esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 15 Assignment of Processing Tasks Across Nodes in a WSN
### Initial Optimization Problem Formulation  
A max-min problem that maximizes the minimum lifetime across all nodes was initially formulated as a binary non-linear program. A brief explanation of the formulation is given as follows:  
* Decision variables: are given by a binary state vector \(\mathbf{s}_{i}\in\left\{0,1\right\}^{L}\) which represents assignment of the \(L\) processing tasks for an operation for each node \(i\).
* Objective function: is a max-min nonlinear function in the binary decision variables given by the equation, \[\underset{\mathbf{s}_{i}}{max}\underset{\forall i\in X}{min}\frac{\gamma_{i}} {E_{i}\left(\mathbf{S}\right)}\] (35) where \(\gamma_{i}\) is the initial battery residuary energy for node \(i\), \(X\) is the set of nodes and \(E_{i}\left(\mathbf{S}\right)\) is the energy expended by node \(i\) for task processing and packet radio transmission. \(E_{i}\left(\mathbf{S}\right)\) is linearly dependent on the binary matrix \(\mathbf{S}\) which is a concatenation of all the state vectors of all nodes \(\mathbf{s}_{i}\in\left\{0,1\right\}^{L}\).
* Constraint sets: the only constraint set aside from the binary nature of the decision variables is an upper bound binary constraint, \(\mathbf{d}_{i}\geq\mathbf{s}_{i}\;\;\forall i\in X\), which ensures that no task gets assigned to a node except if it is allowed to (or capable to) do it by enforcing a zero value for any variable \(s_{il}\) if the corresponding \(d_{il}\) is zero.  
This problem is easily converted to a mixed binary integer program by replacing the term \(\underset{i\in X}{min}\frac{\gamma_{i}}{E_{i}\left(\mathbf{S}\right)}\) in the objective function by an auxiliary variable \(\alpha\) and minimizing it instead while adding the linear constraint \(\frac{E_{i}\left(\mathbf{S}\right)}{\gamma_{i}}\geq\alpha\) to the constraint set. This problem is centralized and has a complexity that scales with both the number of nodes and the number of tasks.
# BIM Policy Trends in Europe: Insights from a Multi-Stage Analysis
## 3 Results
### BIM Policy in Europe
#### 3.2.23 Spain  
BIM is not mandatory. In 2015, the European BIM Summit issued a letter of intent and proposed a schedule for BIM implementation goals in Catalonia, such as requiring BIM in public procurement for projects with a budget over EUR 2 million in the design and construction phase starting in 2018 and in all stages of the investment process from 2020 [96]. That the same year, the Commission for BIM adoption in Spain was established. Since 2017, data has been collected by the CIBIM BIM Tender Observatory (_El Observatorio de Licitaciones BIM de la CIBIM_), which publishes quarterly reports outlining the BIM requirements in public investments. According to the 2023 report, 45% of tenders mandate BIM requirements as essential criteria, although their maturity levels vary [97].  
In 2018, the Government of Catalonia Agreement (_Generalitat de Catalunya Acord_) mandated BIM requirements in their tenders for construction works and construction contracts with planned budgets equal to or greater than the harmonized regulation [98] (reduced to EUR 2 million in 2023 [99]). In the same year, the White paper on the definition of a BIM implementation strategy was published [100], followed by the implementation manual in 2021 [101]. In 2023, the BIM PLAN for public procurement in Spain (_PLAN BIM en la contratacion publica_) was released [102], based on a proposal prepared by CIBIM, the Interministerial Commission for the Incorporation of the BIM Methodology in Public Procurement (_Comision Interministerial para la incorporacion de la metodologia BIM en la contratacion publica_). The plan outlines four stages, starting on 1 April 2024 and concluding on 1 April 2030, with gradual implementation. For projects exceeding the EU threshold, the initial phase, termed the early level (_nivel inicial_), progresses the integrated level (_nivelintegrado_) to the final stage. For projects below the EU threshold but exceeding EUR 2 million, progression from the recommended early level to an advanced level (_nived avanzado_) is anticipated. Each level specifies requirements, such as the use of CAD drawings and BIM models for coordination, the CDE as a repository, at least one trained BIM contract manager on the investor's team, a procedure certified under UNE-EN ISO19650 or equivalent, data exchange via the CDE, and the integration of cross-industry BIM models, with all staff trained in BIM and adherence to open standards.
## 3 Results and Discussion
### Red Areas  
The studies aimed to find that the red lake did not produce satisfactory results. It should be highlighted that a clear attribution of some of the peaks present in the mass spectra related to the red areas was difficult to obtain due to the presence of the ion peaks with a high intensity related to the fatty acids and lead oxides present on the underlying gold varnish. A further investigation of the red areas must be necessary to define their nature. Similarly, ATR-FTIR spectra resulted in the same spectral features shown for the gold varnish for both the spectral ranges (Figure 13). On the whole, it can be claimed that gold-yellow and red areas do not show characteristic features. The reason for this evidence could lie on the nature of the colored oils for painting, which are a very fine dispersion of solid granules of pigments in the mass of the medium. This is, in part, confirmed by the fact that the areas where signals were more efficiently collected are covered with the green and blue painting, which are much denser and opaqueer by eye, as well. A further investigation of the red areas must be necessary to define their nature first via in situ XRF measurements with a portable instrument for a tailored sampling and, then, via investigations of cross-sectioned samples carried out by means of scanning electron microscopy coupled with energy dispersive spectroscopy (SEM-EDS) [10,42].  
Figure 12: ATR-FTIR spectra of the green areas in the range of 3500 to 500 cm\({}^{-1}\) (**a**) and in the range of 1600 to 200 cm\({}^{-1}\) (**b**). In addition to spectral features common with gold varnish, characteristic peaks (1609, 1362, 821, 494, and 376 cm\({}^{-1}\)) attributed the green pigment as verdigris (copper acetate), which, in part, was altered, which formed copper oxalates.  
Figure 13: ATR-FTIR spectra of the red areas ranging from 3500 to 500 cm\({}^{-1}\) (**a**) and in the range of 1600 to 200 cm\({}^{-1}\) (**b**). All the spectral features are in common with the gold varnish and it was not possible to identify the nature of the pigment or coloring agent.
## 3 Background and Related Works
### HIP DEX Packet's Format Analysis and Proposed Compression  
In this section, we present the proposed compression in our previous work in [9], then we explain the possible optimization in compression with the LC-DEX solution, comparing with [9]. We distinguish three types of packet content: (a) static information that can be omitted, (b) redundant information that can be compressed and (c) information that is not compressible or should not be compressed. All HIP packets have a fixed header of 40 bytes. As presented in Figure 2, each packet is composed of a fixed protocol header and a varying number of parameters. These parameters reach the actual protocol information. Fixed header and HIP parameters both contain information that is useful in the context of Internet-based communication, but that is unnecessary during packet transferal in constrained network environments.
## 3 Aeroacoustic Model
### Engine Acoustic Source Model  
The modeling of the RSI noise and the impact of the inflow distortion on this noise source is model-led and investigated, whereas, the modeling of the boundary layer interaction noise is not a subject of the present study.  
The engine acoustic source modeling can be subdivided into three successive steps: the actual source modeling informed by computational fluid dynamics (CFD); the propagation of the modal solution of the sound field to the engines inlet and outlet plane; and finally the computation of all necessary physical quantities, which are required as boundary conditions for the BEM solver, i.e., the reconstruction of the sound pressure and its normal derivative based on the propagated modal solution.
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 2 Related Work
Furthermore, in [33], the Safe Tunnel-based FMT* (ST-FMT*) method generates a preprocessed initial path before method expansion and then constructs a safe tunnel for sampling, which accelerates the convergence speed of the algorithm. Among the sampling-based path planning algorithms, the RRT planning method with a better search capability is suitable for use in complex environments, and the method only requires real-time local data to dynamically construct the search tree. However, RRT node expansion slows down its search efficiency due to the large number of collision detections required. In addition, RRT-generated paths are often redundant and convoluted, making them difficult for mobile robots to execute. To address these issues, an efficient path planning method based on Dynamic Bridging RRT (DBR-RRT) is proposed, aiming to optimize the efficiency of the RRT-Connect algorithm, and, at the same time, to reduce the speed mutation phenomenon that occurs in the rapid operation of the mobile robot, and to achieve a good balance between the speed and the smoothness of the operation. The main contributions are as follows:  
1. Firstly, a heuristic discrimination method is used in path planning to solve the problem of a slow search speed due to node extension collision detection by evaluating whether the sampling points are in free space and assessing the spatial location relationship between neighboring obstacles and setup assistance points. 2. Then, for the extended search phase, the sampling points with the slowest extension speed are searched for, and the set of sampling points is generated in their vicinity to improve the search efficiency. 3. Finally, for the path optimization and pruning phases, different priorities are assigned to the nodes by comprehensively evaluating the sampling point connectivity and proximity to obstacles, which ultimately makes the generated path smoother and easier to execute.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 5 Discussion
Although the generated phase hologram is not focused over the reflector surface at all points, it still gives an idea of surface errors, showing that no large surface distortions can be observed as the phase looks uniform within reflector area. Validation of the deployable mesh parabolic reflector presented overall good results, but further analysis on the strut design must be conducted to minimize scattering effects. Additionally, future work should focus on the development of the feed assembly for a full polarimetric configuration, and later tested with the reflector.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## The Model of Room Monitoring System  
### System Architecture  
Figure 1 presents an overview of the proposed system. This Figure 1 presents an overview of the proposed system. This system uses consecutive video frames captured using a monitoring camera as the input images. This system involves two main processes: perspective correction and person detection.  
In the first process, perspective correction is used to transform an input image into a 2D top-view image to produce a perspective transform matrix. This approach enables users to use the system more efficiently. The second process is person detection. We propose a new person detection scheme that combines the Mask R-CNN scheme and tile technique. Our scheme achieves excellent performance, especially for small-sized people who are far from the camera. A copy of the input image is generated to implement person detection. The objects in the upper part of the image look small owing to their distance from the camera. Therefore, only that part of the image is extracted to create tile images of identical size. Next, the Mask R-CNN is used to detect objects on each tile image and the full-frame image. We also add a filter that helps obtain objects from the person class in the output layer. After initial detection using the Mask R-CNN, the bounding boxes for each tile image and the full-size images are collected as the initial results. Finally, the initial results are merged using our proposed scheme to generate a final detection result, which is stored in a cloud database. Moreover, we use the bounding boxes in the final result to calculate the coordinates of persons; these are then transformed into new person coordinates in 2D images by using the perspective transform matrix. Therefore, the system can be observed quickly in different views.
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
## 7 Evaluation of the Encryption Efficiency  
The theoretical efficiency analysis of UCP-WABE is first given, then we carry out the experiments, and the results show that UCP-WABE has better efficiency than other schemes.
## 2 Related Works  
### Preliminaries  
We start by introducing a few notations. Let \(D=\{\mathbf{x}_{i}\}\subset\mathbb{R}^{n}\), \(1\leq i\leq N\) be a dataset of \(n\)-dimensional data points (samples) \(\mathbf{x}_{i}=(x_{1}^{j},x_{1}^{j},\ldots,x_{N}^{n})\), with the corresponding labels \(y_{i}\in C\). Let \(\mathbf{x}^{j}=\{x_{1}^{j},x_{2}^{j},\cdots,x_{N}^{j}\}\), \(1\leq j\leq n\) be the \(j\)-th feature, or dimension, of \(D\). Thus, \(D\) can be seen as a table with \(N\) rows (samples) and \(n\) columns (dimensions), with \(\mathbf{y}=\{y_{1},y_{2},\ldots,y_{N}\}\) being their label vector. Given a dataset \(D\) and its corresponding labels \(\mathbf{y}\), a _classifier_ constructs a function \(f\colon\mathbb{R}^{n}\to C\) so that \(f(\mathbf{x}_{i})=y_{i}\) ideally for all \(\mathbf{x}_{i}\in D_{t}\), where \(D_{t}\subseteq D\) is the training set. After training, the classifier \(f\) is typically further evaluated in the same way on a test set \(D_{T}\subset D\), \(D_{T}\cap D_{t}=\emptyset\). After training and testing, the classifier \(f\) can be used to predict the labels of new samples \(\mathbf{x}\in\mathbb{R}^{n}\setminus D\).  
A dimensionality reduction (DR) method, also called a _projection_, is a function \(P:\mathbb{R}^{n}\to\mathbb{R}^{q}\) that maps an \(n\)-dimensional sample \(\mathbf{x}\in\mathbb{R}^{n}\) to a \(q\)-dimensional sample \(P(\mathbf{x})\in\mathbb{R}^{q}\), where \(q\ll n\). Typically, one uses \(q=2\) or \(3\) for visualization purposes. An _inverse projection_\(P^{-1}:\mathbb{R}^{q}\to\mathbb{R}^{n}\), also called backprojection or unprojection [25], is a function that maps a \(q\)-dimensional sample \(\mathbf{z}\in\mathbb{R}^{q}\) to an \(n\)-dimensional sample \(\mathbf{x}\in\mathbb{R}^{n}\) so that it approximates the inverse of \(P\), i.e., \(P^{-1}(P(\mathbf{x}))\approx\mathbf{x}\). We next denote as \(P(D)\) the application of \(P\) to all points of a dataset \(D\) (and analogously for \(P^{-1}\)).
###### Abstract  
Over the past decades, additive manufacturing has rapidly advanced due to its advantages in enabling diverse material usage and complex design production. Nevertheless, the technology has limitations in terms of quality, as printed products are sometimes different from their desired designs or are inconsistent due to defects. Warping deformation, a defect involving layer shrinkage induced by the thermal residual stress generated during manufacturing processes, is a major factor in lowering the quality and raising the cost of printed products. This study utilized a variety of thermal time series data and the K-nearest neighbors (KNN) algorithm with dynamic time warping (DTW) to detect and predict the warping deformation in the printed parts using fused deposition modeling (FDM) printers. Multivariate thermal time series data extracted from thermocouples were trained using DTW-based KNN to classify warping deformation. The results showed that the proposed approach can predict warping deformation with an accuracy of over 80% by only using thermal time series data corresponding to 20% of the whole printing process. Additionally, the classification accuracy exhibited the promising potential of the proposed approach in warping prediction and in actual manufacturing processes, so the additional time and cost resulting from defective processes can be reduced.  
Keywords:additive manufacturing; warping deformation; K-nearest neighbors; thermal time series; quality prediction +
Footnote †: journal: Applied Sciences
## 2 Vibration Control Methods
### Single-Shot Vibration Waveform Reconstruction and Multi-Hole Vibration Superposition Method
\begin{split} f_{i}(t)&=A(Q_{0},R_{0},Q_{i},R_{i}) f_{0}(t)=(Q_{i}/Q_{0})^{\pi}/(R_{0}/R_{i})^{\beta}f_{0}(t)\\ F_{i}(t)&=\begin{cases}\delta_{i}(t)&t<t_{1}(t)\\ f_{i}(t)&t_{1}(t)\leq t\leq t_{1}(t)+t_{dur}\\ \varphi_{i}(t)&t>t_{1}(t)+t_{dur}\end{cases}\end{split}  \tag{6}\]  
where \(f_{i}(t)\) represents the reconstruction function of the vibration curve for the blast hole at an arbitrary position and \(F_{i}(t)\) is the extension of \(f_{i}(t)\) over the entire time domain;\(f_{0}(t)\) is the functional expression of the measured vibration curve for the test hole, obtained by Fourier sequence fitting; \(t_{dur}\) is the duration of the single-shot vibration curve; \(\delta_{i}(t)\) represents the noise prior to \(f_{i}(t)\); and \(\varphi_{i}(t)\) represents the residual wave following\(f_{i}(t)\). (3) _Method for the superposition of vibration waveforms in multi-hole blasting._  
Based on the Anderson principle, the reconstructed vibration curves for single holes are superposed according to the arrival time, to obtain the predicted vibration curve for the multi-hole delay blasting. Equation (7) is a superposition formula for multi-hole blasting vibration. \[F^{\prime}(t)=\sum_{i=1}^{l}F_{i}\big{(}t-t^{\prime}(i)\big{)} \tag{7}\]  
where \(l\) represents the number of holes in the blasting area. The methods of wave arrival time calculation, vibration wave superposition and the arrangement of monitoring points around the blast area have been written into the MATLAB_R2021b program. After calculating the number of simultaneous wave arrivals at all monitoring points and obtaining the adjusted delay scheme by adjusting the number of simultaneous wave arrivals, the vibration superposition of multi-hole blasting is used to verify the control effect of blasting vibration after delay adjustment. Figure 3 shows the overall idea of the bench blasting vibration control method proposed in the paper.
## 6 ML-Based Analysis of ReMouse Dataset: Focusing on Sessions Generated by Different Users  
The objective of our ML-based analysis of the curated image-based ReMouse dataset (as explained in Section 5) was to investigate the (dis)similarities between comparable (same-order number) web sessions generated by different users. We specifically decided to look at the third session generated by each of the 100 participating ReMouse users (forming one data subset, which we will refer to as 'ReMouse Subset-3' in the reminder of this article), as well as the fifth session generated by each of the 100 participating ReMouse users (forming the second data subset, which we will refer to as 'ReMouse Subset-5'). We opted to look at the third and fifth sessions due to our observation that for most ReMouse users some of the originally exhibited 'erratic' mouse behavior largely disappears after the first two rounds/repetitions of the 'Catch Me If You Can!' game (see Section 3). In other words, the user behavior and mouse trajectory in these sessions are generally'stable' and thus likely to produce more accurate results. To conduct the cross-user session (dis)similarity analysis, we specifically decided to deploy unsupervised ML learning, including the Self-Organizing Map (SOM) and several unsupervised clustering ML algorithms.  
The SOM algorithm is typically used to build a topology-preserving mapping of high-dimensional input data to 2D or 3D space, where the similarity of individual input points can be assessed in more intuitive (visual and non-visual) ways. Unsupervised clustering is known for its ability to decompose a dataset into subgroups based on their similarity so that data points in the same cluster are more closely related to each other than data points in different clusters [45].  
According to our knowledge, this is the first research study that has looked into the use of unsupervised clustering on the image representation of user sessions for the purpose of cross-user session (dis)similarity analysis. Additionally, the only other work that has pursued image-based web-session representation and analysis [22] was specifically concerned with the problem of malicious web-bot detection through session classification,  
Figure 10: The number of components needed to explain the variance.  
and thus ultimately opted for the use of supervised deep learning--as opposed to the question of session similarity, which is the focus of our work and requires the use of unsupervised techniques.
## 4 Model Calibration and Square Lattice Surrogate Models  
### Commitment Index  
For random walks in our continuous model, a particle may cross, return and then recross a subdomain boundary several times before \"committing\" to a new subdomain. This leads to inaccuracy in the calculation of transition probabilities as stationary quantities in the lattice-based surrogate models. Consequently, we introduce a _commitment index_, \(M_{C}\), to indicate the number of successive time steps after which a particle is deemed to commit to a new subdomain. The role of this parameter is illustrated in Figure 6 for the case \(M_{C}=4\). While the value of \(M_{C}\) requires calibration via direct comparison of simulated results for mean-squared displacement in the continuous and lattice-based surrogate models, we now explain its narrow range of variation.  
1. Consider a particle, committed to a subdomain, that has just crossed the boundary into a neighboring subdomain (first-step location). A (black) ring indicates all possible particle locations at the next step, including some locations that result in recrossing the same boundary (Figure 6a).
2. We next illustrate a few possible steps relative to the particle's first-step location, thus indicating possible second-step locations. A (red) ring of possible 3 rd-step locations is shown around each such second-step location (Figure 6b).
3. Choosing one of the possible second-step locations that is in the new subdomain (gray ring), the resulting set of possible third-step locations is shown in red (Figure 6c).
4. Lastly, the larger the number of successive steps for which a particle remains in the new subdomain, the smaller the probability that it will recross back into the subdomain to which it was previously committed; in Figure 6d, blue rings depict fourth-step locations.  
Figure 6: Illustration of the role of the commitment index (calibration) parameter \(M_{C}\): (**a**) The particle (solid black circle) has just crossed the boundary between subdomains (dashed gray line) to reach its 1 st-step location. Possible 2 nd-step locations are indicated by the solid black ring. (**b**) Three possible 2 nd-step locations are indicated by solid red circles. Some possible next steps (3 rd-step locations) are shown with red rings. (**c**) A red ring illustrates the particle’s remaining possible 3 rd-step locations when it has remained in the current subdomain at the prior step. (**d**) Possible 4 th-step locations are indicated with blue rings.  
For each of our two obstacle arrangements, \(M_{\mathbb{C}}\) is calibrated by constructing transition probabilities from continuous model simulations over a range of \(M_{\mathbb{C}}\) values and by comparing mean-squared displacement for the continuous and lattice-based surrogate models to identify the case with the best agreement (see Section 5.1).