esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Additive Manufacturing of Fibre-Reinforced Materials: A Comprehensive Review
###### Abstract  
Additive manufacturing (AM) plays a significant role in the 4 th Industrial Revolution due to its flexibility, allowing AM equipment to be connected, monitored, and controlled in real time. In advance, the minimum waste of material, the agility of manufacturing complex geometries, and the ability to use recycled materials can provide an advantage to this manufacturing method. On the other hand, the poor strength and durability of the thermoplastics used in the manufacturing process are the major drawback that keeps AM behind common production methods such as casting and machining. Fibre-reinforced polymers can enhance mechanical properties, advance AM from the commonly used polymers, and make AM competitive against conventional production methods. The main focus of the current review is to examine the work conducted in the field of reinforced additively manufactured technologies in the literature of recent years. More specifically, this review discusses the conducted research in the composite fibre coextrusion (CFC) additive manufacturing techniques developed over the past years and the materials that can be used. In addition, this study includes an up-to-date comprehensive review of the evaluation of fibre-reinforced 3D printing along with its benefits in terms of mechanical response, namely tensile, flexural, compression and energy absorption, anisotropy, and dynamic properties. Finally, this review highlights possible research gaps regarding fibre-reinforced AM and proposes future directions, such as deeper investigations into energy absorption and anisotropy, to position fibre-reinforced AM as a preferred fabrication method for ready-to-use parts in cutting-edge industries, including automotive, aerospace, and biomedical sectors.  
carbon fibre; additive manufacturing; composites; fibre reinforcement; continuous filament fabrication +
Footnote †: journal: Applied sciences
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Literature** & **Wavelength** & \
\begin{tabular}{c} **Pulse** \\ **Duration** \\ \end{tabular} & **Spot Size** &
\begin{tabular}{c} **Single Pulse** \\ **Energy** \\ \end{tabular} & **Frequency** & **Weight** & **Power** \\ \hline
[10,11] & 1064 nm & 0.66 ns & 15 \(\upmu\)m & 12 \(\upmu\)J & 7 kHz &
\begin{tabular}{c} 500 g \\ (whole system) \\ \end{tabular} & 3 W \\
[12] & 1064 nm & 7 ns & 30 \(\upmu\)m & 15 mJ & 0.1 Hz &
\begin{tabular}{c} 2.7 kg \\ (whole system) \\ \end{tabular} & 8 W \\
[13] & 532 nm & 1.4 ns & 30 \(\upmu\)m & 40 \(\upmu\)J & 0.1 kHz & 100 mm and width & - \\  & & & & & 34 mm & \\ This paper & 1064 nm & 0.41 ns & 15 \(\upmu\)m & 16.7 \(\upmu\)J &
\begin{tabular}{c} 0.1 k–1 kHz \\ Adjustable \\ \end{tabular} & 106 g & 3 W \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature research. to achieve micro-focused laser output. At a focal length of 30 mm, representing near field focusing, the spot size is 15 \(\upmu\)m, while also providing a solution for adjusting the output power. These superior performance characteristics make this microchip laser an ideal choice for planetary mass spectrometry, providing a more precise and efficient tool for planetary science research.
# Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder
## 1 Introduction
The function of the softmax classifier is to give the probabilities for each class label [18]. The LOF algorithm does not require prelabeled samples for its learning process, and therefore is very suitable for fault diagnosis of point machines, because the samples gathered in practice related to such machines typically are in lack of labels. Further, the fault datasets integrated by the researchers in general also lack completeness, as not all types of possible unhealthy states occur within the datasets. To verify the effectiveness of the proposed fault diagnosis scheme, we collected 3300 in-field current signals to be used for fault analysis by various algorithms. The signals are generated from point machines that are installed and currently being used in operation lines. By aid of this dataset, we evaluate the prediction performance of the proposed algorithm by comparing its prediction skills with respect to existing methods, such as the feature-based LOF algorithm, the stacked denoising autoencoder, the sparse denoising autoencoder, and the gated recurrent unit-based sequence to sequence autoencoder. The results show that the proposed scheme not only has the ability to automatically capture most effective features from the raw current signals, but also achieves superior diagnosis accuracy compared to the existing methods. The fault dataset, which we have termed Cu-3300, as well as the code file of the proposed scheme have been made available online at [[https://github.com/[PERSON]/SignalRepresentationAnalysis](https://github.com/[PERSON]/SignalRepresentationAnalysis)]([https://github.com/[PERSON]/SignalRepresentationAnalysis](https://github.com/[PERSON]/SignalRepresentationAnalysis)). To the best of our knowledge, Cu-3300 is the first open source dataset in the area of railway point machines and our fault analysis is the first to investigate the use of autoencoders for fault diagnosis of point machines. The rest of the paper is organized as follows. Section 2 discusses the related work. The proposed fault diagnosis scheme is detailed in Section 3. Section 4 verifies the superior performance of the proposed scheme. Section 5 concludes the paper.
# Multimodal Tucker Decomposition for Gated RBM Inference
## 2 Restricted Boltzmann Machines
### RBM Training  
[PERSON] and [PERSON] [13] showed that the derivative of the log-likelihood of the data under the RBM with respect to its parameters is:  
\[\frac{\partial\log p(\mathbf{v},\mathbf{h})}{\partial\theta}=-\left\langle \frac{\partial\log E(\mathbf{v},\mathbf{h})}{\partial\theta}\right\rangle_{ data}+\left\langle\frac{\partial\log E(\mathbf{v},\mathbf{h})}{\partial \theta}\right\rangle_{model} \tag{4}\]  
where \(\left\langle\cdot\right\rangle_{data}\) denotes the expectation over the data, or in other words the distribution \(p(\mathbf{h}\mid\mathbf{v}^{(t)},\theta)\). In the same way, \(\left\langle\cdot\right\rangle_{model}\) denotes the expectation over the model distribution \(p(\mathbf{v},\mathbf{h}\mid\theta)\). However, directly calculating the sums that run over all values of \(\mathbf{v}\) and \(\mathbf{h}\) in the second term in (4) leads to a computational complexity, which is in general exponential in the number of variables.  
The \(\left\langle\cdot\right\rangle_{model}\) expectation can be approximated with samples from the model distribution. These samples can be obtained via Gibbs sampling, iteratively sampling all units in one layer at once given the other layer using (2) and (3) alternately. However, this requires running the Markov chain for an infinite time to ensure convergence to a stationary state, making it an unfeasible solution.  
Obtaining an unbiased sample of \(\left\langle\cdot\right\rangle_{model}\) is extremely difficult. [PERSON] [3] approximates the second term in the true gradient in (4) by using an approximation of the derivative called Contrastive Divergence: the goal of CD is to replace the average \(\left\langle\cdot\right\rangle_{model}\) with samples \(\left\langle\cdot\right\rangle_{k}\) obtained after running \(k\) steps of Gibbs sampling starting from each data sample. This is illustrated in Figure 3. A typical value used in the literature is \(k=1\). Moreover, this way of updating the parameters has become a standard way of training RBMs. Although it has proven to work well in practice, CD does not yield the best approximation of the log-likelihood gradient [13; 14]. There has been much research dedicated to better understanding this approach and the reasoning behind its success [13; 14; 15], leading to many variations being proposed from the perspective of improving the Markov chain Monte Carlo approximation to the gradient, namely, Persistent CD [16], Fast Persistent CD [17], and Parallel Tempering [18].
# A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
## 6 Performance Analysis and Comparison
### Transmission Delay
In conclusion, the delay improvements are related to the network reliability. As the distance to the sink increases, a fixed larger transmission distance can bring about a smaller delay. While in the COOR(P) method, the transmission distance even increases with the distance to the sink, the COOR(P) method significantly reduces the delay of the nodes in the far-sink region of the network.
# Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
###### Abstract  
Electrical capacitance tomography holds significant potential for multiphase flow parameter measurements, but its application has been limited by the challenge of reconstructing high-quality images, especially under complex and uncertain conditions. We propose an innovative multi-objective robust optimization model to alleviate this limitation. This model integrates advanced optimization methods, multimodal learning, and measurement physics, structured as a nested upper-level optimization problem and lower-level optimization problem to tackle the challenges of complex image reconstruction. By integrating supervised learning methodologies with optimization principles, our framework synchronously achieves parameter tuning and performance enhancement. Utilizing the regularization theory, the multimodal learning prior image, sparsity prior, and measurement physics are incorporated into a novel lower-level optimization problem. To enhance the inference accuracy of the prior image, a new multimodal neural network leveraging multimodal data is developed. An innovative nested algorithm that mitigates computational difficulties arising from the interactions between the upper- and lower-level optimization problems is proposed to solve the proposed multi-objective robust optimization model. Qualitative and quantitative evaluation results demonstrate that the proposed method surpasses mainstream imaging algorithms, enhancing the automation level of the reconstruction process and image quality while exhibiting exceptional robustness. This study pioneers a novel imaging framework for enhancing overall reconstruction performance.  
imaging method; multi-objective robust optimization; multimodal learning; information fusion; electrical capacitance tomography +
Footnote †: journal: Applied Sciences  
0000-0001-9993-3225-2226-1
## 2 Aims and Objectives
### Propulsion System Aims and Objectives  
The propulsion systems role is to maintain the craft's orbiting altitude, as well as allowing it to perform orbital maneuvers and reach a safe graveyard orbit once the mission is complete [19]. This report will research previously incorporated propulsion systems as well as analyzing readily available off-the-shelf systems. Suitable technologies will be compared and selected using engineering design tools as a justification based on performance data from manufacturers. The chosen systems operation will be analyzed in mathematical detail. The mass, volume, power and fuel consumption and cost will all be considered during selection.  
Propulsion System Objectives
* Research suitable propulsion systems for a 3U CubeSat gathering performance data and operational complexity data.
* Analyze the researched systems, using Engineering Design tools to specify baseline requirements based on the missions needs and applicable safety regulations.
* Select a suitable propulsion system to power the craft, then further select an off-the-shelf component incorporating this technology.
* Verify the suitability of the chosen system, ensuring it can meet the mission and safety requirements.
* Ensure achievable power consumption from the electrical system throughout the orbit cycle.
## 8 Application Challenges and Strategic Directions for Digital Twin Integration
### Strategic Directions for Scalable DT Integration
#### 8.4.2 DT-Driven Certification and Homologation  
One of the most transformative directions for DT is its potential use in regulatory compliance. By digitally signing simulation results and ensuring a version-controlled traceability of test cases, DTs could become part of certification dossiers [54]. This would reduce the physical testing burden while improving repeatability and auditability of functional safety evaluations.  
Simulation-based certification is particularly promising for  
Adaptive headlight performance under variable weather conditions;  
Failure mode validation in redundant lighting architectures;  
System behavior in pre-defined regulatory scenarios.
## 2 Related Work
### Development Framework  
The framework for the future development of 5G and beyond is described in recommendation ITU-R M-R 0-2083.M-R in detail and this means that IMT systems are contributing as follows:  
_Wireless Infrastructure:_ Broadband connectivity became as important as getting electricity. IEC in 5G technologies plays a significant role in enabling service to deliver and exchange information. In addition, different users would be enjoying a wide variety of applications and services, starting of entertainment media services to professional applications.  
_New ICT Market:_ The prospective improvement of IEC systems works to enhance emergence of integration ICT industry that enable economies around the world. Among the potential areas are accumulation, analysis of big data, provision of customized services for networking for enterprises, aggregation, companies and groups of social networks on wireless networks.  
_Bridging the digital Gap:_ 5G technologies are still helping to bridge some scarcity caused by the growing digital gaps. According to its ease for deploying mobile and wireless communication, IEC's systems can support this goal, while at the same time, saving energy and maximizing efficiency.  
_New communication Methods:_ IEC allows any type of content to be exchanged at anytime and anywhere from any device. Users will generate and share more content without being restricted by time and location.  
_New forms of Education:_ IEC in 5G changes teaching methods by providing easy access to digital libraries. Internet sources Martial or storage of knowledge in the cloud, in addition to, promoting applications such as e-learning, distance learning, and teleconferencing [47].  
_Enhancing Energy effectiveness:_ IEC has a significant role across a range of economic and energy sectors, including supporting machine-to-machine (M2M) communications and providing solutions for smart energy grid, logistics, smart transportation, and teleconferencing.  
_Social Changes:_ Broadband networks facilitate the rapid formation and public exchange of a political or social issue through a social networking service. Creating the opinions of many connected people due to the ability to exchange information at any time and any place is considered to be the main reason for social change.  
_Culture and Art:_ 5G technologies play a significant role in supporting arts and artists in participating and performances such as group activities, creating a virtual singing group, catching eyes, co-authoring, or writing songs. In addition, 5G technologies enhance the connection throughout virtual platforms for people to exhibit culture.
## 4 Conclusions  
A coordination study was presented and applied to the IEEE 13-node test feeder to evaluate the effect of the PV DG penetration on the protection devices coordination. Different cases were studied by changing DG penetration levels and locations for each possible fault location. A protection coordination assessment was carried out by analyzing the location of the faults (devices see fault currents for downstream and upstream faults). The most relevant conclusions from this work are:  
* Faults upstream in relation to the last DG were not seen by the protection devices located downstream from this DG. Even for larger DG penetration these downstream devices did not have coordination problems between them (as they did not see any fault);
* On the other hand, although devices saw fault currents for upstream faults, coordination was lost if they saw the same fault current for a fault downstream as well as for a fault upstream.  
Additionally, the present protection procedures need to be revised, as the presence of DG sources might represent a quite serious effect on the current and triggering time reaction of the protection relays. According to the results and depending on the distance to the substation, the tripping time with DG was increased up to 167% (for 25% PV DG penetration) and 180% (for 50% and 75% PV DG penetration).  
The results from this work can be reasonably extrapolated to grid networks, bearing in mind the current direction effects on the protection relays, and the DG penetration.  
Future works of the research team that carried out the present work are the development of simplified techniques to locate faults in distribution networks with DG, new methods to select the appropriate fuses in these networks (to avoid unexpected fuse tripping), and a procedure to successfully reprogram overcurrent relays.  
Finally, it should also be noted that the present work will be followed by others that will include research on the dynamic modeling of power supplies from DG in distribution networks, and simplified methodologies on fault locations in grids with DG.  
Conceptualization, D.A.-G. and S.P.; methodology, D.A.-G., E.M.G.d.T., M.I.M.-L. and S.P.; software, D.A.-G.; validation, D.A.-G., E.M.G.d.T. and M.I.M.-L.; formal analysis, D.A.-G.; investigation, D.A.-G., E.M.G.d.T., M.I.M.-L. and S.P.; data curation, D.A.-G.; writing--original draft preparation, D.A.-G. and S.P.; writing--review and editing, D.A.-G. and S.P.; supervision, D.A.-G. and S.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors are indebted to the IDR/UPM Institute [PERSON] for its kind support regarding the research related to power systems at the research institute. The authors are also grateful to [PERSON] and SIELEC for the kind supervision and advise in relation to power distribution networks and protection systems. Finally, the authors are grateful to the reviewers, whose comments helped in improving the present work. The authors declare no conflict of interest.
# Projection onto the Set of Rank-Constrained Structured Matrices for Reduced-Order Controller Design
## 3 Algorithms
### Proposed Algorithm  
First, we define function \(F(X_{1},X_{2})\) such that \(F(X_{1},X_{2})\preceq 0\) is equivalent to the LMIs to be solved. For Problem 1 for example, \(F(X_{1},X_{2})\preceq 0\) means that the LMIs (7)-(9) hold. Namely, we consider the following problem including Problems 1 and 2.  
**Problem 3**.: _Find a pair of matrices \((X_{1},X_{2})\in\mathcal{S}_{n}^{2}\) such that_  
\[\operatorname{rank}\begin{bmatrix}X_{1}&I\\ I&X_{2}\end{bmatrix}\leq r,\quad F(X_{1},X_{2})\preceq 0. \tag{13}\]Then, we propose alternating projection [31] to solve the rank-constrained LMI problems. For this, we define the two closed subsets of \(\mathcal{S}_{n}^{2}\):  
\[\Omega_{r} \triangleq\bigg{\{}(X_{1},X_{2})\in\mathcal{S}_{n}^{2}:\text{rank} \begin{bmatrix}X_{1}&I\\ I&X_{2}\end{bmatrix}\leq r\bigg{\}}, \tag{14}\] \[\Lambda \triangleq\Big{\{}(X_{1},X_{2})\in\mathcal{S}_{n}^{2}:F(X_{1},X_ {2})\preceq 0\Big{\}}. \tag{15}\]  
Then, the problem is that of finding a pair of matrices \((X_{1},X_{2})\) in \(\Omega_{r}\cap\Lambda\) with \(r=n+n_{c}\). For this, we adapt alternating projection between \(\Omega_{r}\) and \(\Lambda\). The iterative algorithm is given by  
\[\begin{split} Z[k]&=\Pi_{\Omega_{r}}(X[k]),\\ X[k+1]&=\Pi_{\Lambda}(Z[k]),\quad k=0,1,2,\ldots,\end{split} \tag{16}\]  
where \(X[0]=(X_{1}[0],X_{2}[0])\in\mathcal{S}_{n}^{2}\) is a given initial guess of \((X_{1},X_{2})\). The computation of the projection operators \(\Pi_{\Omega_{r}}\) and \(\Pi_{\Lambda}\) are shown in the next following subsections.  
We show a stability result for this algorithm as follows:  
**Lemma 1**.: _The sequences \(\{X[k]\}\) and \(\{Z[k]\}\) generated by (16) satisfy the following inequalities:_  
\[\begin{split}\|X[k+1]-Z[k+1]\|&\leq\|X[k]-Z[k]\|,\\ \text{\rm dist}(X[k+1],\Omega_{r})&\leq\text{\rm dist }(X[k],\Omega_{r}),\\ \text{\rm dist}(Z[k+1],\Lambda)&\leq\text{\rm dist }(Z[k],\Lambda),\quad k=0,1,2,\ldots\end{split} \tag{17}\]  
_where dist is the distance function defined in (4)._  
The proof is given in Appendix A.  
**Remark 1**.: _We can also adopt the Dykstra algorithm [32] that gives an element in \(\Omega_{r}\cap\Lambda\) that is a projection (i.e., one of the nearest points) on \(\Omega_{r}\cap\Lambda\) from the initial guess \(X[0]\in\mathcal{S}_{n}^{2}\). The algorithm is described as follows:_  
\[\begin{split} Z[k]&=\Pi_{\Omega_{r}}(X[k]+P[k]),\\ P[k+1]&=X[k]+P[k]-Z[k],\\ X[k+1]&=\Pi_{\Lambda}(Z[k]+Q[k]),\\ Q[k+1]&=Z[k]+Q[k]-X[k+1],\quad k=0,1,2,\ldots,\end{split} \tag{18}\]  
_where we set \(P[0]=Q[0]=0\)._
# Stationary Target Identification in a Traffic Monitoring Radar System
## 2 Traffic Monitoring Radar System
### Radar Signal Processing for Target Information Extraction
In \(A_{2}\), the transmitted waveform also can be expressed as:  
Figure 5.1  
This waveform also passes through the frequency mixer and the filter, and the filter output can be expressed as:  
Figure 5.2  
If we apply the Fourier transform to \(s_{L}^{(2)}(t)\), we can estimate a frequency different from Equation (4). The new frequency can be expressed as:  
Figure 5.3 Figure 5.4  
Therefore, we can finally estimate \(R_{l}\) and \(v_{l}\) using Equations (4) and (7) as follows:  
Figure 5.5  
and:  
Figure 5.6  
If multiple targets exist in the field of view (FOV) of the radar, it may be difficult to effectively pair \(\hat{f}_{l}^{(1)}\) and \(\hat{f}_{l}^{(2)}\) for the \(l_{th}\) target. In other words, ambiguity occurs when pairing multiple beat frequencies [17]. Therefore, we additionally use the waveforms in \(A_{3}\) and \(A_{4}\)[18], shown in Figure 5.7 The waveforms in \(A_{3}\) and \(A_{4}\) can be expressed as:  
\[s_{t}^{(3)}(t)=A_{T}\cos\left(2\pi\left(f_{c}-\frac{3\Delta B}{2}\right)t+\pi \frac{\Delta B}{2\Delta T}t^{2}\right)\ \left(2\Delta T\leq t\leq 4\Delta T\  
Finally, using the four beat frequencies (i.e., \(\hat{f}_{l}^{(1)}\), \(\hat{f}_{l}^{(2)}\), \(\hat{f}_{l}^{(3)}\), and \(\hat{f}_{l}^{(4)}\)), we can effectively estimate the relative distance and relative velocity of each target even in cases where multiple targets exist. To obtain accurate position information of a target, not only distance information, but also angle information must be accurately estimated. The radar we designed utilizes an array antenna system with four receiving antenna elements, as shown in Figure 5.8 In the array antenna system, angle information of the \(l_{th}\) target, \(\theta_{l}\), can be estimated using the phase difference between signals received from each receiving antenna channel [19]. Many estimation algorithms, such as the fast Fourier transform [20], the Bartlett method [21], and the multiple signal classification [22; 23], can be used to find the phase difference between the receiving channels. In our radar system, we use the Bartlett algorithm as the angle estimation algorithm. The Bartlett algorithm has the advantage of having good angular resolution while being able to be applied in real time due to its low computational complexity [24].