaoning, China.  
A database was constructed using a ZED camera [23] mounted on the front of an SUV, as shown in Figure 2. The tour to take the photographs was conducted in the city of Celaya, Guanajuato, Mexico. A total of 179 images of bumps were collected and stored in folder 0 (134 yellow speed bumps and 35 unmarked speed bumps); 231 images of potholes were stored in folder 1; and 304 images of streets without bumps or potholes were stored in folder 2. The images were captured at a resolution of 376 \(\times\) 672 pixels. One of the most important factors when selecting the images was the position of the sun. For example, around six in the afternoon, the sun sometimes shone directly into the camera and hindered the capture of good-quality images. Therefore, it was necessary to eliminate such images, as well as some images that did not contain speed bumps or potholes, to balance the number of images in each class.  
Three categories stored in different folders were used to label the images: an image with label 1 has a speed bump, an image with label 2 has a pothole, and an image with  
Figure 1: Proposal flowchart.  
Figure 2: ZED camera sitting on the car’s front.  
label 3 has no speed bump or pothole. Examples of this type of image are shown in Figure 3. It is important to note that, in the database, there are speed bumps that are not marked and others that are marked in yellow. Some have yellow stripes, and others are completely painted yellow. The potholes, which are not desired characteristics of the streets, are not marked and could be confused with puddles of water on the streets.
## 2 Constraint-Handling Technique Based on a Neural Network for the Differential Evolution Algorithm
### General Overview of the Differential Evolution Algorithm  
In this work, the optimization strategy for the solution of synthesis problems is based on the differential evolution variant called DE/RAND/1/BIN [58]. The term \"DE\" in the name of the variant DE/RAND/1/BIN relates to the Differential Evolution algorithm. The term \"RAND\" indicates the inclusion of a random mutation with one \"1\" pair of individuals, meaning that the base vector is randomly selected from the population and one difference vector is added. The last term \"BIN\" implies the incorporation of a binomial crossover.  
DE/RAND/1/BIN initializes with a population \(\mathbf{X}^{G}=\{X_{1}^{G},X_{2}^{G},\ldots,X_{NP}^{G}\}\) in the generation \(G=0\) with \(NP\) individuals. Each individual \(X_{p}^{G}\ \forall\ p=\{1,2,\ldots,NP\}\) in the population \(\mathbf{X}^{G}\) has \(D\) genotypic characteristics represented by the design variable \(x_{p,q}\) i.e., \(X_{p}^{G}=[x_{p,1},x_{p,2},\ldots,x_{p,q}]\ \forall\ q=\{1,2,\ldots,D\}\). In the initial generation \(G=0\), the design variable vector is determined by random values in the range \([X_{min},X_{max}]\).  
The individuals in the population \(\mathbf{X}^{G}\) undergo the mutation and crossover processes to generate a population of offspring individuals \(\mathbf{U}^{G}=\{U_{1}^{G},U_{2}^{G},U_{3}^{G},\ldots,U_{p}^{G}\}\) in each generation \(G\). These processes aim to improve the fitness of individuals \(\mathbf{X}^{G}\) for the next generation of parents \(\mathbf{X}^{G+1}\). The mutation and crossover processes of the variant DE/RAND/1/BIN are detailed in (5):  
\[u_{p,q}^{G} = \overbrace{\left\{\underbrace{x_{r_{3,q}}^{G}}_{\text{Base vector}}\ \  
where \(CR\in[0,1]\) is the crossover factor, \(F\in[0,1]\) is a scale factor, and \(r_{1}\), \(r_{2}\) and \(r_{3}\) are random numbers that represent the information of three individuals; these must be different from the individual \(p\) and, among them, and \(q_{rand}\in\{1,2,\ldots,D\}\) is a random value that controls the crossover point.  
Each new offspring's individual \(U_{p}^{G}\) is compared with their parent \(X_{p}^{G}\). Both individuals compete and only the fittest individuals pass on to the next generation \(G+1\). In this work, the selection between the parent and the offspring vectors is based on the Neuronal Constraint-Handling (NCH) technique. This technique is described in the following subsection.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Wireless Sensor Networks (WSN) Technology
#### 2.1.1 Structure of a Typical Sensor Node  
To understand the WSN technology, a study of a typical sensor node is important. Figure 3 shows the four major units of a node: power, sensing, processing, and transceiver units. The power unit provides the energy required by other units in the node. This power source in most cases is provided by a battery. The sensing unit monitors the physical quantities that are presented in analog form or signals. With the aid of an Analog-to-Digital converter (ADC), the analog signal is converted to a digital signal. Both the sensor and ADC form the sensing unit. The digital signal is then processed by a micro-controller or processor. The processed data are stored temporarily in the storage section of the processing unit. The transceiver unit enables the node to connect with other nodes or the gateway in the network [42].
## 2 Numerical Methods
### Numerical Algorithm and Schemes  
The transient Reynolds-averaged Navier-Stokes (RANS) and species transport equations were numerically solved by discretizing the finite volume method. As for the RANS simulations, a turbulence model for the realizable \(k\)-\(\epsilon\) model [16] was adopted to accurately predict the spreading rate of both planar and round jets, since the realizable \(k\)-\(\epsilon\) model yielded favorable results in a parametric study pertaining to exhaust smoke from a ship based on turbulence models confirmed by the authors' previous studies [1; 2].  
The velocity and pressure were coupled using a semi-implicit method for a pressure-linked equation algorithm. The convection and diffusion terms of the governing and \(k\)-\(\epsilon\)transport equations were discretized via a second-order upwind and a central differencing scheme, respectively. The temporal term was discretized using the Crank-Nicholson scheme. An algebraic multigrid method was adapted to accelerate solution convergence. The computations were continued until the residuals of all solution variables were less than \(10^{-6}\). The numerical solver, schemes, and models used in this study were the same as those applied in the authors' previous studies [1; 2]. In those studies, the analysis focused on exhaust smoke from a navigating ship, and the results were validated through comparison with experimental data.
# A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study
## Maintenance Ontology and Implementation
### Implementation of the Maintenance Ontology
The KBS makes it possible for all data on the platform to have semantics following the rules provided by the maintenance ontology. It stores data in the form of graphs, making links between the data and providing context to the maintenance ontology. The KBS also facilitates describing the data in a triple format (Subject, Predicate, Object) by using a resource description framework (RDF). In addition, it provides semantic services, such as search, authentication, dataset, revision, and SPARQL (Figure 7). Thus, it facilitates governance of the maintenance ontology, controlling all the data on the platform and the semantic enrichment of the Z-BRE4K platform. Figure 5: Structure of the compression molding machine. Figure 6: Semantic description of an example scenario. Figure 7: The knowledge base system’s component diagram. The implementation details of the Z-BRE4K semantic model as an ontology provide some insights based on the practical implementation issues that developers have encountered:  
* The competency question methodology proved to be a useful tool for meeting the requirements of stakeholders in the domain field. Future efforts to develop ontologies for smart manufacturing applications should consider this method to standardize the ontology design and development process. * The BFO framework was successfully implemented to create ontologies in the maintenance engineering domain, and other domains should follow (e.g., zero-defect manufacturing, product-service systems, energy-efficient manufacturing). * Ontology engineering requires iterative processes. Hence, updates to the classes and their relationships from iterative feedback from end-users and technical providers in different use cases may help developers consolidate the ontology for application to further scenarios. A preliminary assessment of the evidence-based effectiveness of combining the data and this management approach is confirmed by the successful implementation of the Z-BRE4K platform, as presented in detail by [PERSON] et al. (2022) [50]. The Z-BRE4K system, which was developed following the principles of the semantic model provided, has contributed to industrial digitization and the creation of smart factories. It provides a generic platform as a complete solution, supporting standards-based factory connectivity, data management, various AI models' training and comparisons, live predictions, and real-time visualizations. Finally, Table 13 (in Appendix A) presents the Z-BRE4K ontology's object properties.
## 2 Materials and Methods: Ten-Koh Mission Description
### Design of Mission Operations  
The Japanese Aerospace Agency (JAXA) offered free launch slots for the GOSAT-2 launch in early 2017, and the Ten-Koh mission design was based on general estimates and assumptions for a Sun-synchronous polar orbit with an altitude of 613 km and 97.8 degrees inclination (the final orbit parameters were not confirmed until a few months prior to the launch). The previously described experiments and the associated six instruments were conceptualized during a brainstorming process and an official proposal was submitted to the JAXA as a bid for the GOSAT-2 launch opportunity, which was offered to Japanese universities. After the launch, the final orbit was confirmed to have the following parameters:  
* Perigee: 593 km.
* Apogee: 615 km.
* Inclination: 97.8 degree (retrograde orbit).
* Semimajor axis: 6975 km.
* Orbital period: 96.6 min.
* Eclipse duration: 47.7 min.
* Number of revisit times per day over Kyutech (Japan): 4.
* Revisit cycle: 9 days.
* Local equator crossing time of descending node: 13:00 h.  
Even though Ten-Koh can pass over the Kyutech ground station in Japan up to four times per day (twice at around 13:00 and twice at 01:00), only passes with more than 15 degrees of elevation are used. This is done in order to establish reliable communication between the ground station and the satellite (i.e., to avoid the loss of data packets). Ultimately, an average of two passes per day are usable.
# An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image
## 2 Theoretical Analysis and Methods
### Weak and Small Object Detection Algorithm
Based on the characteristics of the aircraft object, the data can be determined whether it is an aircraft object. As shown in Figure 5, weak and small aircraft can be detected using the proposed algorithm. In Figure 5b, the circumferential radius of the detected aircraft is only 2 pixels, and the minimum difference between objects and background in Figure 5d is only 1 pixel. In Figure 5a,b, some of the detection circumferences are not located at the center of the aircraft. Therefore, mean shift clustering is used to cluster the detection points to the center of the aircraft.
# Data Imputation Based on Retrieval-Augmented Generation
## 4 Experimental Verification
### Evaluation of Code Generator Effectiveness
#### 4.4.1 Dataset Preparation  
**Data Configuration Plan.** Data imputation typically benefits from rule-based methods. Therefore, its data management plan supports code and data access modules. Additionally, we briefly discuss the performance of small models on this task to confirm the superiority of the code and model in this area.  
**Datasets.** We use the Buy dataset and the Restaurant dataset for evaluation. The Buy dataset has three attributes: product name, product description, and manufacturer. In this dataset, the manufacturer is masked and considered the missing attribute to estimate. The Restaurant dataset has five attributes: restaurant name, address, city, phone number, and food type. In this dataset, the city is masked and regarded as the missing attribute to estimate. These two datasets are drawn from real-world domains. The _Buy_ dataset comprises product listings collected from an e-commerce platform, while the _Restaurant_ dataset contains restaurant records sourced from a public directory. Both have one key attribute masked (manufacturer or city) to create a realistic imputation task.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Re-ranker**} & \multicolumn{2}{c}{**WikiTuples**} & \multicolumn{2}{c}{**Education**} & \multicolumn{2}{c}{**Cricket Players**} \\ \cline{2-7}  & **S@1** & **S@5** & **S@1** & **S@5** & **S@1** & **S@5** \\ \hline Initial Retrieval & 0.231 \(\pm\) 0.005 & 0.733 \(\pm\) 0.004 & 0.577 \(\pm\) 0.004 & 0.923 \(\pm\) 0.003 & 0.830 \(\pm\) 0.003 & 0.989 \(\pm\) 0.001 \\ GPT-3.5/Pairwise & 0.470 \(\pm\) 0.006 & 0.756 \(\pm\) 0.004 & 0.260 \(\pm\) 0.005 & 0.960 \(\pm\) 0.002 & 0.932 \(\pm\) 0.002 & 0.989 \(\pm\) 0.001 \\ GPT-3.5/Listwise & 0.210 \(\pm\) 0.007 & 0.676 \(\pm\) 0.005 & 0.255 \(\pm\) 0.006 & 0.825 \(\pm\) 0.003 & 0.750 \(\pm\) 0.003 & 0.841 \(\pm\) 0.002 \\
**Re-ranker (ours)** & **0.630 \(\pm\) 0.005** & **0.915 \(\pm\) 0.003** & **0.976 \(\pm\) 0.002** & **0.986 \(\pm\) 0.001** & **0.941 \(\pm\) 0.002** & **1.000 \(\pm\) 0.000** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of performance on re-ranker subsets (average \(\pm\) standard deviation). The best results are bolded. Each experiment was repeated three times.
# Electric Field Distribution Induced by TMS: Differences Due to Anatomical Variation
## 4 Discussion
### Results Synopsis and Interpretation  
Due to the unique morphology of the human brain, the interindividual comparison of distributions is a challenging task. To overcome this challenge, the induced EFs were analyzed at multiple levels. At first, they were visualized over the individual brains, to identify the qualitative range of anatomical variability. Then the distributions were compared across subjects, in terms of stimulation metrics (E\({}_{\text{max}}\), E\({}_{\text{median}}\), V\({}_{\text{st}}\), V\({}_{1/2}\)), and between groups (anatomical and homogeneous versions for the same coil position), in terms of IQRs and averaged metrics. It was found that, at a level of \(p<0.05\), the IQRs for the anatomical models are larger than the ones of the corresponding homogeneous models, indicating that the anatomical distribution of electrical conductivity (resulting from tissue distribution) enhances inter-subject variation of the induced EFs.  
[PERSON] et al. [41] used two anatomical models in their calculations and claimed a difference of \(11\%\) between the \(99\)th percentiles of EF distributions, which they attributed to individual anatomy. In the current study, using the same approach for the \(99.9\)th percentile, the variation ranges between \(14\%\) and \(23\%\), depending on the location of the coil and the head model type. In another study [20], the inter-subject variation was estimated at \(38\%\) based on the maximum EF. Therefore, it is preferable for the impact of individual anatomy on the EF distribution to be determined by the statistical comparison of IQRs, which express the variability of the EF distribution in subjects, instead of a comparison of single points from the EF statistical distribution.  
Finally, the stimulation metrics were tested for linear correlation with distinctive anatomical parameters, including brain, head and CSF volumes, and age. It should be noted that a parameter was considered as a predictor variable only in the case a statistically significant correlation (\(p<0.05\)) was found with a simulation metric (response variable) for all investigated scenarios (two coil positions \(\times\) two head models). The correlation analysis indicated a significant negative correlation between the distribution's characteristics (median and quartiles) and brain/head volumes, suggesting that higher EFs are induced in smaller brains and heads. In addition, our results showed a moderate negative correlation between stimulated volume and age. However, the correlation of these two variables seems irrelevant to brain volume, since there was no equivalent correlation between normalized stimulated volume and age, implying a more complex mechanism for their association. Interestingly, no correlation was found for the maximal EF metric or the CSF volume parameter, raising the issue of whether the former should be defined as a different percentile of the distribution or whether the latter should be replaced by a CSF thickness parameter [33; 42]. Both issues remain to be addressed in potential future studies.
## 3 Formulation for a Mathematical Model of the DCBAP
### Estimation of Increased Handling Times for a Ship  
In this research, it is assumed that the berthing position of a ship will affect the ship's handling time. In Figure 3, we give each ship \(j\) or \(k\) an initial handling time based on their respective desired berthing positions. Thus, if any of the ships deviates from its desired berthing position, then the ship's handling time will increase due to the greater distance that a truck will need to move a container.  
Equation (4) is the formula for calculating the distance (\(\Delta b_{j}\)) for a ship \(j\) deviating from its desired berthing position.  
\[\Delta b_{j}=\left|B_{j}-d_{j}\right| \tag{4}\]  
where  
\(B_{j}\): is the actual berthing position of ship \(j\) alongside the quay;  
\(d_{j}\): is the desired berthing position of ship \(j\) alongside the quay.  
Then, the increased handling times (\(\Delta H_{j}\)) for the ship \(j\) is determined by Equation (5)  
\[\Delta H_{j}=\beta\cdot\Delta b_{j} \tag{5}\]  
where  
\(\beta\): is the berth deviation factor; a rate estimating the increase of handling time due to the deviation from the desired berthing position (\(d_{j}\)).  
Figure 3: The coordinates of two corners of the ships \(j\) and \(k\).  
Equation (6) is used to estimate \(T_{j}\), the actual handling times of a ship \(j\).  
\[T_{j}=h_{j}+\Delta H \tag{6}\]  
where  
\(h_{j}\) is the original handling time for ship \(j\) (this is not affected by berthing position).  
Finally, given the actual start berthing time of ship \(j\) (\(S_{j}\)), the completion time of a ship \(j\) can be estimated by Equation (7).  
\[C_{j}=S_{j}+T_{j},\ \ \forall j\in J \tag{7}\]
## 3 Results and Discussion
### Thermal Properties
These stages were more evident in the reinforced samples with high filler contents (40 wt%). In the first stage, BioEP resin was thermally degraded along with the low molecular weight components of FF such as hemicelluloses, while in the second stage the thermal degradation of cellulose and lignin occurred. For this reason, this stage was more evident in composites with high filler content. Regarding the maximum degradation temperature (\(T_{\max}\)), obtained from the peak of the first degradation stage of the DTG curves, it can be seen that the incorporation of the reinforcement into the BioEP matrix slightly decreases this temperature (it almost remains constant). In this case, the \(T_{\max}\) of composites did not vary significantly with the particle size or content, obtaining a \(T_{\max}\) of around 322 \({}^{\circ}\)C for all developed composites, except for the composite reinforced with 40 wt% of FFF, which has a \(T_{\max}\) slightly lower of about 319 \({}^{\circ}\)C. Therefore, after thermogravimetric analysis, the results suggest that as the lignocellulosic reinforcement  
Figure 4: Thermal degradation of BioEP, FF, and BioEP/FF composites reinforced with a different content of coarse (CFF) and fine (FFF) flaxseed flour (**a**) thermogravimetry (TG) weight loss and (**b**) differential thermogravimetry (DTG) first derivative curves. content increases, the thermal stability of the composites decreases, while the residual mass at high temperatures (700 \({}^{\circ}\)C) increases. Nevertheless, the overall thermal stability of these composites is not compromised by incorporating FF in both coarse and fine particle size.
## 2 Review of Kernel-Based TF Methods
### The Modified B Distribution  
The separable kernel TFDs have flexibility to independently adjust smoothing along the time- and frequency axes. Therefore, these methods generally perform better than the spectrogram [26]. The smoothing function of such a TFD is usually expressed as \[\gamma(t,f)=g_{1}(t)G_{2}(f), \tag{14}\]  
where \(g_{1}(t)\) performs smoothing along the time axis and \(G_{2}(f)\) performs smoothing along the frequency axis. The separable kernel TFDs can be expressed as  
\[\rho(t,f)=g_{1}(t)\underset{t}{*}W_{2}(t,f)\underset{f}{*}G_{2}(f), \tag{15}\]  
where \(W_{2}(t,f)\) now is the Wigner-Ville distribution computed from the analytic signal \(z(t)\), using the following expression:  
\[W_{2}(t,f)=\int z(t+\tau/2)z^{*}(t-\tau/2)e^{-\beta\pi f\tau}d\tau. \tag{16}\]  
The analytic signal \(z(t)\) is obtained from real-valued signal \(x(t)\) using the Hilbert Transform, i.e.,  
\[z(t)=x(t)+jH\left\{x(t)\right\}. \tag{17}\]  
Equation (15) can be more efficiently implemented in the time-lag domain as [26]:  
\[\rho(t,f)=\int g_{1}(t)\underset{t}{*}R_{2}(t,\tau)g_{2}(\tau)e^{-\beta\pi f \tau}d\tau, \tag{18}\]  
where \(g_{2}(\tau)=\int G_{2}(f)e^{\beta\pi f\tau}df\). The modified B distribution (MBD) uses a time-only smoothing kernel defined as [27]:  
\[g_{1}(t)=\frac{cosh^{-2\beta}(t)}{\int cosh^{-2\beta}(t)dt}. \tag{19}\]  
The aforementioned formulation of the MBD does not have any parameter to control smoothing along the frequency axis, but, in practice, a rectangular lag-window is used to restrict the integration of Equation (18) to finite limits.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Prediction Intervals Formulation and Evaluation Metrics
#### 2.5.4 Prediction Intervals Normalised Average Width (PINAW)  
PINAW is one of the indices for assessing the PI. It gives quantitative width of the PIs and is given in Equation (23)  
\[PINAW=\frac{1}{mR}\sum_{i=1}^{m}PIW_{i}, \tag{23}\]  
where \(R=y_{\text{max}}-y_{\text{min}}\) represents the range of the highest and lowest values of the actual \(y_{i}\).