esa-sceva/satcom-chunk-collection huggingface dataset:
# Operationalisation of Building Inspections and Repair: Systematisation-Based Approach
### Scope and Objectives  
The presented study is included within the scope of inspection of the envelope of current buildings as a support task to proactive and reactive maintenance strategies. Considering the categories roofs, facades and floorings, the research objectives are as follows (Figure 1):  
* understanding if and which defects can be diagnosed using the same means, considering visual observation as the base procedure and the use of tools and equipment as specific diagnosis methods;
* understanding if and which defects can be repaired using the same techniques and the number of resources associated with those practices.  
To achieve the mentioned objectives, the authors will develop two new inter-defects correlation matrices, this time based on the (i) defects-diagnosis methods and (ii) defects-repair techniques correlation matrices, which, until now, were excluded from previous research in the scope of the development of the inspection protocol for the non-structural building envelope [12; 13; 34]. Based on those two new matrices, an analysis of the most  
Figure 1: Diagram of the scope and objectives of the present research.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Code** & **Category** & **Code** & **Defect** \\ \hline \multirow{8}{*}{A-D} & & A-D7 & Absence/loss of filling material in connecting elements or current joints \\  & & A-D8 & Inadequate operation of expansion joints in flat roofs \\  & & A-D9 & Insufficient or excessive overlap of the cladding elements in roofs \\  & & A-D10 & Clearances/gaps in door and window frames \\  & & A-D11 & Absent or damaged hinges or locks in door and window frames \\  & & A-D12 & Ponding/insufficient or excessive slope in roofs \\  & & A-D13 & Inadequate operation of elements of the rainwater drainage system \\  & & A-D14 & Deficient capping adjacent to flat roofs \\  & & A-D15 & Incorrect or deficient interventions in claddings of pitched roofs \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._relevant and useful diagnosis methods and repair techniques for the building envelope is provided, taking the most common and severe defects into account.  
The mentioned objectives are framed within an approach of considering building pathology from the surveyor's perspective, preparing an inspection report with preventive recommendations, acting on the causes of the defects and aiming at cost-effective results.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 3 Sparse Codes
### Duty-Cycling Operation  
As sparse codes are applied to batteryless passive tags, the design of the codebook affects the energy state of RF tags relying on the duty-cycling operation. For the duty-cycling operation, the incident power received at the antenna of tag \(n\) can be expressed as  
\[E_{n}=\mathbb{E}\left[\sum_{k=1}^{K}\|\mathbf{y}_{k,n}\|^{2}\right]. \tag{20}\]  
Then the energy harvesting (EH) constraint can be formulated as [40]  
\[E_{n}\geq\theta=\frac{P_{c}}{\eta(1-\beta)},\quad P_{c}=R_{s}\sigma_{c}^{2} \quad\beta=D\sigma_{b}^{2},\quad\sigma_{b}^{2}=\frac{1}{MK_{1}}\sum_{m=1}^{M} \|\tilde{\mathbf{f}}_{m}\|^{2}=\alpha, \tag{21}\]  
where \(\sigma_{c}^{2}\) is the energy per symbol (e.g., 0.58 pJ/symbol [18]), \(\eta\) the energy harvesting efficiency (e.g., 25% [41]), \(\sigma_{b}^{2}\) the average backscatter symbol power, and \(R_{s}=\frac{1}{t_{s}}\) represents the backscatter symbol rate. It is noteworthy that since there are \(U\) multiple ambient sources with the OFDM modulation, tags can harvest sufficient energy from these sources nearby in dense network scenarios and be activated to  
Figure 6: Example of the factor graph design when \(N=10\).  
upload data to the reader. The state of \(\text{tag}\ \mathbf{a}=[a_{1},\cdots,a_{N}]\in\mathbb{B}^{1\times N}\) (i.e., 0: idle, 1: active) with EH threshold \(\theta\) can be obtained by  
\[a_{n}=\mathbb{I}\{E_{n}\geq\theta\},\quad\text{for}\ n=1,\cdots,N. \tag{22}\]  
Given the state \(\mathbf{a}\), the reflection coefficients and backscattered signals at RF tag \(n=1,\cdots,N\) over \(\mathbb{I}\) OFDM samples in time slot \(k=1,\cdots,K\) can be represented by  
\[\mathbf{x}_{k,n}=\Gamma_{k,n}\mathbf{y}_{k,n}\in\mathbb{C}^{L\times 1},\quad \text{where}\ \Gamma_{k,n}\in\begin{cases}\left\{0\right\},&\quad\text{if}\ a_{n}=0,\\ \tilde{b}_{0,k,n}(m),&\quad\text{if}\ a_{n}=1.\end{cases} \tag{23}\]  
Then, the \(N\) sparse-coded AmBC signals over \(K\) time slots are backscattered to the reader. In the following section, we discuss a scheme for detecting these signals when both the OFDM structure and sparse codebooks are known to the reader.
# A Deep Learning-Based Mapping Model for Three-Dimensional Propeller RANS and LES Flow Fields
## 4 Experiments
### Experiment of Regression of Flow Fields
Ultimately, by meeting the time requirements and achieving a certain level of accuracy, the feasibility and practicality for the mapping of flow fields can be achieved. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Error Rate** & **V\_axial** & **V\_tangential** & **V\_radial** \\ \hline mapping between & & & \\ RANS and LES & 2.73\% & 27.59\% & 22.73\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The error rate of the mapping between RANS and LES for the dataset. Figure 9: The distribution of error rates at \(x=0.012\) for the three velocity components. Compared to the three-dimensional model, the two-dimensional mapping model has a higher time cost because it processes each point individually using a regression network, enhancing accuracy but increasing computational time. In contrast, the three-dimensional model handles larger data volumes and greater complexity by using an integrated mapping network with a regression module, improving time efficiency. To manage more points and complex structures, the three-dimensional model adopts a simplified processing strategy, sacrificing some accuracy for speed. Thus, the two-dimensional model's pursuit of high precision results in significantly longer processing times.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## Appendix B Maximum Likelihood Estimations of the Signal Parameters and Noise  
To calculate the best estimates for the signal parameter and noise, we maximize the log-likelihood equation. Equation  
\[\log(f(z;\chi))=-N\log\!\left({\sigma_{n}}^{2}2\pi\right)-\frac{1}{2{\sigma_{n} }^{2}}\sum_{n=0}^{N-1}(z_{n}-\mu_{n})^{*}(z_{n}-\mu_{n}) \tag{111}\]  
can be maximized with regard to \(\chi\)  
\[0 = \frac{\partial}{\partial\chi}\!\left[-N\log({\sigma_{n}}^{2}2\pi) -\frac{1}{2{\sigma_{n}}^{2}}\sum_{n=0}^{N-1}(z_{n}-\mu_{n})^{*}(z_{n}-\mu_{n})\right] \tag{112}\] \[= \frac{\partial}{\partial\chi}\!\left[\sum_{n=0}^{N-1}(z_{n}-\mu_ {n})^{*}(z_{n}-\mu_{n})\right]\]  
In the last step, we truncated all parameters that do not depend on \(\chi\). The likelihood equation is maximized by minimizing the distance of \(\mu\) from the measurements. Equation (27) can now be inserted into Equation (112).  
\[0 = \frac{\partial}{\partial\chi}\!\left[\sum_{n=0}^{N-1}(z_{n}^{*}z_ {n}-z_{n}^{*}\mu_{n}-z_{n}\mu_{n}^{*}+\mu_{n}^{*}\mu_{n})\right] \tag{113}\] \[= \frac{\partial}{\partial\chi}\!\left[\sum_{n=0}^{N-1}(-2\cdot \Re[z_{n}\mu_{n}^{*}]+\mu_{n}^{*}\mu_{n})\right]\] \[= \frac{\partial}{\partial\chi}\!\left[\sum_{n=0}^{N-1}\!\left(-2 \cdot\Re[z_{n}\tilde{b}e^{n\cdot(-\alpha-j\hat{\omega})T-j\hat{\varphi}}\right) +\tilde{b}e^{n\cdot(-\alpha-j\hat{\omega})T-j\hat{\varphi}}\tilde{b}e^{n\cdot( -\alpha+j\hat{\omega})T+j\hat{\varphi}}\right)\right]\] \[= \frac{\partial}{\partial\chi}\!\left[\Re\!\left\{-2\tilde{b}e^{- j\hat{\varphi}}\sum_{n=0}^{N-1}z_{n}e^{n\cdot(-\alpha-j\hat{\omega})T}\right\}+ \tilde{b}^{2}\sum_{n=0}^{N-1}e^{-2n\alpha T}\right]\] \[= \frac{\partial}{\partial\chi}\!\left[-2\tilde{b}\cdot\Re\!\left\{ e^{-j\hat{\varphi}}\sum_{n=0}^{N-1}z_{n}e^{-\alpha nT}e^{-j\partial nT}\right\}+ \tilde{b}^{2}\frac{1-e^{-2N\alpha T}}{1-e^{-2\alpha T}}\right]\]  
In the first sum of Equation (113), analogous to a matched filter, we weight each measuring point with the amplitude of the decaying resonator and turn out the carrier frequency. Then, we rotate out the initial phase. If the parameters in \(\chi\) are optimally chosen, the measuring points are then Gaussian distributed along the real axis.
# A 6 DOF Virtual Environment Space Docking Operation with Human Supervision
## 1 Introduction  
There are many possible applications for the six degree of freedom (6 DOF) ball and plate platform, ranging from flight simulators, gaming and mimicking space rendezvous for docking and berthing mechanisms. Space engineering applications have increased degrees of LPV (linear parameter varying) dynamics due to their size, relative position, remote location and high instrumentation complexity [1]. The mechanical design of such systems may not be optimal from a control point of view, but rather from a practical point of view (e.g., remote access, testing, validation protocols, etc.). Thus, the control task becomes more challenging within the limitations imposed by the hardware and context [2, 3, 4].  
Space rendezvous missions are characterized by a high degree of uncertainty in relative inertial load and relative mass distribution depending on the angles of contact between the meeting parts. For example, a leg has to put less effort into accelerating the platform in a vertical translation when the platform is at maximal height than when the platform is at its lowest level. Since the experienced loads do not correspond to tangible masses, the experienced load is referred to as a reflected mass. Therefore, the load on top of an LEMA (linear electro-mechanical actuator) is a non-fixed parameter. Since the dynamic models corresponding to these variable reflected masses change significantly, the variation of the dynamics should be taken into the consideration during the controller tuning process. A specific application is introduced in this paper, as it has the advantage to mimic real-life conditions in a lab scale environment. The challenge of model uncertaintyhas been addressed previously with a model-based control scheduling scheme and a robust control algorithm [5; 6; 7].  
This paper presents a co-simulation between a real world model and a virtual world model of a Stewart platform simulator. The purpose is to combine real-world parts (a 6 DOF platform) with a virtual model (an equivalent docking 6 DOF platform) and mimic a space rendezvous. There is no one-controller-fits-all solution available due to the various orders of magnitude of the difference between dynamic values of the system operation. An adaptive control algorithm ensures the system can abide to aforementioned performance specifications in the presence of high LPV conditions.  
The paper is organized as follows. The real world and virtual environment setup, afferent software and hardware facilities and the mimicking co-simulation of the virtual-reality part of the system are described in Section 2. Next, an adaptive controller is proposed in Section 3, allowing human-in-the-loop interaction. The results and discussion are given in Section 4, and Section 5 presents the conclusions and summarizes the main outcome of this work.
# Experimental Study of Material Proportioning for Similar Modeling of Brittle Rocks
## 2 Materials and Methods  
### Selection of Material Components  
Simulation materials similar to rocks are generally composed of coarse aggregates, fine aggregates, and binders, and they always possess characteristics such as high density and low strength. However, materials prepared using the slurry casting method [18; 20; 27] often have issues such as low density, high strength, unstable mechanical properties, and long production times. [PERSON] et al. [28] developed a novel rock-similar material (IBSCM) that exhibits characteristics such as high density, low strength, stable performance, and short drying time. Building on previous research, this study selected refined iron powder and barite powder as fine aggregates, quartz sand as a coarse aggregate, and rosin-alcohol solution as the binder to formulate similar materials. Gypsum powder was added to adjust the brittleness of analogous materials. The material components are shown in Figure 1. In the figure, the barite powder is a mixture of equal proportions of 400-mesh, 600-mesh, and 800-mesh particle sizes, while the quartz sand is a mixture of equal proportions of 70-110-mesh and 110-160-mesh particle sizes; furthermore, the iron powder has a 200-300-mesh specification, the gypsum powder is \(\alpha\)-type high-strength gypsum with a fineness of 1500 mesh, and the rosin used is premium-grade rosin from the Guangxi Masson pine.
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 5 Analysis of the Fault Diagnosis Algorithm
### PCA and Entropy-Based Fault Detection  
By comparing the changes in the data under the fault conditions obtained from the water-tank experiments described in Section 3, it was confirmed that values for vibration  
Figure 8: Fault diagnosis algorithm based on PCA and entropy.  
and current were sensitive to fault occurrence. The magnitude of vibration increased under the fault conditions, and especially when floating objects were entangled, the values of current data increased. Furthermore, when the objects were entangled in a thick rope or net, the changes in the values of vibration and current were even more significant. Therefore, in this study, the PCA results were orthogonally projected onto the plane of the vibration axis and the current axis. Then, the entropy values were calculated for the planes with orthogonal projection.  
Table 3 presents a summary of the ranges of the entropy values in the vibration and current planes and the mean values for each condition, respectively, for Case 1 and Case 2, the conditions of different battery voltage values. The mean values of the entropy for the results of a normal blade were 0.9035 and 0.8837, which are smaller than 1. Therefore, in this study, the threshold value was set to 1; the entropy value was defined as normal or abnormal, depending on whether it fell below or above 1, to perform fault detection.  
The vibration and current values changed more significantly in the case of entanglement in the thick rope and the net compared to the other fault conditions. The entropy values also reflected these characteristics. From Table 3, it can be observed that the entropy values for the thick rope and the net in the entanglement conditions were greater than 1.6. Therefore, in this study, the thick-rope and net entanglement conditions were classified as conditions with relatively large variations in data owing to fault occurrence, whereas the breakage conditions and the thin-rope entanglement condition, with entropy values between 1 and 1.6, were classified as conditions with relatively small changes in data.
## 4 Discussion and Conclusions
### Limitations and Future Research Directions
This requires analyzing emergent behaviors in long-term learning processes, modeling the interactions between attacks and defense using dynamic game theory, and combining advanced methodologies such as multi-party computation-based privacy-preserving outsourcing [14]. Additionally, ensuring the reliability of the validation datasets used for evaluating the performance of the global model, possibly through the participation of external agencies or blockchain-based data verification systems, is also crucial. In summary, addressing these limitations and future research directions indicates that technological advancements, as well as their acceptability in industrial settings, are crucial for the practical application of the proposed framework. Therefore, efforts to advance technology through phased empirical validations based on academia-industry collaboration and to lay the groundwork for activating the data economy through legislative adjustments are required [15]. In this process, establishing a governance system to coordinate and foster cooperation among stakeholders in the energy sector will also emerge as a key task. Despite these challenges, the vision of blockchain-federated learning integration and the data utilization framework presented in this research provide a foundation for leading a paradigm shift across the energy system, extending beyond the EV industry, which holds significant academic and practical significance. It is hoped that the issues raised and future research directions will serve as a stepping stone for enhancing sustainability in the energy and transportation sectors and achieving carbon neutrality goals. Particularly, leveraging the research outcomes as a bridge to enhance the societal acceptance of energy data utilization and contribute to creating an open innovation ecosystem is highly anticipated. **Author Contributions:** Conceptualization, [PERSON] and [PERSON]; methodology, J.-H.P. ; software, J.-H.P. ; validation, [PERSON]-H.P. and [PERSON]; formal analysis, [PERSON]-H.P. ; data curation, J.-H.P. ; writing--original draft preparation, J.-H.P. ; writing--review and editing, [PERSON]-H.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions. The authors declare no conflicts of interest.
## 3 Finite Element Modeling (FEM) of Human-Seat Interactions
### _Material Modeling and Challenges_
#### 3.3.1 Hyperelastic Modeling of Polyurethane Foam  
To characterize the material properties of PU foam, compression tests were conducted, reflecting the real-world compressive forces that car-seat cushions endure during use.  
Figure 5: Geometry and mesh of the right half-symmetric model of the car-seat cushion (**a**) and with the simplified pneumatic spring integrated inside (**b**). Colors were used to distinguish different component, including geometry (gray) and mesh (blue) of the right half-symmetric model, mesh of the rectangular block (green), mesh of the pneumatic spring integrated inside (yellow) (**c**).  
The objective was to determine the stress-strain behavior of PU foam under uniaxial stress and derive parameters for the hyperelastic material model. Test samples, measuring \(100\times 100\times 80\) mm, were prepared from seat cushion materials. The tests were performed on an Instron E3000 machine (Figure 6a), with the displacement rate set at 1 mm/min and a maximum compression strain of 75%. Figure 6a illustrates the detailed explanation of the compression experiment and its simulation in MSC.Marc. The primary objective was to assess the correlation between experimental and simulated stress-strain and force-displacement curves, confirming the suitability of the selected material model for polyurethane (PU) foam.  
The experimental deformation-force data were processed using MATLAB 2017 to generate stress-strain curves (Figure 6b). These curves provided input for identifying suitable material parameters for the hyperelastic model, which was implemented and validated through simulations in MSC.Marc software. According to the input requirements for analyzing and calculating the hyperelastic material model in MSC.Marc software, the stress-strain curve was computed based on the average values of the deformation-force curve obtained from the loading and unloading process in the compression test.  
Figure 6: The compression test of polyurethane foam (**a**) and experimental results (**b**).  
The selected constitutive model for PU foam was defined by the strain energy density function:  
\[W=\sum_{n=1}^{N}\frac{\mu_{n}}{\alpha_{n}}\big{[}\big{(}\lambda_{1}^{\alpha_{n}}+ \lambda_{2}^{\alpha_{n}}+\lambda_{3}^{\alpha_{n}}\big{)}-3\big{]}+\sum_{n=1}^{N} \frac{\mu_{n}}{\beta_{n}}\Big{(}1-J^{\beta_{n}}\Big{)} \tag{2}\]  
where \(\mu_{n},\alpha_{n},\beta_{n}\) are material constants with two terms (\(N\) = 2); and \(\lambda_{1}=1,\lambda_{2}=1,\lambda_{3}=\frac{L}{L_{0}}\) are the stretch ratios along the three principal axes. The volumetric ratio \(J\), indicating volumetric change, is defined as \(J=\lambda_{1}\lambda_{2}\lambda_{3}\). The simulations and parameter calculations conducted using MSC.Marc software yielded the following results: \(\mu_{1}=3.2\ \times\ 10^{6}\ \mathrm{Pa}\), \(\alpha_{1}=0.00157295\), \(\beta_{1}=1.19689\times 10^{-5}\); and \(\mu_{2}=4058.48\ \mathrm{Pa},\alpha_{2}=22.9999\), \(\beta_{2}=1.23469\)
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 4 Core Framework Components
### User Interface and Proxy Access  
Memory areas [B] and [C] of Figure 2 provide support for data sharing between the real-time algorithms and other parts of the system, namely, UI and proxy tasks. Being shared between multiple groups of tasks, these memory areas must be protected against concurrent access by means of mutual exclusion locks with definite timeouts on the real-time side. The choice of having two memory areas, each supporting unidirectional data flows 3-4 and 5-6, respectively, is useful to reduce lock contention. At the same time, keeping memory [A] disjoint from [B] and [C] also provides a safeguard against unintentional or unauthorized access to PI variables by non-real-time tasks, on systems equipped with a Memory Protection Unit (MPU) or similar memory protection mechanisms.  
Data transfer to and from these shared memory areas may be performed explicitly by the control function on the real-time side and by UI and proxy tasks on the other side. This approach is convenient when some processing is required besides the transfer. Alternatively, when a mere copy is sufficient, RTfw itself can transparently mirror a pre-configured subset of PI variables in [A] to/from shared memories [B] and [C].  
A typical UI consists of multiple UI tasks, which are completely user-written and are outside the scope of RTfw, except for what concerns their access method to shared memories [B] and [C]. Proxy tasks are part of RTfw, instead, and have access to a configurable subset of information in the shared memories. Most importantly, they enable non-real-time agents, such as those performing Internet-based monitoring, diagnostics, and troubleshooting, to communicate with an RTfw-based system, as described in Section 7.  
Internet communication can be based upon a variety of protocols and corresponds to data flow [in] in Figure 2. Currently, RTfw implements a proxy based on the Modbus-TCP protocol [43] for this but, due to its modular architecture, it supports the addition of proxies for other user-defined communication protocols without changes to its core components.