esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Vibration-Mode-Induced Noise of Structure-Acoustic Coupled Systems
## 3 Modeling  
To simulate the structure-acoustic coupling system, a system consisting of a reverberation chamber with a shell on one side was configured. COMSOL was used to perform the time-domain coupling analysis. The modeled structure is shown in Figure 2. Figure 2a shows the shell (3.8 \(\times\) 2.6 m) on one side of the reverberation chamber, locations of nine accelerometers installed at the intersections of the four quarters horizontally and vertically on the shell plane, and vibration point (red triangle) in the bottom left part of the shell. Figure 2b shows the plane of the reverberation chamber, along with the locations of five microphones (at a height of 1.2 m). Table 1 presents the properties of the material used for modeling. The shell thickness was varied to examine the vibration behaviors associated with the 10 Hz band, and the primary natural frequency was set as approximately 17 Hz. The Rayleigh damping parameters \(\alpha\), \(\beta\) were used to represent the shell's damping, and the modal damping ratio in the band of 10-100 Hz was set as 2.5-4.5%. The reverberation chamber's damping was implemented as an impedance (wall side) and set as 400 times the speed of sound. Table 2 lists the elements of the FEM model used in the analysis, mesh maximum size, and time gap for the time history analysis. The frequency range was up to 170 Hz, set considering the mesh size. The impact load was a pulse in the half wave form of a sine wave, the maximum load was 400 N, and the loading duration was 20 ms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Item** & **Value** \\ \hline \multirow{4}{*}{Acoustic} & Density & 1.225 kg/m\({}^{3}\) \\ \cline{2-3}  & Speed of Sound & c = 340 m/s \\ \cline{2-3}  & Impedance & 400 c Pa s/m \\ \hline \multirow{4}{*}{Shell} & Density & 2400 kg/m\({}^{3}\) \\ \cline{2-3}  & [PERSON]’s ratio & 0.17 \\ \cline{2-3}  & Young’s modulus & \(2\times e^{10}\) Pa \\ \cline{2-3}  & Size & 3.8 \(\times\) 2.6 m \\ \cline{2-3}  & Thickness & 50 mm \\ \cline{2-3}  & Rayleigh Damping & \(\alpha\) = 4.5, \(\beta\) = 1.5 \(\times e^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material property information of the modeled structure, which was necessary for FEM analysis.  
Figure 1: Covariance ratio: The graph was given by normalization from theory. If the difference between vibration mode and acoustic mode was larger, the frequency ratio became larger numerically.
# Potentiometric Biosensor Based on Artificial Antibodies for an Alzheimer Biomarker Detection
## 2 Materials and Methods
### Reagents and Solutions  
All chemicals were of analytical grade and de-ionized water (conductivity 0.1 \(\upmu\)S/cm). 3-Morpholinopropane-1-sulfonic acid (MOPS) from Applichem; A\(\beta\)-amyloid 42 (A\(\beta\)-42) from GeneScript; SWCNTs, \(o\)-nitrophenylolytl (oNPOE), high molecular weight poly (vinyl chloride) (PVC), acrylamide (AAM), N,N\({}^{\prime}\)-methylenebisacrylamide (NNMBA), ammonium persulfate (APS), oxalic acid (Oac), N-hydroxysuccinimide (NHS), N-ethyl-N\({}^{\prime}\)-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDAC) and sodium chloride (NaCl), creatinine (Crea), albumin (Alb), 2-aminoethyl methacrylate hydrochloride (AEMA), proteinase K and sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) were purchased from Fluka; tetrahydrofuran (THF) was purchased from Riedel-deHaeen; hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) and potassium chloride from MERCK; potassium dihydrogen phosphate (KH\({}_{2}\)PO\({}_{4}\)) and sodium phosphate dibasic dihydrate (Na\({}_{2}\)HPO\({}_{4}\)) from Panreac; tetramethylenediamine (TEMED) and urea from TLC; and CORMAY serum from Cormay diagnostics.  
Phosphate buffer solution (PBS) buffer was prepared with the following composition: NaCl, 8 g/L; KCl, 0.2 g/L, Na\({}_{2}\)HPO\({}_{4}\), 1.42 g/L; KH\({}_{2}\)PO\({}_{4}\), 0.24 g/L. The pH was adjusted to a value of 8.0. The standard solutions of A\(\beta\)-42 were prepared from a successive dilution of the stock solution (0.05 mg/mL) previously prepared in ultrapure water. The MOPS 1.0 \(\times\) 10\({}^{-3}\) mol/L were prepared in deionized water.  
The interfering species were prepared in MOPS buffer 1.0 \(\times\) 10\({}^{-3}\) mol/L, pH 8 with the following concentrations: Crea 1.2 \(\times\) 10\({}^{-5}\) g/mL, Alb 5.2 \(\times\) 10\({}^{-3}\) g/mL and urea 4.0 \(\times\) 10\({}^{-4}\) g/mL.
# Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges
## 4 Towards Software-Defined Heterogeneous Vehicular Networks
### Architectural Challenges
#### 4.2.2 Heterogeneous Multi-Hop Routing  
Vehicles typically disseminate safety-critical information to other vehicles in their immediate vicinity via multi-hop V2V communication or by relaying the messages through roadside units, each possessing a different radio access technology. Nevertheless, these safety-critical messages may get lost if the serving radio access technology fails or next hop becomes unavailable (especially in sparse traffic conditions), thus resulting in a communication breakdown and wastage of precious network resources, which may lead to fatal accidents on the roads. Furthermore, some of these radio access technologies are comparatively more expensive than the others, i.e., LTE is fairly expensive in contrast to WiFi and DSRC. Thus, a highly reliable and cost-efficacious communication via multi-hop routing is indispensable in a VANET context. It is also crucial in restoring the network connectivity, especially in case the serving radio access technology fails, but an overlapping radio access technology is accessible. Likewise, in the case of vehicles outside the coverage range of serving roadside units, the network packets could still be shared (with them) through multi-hop V2V communication by vehicles associated with the roadside units, and centralized SDN controllers with a global view of the underlying network can map the shortest, yet optimal multi-hop routes for the said purpose.  
Moreover, SDHVNets via their characteristics of reconfigurable and reprogrammable networking infrastructure can reduce the service costs either related to downloading of a particular application or upgrading of a particular service. Let us assume that 50 vehicles are traversing through a particular geographical region with only LTE coverage and intend to perform service upgrades. If the packet size is 20 MB with the cost of service upgrade as US$ 0.15 per MB, then the entire service upgrade cost would be 50 \(\times\) 20 \(\times\) 0.15 = 150$. SDHVNets, on the other hand, could facilitate cooperative sharing by enabling 1/3 of the vehicles to perform service upgrades via LTE and the remaining 2/3 to request the same content via V2V communication, thereby cutting down the cost to 50 \(\times\) 20 \(\times\) 0.15 \(\times\) 1/3 + 0 \(\times\) 2/3 = 50$.
# Design and Performance Analysis of Navigation Signal Based on OFDM
###### Abstract
[PERSON] et al. proposed a modulation mode of the OFDM signal based on a subchirp bandwidth overlap and piecewise transceiver. The modulation scheme reduces the ASP of a signal by designing a reasonable sweep bandwidth range and piecewise transceiver mode, and the designed signal had good detection performance in MIMO radar monitoring [23]. In addition, from the perspective of the signal design system, [PERSON] et al. proposed a future-oriented, frequency-hopping binary offset carrier modulation scheme with integrated on-conduction, aiming to improve the anti-interference ability of signal navigation [24]. [PERSON] et al. proposed a multifunctional signal based on the modulation of binary offset carrier (BOC) and binary phase shift keying (BPSK), which contained measurement, navigation, and communication functions, and can effectively simplify the receiver structure and improve spectral utilization. The different signals are quadrature in the frequency domain, so the data transmission signal has no impact on the BOC signals. It is a 1 reference for communication and navigaiton integration [25]. These designs can also be seen as integrated reference solutions for communication and guidance. The main reason why OFDM modulation technology can be widely used is that it is very effective against multipath delay expansion. The OFDM modulation mode has high data transmission, spectrum utilization, and anti-multipath fading characteristics. This paper proposes an navigation signal modulation method based on OFDM, combining the direct sequence spread spectrum (DSSS) and OFDM modulation technologies. High tracking accuracy, accomplished by using a high-frequency subcarrier to increase the energy of the navigation signal at high frequency, is conducive to improving the tracking performance of the code ring. Other obvious advantages are that the tracking accuracy can reach 0.2 m, and has a strong robustness, good anti-interference performance, and anti-multipath ability. In this paper, the mathematical model of the OFDM-modulated navigation signal is firstly proposed, and the analytical expressions of the autocorrelation function (ACF) and PSD of the OFDM-modulated navigation signal are deduced and verified in detail. The influence of each modulation parameter on the performance of the OFDM-modulated navigation signal is also systematically analyzed. The performance of different types of navigation signals is compared and evaluated. On the basis of a qualitative analysis, we carried out a quantitative analysis and evaluation of the OFDM-modulated navigation signals using the aspects of tracking accuracy, code loop parameters, and anti-interference ability. We examined the influence of the signal in the conduction integration. The simulation showed that the OFDM navigation signal showed good compatibility in the fusion of communication signals, and thus has wide application prospects, which provides a feasible reference scheme for the integration of communication and conduction.
# Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
## 5 Experiments  
A possible application of PP-Overgrid is to compensate the variability of energy production from unpredictable sources, such as wind and solar. For example, Figure 6 shows the total wind and solar energy production in Belgium, obtained by analyzing publicly available data of the Belgium's transmission system operator [31]. From the figure, it is clear that such energy sources, and particularly wind, are highly variable and difficult to predict [32]. In our experiments, we will derive the power constraint \(P_{DSO}\) applied by the operator by considering these data about renewable energy production.  
PP-Overgrid has been implemented in Java exploiting the PeerSim simulator [33], which provides the basic tools to simulate the gossip P2P overlay. The topology is generated randomly by PeerSim, where the number of nodes represent the number of smart buildings, while, for the power consumption data, we used the traces described in Section 5.2. Running independently on each node, the gossiping protocol exchanges messages with the neighbor nodes, transmitting the information required for the Flow Updating algorithm. At startup, the emulator generates the random topology of the P2P network, with average node degree D the number of neighbors per node. It builds the connections between the nodes (each node has a neighbor \"list\"), so that each node then communicates to the neighboring nodes based on this topology. The list of neighbors can be statically configured or can change dynamically as  
Figure 5: Convergence of the PP-Overgrid algorithm with \(N=100\) nodes, subnodes \(n=\{1,5,10\}\) and graph degree \(D=\{5,10,20\}\).  
nodes join and leave the overlay. For easiness of presentation, however, in our experiments, we limit our analysis to a static environment.
# BIM-Based Model Checking: A Scientometric Analysis and Critical Review
## 4 Critical Review
### Application Cases of Platforms and Projects  
The applications of BMC can be roughly categorized into three types: code checking, model checking, and complex rule checking. The basic object of BMC is computer code, but it differs from the code checking of computer programs. Most BIM data is created by modeling software, like Autodesk Revit V2025, Bentley AECOsim Building Designer V8i, and Dassault Systemes CATIA V5 6R 2023, rather than being manually written as program code. This means that the final model data already conforms to the constraints set by these software systems, making additional code checking unnecessary. However, code checking becomes meaningful and necessary for custom information added by users, such as incorrect statements, unauthorized interfaces, and invalid values. For example, Express Data Manager involves semantic and structural validation of model data in certain cases, but it is still not a tool directly used for checking code errors. In other applications, one feature is model checking, including the completeness and logical consistency of information. For example, the collision detection function that most BMC systems already have is a simple rule, i.e., that the co-ordinates of points contained in different components of the model cannot be repeated in space. On the other hand, complex rule checking is a more form of advanced model checking, involving more complex logic. Both comprise judgments about the correctness or errors in model information, i.e., comparing the differences between existing model information and rules, rather than validating the code statements themselves.  
Some BMC systems have been successfully developed and applied in actual engineering project management. This study surveyed the software and systems mentioned in the 145 articles that are capable of implementing different functionalities. Dozens of outcomes have been achieved worldwide, with the top ten most cited ones shown in Table 6. Among them, the Solibri Model Checker (SMC) is currently the most popular checking system, which continues to expand. The CORENET and ePlanCheck projects, developed by the Singapore government based on FORNAX, have the most significant impact on BMC practice. Admittedly, this survey cannot cover all BMC-related systems, and their frequency of appearance in the surveyed articles does not fully represent their popularity and performance. The systems ranking higher could have been developed earlier, resulting in broader influence and more citations.
## 4 Simulations and Results
### Maintainability and Mean Time to Repair (MTTR)  
The capacity of an entity to continue being in or return to a state where it can carry out a required function under set conditions and utilizing the advised processes and resources is known as maintainability [30].  
The MTTR is the average time spent eliminating a fault and restoring the drive to service (time for all activities between fault clearing and restart). This time depends on proper maintainability, like an adequate diagnostic system, available spare parts, and the competence level of the personnel involved [33]. We suppose that, in our system, a repair starts immediately after the occurrence of a failure and that the RBS restarts instantaneously after a repair.  
We apply data collector monitors in each repair transition (**RMC** for the master cylinder and **System_reparation** for the whole system) to obtain the MTTR. If the transitions are enabled then the monitor should collect the repair time **waitrep**.  
The RBS MTTR empirical PDF distribution, depicted in Figure 12, is close to an exponential distribution.
# A Comparative Study of the Application of Different Commercial Software for Topology Optimization
## 5 Results
### Optimization of a Small Bridge  
The intention of the authors here was to optimize a bigger structure. In addition, they used an increased design space in order to increase the algorithm's flexibility. The weight of the initial design was 2274.1 Kg, which is higher compared to the weight of the previous case studies. The design solutions of SolidWorks and ABAQUS were alike, as is shown in Figure 10. However, some differences can be observed in this case study. For example, in the optimization case with a 50% weight reduction, SolidWorks' design contains six beam-like geometries on each side of the bridge, while the ABAQUS design has eight. In addition, both designs have the same weight, but their beams have different diameters. It seems that the software's TO algorithm removed material from different regions of the  
Figure 8: The optimized designs of pillow bracket for 50% and maximum weight reduction in (a) SolidWorks, (b) ANSYS Mechanical, and (c) ABAQUS.  
Figure 9: The validation results of the pillow bracket for 50% and maximum weight reduction in (a) SolidWorks, (b) ANSYS Mechanical, and (c) ABAQUS.  
structure despite the fact that they are both using the SIMP method. Furthermore, their designs in the second optimization case are also quite different. ANSYS Mechanical again resulted in different design solutions, especially in the first optimization case. ABAQUS came up with the heaviest bridge, 633 Kg, which corresponds to a 72.2% weight reduction, while SolidWorks created the lightest design with 477.2 Kg (79%) and ANSYS Mechanical with 500.6 Kg (78%).  
At the FEA, the CAD model has been discretized into 30 mm tetrahedral elements, resulting in 101,108 finite elements in total. The maximum identified Von Mises stress was 157 MPa. In the first optimization case with a 50% weight reduction, all the designs had smaller maximum stresses, as shown in Figure 11. Among them, SolidWorks resulted in the strongest design solution. In addition, the derived SolidWorks solution in the second optimization case had the higher maximum stress, 571.9 MPa. ANSYS Mechanical and ABAQUS came up with stronger solutions with maximum stresses of 527.3 and 414.2 MPa, respectively, but much heavier designs (see Figure 11). Thus, SolidWorks' design is preferred in terms of weight and acceptable yield strength.
# Privacy Preserving Classification of EEG Data Using Machine Learning and Homomorphic Encryption
## 3 Results
### Use Case 1--Seizure Detection
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Initial Data**} & \multicolumn{2}{c}{**Quadratic Data**} & \multicolumn{2}{c}{**Resampled Data**} \\  & **178 Features** & \multicolumn{2}{c}{**356 Features**} & \multicolumn{2}{c}{**80 Features**} & \\ \cline{2-7}  & **PR** & **SVM** & **PR** & **SVM** & **PR Plaintext** & **PR Ciphertext** & **SVM** \\ \hline Accuracy & 80.59 & 95.78 & 89.22 & 95.86 & 92.26 & 92.26 & 95.85 \\ Sensitivity & 34.25 & 98.82 & 84.40 & 98.60 & 86.56 & 86.56 & 98.70 \\ Specificity & 92.17 & 95.02 & 90.43 & 95.17 & 93.68 & 93.68 & 95.14 \\ PPV & 52.46 & 83.23 & 68.81 & 83.62 & 77.44 & 77.44 & 83.54 \\ NPV & 84.86 & 99.69 & 95.86 & 99.63 & 95.54 & 95.54 & 99.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison when using different input data configurations in the EEG-based seizure detection use case. Considering the scenario where the model is used only on ciphertext data, without any previous analysis regarding its performance, we chose the precision parameter to be large enough (35 powers) to ensure zero loss in accuracy. This led indeed to the same results as those obtained on plaintext data, but the runtime was significantly larger. To determine the influence of choosing a lower precision parameter, we ran multiple experiments and analyzed the computational time and performance loss. As displayed in Table 3, the runtime can be drastically improved by choosing a smaller number of terms for the encoding step, but after a certain threshold is exceeded (21 powers for this use case), the performance decreases.
## 3 Target Discrimination
### SVM Training
Here, the radial basis function (RBF) is used as the kernel function of the SVM binary classifier, \(\mathrm{K}(x,x^{\prime})=\exp(-\|x-x^{\prime}\|^{2}/o^{2})\),\(\sigma>0\). Before training, each feature is first normalized to the range of 0-1 to reduce the dominant role of a certain dimension feature due to its large magnitude. The RBF function has two key parameters: the penalty factor \(c\) and the kernel parameter \(\sigma\). We obtained the best parameter through the cross-validation method and set \(c=1\) and \(\sigma=0.7\)[29]. Training images come from the satellites GF-2 and ZY-3 2 m resolution.
# Quantitative Model for the Value of the 3D Product Model Use in Production Processes
## 2 Materials and Methods
### Less Time Updating Work Instructions  
When an engineering change order (ECO) is released, it creates an update to the product model and often requires an update to the associated work instruction for the production station effected by that ECO. When the 3D product model is linked to the production and assembly precedence diagram, a change to either the 3D product model or the precedence generates an update to the visual work instructions automatically when the ECO is processed. This then saves the engineer time they would have spent manually updating written work instructions. The model for the value created through less time updating work instructions by using 3D product model production process software is demonstrated as:  
\(W_{m,m,k}=\text{Time to update work instruction }k\text{ for station }n\text{ on production line }m\)  
\(K_{m,n}=\text{Number of work instructions updated per year per station }n\text{ on production line }m\)  
line \(m\)  
\(k=\text{A given work instruction update on station }n\text{ on production line }m\text{ (}1-K_{m,n}\text{)}\)  
\(K_{m}=\text{Number of work instructions updated per year on production line }m\)  
\[K_{m}=\sum_{n=1}^{N}K_{m,n} \tag{13}\]\(\overline{W}_{m}=\) Average time to update a work instruction for production line \(m\)  
\[\overline{W}_{m}=(\sum_{n=1}^{N}\frac{\sum_{k=1}^{K_{m}}W_{m,n,k}}{K_{m,n}})/N \tag{14}\]  
\(\overline{\Gamma}=\) Average cost of labor per hour to update work instructions  
\(C_{m}=\) Cost to update work instructions on production line \(m\)  
\[C_{m}=\overline{W}_{m}\times K_{m}\times\overline{\Gamma} \tag{15}\]  
\(C_{Without3D,m}=\) Cost to update work instructions (\(C_{m}\)) for production line \(m\) prior to 3D product model implementation in production processes  
\(C_{With3D,m}=\) Cost to update work instructions (\(C_{m}\)) for production line \(m\) after 3D product model implementation in production processes  
\(P_{W_{m}}=\) Incremental profitability via reduction in time to update work instructions  
\[P_{W_{m}}=C_{Without3D,m}-C_{With3D,m} \tag{16}\]