esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To evaluate this hypothesis, we propose the BuMPeT (Building Metadata Performance Testing) method which combines PTing and building metadata models, and describes a workflow that supports fault and error discovery. We introduce four procedures that use building metadata models and results from PTs to localize or identify a set of causes for failed performance tests. Availability of a metadata model is essential for our method, both for initial configuration of PTs, and for feasibility of examining the results from aforementioned procedures. The BuMPeT method describes errors that may occur in the workflow and thus classifies them for each scenario. This is a clear step towards advancing energy informatics to enable assessable improvements of energy performance in buildings [18]. The BuMPeT method is superior to existing work by its flexibility to use various models for predicting building's expected behavior, as well as the coverage of all subsystems in the building. This flexibility enhances our method's simplicity and applicability. The contributions of the paper are as follows:  
* Analysis of the strengths of using building metadata models with PTing;
* The BuMPeT method for combining building metadata models with PTing, to discover and classify causes for failed PTs;
* Implementation of the BuMPeT method and results for a case study building covering 8500 m\({}^{2}\). Within the case study, the focus is on the lighting subsystem given its simplicity of implementation, yet sufficient complexity to perform the experimentation. The BuMPeT method has been applied on both a real and a hypothetical scenario, yielding successful results. Though the BuMPeT method is designed predominantly for buildings, its methodology of combining PTing results with outcomes from queries to metadata models, may also be applied to additional fields. As an example, the authors in [19] discuss energy consumption issues in telecommunication networks. As a network is comprised of various components, each with differing needs and energy consumption, it becomes possible to apply the concept of PTing. Further, describingthe network ontologically allows for semantic expression of its metadata, which in turns allows for easier diagnosis in cases of failure. A similar approach can be applied to smart home energy management systems [20] or cellular access networks [21].
# Spatial Data Analysis for Deformation Monitoring of Bridge Structures
###### Abstract  
Weather conditions and different operational loads often cause changes in essential parts of engineering structures, and this affects the static and dynamic behavior and reliability of these structures. Therefore, geodetic monitoring is an integral part of the diagnosis of engineering structures and provides essential information about the current state (condition) of the structure. The development of measuring instruments enables deformation analyses of engineering structures using non-conventional surveying methods. Nowadays, one of the most effective techniques for spatial data collection is terrestrial laser scanning (TLS). TLS is frequently used for data acquisition in cases where three-dimensional (3D) data with high resolution is needed. Using suitable data processing, TLS can be used for static deformation analysis of the structure being monitored. For dynamic deformation measurements (structural health monitoring) of bridge structures, ground-based radar interferometry and accelerometers are often used for vibration mode determination using spectral analysis of frequencies. This paper describes experimental deformation monitoring of structures performed using TLS and ground-based radar interferometry. The procedure of measurement, the analysis of the acquired spatial data, and the results of deformation monitoring are explained and described.  
terrestrial laser scanning; ground-based radar; spatial data analysis; deformation monitoring; orthogonal regression; Fourier transformation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Testing Scheme
#### 2.4.2 Cyclic Loading Test  
This part of the test was divided into a constant amplitude cyclic loading test and an increased amplitude cyclic loading test, with 15 cycles in each cyclic group. It featured rates of 2 kN/s (f = 0.006 Hz) for the first five cycles, 4 kN/s (f = 0.012 Hz) for the middle five cycles, and 6 kN/s (f = 0.018 Hz) for the last five cycles. The amplitude intervals for the three specimens of the constant amplitude loading test were as follows: 50%\(\alpha_{\text{m}}\)-100%\(\alpha_{\text{m}}\) for mode 1, 60%\(\alpha_{\text{m}}\)-110%\(\alpha_{\text{m}}\) for mode 2, and 70%\(\alpha_{\text{m}}\)-120%\(\alpha_{\text{m}}\) for mode 3; each was loaded monotonically at a rate of 0.2 kN/s until specimen damage occurred after surviving more than six cycle sets within the designated cyclic interval. The increasing value cyclic loading test was mode 4, with the lower amplitude starting at a constant 10%\(\alpha_{\text{m}}\) and increasing by 15%\(\alpha_{\text{m}}\), continuing until specimen destruction. The peak strengths of the four specimens were 206.8 MPa, 258.8 MPa, 260.3 MPa, and 163.5 MPa, respectively. The loading paths for the four modes are depicted in Figure 5.  
Figure 4: Uniaxial stress–strain curve.  
Figure 5: \(Cont.\)
# Urban Free-Space Optical Network Optimization
## 2 Problems Statement
### Connected Network with Minimum Number of Intermediate Points  
This scenario includes some terminal points (blue vertices) in the city that need to be connected and some optional intermediate points (red vertices) that may be used as relays to enable connectivity. This problem is motivated by the need to place new infrastructure at the chosen intermediate points.  
**Definition 2**.: _Given a red-blue visibility graph \(G_{rb}=(B,R,E)\), find a spanning tree that contains all vertices in \(B\) and a minimum number of vertices in \(R\). The solution for this problem is a \(\hat{G}_{rb}\) with minimum number of red vertices \(R^{\prime}\) (\(E^{\prime}\) depicts the induced edges of graph \(B\cup R^{\prime}\)). Figure 3 provides an example of such a problem. Two possible solutions are depicted in Figure 4._  
Figure 3: A simple visibility graph that displays a terrain with a set of (green and gray) buildings: (**a**) an example of buildings in a “real” city map; (**b**) an enlarged part of the map, where the red lines represent FSO links between two buildings, and the gray dashed lines are non-FSO links; and (**c**) the red—blue visibility graph of the terrain, where each point in the graph corresponds to a building.  
Figure 2: An example of two different spanning trees of \(K5\) (full graph with 5 nodes). Graph A is \(K5\), Graph B has only two leaves, and Graph C has the maximum leaf number (four leaves).  
The complexity of this problem (on general graphs) is known to be NP-hard [14].
## 1 Introduction
The second approach [28] is based on the analytical properties of the Riccati equation, the use of which allows for an equivalent transition from the initial nonlinear optimization problems to control problems of a linear dynamic system in phase variables, which is a projection of the so-called Hamiltonian system onto a vector space [28]. For each specific problem and the terminal optimality criterion, the type of projection is determined, that is, the number of Hamiltonian variables sufficient to form the initial criterion. This approach is also based on the application of the Pontryagin maximum principle. It makes it possible to obtain numerical algorithms for constructing optimal observation control laws that have monotonic convergence and allow to build consistently improving measurement strategies. Of great practical interest is the second approach, which allows us to obtain constructive results when solving problems of optimizing observations. It is this approach that is used in the framework of the studies described in this article. As part of this research, it is assumed that the intrusion detection system monitors the state of several controlled objects included in the telecommunications network. It is assumed that the Kalman filter is used in the system to improve the quality of the assessment of the state of objects. It should be noted that this article does not consider the issues of filtering and searching for anomalies in the behavior of objects, since they have been researched quite extensively to date. It seems relevant to study the possibility of using the mathematical apparatus for optimizing observations in relation to the tasks of resource management of IDS. The mathematical formulation of the problem described above is provided in the second section. The general algorithm for solving this problem is also considered there. Later in the article, in the third section, the results of mathematical modeling are presented, confirming the effectiveness of the proposed method of optimizing AIDS resources. The fourth section contains a discussion of the results obtained in the framework of this work. The fifth section summarizes the results and makes a general conclusion.
## 4 Proof-of-Concept Implementation  
An implementation of the model introduced in Section 3 is discussed below. This implementation was devised as a constructive proof of concept with the objective of confirming the feasibility of the proposed conceptual framework. Only available state-of-the-art technologies are involved in this implementation, and no assumptions are made about future technologies or upcoming existing ones.  
Blockchain is a relatively recent set of technologies that is in a process of maturing and continuous improvement in general, and, in particular, in the aspect of interconnecting blockchains such as designed in this proposal. Some initiatives related to Ethereum, Polka-dot, or Hyperledger, for example, work in this line [62; 63]. All potential implementations of the design proposed in Section 3 must consider the security of the chosen blockchain platform (e.g., Bitcoin [65], Ethereum [66], Hyperledger [67], etc.), its protocols and features, especially when exposed in a production environment [68; 69; 70; 71] and in the educational field [72].  
To demonstrate and exemplify the feasibility of the proposed model with current technology, the system was designed using Hyperledger Fabric as a framework. Hyperledger Fabric [73] is a permission platform for distributed ledger solutions built upon a modular architecture that provides high degrees of confidentiality, flexibility, resiliency, and scalability, besides supporting pluggable implementations of several components such as consensus mechanisms or membership services for identity management.  
The reasons to select Hyperledger Fabric for this initial implementation are as follows:  
* It is a private, efficient processing blockchain, enabling parallel processing with thousands of transactions per second depending on the topology and configuration [74];
* It provides a membership identity service that manages user ID and authenticates all participants in the network, allowing the creation of access control lists to provide different levels of permission depending on the role;
* It enables the creation of private channels to assure privacy and confidentiality; hence, information cannot be accessed without proper permissions to the network participant;
* It allows the creation of smart contracts called \"chaincode\" in the Hyperledger Fabric network required by the proposed design;
* It implements a modular architecture with pluggable components for different types of identity, consensus mechanisms, or encryption algorithms.  
To model the implementation of this proposal, a five-element system was devised.
# Study on Dynamic Strain Distribution in Rock Broken by Abrasive Slurry Jet
## 5 Comparative Analysis of Theoretical and Experimental Results  
In this section, theoretical and experimental results are compared and analyzed to obtain a unified dynamic strain distribution in rock broken by ASJ.  
Figure 10: Comparison diagrams of theoretical and experimental results.  
Figure 9: Numerical solutions for the formula for stress wave transmission in rock.  
Figure 10 shows the comparison diagrams of the theoretical and experimental curves of the rock dynamic strain. The figure shows that there are two similarities and two differences between the theoretical curves and experimental curves. One of the similarities is that the maximum values of rock dynamic strain in the two curves are approximately equal under the same jet pressure and propagation distance. The other similarity is the effects of jet pressure and propagation distance on the maximum strain. The maximum value of rock strain increases linearly with the increase of jet pressure, while decreases sharply with the increase of propagation distance.  
One of the differences is that the increase in the process of rock dynamic strain in the two curves cannot be fitted. The initial time point in the theoretical curves is the moment when the ASJ touches the rock, while the one in the experimental curves is not that moment. It is difficult to capture the strain of rock under impacting of the stable and high-speed ASJ at the initial time as the booster process of the ASJ system takes time. Therefore, the theoretical curves need to be shifted on the timeline to fit the experimental curves, which are shown in Figure 10. The other difference is that the experimental curves are tortuous, while the theoretical curves are very smooth. The theoretical equation was established with effects of rock failure on dynamic strain ignored. However, in the process of strain measurement, the rock will break continuously, releasing the rock stress in an instant. When the breaking stops, rock stress will increase under the impacting of ASJ, forming the fluctuating rock dynamic stress finally. This also reflects that rock impacted by ASJ is broken in the form of stepped-failure indirectly.  
Figure 10 also shows that theoretical curves shifted on the timeline are fitted appropriately with the experimental curves under various jet pressures and propagation distances, which can prove the correctness of theoretical and experimental results.
## 2 Test Exploration
### CFD Theory  
#### Governing Equation  
The fluid flow pattern in the double-row TBR is relatively complicated; however, it still satisfies the Navier-Stokes governing equation, including the mass conservation equation, momentum conservation equation, and energy conservation equation [20]. In this work, only the fluid flow in the bearing was studied, and the working temperature was predefined. Therefore, the energy equation is not considered here [21].  
\[\frac{\partial\rho}{\partial t}+\
abla(\rho u)=0 \tag{1}\]  
\[\frac{\partial(\rho u_{i})}{\partial t}+\
abla(\rho u_{i}u)=\
abla(\mu\cdot grad (u_{i}))-\frac{\partial p}{\partial x_{i}}+S_{i} \tag{2}\]  
where \(\rho\) is the lubricating oil density, kg/m\({}^{3}\); \(t\) is time, s; \(u\) is the velocity vector, m/s, \(u=[u_{1},u_{2},u_{3}]\); \(\mu\) is the dynamic viscosity of the lubricating oil, Pa-s; \(p\) is the pressure on the lubricating oil infinitesimal body, Pa; \(x_{i}\) represents the coordinate in all directions, where  
Figure 3: Bearing CFD analysis flow chart.  
Figure 2: Disassembling inspection diagram after the bearing oil loss test. (**a**) Outer ring rib structure; (**b**) Inner ring rib structure.  
\(i=1\), 2, and 3, corresponding to the x, y, and z directions, respectively. \(S_{i}\) is the general source term, kg/(m\({}^{2}\)-s\({}^{2}\)).
# An Early Warning System to Detect At-Risk Students in Online Higher Education
## 8 Conclusions and Future Work
** \\ \hline
10\% & 80.59 & 80.70 & **84.20** & 91.39 & 90.02 & **91.71** & 44.14 & 47.88 & **58.79** & 46.54 & 49.19 & **59.75** \\
15\% & 82.58 & 82.88 & **85.44** & 92.02 & 91.14 & **92.17** & 49.14 & 52.45 & **62.39** & 51.03 & 53.64 & **62.68** \\
20\% & 84.33 & 84.81 & **87.00** & 92.54 & 92.12 & **92.65** & 59.33 & 56.57 & **66.04** & 55.37 & 57.96 & **65.76** \\
20\% & 85.82 & 86.52 & **88.30** & 92.94 & 92.95 & **93.15** & 58.51 & 61.12 & **69.70** & 59.51 & 62.11 & **68.96** \\
30\% & 87.12 & 88.01 & **93.32** & 92.36 & 93.68 & 93.64 & 2.79 & 63.55 & 73.41 & 63.39 & 66.02 & 72.25 \\
35\% & 88.15 & 82.95 & **96.05** & 94.33 & **94.18** & 94.13 & 66.65 & 68.09 & **76.68** & 67.07 & 69.75 & 78.50 \\
40\% & 89.00 & 90.29 & **91.72** & 93.62 & 94.65 & **94.70** & 74.92 & 72.52 & **80.32** & 70.42 & 73.18 & **78.94** \\
45\% & 89.81 & 91.23 & **92.68** & 93.93 & 90.59 & **95.22** & 77.33 & 75.82 & **83.73** & 73.39 & 73.31 & **82.06** \\
50\% & 90.64 & 92.04 & **93.54** & 94.31 & 95.04 & 95.85 & 76.35 & 78.58 & 86.45 & 75.78 & 78.77 & **84.59** \\
55\% & 91.30 & 92.62 & **94.16** & 94.66 & 96.63 & 95.81 & 72.85 & 80.71 & **88.82** & 77.57 & 80.6
## 2 Research on UAV Path-Planning Problem
### Environmental Modeling
#### 2.3.4 Wind-Field Model  
Wind speed and direction can have an impact on UAV energy consumption, flight stability, and path accuracy. Therefore, the effects of wind need to be taken into account when performing UAV path planning, which may change speed, altitude, or direction of the UAV [34]. This paper adds a wind-field model to the three-dimensional map, and the wind vector \(\overrightarrow{w}=\left(w_{x},w_{y},w_{z}\right)\) is [35; 36]:  
\[\left\{\begin{array}{l}w_{x}=v_{w}\cdot sin\theta\\ w_{y}=v_{w}\cdot cos\theta\\ w_{z}=v_{w}\cdot e^{-\left(\frac{\left(x-x_{1}\right)^{2}+\left(y-y_{1}\right) ^{2}}{\left(r_{1}\right)^{2}}\right)}\left[1-\left(\frac{\left(x-x_{1}\right) ^{2}+\left(y-y_{1}\right)^{2}}{\left(r_{1}\right)^{2}}\right)\right]\end{array}\right. \tag{14}\]  
The relative airspeed and ground speed of the drone were [37]:  
\[\overrightarrow{v}_{a}=\left(\begin{array}{l}v_{a}cos\psi cos\gamma\\ v_{a}sin\psi cos\gamma\\ -v_{a}sin\gamma\end{array}\right) \tag{15}\]  
\[\overrightarrow{r}=\overrightarrow{v}_{a}+\overrightarrow{w} \tag{16}\]  
where \(w_{x},w_{y},w_{z}\) denote the components of the wind field in the \(x,y,z\) directions; \(v_{w}\) and \(v_{a}\) are the wind speed at the center of the updraft \(\left(x_{1},y_{1}\right)\) and the initial speed of the UAV, respectively; \(r_{1}\) is the radius of influence of the airflow center; \(\theta\) is the angle between the point \(\left(x,y\right)\) and the line connecting the center of the airflow \(\left(x_{1},y_{1}\right)\) relative to the north direction; \(\overrightarrow{v}_{a}\) and \(\overrightarrow{r}\) are the relative airspeed and ground-speed vectors of the UAV, respectively; \(\psi\) and \(\gamma\) are the yaw and pitch angles. Figure 2 shows the mountain model and the city model with wind field, respectively. The number of peaks or buildings in this paper is chosen to be 20 and 25 (i.e., the number of peaks is taken to be 20 and 25, and the number of buildings in the city is taken to be 20 and 25, for a total of four terrains), and this number is determined in the initialization phase. The heights of the peaks or buildings are randomly generated, and the terrain parameters are recorded after generation to repeat the experiment. Only mountainous and urban terrain with a peak or building count of 25 is shown in Figure 2.  
Figure 2: (a,b) represent the mountain and city maps with wind fields, respectively, where the red arrow indicates the direction of the wind field, the orange sphere represents the exclusion zone, and the green terrain represents the peaks or urban buildings.
# Optimal Robust PID Control for First- and Second-Order Plus Dead-Time Processes
## 2 Control System and Design Objective
### Performance Function and Constraint  
First, the integral of absolute error (IAE) is used in the performance function, which is defined as follows:  
\[J = \int_{0}^{\infty}|e(t)|dt=\int_{0}^{\infty}|r(t)-y(t)|dt. \tag{5}\]  
Next, the constraint condition for assigning robust stability is also introduced. The sensitivity function of the discussed system, \(S(s)\), is defined as follows:  
\[S(s) = \frac{1}{1+P(s)C(s)}, \tag{6}\] \[C(s) = C_{1}(s)+C_{2}(s). \tag{7}\]  
The maximum value of Equation (6), \(M_{s}\), is decided as follows:  
\[M_{s} = \max_{\omega}\frac{1}{|1+P(j\omega)C(j\omega)|}. \tag{8}\]  
The relationship between \(M_{s}\) and the gain margin (\(g_{m}\)) and that between \(M_{s}\) and the phase margin (\(\phi_{m}\)) are as follows [3; 4]:  
\[g_{m} \geq \frac{M_{s}}{M_{s}-1}, \tag{9}\] \[\phi_{m} \geq 2\arcsin\frac{1}{2M_{s}}. \tag{10}\]  
Therefore, robust stability is achieved by satisfying the following constraint:  
\[|M_{s}^{d}-M_{s}|\cong 0, \tag{11}\]  
where \(M_{s}^{d}\) denotes the desired (prescribed) robustness. In the present study, the prescribed robustness is selected in the rage from 1.4 to 2.0 [18; 19].
## 3 Results and Discussion
### BLUF Sequences, Modular Domains and Phylogenetic Analysis
Our analysis further confirms that tyrosine, glutamine, and tryptophan (or methionine) are critical for the substrate specificity of the BLUF sequences, as has been reported earlier [6; 11; 33; 57]. The photo-activation of the BLUF domains actually involves conserved glutamine and tyrosine residues, where glutamine contributes to hydrogen bond formation in a dark state [11; 12; 13; 14]. However, in light adapted conditions, a hydrogen bond rearrangement (tautomerization) occurs to form a new hydrogen bond with tyrosine [11]. A motif analysis revealed three different conserved motifs (Figure 2) among different BLUF sequences. Each motif has its importance, as each of them was comprised of an essential amino acid residue involved in the regulation of the BLUF photocycle and photodynamics [6; 11]. The phylogenetic analysis of the selected sequences of 34 BLUF domains divides them into four distinct clusters (I-IV), which seem to differ from each other mainly by the presence of eukaryotic and prokaryotic BLUF proteins in them (Figure 3). As observed from the phylogenetic analysis, the BLUF domain sequences of eukaryotic and prokaryotic origins are evolutionarily intermixed. Each cluster (except cluster IV) was further subdivided into sub-clusters, which represent closely related BLUF domain sequences broadly associated with similarkind of eff  
Figure 2: Multiple sequence alignment of the different BLUF modular domains depicting conserved amino acids. The black arrow indicates conserved amino acids crucial for regulating the flavin binding pocket, photocycle and photodynamics of the BLUF domain containing proteins [6; 11]. The sequences under the solid boxes represent the conserved motifs of the BLUF domain. The conserved motifs were predicted using the Multiple EM for the Motif Elicitation (MEME) suite.
# Improving Multi-Label Learning by Correlation Embedding
## 3 The Proposed Method  
In multi-label learning, \(\mathbf{X}=[\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{n}]^{T}\in \mathbb{R}^{n\times d}\) is the feature matrix and \(\mathbf{Y}\in\{0,1\}^{n\times q}\) is the label matrix, where \(n\) is the number of instances, \(d\) is the dimension and \(q\) is the number of class labels. The \(i\)-th example is denoted by a vector with \(d\) attribute values\(\mathbf{x}_{i}=[x_{i1},x_{i2},\dots,x_{id}]\), and \(\mathbf{y}_{i}=[y_{1i},y_{2},\dots,y_{ij}]\) is a set of possible labels for \(\mathbf{x}_{i}\), where \(y_{ij}=1\) indicates the \(i\)-th instance belonging to the \(j\)-th label, otherwise, \(y_{ij}=0\).  
In this paper, we integrate the feature space dimension reduction and the multi-label classification into a unified framework. The learning framework of our proposed method MLLCE is shown in Figure 1. First, we project the original high-dimensional feature space to a low-dimensional latent space by a mapping matrix. To model label correlation, we learn an embedding matrix from the pre-defined label correlation graph by graph embedding. Then, we use the embedding matrix as the model coefficients to construct a multi-label classifier from the low-dimensional latent feature space to the label space. In this way, the inherent correlations between different labels will be directly kept in the model coefficients. Finally, we extend the proposed method MLLCE to the nonlinear version, i.e., NL-MLLCE.