esa-sceva/satcom-chunk-collection huggingface dataset:
# A Computer Vision Milky Way Compass
## 2 Materials and Methods
### Methodology
#### 2.2.1 Image Thresholding  
The only discernible large object in a clear, moonless night sky is the MW. This suites thresholding techniques that assume that the image contains two pixel groups, those belonging to the foreground and those belonging to the background. Because of the simple computation, robustness and adaptability, [PERSON]'s technique [35] is extensively used for computer vision applications. The algorithm is used to automatically determine optimal threshold values to segment the background from the foreground area [36;37]. Figure 8 shows a comparison of two additional common thresholding techniques, [PERSON]'s method [38] and [PERSON]'s entropy method [39]. All three popular image-thresholding approaches achieve similar results in this situation.  
The following equations represent [PERSON]'s thresholding method of an image with \(L\) gray levels. Suppose we split the image pixels into two classes \(C_{0},C_{1}\) by threshold at level \(t\). The probability of occurrence of level \(i\) is given by \(\frac{n_{i}}{N}\)  
\[p(i)=\frac{n_{i}}{N},p(i)\geq 0,\sum_{i}^{L-1}p(i)=1 \tag{1}\]  
The probabilities of two class occurrences are given by  
\[\omega_{0}=\mathrm{P}(C_{0})=\sum_{i=0}^{t}p_{i}=\omega(t) \tag{2}\]  
\[\omega_{1}=\mathrm{P}(C_{1})=\sum_{i=t}^{L-1}p_{i}=1-\omega(t) \tag{3}\]  
The mean of the two classes is calculated based on the following equations  
\[\mu_{0}=\sum_{i=1}^{t}\frac{i\cdot p(i)}{\omega_{0}}=\frac{1}{\omega(t)}\sum_ {i=1}^{t}i\cdot p(i) \tag{4}\]  
\[\mu_{1}=\sum_{i=t+1}^{L}\frac{i\cdot p(i)}{\omega_{1}}=\frac{1}{1-\omega(t)} \sum_{i=t+1}^{L}i\cdot p(i) \tag{5}\]  
Total mean can be written as  
\[\mu_{T}=\mu(L-1)=\sum_{i=0}^{L-1}ip_{i} \tag{6}\]  
Figure 8: Thresholding method comparison for real sky images.  
Otsu is defined as the between-class variance (BCV) that is shown as  
\[\sigma_{B}^{2}=\omega_{0}(\mu_{0}-\mu_{T})^{2}+\omega_{1}(\mu_{1}-\mu_{T})^{2} \tag{7}\]  
The optimal thresholding value of Otsu \(t\) is chosen by maximising \(\sigma_{B^{2}}\)  
\[t\ =\ \max\{\sigma_{B^{2}}\} \tag{8}\]  
We used MATLAB R2021a software to calculate the Otsu threshold and to apply the flat morphological structuring element to dilate the object area. The spherical structuring element with a selected radius was used to generate the dilated binary object image. There is an example in Figure 9 that shows the MW detection result of the generated test image shown in Figure 10 with cardinal points.  
In Figure 9, the left image is the detection area result that is overlaid on the original RGB test images. The middle image shows the threshold results for the MW area, and on the right is the edge detection result.
## 3 Fundamental Limits  
In this section, we derive the fundamental limits for synchronous and asynchronous ISAL networks, respectively. According to Equations (3)-(6), both localization and sensing can be considered parameter estimation problems. The parameters to be estimated in the \(n\)th time slot of the synchronous ISAL network can be written as  
\[\mathbf{\theta}^{(n)}=[\mathbf{p}_{\rm r}^{(n)\rm T},\mathbf{p}_{\rm t}^{(n)\rm T }]^{\rm T}, \tag{7}\]  
where \(\mathbf{p}_{\rm r}^{(n)}\) and \(\mathbf{p}_{\rm t}^{(n)}\) are the position vectors of radars and targets in the \(n\)th time slot, respectively.  
The parameters to be estimated in the \(n\)th time slot of the asynchronous ISAL network can be written as  
\[\mathbf{\theta}^{(n)}=[\mathbf{p}_{\rm r}^{(n)\rm T},\mathbf{\tau}^{(n)\rm T},\mathbf{ p}_{\rm t}^{(n)\rm T}]^{\rm T}, \tag{8}\]  
where \(\mathbf{p}_{\rm r}^{(n)},\mathbf{p}_{\rm t}^{(n)}\), and \(\mathbf{\tau}^{(n)}\) are the position vectors of radars and targets and the time offsets between the anchors and different radars in the \(n\)th time slot, respectively.  
According to [16], CRLB is often used as the lower bound of the variance of an unbiased estimator. In the ISAL networks, we introduce the squared position error bound (SPEB) instead of CRLB as the lower bound of the mean square error (MSE), which is defined as  
\[\mathcal{P}(\mathbf{\theta}_{1})\triangleq\text{tr}(\mathbf{J}_{\rm b}^{-1}(\bm {\theta}_{1}))\leq\mathbb{E}(\|\mathbf{\theta}_{1}-\mathbf{\theta}_{1}\|^{2}), \tag{9}\]  
where \(\mathbf{\theta}_{1}\) and \(\mathbf{\theta}_{1}\), respectively, represent one of the parameters in \(\mathbf{\theta}\) and an estimate of the parameter vector \(\mathbf{\theta}_{1}\) based on an observation of the received signals, and \(\mathbf{J}_{\rm e}(\mathbf{\theta}_{1})\) represents the EFIM of the parameter \(\mathbf{\theta}_{1}\)[17].
## 3 Preliminaries
### Feature Selection  
Information theory feature selection algorithms measure the randomness of the feature \(F\) through entropy. The higher the entropy, the higher the uncertainty, which makes the feature become less informative. Feature selection methods derived from information theory measure the mutual information that exists between the features to reduce the uncertainty and select only the highly informative features [53]. The proposed work explores various feature selection algorithms using mutual information as relationship measure for computing a robust feature subset. The optimal feature subset will serve as the input to the machine learning classifier.
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 2 Feature Extraction Method Based on ICEEMD-Time-Frequency Information Entropy  
Vibration signals of planetary gearboxes are complex, with multiple components and nonlinear and nonstationary characteristics that are obviously interfered with by noise [17]. How to obtain fault characteristic information effectively is an important part of fault diagnosis of planetary gearboxes. In this study, the vibration signal feature is extracted by ICEEMD-time-frequency information entropy to characterize the operating state of the planetary gearbox.
# Post-Digital Learning for Rural Development: A Case Study of _Open Biotec_ MOOCs in Colombia
## 2 Materials and Methods
### MOOCs Creation  
The creation of MOOCs for the biotechnology training route utilized Action Research methodology, grounded in the connectivism learning theory [54; 55]. This approach allowed for an iterative cycle of planning, action, observation, and reflection to address the specific problem of developing effective online learning experiences.  
To design the MOOCs, the researchers conducted a previous analysis of the target community, utilizing the information collected in a prior diagnosis. The team then defined the competencies, learning objectives, performance criteria, and structure of the course based on the principles of connectivism learning theory.  
For the construction of the courses, a script was created, incorporating dialogues, graphic scenarios, illustrations, and characters (avatars) for each lesson. This approach allowed for the creation of engaging and interactive learning experiences [44; 56].  
Afterward, SCORM packages were developed for each course and hosted in Moodle as the Learning Management System (LMS). The use of Moodle as the LMS allowed for the tracking of student progress and the provision of feedback to learners based on their performance.  
Finally, a website was implemented in WordPress for the management of the LMS, the user accounts, and tracking the platform usability, under the domain name [[https://openbiotec.com](https://openbiotec.com)]([https://openbiotec.com](https://openbiotec.com)) (accessed on 20 March 2023). This website provided a user-friendly interface for learners to access the MOOCs and for researchers to track platform usage and make improvements to the courses based on data-driven insights.
## 6 Conclusions  
Our results showed that an online ultrasound training method for physiotherapists is valid and reliable when compared to an onsite ultrasound training method. Our results also showed that the Online 2 group obtained better scores for the theoretical and the practical exams than the Onsite 2 group, while in the Online 1 group, scores for the theoretical exam were similar to those of the Onsite 2 group. The online ultrasound training method showed excellent ICC for the Online 2 group, meaning that future online courses should be based on a minimum of 36 h of training for optimal education.  
Conceptualization, S.F.-C.; methodology, S.F.-C., J.N.C.-Z., D.P.-M., A.A.-O., A.F.-G., and T.G.-I.; software, S.F.-C., J.N.C.-Z.; validation, S.F.-C., J.N.C.-Z., D.P.-M., A.A.-O., A.F.-G. and T.G.-I.; formal analysis, S.F.-C., J.N.C.-Z., D.P.-M., A.A.-O., A.F.-G. and T.G.-I.; investigation, S.F.-C., J.N.C.-Z., D.P.-M., A.A.-O., A.F.-G. and T.G.-I.; resources, S.F.-C., D.P.-M. and T.G.-I.; data curation, S.F.-C., J.N.C.-Z., D.P.-M., A.A.-O., A.F.-G. and T.G.-I.; writing-original draft preparation, S.F.-C., J.N.C.-Z., D.P.-M., A.A.-O., A.F.-G. and T.G.-I.; writing-review and editing, S.F.-C., J.N.C.-Z., D.P.-M., A.A.-O., A.F.-G. and T.G.-I.; visualisation, S.F.-C., J.N.C.-Z., [PERSON]-M., [PERSON]-O., [PERSON]-[PERSON] and [PERSON]-I.; supervision, [PERSON]-C., [PERSON]-Z., [PERSON]-M., [PERSON]-O., [PERSON]-G. and [PERSON]-I.; project administration, [PERSON]-C., J.N.C.-Z., [PERSON]-M., [PERSON]-O., [PERSON]-G. and T.G.-I.; funding acquisition, [PERSON]-C., [PERSON]-M. and T.G.-I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Department of Docent Innovation of the Francisco de Vitoria University.  
We acknowledge the facilities and devices borrowed by the enterprise Helios Electromedicina (Madrid) for the philanthropic collaboration to develop the sampling.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Hollow Structure** & **Solid Structure** & \begin{tabular}{c} **Distance between** \\ **Structures** \\ \end{tabular} & \
\begin{tabular}{c} **Solid Structure** \\ **Surface Distance** \\ \end{tabular} &
\begin{tabular}{c} **Hollow Structure** \\ **Surface Distance** \\ \end{tabular} \\ \hline Onsite 1 & 0.048 & 0.061 & 0.019 & 0.015 & 0.016 \\ Onsite 2 & 0.047 & 0.006 & 0.029 & 0.015 & 0.044 \\ Online 1 & 0.143 & 0.142 & 0.122 & 0.015 & 0.000 \\ Online 2 & 0.350 & 0.062 & 0.943 & 0.015 & 0.077 \\ \hline \hline \end{tabular} ICC: intraclass correlation coefficient.  
\end{table}
Table 3: ICC for each group and measurement.
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 4 Results
### Heuristic Methods Benchmark Results
#### 4.3.1 Parameters Optimization Results
The implementation of these methods exhibited relatively low memory demands on current computing systems, with a maximum observed peak of 233 MB. Therefore, memory usage does not present a significant concern. However, as will be seen in Section 4.3.2, the fastest method does not necessarily yield the lowest \(FF_{\text{Optimized}}\). \begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Min**} & \multicolumn{2}{c}{**First**} & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**Median**} & \multicolumn{2}{c}{**Third**} & \multirow{2}{*}{**Max**} \\  & & & & & & & **Quartile** & \\ \hline \multirow{2}{*}{EDA} & Population Size & 12 & 30 & 31 & 35 & 35 & 35 \\  & Sigma & 0.121 & 0.330 & 0.348 & 0.358 & 0.365 & 0.632 \\ \hline \multirow{2}{*}{CE} & Population Size & 14 & 21 & 22 & 23 & 23 & 24 \\  & Elite Fraction & 0.355 & 0.400 & 0.440 & 0.416 & 0.441 & 0.678 \\ \hline \multirow{2}{*}{ABC} & Num Employed Bees & 10 & 24 & 24 & 26 & 26 & 27 \\  & Num Onlooker Bees & 24 & 24.0 & 27 & 26.0 & 29.0 & 34 \\  & Num Scout Bees & 1 & 3.000 & 4 & 3.500 & 4.250 & 5 \\ \hline \multirow{2}{*}{CS} & Population Size & 11 & 27 & 26 & 28 & 28 & 28 \\  & pa & 0.172 & 0.230 & 0.262 & 0.246 & 0.277 & 0.441 \\  & Step Size & 0.021 & 0.037 & 0.039 & 0.038 & 0.042 & 0.051 \\ \hline \multirow{2}{*}{APSO} & Num Particles & 11 & 28 & 26 & 28 & 29 & 29 \\  & W Max & 0.764 & 0.813 & 0.834 & 0.824 & 0.831 & 0.986 \\  & W Min & 0.203 & 0.238 & 0.269 & 0.248 & 0.290 & 0.386 \\  & C1 & 1.301 & 1.749 & 1.782 & 1.886 & 1.915 & 1.931 \\  & C2 & 1.623 & 1.734 & 1.799 & 1.802 & 1.855 & 2.114 \\  & V Max & 0.640 & 0.660 & 0.814 & 0.684 & 0.748 & 1.831 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 9: Average time per generation/iteration for each method.
## 4 Problem Formulation and Algorithm Proposed
### Problem of Minimum Network Cost Transducer Orientation  
#### 4.1.1 NP-Complete of Transducer Orientation Problem  
Firstly, we consider the fixed orientation problem of transducers with \(\pi/3\), whose orientations are stabilized after the network is established.  
Assuming a symmetric connected network composed of nodes set \(V\) can be expressed as an undirected graph \(G=(V,E)\), \(E\) is the edges set that \(\forall u,v\in V,if\;\;e_{u,v}\in E,u\stackrel{{\leftrightarrow} }{{\leftrightarrow}}v\). The nodes are equipped with a directional transducer of beamwidth \(\pi/3\), and its transmission range is \(r\). So the first problem is formulated as follows.  
**Problem 1**.: _Given a transmission range \(R\) and a set of nodes \(V\) with a \(\pi/3\) beamwidth transducer, can a network be symmetrically connected by assigning to each node a suitable orientation?_  
Problem 1 is called the transducer orientation problem (TOP), which is proven to be NP-complete in the following.  
**Theorem 1**.: _The TOP is NP-complete._  
Proof.: In order to prove that the TOP is NP-complete, we firstly prove \(TOP\in NP\), and then prove that TOP is NP-hard by reducing \(HPHGG\leq_{P}TOP\).  
For the proof of \(NP\), given a certification \(\mathcal{C}=[C_{1},C_{2},...,C_{n}],n=\|V\|\), denoting the set of nodes' orientations, we can easily determine if the network, including all nodes, is fully connected in polynomial time. Therefore, the TOP is NP.  
Before the reduction is commenced, an equilateral triangular widget of side length \(r\) is introduced at first. \(r\) is infinitesimally larger than the transmission range \(R\), as is shown in Figure 5(a)The vertexes of the outer triangle \(x,y,z\) represent nodes provided with the \(\pi/3\) beamwidth transducer, and then we add three inner nodes \(u,v,w\) located in the midpoint of each edge, so each node can be assigned an orientation so that the triangle vertexes are symmetrically connected, as the triangles in Figure 5(b) show. We call the vertex in the outer triangle whose beamforms form inward as the _Emitting Node_, while the other forms as the _Contacting Node_.  
Each vertex in a hexagon of HPHGG of side length \(r\) can be replaced with the above widgets in order to construct a reduction, as is depicted in Figure 5(b). A connected HPHGG instance with vertexes replaced by equilateral triangular widgets is thus achieved in the same way, as Figure 7 shows, according to Appendix A Lemma A1, in which a few widgets need to be turned round.  
From the picture above, we can see that a HPHGG with the largest node degree 3 is found; the paths are highlighted with bold black arrows. In fact, according to [18], a Hamiltonian path can always be found in a hexagon grid graph.  
In summary, given a HPHGG, we can reduce it to a TOP by adding some equilateral triangular widgets, which can be completed in polynomial time; thus, the reduction is completed.  
Figure 6: An equilateral triangular widget and a replacement of the hexagon.  
Figure 7: Reduction from HPHGG to TOP.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
###### Abstract  
The rapid evolution of 5G and beyond technologies has sparked an unprecedented surge in the need for networking infrastructure that can deliver high speed, minimal latency, and remarkable flexibility. The programmable data plane, which enables the dynamic reconfiguration of network functions and protocols, is becoming increasingly important in meeting these requirements. This paper provides an overview of the current state of the art in programmable data planes implemented in 5G and beyond architectures. It proposes a classification of the reviewed studies based on system architecture and specific use cases. Furthermore, the article surveys the primary applications of programmable devices in emerging telecommunication networks, such as tunneling and forwarding, network slicing, cybersecurity, and in-band telemetry. Finally, this publication summarizes the open research challenges and future directions. In addition to offering a comprehensive review of programmable data plane applications in telecommunication networks, this article aims to guide further research in this promising field for network operators and researchers alike.  
programmable data plane; 5G and beyond; architectures; programmable devices; P4; SDN 2023 23 23 6955 1
# An Improved FFIP Method Based on Mathematical Logic and SysML
## 7 Conclusions  
In this paper, an improved method of safety analysis is proposed by combining FFIP with mathematical logic and SysML. The following problems are solved.  
1. The FFL in FFIP is extended, the concept of deviation is introduced. Now the FFIP method is suitable for failure analysis in every stage of the system.
2. Mathematical logic is used to describe the failure logic, solved the problem of expressing FFL in FFIP.
3. FFIP is combined with SysML. SysML is used to help the FFIP to establish the structure, function, and behavior model. At the same time, the SMD in SysML is improved by extending the elements in SysML to analyze and describe the failure logic.
4. A more detailed path of fault propagation is obtained by simulating the single failure and double failures of the brake system. Additionally, the fault tree is generated, and the key failure locations are calculated according to the fault propagation paths of FFIP.  
In this paper, we have solved the problems of component failure and failure propagation of the system. Next, we will focus on the failure events that:  
1. do not follow nominal flow paths; and
2. do not follow any established flow paths.  
These researches will help people to predict more failure situations and propose improvement measures in the early design stage of the system.  
[PERSON] and [PERSON] prepared the conception and the formal description of the proposed solution; they both performed the literature review; [PERSON] and [PERSON] studied the case; [PERSON] and [PERSON] implemented the proposed solution and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 10: Simplified fault tree of brake system.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 2 Technical Introduction to AI for Clinicians
### Deep Learning (DL)
#### 2.4.2 Learning by Transfer  
In medical image processing, many DL architectures use _transfer learning_, a method that enables designers to use previously trained deep NNs.  
Certain widely used DL structures in fundus examination are Inception V1 and V3 [19]. These networks were initially qualified with ImageNet [20], which is one of the largest datasets of natural images. The above architectures have yielded satisfactory outcomes, but their direct application in medical image analysis is not feasible for many reasons:  
* Natural and medical images have different and particular statistical characteristics;
* Moreover, training these deep NNs with medical image datasets from scratch is not efficient because these datasets are often small. For fundus images, for instance, one of the largest freely available databases is EyePACS, which has just 35,126 training images;
* Due to the large quantity of factors that deep NNs have to optimize, their hit relies on large volumes of available data [21];
* Transfer learning takes models with parameters learned from ImageNet (or another natural image set like CIFAR) and then performs a fine-tuning process.  
This process is helpful for feature extraction, which may later be implied in another ML paradigm, but also for the classification issue.  
While it is feasible to create our own models, there is research that has demonstrated that under special conditions (like small training sets), the fine-tuning method offers better outcomes than training from scratch.  
The best CNN models for medical imaging are _classification models, segmentation models_, and _multimodal architectures_[22; 23].  
Figure 4: Example of feature selection by a CNN [18].
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 2 Background  
### Overview of Knock  
SI engines currently power the majority of passenger automobiles in the world. Although alternatives, such as fuel cells and advanced batteries, are becoming available, the SI engine is still positioned to be the primary means of powering both consumer and commercial vehicles, especially in rapidly growing emerging markets [1]. In particular, as the global fleets start to move to electrification, hybrid vehicles that combine an electric powertrain with an SI engine will become increasingly prevalent [2]. Therefore, it is imperative that the field of SI engine technology be advanced further. Since the maximum efficiency of an engine is limited by knock, advancing the knock limits is crucial to advance ICE technology.  
The term \"knock\" signifies the engine noise resulting from the rapid self-ignition of unburned fuel and air as the flame traverses the combustion chamber [3]. Figure 1 displays a regular engine cycle and a knocking engine cycle. Initially, in Figure 1, the intake valve opens at point A, allowing for the entry of fuel and air into the cylinder. The piston's downward movement draws this mixture through the open valve into the cylinder, followed by the closure of the intake valve at point B, trapping the fuel and air inside. Subsequently, as the piston moves upwards, compressing the fuel-air mixture leads to an increase in the in-cylinder pressure due to a decreased cylinder volume. At point C, the spark plug initiates combustion by creating a flame kernel. This flame propagates across the chamber, elevating the pressure from point C to point D. In Figure 1, the combustion ends at point E, with the flame being quenched against the cylinder walls. The resultant pressure surge from the combustion thrusts the piston downward, generating mechanical work.  
The knocking cycle, illustrated in Figure 1, follows a similar progression to the normal cycle from point A to point D. However, with the increasing cylinder pressure, the temperature within the end-gas, comprising unburned fuel and air ahead of the flame front, rises. At point E, the heightened pressure and temperature of the end-gas lead to autoignition. This rapid autoignition results in a pressure increase within the region, outpacing the expansion rate of that region. Consequently, the energy release manifests as a rapid pressure rise, followed by pressure fluctuations resonating within the combustion chamber's frequencies. These pressure oscillations induce vibrations in the engine block, generating engine noise--a characteristic knocking sound that is emitted by the engine [4].  
Figure 1: (**a**) Image and pressure trace for a standard SI engine cycle and (**b**) a knocking cycle.