esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis of Shape-Memory Polyurethanes: Combined Experimental and Simulation Studies
## 3 Results and Discussion
### Shape-Memory Properties  
Shape-memory properties of the synthesized materials were investigated using the \(\varepsilon_{\rm prog}=100\%\) (Table 6). Data show that there is a strong correspondence between the OCD average chain length and shape recovery and fixity ratios, which was reported previously [20]. In the case of E_PCUU5000_3 and E_PCUU5000_2, thermocycle measurements were conducted with the use of a tensile testing device due to the inability to determine the parameters that would allow the tests to be carried out using a DMTA analyzer. All samples have exhibited satisfactory R\({}_{\rm f}\) and R\({}_{\rm r}\) parameters of around 99% (Table 6 and Figures S30-S35 in the Supplementary Materials). It is important to highlight the fact that, in the case of longer polymer chains, the degree of crystallinity is also higher. This conclusion supports the fact that using longer switching segment chains leads to a greater possibility of crystallization within the switching segment domains. What is more, the R\({}_{\rm f}\) values increases and R\({}_{\rm r}\) slightly decreases with a lower content of hard segments in PCUUs based on the E_OCD_3000.  
Recently we showed that higher values of \(\varepsilon_{\rm prog}\) of PCUUs led to an increase in crystallinity, and values of T\({}_{\rm m}\) and \(\Delta\)H\({}_{\rm m}\)[20]. Here, we investigated samples elongated to 100%. As can be seen from Table 5, the reported T\({}_{\rm m}\) has not changed significantly. However, for samples from E_PCUU3000_3.5 to E_PCUU3000_2, an increase in \(\Delta\)H\({}_{\rm m}\) can be observed in the first cycle, which can suggest elongation-induced crystallization related to the orientation of polymer chains. On the other hand, lower \(\Delta\)H\({}_{\rm m}\) values were recorded in the  
Figure 10: Reversible heat flow curves based on MDSC measurements of (**a**) E_PCUU3000_1.5; (**b**) E_PCUU3000_2.5; and (**c**) E_PCUU_3.5. The * symbol refers to sample elongated with 100% strain (1 st run *) and the second heating run of the previously elongated sample after its return to the primary shape (2 nd run *).  
second run for all samples. In addition, for almost all stretched samples, smaller values of \(\Delta\mathrm{H_{m}}\) can be observed than for cast films. Most probably, this originates from the fact that deformed samples need more time after heating and melting to recrystallize under the measurement conditions. Furthermore, the majority of samples stretched by \(\varepsilon_{\mathrm{prog}}=100\%\) had lower values of \(\mathrm{T_{g}}\) in comparison to the initial samples but the differences were not significant. In Figure 10, reversible heat flow curves based on MDSC show that, in the second heating cycle, the thermal history of the sample is removed, thus reducing the degree of crystallinity induced by stretching the sample. It can be observed that with the increase in HS, the differences for the cast and stretched samples are more visible.
## 5 Conclusions  
This work described a 1D flow network solver developed to simulate two-phase multi-component flows inside cryogenic turbopumps used for liquid rocket engines. The flow network solver provides the capability to analyse very complex geometries, involving very narrow passages and fittings with mixing of different fluids. In particular:  
1. A general-purpose finite volume procedure was implemented for solving the mass, momentum, and energy conservation equations of fluids and solids. The pressure drop and the convective single- and two-phase heat transfer, involved in the momentum and energy conservation equations, were calculated using empirical correlations.
2. The network solver was based on an extension of a classic 1D homogeneous model, originally developed for a pure substance, to the case of two-phase multi-component flow. Classical two-phase heat transfer and pressure drop models were adapted to multi-component flows by using an overall gas fraction.
3. The validation of the numerical tool was conducted by means of experimental data obtained for the secondary circuit of an oxidizer turbopump developed at Avio S.p.A.; the Dynamic Seal Package was considered, which includes leakage flow from the oxidizer pump discharge and mixing of the propellant with He.
4. The numerical predictions of fluid temperatures were compared with the measured data. In most cases, a very good agreement was observed between the measurements and predictions, with a maximum error lower than 7%, observed at the He/LOx drain.
5. The temperature discrepancies may be due to several factors. The assumed homogeneous model and the adopted correlations may be inadequate to capture the two-phase phenomena of a bi-component mixture. However, the use of a more sophisticated model, such as the drift-flux model or two-fluid model, would imply a larger computational cost without guaranteeing a considerable accuracy improvement due to the implicit uncertainties in predicting the interfacial interaction terms between the two phases.  
Conceptualization, M.A., C.B., G.C., F.M.M., L.M. and D.P.; investigation, L.M. and F.M.M.; writing--original draft preparation, L.M. and F.M.M.; writing--review and editing, M.A., C.B., G.C., F.M.M., L.M. and D.P.; supervision, M.A., C.B., G.C., F.M.M. and D.P.; project administration, D.P. and G.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Avio S.p.A. (Contract No. C119347-72/2021).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results
### Comparison of Different Algorithms  
In order to verify the convergence of the IGWO, based on Equations (1)-(3), this paper utilizes the random function to randomly define the center point \(x_{i},y_{i}\) and height \(z_{i}\) of obstacle peaks, the expansion range _range_ of the peaks, as well as the number of peaks \(n\). Ten instances of mountainous obstacle environments were randomly generated, and for each randomly generated task environment, the convergence of the IGWO algorithm, GWO algorithm, particle swarm optimization (PSO) algorithm, genetic algorithms (GA), and wolf pack algorithm (WPA) were simulated and computed. Each environment underwent 20 simulation experiments, and the average results of each algorithm were obtained. The fitness iteration curves for each algorithm are shown in Figure 10, with the number of iterations on the x-axis and the best fitness on the y-axis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Meaning** & **Value** \\ \hline \(K_{att}\) & Gravitational gain coefficient & 0.05 \\ \(K\_{line}\) & Gravity gain coefficient of flight route & 500 \\ \(K_{rep}\) & Repulsive gain coefficient & 100 \\ \(\rho_{0}\) & Distance affected by obstacles & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 4: IAPF parameter settings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Meaning** & **Value** \\ \hline \(N\) & The number of grey wolf populations & 20 \\ \(T\) & Maximum number of iterations & 10 \\ \(N\_{\text{length}}\) & The movement step size for each & 0.5 \\ \(\rho_{0}\) & Distance affected by obstacles & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: IGWO parameter settings.  
From Figure 10, it can be observed that after 50 iterations, the IGWO algorithm achieves a better optimal fitness value compared to the GWO algorithm, PSO algorithm, GA, and WPA. The optimal fitness obtained by the IGWO algorithm is approximately 2239.36.
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 6 Discussion
### Summary of the Results and Suggestions for Future Work
#### 6.1.3 Main Approaches Used for IFC Extensions and Validation  
Implementation approaches for translating the extended IFC schema to an IPF varied based on the extension's intent. Entity extension cases typically utilized STEP toolkits, while property extensions directly used BIM authoring tools. Other cases showcased extension ideas without an actual IPF implementation, representing these ideas in textual form through EXPRESS-X, or visually via EXPRESS-G.  
For entity and property extension cases, validation was primarily performed through demonstrations of interoperability between export and import applications. In contrast, property extension cases targeting process representations not only verified their interoperability but also needed their extensions by demonstrating their utility in receiving applications.
# Evaluation Model of Holographic Communication Experience in the 6G Era Based on Light Field Display
## 3 Method  
The main process of the method in this article is shown in Figure 1. Light-field image data are constructed that are suitable for display devices. Then, by controlling the test environment, the subjects watch the images through the display device and score them in the three dimensions of space, comfort, and reality based on their real feelings. The user rating result and the image are used as a set of training data pairs, and the mapping relationship is learned through the deep learning model. Next, each link is introduced in detail.
# Parametric Study of Three Types of Timber Connections with Metal Fasteners Using Eurocode 5
## 2 Mechanical Properties: Glutam and Steel  
Wood is a porous and fibrous material, which consists of cellulose, hemicellulose, lignin, and small amounts (from 5% up to 10%) of foreign materials incorporated into the cellular structure [4]. The cellular structure allows woods to be heavy or light, stiff or flexible, and hard or soft [4]. The properties of a wood species are relatively constant within its limits [3; 19]. Wood is a complex and an anisotropic material. In engineering practice, wood is defined as an orthotropic material.  
In this work, three different wood species in homogeneous glued laminated (glulam) were considered for the designed connections, where the density properties vary between 370 and 480 kg/m\({}^{3}\)[3; 5; 6; 7; 10].  
The three types of glued laminated wood are GL20H, GL24H, and GL32H, which are commonly applied in building construction. CEN BS EN1194, 1999 [3; 5; 7; 10] lists eight gillam strength classes. To differentiate, they are designated by GLxH as a homogeneous lay-up, which means that all the laminations are of the same strength class and species combinations, or GLxC as combined, where the cross section comprises internal and external laminations of strength classes and species combinations [3; 5; 6; 7; 19; 20]. The designation GL refers to a glued laminated wood; the number (x), which appears in the designation, defines its flexural resistance.  
The mechanical properties for the chosen gillam wood used in this study are shown in Table 1[9; 20; 21], with the Poisson ratio value equal to 0.4. Glulam wood has high-quality and is characterized by a high structural capacity, aesthetic aspects, allowing possibilities of free form in construction [22].  
For metal fasteners, the steel material S275 was considered, according to Eurocode 3 part 1-1 [23], where the yield strength \(f_{y}\) is equal to 275 MPa, the ultimate tensile strength \(f_{u}\) equal to 430 MPa, the Young's Modulus is equal to 210 GPa, the Poisson's ratio is equal to 0.3 and density equal to 7850 kg/m\({}^{3}\).
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 2 Materials and Methods
### State of-the-Art Onset Detection Algorithms
#### 2.2.3 Aubio  
This real-time library uses a window size of 2048 frequency samples to detect onsets [30]. In addition, Aubio sets a threshold value to mark quiet regions. Finally, it constructs a function based on successive spectral frames with a window size of 2048 and a hop-size of 1024, meaning the frame duration was approximately 23.22 ms for a 44,100 Hz sample rate. The dynamic thresholding and peak selections return the onset frames.
## 4 Architecture of Real-Time AI Agents for Autonomous Ships
### Sensor Fusion and Perception Infrastructure  
The ability of AI agents to perceive and interpret their environment in real time depends fundamentally on the quality and integration of sensor systems. Autonomous maritime platforms are equipped with a variety of sensing modalities, with each pro  
Figure 2: High-level architecture of autonomous maritime systems integrating AI agents (own elaboration).  
viding complementary data critical to maintaining situational awareness and supporting intelligent navigation [70].  
Key sensors include LIDAR, radar, sonar, cameras (both optical and infrared), and AIS receivers. LIDAR systems offer precise 3D environmental mapping, enabling the accurate detection of nearby obstacles, particularly in cluttered or port environments. Radar systems, by contrast, maintain robust performance in adverse weather conditions such as fog, rain, or darkness and are thus essential for all-weather operations [70]. Sonar enhances underwater awareness, while AIS data provide vital information about nearby vessels, including position, speed, and course, enabling cooperative navigation and collision avoidance.  
Cameras feed visual data into computer vision subsystems for detecting and classifying objects, reading maritime signage, and interpreting environmental indicators [71]. The diversity of input sources ensures both redundancy and resilience; no single sensor type can reliably function under all operational conditions.  
To synthesize these multisource data into coherent, reliable input for the AI agent, sensor fusion algorithms are applied. These include probabilistic techniques such as Kalman filters and Bayesian networks, which integrate asynchronous and heterogeneous data into a unified spatial-temporal model. This fusion process compensates for the limitations of individual sensors and improves the accuracy and robustness of perception [72].  
Beyond fusion, temporal and spatial synchronization is a key challenge. Sensor signals must be accurately aligned to prevent temporal drift or spatial miscalibration, both of which can lead to faulty perception or unsafe decisions. Robust calibration procedures and real-time correction mechanisms are thus integrated into the perception layer.  
By integrating multimodal sensor data, the perception infrastructure enables the AI agent to construct a dynamic, real-time model of the ship's surroundings. This model serves as the foundation for all downstream decision-making, route planning, and safety protocols.
## 3 Trilateration and PSO Algorithms
### Obtaining an Initial Solution  
To obtain an initial solution, the X-axis is discretized into \(k\) equidistant points and the Y-axis is divided into \(t\) equidistant points for each segment. This approach initially results in a total of \(k\times t\) points. The point \(POS_{p}\) with coordinates \((x_{p},y_{p},h)\) represents the initial position of the particle \(p\).  
After obtaining the initial particles, the objective function is computed for each of them. Subsequently, the initial velocity of each particle is defined by using a uniformly distributed random number within the range \([0,1]\). Furthermore, the position and value of the objective function where the best value is achieved among all particles are stored as \(gbest\). Lastly, for each particle, the best position and the corresponding value of the objective function achieved by that particle are recorded as \(gbest_{p}\).
## 3 Results
### Raman Spectroscopy  
The spectra obtained by structural measurements using Raman spectroscopy (Figure 12) confirm the results obtained from mid-infrared spectroscopic measurements (Figure 11). For glazes 1 and 2, sharp bands of very high intensity associated with the occurrence of crystalline phases can be seen (Figure 13): ZrSiO\({}_{4}\)--about 1000 and 450 cm\({}^{-1}\) and ZrO\({}_{2}\)--group of bands in the range of 400-600 cm\({}^{-1}\) and low-intensity bands about 1000 cm\({}^{-1}\) (Zr-O bonds). It is noticed that the integral intensity of the band from the crystalline phases of zirconium silicate (at about 1000 cm\({}^{-1}\)) changed. It is probably caused by the measurement point, which could be covered by a very thin layer of the glassy phase, or the size of this crystal was very small, and the amorphous phase was measured as well.  
A decrease in the integral intensity of the bands originating from the crystalline phases was observed, favored by the appearance of bands with a larger half-width. These are probably bands related to Q\({}^{n}\) units, which characterize the order of the silica subnetwork of the amorphous phase. n means the number of bridging oxygen atoms linked to the silica atom, and the higher n number means a more ordered structure. An increase in the content of potassium oxide causes the half-width of the bands present, indicating an increase in the disorder of the structure [11; 12; 13].  
On the spectra obtained by means of Raman spectroscopy, it is also possible to distinguish bands corresponding to the vibrations of Q units. Due to the presence of crystalline phases in the structure occurring in large numbers and the fact that their location coincides with the location of Q\({}^{2}\) and Q\({}^{3}\) units, the analysis covers only the differences in position and width Q\({}^{4}\) and Q\({}^{1}\) units.  
Figure 12: Raman spectrum of glazes 1–5.  
The position of both Q\({}^{4}\) and Q\({}^{1}\) units is slightly shifted toward higher wavenumbers. This phenomenon suggests that with an increase in the amount of potassium oxide in the glaze composition, its internal structure is rearranged. However, there is no change in the integral intensity of the bands, which may indicate that this increase in alignment is very small.
# Multiband and Lossless Compression of Hyperspectral Images
## 4 Experimental Results
### 1997 AVIRIS Dataset
In addition, LUT obtains better results of our approach on two of four compared hyperspectral images: Lunar Lake and Jasper Ridge. The high flexibility and adaptability of our approach makes it considerable for on board implementations. In fact, the coding parameters can be customized depending on the hardware available.
# Transmission Power Control in Wireless Sensor Networks Using Fuzzy Adaptive Data Rate
## 4 Experimental Results and Discussions  
### Allocation of Bridge and Sensor Nodes  
The TDMA polling period is 10 s in the experiments. The root, bridge, and 10 sensor nodes are placed in different positions. The sensor nodes are divided into experimental and control groups, and placed in five different locations for testing. The experimental site is located in the Sixth Hall of Engineering, National Yunlin University of Science and Technology, Taiwan. Figure 10 illustrates the configuration diagram of the nodes' position. The red dots are the root and bridge, and the green dots are the sensor nodes. The sensor nodes are set in five different positions, and there are two sensor nodes for the experimental and control groups in each position. Each sensor node is numbered 1 to 10 for analyzing experimental results easily; odd numbers 1, 3, 5, 7, 9 are experimental groups, and even numbers 2, 4, 6, 8, 10 are control groups.  
Figure 10: Configuration diagram of the nodes’ positions.  
Figure 9: Flow chart for the control algorithm.  
Table 7 presents the data rate and transmission power used by each sensor node in the long-term test. The parameter combination of each control group is the most commonly used combination under PER 1% after being tested by the algorithm group for a period of time.
# Game Approach to HDR-TS-PV Hybrid Power System Dispatching
## 3 **Hdr-Ts-PV** Game Model
### HDR-TS-PV Non-Cooperative Game and Payoff Functions
#### 3.2.3 Payoff Function of \(\mathit{PV}\) Plant  
The output power of the \(\mathit{PV}\) plants can be modeled using solar irradiance and the installed capacity [39]. According to the weather forecast, the \(\mathit{PV}\) plant obtains the predicted output power as the credible power generation in the first stage. The curtailed electricity and the abandoned loads are obtained from the actual solar irradiance in the second stage. This type of problem can usually be described by a Bayesian game model [34]: the two players of the game are the \(\mathit{PV}\) plants and nature, where the \(\mathit{PV}\) plants make decisions based on predicted values, and nature determines the joint probability of the actual solar irradiance. The payoff function of the \(\mathit{PV}\) plant is  
\[I_{\mathrm{P}}=\sum_{l=1}^{T}[c_{e}^{l}\overline{P}_{\mathit{PV}}^{t}-\sum_{ k=1}^{K}\pi_{k}(c_{e}^{l}P_{\mathit{PV}cur}^{k,t}-c_{ph}^{t}P_{\mathit{PV}al}^{k,t})], \tag{15}\]  
where \(k\) is the number of types of solar irradiance, \(\pi_{k}\) is the probability of each type, \(\overline{P}_{\mathit{PV}}^{t}\) represents the predicted value of the output power based on the solar irradiance in the non-cooperative game. \(P_{\mathit{PV}cur}^{k,t}\) and \(P_{\mathit{PV}al}^{k,t}\) are the curtailed power and the abandoned loads under each type, and \(c_{ph}^{t}\) is the penalty price for abandoned loads. The models of \(\overline{P}_{\mathit{PV}}^{t}\), \(P_{\mathit{PV}cur}^{k,t}\), and \(P_{\mathit{PV}al}^{k,t}\) are  
\[\overline{P}_{\mathit{PV}}^{t}=\lambda_{\mathit{PV}}^{t}\overline{P}_{\mathit{ PV}}, \tag{16}\]  
\[P_{\mathit{PV}cur}^{k,t}=\max\{\overline{\xi}_{\mathit{PV}}^{k,t}-\overline{P} _{\mathit{PV}}^{t},0\}, \tag{17}\]  
\[P_{\mathit{PV}al}^{k,t}=\min\{\overline{\xi}_{\mathit{PV}}^{k,t}-\overline{P} _{\mathit{PV}}^{t},0\}, \tag{18}\]  
\[\left|\overline{\xi}_{\mathit{PV}}^{k,t}-\overline{P}_{\mathit{PV}}^{t} \right|\leq\varphi \tag{19}\]  
where \(\lambda_{\mathit{PV}}^{t}\) is the power generation coefficient of the \(\mathit{PV}\) plant predicted according to the solar irradiance [39] and \(\overline{\xi}_{\mathit{PV}}^{k,t}\) is the actual output power corresponding to the non-cooperative part of the \(\mathit{PV}\) plant. \(\varphi\) is the power predicted error of the \(\mathit{PV}\) plant.  
The penalty price for abandoning loads, \(c_{ph}^{t}\) is expressed as the product of the penalty coefficient for abandoning loads \(p\) and the \(\mathit{TOU}\) price \(c_{e}^{t}\), namely,  
\[c_{ph}^{t}=pc_{e}^{t}. \tag{20}\]