esa-sceva/satcom-chunk-collection huggingface dataset:
# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
###### Abstract  
This paper presents a deep learning-based Scan-vs-BIM methodology for evaluating structural integrity through the extraction of features from As-Built scan and As-Planned Building Information Modeling (BIM) comparison data. Traditional Scan-vs-BIM frameworks often rely on Scan-to-BIM processes to generate point cloud-based mesh models for comparison, which significantly impairs computational efficiency. In contrast, the proposed streamlined Scan-vs-BIM framework incorporates a deep neural network (DNN) model consisting of two neural networks: one for structural integrity assessment and another for error type analysis. The model evaluates the structural integrity of individual components in a sequential manner, repeating the process across all elements to comprehensively assess the entire structure. Rather than converting point cloud data into mesh models for comparison, this approach directly measures the spatial discrepancies between the As-Built point cloud and As-Planned BIM, analyzing the distribution tendencies of these distance values. Experimental validation on actual steel structures demonstrated that the proposed method effectively predicts structural integrity, providing significant improvements in both accuracy and computational performance.  
scan-vs-BIM; steel structure; deep learning; deep neural network (DNN); integrity evaluation; 3D scanning +
Footnote †: journal: applied sciences
## 2 Modeling Principles  
### General Dynamic Vibration Absorbers (DVA) Modeling Approach  
Modeling the novel SHMD device requires a thorough understanding of the modeling principles and procedures followed in the design of passive, active and semi-active systems. In order to do so, the dynamic behaviour of an \(n\)-DOF system coupled with a DVA (as depicted in Figure 1) under a random dynamic loading needs to be considered through its equation of motion:  
\[M\ddot{x}(t)+C\dot{x}(t)+Kx(t)=Bu(t)+Dd(t) \tag{1}\]  
In Equation (1), each overdot declares single differentiation with respect to time, \(M\), \(C\) and \(K\) are the \(n\times n\) mass, damping, and stiffness matrices, respectively; \(x(t)\) and \(d(t)\) are in order of the displacement, and external force \(n\times 1\) column vectors; \(u(t)\) is the single scalar control force and \(B(n\times 1)\) and \(D(n\times n)\) are the influence matrices assigning the control and external force contributions, respectively, to the individual degree of freedoms (DOFs). For each DOF in \(x(t)\) being the lateral displacement of the \(i\)th \((i=1,...,n)\) mass, \(M\) becomes diagonal, while for the classical viscous damping considered the damping matrix \(C\) attains a form identical to the symmetric stiffness matrix \(K\). Without any loss of generality, the DVA is attached to the \((n-1)\)th DOF and its motion constitutes the \(n\)th DOF. For control implementation purposes, Equation (1) can be represented in the state space domain using a first order differential equation, such that:  
\[\dot{z}(t)=Az(t)+Fu(t)+Ed(t)\]  
Figure 1: Structural configuration and mathematical models for (**a**) tuned mass damper (TMD); (**b**) semi-active tuned mass damper (STMD); (**c**) active mass damper (AMD); (**d**) active-tuned mass damper (ATMD); and (**e**) semi-active hybrid mass damper (SHMD) systems.  
where, \(\dot{z}(t)\) represents the first order time-change of the states \(z(t)=\left[\begin{array}{cc}x(t)&\dot{x}(t)\end{array}\right]^{T}\) of the system, \(A\) is the system block matrix containing the system's mass, damping, stiffness properties, \(F\) is the control force locator block matrix, and \(E\) is the external perturbation locator block matrix, such that:  
\[A=\left[\begin{array}{cc}0&I\\ -M^{-1}K&-M^{-1}C\end{array}\right],F=\left[\begin{array}{cc}0\\ M^{-1}B\end{array}\right],E=\left[\begin{array}{cc}0\\ M^{-1}D\end{array}\right] \tag{3}\]  
With \(I\) being the identity matrix of appropriate dimensions (i.e., \((n\times n)\)).
## 4 MSIMP Based on EMOEAs
### Surrogate Model for EMOEAs
They enhance the adaptability of the algorithm: Through continuously updating the surrogate model to cope with dynamic changes in the environment, such as changes in the target area, changes in the mission requirements, and so on, the algorithm can maintain efficient optimization capabilities in the face of dynamic problems. In MSIMP, this adaptability enables the system to cope with changes and uncertainties in the task requirements and ensures the smooth execution of the task. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Main Ideas** & **Advantages and Disadvantages** \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Cooperative \\ Surrogate Models \\ for Multi-Objective \\ Optimization [98] \\ \end{tabular}  } & Multiple surrogate models are combined for collaborative optimization, and the optimization effect is improved by weighted average prediction results. According to the feedback of the optimization process, the model weight and combination strategy are dynamically adjusted to meet the optimization needs of different stages. & **Advantages:** The synthesis of different surrogate models makes the advantages of each model complementary, reduces the possible bias of a single model, and improves the data generalization ability of the algorithm. & **Disadvantages:** The training and evaluation of multiple surrogate models greatly increases computational complexity and resource consumption. \\ \hline \multirow{4}{*}{\ \begin{tabular}{c} Transfer Learning \\ for \\ Surrogate-Assisted \\ Multi-Objective \\ Optimization [99] \\ \end{tabular}  } & The similarities and differences between the source and target domains are analyzed, effective knowledge migration methods are designed to migrate the surrogate models trained on the source domain to the target domain, and the adaptability of the surrogate models to new tasks is improved. & **Advantages:** Using historical knowledge to construct more accurate initial prediction models and transferring knowledge from the source domain to the target domain to speed up the convergence of EMOEAs. & **Advantages:** The similarity and difference judgments between different tasks are complex and highly dependent on the quality of historical data. \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Parallel Surrogate \\ Models for \\ Multi-Objective \\ Optimization [100] \\ \end{tabular}  } & Multiple surrogate models are trained in parallel, the fitness value of each objective function is \\
## 4 Actuation System  
Several options exist for actuating the MLE system. Besides classical actuators like pneumatic, hydraulic, and electromechanical systems, which are well established for aircraft applications, several novel actuation concepts for morphing systems are discussed in the literature like shape memory alloys [31] and piezoceramic actuators [32]. For the MLE system presented in this paper, an electromechanical actuation system has been selected because their reliability for aircraft applications can be quantified based on experience [33], and they are consistent with a more electric aircraft approach, which is expected to play a major role in future aircraft design [34].  
For deploying the morphing leading edge, the stringers 2, 3, 4, and 5 are positioned by the drive mechanism shown in Figure 12.  
The drive mechanism is driven by a linear electromechanical actuator. Its orientation in spanwise direction allows to design an actuation system that leaves approx. 50% of the cross section as free space for cables, tubes, and other systems to be placed in the leading edge of the wing. A bell crank deflects the linear movement of this actuator into the desired direction of movement for stringer 3, which is an oblique movement pointing downwards and to the front. Stringers 2, 3, and 4 are connected to a crank by links. Stringer 5 is connected to the support structure by a link. The geometry of these links and the crank  
Figure 11: Steel profiles for additional stiffening of the stringers; stringers 1, 2, 4, and 5 are reinforced by a pair of C-section profiles, each, while higher loads at stringer 3 require box-section profiles; cylindrical parts are fittings for the kinematic system; stringer 1 is not connected to the kinematic system and therefore does not possess fittings; the CFRP-portion of each stringers (not shown here) is sandwiched between the two profiles.  
Figure 10: Inverse reserve factors due to thermal residual stresses in the undeformed skin for cooling to \(-45\)\({}^{\circ}\)C; **(a)** ABAQUS-model (linear shell elements) of the CRFP-only stringers largely shows inverse reserve factor (IRF)-values (even without morphing) of ca. 0.35 (green) and above; **(b)** ANSYS-model (linear shell elements—here displayed with their specified thickness) shows IRF-values mostly around 0.1 and below (also without morphing); upper right corner: Design principle of the stringer-reinforcement assumed for the ANSYS-model (this has since been superseded by the design shown in Figure 11).  
together with the stiffness properties of the skin define the relative position of the stringers, resulting in an aerodynamically feasible aerofoil for any actuator position.
## 3 Results and Discussion
### Method Comparison
#### 3.1.1 Limits of Detection  
IDT  
The investigated IDTs are designed to detect amphetamine, methamphetamine, and MDMA. Therefore, IDTs are a suitable example to examine the chosen target compounds. The information gathered from a measurement is that one of the investigated substances is present or not. No statement is made what specific target compound is found if a positive result is obtained. To gain further information about the capabilities of IDTs, the LOD was determined for amphetamine, methamphetamine and MDMA separately. The results of Table 1 reveal that clear visible indication/bar is possible down to 2 ng total target compound. In some cases, lower substance amounts down to 1 ng resulted in positive signals (slightly visible bar) as well, but were discarded due to the inconsistency of the results. In addition, all three substances yielded the same LOD. This is interesting because in most cases of on-site analysis this test only informs about a sum parameter. The knowledge that all investigated compounds can be detected, at a similar amount, eases the practical use for first responders. Cross-reactions were tested with all listed drug precursors and caffeine. No false positive results were obtained up to the investigated limit of one thousand times the LOD (2000 ng) per substance.  
#### 3.1.1 Limits of Detection  
IDT  
The investigated IDTs are designed to detect amphetamine, methamphetamine, and MDMA. Therefore, IDTs are a suitable example to examine the chosen target compounds. The information gathered from a measurement is that one of the investigated substances is present or not. No statement is made what specific target compound is found if a positive result is obtained. To gain further information about the capabilities of IDTs, the LOD was determined for amphetamine, methamphetamine and MDMA separately. The results of Table 1 reveal that clear visible indication/bar is possible down to 2 ng total target compound. In some cases, lower substance amounts down to 1 ng resulted in positive signals (slightly visible bar) as well, but were discarded due to the inconsistency of the results. In addition, all three substances yielded the same LOD. This is interesting because in most cases of on-site analysis this test only informs about a sum parameter. The knowledge that all investigated compounds can be detected, at a similar amount, eases the practical use for first responders. Cross-reactions were tested with all listed drug precursors and caffeine. No false positive results were obtained up to the investigated limit of one thousand times the LOD (2000 ng) per substance.
# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 4 Conclusions and Future Research Lines  
In this paper we provided a comprehensive survey of the membrane computing models, i.e., spiking neural P systems used for fault diagnosis of power systems. We also discussed corresponding reasoning algorithms based on these models. However the investigation regarding the construction of fault diagnosis models based on membrane computing models is still in preliminary stage. Hence there exists many topics which need to be investigated further. We discuss some of the topics which can be considered as future topic of research below.  
(1) The FRSNPs models and their variants have been very useful for fault diagnosis in power systems. Constructing new variants of FRSNPs models by incorporating the properties of neural networks or deep neural networks such that these models have the capability of handling fuzzy, vague or incomplete information could be a future research direction.  
(2) The reasoning algorithms discussed in this paper are efficient. However, there has been no known study discussing the complexity of these algorithms. It is not known whether the time and space complexity of these algorithms are optimal or not. Investigating optimality of these algorithms and constructing reasoning algorithms with better bounds could also be a future research direction.  
(3) In this paper we discussed the use of a supervised learning algorithm in the framework of SNPS for identification of faults in transformers. However, it remains to be investigated whether any unsupervised learning algorithm in the framework of SNPS can be constructed such that it can be used for fault diagnosis in power systems.  
(4) The investigations on the use of FRSNPS models for power systems fault diagnosis in the literature mentioned in this paper indicate that FRSNPS models have the potential to diagnose faults in large-scale power systems with a linear or polynomial time. However, the proof or experimental verification is a future work.  
Conceptualization, Y.L. and G.Z.; investigation, P.P. and Y.L.; writing--original draft preparation, P.P.; writing--review and editing, Y.L., P.P., Y.C., S.F., X.M. and G.Z.; supervision, G.Z.; funding acquisition, Y.L., Y.C., S.F. and X.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Science and Technology Project of State Grid Sichuan Electric Power Company (No. 521997180016).  
The authors declare no conflict of interest.
# Biologically Derived Gels for the Cleaning of Historical and Artistic Metal Heritage
## 4 Organogels for Organic Coatings Removal
### Bio-Based Solvents
#### 4.2.5 Biodiesel and Derivatives  
To obviate the traditional reliance on fossil-fuels, biodiesel has been developed as an innovative green option. Nowadays it can be extracted from vegetable oils or algae, labeling it as renewable, bio-sourced, and biodegradable [90; 99].  
From the chemical point of view, biodiesel is a blend of alkyl esters and long-chain fatty acids [84]: this makes it an appropriate solvent for non-polar substances.  
In the field of metal conservation, biodiesel evidenced the capability of acting as a cleaning agent for the removal of wax from a bronze sculpture [84]. Regrettably, besides the solvent efficiency on non-polar coatings, the research pointed out the non-volatility of biodiesel, which appears as the main drawback of such application. Even if englobed in a gel system, the solvent tends towards leaving residues on the artifact. Given the fact that biodiesel could be a potential carbon source for microorganisms, eventual biodegradationbacteria and fungi present could induce a biodeterioration of organic materials such as canvas, textile, or wood. Therefore, despite its satisfactory application in this specific case, this protocol gives the impression of being unadvisable for the more sensitive substrates mentioned above, no less than metals in a negative state of conservation (e.g., highly-porous corroded).  
Derived as a by-product of biodiesel, glycerol arises as a promising green solvent: renewable, biodegradable, non-volatile, non-toxic, and affordable [90, 99]. It is classified as an intermediately polar solvent [108]; however, its use in industrial activities and reactions have been often limited due to its high viscosity [99]. Even in combination with choline chloride, creating a Deep Eutectic Solvent (DES) [90], the derived chemical is still too viscous for certain applications [99], in addition to the inherent non-volatility of DES systems.  
In art conservation, it used to be employed chiefly in paintings as a moistening and plasticizing medium [96]. However, for the same reasons narrowing down its application in industry, glycerol does not seem an appealing solvent in this field. Indeed, the non-volatility, one of its main green advantages, turns into a weakness in conservation practices, being unable to evaporate from the dissolved substance or the targeted surface.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 3 Preliminaries
### System Model and Assumptions  
Our model is based on graph theory [62], represented as an undirected graph \(G=(V,E)\). The graph consists of a set of vertices \(V\) representing the network's nodes \(v_{i}\in V\) and a set of edges \(E\) that connect nodes together. Any edge \(e_{ij}\in E\) is formed between any two nodes, \(v_{i}\) and \(v_{j}\), when the two nodes are in the transmission range of each other.  
This paper relies on assumptions to establish, model, and analyze our network. The first assumption is that all network nodes are stationary, meaning their positions and network topology remain fixed throughout the analysis. Except for the sink node, all nodes are assumed to have the same capabilities, equal importance, and initial energy levels. Data transmission is categorized into one-hop for critical data and two-hop for normal data. Moreover, the nodes, excluding the sink node, are equipped with routing functionality but are constrained regarding resources, encompassing energy, and processing capabilities.
## 2 MSFGWO Algorithm Model
### Hybrid Algorithm  
#### 2.3.1 Improvement of the Search Mechanism  
In the position update process of the GWO algorithm, the majority of grey wolf individuals within the population converge towards the optimal, sub-optimal, and third-best solutions. This convergent behavior results in the individual grey wolves within the population gradually becoming homogeneous, thereby diminishing the population diversity and narrowing the scope of exploration. Consequently, this hinders the discovery of new, potentially superior solution spaces, ultimately limiting the algorithm's performance when considering complex optimization problems. Furthermore, while the GWO algorithm establishes an effective mechanism for balancing exploration and exploitation through the parameters \(\overset{\rightarrow}{a}\) and \(\overset{\rightarrow}{A}\), its exploration capability remains heavily reliant on the parameter C. This singular dependence on \(\overset{\rightarrow}{C}\) for exploration increases the likelihood of the algorithm converging to local optima, thereby hindering its ability to effectively search for the global optimum. To address the aforementioned challenges, the proposed algorithm incorporates the perturbation mechanism of the VNS algorithm and the mutation strategy of the GA.  
1. Shaking Procedure in VNS Algorithm  
The shaking procedure within the VNS algorithm serves to modify the spatial configuration among the grey wolf individuals. This study employs the satellite exchange method to generate new grey wolf individuals, effectively mitigating the tendency of grey wolf individuals in the GWO algorithm to gradually converge. This enhancement improves the algorithm's capability to explore a broader solution space. This operation not only enhances the GWO algorithm's ability to avoid local optima but also substantially increases the population diversity. The shaking procedure in the VNS algorithm is depicted in Figure 2.  
Figure 2: Shaking procedure in the VNS algorithm.  
The mutation strategy in the GA is implemented by randomly altering elements within the individual grey wolf representations. In this study, the satellite combinations associated with each grey wolf are stochastically reconfigured to generate novel grey wolf entities. This process not only effectively enhances the population diversity but also substantially improves the algorithm's capability to explore a broader solution space. Consequently, this assists the GWO algorithm in avoiding entrapment in local optima. The mutation strategy in the GA is shown in Figure 3.
# Reconfigurable Diplexer-Based Filtenna for Tx/Rx Operation in Mobile Satellite Terminals
So, depending on the diodes state (on or off), the antenna can change between the two frequencies. Another reconfigurable antenna is shown in [7]. This one has two ports: one for the reception (23 GHz until 24 GHz) and another for transmission (25 GHz until 27 GHz and 27 GHz until 30 GHz). Two band pass filters with radial stubs were designed to block the frequencies outside from the bands mentioned above. Since for transmission two bands were allowed, a PIN diode was inserted inside the filter to allow the change between both bands. In [8], the authors present four models of microstrip patch antennas operating at 60 GHz. Even though these antennas were designed on the same substrate, the patches and ground plane are different. Besides that, they have slots using different shapes with variable resistors inside, and changing the value of the resistors, it is possible to modify some of antenna's characteristics, such as resonance frequency, bandwidth, or gain. An ultra-wide bandwidth antenna that is capable of operating within the range of 3.1 to 10.6 GHz is reported in [9], using a band pass filter consisting of short-circuited shunt stubs and inter digital edge coupled microstrip lines, to improve the selectivity, since the frequencies out of this band can generate interference with others communication systems. In [10], a novel broadband antenna is presented using a band pass hairpin filter that is integrated in the structure, filtering between 2.03 and 2.3 GHz. The microstrip patch acts both as a radiating element and as the last resonator of the band pass filter, allowing the antenna to radiate within the filter frequencies. In this paper, a small reconfigurable microstrip patch filtenna is presented that can be integrated into mobile devices to communicate with low-orbit satellites. The filtenna can operate in two different bands, 20 GHz and 29 GHz. To change the operating frequency, two different metal structures can be connected via a PIN diode. Two hairpin filters were used to eliminate the undesired frequencies, and they also work as a DC blocker. This paper is organized in five sections. In Section 1, an introduction is presented, followed by Section 2, where the choice of the filters and the switching element is described. In Section 3, the reconfigurable filtenna design is explained, and in Section 4, the measured and simulated results are shown. Finally, the conclusion of this work is presented in Section 5.