esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph Theory-Based Characterization and Classification of Household Photovoltaics
## 3 Methodology
### Construction  
The goal in this section is to develop an algorithm that builds all digraphs (of size \(p\)) that satisfy all three array-digraph conditions. To do so, it is desirable for all size \(p\) digraphs to be obtained as subgraphs of some kind of underlying \"complete graph\" that contains them all. We define the complete mixed structure of order \(2p\), denoted as \(G_{2p}\), as the mixed graph whose vertex set is \(\{0,1,\ldots,2p-1\}\), such that:  
1. Vertex \(2k\) is adjacent to \(2k+1\) through an arc \((2k\to 2k+1)\), and adjacent to all other vertices through an edge for all \(k\in\left[0,\left\lfloor(p+3)/2\right\rfloor\right]\).
2. Vertex \(2k+1\) is adjacent from \(2k\) through the arc \((2k\to 2k+1)\), and adjacent to all other vertices through an edge for all \(k\in\left[0,\left\lfloor(p+3)/2\right\rfloor\right]\).  
Here, \(\lfloor m\rfloor\) means the floor function of \(m\in\mathbb{Z}\), i.e., the greatest integer not greater than \(m\). The complete mixed structure is the one that contains all possible edge connections between all heads and tails of all panels, as shown in Figure 4 for the case \(p=4\).  
Given a spanning mixed subgraph \(H\) of \(G_{2p}\), let \(\sim\) be an equivalence relation in the vertex set such that, given \(u\),\(v\in V(H)\), we say that \(u\) is related to \(v\) (\(u\sim v\)) if, and only if there exists a path from \(u\) to \(v\) (made up of edges) contained in \(H\). The quotient set \(V(H)/\sim\) defines a partition on the vertex set given by the equivalence classes. For every spanning mixed subgraph \(H\) we build a digraph \(D\) such that:  
1. Every vertex is an equivalence class of \(V(H)/\sim\).
2. Arc \([u]\to[v]\) is drawn for every \(w\in[u]\) and \(z\in[v]\) such that \(w\to z\) is an arc of \(H\).  
Figure 4: Complete mixed structure of four panels \(G_{8}\).  
Figure 3: Example of a PV panel structure and its associated array-digraph.  
Here, \([v]\) represents the equivalence class to which vertex \(v\) belongs. An example of this construction is found in Figure 5.  
Given the association between array-digraphs and partitions, we propose a method to obtain all array-digraphs with \(p\) arcs that exploits this relation. In Figure 6, we present a sketch of the algorithm. It constructs all possible partitions of the set \(\{0,1,\ldots,2p-1\}\), builds its associated graphs and tests the array-digraph conditions, and also checks isomorphisms with the saved digraphs.  
This algorithm is not computationally efficient since, on one side, the number of partitions of \(2p\) elements increases exponentially. These are known as Bell numbers. On the other side, checking isomorphy between graphs is an NP problem [28].
## 5 Discussion
### Comparison of Results
Indeed, if we were to restrict the permeability analysis to the Kozeny-Carmen fits through the core-averaged porosity of 0.18 (Figure 15), the resulting permeabilities are much more similar to those determined by [PERSON] et al. [11]. The differences between the permeabilities from the Mezobereny well tests, our numerical flow simulations, and the results published by [PERSON] et al. [11] might also have a sedimentological origin. Variations in permeability might indeed be anticipated on a variety of scales. First, permeability within fluvial sand bodies has been shown by many workers to vary on a very local scale in different fluvial sedimentary structures (e.g., [46; 47; 48]). Porosity and permeability can also be expected to differ between turbiditic, deltaic, and fluvial sandstones, so the overall average values presented by [PERSON] [23] do not necessarily represent the properties of any of these sedimentary facies. Second, hydrocarbon fields in the Pannonian Basin are typically found in anticlinal traps or stratigraphic traps. The anticlines are often palaeo-highs, the structural traps being mainly found towards the margins of the Bekes Basin [10; 11]. In contrast, Mezobereny is located near the centre  
Figure 16: Comparison of results of this study with those of [PERSON] et al. [11]. of this basin (Figure 1). Palae-topography may well have impacted sediment supply and the development of accommodation space, which could have affected sedimentation and hence reservoir properties [13]. Suitably-designed production tests would have to be conducted to determine any variation of reservoir properties across this basin. Our results nevertheless raise the possibility that permeability may well have significant lateral variations across the basin. Comparison is also possible for sandstone sample F3 between the porosities determined from MIP measurements and those deduced from X-CT analysis. The former measurements yielded \(\sim\)0.32 0.03 (Section 4.2), the latter \(\sim\)0.180 0.006 (Section 4.4). However, as is illustrated in Table 5, the high porosity from the MIP analysis yields values for the grain density that greatly exceed the \(\sim\)2650 kg m 3 value expected for silica. On the other hand, the mean bulk density of pieces of sample F3, 2199 139 kg m 3, adjusts, using our porosity from X-CT analysis and Equation (3), to a grain density of 2680 190 kg m 3, a plausible value for silica. We thus conclude that the porosities determined by MIP measurement are unreliable and those determined from X-CT analysis are reliable, justifying our use of these in our investigation of permeability. In this instance, the MIP analysis has not produced reliable results, possibly because the apparatus operated on the basis of theory for cylindrical pores [35], the actual pore spaces being not cylindrical (Figures 10 and 14).
## 1 Introduction
Specifically, the essence of ICSPB-IoT is to achieve the efficient management of engineering quality, safety, cost and schedule through the intelligent interconnection and collaborative interaction of \"entities\" (i.e., things involved in completing a certain task, such as resource elements, subsystems, etc.) in the process of prefabricated buildings by comprehensively applying technologies including RFID, machine vision, sensor, GPS, and network communication. For instance, through the integrated application of different IoT technologies, it is possible to achieve an intelligent operation of the position tracking of prefabricated buildings components [12], quality traceability [6], component manufacturing [21; 22], storage [23], transportation and field lifting [8; 14]. In particular, the monitoring of the whole construction process effectively strengthens the coordination and communication between all stages of prefabricated buildings, which significantly improves the work efficiency and reduces the occurrence of information asymmetry and interruption. The IoT helps prefabricated buildings transition toward intelligence and information-oriented development. Therefore, the systematic exploration into the principles of ICSPB-IoT and operating mechanisms of ICSPB-IoT is of great theoretical and practical significance for reducing the cost, ensuring the quality and safety, and promoting the intelligent construction of prefabricated buildings. This paper is arranged as follows: First, in Section 2, the literature about intelligent developments in prefabricated buildings, the application of IoT in prefabricated buildings, and the development of intelligent construction systems is reviewed. Then, the conceptual framework of the ICSPB-IoT is introduced, and its system compositions are analyzed in Section 3. The operating mechanism of the ICSPB-IoT in each stage is analyzed in Section 4. Finally, in Section 5, the implementation path of the ICSPB-IoT is proposed and the difficulties that may be encountered are discussed, which is followed by a conclusion section in Section 6.
# A Robust Control Algorithm of a Descent Vehicle Angular Motion in the Earth's Atmosphere
## 5 Linearization of Angular Motion Model of the Lander
, \tag{12}\]  
where \(\mathbf{A}_{[1,1]}(t)\), \(\mathbf{A}_{[1,2]}(t)\), \(\mathbf{A}_{[2,1]}(t)\), \(\mathbf{A}_{[2,2]}(t)\in\mathbf{R}^{3\times 3}\) - blocks of the state matrix (in the general case, not zero), \(\mathbf{c}(t)=\big{[}\ c_{\gamma}(t)\ \ \ c_{\beta}(t)\ \ \ c_{\alpha}(t)\ \ \big{]}^{T}-\) vector of measured combinations of kinematic parameters, \(\mathbf{J}=I_{E_{\
u}}\). Based on the simulation results in MATLAB for the full linear model (9), the values of the variable coefficients of the matrices \(\mathbf{A}(t)\) and \(\mathbf{C}(t)\) from the record (12) were estimated. It turned out that many coefficients change insignificantly during the motion of the lander, and are close to zero or one in magnitude. The most significant changes are the coefficients \(a_{4,2}(t)\), \(a_{5,2}(t)\), and \(a_{6,3}(t)\) of the state matrix \(\mathbf{A}(t)=\big{[}a_{i,j}(t)\big{]}\). Let us form a simplified linear model (Figure 1) by changing the record of the state and observation matrices in comparison with the record (12) and introducing an underscore for the simplified matrices:  
\[\mathbf{A}_{-}(t)= \begin{bmatrix}\mathbf{0}_{3\times 3}&\mathbf{I}_{3}\\ \mathbf{A}_{-[2,1]}(t)&\mathbf{0}_{3\times 3}\end{bmatrix} ,\ \ \ \mathbf{B}= \begin{bmatrix}\mathbf{0}_{3\times 3}\\ \mathbf{J}^{-1}\end{bmatrix} ,\ \ \ \mathbf{C}= \begin{bmatrix}\mathbf{e}_{1}^{T}&\mathbf{0}_{1 \times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{I}_{3}\end{bmatrix} , \tag{13}\]  
where  
\[\mathbf{A}_{-[2,1]}(t)= \begin{bmatrix}0&-a_{4,2}(t)&0\\ 0&-a_{5,2}(t)&0\\ 0&0&-a_{6,3}(t)\end{bmatrix} =-\tilde{q}(t) \begin{bmatrix}0&\bar{a}_{4,2}&0\\ 0&\bar{a}_{5,2}&0\\ 0&0&\bar{a}_{6,3}\end{bmatrix} ;\]  
\(\mathbf{e}_{1}= \begin{bmatrix}1&0&0\end{bmatrix} ^{T}\); \(\tilde{q}(t)>0\) - scaled value of the velocity head; \(\bar{a}_{4,2}>0\), \(\bar{a}_{5,2}>0\), \(\bar{a}_{6,3}>0\) - constant of linearization of the scaled aerodynamic moment. The static addition \(\mathbf{u}_{0}\) in the control law (5) for the simplified model instead of formula (11), is calculated in a simplified way: \(\mathbf{u}_{0}=\underset{-0}{\mathbf{u}}\), using formulas (6) and (7). Since the coefficients \(\tilde{a}_{4,2}\), \(\tilde{a}_{5,2}\), \(\tilde{a}_{6,3}\), and \(\tilde{q}(t)\) are stochastic and difficult to determine, the problem arises for object (9) with matrices \(\mathbf{A}=\mathbf{A}\) and \(\mathbf{C}=\mathbf{C}\) to synthesize a robust output controller (5) with a stationary matrix \(\mathbf{F}\) independent of the matrix components \(\mathbf{A}\), which nevertheless ensures that condition (10) is satisfied.
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 4 Simulation Setup and Experimental Results
### Performance Evaluation of Throughput  
The throughput evaluation, conducted over 100 communication rounds for each dataset and channel condition, reveals the superior performance of the QESIF, as shown in Figure 5. In clean channel scenarios, the QESIF achieved an average throughput of 868 kbit/s, outperforming QuIDS (832 kbit/s), QRHE (798 kbit/s), HyQuSec (762 kbit/s), and QLSN (722 kbit/s). Under noisy channel conditions, the QESIF maintained a strong performance at 784 kbit/s, while the closest competitor, QuIDS, reached 755 kbit/s. QRHE and HyQuSec delivered 728 kbit/s and 705 kbit/s, respectively, with QLSN dropping to 684 kbit/s. In the eavesdropped scenario, the QESIF still led with 725 kbit/s, followed by  
Figure 4: Latency trends of QESIF and baseline approaches across datasets and channel conditions.  
Figure 3: Attack Detection Rate (ADR) comparison across multiple datasets.  
QuIDS at 695 kbit/s, QRHE at 674 kbit/s, HyQuSec at 648 kbit/s, and QLSN at 628 kbit/s. These results underscore the QESIF's ability to sustain high-throughput performance even under degraded quantum channels or active interference.
## 2 MDD/GNSS Installations and Measurement Methods
### The MDD/GNSS Data Transferring System  
Figure 7 illustrates the MDD/GNSS data transferring system that is composed of GNSS (GPS) antennas and receiver boards (Swift Navigation's Piksi model), laser module controllers, an ADC/Raspberry Pi(r) Board and connection wires between them. It also shows the data wiring connection between the MDD and the Raspberry Pi(r) 4B board where the data are being transmitted to a cloud-based Firebase server. The developed software was employed to receive the data and store them on a computer located at PaiChai University's laboratory. Figure 8 also depicts 4G wireless communication for the MDD/GNSS monitoring system. Each laser sensor was linked to a channel on an Analog Digital Converter Board (ADC), which then transmitted the data to the Raspberry Pi board. A real-time monitoring program running on the laboratory computer recorded and saved the data on its local hard disk storage for subsequent analysis and processing.  
Figure 5: GNSS observation streaming system.  
Figure 6: The GNSS Monitoring System: VRS application for determining base station coordinates.  
Figure 8: The whole system of MDD-GNSS for measurement of track layer displacement.  
Figure 7: The MDD/GNSS data transferring system: (**a**) MDD data wire connection, (**b**) MDD/GNSS wireless data transferring.  
Figure 8 illustrates the complete configuration and functionality of this system, offering a comprehensive overview of its interconnected components. It emphasizes the system's important components used in track layer displacement monitoring.
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 3 SPinDP: Stream Purifier in Distributed Platform
### Shared Storage  
The fourth component, Shared Storage, consists of a relational database and an in-memory database. Various information generated within the SPinDP platform must be shareable among the components as needed. Therefore, in this paper, we classify the information stored in each database based on the importance, processing, and transmission speed of the information.  
In SPinDP, MariaDB [29] is used as a relational database, and Redis [30] is used as an in-memory database. MariaDB is used to store and manage metadata such as user information, source, plan, destination names, and creation dates for the operation of the integrated interface. The information stored in MariaDB is used to operate and manage Web-based user interfaces in conjunction with Plan Manager. Redis, an in-memory database, is mainly used to store the variables or intermediate operation results of sampling and filtering algorithms implemented in Apache Storm. Since SPinDP is based on stream processing, the processing results also change very quickly. Therefore, if the necessary variables or data shared during algorithm processing are managed in disk-based databases, there is a high possibility of additional latencies in reading and writing; therefore, we adopt an in-memory database to manage such intermediate results.
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
## 2 Vertical Plane Modelling
### EMAT-EM Model  
This section introduces the EMAT-EM model to analyse the distribution of **F** (Lorentz force density). Firstly, the classic Dodd and Deeds solution [20] to the vector potential is described, and the strategy of adapting the circular analytical solutions for a straight wire is introduced (Section 2.1.1). The FEM is employed to validate the adapted solution (Section 2.1.2). Finally, based on the adapted analytical solutions, the distribution of Lorentz force density is presented (Section 2.1.3).
# Virtual Scene Construction for Seismic Damage of Building Ceilings and Furniture
###### Abstract  
A valid seismic damage scene for indoor nonstructural components is critical for virtual earthquake safety drills which can teach occupants how to survive in earthquakes. A virtual scene construction method for the seismic damage of suspended ceilings and moveable furniture is proposed based on FEMA P-58 and a physics engine. First, a modeling framework is designed based on building information modeling (BIM) to create consistent structural and scene models for the subsequent structural time-history analysis (THA) and scene construction. Subsequently, FEMA P-58 is employed to determine the damage states of nonstructural components based on the results of the THA. Finally, the physical models on the movements of the damaged components are designed using a physics engine and are also validated through the experiments such as an existing shaking table test. Considering a six-story building as a case study, a virtual earthquake scene of the indoor nonstructural components is constructed and applied in an earthquake safety drill. The outcome of this study provides well-founded scenes of the seismic damage to indoor nonstructural components for performing virtual earthquake safety drills.  
Keywords:virtual scene; nonstructural components; seismic damage; physics engine; FEMA P-58 +
Footnote †: journal: Applied Sciences
## 11 Multifunctionality of the Newly Characterized Processive Endocellases
[118] reported a novel single modular processive GH5 cellulase Cel5A with a molecular weight at 42 kDa from the brown rot basidomycete _Postia placenta_, which had about 57-fold lower activity on Avicel in comparison with the novel GH5-p4818 Cel5_2A endocellulae reported by [PERSON] et al. [45]. [PERSON] et al. [133] reported that the _Saccharomyces degradans_ GH5 processive endoglucanase Cel5H had a molecular weight of 68 kDa and was bi-modular. [PERSON] et al. [54] reported a novel single modular processive GH5 cellulase with a predicted molecular weight of 40 kDa from yak rumen metagenome with activity (0.200 U/mg protein) on Avicel as well as hydrolysis of \(\beta\)-glucans, which is, in our review, the only reported endocellulae with a small molecular weight and activity in hydrolyzing crystalline celluloses comparable to this novel GH5-p4818 Cel5_2A. However,it should be pointed out that this yak ruminal processing GH5 cellulase only had activities on \(\beta\)-1.3/1.4-glucans including soluble and insoluble celluloses and had no activities towards other hemicelluloses [54]. [PERSON] et al. [55] reported a novel GH5 cellulase with a molecular weight of 60 kDa from buffalo rumen metagenome without specifying if this enzyme had a distinct second CBM. Several other well characterized GH9 cellulases were also bi-modular, including the _Thermobifida fusca_ processive endoglucanase Cel9A with a molecular weight of 90.2 kDa [120], the _Clostridium thermocellum_ noncellulosomal GH9 processive endocellase with a molecular weight of 93 kDa [131], the ruminal _Fibrobacter succinogenes_ exocellulae GH9D with a molecular weight of 76 kDa [69], and the _Clostridium Phytorementans_ GH9 thermophilic processive endocellase with a molecular weight of 104.9 kDa [135]. Moreover, a bi-modular, bi-functional endo-acting and exo-acting procesive thermophilic _Caldicellulosirupt besci_ endocellase Ce1A was characterized with a large molecular weight of 230 kDa by [PERSON] et al. [103]. This thermophilic _Caldicellus disruptor besci_ endocellase Ce1A was further characterized to have an estimated effective size of 10-35 nm, and was highly regarded as an outstanding potential industrial cellulase for digesting raw feedstocks because of its unique mechanism of cellulose degradation [77]. Therefore, to the best of our knowledge, these novel GH5-Cel5A1 and GH5-p4818 Cel5_2A processive endcellulases are among the only reported processive endcellulases documented so far that are featured to be a monomodular and a small molecular weight endocellase with high activity on crystalline celluloses and multi-functional hydrolytic activities towards hemicelluloses, and should be highly penetrating in effectively digesting raw biomass materials such as fibre feeds and feedstocks as ideal industrial biocatalysts.
# Reduced Complexity Detection in MIMO Systems with SC-FDE Modulations and Iterative DFE Receivers
## 5 Conclusions  
In this paper, we presented a MIMO system where \(P\) antennas transmit to \(R\) receiving antennas, with the employment of SC-FDE modulations and IB-DFE-based nonlinear receivers. Moreover, we analyzed the effect of different noise characteristics in different receive branches. We presented an efficient method for obtaining the receiver parameters that requires the inversion of size-\(P\) matrices, contrary to conventional schemes that require the inversion of size-\(R\) matrices. This method is particularly interesting for the cases where \(R\gg P\gg 1\). Our implementation considers low resolution ADCs, which is relevant for massive MIMO systems. Additionally, ADCs with reduced resolutions are an advantage in distributed MIMO schemes, such as BS cooperation systems, decreasing the signaling requirements between the receive antennas and central unit. The implementation of low resolution ADCs is a starting point for studying lower complexity systems and can be extended to schemes where quantization is not employed, such as Radio-over-Fiber (RoF) [20]. Moreover, it also can be considered as a technique that does not require the inversion of matrices, as Equal Gain Combiner (EGC) [21] and Maximum Ratio Combiner (MRC) [22]. An additional issue would be the extension of these techniques to hybrid analog/digital massive MIMO schemes where several antennas share a single RF chain, as in [23; 24].  
**Acknowledgments:** The authors would like to thank the editor and the anonymous reviewers for their valuable comments and suggestions which improved the quality of the paper. This work was supported by Fundacao para a Ciencia e Tecnologia and Instituto de Telecomunicacoes through project UID/EEA/50008/2013.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] were involved in the mathematical developments and writing of the paper. The computer simulations were carried out by [PERSON].  
**Conflicts of Interest:** The authors declare no conflicts of interest.